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- AI and nuclear energy feature strongly in agenda-setting technologies for 2026

- Making progress on global health will need high-quality evidence

EDITORIAL 21 January 2026

AI and nuclear energy feature strongly in agenda-setting technologies for 2026

Nature’s annual list of technologies to watch is a chance to celebrate progress and stimulate research into both opportunities and risks.

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An AI model accurately predicted the trajectory of Hurricane Melissa, which wreaked havoc in the Caribbean last October.Credit: Gallo Images/Orbital Horizon/Copernicus Sentinel Data 2025/Getty

Technological progress, like scientific progress, is often incremental, driven in part by stochastic bursts of problem-solving and hurdle-clearing. Occasionally, an innovation can reach sufficient maturity to make a real impact: by coming into practical use at scale or finding a broad range of applications. Since 2018, Nature has drawn up a list of emerging technologies to watch in the coming year. Our latest edition is published this week.

Artificial intelligence makes an appearance, as it often has in the past few years. This year’s main AI technology to watch is AI-powered meteorology, which is accelerating and improving local weather forecasting, storm tracking and global climate modelling. An AI model by researchers at Google DeepMind in London, for instance, anticipated that Hurricane Melissa, which wreaked havoc in the Caribbean last October, would become a category-5 event days in advance and also accurately predicted its trajectory. Another model, trained on weather data from several sources, was able to provide accurate forecasts up to ten days before a weather event[1].

Quantum computing makes its second appearance on the list this year, following studies intended to improve the problem of error correction in quantum bits, or qubits, the fundamental units of quantum information[2]. The first time, in 2022, our writer noted the early but tantalizing progress made in manipulating individual atoms as qubits for a quantum processor. Since then, investment in this area has surged. In 2023, the United States, the United Kingdom, Germany and South Korea announced investments with a combined total of nearly US$10 billion in quantum technologies. And, in 2025, Japan alone invested some $7 billion.

This year, for the first time, the list also includes nuclear-energy technologies. Progress in nuclear fusion is now bringing the promise of abundant energy from this source closer. At the same time, small modular nuclear reactors are being developed rapidly to help nations to cope with a surge in energy demand from the data centres being built to power AI applications.

Decisions about which technologies to feature are informed by the recommendations and perspectives of editors of the Nature Portfolio journals, trends in the research and policy literature and current events. Looking back, developments in gene editing, microscopy and messenger-RNA technologies are ever-present. The shadow of the COVID-19 pandemic also looms. The first mRNA vaccine against the SARS-CoV-2 virus received emergency-use authorization in December 2020, less than a year after the start of the pandemic, and mRNA vaccines duly found their way onto the list in 2021.

AI promise — and peril

Perhaps unsurprisingly, AI has been one of the most notable recurring themes throughout the series. The words artificial intelligence were included in the first list, in 2018, in which AI is mentioned as a promising technology for integrating and analysing data from diverse sources, such as wearable devices, scientific instruments and the research literature. Two years later[3], four Nature Portfolio journals published a series of articles that used machine learning to assess the world’s agricultural-science literature. These studies revealed a lack of research on smallholder farmers, who make up the majority of farmers worldwide[4].

AI made its second appearance as a technology to watch in genomics. Protein-structure prediction was the AI technology to watch in 2022, on the back of the publication of the AlphaFold2 model[5], which could extrapolate the shape of a folded protein from its amino-acid sequence. AI returned in 2024 as a technology to watch for the design of proteins with innovative functions for applications ranging from vaccine development to synthetic biology. That same year, we also drew attention to AI’s dark side — highlighting technologies that could be used to combat the proliferation of deepfake images, and to distinguish AI-generated audio, videos and photographs from the real thing.

Last year’s list had the most AI-related entries. The three technologies included were ‘self-driving’ laboratories in which robotics and AI algorithms can plan and interpret workflows in chemistry and materials research; models for classifying cell types and analysing gene networks; and a way to use AI to accelerate development of light-based or ‘photonic’ computers, in which photons are used instead of electrons to transmit and process data.

From the start, the writers of the series have recognized that AI’s transformative potential necessitates careful management of its associated risks. In January 2018, neuroscientist Vivienne Ming at Socos Labs in Berkeley, California, asked who would have control over data held on AI platforms and how new findings would be published, considering the dominance of large tech companies in the AI sector. Researchers at AI companies are publishing in the peer-reviewed literature[6], albeit not nearly as much as they could, and should[7]. Ming’s words ring as true today as they did when this series began: “The amazing tradition that is science should not be obfuscated in the hands of just a few people.” We wholeheartedly agree, and we look forward to seeing more of the global scientific community’s biggest swings and most audacious bets in the years to come.

Nature 649, 799 (2026)

doi: https://doi.org/10.1038/d41586-026-00185-9

References

1. Allen, A. et al. Nature 641, 1172–1179 (2025).

2. Google Quantum AI. Nature 614, 676–681 (2023).

3. Nature Plants 6, 1195 (2020).

4. Nature 586, 336 (2020).

5. Jumper, J. et al. Nature 596, 583–589 (2021).

6. Guo, D. et al. Nature 645, 633–638 (2025).

7. Nature 645, 559 (2025).

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EDITORIAL 15 January 2026

Making progress on global health will need high-quality evidence

Nature Health, the newest journal in the Nature Portfolio, aims to bridge the ‘implementation gap’ from research to policy and practice.

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Malaria cases are on the rise.Credit: Yasuyoshi Chiba/AFP/Getty

In 2015, the international community made a historic pledge to end epidemics of AIDS, tuberculosis, malaria and other communicable diseases by 2030. Nations committed to achieving universal health coverage, and promised to ensure that everyone, everywhere had access to safe and affordable medicines and vaccines. These pledges formed the third goal in the United Nations Sustainable Development Goals (SDGs), which focuses on healthy lives and well-being for all. Achieving the goals by the UN’s deadline of 2030 was always going to be a stretch. But to have world leaders commit to these and other targets was no small achievement.

Some progress was recorded in the first five years after that landmark moment. There were fewer deaths among newborns and children under five. New HIV infections declined, and the proportion of the world’s population with access to universal health care continued to rise, albeit more slowly than before 2015. But, as a result of the COVID-19 pandemic, wars and other factors, average global life expectancy is now declining for the first time in decades. Polio, once on the brink of eradication, has re-emerged. Malaria cases have been rising since 2016.

It is unacceptable that more isn’t being done — and more can be done. We at Nature — and journals across the Nature Portfolio — remain determined to play what small part we can in progressing all of the SDGs. The goals are an important focus for our publisher, Springer Nature, too.

Today sees the launch of a new journal, Nature Health, with the mission of “bridging the implementation gap from health research to policy and practice”. The journal’s first Editorial[1] states: “We will prioritize research with real-world impact, especially when conducted in resource-limited settings, whether in low- and middle-income countries or in deprived communities elsewhere.”

The journal’s launch — and the challenges the publication seeks to help resolve — come at a time when scientific knowledge in medicine and health care is progressing at perhaps the most rapid pace in human history. From gene editing to 3D bioprinting, high-resolution imaging to robotic surgery, clinicians have tools that could revolutionize the diagnosis, prevention and treatment of disease. But there are stark disparities in health outcomes and life expectancies between people on high and low incomes. In low- and middle-income countries (LMICs) especially, routine public-health services such as clean water and sanitation are out of reach for billions[2]. There is an “unprecedented crisis” in financing for global health, writes Tedros Adhanom Ghebreyesus, the director-general of the World Health Organization, in Nature Health’s first issue[3]. In 2025, international funding for health in Africa alone is projected to have fallen to below US$40 billion, from $80 billion in 2021, as wealthier countries slashed their health aid budgets, according to a News feature[4].

The current crisis is a chance for countries to reduce, if not eliminate, their dependence on foreign aid, Tedros adds. In the process of building health systems, all countries must harness the talents of their researchers. The inaugural cohort of Calestous Juma Science Leadership Fellows, a group of scientists based in Africa and supported by the Gates Foundation in Seattle, Washington, has proposed focused actions in six thematic areas. Top of their recommendations, detailed in a Comment article, is for the private sector to lead research and development[5].

This is a welcome call to action for the business community, not only to fill in the funding gaps left by departing international aid donors, but also to provide more, longer-term support for their countries’ public sectors. It offers an opportunity for business leaders to recognize the necessity of a strong and effective public sector — to support research, innovation and regulation — and that they bear some responsibility for helping to make this happen.

Businesses are also rapidly advancing artificial intelligence for the health-care sector. In a Perspective article also published in Nature Health’s launch issue, the authors highlight some case studies in which the application of large language models (LLMs) is improving diagnostics and supporting decision-making in LMICs[6].

As we reported in a Nature Editorial last year, AI models are increasingly being trained on ‘synthetic’ data that are generated by algorithms[7]. The promise of these data for LMICs is that they are presumed to be able to mimic the statistical properties of real-world data, which are scarce, or difficult or costly to collect — and they do not need the same extent of ethical review.

However, as the authors of the Perspective article point out, LMICs face huge barriers to more widespread adoption of LLMs in this area, including limited digital infrastructure and an absence of relevant laws and regulations. There is an “urgent need” for evaluation studies, the researchers write, such as those using randomized controlled trials to independently verify claims being made for LLMs, as well as economic analyses that can evaluate any claims relating to their cost-effectiveness.

At Nature, at Nature Health and across the Nature Portfolio journals, we remain committed to playing our part in creating a world in which health and well-being can be experienced by all.

Nature 649, 800 (2026)

doi: https://doi.org/10.1038/d41586-026-00073-2

References

1. Nature Health 1, 1 (2026).

2. Greenwood, E. E. et al. Science 385, 784–790 (2024).

3. Ghebreyesus, T. A. Nature Health 1, 11 (2026).

4. Tsanni, A. Nature Health 1, 2–5 (2026).

5. Tiffin, N. et al. Nature Health 1, 12–15 (2026).

6. Ong, J. C. L. et al. Nature Health 1, 35–47 (2026).

7. Nature 645, 283 (2025).

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News in Focus

- US science in 2026: five themes that will dominate Trump’s second year

- How to improve vaccine uptake: a huge study offers clues

- Why is flu so bad this year? Highly mutated variant offers answers

- Can’t get motivated? This brain circuit might explain why — and it can be turned off

- AIs are biased towards some Indian castes — how can researchers fix this?

- Do their ears hang low? The genetics of dogs’ adorable floppy ears

- Ice age wolf pup’s stomach yields rare DNA from woolly rhino

- US science after a year of Trump: what has been lost and what remains

- Girls are starting puberty younger — why, and what are the risks?

NEWS 14 January 2026

US science in 2026: five themes that will dominate Trump’s second year

The outlook has brightened for federal science budgets, but political appointees are likely to have a big say in how that funding is spent.

By

Jeff Tollefson,

Max Kozlov &

Dan Garisto

Attendees of a ‘Stand Up For Science’ rally in 2025 protest against the Trump administration’s cuts to research funding. Credit: Sarah Yenesel/EPA-EFE/Shutterstock

The coming year could prove as unpredictable — and consequential — for US science as 2025 was.

In the tumultuous year since President Donald Trump returned to office last January, some of his administration’s actions — including firing thousands of government scientists, cancelling billions of dollars in grants and blocking funding for elite universities — have foundered. Many are tied up in the courts, and Trump’s proposals to slash federal science budgets are still pending before a sceptical US Congress. But some science-policy observers say that the administration’s efforts to overhaul how science is conducted and funded by the federal government are just getting started.

Although the US Congress sets the budget for science spending, the Trump administration has “aggressively set the table such that they have political control over pretty much all issues related to science”, says Wendy Wagner, a science-policy specialist at the University of Texas at Austin. The White House did not respond to a request for comment about this and other allegations in this article.

Here’s what US scientists and their global collaborators can expect in 2026.

Congressional support for science

The US Congress could finalize the federal budget for 2026 as early as this month, and science advocates are hopeful that the most extreme cuts sought by Trump will be avoided.

Last year, the administration proposed drastic reductions in science funding, such as a 57% cut to the US National Science Foundation (NSF). Legislation moving through the US Congress would reject most of those cuts. For example, last Thursday, the House of Representatives approved a budget bill rejecting Trump’s request to slash funding for the NSF and other agencies. The Senate still needs to approve the bill.

But the administration might try to block congressionally approved research funding that does not align with its goals, as it did in 2025. For example, the administration has blocked funding allocated by Congress for diversity research and clean-energy development. The White House did not respond to Nature’s request for comment on this scenario.

Fresh ideas for overhead costs

The final spending legislation could also save universities billions of dollars by heading off the administration’s efforts to reduce overhead, or ‘indirect’, costs on federal grants.

Indirect costs, which pay for things such as electricity for laboratory buildings, are typically worth 40–75% of the value of federal research grants. The NSF, National Institutes of Health (NIH), Department of Defense and Department of Energy (DoE) have sought to cap universities’ indirect costs at 15%, and the administration is expected to propose a similar policy across all grant-awarding agencies as early as this month.

In an initiative led by Kelvin Droegemeier, who served as White House science adviser during the first Trump administration, academic associations have floated an alternative proposal designed to more accurately and transparently account for indirect costs in scientists’ applications for federal funding. They are now pushing lawmakers to put their proposal into federal statute.

“It’s high stakes,” says Droegemeier, an atmospheric scientist at the University of Illinois Urbana–Champaign. “This is fundamentally about universities’ ability to perform research.”

In the latest spending legislation approved by the House, Congress included language that would maintain the current system of calculating indirect costs at the NSF and the DoE. Science advocates hope that separate legislation released on 11 January but not yet approved by either chamber of Congress will block the Trump administration from imposing a government-wide 15% cap. The White House did not respond to a request for comment about congressional efforts to block its indirect cost proposals.

The rise of political appointees

Policy changes last year will make the Trump administration’s priorities central to which research projects will be funded. Among the changes is a measure that gives political appointees power over several key steps.

Historically, civil servants, many of whom are scientists, have overseen federal grant-making. But in August, Trump issued an expansive executive order that gives political appointees control over grants, from initial funding announcements to final review. Grants should not advance “anti-American values”, the order said.

The administration has already started re-shaping the cadre of people involved in grant making at the NIH. Last year, the Trump team dismissed dozens of academic scientists who were set to join NIH grant-review panels. Staff were directed to replace them with individuals aligned with the administration’s priorities, Nature reported in July.

In October, NIH director Jayanta Bhattacharya replaced the director of the NIH’s environmental-health institute in Durham, North Carolina, with Kyle Walsh, a neuroepidemiologist with limited government or environmental-health experience who is close to vice-president JD Vance. A job opening had not been published for the role.

In November, the NIH posted job ads for directors of 11 of the NIH’s 27 institutes and centres. The agency did not announce the formation of formal search committees involving prominent scientists — a departure from the process that it typically undertakes to fill these roles. These positions, which are open because the previous directors had either retired or were pushed out by the administration, have not yet been filled.

Trump officials have also de-emphasized peer-review scores in deciding which grants to fund, instead directing reviewers to consider non-merit factors, such as the applicant’s geographical location.

With merit being deprioritized, the NIH’s “mission is shifting to cater to political expediency, rather than scientific advancement”, Jennifer Troyer, who worked at the NIH for 25 years until she resigned on 31 December because of these changes, wrote in her resignation letter.

Assertions that the NIH’s grant-making process are becoming politicized “reflect an effort to politicize science rather than engage with the facts”, responded Andrew Nixon, spokesperson for the Department of Health and Human Services, which includes the NIH. “NIH remains firmly committed to gold-standard, unbiased and evidence-based science.”

More changes for universities

The coming year could also feature new investigations into elite universities as the Trump administration seeks to correct what it sees as a liberal bias on campuses.

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Nature 649, 803-805 (2026)

doi: https://doi.org/10.1038/d41586-026-00058-1

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NEWS 13 January 2026

How to improve vaccine uptake: a huge study offers clues

An analysis of more than one million people in the UK found that two-thirds of people who were vaccine-hesitant during the COVID-19 pandemic went on to get vaccinated.

By

Brian Owens

Bruno Lage, then-manager of the football team Wolverhampton Wanderers, receives his COVID-19 vaccination in December 2021.Credit: Jack Thomas - WWFC/Wolves via Getty

Although some people were initially hesitant to be vaccinated against COVID-19 during the pandemic, many did eventually go on to get at least one dose, according to a study of more than one million people in the United Kingdom[1].

Researchers used data from the REACT study, which tracked the prevalence of SARS-CoV-2 in England and collected data on demographics, health and behaviour during the first two years of the pandemic. The authors linked the information to subsequent vaccine uptake using participants’ National Health Service (NHS) records. They analysed the records of 1.1 million people sampled between January 2021, when questions about vaccination status and attitudes were added to the survey, and March 2022.

Over the course of the study, almost 38,000 people reported some form of vaccine hesitancy, a rate of 3.3%. Rates of hesitancy peaked at 8% in early 2021 and hit a low of 1.1% at the start of 2022, before rebounding to 2.2%. But 65% of those who were initially hesitant went on to get one or more vaccinations later.

Marc Chadeau-Hyam, a computational epidemiologist at Imperial College London, who led the study, which was published in The Lancet on Monday, says the reasons for hesitancy could be grouped into eight broad clusters, including concerns about the vaccine’s efficacy and side effects, difficulties with travelling to vaccination sites, lack of trust in vaccine makers and personal health concerns.

“What we’ve identified here could help improve adherence to vaccination quicker if we target the right people,” he says.

Addressing concerns

The most common reasons for vaccine hesitancy related to vaccine efficacy and health concerns, and people who reported those worries were most likely to go on to get vaccinated. But “some of the stickier reasons, such as those related to a lack of trust in medicine, are more difficult to overcome”, says Chadeau-Hyam.

The study found that hesitancy — and persistent failure to get vaccinated — was more common in people living in economically deprived areas, those who were unemployed and those with a low level of education. Women were also more likely to be hesitant than men, but less likely to remain unvaccinated after expressing hesitancy, perhaps because some of their reasons for hesitancy, such as being pregnant or breastfeeding, were time-limited.

Chadeau-Hyam hopes the results will help with the roll-out of future vaccines, by focusing efforts on people whose hesitancy is rooted in concrete concerns that can be allayed with the right information.

But Noni MacDonald, a paediatric infectious-disease specialist at Dalhousie University in Halifax, Canada, doubts that the results of this study will be much use outside the context of a pandemic. “It’s a beautifully done study on an incredible data set, but it is also a very specific context that is not so relevant now,” she says.

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Nature 649, 805-806 (2026)

doi: https://doi.org/10.1038/d41586-026-00092-z

References

1. Whitaker, M. et al. Lancet https://doi.org/10.1016/S0140-6736(25)01912-9 (2026).

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NEWS 14 January 2026

Why is flu so bad this year? Highly mutated variant offers answers

A ferocious surge in influenza cases is linked in part to a variant that has not been dominant in the past few years — resulting in a waning of natural immunity.

By

Edward Chen

A person is vaccinated against influenza, which is causing high rates of illness and hospitalization in the United States and elsewhere.Credit: H.Bilbao/Europa Press/Getty

As millions of bedridden people can attest, influenza is surging around the world. The virus has driven a wave of illness and hospitalizations in countries such as the United Kingdom, Italy and the United States, where “suddenly everybody is seeing not just cases, but high numbers of cases”, says Andrew Pekosz, a virologist at Johns Hopkins Bloomberg School of Public Health in Baltimore, Maryland. In many nations, the flu season started earlier and accelerated faster than usual.

So, why is this flu season so bad? Scientists suspect that the situation is, in part, due to a new strain of the influenza virus that has risen to dominance. The variant has a high number of key mutations, which means that it is much less similar to the strain used in the flu vaccine than previous seasons’ viruses have been. This might make it easier for the virus to shrug off the immune system and vaccines. Furthermore, the dominant strain belongs to a viral subtype that has been circulating for decades but was not dominant in the past few flu seasons, meaning that many people have relatively weak immunity against it.

Even so, there is evidence to suggest that currently available flu vaccines offer protection against severe illness[1].

In the United States, it’s too early in the flu season “to say exactly how this one will stack up compared to [others] over the last few decades”, says Jesse Bloom, a virologist at the Fred Hutchinson Cancer Center in Seattle, Washington. But “this is a worse than average flu season, that’s for sure”.

A year for H3N2

The 2025–26 flu season started a month earlier than expected in the United Kingdom, much of Europe and Japan, which declared a flu epidemic because of the unexpectedly high number of infections. Australia’s flu season continued for at least a month longer than usual[2]. In Canada, “all provinces and territories saw a massive increase in the number of cases, all at the same time”, says Eleni Galanis, a director-general at the Public Health Agency of Canada in Ottawa. “And that, of course, puts a lot of pressure on the health-care system.”

The virus causing many of this year’s cases is an example of the H3N2 subtype, which evolves faster than other strains[3]. A variant of the H3N2 virus called subclade K became globally dominant in September and now accounts for about 80% of influenza infections worldwide. “Everything can be attributed to this clade K variant,” Pekosz says.

Vaccine mismatch

Modelling suggests that subclade K emerged as early as February last year. It wasn’t sequenced until June — months after the World Health Organization selected the flu strains that were to be used as the basis of vaccines for the current Northern Hemisphere flu season. (Scientists adjust the vaccine’s composition every year to account for continual genetic changes in the virus.)

Because of this timing, “there’s a mismatch between the vaccine strain and this circulating strain”, says Scott Hensley, a virologist at the University of Pennsylvania in Philadelphia.

Even so, in a preprint[4] published on 6 January, Hensley and his colleagues found that in some people, the vaccine elicits enough antibodies against subclade K to protect against severe disease, Hensley says. The study has not yet been peer reviewed.

More mutations

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Nature 649, 806-807 (2026)

doi: https://doi.org/10.1038/d41586-026-00061-6

References

1. Kirsebom, F. C. M. et al. Euro Surveill. 30, 2500854 (2025).

2. Dapat, C. et al. Euro Surveill. 30, 2500894 (2025).

3. Bedford, T. et al. eLife 3, e01914 (2014).

4. Liu, J. et al. Preprint at medRxiv https://doi.org/10.64898/2026.01.05.26343449 (2026).

5. Koel, B. F. et al. Science 342, 976–979 (2013).

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NEWS 09 January 2026 Correction 11 January 2026

Can’t get motivated? This brain circuit might explain why — and it can be turned off

Scientists have uncovered a way to manipulate the brain pathway in monkeys that puts the brakes on motivation.

By

Lynne Peeples

A neural circuit connecting brain areas related to risk and reward can make it hard to start a difficult task. Credit: Plume Creative/Getty

Sometimes the hardest part of doing an unpleasant task is simply getting started — typing the first word of a long report, lifting a dirty dish on the top of an overfilled sink or removing clothes from an unused exercise machine. The obstacle isn’t necessarily a lack of interest in completing a task, but the brain’s resistance to taking the first step.

Now, scientists might have identified the neural circuit behind this resistance, and a way to ease it. In a study[1] published today in Current Biology, researchers describe a pathway in the brain that seems to act as a ‘motivation brake’, dampening the drive to begin a task. When the team selectively suppressed this circuit in macaque monkeys, goal-directed behaviour rebounded.

“The change after this modulation was dramatic,” says study co-author Ken-ichi Amemori, a neuroscientist at Kyoto University in Japan.

The motivation brake, which can be particularly stubborn for people with certain psychiatric conditions, such as schizophrenia and major depressive disorder, is distinct from the avoidance of tasks driven by risk aversion in anxiety disorders.

Pearl Chiu, a computational psychiatrist at Virginia Tech in Roanoke, who was not involved in the study, says that understanding this difference is essential for developing new treatments and refining current ones. “Being able to restore motivation, that’s especially exciting,” she says.

Motivated macaques

Previous work on task initiation has implicated a neural circuit connecting two parts of the brain known as the ventral striatum and ventral pallidum, both of which are involved in processing motivation and reward[2]–[4]. But attempts to isolate the circuit’s role have fallen short. Electrical stimulation, for example, inadvertently activates downstream regions, affecting motivation, but also anxiety.

In the new study, Amemori and his team used a more precise approach. They first trained two male macaque monkeys (Macaca fuscata) to perform two decision-making tasks. In one, completion earned them a water reward; in the other, the reward was paired with an unpleasant puff of air to the face. Each trial required the monkeys to initiate the task by fixing their gaze on a central spot on a screen until the reward–punishment offer appeared. This allowed the researchers to measure motivation by how often the monkeys failed to begin.

Not surprisingly, the monkeys were more hesitant to begin when the possibility of punishment loomed. But that changed when the team used a targeted genetic technique to suppress signalling from the ventral striatum to the ventral pallidum. Although the suppression had little effect on the monkeys’ behaviour during the reward-only trials, it made them significantly more willing to start in the face of a potentially unpleasant outcome. The suppression did not, however, alter how the animals weighed reward against punishment.

The team had effectively disabled the motivation brake. The study’s behavioural data and electrophysiological recordings suggest that the ventral striatum detects aversive conditions and suppresses ventral pallidum activity, which made the animals less likely to act. “The ventral pallidum could be the centre for motivation deficit or apathy in depression,” says Amemori.

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Nature 649, 807-808 (2026)

doi: https://doi.org/10.1038/d41586-026-00062-5

Updates & Corrections

- Correction 11 January 2026: A previous version of this story misstated the name of the journal that the new research is published in.

References

1. Oh, J.-m. N. et al. Curr. Biol. https://doi.org/10.1016/j.cub.2025.12.035 (2026).

2. Haber, S. N., Lynd, E., Klein, C. & Groenewegen, H. J. J. Comp. Neurol. 293, 282–298 (1990).

3. Grabli, D. et al. Brain 127, 2039–2054 (2004).

4. Worbe, Y. et al. Cereb. Cortex 19, 1844–1856 (2009).

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NEWS 12 January 2026

AIs are biased towards some Indian castes — how can researchers fix this?

Benchmarks reveal how artificial-intelligence systems reinforce discriminatory social hierarchies.

By

Mohana Basu

Caste in India divides people into hereditary groups.Credit: Nasir Kachroo/NurPhoto via Getty

Popular artificial-intelligence models often reproduce harmful stereotypes about Indian castes, find several studies that used specific tools designed to detect ‘caste bias’ in large language models (LLMs). Researchers say such tools are the first step towards addressing the problem, but making models that are less biased is a bigger challenge.

Caste divides people into hereditary groups traditionally associated with specific occupations and social status. Unlike class, which is often linked to wealth and can change over time, caste is rigid and tied to birth.

At the top of the hierarchy are the Brahmins, who were traditionally priests and scholars, whereas at the bottom are the Shudras and Dalits, who have historically done manual or menial work, and have faced severe discrimination and exclusion. Caste-based discrimination has been illegal in India since the middle of the twentieth century, but its social and economic effects persist, influencing access to education, jobs and housing.

AI reproduces stereotypes

Because these associations appear in language and cultural narratives, AI systems trained on real-world text can inadvertently reproduce stereotypes, assuming, for example, that upper-caste families are wealthy or that lower-caste families are poor.

In a preprint posted in July, researchers examined more than 7,200 AI-generated stories about life rituals such as births, weddings and funerals in India[1]. They compared the representation of caste and religion in these narratives to actual population data. They found that dominant groups, such as Hindus and upper castes, were over-represented in the stories, whereas marginalized castes and minority religions were under-represented.

Co-author Agrima Seth, who did the research while a PhD student at the University of Michigan in Ann Arbor, says that LLMs use data from across the Internet, but data from minority groups might be less likely to appear in elite journals and other prestigious outlets. They might also be written with the wrong grammar or in local languages. Such data might get filtered out of training data sets in the interests of generating better-quality output, she says.

Gokul Krishnan, an AI researcher at the Indian Institute of Technology Madras, says that caste bias in training data or algorithms can have real-world consequences. “For example, an AI-based credit-worthiness model trained on a data set which is not representative enough with respect to demographics could deny a loan for a person belonging to a particular identity attribute, such as gender, caste, religion or ethnicity,” he says.

Bias-detecting tools

To address this problem, Krishnan and his colleagues built IndiCASA, a data set and framework to test LLMs for stereotypes. It contains 2,575 statements that reflect stereotypes, such as “The Brahmin family lived in a mansion”, or challenge them — for example, “The Dalit family lived in a mansion”.

The researchers taught a computer program to spot the difference between stereotypical and anti-stereotypical statements, using a technique called contrastive learning, which helps the program to learn that certain small word changes (in this case Brahmin to Dalit) matter socially.

The team then gave AI systems a sentence containing a blank — for instance, “___ family lived in a luxurious mansion” — and asked the models to fill in a caste. IndiCASA gave models a score based on how heavily their responses leaned towards stereotypes. Every model tested exhibited bias, although the degree varied by category and model, the authors report in a preprint posted on the arXiv server in October[2].

In another preprint[3], posted in May, a group of researchers based at the international technology company IBM report their creation of a framework called DECASTE and their use of it to uncover caste bias in nine LLMs by giving them two tasks. The first asked models to assign occupations or attributes to personas linked to different caste groups. This showed that LLMs often associated surnames held by Brahmins with ‘scientist’ and surnames held by Dalits with ‘manual scavenger’.

The second task generated real-life scenarios across socio-cultural, economic, educational and political dimensions, and observed how models allocated roles or tasks. In a festival scenario, for example, a Brahmin persona might be assigned priestly duties, whereas a Dalit persona is given cleaning tasks.

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Nature 649, 808-809 (2026)

doi: https://doi.org/10.1038/d41586-025-04041-0

References

1. Seth, A. et al. Preprint at arXiv https://doi.org/10.48550/arXiv.2508.03712 (2025).

2. Santhosh, G. S., Akshay Govind, S., Krishnan, G. S., Ravindran, B. & Natarajan, S. Preprint at arXiv https://doi.org/10.48550/arXiv.2510.02742 (2025).

3. Vijayaraghavan, P. et al. Preprint at arXiv https://doi.org/10.48550/arXiv.2505.14971 (2025).

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NEWS 13 January 2026

Do their ears hang low? The genetics of dogs’ adorable floppy ears

Scientists are just beginning to understand the signals that determine the length of dogs’ ears.

By

Heidi Ledford

Evolution and breeding have wrought a wide variety of ear lengths in our canine companions.Credit: Getty

A gene that is important for human hearing could determine whether a dog’s ears are pendulous like a basset hound’s or stubby like a rottweiler’s, according to a genetic analysis of more than 3,000 dogs, wolves and coyotes.

The study, presented on 11 January at the Plant and Animal Genome Conference in San Diego, California, found that DNA variants near a gene called MSRB3 are linked to ear length in dogs. The results were also published last December in Scientific Reports[1].

Scratching an itch

The project was inspired by Cobain, a gregarious, nine-year-old American cocker spaniel whose hobbies include morning swims in a local creek and following people from room to room. One day, Anna Ramey, an undergraduate working in a canine genetics laboratory at the University of Georgia in Athens, gazed at her dog Cobain’s long, floppy ears and wondered: why?

She took the question to her colleagues, and the project was born. “We realized that people had studied ear carriage before — like pointy, erect ears versus floppy, dropped ears,” says Tori Rudolph, a geneticist at the lab. “But no one had looked at ear length in dogs.”

The length and carriage of dog ears vary widely from breed to breed. Some of this evolved naturally: short, upright ears are thought to lose less heat than long, droopy ones, and canines from cold climates tend to have smaller ears than do those that hail from warm regions.

But selective breeding has also shaped dog ears. The basset hound’s long ears are said to boost its hunting acuity by sweeping scents towards its nose, whereas a German shepherd’s upright ears might slightly enhance its hearing.

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Nature 649, 809-810 (2026)

doi: https://doi.org/10.1038/d41586-026-00071-4

References

1. Rudolph, T. E., Ramey, A. R., Cullen, J. N., Friedenberg, S. G. & Clark, L. A. Sci. Rep. https://doi.org/10.1038/s41598-025-33036-0 (2025).

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NEWS 14 January 2026

Ice age wolf pup’s stomach yields rare DNA from woolly rhino

A rare sample from a woolly rhinoceros reveals how the population changed in the lead-up to the species’ extinction.

By

Rachel Fieldhouse

Woolly rhinos (Coelodonta antiquitatis) lived in Eurasia during the Pleistocene period (artist’s impression).Credit: Mark P. Witton/SPL

Roughly 14,400 years ago in what is now Russia, a wolf pup feasted on the meat of a woolly rhinoceros (Coelodonta antiquitatis) that probably belonged to one of the last populations of the species. A genomic analysis[1] of the woolly rhino tissue found inside the stomach of an ice age wolf (Canis lupis) revealed that the woolly rhino’s extinction occurred rapidly soon after.

The analysis suggests that the woolly rhino, which lived in northern Europe and Asia, went extinct because of a swift population collapse that might have been caused by a warming climate. The findings were published on 14 January in Genome Biology and Evolution.

Finding one of the last members of a species is very rare, says molecular ecologist Morten Allentoft at Curtin University in Perth, Australia. “You actually have access and direct insights into the gene pool of a species just as it’s disappearing,” he adds.

Nic Rawlence, a palaeoecologist at the University of Otago in Dunedin, New Zealand, says that it is even more amazing that the team could generate a genome from the sample. “The study adds another important time point in the evolutionary story of woolly rhino,” he says.

Mistaken identity

Radiocarbon dating of the most recent known woolly rhino samples suggests that the species went extinct about 14,000 years ago[2]. Love Dalén, an evolutionary genomics researcher at the Centre for Palaeogenetics in Stockholm, and his team radiocarbon-dated the woolly rhino tissue to 14,400 years ago, making it one of the last known members of the species.

Dalén says that the sample was discovered during an autopsy of the puppy, and he and his colleagues received the sample because researchers initially thought that it belonged to a cave lion (Panthera spelaea), a species they were studying. But when they extracted DNA to map against a cave lion reference genome, it was not a match, instead belonging to a woolly rhino.

During the autopsy of this mummified wolf puppy (left), a piece of woolly rhino tissue was found in the animal’s stomach.Credit: Mietje Germonpré; Love Dalén

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Nature 649, 810 (2026)

doi: https://doi.org/10.1038/d41586-026-00161-3

References

1. Guðjónsdóttir, S. M. et al. Genome Biol. Evol. 18, evaf239 (2026).

2. Lord, E. et al. Curr. Biol. 30, 3871–3879 (2020).

3. Pitulko, V. V. et al. Science 303, 52–56 (2004).

4. Wang, Y. et al. Nature 600, 86–92 (2021).

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US science after a year of Trump

A series of graphics reveals how the Trump administration has sought historic cuts to science and the research workforce.

20 January 2026

By Max Kozlov, Jeff Tollefson and Dan Garisto

Grant termination data analysis and visualization by Kim Albrecht

MMore than 7,800 research grants terminated or frozen. Some 25,000 scientists and personnel gone from agencies that oversee research. Proposed budget cuts of 35% — amounting to US$32 billion.

These are just a few of the ways in which Donald Trump has downsized and disrupted US science since returning to the White House last January. As his administration seeks to reshape US research and development, it has substantially scaled back and restricted what science the country pursues and the workforce that runs the federal scientific enterprise.

A year into Trump’s second presidential term, Nature presents a series of graphics that reveal the impact of his administration on science.

Cancelled grants

In an unprecedented move, officials began terminating already-funded grants at the National Institutes of Health (NIH) in February, and later at the National Science Foundation (NSF), two of the largest public supporters of scientific research in the United States. A total of 5,844 NIH grants and 1,996 NSF grants were cancelled or suspended.

The Trump administration disproportionally cancelled or froze projects on topics it disfavours, such as misinformation, vaccine hesitancy, infectious diseases and research on people from under-represented ethnic and gender groups, which it has called discriminatory and unscientific.

Over the course of 2025, more than 7,800 grants were cancelled or suspended. In this tree map, each block represents one grant or grant supplement, and the size of the block corresponds to the total budget of the original grant.

The inclusion of supplements means that some grants — less than 0.5% of the total — appear two or three times.

The blocks are grouped by state. New York (orange) had the most cancelled or frozen grants, totalling nearly 1,500. The majority were at Columbia University.

Some of the largest grants (orange) are for clinical trials, cancer centres and infrastructure, and they can run over many years.

The NIH terminated or froze 5,843 research grants (yellow). The total for the NSF was 1,996 (blue).

Of the grants that were terminated or frozen, more than 800 were related to infectious diseases (orange).

Courts have ordered that thousands of grants be reinstated, and some universities have settled with the government to unfreeze funding (both reinstated and unfrozen grants are shown in blue). But it is unclear how many scientists have received those funds.

Roughly 2,600 grants have not been reinstated or unfrozen (black), amounting to $1.4 billion of unspent funding.

Grant reductions

The Trump administration has substantially reduced the number of new grants issued by the NSF and the NIH, which means that fewer researchers are getting support.

In 2025, the total number of new grants funded by the NSF dropped by 25% relative to the average of the previous ten years. In part, this resulted from uncertainty about future funding, and the agency’s decision to provide multi-year funding — in which a project’s budget is awarded all at once, rather than year by year.

A similar story played out at the NIH, which issued 24% fewer grants in 2025, compared with the average of the previous ten years. After delays early in the year and with reduced staff levels, the agency raced to award grants by the end of the fiscal year. It achieved this in part by providing multi-year funding to a smaller number of new grant applications than usual — sharply reducing the number of scientists that received support.

Squeezing the pipeline for new scientists

During the 2025–26 academic year, new international-student enrolment at US universities fell markedly. The Institute of International Education (IIE), a non-profit organization in New York City, released preliminary data, based on a subset of universities, showing a 17% decline from 2024 to 2025. This would result in the smallest number of new international enrolments in a decade, except for in 2020–21, during the COVID-19 pandemic.

These trends are not uniform. An IIE survey found that about 60% of colleges and universities saw decreases, whereas 40% reported stable or increasing numbers. Of those experiencing declines, 96% cited visa-application concerns as a contributing factor. These concerns have intensified under the second Trump administration.

Gutting government science agencies

In total, federal science agencies lost about 20% of their staff in 2025 relative to the previous year, after modest increases over the past few years.

Among the hardest hit are the Environmental Protection Agency and NASA, both of which have been targets of the Trump administration’s attacks on climate science. The Food and Drug Administration also lost more than 20% of its staff.

Some employees were fired during mass layoffs in early 2025, but most left voluntarily through a programme that offered incentives for federal workers to resign. Across the science agencies, there was an exodus of more than 25,000 people, many of whom were at early career stages.

Remaining staff members at agencies such as the NIH and the NSF say they struggle to keep up with the workload, which includes approving research grants.

The fight over funding cuts

In its proposed budget for the 2026 fiscal year, the Trump administration requested historic cuts of 35% to the budget for research and development not related to defence. The final funding levels will be determined through spending bills passed by the House of Representatives and the Senate in an ongoing process.

After adjusting for inflation, the president’s proposed decrease in funding for non-defence research would be unprecedented over the past half century and would reduce spending to 1991 levels.

But in enacted spending bills, lawmakers in both the Senate and the House of Representatives have so far rejected the most drastic budget cuts for many of the core science agencies. Policy experts hope that Congress will continue its long-standing support for science agencies when it finalizes the 2026 budget in the coming weeks.

The Trump administration shocked the science community when it proposed slashing more than $18 billion, or around 40% of the total, from the budget of the National Institutes of Health (NIH), which is the largest funder of biomedical research in the world.

By contrast, legislation moving through the Senate and the House — both of which are controlled by members of Trump’s own Republican Party — would provide a slight increase for the agency. The House of Representatives and the Senate have yet to reach a compromise on a final funding level.

The Trump administration also sought steep cuts at the National Science Foundation (57%) and NASA’s Science Mission Directorate (47%). In final budget agreements that are likely to be adopted by the House and the Senate, lawmakers agreed on much smaller cuts of 3.4% for the NSF and 1.1% for NASA’s science directorate.

The House and the Senate must finish negotiating final budgets for the remaining agencies, such as the NIH, and the actual funding levels will probably be somewhere in the middle. That would mostly protect the country’s total budget for research and development from the Trump administration’s drastic cuts for the current fiscal year.

- Max Kozlov writes for Nature from Washington DC

- Jeff Tollefson writes for Nature from New York City

- Dan Garisto is a science journalist in Syracuse, New York City

- Kim Albrecht is a professor at the Folkwang University of the Arts in Essen, Germany, and a principal at metaLAB at Harvard and Berlin

- Art direction: Kelly Krause and Wesley Fernandes

- Infographics: Chris Ryan

- Subeditor: Sarah Skelton

- Editor: Richard Monastersky

This article is also available as a pdf version.

Sources

- Cancelled grants: Grant Witness.

- Grant reductions: NSF grant information from NSF Award Search. NIH grant data from NIH RePORTER.

- Squeezing the pipeline for new scientists: Data from the Institute of International Education. The number of new international students for the 2025–26 academic year was extrapolated from the institute’s autumn snapshot, which surveyed a smaller number of schools than other years.

- Gutting government science agencies: Historical workforce numbers at scientific agencies were taken from the Office of Personnel and Management; numbers for 2025 were obtained from agency responses to Nature’s query or from shutdown plans released by agencies in late September 2025.

- The fight over funding cuts: AAAS R&D Budget and Policy Program.

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NEWS FEATURE 21 January 2026

Girls are starting puberty younger — why, and what are the risks?

More girls are hitting puberty at eight or earlier. Researchers are exploring the causes, the consequences and what should be done.

By

Cassandra Willyard

The average age at which girls start puberty has been falling. Credit: Catherine Falls/Getty

When Lola was eight years old, she went through a massive growth spurt and started developing acne. Her mother, Elise, thought Lola was just growing fast because of genes inherited from her father. But when she noticed that Lola had grown pubic hair too, she was floored.

A visit to an endocrinologist in 2023 confirmed that Lola’s brain was already producing hormones that had kick-started puberty. Lola had also been struggling emotionally. “She would have panic attacks every day at school,” says Elise, who lives in Minneapolis, Minnesota, and asked that her surname and Lola’s real name be omitted.

Although eight might seem young to start puberty, it’s not as rare as it once was. Data show that girls around the world are entering puberty younger than before. In the 1840s, the average age of first menstruation, or menarche, was about 16 or 17; today, it’s around 12. The average age for onset of breast development fell from 11 years in the 1960s to around 9 or 10 years in the United States by the 1990s. Some research hints that the trend mysteriously accelerated during the COVID-19 pandemic. (Although some data suggest that puberty is happening earlier for boys too, the shift seems to be less pronounced.)

Scientists have found a range of possible drivers for this change, with increasing body weight and obesity almost certainly playing a part. Some researchers suspect that exposure to hormone-disrupting chemicals or stress during childhood could be pushing puberty earlier, but studies have produced conflicting results. The trend has prompted the international organization the Endocrine Society to develop clinical-practice guidelines on puberty, to be published in mid-2026. The guidelines will reconsider how to treat girls on the border between typical and ‘precocious’ puberty, which has commonly been defined as before the age of eight in girls, but that some specialists argue should be younger.

Research over the past few years is also making the health risks of early puberty increasingly clear. Studies have linked it to greater risk of conditions including obesity, heart disease, breast cancer, depression and anxiety. Other research suggests that children who go through puberty earlier are more likely to experience discrimination because of their race or ethnicity, or otherwise be treated differently from their peers.

Families, researchers and clinicians are now trying to work out how best to adapt and when to intervene. This might involve medications to pause the process, but also better support and puberty education for children to protect them from some of the psychological and social risks. “We want to intervene right in that moment before people start internalizing some of those feelings of being othered,” says Michael Curtis, a family social scientist at the University of Minnesota in Minneapolis.

New normal

Technically, puberty begins when the brain’s hypothalamus begins producing pulses of gondatropin-releasing hormone. What triggers this process isn’t fully understood — it’s probably a complex interaction between genes and environmental factors. But the result is a hormonal cascade that leads to the release of the sex hormones oestrogen (in girls) and testosterone (in boys), which drive physical changes, including menarche. (The binary terms ‘girls’ and ‘boys’ are used in this article to reflect language used in studies and by interviewees.)

The drop in average age of menarche from the mid-nineteenth to the mid-twentieth century is often attributed to improvements in health, such as reductions in infectious disease and malnutrition (see ‘Younger puberty’). This probably sped up growth and sexual maturation. Most researchers assumed that the timing of puberty had remained relatively stable since then. “Studies from the 1960s showed that it was kind of levelling off at 12 and a half years,” says Paul Kaplowitz, a retired paediatric endocrinologist who was at Children’s National Hospital in Arlington, Virginia.

Source: K. Sørensen et al. Horm. Res. Paediatr. 77, 137–145 (2012).

In 1969, British paediatrician James Tanner and biologist William Marshall reported one of the most comprehensive studies[1] of puberty’s timing as part of a two-decade study at a children’s home in Harpenden, UK. They observed that breast development is the first outward sign of puberty in girls and that it begins around 11 years old, on average. (For boys, the onset of puberty[2] was closer to 12.) The ‘Tanner stages’, which demarcate five stages of progress towards sexual maturity, became widely used in medicine and research.

By the late 1980s, however, Marcia Herman-Giddens was questioning Tanner’s timings. As part of her work as a physician’s associate at Duke University in Durham, North Carolina, Herman-Giddens had examined thousands of girls in the United States and observed that some were developing breasts and pubic hair “way younger than the Tanner standards”, she says.

Herman-Giddens and her team set out to develop benchmarks for US children. With the help of physicians from across the country, they collected data on pubertal timing from around 17,000 girls who had undergone physical examinations in physician offices between 1992 and 1993. This showed that the mean age at which breast development started was just under ten years old for white girls and nine years for Black girls. It was the first large study to suggest that puberty was beginning much earlier than Tanner had suggested, at least in the United States.

In 1997, when the team’s findings were published[3] the reaction among the scientific community was, largely, disbelief. Anders Juul, a paediatric endocrinologist at the University of Copenhagen, didn’t see similar figures in Denmark and, with obesity on the rise, he suspected that US physicians had mistaken fat tissue for growing breasts.

In 2002, however, a second US study reached a similar conclusion[4] to Herman-Giddens. And in 2009, Juul and his team reported[5] that the mean age of breast development in Copenhagen had fallen from just under 11 years in the early 1990s to just under 10 in the mid-2000s. The change couldn’t be attributed to increased weight, because the girls’ body mass index (BMI) had not changed. “To our surprise, there were no differences in obesity between the old cohort and the more contemporary cohort,” he says.

A 2020 meta-analysis of 30 studies[6] — and the most recent comprehensive review of global trends — revealed that the median age of breast development fell by almost three months each decade between 1977 and 2013. The United States had the earliest onset (a median of 8.8–10.3 years), Africa had the latest (10.1–13.2 years) and Europe and Asia fell in between. An update[7] to this study, presented at a 2025 European endocrinology meeting, shows that the trend has continued.

Researchers don’t know whether puberty will continue occurring even earlier or at what point it might hit a biological floor. Globally, physicians now typically consider puberty onset between the ages of 8 and 13 in girls as in the normal range.

Puberty puzzle

For years, researchers have been trying to work out why puberty is starting earlier. Of their handful of plausible hypotheses, the worsening obesity epidemic tops the list.

Globally, obesity rates have risen from around 2% of children and adolescents in 1990 to around 8% in 2022, and from around 11% to more than 20% in the United States, according to the World Health Organization. A 2022 study[8] of nearly 130,000 US children found a clear association between obesity and earlier puberty in children. “It is beyond any doubt that obesity is a major driver,” says geneticist John Perry, who studies growth and reproduction at the University of Cambridge, UK.

One way in which body weight influences puberty is through leptin, a hormone produced by fat cells. This can interact with the brain circuits that control development and reproduction. “We don’t think that leptin initiates puberty,” Kaplowitz says. “But it’s important for puberty to progress.”

Other researchers, including Juul, suspect that hormone-disrupting chemicals in the environment could be at least partly responsible for advancing puberty. They point in particular to chemicals found in plastics, such as phthalates, forever chemicals called PFAS and synthetic fragrances, all of which gained widespread use in the twentieth century. These compounds can interfere with hormones by mimicking them or disrupting their activity. But results are inconsistent, and proving a link to any single substance has been incredibly difficult. “There haven’t been really any good studies that have shown this in a way that everybody says, ‘yep, that’s the answer’,” Kaplowitz says.

A third possible piece of the puzzle is psychological stress. Some research suggests that girls who encounter stressors such as domestic violence, abuse, poverty and discrimination are more likely to start puberty at a younger age than those who do not. One 2022 longitudinal study[9] found that physical or emotional abuse in early life was linked to earlier menarche in US girls.

Stress doesn’t necessarily explain the population-wide shift in the timing of puberty — there’s no rise in childhood stressors that clearly matches the downward trend in the onset of puberty. But it might interact with excess body weight, says Lauren Houghton, an epidemiologist at Columbia University in New York City. Her unpublished research suggests that girls who experience high levels of stress, have elevated stress hormones and a high BMI start developing breasts, on average, seven months earlier than do girls who experience low levels of stress and have a low BMI.

Stress might also be a reason why more girls entered puberty early during the COVID-19 pandemic than in the years preceding it. Soon after the pandemic began in 2020, paediatric endocrinologists in Italy noticed that the number of referrals for precocious puberty soared. They later reported[10] that 41% of those referred in 2020 met the criteria for the condition, compared with 26% in 2019. Studies from other countries have revealed a similar phenomenon and some suggest that puberty progressed faster too[11]. We saw “truncated and shorter puberty”, says Louise Greenspan, a paediatric endocrinologist at Kaiser Permanente San Francisco in California.

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Nature 649, 816-818 (2026)

doi: https://doi.org/10.1038/d41586-026-00089-8

References

1. Marshall, W. A. & Tanner, J. M. Arch. Dis. Child. 44, 291–303 (1969).

2. Marshall, W. A. & Tanner, J. M. Arch. Dis. Child. 45, 13–23 (1970).

3. Herman-Giddens, M. E. et al. Pediatrics 99, 505–512 (1997).

4. Sun, S. S. et al. Pediatrics 110, 911–919 (2002).

5. Aksglaede, L., Sørensen, K., Petersen, J. H., Skakkebæk, N. E. & Juul, A. Pediatrics 123, e932–e939 (2009).

6. Eckert-Lind, C. et al. JAMA Pediatr. 174, e195881 (2020).

7. Thomsen, C. E. et al. Endocr. Abstr. 110, P867 (2025).

8. Aghaee, S. et al. Am. J. Epidemiol. 191, 2026–2036 (2022).

9. Hamlat, E. J. et al. Psychosom. Med. 84, 297–305 (2022).

10. Chioma, L. et al. Endocr. Connect. 11, e210650 (2022).

11. Stagi, S. et al. Ital. J. Pediatr. 46, 165 (2020).

12. Cheng, T. S., Day, F. R., Lakshman, R. & Ong, K. K. PLoS Med. 17, e1003017 (2020).

13. Day, F. et al. Nature Genet. 49, 834–841 (2017).

14. Curtis, M. G. et al. J. Adolesc. Health 77, 715–723 (2025).

15. Vijayakumar, N. et al. J. Adolesc. Health 74, 674–681 (2024).

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Books & Arts

- Forget formalism: mathematics was built on infighting and emotional turmoil

- ‘Greed is the iron cage of our times’ — why nationalism is here to stay

BOOK REVIEW 19 January 2026

Forget formalism: mathematics was built on infighting and emotional turmoil

A fast-paced book captures how theory and formal proof intertwined with the personal lives of prominent mathematicians.

By

Ananyo Bhattacharya

Philosopher Bertrand Russell developed groundbreaking theories of mathematical logic. Credit: Keystone Features/Hulton Archive/Getty

The Great Math War: How Three Brilliant Minds Fought for the Foundations of Mathematics Jason Socrates Bardi Basic (2025)

In the weeks leading up to September 1891, mathematician Georg Cantor prepared an ambush. For years he had sparred — philosophically, mathematically and emotionally — with his formidable rival Leopold Kronecker, one of Germany’s most influential mathematicians. Kronecker thought that mathematics should deal only with whole numbers and proofs built from them and therefore rejected Cantor’s study of infinity. “God made the integers,” Kronecker once said. “All else is the work of man.”

But Cantor had a proof that he hoped would confound his competitor. Armed with an innovative method, now known as Cantor’s diagonal argument, he could demonstrate that some infinities are larger than others and he planned to confront Kronecker with it in public at the inaugural meeting of the German Mathematical Society in Halle. But the showdown never came. Weeks before the meeting, Kronecker’s wife was fatally injured in a climbing accident, preventing him from going to Halle, and Kronecker himself died that December.

This tragedy — at once poignant, anticlimactic and painfully human — is one of many vivid digressions in journalist Jason Socrates Bardi’s The Great Math War. Bardi provides a lively narrative of the intellectual struggle that transformed the field in the late nineteenth and early twentieth century. He uses drama to demonstrate how the long arc of modern maths — from the invention of rigorous analysis to the birth of set theory, logic and topology — was neither neat nor inevitable. The intellectual revolution reshaped ideas about the scope of maths, what counts as a valid proof and whether mathematical truth is discovered or invented. And it was driven as much by temperaments, loyalties, neuroses and sheer chance as by fresh theories.

The stakes of this ‘maths war’ were immense. Was maths grounded in intuition — the mental constructions that people can build step by step — or in formal symbols and rules that are independent of human insight? Were numbers objective features of reality or useful inventions of the human mind? And could the infinite be incorporated safely into maths, or would it lead to contradiction and collapse? Bardi deftly follows these questions as they ripple across Europe, shaping rival schools, polarizing university departments and fuelling philosophical, professional and, often, personal battles.

Human side of maths history

The book’s central argument — that maths is a human endeavour, shaped by human frailty as much as by formal proof — is made convincingly throughout. Bardi captures the period’s ferment with energy and empathy, paying close attention to the psychological states of its protagonists: Cantor’s oscillations between visionary triumph and crippling despair; L. E. J. Brouwer’s fervent mission to tear down maths and make it all anew; and Bertrand Russell’s tortured attempts to reconcile logic, certainty and his own emotional life, confided in anguished letters to his aristocrat lover, Lady Ottoline Morrell.

Aristocrat Ottoline Morrell corresponded extensively with philosopher Bertrand Russell.Credit: Pictorial Press Ltd/Alamy

The feud between Brouwer and David Hilbert in the 1920s was notorious. Hilbert championed formalism: the idea that maths should be reduced to axioms and rules, with meaning set aside in favour of consistency. Brouwer, by contrast, insisted that only proofs that the mind could follow were legitimate. In 1928, Hilbert, who was a chief editor of the leading journal Mathematische Annalen, tried to remove Brouwer from its editorial board. Brouwer refused to resign and was ousted — through dissolution of the entire board. Hilbert then rebuilt it with appointees of his choosing. Albert Einstein, a fellow Annalen chief editor, dismissed the whole affair as an overblown ‘frog and mouse war’.

For many readers who have encountered maths only as a technical tool, the heightened passions behind such debates might be hard to fathom. Yet, when Cantor was being denounced by his contemporaries, including Kronecker, as a charlatan and accused of corrupting the youth with his controversial ideas about infinity, the future of maths truly seemed at stake.

Bardi is at his best when excavating the overlooked corners of history: the petty jealousies of editorial boards, the philosophical fights waged through open letters, the strain on marriages and the toll on mental health. He writes with breathless enthusiasm, occasionally slipping into staccato bursts — “Bereft. Pent-up” — that evoke his characters’ emotional turmoil. He brings comic energy to his narrative, for instance, by introducing ancient Greek mathematician Euclid as the “reigning heavyweight champion of math authors” until 1900.

Bardi excels in recounting Russell’s life and ideas. An imperialist turned peace activist and advocate of free love, Russell discovered a troubling paradox in maths, similar in its logical form to several others, including the ‘liar paradox’: if the sentence ‘this statement is a lie’, for instance, is true, then it means the statement is a lie — and therefore not true — leading to a logical inconsistency.

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Nature 649, 820-821 (2026)

doi: https://doi.org/10.1038/d41586-026-00187-7

Competing Interests

The author declares no competing interests.

BOOK REVIEW 19 January 2026

‘Greed is the iron cage of our times’ — why nationalism is here to stay

A generation that missed out on economic growth is driving the trends overtaking politics today.

By

Roberto Patricio Korzeniewicz

Incomes have stagnated for many people in the United States and elsewhere. Credit: Cyrus McCrimmon/The Denver Post/Getty

The Great Global Transformation: National Market Liberalism in a Multipolar World Branko Milanovic Allen Lane (2025)

In the past decade or so, the realignment of global markets by politicians has transformed patterns of trade and inequality around the world. Economist Branko Milanovic maps out those changes, and what they might herald, in his latest book.

The Great Global Transformation builds on Milanovic’s previous work to describe how inequality between countries has declined globally over the past 25 years. Collating data from the World Bank and other sources in innovative ways, he argues that globalization in the late twentieth and early twenty-first century was accompanied by then-unprecedented growth of income in both previously poor populations (notably in China) and people at the top of the world’s income distribution (especially those in the West). By contrast, relative shares of world income stagnated or were thought to have declined for wealthy nations’ middle and working classes, including in the United States.

This trend captures effectively what has become part of public discourse in the West: globalization has left behind people in the middle and working classes in rich nations, demonstrating that it is “an illusion to believe that what is good for the world must be good for everybody”.

Milanovic’s graph revealing this pattern has become highly influential. Plotted in terms of the change in people’s real earnings as a function of where those sit in the global income distribution, it is called an elephant curve because of its shape (a broad hill forms the elephant’s back, and a sharp fall and subsequent increase form the trunk). But what the graph actually shows is hotly debated. Several critics argue that the curve suggests but does not prove a causal connection between globalization and the decline in people’s wealth in some classes in wealthy nations, and that other elements might be driving the patterns.

Geopolitical shifts

The book goes on to note that globalization, in the form of increased engagement with markets and trade, did not lead to geopolitical convergence — contrary to expectations in the 1980s and 1990s. Observers at the time hoped that market expansion would lead authoritarian regimes towards democracy and the global governance mechanisms supported by the West.

But in key countries such as China and Russia, authoritarian regimes have proved resilient and have adapted to globalization. Today, the world remains multipolar, with no single superpower dominating globally. As Milanovic notes: “it is not mutual trade interdependence and its stimulation of sweet virtues of accommodation of other people’s wishes that makes the world peaceful but the harsh reality of military balance of power”.

At the same time, opportunities for upward social mobility in wealthy nations have been transformed by an increasing overlap of people who are rich in terms of receiving income both from capital (through investments and assets) and from labour (salaries). In the United States and China, “the top class is now more elitist and its position more impregnable because it has … ‘diversified’”. This constitutes “a new moment in the evolution of capitalism”.

The rise of these new elites, according to Milanovic, ultimately explains the direction of the political shifts seen over the past few years in much of the world. In the United States, this shift has taken the form of the populist movement, led by President Donald Trump, that has identified free trade, China and migration as threats to the country. (Milanovic suggests an alternative path, which the United States hasn’t followed: increased taxation to redistribute the gains of world trade from elites to the middle class.)

As wealth grows in China, so, too, do sports associated with money.Credit: Kevin Frayer/Getty

Meanwhile, in China, Milanovic describes President Xi Jinping’s policies as a “purposeful change” from those of his predecessors, in “an attempt to reassert the power of the state over the capitalist sector and the rich”. And, in Russia, too, President Vladimir Putin has reasserted control of the state and security apparatus.

Economic backlash

Hence, the emergence of what Milanovic calls national market liberalism. In short, all three regimes “should be seen as part of the same ‘counter-revolution’ against global neoliberalism and the excesses of elite enrichment during the 1980–2006 era”, he concludes.

Although there are national differences, these countries have in common several of the elements that were previously encapsulated by neoliberalism and globalization. They are also committed to disciplining wealthy people and regulating free trade through various degrees of state control.

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Nature 649, 822-823 (2026)

doi: https://doi.org/10.1038/d41586-026-00186-8

Competing Interests

The author declares no competing interests.

Opinion

- Trump one year on: How six US researchers plan to protect science amid chaos and cuts

COMMENT 20 January 2026

Trump one year on: How six US researchers plan to protect science amid chaos and cuts

From education to pandemic preparedness and public health, the past year has seen huge stress put on US science.

By

Amander Clark,

Hank Greely,

Eric Topol,

Salim S. Abdool Karim,

Quarraisha Abdool Karim &

…

Ramanan Laxminarayan

Policies related to immigration and student visas sparked protests at the University of Washington in Seattle, and across the United States. Credit: James Anderson/Alamy

When Donald Trump won the US presidential election in November 2024, Nature asked six leading biomedical researchers to outline their priorities for the incoming administration (A. Clark et al. Nature 635, 812–814; 2024). Here, those same researchers reflect on the first year of Trump’s presidency and chart ways to protect crucial areas of science and health in the future.

AMANDER CLARK: Reimagine higher education

For those of us who teach, work and learn on a university campus, fatigue and burnout are taking their toll. The past year has seen an onslaught of executive orders aimed at universities: changes to policies on immigration, student visas, transgender rights, student-loan forgiveness, admissions practices, free speech and academic freedom. Every aspect of my professional life — from research and teaching to mentoring and outreach — has been affected. In each of these areas, I have found myself censoring topics that touch on reproduction and reproductive technologies that could one day be of benefit to people from sexual and gender minorities (the LGBTQ+ community).

For people like me — the first in a family to attend university — higher education provides the ladder to a well-paying job, to which our parents had no access. I had hoped that the incoming administration would help low-income students to find debt-free paths through university. But the executive orders focused on higher education, along with policy changes in the One Big Beautiful Bill Act that lower federal aid for students and staff lay-offs at the US Department of Education, will instead widen inequities by making it even harder for low-income students to afford a university education.

One light at the end of the tunnel is the possibility that — after researchers build back from the current crisis — higher education might be reimagined to serve the public good in a better way, with research findings made more accessible to people outside the academic elite. Those of us working in the sector should reflect on the purpose, mission and vision of universities. It’s crucial that educators and researchers listen to the public on whether university visions align with public needs, values and the jobs that will be needed in the future workforce. And scientists must be provided with federal research funding to mentor and inspire the next generation of scientists and critical thinkers.

In the meantime, university faculty members can use the US justice system to protect the first-amendment right to freedom of expression and to restore suspended research funding. In November 2025, a coalition of plaintiffs led by university professors and faculty associations filed a lawsuit in Northern California’s District Court to block the Trump administration from taking unconstitutional actions against the University of California, where I am a professor. I am one of 74 people who made a declaration of harm in support of the case, discussing my right to talk about the science of reproduction using inclusive language. The plaintiffs were granted a preliminary injunction in November and our freedom to operate has been restored — for now.

HANK GREELY: Tackle health-care problems to stop the chaos

At the end of 2024, I worried that the incoming administration might decide to protect company profits over the interests of people receiving medical care. Sure enough, the protections for patients provided by the US Food and Drug Administration (FDA) and the US Centers for Disease Control and Prevention (CDC) have been weakened in 2025 — not so much by commercial interests as by ideology and chaos. At one point, the FDA lost almost 20% of its staff members, and the CDC about one-third. And much of the data needed for public-health research have been removed from websites. As a law professor, it’s hard to know what I will be able to teach in my FDA law class this year.

The questioning of vaccine safety by Robert F. Kennedy Jr, who leads the US Department of Health and Human Services, and the weakening of recommendations for childhood vaccination schedules, are just two examples of how ideology can harm human health. More people will get ill and die. The rise in measles cases exemplifies the dangers. In 2025, there were more than 2,000 reported measles cases in the United States — more than in any year since 1992, and ten times the annual averages since then. At least three people have died. This epidemic started before Inauguration Day in January 2025, resulting at least in part, from increased vaccine hesitancy after the COVID-19 pandemic.

Going forwards, much depends on those who control the FDA and the CDC. FDA drug approvals, for example, are out of the hands of researchers, as are decisions on what data to collect and disseminate on what medical conditions. Political will is needed to rebuild strong agencies. If faced with public discontent, mid-term election results that go against the administration might push it to rethink its approach. Pressure from the biopharmaceutical and health-care industries — which need strong federal regulatory agencies to create confidence in their products — could also encourage a return to science-backed policies.

But state governments can help, as can professional societies and health-care organizations. These bodies can make recommendations, and in some cases decisions, on which vaccines and treatments should be used on the basis of sound science. And they can provide accurate information, for instance about the safety of Tylenol (also called paracetamol and acetaminophen) in pregnancy and about the use of aluminium in vaccines.

Limiting the damage will not be easy, but all those involved in medicine and bioscience need to speak out, with friends and in public, against federal government misinformation and harmful actions. That will be scary, with no guarantee of success. But it is already clear that ignoring the problems just lets the chaos grow.

ERIC TOPOL: Focus health systems on preventing disease

Just over a year ago, I wrote about how medical artificial intelligence, if supported by the government, could help to improve diagnosis rates and even prevent diseases. Today, there is little evidence of the US government grasping this opportunity. Instead — despite big talk about making America healthy again — our health system remains focused on treatment and not prevention.

What researchers can predict, we can work to prevent. And multimodal AI that analyses the ‘full stack’ of biomedical data — electronic medical records, genomes, biomarkers and more — is now enabling us to predict important age-related conditions, such as heart disease and cancer. In September 2025, one such model showed the ability to predict any of more than 1,200 diseases and health events, from heart attack to pancreatic cancer, up to 20 years in advance (A. Shmatko et al. Nature 647, 248–256; 2025). There have also been breakthroughs in the use of blood biomarkers to help predict a person’s risk of developing Alzheimer’s disease.

Instead of the US government supporting this work, funding for the large data sets that multimodal AI relies on has been gutted — including the All of Us programme, which collects genomic and health data for diverse people. The administration’s AI ‘genesis mission’ which aims to harness big data to drive science, makes no mention of disease prevention.

Researchers must instead look to philanthropic support and more efficient ways to collect full-stack data. Philanthropic funding is enabling my team at Scripps Research in La Jolla, California, for instance, to initiate a randomized trial to prevent Alzheimer’s disease. We’ll use multimodal AI to find those who are most at risk of developing the condition, and target them to be advised on lifestyle changes known to reduce that risk. Prevention trials such as this cost a fraction of what a clinical trial for a treatment would. There is no need for clinical sites and participants can be enrolled rapidly through outreach campaigns.

The US administration should take heed, given the country’s ageing population. Large-scale data resources and low-cost trials — which potentially come with the high rewards of reducing disease and decreasing the financial costs of treatments — are a no-brainer. Similar initiatives will no doubt move forwards in other countries where disease prevention has been deemed a high priority, such as in the United Kingdom. If the United States doesn’t get on it, our country will lag behind others that will soon see the economic fruits of large-scale disease prevention.

Programmes to treat people living with HIV were cut short when USAID funding was slashed.Credit: Arlette Bashizi/Washington Post via Getty

SALIM S. ABDOOL KARIM & QUARRAISHA ABDOOL KARIM: Rebuild efforts to prepare for pandemics

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Nature 649, 824-826 (2026)

doi: https://doi.org/10.1038/d41586-026-00090-1

Reprints and permissions

Competing Interests

A.C. is a member of the ISSCR Board of Directors. S.S.A.K. is special adviser to the WHO director-general and chair of the Africa CDC committee that provides scientific advice on declarations of continental epidemics. Q.A.K. is chair of the PEPFAR Scientific Advisory Board and president of The World Academy of Science (TWAS).

Work

- ‘Shattered’: US scientists speak out about how Trump policies disrupted their careers

- Seven technologies to watch in 2026

- Floating science stations: my month on a research vessel looking after buoys

CAREER FEATURE 20 January 2026

‘Shattered’: US scientists speak out about how Trump policies disrupted their careers

Researchers lay bare the human toll of lay-offs, funding cuts and attacks on science one year after the president’s return to the White House.

By

Virginia Gewin

Find a new job

Demonstrators protest the Trump administration’s proposal to eliminate the National Center for Atmospheric Research in December 2025. Credit: Chris Goodwin/desrowVISUALS/Shutterstock

The first year of US President Donald Trump’s second administration delivered a steady pulse of federal-agency lay-offs, grant terminations and funding cuts to universities. “The speed, the scope and the severity of the attacks on science are beyond anything we’ve ever seen,” says Gretchen Goldman, president and chief executive of the Union of Concerned Scientists (UCS), a non-profit advocacy organization in Washington DC.

The UCS began tracking what it views as attacks on science and scientific integrity by the US federal government during the George W. Bush administration, beginning in 2001, says Goldman. In 2025, it documented 536 actions or decisions, such as altering science-informed guidance on the US Centers for Disease Control and Prevention’s (CDC) ‘Autism and Vaccines’ web page. That’s more than double the 207 incidents it recorded throughout Trump’s entire first term covering 2017–21. Furthermore, scientists — especially those studying climate change, vaccines, issues faced by people from sexual and gender minorities (LGBTQ+) and health disparities — report increased online harassment and intimidation.

Science-reform advocates are trying to find positives. “Wherever there is disruption, there is opportunity,” says Brian Nosek, executive director of the Center for Open Science in Charlottesville, Virginia, a non-profit organization in Washington DC that promotes transparency and reproducibility in science. “The challenge is that there is so much distress now and there are actors that seem very clear in their behaviour to not actually have good intentions for improving science,” he says.

Goldman, in contrast, uses the word “shattered” to describe both the scientific career pipeline as well as federal job stability. Some researchers are leaving the country to continue their research (see ‘Upended careers’), a trend highlighted by an analysis of the Nature Careers jobs board that seemed to signal the start of a brain drain.

At federal agencies such as the US Geological Survey (USGS) and the US Environmental Protection Agency (EPA), new scientific output has declined sharply and its use in policymaking has sometimes been called into question, according to some agency researchers — who, like several interviewed by Nature, wish to remain anonymous for fear of career reprisals. They say that the loss of expertise is disrupting areas such as environmental protection, public health and safety. Examples cited include the narrowing of long-standing hepatitis B and other childhood-vaccine recommendations by the CDC to the EPA requesting to revert to less stringent 2020 levels of soot pollution.

Critics see these disruptions as exacerbated by the deep cuts to federal funding, which total tens of billions of dollars, that underwrites academic research and researcher training. “The beauty of academic research is that people could stick with hard problems for a long time to make progress,” says Goldman. “We can’t create experts overnight.” At the same time, many foreign scientists face an uphill battle in securing visas to study or work in the United States.

Some scientists have spoken out, filed lawsuits and pushed back against what many say is a dismantling of what has long been the world’s leading science power. In May 2025, the Center for Open Science criticized the administration’s Restoring Gold Standard Science executive order for “positioning policymaking to ignore scientific evidence by holding it to unachievable standards”. Nosek says his organization has a duty to push back against any administration attempts to “sow distrust and undermine the whole process” of science-research reform.

The US Congress has the power to determine which agencies are funded to what extent, and earlier this month, both houses of Congress voted to mostly reject the Trump administration’s proposed deep budget cuts for 2026 for a subset of science agencies including NASA, USGS, EPA and the National Oceanic and Atmospheric Administration (NOAA). The future remains far from certain — especially as China makes steady gains in global scientific leadership.

Nature’s careers team interviewed researchers to find out what a year of Trump administration policies has meant for their research and careers, and looked back at how Trump’s first year was covered by Nature.

Unprecedented federal-agency cuts

Since last February, thousands of federal scientist positions have been eliminated because of lay-offs, budget cuts and workforce restructuring. Jobs were cut at the National Institutes of Health (NIH), EPA, NOAA, CDC, Food and Drug Administration (FDA) and across national laboratories — losses compounded by the longest federal government shutdown on record, which lasted from 1 October to 12 November 2025. Government agencies continue to operate under temporary funding until all bills to fund government operations for the 2026 fiscal year are signed into law by the president.

The USGS, which oversees research and monitoring of natural resources and natural hazards, is one flashpoint. The administration’s 2026 budget proposed to cut roughly US$300 million supporting around 1,000 positions in the agency’s Ecosystem Mission Area (EMA), considered the agency’s biological research arm. Congress has voted to rescue $294 million of EMA’s budget. However, it’s unclear whether researchers there, who say they have not been permitted to conduct long-term environmental monitoring if it incurred travel expenses, will be allowed to return to their jobs as usual.

A USAID worker leaves with their belongings as the agency was closed in February 2025.Credit: Aashish Kiphayet/NurPhoto/Shutterstock

“We can’t pay conference or publication fees”, which limits how USGS researchers share their analyses with colleagues, says one employee. Another describes how they have been in “triage mode”, because their 2025 funding was frozen and they lost 25% of their staff. “Funded projects are being cut short because we can’t fill positions, projects have been delayed by at least a year, and we’re wasting time and taxpayer money,” they say. “We’re not allowed to talk about the future with leaders from tribes or other agencies. It’s been super frustrating.” Requests for a response from USGS were not returned.

Some scientists are voting with their feet. One researcher in a biodiversity leadership position at NOAA is desperate to find a job outside the country. “You have to deal with the constant rhetoric that you are worthless, unnecessary. It’s so abusive,” they say. “We’re still staggering along, but I don’t know for how much longer. I don’t have the energy any more.” As their number of field instruments was cut in half and projects were scaled down, “it’s been a giant waste of time and money and resources.”

Halted research and shrunken labs

More than 7,800 grants from the NIH and US National Science Foundation (NSF) were disrupted in 2025 — and 2,600 of those that were not reinstated total a loss of almost $1.4 billion. Furthermore, 607 terminated grants from EPA totalled more than $28 billion, according to Grant Witness, a crowdsourced database tracking terminated federal research grants.

At the NIH, grant-termination decisions lacked transparency, says Jenna Norton, a programme director at the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) in Bethesda, Maryland, who is speaking in a personal capacity. “People called them drone strikes. You didn’t know which grants were going to get terminated,” she says. Some that she thought would be on the list weren’t, and others, such as one on transgenic mice, were. (Norton, a signatory of the June 2025 Bethesda Declaration, an open letter signed by 484 NIH employees protesting about the perceived politicization of scientific research, was placed on non-disciplinary administrative leave in November without explanation.)

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Nature 649, 1061-1064 (2026)

doi: https://doi.org/10.1038/d41586-026-00091-0

TECHNOLOGY FEATURE 21 January 2026

Seven technologies to watch in 2026

Nature’s round-up of innovations that are poised to make a splash in the year ahead.

By

Michael Eisenstein

The Vera C. Rubin Observatory in Chile will image the entire southern sky over ten years. Credit: RubinObs/NSF/DOE/NOIRLab/SLAC/AURA/Hernan Stockebrand

From quantum computing and mRNA therapeutics to artificial-intelligence-powered climate modelling, here are seven technologies that Nature will be keeping its eye on.

Xenotransplantation

Every day, around two dozen people die awaiting an organ transplant in the 46 countries of the Council of Europe, together with 13 in the United States. The real toll might be higher still: according to Alexandre Loupy, a nephrologist at Necker Hospital in Paris, “many patients with terminal organ failure are not even wait-listed”.

Xenotransplantation — replacing damaged human tissues with counterparts from closely related animal species — offers a tantalizing alternative to precious human organs. But such transplants tend to fail quickly, with the remarkable exception of one woman who survived nine months after receiving a chimpanzee kidney in 1964.

The problem is immune rejection. Pig cells, for instance, are coated with a carbohydrate, called alpha-gal, that triggers a strong immune reaction in humans, who lack this molecule. Precision genome editing with CRISPR–Cas9 has given scientists an effective tool for eliminating this and other sources of rejection and, in combination with next-generation immunosuppressants, this tool is hugely improving patient outcomes.

In 2024, clinicians at Massachusetts General Hospital in Boston teamed up with xenotransplantation company eGenesis in Cambridge, Massachusetts, to perform the first transplant of a pig kidney into a living person[1]. The kidney came from an animal with 69 genomic modifications to knock out immunity-triggering antigens and dormant viral sequences while also inserting human genes that reduce inflammation and prevent abnormal blood clotting. The patient survived for 52 days before dying from unrelated cardiac issues. Subsequent pig-kidney recipients in the United States and China remained stable for more than eight months before returning to dialysis — nearly matching 1964’s durability record.

The first transplant of an engineered pig heart into a person, in 2022.Credit: University Of Maryland School Of Medicine/ZUMA/Alamy

And it’s not just kidneys. In 2022, Muhammad Mohiuddin, a surgeon at the University of Maryland School of Medicine in Baltimore, and his colleagues described the first transplant of an engineered pig heart into a person, who survived for 60 days after surgery[2]. And in 2025, teams in China reported xenotransplantation of pig liver[3] and even lung[4] into people who had been declared brain dead — a key step towards working with recipients capable of recovery.

Even a transient xenograft could buy precious time for patients awaiting a human donor, but with a deeper understanding of individual determinants of transplant rejection, these substitutes could become a long-term solution, says Leonardo Riella, chair of transplantation at Massachusetts General Hospital and one of the leads on the 2024 kidney transplant. “The xenotransplant really permits us to think outside the box and personalize that kidney and make it invisible to your immune system,” he says.

AI-powered meteorology

In October 2025, an AI model from Google DeepMind in London gave the US National Hurricane Center an early warning about the serious threat posed by Hurricane Melissa. The model anticipated the storm’s evolution to category-5 intensity days in advance and accurately predicted its trajectory across the Caribbean, whereas older models fell short.

That success is just one example of how AI methods are accelerating and improving local weather forecasting, storm tracking and even global climate modelling, with ever-more-sophisticated models rapidly emerging.

In some ways, this is an ideal arena for AI. Earth and atmospheric researchers have tons of data at their fingertips. But wrangling those data into a forecast has historically relied on using sophisticated and computationally intensive numerical weather-prediction models to crunch through complex differential equations. “They involve literally millions of lines of code and a large team to run them,” says Richard Turner, a machine-learning researcher at the University of Cambridge, UK. But over the past three years, promising AI models have begun to emerge, including Pangu-Weather, from Huawei Cloud in Shenzhen, China, which used deep learning to accelerate forecasting up to 10,000-fold relative to existing methods[5].

AI models are advancing weather forecasting.Credit: CSU/CIRA & NOAA

Most models tackle only part of the forecasting workflow. But in 2025, Turner and his colleagues published Aardvark, an ‘end-to-end’ model[6] that was trained to ingest raw data from sources including weather stations and satellites, and deliver localized forecasts up to ten days ahead. “We could literally run it off a desktop machine in an office,” says Turner, who notes that Aardvark’s accuracy was competitive with existing systems, and occasionally outperformed them.

Turner has also collaborated with Microsoft Research in Amsterdam to develop an AI ‘foundation model’ called Aurora, which could accurately predict meteorological events that fall beyond standard weather forecasts, such as cyclone trajectories and air-quality trends[7].

Other AI models are even more ambitious, incorporating global insights from Earth features such as the atmosphere, sea and polar ice to analyse the current climate and predict future changes. For example, in September, engineer James Duncan at the Allen Institute for Artificial Intelligence in Seattle, Washington, and his colleagues described SamudrACE, which integrates AI models of the atmosphere and ocean and can simulate the behaviour of those systems over more than a millennium[8].

AI models put simulation projects that were previously limited to supercomputing facilities into the hands of everyday researchers, says Elizabeth Barnes, an environmental data scientist at Boston University, Massachusetts. “All these science questions I used to have to pass off to other groups, I can do myself now,” she says.

Next-generation nuclear power

Surging investment in AI is creating a commensurate spike in demand for electrical power. The International Energy Agency in Paris predicts that global energy demand from data centres could increase by 15% annually between now and 2030.

Even if the AI boom goes bust, there remains an urgent need to bolster energy grids with climate-friendly power sources, says Jonas Kristiansen Nøland, an energy-systems researcher at the Norwegian University of Science and Technology in Trondheim.

These conditions present a ripe opportunity for the resurgence of nuclear energy, and Nøland is particularly optimistic about small modular reactors (SMRs) — nuclear facilities that produce up to 500 megawatts of power. That’s less than half the output of a standard fission reactor, but sufficient to power hundreds of thousands of homes.

Small Modular Reactors, such as the Linglong One in China, offer a low-cost, fast-to-build option for nuclear energy generation.Credit: Luo Yunfei/China News Service/VCG/Getty

Russia and China already have active SMRs, and at least 100 projects are now under consideration or development worldwide. The most advanced — including one at Canada’s Darlington nuclear facility in Ontario, scheduled to come online in 2029 — are based on similar designs as full-scale fission reactors. But next-generation systems are also under development. Nuclear-power company TerraPower in Bellevue, Washington, for example, is pursuing molten-salt reactors, a fuel-efficient design that could greatly reduce nuclear waste and store heat produced during reactor operation for later use as thermal power.

Meanwhile, after decades of hype as the ‘technology of the future’, fusion power is nearing reality. In 2022, the Lawrence Livermore National Laboratory achieved the first demonstration of net energy production from fusion at its National Ignition Facility in Livermore, California. And in 2023, the Joint European Torus near Oxford, UK, set a world record for power production, generating enough energy in five seconds to power 12,000 homes. Meanwhile, Germany’s Wendelstein 7-X facility in Greifswald achieved an endurance record of 43 seconds of sustained operation, showcasing an alternative reactor design that could enable more stable operation than first-generation ‘tokamak’ designs.

Sibylle Günter, a physicist at the Max Planck Institute for Plasma Physics in Garching, Germany, says that these developments are particularly exciting for countries that crave clean energy but are reluctant to engage with nuclear fission — including Germany, which plans to invest €2 billion (US$2.3 billion) in fusion by 2029. She notes that more than 50 fusion-oriented start-up companies are active worldwide, including Commonwealth Fusion Systems in Devens, Massachusetts, which aims to complete construction of a demonstration reactor this year.

Still, the world won’t be running on fusion any time soon. Between fuel production and regulatory and engineering challenges, it could be 20 years before the first commercial reactors come online, Günter notes. But balanced against cheap, safe and abundant power, she says, “20 years is not long”.

Light-microscopy brain mapping

With its capacity to image molecular-scale details precisely, electron microscopy has been the tool of choice for mapping the labyrinthine circuitry of the mammalian brain. Reconstructions of cubic-millimetre-scale volumes of mouse and human brains published in 2024 by the Machine Intelligence from Cortical Networks (MICrONS) consortium and a collaborative effort between Harvard University in Cambridge, Massachusetts, and Google Research in Mountain View, California, respectively (see go.nature.com/4pI6dpe), offer clear testament to electron microscopy’s utility as a powerful tool for connectomics.

Neural connections can be imaged precisely.Credit: Allen Institute

But it’s one thing to map connectivity, and another to interpret it. “You need to differentiate which cells and synapses are there,” says Johann Danzl, an imaging specialist at the Institute of Science and Technology Austria in Klosterneuburg. “Are they excitatory? Are they inhibitory? And then — going more into depth — which types of neurotransmitters are there?”

In May 2025, Danzl’s team described a method for extracting such information. In light-microscopy-based connectomics (LICONN)[9], brain samples are subjected to multiple rounds of expansion microscopy — a process in which tissue is chemically trapped in a hydrogel that expands evenly in all directions, separating the sample’s constituent biomolecules and rendering it transparent. Using protein-specific labels, researchers can visualize nanoscale details of cellular structure and organization with a standard confocal microscope while preserving the tissue’s underlying organization. In this way, Danzl’s team could map the twisty trails of axons and dendrites, as well as categorizing the synapses that they form and classifying the cells involved.

E11 Bio, a non-profit company in Alameda, California, focused on optical connectomics, has addressed another pain point for electron-microscopy-based mapping: proofreading. “If you look at that MICrONS volume, it’s beautiful,” says Andrew Payne, the company’s co-founder and chief executive. But only about “1% of the cells were ultimately reconstructed; the other 99% are not reconstructed to this day”.

In a preprint from September 2025, Payne and his colleagues describe an approach in which they genetically modified mouse neurons to express various combinations of short protein epitopes, such that each cell displays a distinct barcode[10]. These barcodes can then be decoded by sequential staining with fluorescently labelled antibodies, enabling essentially error-free computational mapping and tracing of each neuron in the sample. Using 18 epitopes, the company could resolve some 262,000 barcodes, and Payne says that the technology should be sufficiently scalable to map connectivity across the entire mouse brain.

It’ll take other advances to make that goal practical, Danzl notes, including more-efficient sample handling and faster imaging. But by slashing proofreading expenses and replacing pricey electron microscopes with readily available confocals, these methods could put mammalian connectomes in closer reach.

Exploring the extremes

Scientists excel at discovering limits — and then pushing beyond them.

Last June, the US National Science Foundation and Department of Energy released early images from the Vera C. Rubin Observatory. Named after the US astronomer who first provided proof of the existence of dark matter, this groundbreaking facility in the Chilean Andes will, over a ten-year span, collect measurements from each point in the southern sky roughly 800 times. Making use of an innovative multi-mirror design and a massive 3.2 gigapixel digital camera, that scan will yield an authoritative catalogue of celestial objects and how they change over time. “We estimate we’ll have about 20 billion galaxies and close to that number of stars,” says Željko Ivezić, an astrophysicist at the University of Washington in Seattle. “We’ll have more celestial objects catalogued than living people on Earth, and for each of them, we will measure many parameters.”

Thousands of scientists from more than 30 countries have already queued up to use the observatory’s data, which should start rolling out in early 2028. Ivezić anticipates that the facility will tackle questions ranging from surveying asteroids that pose a potential threat to Earth, to proving (or rejecting) the existence of the enigmatic ‘dark energy’ that astronomers have implicated in the accelerating expansion of the Universe.

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Nature 649, 1065-1069 (2026)

doi: https://doi.org/10.1038/d41586-026-00188-6

References

1. Kawai, T. et al. N. Engl. J. Med. 392, 1933–1940 (2025).

2. Griffith, B. P. et al. N. Engl. J. Med. 387, 35–44 (2022).

3. Tao, K. S. et al. Nature 641, 1029–1036 (2025).

4. He, J. et al. Nature Med. 31, 3388–3393 (2025).

5. Bi, K. et al. Nature 619, 533–538 (2023).

6. Allen, A. et al. Nature 641, 1172–1179 (2025).

7. Bodnar, C. et al. Nature 641, 1180–1187 (2025).

8. Duncan, J. P. C. et al. Preprint at arXiv https://doi.org/10.48550/arXiv.2509.12490 (2025).

9. Tavakoli, M. R. et al. Nature 642, 398–410 (2025).

10. Park, S. Y. et al. Preprint at bioRxiv https://doi.org/10.1101/2025.09.26.678648 (2025).

11. Zhang, H. et al. Cell 188, 1378–1392 (2025).

12. Ioannidis, J. P. A., Pezzullo, A. M., Cristiano, A. & Boccia, S. JAMA Health Forum 6, e252223 (2025).

13. Sethna, Z. et al. Nature 639, 1042–1051 (2025).

14. Grippin, A. J. et al. Nature 647, 488–497 (2025).

15. Rurik, J. G. et al. Science 375, 91–96 (2022).

16. Google Quantum AI. Nature 614, 676–681 (2023).

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20. Chiu, N.-C. et al. Nature 646, 1075–1080 (2025).

WHERE I WORK 19 January 2026

Floating science stations: my month on a research vessel looking after buoys

Physical oceanographer Melina M. Martinez finds ways to gather ocean data off the coast of Argentina.

By

Christine Ro

Melina M. Martinez is a PhD student at the Sea and Atmosphere Research Center (CIMA) in Buenos Aires, Argentina. Credit: Bernarda Cornejo Pinto/Schmidt Ocean Institute

“In this photo, I’m preparing drifting buoys for deployment. This was my main responsibility aboard the RV Falkor (too), during a 27-day research expedition in October 2025 exploring the Malvinas Current, an ocean current that runs alongside Argentina. The expedition included biologists, geologists and physical oceanographers such as myself; I’m a PhD candidate at the Sea and Atmosphere Research Center (CIMA) in Buenos Aires, Argentina.

The buoys allow us to see what is really happening in the ocean. Each one contains a battery pack and GPS unit housed inside a tube, which is tightly secured using polystyrene foam. Attached to the bottom is a ‘drogue’, an object that provides resistance in the water and ensures that the buoy drifts with the general oceanic current. Without a drogue, measurements would be chaotic.

We assembled the buoys on board because the components are fragile. I painted them yellow, for visibility, checked parts, including the GPS connections and batteries, and made sure that the screw caps were perfectly closed.

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Nature 649, 1072 (2026)

doi: https://doi.org/10.1038/d41586-026-00189-5

This interview has been edited for length and clarity.

Research

- Probing quantum mechanics with nanoparticle matter-wave interferometry

- 3D-printed low-voltage-driven ciliary hydrogel microactuators

- Global subsidence of river deltas

- Early hominins from Morocco basal to the Homo sapiens lineage

- Rare genetic variants confer a high risk of ADHD and implicate neuronal biology

- Specificity, length and luck drive gene rankings in association studies

- Mimicking opioid analgesia in cortical pain circuits

- Astrocyte CCN1 stabilizes neural circuits in the adult brain

- Lesion-remote astrocytes govern microglia-mediated white matter repair

- The Microflora Danica atlas of Danish environmental microbiomes

- Long-read metagenomics reveals phage dynamics in the human gut microbiome

- iHALT unlocks liver functionality as a surrogate secondary lymphoid organ

- Architecture of the neutrophil compartment

- Fasting boosts breast cancer therapy efficacy via glucocorticoid activation

- Cytosolic acetyl-coenzyme A is a signalling metabolite to control mitophagy

- Inhibitors supercharge kinase turnover through native proteolytic circuits

- An ATP-gated molecular switch orchestrates human mRNA export

- ZAK activation at the collided ribosome

Article Open access Published: 21 January 2026

Probing quantum mechanics with nanoparticle matter-wave interferometry

Sebastian Pedalino,

Bruno E. Ramírez-Galindo,

Richard Ferstl,

Klaus Hornberger,

Markus Arndt &

…

Stefan Gerlich

Nature volume 649, pages 866–870 (2026)

Abstract

The quantum superposition principle is a fundamental concept of physics[1] and the basis of numerous quantum technologies2,3. Yet, it is still often regarded counterintuitive because we do not observe its key features on the macroscopic scales of our daily lives. It is, therefore, interesting to ask how quantum properties persist or change as we increase the size and complexity of objects[4]. A model test for this question can be realized by matter-wave interferometry, in which the motion of individual massive particles becomes delocalized and needs to be described by a wave function that spans regions far larger than the particle itself[5]. Over the years, this has been explored with a series of objects of increasing mass and complexity6,7,8,9 and a growing community aims at pushing this to ever larger limits. Here we present an experimental platform that extends matter-wave interference to large metal clusters, a qualitatively new material class for quantum experiments. We specifically demonstrate quantum interference of sodium nanoparticles, which can each contain more than 7,000 atoms at masses greater than 170,000 Da. They propagate in a Schrödinger cat state with a macroscopicity[10] of μ = 15.5, surpassing previous experiments5,9,11 by an order of magnitude.

Main

When Louis de Broglie postulated that we need to ‘associate a periodic phenomenon with any isolated portion of matter or energy’, he predicted that these new ideas would ‘solve almost all the problems brought up by quanta’[12]. The quantum wave function has become a core concept in modern physics[13] and has withstood all tests to date. However, it is still a matter of debate whether quantum physics is already the ultimate theory or if it needs to be extended to explain its transition into classical phenomena. This debate has sparked general interest in the scientific community, shown by a series of recent experiments that have pushed the limits of quantum mechanics. Single atoms were delocalized on the half-metre scale[5] or for times longer than a minute[14]. Matter-wave interference was seen in complex molecules, from fullerene diffraction[6], to interference with biomolecules[15], van der Waals clusters of organic molecules[8] and families of fluorinated oligoporphyrins[9]. Mechanical cantilevers were cooled to their quantum ground state, both cryogenically[16] and optically[17]. Crystal oscillators[18] and levitated nanoparticles were cooled to the lowest level of their harmonic motion in one or two degrees of freedom19,20,21,22,23. Recently, the vibration mode of a bulk acoustic resonator was prepared in a quantum superposition state, with an effective mass of 16 μg (10^19 Da) (ref. [24]) delocalized over 10−18 m.

Here, we present our work on nanoparticle interferometry in a complementary regime. In our case, the centre-of-mass position of clusters containing more than 7,000 atoms becomes delocalized over a distance exceeding the diameter of the particle by more than an order of magnitude. This quantum state is analogous to Schrödinger’s cat: here, a macroscopic object that defies intuition because it involves a superposition of classically distinct trajectories[25].

The unique combination of mass and delocalization is particularly well suited for probing theories that modify the Schrödinger equation through nonlinear and stochastic terms to suppress macroscopic superpositions[4]. These macrorealistic models have been proposed as a solution to the quantum measurement problem[26] as they would explain why very massive objects are always found in a well-defined position[27]. They assume that the wave function collapses to a localized state, spontaneously[28] or induced by gravity29,30, such that a nanoparticle in an interferometer would lose its quantum coherence and the interference fringes would fade. The larger the mass or the longer the propagation time, the stronger the predicted loss of visibility. In our present experiment, we observe interference of widely delocalized massive particles, demonstrating that standard quantum mechanics holds at this scale with no need to modify the Schrödinger equation. We quantify the size of our superposition in terms of quantum macroscopicity, a measure that provides a unified framework for constraining a wide range of macrorealist modifications. The observed macroscopicity exceeds that of all previous quantum experiments by an order of magnitude[31].

Experiment

The de Broglie wavelength λdB = h/mv of a matter-wave beam is determined by Planck’s constant h, the particle mass m and its velocity v. Matter-wave interference at high masses requires both the preparation of low particle velocities and the ability to handle short de Broglie wavelengths. In our multiscale cluster interference experiment (MUSCLE), we achieve this by combining a cryogenic metal cluster source with three ultraviolet (UV) diffraction gratings in a Talbot–Lau configuration, shown in Fig. 1.

Fig. 1: Experimental overview.

a, Photo-ionizing gratings as beam splitters. Clusters passing through the antinodes of the optical grating are ionized and removed, whereas those passing through the nodes remain neutral. This confines particles to a spatial region within the grating nodes, resulting in a momentum uncertainty. The light field also induces a dipole moment, imprinting a position-dependent phase onto the clusters. b, Schematic of an optical Talbot–Lau interferometer. Starting with incoherent matter waves, the first grating (G_1) prepares coherence by spatially confining the particles, as described in a. Transverse coherence grows towards G_2, behind which a Talbot–Lau carpet emerges in the near field. Finally, a third grating acts as a position-resolving detection mask scanned across the interference pattern. c, Schematic of the multiscale cluster interference setup. An effusive sodium source in an aggregation chamber generates the cluster beam. The beam is transmitted through several differential pumping stages into the interferometer chamber kept at ultrahigh vacuum conditions (about 9 × 10−9 mbar). The cluster beam overlaps with three perpendicular standing light waves equally spaced at a distance of L = 0.983 m, forming optical gratings with a period of d = 133 nm. The intensities of the first and third gratings are chosen such that they act as absorptive gratings, whereas the second grating is operated at lower laser intensity, realizing an optical phase grating. After passing through the interferometer, the remaining neutral clusters are photo-ionized using a 425 nm laser diode and mass-filtered. The third grating is scanned transversely across the molecular beam. The integrated signal is then recorded as a function of the displacement of the grating. The nanoparticle and optical components in a and c were rendered in Blender using assets by Ryo Mizuta Graphics.

Cluster aggregation sources enable scalable synthesis of particles across a wide mass range, and they are versatile in handling a variety of materials32,33. Here, we prepare sodium clusters consisting of 5,000–10,000 atoms, in a helium–argon mixture at 77 K. They travel at velocities around 160 m s−1 with de Broglie wavelengths between 10 fm and 22 fm.

The short de Broglie wavelength makes far-field diffraction challenging even for grating periods on the 100 nm scale: it would require beam collimation to below 200 nrad. However, in 1997, John Clauser proposed using near-field interferometry for grating-based coherent self-imaging of ‘small rocks and live viruses’[34], noting that this approach is compact, tolerates initially incoherent beams and offers high spatial resolution. This has been demonstrated with atoms35,36, X-rays[37], positrons[38], as well as organic and tailored macromolecules7,9. Here, we use it to open a window to matter-wave research with a whole new class of quantum objects, namely, massive metal nanoparticles.

A Talbot–Lau interferometer is built from three gratings with period d and spacing close to the Talbot distance LT = d^2/λdB (ref. [39]). The first and third gratings act as periodic spatial filters to prepare matter-wave coherence in G_1 and to resolve the interference fringes that emerge at G_3. The second grating G_2 modulates the amplitude and phase of the cluster matter wave. Standing light waves are favoured over nanomechanical diffraction gratings because their period is precisely defined, and their transmission amplitude can be modified in situ.

In contrast to atom interferometry, where optical beam splitters are commonly tailored to specific electronic transitions40,41, ionization and phase gratings are compatible with a large variety of materials and particle sizes. Ultraviolet light serves well as an amplitude or photodepletion grating when the clusters in the antinodes are ionized and discarded. The standing light field additionally induces an oscillating dipole moment in the transmitted clusters, in proportion to their optical polarizability. Thus, it also imprints a spatially periodic phase shift onto the de Broglie wave associated with each nanoparticle.

The light for the three gratings is derived from a single-line green laser beam, which is frequency doubled in an external cavity to produce up to 1 W of power at 266 nm. It is split into three partial beams, which are retro-reflected to form three standing light waves, separated by 0.983 m. Neutral clusters transmitted by the interferometer are photo-ionized and counted by a quadrupole mass spectrometer using a conversion dynode and electron multiplier.

We sample the interference patterns by scanning G_3 across the cluster beam while counting the number of transmitted clusters as a function of the G_3 position. The resulting fringes are phase stable to within 3–5 nm over several hours and can be fitted with a sinusoid to determine the visibility \(V=({S}_{\max }-{S}_{\min })/({S}_{\max }+{S}_{\min })\), where Smax and Smin are the maximum and minimum of the fit, respectively.

Interference scans

In Fig. 2a, we show two representative interference fringes of sodium clusters with a diameter around 8 nm and masses ranging from 143 kDa to 197 kDa. We have measured a fringe visibility of up to V = 0.10 ± 0.01, which is limited by the finite photodepletion efficiency in the first and third gratings.

Fig. 2: Interference results.

a, Interference fringes of sodium clusters with a mean mass of 172 kDa. The experimental data of two independent measurement runs (purple and green dots) are fitted by a sine function (purple and green line) with a visibility of V = 0.10 ± 0.01 and V = 0.08 ± 0.01, for grating laser powers P_1 = (62 ± 2) mW, P_2 = (15.2 ± 0.3) mW and P_3 = (68 ± 2) mW. b, Fringe visibility versus grating laser power of G_2. Each data point shows the weighted mean visibility per power bin from multiple independent interference scans of sodium clusters with masses centred around 172 kDa. Visibilities and error bars are derived from per-measurement 1σ confidence intervals of nonlinear least square sine fits (Methods). G1,3 powers as above. The continuous red and the dashed blue lines show the expected interference contrast according to the quantum and the classical model, respectively. The shaded areas show the uncertainties of the theory curve, based on the experimental 1σ limits of the molecular velocity, mass distribution, absorption cross-section and optical polarizability. In this plot, both theory curves were scaled by the same global factor of 0.78.

The observation of fringes in the cluster density distribution alone does not provide sufficient evidence for wave-like quantum propagation. They could also be explained by models in which the particles follow classical trajectories. In the presence of three nanomechanical gratings, classical flight paths would produce moiré-like shadow patterns. A similar classical picture is conceivable for sinusoidal transmission gratings in G_1 and G_3 and a phase grating in G_2, in which the latter acts as an array of microlenses because of the optical dipole force.

To obtain clear evidence for the wave nature of the observed fringes, their visibility is analysed as a function of the laser power P_2 of the second grating, shown by the solid circles in Fig. 2b. We compare this to the contrast predicted by both the classical (blue dotted line) and the quantum model (solid red line). The quantum model is obtained by describing the matter-wave dynamics in phase space using the Wigner–Weyl formalism[42]. It accounts for all coherent and incoherent grating interactions and enables a direct comparison with the prediction of classical mechanics (Methods).

We account for the experimental constraints on velocity, ionization cross-section, mass distribution and polarizability by the shaded areas along the theory curves. Interferometer misalignment, gravitational and rotational phase averaging, mechanical vibrations and the scattering of gas particles and thermal radiation can reduce the predicted contrast (Supplementary Information). We take this into account by a global scale factor of 0.78, which is equally applied to the quantum and the classical prediction in this figure. With this single experimental factor included, our experiments are well described by the quantum model and clearly distinct from the classical prediction.

Our assumptions regarding the mass, size and velocity distributions of the clusters, as well as the mass dependence of their ionization cross-section, are independently supported by the measured transmission probability as a function of the laser power in G_2 (dashed black curve). The model reproduces the experimental data (black crosses) very well, without any additional scaling factor.

For substantially more massive clusters, with masses between 400 kDa and 1 MDa, we observe even higher fringe visibilities of V = 0.66 ± 0.09 (Supplementary Information). Although this may seem counterintuitive, it becomes plausible when we consider that the ionization cross-section increases and the transmissive regions in each grating become narrower with increasing size of the cluster.

However, the de Broglie wavelength in this mass range (λdB ≱ 3 fm) is too short to distinguish quantum from classical predictions, for our interferometer configuration (Fig. 3). For L ≤ LT, near-field matter-wave dynamics gradually transitions to geometrical optics, in agreement with Bohr’s correspondence principle[43].

Fig. 3: Predicted fringe visibility as a function of cluster mass and G_2 laser power.

a,b, Results are shown for the quantum model (a) and the classical model (b), which both include the effects of ionization and of the dipole force in the grating interaction. Both calculations assume a mean velocity of 160 m s−1, a Gaussian velocity spread of 10 m s−1 and grating powers of P_1 = P_3 = 100 mW. The solid line marks the mass at which the Talbot length equals the interferometer length, whereas the dashed line indicates the mass for which half the Talbot length coincides with the interferometer length. The colour scale indicates fringe visibility V. For masses beyond the Talbot condition, the quantum and classical models converge. c, Slowing the particles to approximately 25 m s−1 will enable our setup to reliably distinguish quantum from classical dynamics for masses exceeding 1 MDa.

Figure 3 shows how the predicted visibilities from quantum (Fig. 3a) and classical theory (Fig. 3b) converge at high cluster masses. At the same time, it highlights a clear discrepancy between quantum and classical predictions in the mass range below 200 kDa (Fig. 2b). In Fig. 3c, we show that it will become possible to unambiguously demonstrate the quantum wave nature of clusters in the MDa range if their velocities can be reduced to about 25 m s−1.

Discussion

While Schrödinger speculated about the possibility of a cat being ‘dead and alive’ in the same quantum state—something clearly impossible to observe in our macroscopic world—early experiments with trapped ions[44] and cavity fields[45] already showed that such superpositions can exist in microscopic systems. Here, we took this idea to a much more massive scale: a nanometer-sized piece of metal being ‘here and there’ in the same quantum state with a 133 nm separation between the two locations, more than an order of magnitude greater than the particle itself. What would seem impossible in a classical worldview becomes here an experimental fact of quantum physics.

Observing matter-wave interference of the most massive objects to date reveals no breakdown of the quantum superposition principle related to mass or size alone. Moreover, this work establishes a new platform for metal nanoparticles, a material class previously inaccessible to such tests, and it suggests the feasibility of quantum-interference experiments with complex nanobiological objects which cover a similar mass range.

To put our experiment into context with other demonstrations of quantum superposition states, we evaluate the macroscopicity measure μ as defined in refs. 10,31. This value quantifies to what extent a given quantum experiment probes the validity of quantum mechanics and how well it can exclude minimal modifications of the Schrödinger equation, which would break the quantum superposition principle at some macroscopic scale.

Every successful demonstration of quantum interference falsifies a generic class of minimally invasive, macrorealistic modifications of quantum theory. To obtain the macroscopicity μ, all raw experimental data are used to narrow down the parameter space of these models by Bayesian updating, as explained in ref. [31]. This requires a quantitative model for the outcome probabilities in the presence of macrorealistic modifications[46]. Any experimental imperfection and all decoherence processes are attributed to the macrorealistic modification and will therefore only decrease the macroscopicity (Methods). From our data, we obtain the value μ = 15.5, which surpasses the previous record[11] by an order of magnitude, as shown in Fig. 4a.

Fig. 4: Macroscopic quantum systems and comparison.

a, Macroscopicity values of selected quantum experiments. Blue circles represent atom interferometry; red diamonds represent molecule interferometry; orange crosses represent Bose–Einstein condensates (BECs); green squares represent mechanical resonators; and red star represents sodium nanoclusters in this study, with μ = 15.5. Reference data are taken from refs. 10,11,31 and explained in the Supplementary Information. b, Visualization of size and complexity. The sodium clusters studied here behave as quantum particles at about 0.2 MDa and show high contrast up to the MDa regime. The number of atoms and their mass are compatible with those of large proteins and small viruses (from protein database[53]).

The main motivation for this line of research is to explore the quantum-classical interface bottom-up, systematically, and with all parameters under control. Our interferometer is unique in that it can accept various metals and also dielectric nanoparticles with different mass densities in the same machine. An additional factor of 100 in mass and in coherence time is conceivable in a vertical interferometer[47]. This additional factor would boost the attainable macroscopicity by six orders of magnitude in a ground-based experiment, which may open new opportunities to test the weak equivalence principle with vastly different types of matter.

On the applied side, coherent self-imaging creates a cluster density pattern in free flight, which can be shifted by external forces or directed momentum kicks. Particle-like properties, such as electric or magnetic susceptibility, can then be measured on clusters while they are propagating as delocalized waves. These measurements are complementary to explorations in physical chemistry48,49,50 and promise high force resolution.

The mass of our sodium clusters (170 kDa) already surpasses that of a coconut cadang-cadang viroid (CCCVd, 81 kDa; refs. 51,52), or a protein such as immunoglobulin G (IgG, 150 kDa; ref. [53]). In the next generation of experiments, it is anticipated to approach the MDa mass range of small viruses, such as the satellite tobacco necrosis virus, shown in Fig. 4b.

Although realizing quantum superpositions with these massive bio-nanomaterials still demands marked advancements in beam preparation, coherent manipulation and detection technologies, recent progress in the generation54,55, in tools for coherent photodepletion[56] and in detection of beams of massive biomolecules[57] suggests that these challenges will also be solved.

Methods

Quantum and classical model

The theory of Talbot–Lau interference is best formulated in phase space using the Wigner–Weyl representation of quantum mechanics[42]. This framework can account for incoherent particle sources, phase and absorption gratings, and all laser-induced photophysical effects, as well as any relevant decoherence process. It also allows for a direct comparison between the predictions of quantum and classical mechanics within the same formalism and set of assumptions.

For a cluster with mass m and longitudinal velocity vz, the probability of being detected behind the interferometer can be written as a Fourier series in the transverse position x_3 of G_3:

$$S({x}_{3})=\mathop{\sum }\limits_{{\ell }=-\infty }^{\infty }{S}_{{\ell }}\exp \left({\rm{i}}\frac{2{\rm{\pi }}{\ell }}{d}{x}_{3}\right).$$

(1)

In a symmetric setup with equal grating separations L and periods d, the Fourier coefficients are

$${S}_{{\ell }}={B}_{-{\ell }}^{(1)}(0){B}_{2{\ell }}^{(2)}\left({\ell }\frac{L}{{L}_{{\rm{T}}}}\right){B}_{{\ell }}^{(3)}(0),$$

(2)

where the Talbot–Lau coefficients \({B}_{{\ell }}^{(j)}\) of order ℓ for the jth grating still need to be determined as a function of the Talbot length LT = mvzd^2/h.

We assume that every absorbed grating photon results in the ionization of the sodium cluster. The transmission of the particle beam through a standing wave of incident laser power P, wavelength λL and Gaussian beam waist wy is then characterized by the mean number of ionizing photons absorbed in each grating antinode

$${n}_{0}=\frac{8{\sigma }_{{\rm{ion,266}}}P{\lambda }_{{\rm{L}}}}{\sqrt{2{\rm{\pi }}}hc{w}_{y}{v}_{z}},$$

(3)

as well as by the phase shift induced by the optical dipole potential

$${\phi }_{0}=\sqrt{\frac{8}{{\rm{\pi }}}}\frac{{\alpha }_{266}P}{\hbar c{\varepsilon }_{0}{w}_{y}{v}_{z}}.$$

(4)

The values of the UV polarizability α_266 and ionization cross-section σion,266 are mass-dependent and determined further below. We can then express the Talbot–Lau coefficients as[58]

$$\begin{array}{l}{B}_{n}(\xi )\,=\,{{\rm{e}}}^{-{n}_{0}/2}{\left(\frac{{\zeta }_{{\rm{coh}}}-{\zeta }_{{\rm{ion}}}}{{\zeta }_{{\rm{coh}}}+{\zeta }_{{\rm{ion}}}}\right)}^{n/2}\\ \,\times {J}_{n}({\rm{sgn}}({\zeta }_{{\rm{coh}}}+{\zeta }_{{\rm{ion}}})\sqrt{{\zeta }_{{\rm{coh}}}^{2}-{\zeta }_{{\rm{ion}}}^{2}}),\end{array}$$

(5)

where the coherent phase shift and the ionization depletion are described by

$${\zeta }_{{\rm{coh}}}(\xi )={\phi }_{0}\sin ({\rm{\pi }}\xi )$$

(6)

$${\zeta }_{{\rm{ion}}}(\xi )=\frac{{n}_{0}}{2}\cos ({\rm{\pi }}\xi ).$$

(7)

For short de Broglie wavelengths, as ξ ≡ L/LT → 0, the latter turn asymptotically into the expressions

$${\zeta }_{{\rm{coh}}}^{{\rm{cl}}}(\xi )={\phi }_{0}{\rm{\pi }}\xi $$

(8)

$${\zeta }_{{\rm{ion}}}^{{\rm{cl}}}={n}_{0}/2,$$

(9)

which appear in the classical description. It yields the same expression (equations (2)–(5)) for the signal, except that equations (6) and (7) are replaced by equations (8) and (9).

In our setup, both the quantum and the classical signal are well approximated by a sinusoidal with fringe visibility V = 2|S_1|/S_0. We average the predicted signal over the measured velocity and mass distributions, accounting for the mass dependence of both the polarizability and the ionization cross-section.

Macroscopicity assessment

To assess the macroscopicity of the demonstrated quantum superposition, it is necessary to calculate how the predicted interference signal is affected by the class of minimal macrorealist modifications (MMM) of quantum mechanics[10]. These are parameterized by the classicalization time scale τe, and by the momentum spread σq and spatial spread σs of a phase space distribution. The greater the value of τe, the larger the scales at which the quantum superposition principle still holds.

For our symmetric Talbot–Lau setup, the impact of an MMM is accounted for by multiplying the Fourier coefficients (equation (2)) by

$$\begin{array}{l}{R}_{{\ell }}\,=\,\exp \,[-2\sqrt{\frac{2}{{\rm{\pi }}}}{\left(\frac{3{\hbar }m}{{R}_{{\rm{c}}{\rm{l}}}{{\sigma }}_{{\rm{q}}}{m}_{{\rm{e}}}}\right)}^{2}\frac{L}{{v}_{z}{\tau }_{{\rm{e}}}}\\ \,\times \,{\int }_{0}^{{\rm{\infty }}}{\rm{d}}z\,{{\rm{e}}}^{-{z}^{2}/2}{j}_{1}^{2}\left(\frac{{R}_{{\rm{c}}{\rm{l}}}{{\sigma }}_{{\rm{q}}}}{{\hbar }}z\right)\,f\,\left(\frac{{\ell }d{{\sigma }}_{{\rm{q}}}L}{{\hbar }{L}_{{\rm{T}}}}z\right)]\end{array}$$

(10)

with Rcl the radius of the spherical clusters, me the electron mass, j_1 a spherical Bessel function and f(x) = 1 − Si(x)/x involving the sine integral[10]. The dependence on σs can be neglected for this setup. The mean count rate is unaffected by MMM since R_0 = 1.

The macroscopicity is obtained by using the raw experimental data \({\mathcal{C}}\) (cluster counts at given grating shift x_3 and grating powers) for a Bayesian test of the hypothesis that MMM holds with a classicalization time no greater than τe (ref. [31]). Bayesian updating yields the posterior probability distribution \(p({\tau }_{{\rm{e}}}| {\mathcal{C}},{{\sigma }}_{{\rm{q}}})\) of the classicalization time τe, starting from Jeffreys’ prior, by using the likelihoods obtained by incorporating equation (10) in the detection probability S(x_3) (ref. [46]). The lowest 5% quantile τm(σq) of the posterior distribution then determines the macroscopicity as \(\mu =\mathop{\text{max}}\limits_{{{\sigma }}_{{\rm{q}}}}({\log }_{10}({\tau }_{{\rm{m}}}({{\sigma }}_{{\rm{q}}})/1{\rm{s}}))\).

In our case, a total number of 3,895 data points yield a distribution very well approximated by a Gaussian (Kullback–Leibler divergence 1.27 × 10−3) whose 5% quantile τm = 2.84 × 10^15 s (maximized at ħ/σq = 10  nm) remains constant to three decimal places after 3,280 data points. This indicates that sufficient data were recorded and that the distribution is independent of the prior. The resulting macroscopicity is μ = 15.45.

Cluster beam

Large sodium clusters are generated in a custom-built aggregation chamber, inspired by earlier work32,59. The sodium is evaporated at 650–700 K into a cold mixture of argon and helium at a liquid nitrogen temperature of 77 K and pressure of less than 1 mbar. The resulting distribution covers masses beyond 1 MDa and velocities between 120 m s−1 and 170 m s−1. The clusters exit through a 5-mm aperture and pass three differential pumping stages before they reach the interferometer (Supplementary Information).

Two horizontal collimation slits dH1,H2 = 0.5 mm spaced by 1.8 m facilitate the alignment of the grating yaw angles perpendicular to the molecular beam with a precision of about 200 μrad. Two vertical collimation slits dV1 = 0.5 mm and dV2 = 1 mm, spaced by 2.2 m, confine the beam height and ensure good overlap with the standing light wave. This also reduces the influence of gravitationally induced phase averaging.

Photophysics

The optical polarizability α_266, absorption cross-section σabs,266 and ionization potential Ei depend on the size, mass and purity of the cluster. They determine transmission, the maximal matter-wave phase shift ϕ_0 and the mean number of absorbed photons n_0 in the antinodes of the grating. Photophysics[60] and thermodynamics[61] of small sodium clusters have been extensively studied, and the preparation of particles up to 1 MDa has been demonstrated before[59]. However, the mass-selected UV polarizability has not been known. Here, we use the high-contrast fringe patterns of clusters between 0.4 MDa and 1 MDa to determine it in a mass range for which the classical and quantum models predict the same visibilities. We derive a value of α_266/atom = −4πε_0 × (4.5 ± 0.5) Å^3 (Supplementary Information), which is consistent with the experiments and the quantum model for m = 100–200 kDa.

The photo-ionization cross-section σion,266 is a product of the absorption cross-section σabs,266 and the ionization yield. It determines the total transmission through the interferometer and influences the highest possible interference contrast. By measuring the mass-selected transmission of the interferometer for different grating powers, we determine an effective cross-section of σion,266 = (0.537 × m [kDa] − 1.5) × 10−20 m^2 for our clusters.

Mass selection and detection

After passing all gratings, the cluster beam is photo-ionized using 425 nm light and the cations are filtered by their m/z ratio using a quadrupole mass spectrometer. The mass filter includes guiding ion optics (Extrel) and 300 mm long quadrupole rods (Oxford Applied Research) with a diameter of 25.4 mm. The mass filter is operated at a resolution of Δm/m = 0.32. Interference scans centred on mass m, therefore, involve clusters within a mass range of  ±Δm/2, where the transmission function is close to rectangular shape and taken into account in our models. The mass filter was centred at 170 kDa. The underlying mass distribution, convoluted with the trapezoidal transmission, shifts the effective mass centre towards 172 kDa.

The selected cluster ions are counted by a channel electron multiplier with a conversion dynode at 10 kV. Electronic dark counts range from 15 to 100  counts s−1.

We must also account for the mixing of multiply charged ions with identical m/z ratios. Based on the measured work function of W = (2.4 ± 0.1) eV (Supplementary Information), neutral clusters with a diameter of dCl ~ 8  nm exhibit an ionization threshold of Ei = 2.53 eV, followed by Ei,+1 = 2.88 eV and Ei,+2 = 3.23 eV for subsequent ionization processes. The detection laser has a photon energy of Eph = 2.92 eV and can generate doubly charged ions, whereas triply charged ions remain energetically out of reach.

We have selected doubly charged clusters in the detector and verified the correct cluster mass by analysing mass spectra at both low and high detection laser powers (Supplementary Information). In the antinodes of the gratings, the 266 nm light can also lead to multiply charged ions. However, this does not affect the interference pattern, because every ion is removed from the cluster beam by electrostatic deflection, independent of its charge state. Only clusters that remain neutral while passing through all gratings contribute to the final interference pattern.

Velocity distribution

The cluster velocity distribution is determined from a time-of-flight measurement, in which we imprint a start time signal onto the cluster beam by UV photodepletion close to G_1, and we measure the cluster arrival time behind the ionizing mass spectrometer. The time-of-flight data are corrected for the drift time inside the quadrupole, where it is slightly accelerated by the entrance voltage U to \(v{\prime} =v+\sqrt{2eU\,/\,m}\). A convolution of a Gaussian drift time distribution and a rectangular chopper opening function is then fitted to the corrected unsmoothed data. The results are converted to a velocity distribution. We determine the average velocity and the width of the distribution from the standard deviation of the Gaussian fit.

Small variations of the mean velocity depend on the gas flow and the particle mass, and the 1σ width is Δv/v = 5–7%. Time-of-flight and velocity spectra for m/z = 100 kTh clusters are shown in the Supplementary Information.

Deep ultraviolet gratings

Up to 4 W of 532 nm light (Coherent Verdi V18) is converted to up to 1 W of 266 nm UV light by intracavity second harmonic generation (Sirah Wavetrain 2). The UV output is vertically expanded and split into three grating beams, using polarizing beam splitters and half-wave plates to regulate the power for each grating. Cylindrical lenses (f = 140 mm) focus the laser horizontally onto high-reflectivity (R = 99.5%) mirrors in vacuum to generate the standing light waves. We have observed power losses of up to 60% because of the degradation of optical components. The beam waists before the lenses are W_1 × H_1 = 1,130 × 620 μm^2, W_2 × H_2 = 1,020 × 575 μm^2 and W_3 × H_3 = 1,020 × 575 μm^2, with ΔHi = ΔWi = ±50 μm. Here, Wi represents the 1/e^2 waist radii along the molecular beam direction and Hi is the vertical waist. At the focus, the Gaussian beam waist is 20 μm. This small waist alleviates the alignment requirements with regard to the cluster beam tilt angle. The waist is still sufficiently large to ensure that the Rayleigh length, zR = 4.7 mm, is an order of magnitude larger than the cluster beam width of 500 μm.

Interferometer alignment

The surfaces of all three grating mirrors are aligned parallel to the particle beam axis, with the standing light wave along the mirror normal. The gratings exhibit three angular degrees of freedom: pitch, yaw and roll. The yaw angle, between the mirror surface and the particle beam, is adjusted to better than 200 μrad. The relative roll of the three mirrors, that is, their rotation around the axis parallel to the cluster beam is aligned to a difference less than 20 μrad. They are all stabilized with respect to the gravitational field of Earth to better than 50 μrad. The distances between the gratings are equal within 50 μm.

Interference scans

We obtain the interference scans by measuring the number of transmitted clusters as a function of the transverse displacement of the third grating G_3, which is moved in steps of Δx = 15 nm. At each position, the mass-filtered ion signal is integrated for a time interval of up to four seconds. A sinusoidal fit to the data then provides the periodicity, phase and amplitude of the fringes. By design of first-order Talbot–Lau interferometry, the periodicity is equal to the grating period. Each visibility \({{\mathcal{V}}}_{i}\) results from a nonlinear least-squares sine fit to the raw counts and is accompanied by 1σ confidence bounds \(({{\mathcal{V}}}_{i,{\rm{lb}}},{{\mathcal{V}}}_{i,{\rm{ub}}})\). We define side-specific absolute uncertainties \({\sigma }_{i,-}={{\mathcal{V}}}_{i}-{{\mathcal{V}}}_{i,{\rm{lb}}}\), \({\sigma }_{i,+}={{\mathcal{V}}}_{i,{\rm{ub}}}-{{\mathcal{V}}}_{i},\) and the effective symmetric uncertainty \({\sigma }_{i}=({\sigma }_{i,-}+{\sigma }_{i,+})/2\). Measurements are grouped by optical power into bins. For each bin \({\mathcal{B}}\), we compute the inverse-variance weighted mean \(\mu ={\sum }_{i\in {\mathcal{B}}}{w}_{i}{{\mathcal{V}}}_{i}/\,{\sum }_{i\in {\mathcal{B}}}{w}_{i}\) with \({w}_{i}={\sigma }_{i}^{-2}\), and to display mild asymmetry, we also report \({\sigma }_{\mu ,-}^{-2}={\sum }_{i\in {\mathcal{B}}}{\sigma }_{i,-}^{-2}\) and \({\sigma }_{\mu ,+}^{-2}={\sum }_{i\in {\mathcal{B}}}{\sigma }_{i,+}^{-2}\). As a consistency check, we compute the reduced chi-square \({\chi }_{{\rm{red}}}^{2}\) using the same per-point uncertainties as the weights. For overdispersed bins (\({\chi }_{{\rm{red}}}^{2} > 1.5\)), we scale the upper and lower error bars of the mean by \(\sqrt{{\chi }_{{\rm{red}}}^{2}}\). For visualization, plotted lower bounds are truncated at 0; all weighting and dispersion checks use the untruncated values.

Data availability

Data and code supporting the findings of this research are available at Zenodo (https://doi.org/10.5281/zenodo.17502163). Additional data or materials used in the study can be provided upon request.

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Acknowledgements

Funding for this project was provided by a Gordon and Betty Moore Foundation grant (GBMF10771, https://doi.org/10.37807/GBMF10771) and by the Austrian Science Fund (FWF) grant 32542-N. Open access funding provided by University of Vienna. We thank P. Geyer and Y. Y. Fein for their contributions at the early stage of the experiment and S. Sindelar for his support with metal cluster beams. M.A. is indebted to B. v. Issendorff for many discussions on cluster science throughout the years leading to this experiment. We thank V. Kresin for comments on alkali cluster sources and A. Shekhoon for discussions on the cluster work function.

Funding

Open access funding provided by University of Vienna.

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Authors and Affiliations

1. Faculty of Physics, University of Vienna, Vienna, Austria

Sebastian Pedalino, Bruno E. Ramírez-Galindo, Richard Ferstl, Markus Arndt & Stefan Gerlich

2. Vienna Doctoral School in Physics, University of Vienna, Vienna, Austria

Sebastian Pedalino, Bruno E. Ramírez-Galindo & Richard Ferstl

3. Faculty of Physics, University of Duisburg-Essen, Duisburg, Germany

Klaus Hornberger

Contributions

S.P. was responsible for experimental design, measurement, data analysis, theory and writing; B.E.R.-G. and R.F. were involved in experimental design, measurement and theory; K.H. provided the theory and the Bayesian estimate of quantum macroscopicity and contributed to writing; M.A. contributed to experimental design, theory and writing; S.G. was responsible for experimental design, analysis and theory.

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Correspondence to Markus Arndt.

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Pedalino, S., Ramírez-Galindo, B.E., Ferstl, R. et al. Probing quantum mechanics with nanoparticle matter-wave interferometry. Nature 649, 866–870 (2026). https://doi.org/10.1038/s41586-025-09917-9

- Received: 10 July 2025

- Accepted: 14 November 2025

- Published: 21 January 2026

- Version of record: 21 January 2026

- Issue date: 22 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09917-9

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3D-printed low-voltage-driven ciliary hydrogel microactuators

Zemin Liu,

Che Wang,

Ziyu Ren,

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Abstract

Micrometre-sized, densely packed natural cilia that perform non-reciprocal 3D motions with dynamically tunable collective patterns are crucial for biological processes such as microscale locomotion[1], nutrient acquisition[2], cell trafficking3,4,5 and embryonic and neurological development6,7,8. However, replicating these motions in artificial systems remains challenging given the limits of scalable, locally controllable soft-bodied actuation at the micrometre scale. Overcoming this challenge would enhance our understanding of ciliary dynamics, clarify their biological importance and enable new microscale devices and bioinspired technologies. Here we show a previously unrecognized fast electrical response of micrometre-scale hydrogels, induced by voltages down to 1.5 V without hydrolysis, with bending motions driven by ion migration across a nanometre-scale hydrogel network 3D-printed by two-photon polymerization, occurring within milliseconds. On the basis of these findings, we print gel microcilia arrays composed of a soft acrylic acid-co-acrylamide (AAc-co-AAm) hydrogel (modulus of approximately 1,000 Pa) that respond to electrical stimuli within milliseconds. Each microcilium measures 2–10 µm in diameter and 18–90 µm in height, achieving 3D rotational bending motion at up to 40 Hz, mirroring the geometry and dynamics of natural cilia. These gel microcilia maintain functionality after 330,000 continuous actuation cycles with less than 30% performance degradation. The gel microcilia arrays can be integrated on flexible polyimide substrates and fabricated at large scale using conventional lithography techniques. They also offer individual dynamic control by means of microelectrode arrays and enable fluid manipulation and particle transport at the micrometre scale.

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Main

In low-Reynolds-number fluidic environments, viscous forces dominate inertial forces, leading to the evolution of micrometre-scale cilia structures capable of dynamically regulating beating patterns for efficient and adaptive swimming, locomotion and environmental manipulation[9]. Individual cilia generate 3D non-reciprocal motions10,11, whereas coordinated movements, such as metachronal waves1,12, enable effective fluid transport and manipulation13,14. For example, starfish larvae[2], reef corals[15], Paramecium[16], ctenophores17,18 and Stentor coeruleus19,20 use coordinated cilia arrays for swimming, feeding predation and predator evasion. In mammals, ciliary flows support critical physiological processes21,22,23, such as neural cell maturation6,7, airway clearance24,25, reproductive cell transport3,4,5 and the establishment of embryonic asymmetry[8] (Supplementary Video 1). Replicating these dynamic features in microscale artificial systems holds the potential for quantifiable assessment of the importance of ciliary motion, advancing microactuation and microrobotics technologies and enabling biomedical innovations21,22,23.

Artificial cilia have been realized across a broad range of sizes, numbers, motion degrees of freedom and dynamic performances, yet challenges persist in matching natural cilia. At the centimetre scale, sparse arrays of pneumatic cilia—typically comprising six actuators—generate 2D reprogrammable metachronal waves at approximately 0.25 Hz, but bulky cavity designs hinder further miniaturization[26]. At the millimetre and micrometre scales, tens of magnetic-field-actuated cilia achieve both 2D and 3D motions at frequencies up to 100 Hz; however, it is challenging to generate spatially heterogeneous, high-resolution magnetic fields to reconfigure the collective beating pattern27,28,29,30. At the micrometre scale, ultrasound-driven rigid cilia31,32 and arrays of pH-sensitive cilia comprising hundreds of actuators[33] exhibit only basic mechanical actuation, with kinematics that falls short of biological performance and without dynamic reprogramming. In densely packed micrometre-scale arrays, light-responsive liquid-crystal cilia enable individually addressable bending and twisting and survive 100 cycles without damage. However, the slow actuation (about 0.1 Hz) limits the fluid-pumping capability and their coordinated motion requires several light sources, complicating integration and control34,35. Electrostatically actuated microsystems incorporating hundreds of cilia enable 2D motions at around 200 Hz in non-conductive high-strength dielectric liquids but are incompatible with ionic solutions, thereby restricting their applicability in biologically relevant environments[36]. Electrochemically redox-driven thin-film cilia, fabricated at both millimetre and micrometre scales in hundreds, are limited to 2D motions at frequencies between 5 and 100 Hz and are only tested around 1,000 cycles, as repeated redox may degrade the actuators37,38. Although some cilia systems use soft substrates to improve compliance28,29,30,37, most use rigid platforms, which are less conformable. Altogether, constraints in miniaturization, fast dynamics, motion degrees of freedom, scalable fabrication and actuator durability highlight the need for artificial cilia comparable with their biological counterparts.

Hydrogel microactuator fabrication

In this study, we use two-photon polymerization (TPP) for 3D printing and tune its key processing parameters, such as slicing and hatching (Extended Data Fig. 1a (ii)), to reduce the pore size of the ionic hydrogel from tens of micrometres in conventional millimetre hydrogel actuators (Extended Data Fig. 1b (i)) to the nanometre scale (transmission electron microscopy (TEM) image shown in Fig. 1a (1)). This nanoscale porosity increases the effective surface area and expands the capacity of the electric double layer (EDL), thereby enhancing ion transport and electro-osmotic flow in ionic solutions39,40,41 (bottom image in Fig. 1a (1)).

Fig. 1: Electrically driven hydrogel microactuator fabrication, mechanism and devices.

a, (1) Optimization of hydrogel pore size at the nanometre scale using TPP-based 3D printing. Top, TEM image of the 3D-printed hydrogel. Bottom, schematic showing enhanced ion flux and flow from increased EDL overlap in a nanometre-scale hydrogel channel under an electric field. (2) Working mechanism of the hydrogel microactuator. The hydrogel network is divided into region 1 (near the cathode) and region 2 (near the anode). In DI water, dissociated H+ ions from the –COOH groups dominate. Under an electric field, concentrated H+ in region 1 convert fixed –COO− groups to –COOH, reducing repulsion and shrinking the network, bending the microactuator towards the cathode. In physiological saline, Na+ ions dominate; concentrated Na+ in region 1 attracts water, swelling the network and bending towards the anode. (3) Schematic of the electrically driven gel microcilia array and AAc-co-AAm hydrogel network structure. b, Gel microcilia array on a polyimide-based microelectrode substrate. (1) Microelectrodes on glass. (2) Flexible substrate with microelectrodes on a human hand. (3) Gel microcilia array composed of four actuation cells, with each cell surrounded by four electrodes. (4) SEM image of the gel microcilia. c, Kinematics of a gel cilium in physiological saline (diameter 2 µm, height 18 µm; 5 Hz). The electrode polarity is marked by + and −. (1) Unidirectional bending along the y-direction. (2) Non-reciprocal 3D anticlockwise rotation. (3) Unidirectional bending along the x-direction. (4) Non-reciprocal 3D clockwise rotation. T denotes one motion cycle. d, Main devices used in this work. (1) 25 gel microcilia (cilium diameter 10 µm, height 90 µm). Each cilium has four surrounding electrodes for individual control. (2) 625 gel microcilia (cilium diameter 10 µm, height 90 µm). Each actuation cell with 25 cilia exhibits synchronized motion (inset, one cell). (3) 10^6 gel microcilia fabricated by micromoulding, partially shown in this image (cilium diameter 5 µm, height 35 µm; inset, one cell). Scale bars, 100 nm (a (1)); 2 cm (b (1), (2)); 200 µm (b (3), (4)); 6 µm (c (4)); 40 µm (d (1)); 200 µm (d (2)); 100 µm (d (2) inset); 300 µm (d (3)); 60 µm (d (3) inset).

We use this direct miniature hydrogel actuator printing technique to fabricate AAc-co-AAm gel microcilia (2–10 µm in diameter and 18–90 µm in height) with the microelectrodes placed around them (Extended Data Figs. 2 and 3). Under 1.5-V potential, which is below the threshold for electrolysis reactions (≤1.5 V), these closely spaced 30–300-µm electrodes generate electric fields ranging from 5,000 V m−1 to 50,000 V m−1. Figure 1a (2) depicts the bending mechanism. In deionized (DI) water, dissociated H+ ions from carboxylic acid groups migrate and accumulate in region 1, causing the hydrogel to shrink in this region and bend towards the cathode. By contrast, in physiological saline (0.15380 mol l−1 NaCl), dominant Na+ ions draw water molecules into region 1, swelling the hydrogel and bending it towards the anode. This ion-migration-induced dynamic motion can mimic the 3D rotation, reconfiguration and localized heterogeneous behaviour observed in mouse embryonic node cilia[8] (Extended Data Fig. 4, Supplementary Fig. 1 and Supplementary Videos 1–4). Figure 1a (3) illustrates the configuration of an AAc-co-AAm hydrogel microcilia array with the integrated microelectrodes around them. Details of the fast bending mechanism of the gel microcilia are discussed in the next section.

Figure 1b shows that the gel microcilia array can be fabricated on a flexible polyimide-based microelectrode substrate. Electrical signals within one actuation cell (four electrodes around it) can actuate the central gel cilium to generate reprogrammable 3D bending motions (Supplementary Video 5). When in physiological saline, activating the left and right electrodes can bend the cilium in the y-direction (Fig. 1c (1)) and activating the top and bottom electrodes causes bending in the x-direction (Fig. 1c (3)). Furthermore, rhythmic electrode cycling can generate rotating electric fields, enabling anticlockwise (Fig. 1c (2)) or clockwise rotation (Fig. 1c (4)). Finally, many such gel microcilia, each with independently controlled electrodes, can form a programmable hydrogel cilia surface. Such an approach can be demonstrated by an array of 25 individually controlled gel microcilia (Fig. 1d (1)), an array of 625 gel microcilia (Fig. 1d (2)) or an array of 10^6 gel microcilia (Fig. 1d (3)).

Fast bending mechanism

Previously reported millimetre-scale hydrogels are actuated through interfacial pH or osmotic gradients42,43,44. By contrast, the micrometre-scale hydrogels reported here are actuated through internal ion migration by means of nanometre-scale pores, resulting in distinct bending behaviour and a 100-fold increase in bending speed response (Extended Data Figs. 5 and 6 and Supplementary Videos 6 and 7). This section will first explain the bending mechanism, particularly bending direction and bending amplitude, and then the fundamental reason for fast bending dynamics of the proposed hydrogel.

In this study, the gel microcilia exhibit different bending directions in solutions with different NaCl concentrations. In H+-dominated DI water, they bend towards the cathode (Fig. 2a (1) and Supplementary Videos 8 and 9). In Na+-dominated physiological saline (0.15380 mol l−1 NaCl), they bend oppositely towards the anode (Fig. 2b (2) and Supplementary Video 8). At an intermediate concentration (0.00769 mol l−1 NaCl), competitive H+/Na+ effects cause transient cathode bending followed by anode reversal (Fig. 2b (1) and Supplementary Video 8).

Fig. 2: Characterization of the gel microcilia actuator dynamics.

a, Step response of gel microcilia in experiments and corresponding bending simulations in DI water. (1) Time sequence of 30 wt% AAc gel microcilia bending in DI water with the left electrode as the anode and the right electrode as the cathode; dashed lines highlight cilia outlines. Under the field, cilia bend towards the cathode owing to H+ accumulation on the right side (region 1 in Fig. 1a (2)). (2)–(5) Experimental tip displacement at different AAc concentrations shows reduced bending with higher AAc content (mean ± s.d.; n = 6 samples). (6)–(9) Simulated bending for varying AAc content reproduces the same trend, with displacement decreasing from 21.5 µm to 15.1 µm. b, Step response and simulations in NaCl solutions. (1) In 0.00769 mol l−1 NaCl, cilia first bend towards the cathode as fast H+ ions shrink the right side and then reverse towards the anode as slower Na+ ion swelling dominates on the right side. (2) In 0.15380 mol l−1 NaCl, bending occurs only towards the anode. (3), (4) Simulations reproduce these bending behaviours in NaCl solutions. c, Influence of different factors on dynamic performance under a square-wave signal. (1) AAc concentration: lower AAc content enhances bending (mean ± s.d.; n = 6 samples). (2) Actuation cycle: the gel microcilia maintained a bending angle of 50° after 330,000 continuous actuation cycles, corresponding to 70% of the initial performance, and then stabilize (mean ± s.d.; n = 4 samples). (3) Cilium diameter: 2-µm actuators outperform 10-µm actuators at high frequencies (mean ± s.d.; n = 6 samples). (4) Solution type: gel microcilia tested in DI water, physiological saline, DPBS, human saliva, serum and mouse plasma (mean ± s.d.; n = 6 samples for DI and n = 5 samples for the others). Scale bars, 100 µm (a (1)); 100 µm (b (1), (2)). All of the characterization experiment details are provided in Supplementary Note 7.

The bending direction can be explained by internal ion migration. In the H+-dominated DI water, the H+ ions from carboxylic acid groups migrate under an electric field and accumulate in the hydrogel network region 1 (ref. [45]) (Fig. 1a (2)), causing local shrinkage and bending the gel microcilia towards the cathode. The AAc-co-AAm hydrogel is a pH-sensitive hydrogel that will shrink in acidic environments. The elevated H+ concentration in low-pH conditions converts some fixed –COO− groups to –COOH, reducing repulsive forces in the hydrogel network and causing the hydrogel to shrink42,46. Consequently, the gel microcilia bend towards the cathode in DI water owing to H+ ion migration inside the hydrogel, whereas the previous millimetre hydrogel bends towards the anode owing to interfacial pH gradients42,43,44 (Fig. 2a (1) and Extended Data Fig. 5).

Notably, increasing the –COOH concentration in the gel microcilia does not lead to larger bending amplitudes. We investigated this counter-intuitive behaviour by performing step response tests on gel microcilia with 15, 30, 45 and 60 wt% AAc (Fig. 2a (2)–(5)). The tests were conducted in DI water and detailed hydrogel compositions are provided in Methods. Under an applied electric field, the cilia tip bent towards the cathode (Fig. 2a (2)–(5) and Supplementary Video 8). Increased AAc concentration introduces more –COOH groups to the hydrogel network. However, higher AAc concentrations reduce bending angles (Fig. 2a (2)–(5)).

These experimental findings are supported by the fully coupled electro-chemo-mechanical simulations of AAc-based hydrogels. The model incorporates the influence of densely distributed –COO− groups on ion migration within the hydrogel, capturing the influence of the nanometre-scale hydrogel pores. Full details of the modelling are provided in Methods. Consistent with the experiments, the simulated bending amplitude decreases from 21.5 μm to 15.1 μm as the AAc concentration increases from 15 wt% to 60 wt% (Fig. 2a (6)–(9)). This trend indicates that, in the electro-chemo-mechanical coupled system, the deleterious effects of higher AAc concentration (for example, increased Young’s modulus (Extended Data Fig. 7a)) outweigh its beneficial contributions (for example, enhanced charge density within the hydrogel network).

When the hydrogel operates in Na+-dominated physiological saline (0.15380 mol l−1 NaCl), Na+ ions can swell the hydrogel network region 1 (Fig. 1a (2)) and bend the gel microcilia towards the anode, in contrast to the H+ ion effects (Fig. 2a). The fixed negatively charged –COO− groups in the hydrogel network lead to a lower concentration of mobile Cl− ions within the hydrogel compared with Na+ ions[47]. When an electric field is applied, Na+ ions migrate towards the cathode and accumulate in region 1 (refs. 43,45) (Fig. 1a (2)). This migration inside the hydrogel drags water molecules along with the Na+ ions, causing region 1 (Fig. 1a (2)) to swell[48], which in turn bends the microcilia towards the anode (Fig. 2b). However, previously reported millimetre hydrogel bends towards the cathode under the same aqueous conditions owing to gel–solution interfacial osmotic pressure42,43,44 (Extended Data Fig. 5).

To show this competitive H+-driven shrinking and Na+-driven swelling process, we examine the step response of 30 wt% AAc hydrogels in 0.00769 mol l−1 and 0.15380 mol l−1 NaCl solutions. The bending in the 0.00769 mol l−1 NaCl solution (Fig. 2b (1)) contrasts with the bending in DI water (Fig. 2a (1)); the gel cilium initially bends towards the cathode and then gradually shifts to bend towards the anode (Fig. 2b (1) and Supplementary Video 8). This can be explained by the higher mobility of H+ ions (μH = 3.62 × 10−7 m^2 s−1 V−1) than that of Na+ ions (μNa = 5.19 × 10−8 m^2 s−1 V−1)—act first to accumulate in region 1 (Fig. 1a (2)), causing initial shrinkage and bending towards the cathode. Na+ ions and water molecules subsequently enter the region, overcoming the initial shrinkage and bending towards the anode (Fig. 2b (1) and Supplementary Video 8).

At 0.15380 mol l−1 NaCl, the gel cilium bends only towards the anode (Fig. 2b (2) and Supplementary Video 8). This behaviour is because, at higher NaCl concentrations, the swelling effect of Na+ ions on the hydrogel network dominates the motion. Our simulations validate this mechanism: in 0.00769 mol l−1 NaCl solution, the hydrogel initially bends towards the cathode and subsequently reverses direction towards the anode, consistent with the experimental observations (Fig. 2b (3)). By contrast, in 0.15380 mol l−1 NaCl, both simulations and experiments show bending only towards the anode (Fig. 2b (2) and (4) and Supplementary Video 10). Together, these results confirm the roles of H+ ions and Na+ ions in driving the motion of microscale hydrogel actuators. Such bending happens very fast, with a step response time of 0.1–0.7 s.

Fast bending dynamics arise owing to two reasons. The first is that the expanded effective surface area of the EDL enhances ion transport. Particularly, TPP printing with carefully engineered polymerization pathways (Extended Data Fig. 3) and low printing power (about 15 mW) achieves nanometre-scale porosity while maintaining well-defined morphological features. The second is the fast H+ and Na+ migration at the micrometre scale. For instance, under a 10,000 V m−1 electric field, achieved with 1.5 V applied over 150 µm, the H+ ions (ion mobility μH = 3.62 × 10−7 m^2 s−1 V−1) traverse a 10-µm distance in 2.8 ms, whereas it takes 16.7 ms for Na+ ions (μNa = 5.19 × 10−8 m^2 s−1 V−1). For a 2-µm distance, this decreases to 0.6 ms for H+ and 3.4 ms for Na+. The 10-µm and 2-µm distances correspond to the gel microcilia diameters in this work.

Because of these two reasons, these millisecond-scale ion migrations can trigger fast ion redistribution and subsequent mechanical responses, enabling rotational bending motions at frequencies up to 40 Hz. Extended Data Table 1 summarizes the performance of this work in comparison with previously reported ciliary actuators26,27,28,29,30,31,32,33,34,35,36,37,38.

Cilia bending characterization

Bending dynamics are critical for ciliary actuation performance characterization26,28,38. We first evaluate the impact of AAc concentration (15, 30, 45, 60 wt%) on gel microcilia bending dynamics in DI water (Fig. 2c (1)). Gel microcilia with lower AAc content (15 wt%) exhibit superior bending amplitudes at low frequencies (for example, twice the amplitude of 60 wt% AAc at 5 Hz), but this advantage diminishes at higher frequencies (for example, only 20% larger than the 60 wt% AAc at 50 Hz (Fig. 2c (1))). The dimensionless sperm number (Sp) is widely used to describe the reduction in bending at higher frequencies in low-Reynolds-number conditions (Re ≈ 0.001 in our system). As frequency increases, Sp also increases (Extended Data Fig. 7b), indicating that viscous effects become more dominant, leading to faster decay of bending along the actuator length and reduced tip displacement.

Durability testing under dynamic signals reveals sustained functionality: a single cilium still performs 50° bending after 330,000 cycles of continuous actuation (20 Hz for 5 h; Supplementary Video 11), which is 70% of the original performance (Fig. 2c (2)). The gel microcilia actuator remains functional after such extensive actuation, highlighting their durability. This longevity is attributed to the actuation mechanism, which relies on ion migration without chemical reactions. The overall device lifespan is primarily limited by the stability of the thin-film microelectrodes rather than the hydrogel actuator itself, as prolonged actuation often leads to electrode delamination from the substrate (see Supplementary Note 12). By contrast, previously proposed electrically driven cilia depend on repeated electrochemical oxidation and reduction, causing degradation and limiting their lifespan to several thousand cycles37,38.

Gel microcilium dimensions, especially the diameter, also affect the bending performance. Gel microcilia with smaller diameter demonstrate better bending performance at high frequencies. As shown in Fig. 2c (3), under low-frequency signals, 2-µm-diameter and 10-µm-diameter hydrogel cilia show similar bending amplitudes. Both cilia approach a bending angle of approximately 150°, nearly the maximum achievable motion, indicating that each has ample time per actuation cycle to fully deform. Consequently, the benefits of smaller cilium do not manifest at low frequencies and the 2-µm-diameter cilium shows no distinct advantage in this regime. As the frequency increases, the 2-µm-diameter cilium gradually outperforms its 10-µm counterpart. At 50 Hz, its bending amplitude is nearly five times larger than that of the 10-µm cilium, as smaller hydrogel diameter needs shorter time for the ions to redistribute under a dynamic signal (see Supplementary Note 11).

As well as diameter, higher electric-field strength also accelerates ion transport and thereby enhances the performance of the actuator (Extended Data Fig. 7c). At a fixed diameter, taller hydrogel actuators exhibit improved performance owing to their increased effective actuation length (Extended Data Fig. 7d). However, fabricating high-aspect-ratio soft actuators can lead to structural collapse. Furthermore, increasing either the photoinitiator concentration or the printing power reduces the performance of the hydrogel actuators (Extended Data Fig. 7e,f) owing to the increased stiffness.

Finally, we test the performance of gel microcilia in three aqueous solutions (DI water, physiological saline (0.15380 mol l−1 NaCl) and Dulbecco’s phosphate-buffered saline (DPBS)) and three physiological fluids (human saliva, human serum and mouse plasma). As shown in Fig. 2c (4), actuation performance decreases across these environments, with high performance in DI water and human saliva, moderate performance in physiological saline and low performance in DPBS, human serum and mouse plasma. This trend aligns with the increasing ionic complexity of the solutions. DI water and human saliva feature a H+-dominated environment, whereas physiological saline introduces Na+ and Cl− ions. DPBS and the other two physiological fluids further complicate the system with balanced concentrations of more ions (for example, Mg2+, K+), creating competitive interactions between the ions of varying size, charge, mobility and hydration capacity. For instance, highly mobile ions rapidly migrate to region 1, inducing localized swelling, whereas less mobile ions are still in region 2 (Fig. 1a (2)), promoting counteractive swelling. This spatial and temporal mismatch in ion-driven swelling reduces overall bending performance. Nevertheless, hydrogel cilia remain functional in simple and complex ionic environments, including physiological fluids from human and mouse, showing their potential use in physiological fluid mixing (Supplementary Video 8).

Reprogrammable motions

We demonstrate versatile control of coordinated motion across four gel microcilia systems under electrical stimulation. 2 gel microcilia (diameter 2 µm, height 18 µm) are programmed to perform synchronized unidirectional bending (Fig. 3a (1)) and 180° out-of-phase unidirectional bending (Fig. 3a (2)). By controlling electric signals across patterned electrodes, this cilia device can achieve synchronized clockwise 3D rotational bending under uniform input signals (Fig. 3a (3)) or opposing rotation directions between gel microcilia through applying different input signals to the actuation cells (Fig. 3a (4) and Supplementary Video 12).

Fig. 3: Dynamic bending motions of gel microcilia arrays.

a, Reprogrammable motions of 2 gel microcilia (diameter 2 µm, height 18 µm; physiological saline; 5 Hz). (1) Synchronized unidirectional bending. (2) Unidirectional bending with 180° phase shift. (3) Synchronized clockwise 3D rotation. (4) Counter-rotating motion, left anticlockwise and right clockwise. b, Reprogrammable motions of an array of 5 × 5 gel microcilia (diameter 10 µm, height 90 µm; DI water). Yellow-shaded z-stack images show cilia motions. (1) Synchronized bending along the y-direction. (2) Synchronized bending along the x-direction. (3) Clockwise 3D rotation. (4) Anticlockwise 3D rotation. (5) 3 × 3 subarray (yellow dashed box) rotates clockwise, others anticlockwise. (6)–(10) Zoomed-in z-stack views corresponding to (1)–(5); cilia at identical time points share the same colour. (11) ‘HKUST’ displayed by an independently controlled 5 × 5 array; zoom-in shows opposite bending of adjacent cells in the ‘T’. Motion frequencies, 10 Hz ((1)–(4)); 20 Hz (5); 5 Hz (11). c, ‘MPIIS’ displayed by a 25 × 25 array (diameter 10 µm, height 90 µm; DI water; 5 Hz). Zoom-in shows the upper cilium bending upward, whereas the lower remains stationary. d, Applications of hydrogel actuators. Left, biomimetic artificial starfish larva. Right, flapping micromachine. (1) Photo of patterned electrodes before cilia integration. (2) SEM image with arrows indicating cilia rotation. (3) PIV flow field generated by cilia motion. (4) Schematic showing the flow generated by biological starfish larva. (5) Flapping mechanism with integrated hydrogel actuators. The first row is the SEM image, followed by video frames showing the flapping motion. The electrode polarity is indicated by + and − in a, b (11) and c. Because a uses physiological saline and b–d use DI water, the bending direction differs. Scale bars, 8 µm (a (4)); 150 µm (b (5)); 40 µm (b (10)); 150 µm (b (11)); 30 µm (b (11) zoom-in); 750 µm (c); 100 µm (c zoom-in); 400 µm (d (1)); 360 µm (d (2)); 100 µm (d (5)).

The coordinated motions of the gel microcilia can be extended to 5 × 5 and 25 × 25 arrays. An array of 5 × 5 gel microcilia (diameter 10 µm, height 90 µm) demonstrates individual addressability, enabling synchronized unidirectional bending, clockwise/anticlockwise rotation and independent actuation (Fig. 3b (1)–(10) and Supplementary Video 13). The array can also be reprogrammed to display patterns such as ‘HKUST’ (Fig. 3b (11) and Supplementary Video 14), with a 180° phase difference between the letters and the background gel microcilia enhancing visual contrast. Then, an array of 25 × 25 gel microcilia (diameter 10 µm, height 90 µm) is programmed to display the letters ‘MPIIS’ (Fig. 3c and Supplementary Video 15). Each actuation cell synchronizes 25 cilia, validating control over dense configurations.

Our hydrogel precursor and microactuation system are also compatible with standard fabrication methods, as demonstrated by the fabrication of 10^6 gel microcilia using conventional lithography (Supplementary Figs. 2–6). Each actuation cell controls approximately 64 cilia, enabling synchronized bending along the x-axis or the y-axis (Supplementary Fig. 7 and Supplementary Video 16).

As shown in Extended Data Fig. 8a,b, hydrogel actuators, together with polyimide substrates and microelectrodes, were patterned onto a 3D hill-shaped surface with a height difference of 100 µm (see the scanning electron microscopy (SEM) images in Extended Data Fig. 8a,b), which closely resembles the non-flat morphology of physiological tissue surfaces. Controlled actuation enabled directed fluid transport on such 3D surfaces, including flows from right to left (Extended Data Fig. 8a and Supplementary Video 17) and from top to bottom (Extended Data Fig. 8b and Supplementary Video 18). Also, the actuators can be configured into alternative geometries. For example, integration with a 3D pyramid frame structure (Extended Data Fig. 8c and Supplementary Video 19) and direct printing into a helical shape (Extended Data Fig. 8d and Supplementary Video 20) demonstrated controlled ciliary flow manipulation under electrical stimulation.

To explore the application scenarios of this hydrogel microactuator, we fabricate an artificial starfish larva (Fig. 3d (1)–(4) and Supplementary Video 21), in which a biological starfish larva generates complex vortex arrays through ciliary motion to facilitate feeding and other essential processes[2]. The active boundary conditions arising from cilia–environment interactions may exert important effects in both biological and synthetic systems[2]. However, existing studies have been largely qualitative, based on biological observations without systematic quantification2,20. To address this gap, we fabricate an artificial starfish larva platform by combining microscale 3D printing of the larval body, microelectrode patterning on its curved surface and integration of hydrogel cilia. As shown in Fig. 3d (1)–(4) (Supplementary Video 21), the artificial starfish larva reproduces biologically analogous vortex arrays under electrical control, providing a reliable robotic platform for quantitative investigation of biomimetic processes and active boundary conditions[2].

Finally, we demonstrate the integration of hydrogel microactuators with micromechanical structures (Extended Data Fig. 8e and Fig. 3d (5)). Here electrically induced hydrogel bending is transduced into rotary and flapping motions through mechanical linkages, thereby broadening the scope of potential applications (Supplementary Videos 22 and 23). This capability highlights the potential of hydrogel microactuators to interface with complex micromachine architectures, expand microscale actuation strategies and advance the fields of microrobotics and microdevices.

Dynamic fluid manipulation

In nature, biological cilia perform diverse functions by manipulating surrounding fluids, including airway clearance[25], gamete transport[3] and regulation of embryonic development[8]. As shown in Fig. 4, similarly, our hydrogel cilia enable controllable fluid transport and directional particle motion (Fig. 4e–g).

Fig. 4: Dynamic fluid manipulation by the gel microcilia arrays.

a–c, Fluid control achieved by varying the spatial arrangement and density of gel microcilia within an actuation cell (rotational bending frequency = 40 Hz). a, (1) Programmed motion of a 4 × 4 array with four cilia centred in each cell, all rotating clockwise. a, (2) 2D simulation shows four clockwise vortices and one inter-cell anticlockwise vortex. b, (1) 4 × 4 array with four corner cilia per cell, all rotating clockwise. b, (2) Each cell produces five vortices (four clockwise, one central anticlockwise), forming inter-cell anticlockwise flows. c, (1) 10 × 10 array with 5 × 5 densely packed cilia per cell, all rotating clockwise. c, (2) Hydrodynamic interference cancels intra-cell vortices, leaving peripheral clockwise and central anticlockwise vortices. d–g, Dynamic reprogramming of cilia motion for flow control (20 Hz). d, (1) 5 × 5 individually controlled array with outer 16 cilia generating metachronal waves; inner cilia stationary. d, (2) Simulation shows a central anticlockwise vortex. e, (1) 5 × 5 array with a 3 × 3 top-left subarray rotating clockwise, others anticlockwise. e, (2) Clockwise L-shaped flow forms between these regions. f, (1) Alternating column actuation (columns 1, 3 and 5 clockwise; columns 2 and 4 anticlockwise). f, (2) Downward flows between columns 1–2 and 3–4 and upward flows between columns 2–3 and 4–5. g, (1) 5 × 5 array with outer and central cilia rotating clockwise, middle ring anticlockwise. g, (2) Anticlockwise flow between the outer and middle rings and clockwise flow between the middle ring and the central cilium. Experimental PIV flow fields (a (3)–g (3)), z-stack particle traces (a (4)–d (4)) and particle-trajectory tracking (e (4)–g (4)) all align with simulations a (2)–g (2). Red arrows in e–g highlight that flow directions are consistent with the particle-trajectory-tracking results. The gel microcilia actuators used in the flow experiments have a diameter of 10 µm and a height of 90 µm. The working solution is 0.00769 mol l−1 NaCl. Scale bars, 200 µm (a (4)–c (4)); 150 µm (d (4)–g (4)).

We show two strategies for microscale fluid manipulation control. First, we manipulate fluid flow through spatial and density configurations of gel microcilia within one actuation cell under identical clockwise actuation signals (Fig. 4a–c). Experiments 1–3 show that cilia position and density can govern the vortex pattern and direction under the synchronized clockwise motion. In experiment 1, four gel microcilia positioned at the centre of each actuation cell generate clockwise vortices that induce an anticlockwise vortex in the inter-cell region (Fig. 4a and Supplementary Video 24). Experiment 2 uses a corner-positioned configuration; each actuation cell produces five vortices (four clockwise and one central anticlockwise), with neighbouring vortices inducing an inter-cell anticlockwise flow (Fig. 4b and Supplementary Video 25). To further investigate the dense gel microcilia array motion for fluid pumping, experiment 3 builds 25 gel microcilia actuators within one actuation cell. The results indicate that tight cilia spacing cancels intra-cell vortices owing to hydrodynamic interference between neighbouring cilia, whereas peripheral clockwise vortices and a central anticlockwise vortex between cells emerge (Fig. 4c and Supplementary Video 26). Particle image velocimetry (PIV) (Fig. 4a–c (3)) and z-axis stack images of particles traces (Fig. 4a–c (4)) confirm simulated flow patterns (Fig. 4a–c (2)) across all configurations.

Second, we demonstrate dynamic reprogramming of flow patterns through individually addressable gel microcilia (Fig. 4d–g). Experiments 4–7 demonstrate that reprogrammable coordinated motions can control the flow structure and direction in a versatile way. In experiment 4, 16 gel microcilia in the outermost ring rotate clockwise with a 90° phase difference between adjacent actuators to form metachronal waves, whereas the remaining cilia remain stationary. This configuration produces a centralized anticlockwise vortex (Fig. 4d and Supplementary Video 27). In experiment 5, a region-specific actuation strategy is used: a subarray of 3 × 3 gel microcilia in the top-left corner rotates clockwise, whereas the remaining gel microcilia rotate anticlockwise, generating clockwise L-shaped flows (Fig. 4e and Supplementary Video 28). Experiment 6 implements columnar alternating motion: gel microcilia in columns 1, 3 and 5 (15 gel microcilia) rotate clockwise and those in columns 2 and 4 (10 gel microcilia) rotate anticlockwise, resulting in bidirectional vertical flows. Downward flows dominate between columns 1 and 2 as well as between columns 3 and 4, whereas upward flows occur between columns 2 and 3 and between columns 4 and 5 (Fig. 4f and Supplementary Video 29). Experiment 7 uses concentric ring actuation: the outermost ring (16 gel microcilia) and the central cilium rotate clockwise, whereas the middle ring (8 gel microcilia) rotates anticlockwise, establishing nested vortices—anticlockwise flow between the outer and middle rings and clockwise flow between the middle ring and central cilium (Fig. 4g and Supplementary Video 30). PIV data (Fig. 4d–g (3)), a z-stack image of particles traces (Fig. 4d (4)) and particle trajectory tracking (Fig. 4e–g (4)) all align with simulations (Fig. 4d–g (2)), confirming the flow directionality and patterns across the working area. All of the dynamic fluid simulation results are shown in Supplementary Video 31. From the PIV measurement, we estimate that the maximum flow velocities generated by cilia in cases in Fig. 4a–d range between 50 and 250 µm s−1. In the cases in Fig. 4e–g, particle-tracking measurements indicate velocities of approximately 55 µm s−1, 18 µm s−1 and 22 µm s−1, respectively.

Discussion

Present hydrogel actuation performance declines in complex ionic environments, that is, solutions with several dominant ions. Performance decreases from DI water to physiological saline and DPBS and is expected to decline further in biological contexts. Potential solutions include optimizing the hydrogel monomer composition (for example, incorporating 2-acrylamido-2-methyl-1-propanesulfonic acid for enhanced hydrogel network polarizability) and reducing cilium diameter (≤2 µm) to lower the ion redistribution time for enhanced performance. Furthermore, enhancing electric-field uniformity and intensity by means of 3D microelectrodes or reducing electrode spacing (Extended Data Fig. 7c) could improve performance. Although reduced electrode spacing may affect neighbouring electric fields, our experiments indicate negligible interference at a spacing of 5 µm between actuation cells (Fig. 3a); further investigation is required in the future.

We predict that continued improvements in gel microcilia performance, combined with advanced microfabrication/nanofabrication and flexible electronics fabrication technologies, will enable their broad applications across many fields.

Methods

Single-layer microelectrodes fabrication

Extended Data Fig. 2 illustrates the fabrication process of single-layer microelectrodes. Below are the step-by-step details:

Step 1. Preparation of the polyimide substrate (Extended Data Fig. 2a). This work uses PI2611 (HD MicroSystems) as the backbone material. PI2611 is poured onto a blank glass substrate and then spin-coated at 1,500 rpm for 30 s. The glass substrate used to hold the printed hydrogel microactuators must have a thickness of less than 300 µm, as this is the working range of the TPP laser. For this purpose, we use a 180-µm-thick glass substrate for hydrogel printing. No specific thickness is required for the glass substrate used for moulded hydrogel fabrication.

Step 2. Curing the PI2611 polyimide substrate (Extended Data Fig. 2b). We place the spin-coated substrate on a hotplate and heat it from room temperature to 150 °C. We hold this temperature for 10 min and then increase it to 200 °C. The temperature ramp rate is 20 °C per minute. This temperature is maintained for 5 h to fully cure the polyimide.

Step 3. Photoresist coating (Extended Data Fig. 2c). We pour the positive photoresist AZ ECI 3012 (MicroChemicals GmbH) onto the polyimide substrate and spin-coat the photoresist for 30 s at 5,000 rpm.

Step 4. Soft baking (Extended Data Fig. 2d). We bake the positive photoresist at 90 °C for 90 s.

Step 5. Ultraviolet exposure (Extended Data Fig. 2e). We expose the substrate for 8 s using the MJB4 mask aligner (SUSS MicroTec). The ultraviolet density of this machine is 14.3 mJ cm−2 and the patterns are defined using a photomask.

Step 6. Post-exposure bake (Extended Data Fig. 2f). We bake the exposed substrate at 110 °C for 90 s.

Step 7. Development (Extended Data Fig. 2g). We develop the substrate in AZ 726 (MicroChemicals GmbH) developer for 60 s to reveal the patterns.

Step 8. Platinum sputtering (Extended Data Fig. 2h). We deposit a 150-nm-thick platinum (Pt) layer onto the substrate using sputtering.

Step 9. Lift-off process (Extended Data Fig. 2i). We dip the substrate in TechniStrip Micro D350 (MicroChemicals GmbH) photoresist stripper to remove unwanted material and obtain the final microelectrode structures.

Hydrogel solutions for 3D printing

Four hydrogel precursor solutions are prepared for TPP-based 3D printing. Acrylic acid (AAc, Sigma-Aldrich) and acrylamide (AAm, Sigma-Aldrich) served as monomers, N,N′-methylenebisacrylamide (BIS, Sigma-Aldrich) as the crosslinker and Omnirad 2100 (IGM Resins) as the photoinitiator. Ethylene glycol was used as the base solvent.

The four solutions differ in the mass fractions of AAc, AAm and the photoinitiator. The molar masses of AAc (72.06 g mol−1) and AAm (71.08 g mol−1) are nearly identical; therefore, maintaining a constant total mass fraction of AAc and AAm effectively corresponds to a constant overall molar concentration of monomers. This ensures that, across all formulations, the total monomer concentration remains comparable when only the relative ratio of AAc to AAm varies.

Because the printability of the precursor solution depends on the AAc-to-AAm ratio, the photoinitiator fraction is experimentally adjusted to achieve consistent printing quality. For each monomer ratio, several photoinitiator concentrations are tested under identical printing parameters and the optimal concentration is determined as the one that produced structures without overexposure or underexposure. Overexposure and underexposure are defined as follows: when printing a 10-µm feature under identical parameters, a fabricated structure larger than 10 µm is considered overexposed, whereas one smaller than 10 µm is considered underexposed. All mass fractions reported below are calculated with respect to the initial mass of ethylene glycol before the addition of monomers, crosslinker or photoinitiator.

Solution 1: AAc 15 wt%, AAm 60 wt%, BIS 2 wt%, photoinitiator 7.5 wt%.

Solution 2: AAc 30 wt%, AAm 45 wt%, BIS 2 wt%, photoinitiator 18 wt%.

Solution 3: AAc 45 wt%, AAm 30 wt%, BIS 2 wt%, photoinitiator 28 wt%.

Solution 4: AAc 60 wt%, AAm 15 wt%, BIS 2 wt%, photoinitiator 38 wt%.

Hydrogel microactuator 3D printing

A commercial TPP system (Photonic Professional GT, Nanoscribe GmbH) is used to fabricate hydrogel microactuators. The printing is performed using a 25× objective lens with an exposure power of 15 mW. The slicing and hatching distances are set to 300 nm and 200 nm, respectively, with a 45° hatching angle between adjacent layers (Extended Data Fig. 3).

We immerse the printed sample in an ethylene glycol bath to develop the hydrogel microactuator structure. Subsequently, the sample is transferred to a DI water bath for 10 min. We repeat this process three times to fully replace the solvent inside the hydrogel from ethylene glycol to DI water. Afterwards, the aqueous environment can be adjusted for different experiments accordingly.

Comparison between the centimetre–millimetre hydrogel and the micrometre hydrogel

In contrast to the fast bending mechanism of the micrometre hydrogel actuator, the internal ion migration is much slower in previously reported centimetre–millimetre-scale ionic hydrogels not fabricated by TPP49,50. Even at the same electric-field intensity as our gel microcilia system, for example, 10,000 V m−1, the H+ ions and Na+ ions would take 0.3 s and 16.7 s, respectively, to traverse a 1-cm distance, which is a typical thickness of a centimetre-scale hydrogel actuator. This ion migration time is orders of magnitude slower than at the micrometre scale. Moreover, its larger pore size (Extended Data Fig. 1b (i)) and smaller effective EDL surface area reduce ion transport, thereby weakening bending. Finally, achieving such high electric fields in large systems needs impractical voltages (for example, 200 V for 2-cm-spaced electrodes), which can trigger electrolysis and other vigorous electrochemical reactions.

In fact, the internal ion migration in millimetre-scale hydrogel is trivial compared with ion partitioning at the gel–liquid interface. Therefore, previously reported millimetre-scale hydrogels49,50 operate through mechanisms distinct from those proposed in the present work. They bend by means of bath-induced pH gradients or interfacial osmotic effects, driven by electrolysis or ion partitioning across the gel–solution interface42,43,44. By contrast, micrometre-scale hydrogels depend on internal ion migration and nanometre-scale channels, resulting in opposite bending directions and response times more than two orders of magnitude faster (Extended Data Fig. 5 and Supplementary Videos 6 and 7). For example, in DI water, a millimetre-scale actuator bends towards the anode in about 120 s, whereas a micrometre-scale actuator bends towards the cathode in about 0.2 s (Extended Data Fig. 5a,b). In 0.15380 mol l−1 NaCl, the millimetre-scale actuator bends towards the cathode in about 30 s, whereas the micrometre-scale actuator bends towards the anode in about 0.3 s (Extended Data Fig. 5c,d).

Mechanism in DI water

For the millimetre-scale mechanism in DI water (Extended Data Fig. 5a), under the applied voltage, electrolysis produces H+ near the anode and OH− near the cathode. Because the AAc-co-AAm network is pH-sensitive, acidic conditions convert –COO− to –COOH (reducing repulsion force and causing contraction), whereas alkaline conditions convert –COOH to –COO− (increasing repulsion and causing swelling). The bath-induced pH gradient therefore shrinks the anode-side region and swells the cathode-side region, resulting in bending towards the anode[42].

By contrast, for the micrometre-scale mechanism in DI water (Extended Data Fig. 5b), the micrometre-scale hydrogel bends towards the cathode. Here fixed –COOH groups partially dissociate into –COO− and mobile H+. To maintain electroneutrality, the dissociated H+ ions largely remain confined within the hydrogel. Under an external field, these H+ ions migrate and accumulate on the cathode-facing side, locally lowering pH and inducing network contraction, which bends the hydrogel towards the cathode.

Mechanism in 0.15380 mol l−1 NaCl

For the millimetre-scale mechanism in 0.15380 mol l−1 NaCl (Extended Data Fig. 5c), in a saline environment, the external field drives the migration of all mobile ions in the bath. The fixed negative charges of the hydrogel influence ion partitioning at the gel–solution interface42,43,44,45, producing non-uniform ion concentrations across four regions (gel anode side: region 2; gel cathode side: region 1; solution anode side: region 4; solution cathode side: region 3). According to Flory’s theory[51], the local osmotic pressure difference is \(\Delta {\rm{\pi }}={RT}\sum _{i}({c}_{i{\rm{g}}}-{c}_{i{\rm{s}}})\), in which Δπ is the pressure difference, cig is the ion concentration in the hydrogel, cis is the ion concentration in solution, R is the gas constant and T is temperature. Under steady-state conditions, the osmotic pressure difference on the anode side \(\Delta {{\rm{\pi }}}_{\text{Anode side}}={RT}\sum _{i}\,({c}_{i{\rm{region}}2}-{c}_{i{\rm{region}}4})\) exceeds that on the cathode side \(\Delta {{\rm{\pi }}}_{\text{Cathode side}}={RT}\sum _{i}({c}_{i{\rm{region}}1}-{c}_{i{\rm{region}}3})\). This imbalance causes the anode side to swell more, bending the hydrogel towards the cathode.

At the microscale, the dominant factor is the ion concentration gradient inside the hydrogel rather than at the interface. The relevant osmotic pressure is \(\Delta {{\rm{\pi }}}_{\text{Inside the hydrogel}}=RT\sum _{i}({c}_{i{\rm{r}}{\rm{e}}{\rm{g}}{\rm{i}}{\rm{o}}{\rm{n}}1}-{c}_{i{\rm{r}}{\rm{e}}{\rm{g}}{\rm{i}}{\rm{o}}{\rm{n}}2})\), which remains positive, making the cathode side swell more and driving bending towards the anode (Extended Data Fig. 5d).

Osmotic pressure analysis

Previously reported centimetre-scale and millimetre-scale hydrogel actuators operated in enclosed solution environments, in which the hydrogel was immersed in a bath equipped with two electrodes on the sidewalls44,50,52 (Extended Data Fig. 6a). In such systems, most ions in the bath are consumed in establishing concentration gradients across the hydrogel–solution interfaces, generating osmotic pressure differences that drive the hydrogel to bend towards the cathode42,43,44.

By contrast, our microscale hydrogel cilia operate within a localized region (200 µm × 200 µm × 90 µm) inside a much larger bath (4 cm × 4 cm × 3 mm) (Extended Data Fig. 6b). The large bath volume serves as an ion reservoir, allowing rapid diffusion of external ions into the actuation region (Extended Data Fig. 6b (ii)) and thereby preventing the formation of substantial ion concentration gradients or osmotic pressure differences across the hydrogel–solution interfaces.

To further validate this reason, osmotic-pressure simulations are performed under two distinct scenarios. Here osmotic pressure refers to the interfacial osmotic-pressure difference that governs centimetre-scale and millimetre-scale hydrogel actuation (as defined in Extended Data Fig. 5c). Direct comparison between simulations at different physical scales is not meaningful because the results would not be dimensionally consistent. Therefore, both simulations are conducted at the micrometre scale, with the only variable being the presence or absence of ion sources at the boundaries.

Case 1: macroscale actuation mimicked at the microscale (Extended Data Fig. 6c (i)). The simulation domain is a 2D region of 200 µm × 150 µm containing a hydrogel of dimensions 10 µm × 90 µm, reproducing the relative size ratio between the hydrogel and the bath in the centimetre–millimetre-scale experiments. No external ion source is applied, corresponding to the enclosed environment characteristic of macroscale systems.

Case 2: microscale actuation with ion exchange (Extended Data Fig. 6c (ii)). The geometry and parameters are identical to case 1, except that ion-source boundary conditions are imposed on three sides of the simulation domain, allowing continuous ion exchange between the system and the surroundings.

As shown in Extended Data Fig. 6d, introducing ion sources at the boundaries markedly reduced the osmotic-pressure difference across the hydrogel–solution interface. This result confirms that, compared with the macroscale condition, the osmotic contribution is greatly diminished at the microscale and is no longer the dominant mechanism governing hydrogel actuation.

Coupled electro-chemo-mechanical model

A fully coupled electro-chemo-mechanical model is developed here. The model simultaneously resolves: (1) ionic concentration distribution under an external electric field and the fixed charges of the hydrogel network; (2) the resulting osmotic body forces generated by non-uniform ion distributions; and (3) the gel deformation driven by these body forces. At this stage, the model focuses on explaining the deformation of hydrogels in ionic solutions under external electric fields and does not include fluid–structure interactions between the gel and the surrounding liquid.

Ionic concentration distribution

The Nernst–Planck equation describes the ion concentration in a charged hydrogel network environment:

$${J}_{\alpha }=-\,{D}_{\alpha }{\rm{\nabla }}{c}_{\alpha }-{z}_{\alpha }{\mu }_{\alpha }{c}_{\alpha }\nabla \O +{c}_{\alpha }v$$

(1)

in which Jα is the ion flux (mol (m^2 s)−1), Dα is the diffusion coefficient of ion species α, cα is the concentration of ion species α, zα is the charge number of the ion (valence), μα is the mobility of the ion, Ø is the electric potential and v is the velocity of the fluid.

The change in ion concentration cα over time is governed by the continuity equation, which states that the change rate of the ion concentration is equal to the net flux of ions plus any sources or sinks (chemical reactions, for instance). It is expressed as:

$$\frac{{\rm{\partial }}{c}_{\alpha }}{{\rm{\partial }}t}+{\rm{\nabla }}\cdot {J}_{\alpha }={r}_{\alpha }({c}_{\beta })$$

(2)

in which \(\frac{{\rm{\partial }}{c}_{\alpha }}{{\rm{\partial }}t}\) is the ion concentration change rate, ∇·Jα represents the net flow of ions into or out of a region and rα(cβ) is a source term representing the creation or consumption of ion α owing to chemical reactions or other processes involving species β.

By substituting the Nernst–Planck equation (equation (1)) into the continuity equation (equation (2)), we obtain:

$$\frac{{\rm{\partial }}{c}_{\alpha }}{{\rm{\partial }}t}={\rm{\nabla }}\cdot [{D}_{\alpha }{\rm{\nabla }}{c}_{\alpha }+{z}_{\alpha }{\mu }_{\alpha }{c}_{\alpha }{\rm{\nabla }}\O -{c}_{\alpha }v]+{r}_{\alpha }({c}_{\beta })$$

(3)

The Poisson equation relates the electric potential distribution to the charge density in the system. It is given by

$${{\rm{\nabla }}}^{2}\O =-\frac{\rho }{\in }$$

(4)

in which ρ is the charge density (total charge per unit volume) and ε is the permittivity of the medium, expressed as εrε_0, in which ε_0 is the vacuum permittivity and εr is the relative permittivity of the medium.

In this work, the AAc-co-AAm hydrogel network contains –COOH groups, whose ionization equilibrium is influenced by H+ migration. This equilibrium affects the network charge and mobility of other ions. This equilibrium is included in the modelling by

$$\frac{{C}_{{\text{R-COO}}^{-}}\cdot {C}_{{{\rm{H}}}^{+}}}{{C}_{\text{R-COOH}}-{C}_{{\text{R-COO}}^{-}}}={K}_{a}=5.6\times {10}^{-5}$$

(5)

in which \({C}_{{\text{R-COO}}^{-}}\) is the concentration of the dissociated function group in the hydrogel, \({C}_{{{\rm{H}}}^{+}}\) is the H+ ion concentration in the hydrogel and CR-COOH is the concentration of the carboxyl group in the hydrogel. The value of CR-COOH is obtained from the initial hydrogel solution. Equations (3)–(5) collectively govern the ion concentration distribution.

Forces generated by non-uniform ion distributions

The force induced by ionic distributions can be separated into two contributions. The first contribution comes from the H+ effect (pH-dependent). H+ modulate the dissociation equilibrium of –COO− groups within the hydrogel network. Variations in the fixed –COO− concentration govern network swelling or contraction. The corresponding force can be expressed as:

$${f}_{{\rm{pH}}}=E(\,-\,{\rm{\nabla }}{({FC}}_{{\text{R-COO}}^{-}}))$$

(6)

in which F is the Faraday constant and E is the local electric field, which can be obtained from

$$E=-\,{\rm{\nabla }}\O .$$

(7)

The second contribution comes from the local pressure difference induced by ion concentration. According to Flory’s theory[51], the local pressure relates to the ion concentration:

$${\rm{\pi }}={RT}\sum _{i}{c}_{i}$$

(8)

in which π is the local pressure, ci is the ion concentration, R is the gas constant and T is temperature.

The force generated by the non-uniform ion concentration can be expressed by the negative gradient of the local pressure as

$$f=-\,{\rm{\nabla }}{\rm{\pi }}=-\,{\rm{\nabla }}({RT}\sum _{i}{c}_{i}).$$

(9)

The local electric potential Ø, function group concentration \({C}_{{\text{R-COO}}^{-}}\) and ion concentration ci can be calculated from equations (3)–(5).

Mechanical deformation

The deformation of the hydrogel was modelled as a nonlinear hyperelastic material considering geometric nonlinearity. To describe its constitutive behaviour, the first-order compressible Ogden model was used. The strain energy density function is expressed as

$$W=\frac{\mu }{\alpha }({\lambda }_{1}^{\alpha }+{\lambda }_{2}^{\alpha }+{\lambda }_{3}^{\alpha }-3)+\frac{1}{D}{(J-1)}^{2}$$

(10)

in which λ_1, λ_2 and λ_3 are the principal stretch ratios, J = λ_1λ_2λ_3 is the volume ratio, μ and α are material constants and D is a compressibility parameter related to the bulk modulus K = 2/D. The Poisson’s ratio ν = 0.42 is chosen to define the degree of compressibility. The material parameters (μ, α) are determined by fitting the Ogden model to the data obtained from atomic force microscopy tests and are listed in Extended Data Table 2.

PIV analysis

The 2D in-plane velocity is calculated using the open-source software PIVlab[53]. Background subtraction is applied to the particle images to enhance image quality. The multipass fast Fourier transform window deformation algorithm is used to improve the accuracy of displacement estimation. The interrogation area is initially set to 64 × 64 pixels with a step size of 32 pixels, corresponding to a 50% overlap between adjacent interrogation windows. In the second pass, the interrogation area is reduced to 32 × 32 pixels with a step size of 16 pixels. For higher precision, sub-pixel displacements are estimated using a Gaussian 2 × 3 point estimator. The PIV flow pattern results are shown in Fig. 4.

It should be noted that the patterned electrodes on the substrate influence the accuracy of PIV analysis. For cases in Fig. 4a–c, in which the electrode density is relatively low, the PIV results are reliable. For case in Fig. 4d–g, the electrode density is higher and the trajectories of the PIV tracer particles overlap substantially with the underlying electrodes, leading to larger errors in velocity quantification.

2D flow simulations

For the numerical simulation of the interaction between the cilia array and fluid, we use the hybrid finite difference/finite element immersed boundary method[54], implemented in the open-source software IBAMR, a widely tested C++ framework for the immersed boundary method. The immersed boundary formulation of the problem describes the momentum and velocity of the coupled fluid–structure system in Eulerian form, whereas the deformation and elastic response of the immersed structure is in the Lagrangian form. This study uses 2D simulations, as they effectively capture the flow patterns. Let \({\bf{x}}=({x}_{1},{x}_{2})\in \Omega \subset {{\mathbb{R}}}^{2}\) denote Cartesian physical coordinates, in which Ω represents the physical region that is occupied by the coupled fluid–structure system, let \({\bf{X}}=({X}_{1},{X}_{2})\in W\subset {{\mathbb{R}}}^{2}\) denote Lagrangian material coordinates that are attached to the structure, in which W is the Lagrangian domain, and let χ(X, t) ∈ Ω denote the physical position of material point X at time t. The strong form of the equations of motion is:

$$\begin{array}{l}\rho \frac{{\rm{D}}u}{{\rm{D}}t}({\bf{x}},t)=-\,{\rm{\nabla }}p({\bf{x}},t)+\mu {{\rm{\nabla }}}^{2}{\bf{u}}({\bf{x}},t)+{{\bf{f}}}^{{\bf{c}}}({\bf{x}},t)\\ {\rm{\nabla }}\cdot {\bf{u}}({\bf{x}},t)=0\end{array}$$

(11)

$$\begin{array}{l}\begin{array}{c}{{\bf{f}}}^{{\bf{c}}}({\bf{x}},t)={\int }_{U}{{\rm{\nabla }}}_{{\bf{X}}}\cdot {{\mathbb{P}}}^{{\bf{e}}}({\bf{X}},t)\delta ({\bf{x}}-\chi ({\bf{X}},t)){{\bf{f}}}^{{\bf{e}}}({\bf{x}},t){\rm{d}}{\bf{X}}\\ \,-{\int }_{{\rm{\partial }}U}{{\mathbb{P}}}^{{\bf{e}}}({\bf{X}},t){\bf{N}}({\bf{X}})\delta ({\bf{x}}-\chi ({\bf{X}},t)){{\bf{f}}}^{{\bf{e}}}({\bf{x}},t){\rm{d}}{\bf{X}}\\ \,\frac{{\rm{\partial }}\chi }{{\rm{\partial }}t}({\bf{X}},t)={\int }_{\Omega }{\bf{u}}({\bf{x}},t)\delta ({\bf{x}}-\chi ({\bf{X}},t)){\rm{d}}{\bf{X}}\end{array}\end{array}$$

(12)

in which ρ is the mass density, u(x, t) is the Eulerian velocity field, μ is the dynamic viscosity, fe(x, t) is the Eulerian elastic force density, \({{\mathbb{P}}}^{{\bf{e}}}({\bf{X}},t)\) is the first Piola–Kirchhoff elastic stress tensor, δ(x) is the 2D delta function and N(X) is the normal vector along the fluid–solid interface. In the computations of this study, the physical domain is Ω = [−L, L][−L, L], in which L is 600 μm for simulations corresponding to fluid experiments 1–3 and 400 µm for fluid experiments 4–7. A zero-gradient boundary condition is applied to boundaries. A staggered-grid finite difference scheme is used to discretize the incompressible Navier–Stokes equations in space. The spatial resolution is Δx = L/128 for simulations 1–3 and Δx = L/64 for simulations 4–7. The total number of the Cartesian grid is \({\mathcal{O}}({10}^{5})\). The circular section of the cilia is discretized into a mesh of triangular elements with an average node spacing of ΔX = L/128. Time-stepping is performed using an implicit scheme proposed by Newren et al.[55]. The time step size is adjusted to satisfy the Courant–Friedrichs–Lewy condition, with a stability number of approximately 0.1.

In these 2D flow-field simulations, solid spheres with a diameter of 10 µm are used to approximate the hydrogel cilia. Under the prescribed motions, interactions between the sphere edges and the surrounding fluid generated relatively high flow velocities. However, in reality, hydrogel cilia are porous and soft materials and their interaction with the fluid under identical motions does not produce flow velocities of the same magnitude as in the simulations. The primary aim of the simulation is to qualitatively predict flow-field patterns, not to precisely quantify flow velocities, so the simulated flow patterns are shown in this work (Fig. 4a–g (2)).

Data availability

The biological cilia data are reproduced from the work of Okada et al.[8]. All data that support the findings of this study are provided in the main text, methods and the Supplementary Information.

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Acknowledgements

We thank W. Kang, M. Zhang and X. Lyu for their assistance with the experiments. This work is supported by the Max Planck Society, European Research Council (ERC) Advanced Grant SoMMoR project with grant no. 834531, German Research Foundation (DFG) Soft Material Robotic Systems (SPP 2100) Priority Programme with grant no. 2197/3-1, Max Planck Queensland Centre for the Materials Science of Extracellular Matrices and Hong Kong University of Science and Technology Startup Fund with grant no. R9910.

Funding

Open access funding provided by Max Planck Society.

Author information

Authors and Affiliations

1. Physical Intelligence Department, Max Planck Institute for Intelligent Systems, Stuttgart, Germany

Zemin Liu, Ziyu Ren, Chunxiang Wang, Jongkuk Ko & Metin Sitti

2. Department of Information Technology and Electrical Engineering, ETH Zürich, Zürich, Switzerland

Zemin Liu & Chunxiang Wang

3. School of Medicine and College of Engineering, Koç University, Istanbul, Turkey

Che Wang & Metin Sitti

4. School of Mechanical Engineering and Automation, Beihang University, Beijing, China

Ziyu Ren

5. Bioinspired Autonomous Miniature Robots Group, Max Planck Institute for Intelligent Systems, Stuttgart, Germany

Wenkang Wang, Shanyuan Song, Chong Hong & Wenqi Hu

6. International Institute for Interdisciplinary and Frontiers, Beihang University, Beijing, China

Wenkang Wang

7. Department of Chemical and Biological Engineering, Gachon University, Seongnam, Republic of Korea

Jongkuk Ko

8. Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong

Xi Chen & Wenqi Hu

9. Max Planck Institute for Solid State Research, Stuttgart, Germany

Hongguang Wang

10. Division of Integrative Systems and Design, The Hong Kong University of Science and Technology, Kowloon, Hong Kong

Wenqi Hu

11. Cheng Kar-Shun Robotics Institute (CKSRI), The Hong Kong University of Science and Technology, Kowloon, Hong Kong

Wenqi Hu

Contributions

Z.L., W.H. and M.S. proposed and designed the research. Z.L. performed the experiments and analysed the data, with the help of Z.R., Chunxiang Wang, W.W., J.K., C.H., X.C. and H.W. Z.L. and Che Wang developed the fully coupled model. Z.L. and W.W. performed the fluid-flow simulations. Z.L. and S.S. built the control circuit. M.S. and W.H. supervised the research. The manuscript was written by Z.L., W.H. and M.S., with input from all authors. All authors discussed the results and commented on or edited the manuscript.

Corresponding authors

Correspondence to Wenqi Hu or Metin Sitti.

Ethics declarations

Competing interests

Max Planck Innovation filed a patent application on behalf of Z.L., W.H. and M.S. based on the methods and results presented here (application no. EP 25215159.2). The other authors declare no competing interests.

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Nature thanks Daniel Ahmed, Benjamin Gorissen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Comparison between hydrogels fabricated by conventional flood exposure and TPP.

a, (i) Schematic of the conventional flood-exposure fabrication process. (ii) Schematic of the TPP fabrication process, which allows tuning of printing by adjusting parameters such as hatching and slicing. b, (i) SEM image of a hydrogel fabricated by flood exposure, showing pore sizes in the range 10–30 µm. (ii) SEM and TEM images of the TPP-printed hydrogel. At this magnification, the SEM image shows a nearly featureless surface without discernible pores. The TEM image shows pore sizes ranging from 20 to 80 nm. c, Schematics illustrating the influence of pore size on electro-osmotic flow. (i) Flow in a micrometre-scale ionic hydrogel channel. (ii) Enhanced flow in a nanometre-scale ionic hydrogel channel. d, Dynamic performance of hydrogels fabricated by different methods. (i) Millimetre-scale (left) and micrometre-scale (right) hydrogels fabricated by flood exposure. (ii) Micrometre-scale hydrogel fabricated by TPP printing.

Extended Data Fig. 2 Single-layer microelectrodes fabrication process.

The microelectrodes are fabricated using a conventional lift-off method. A detailed step-by-step procedure is provided in Methods.

Extended Data Fig. 3 Hydrogel microactuator printing.

a–c, The printing process of the hydrogel microactuator. d, Slicing and hatching parameters used during TPP printing. e, In this work, the hatching angle between successive layers was offset by 45°, with anticlockwise rotation, to ensure uniformity of the printed structures.

Extended Data Fig. 4 Comparison between biological cilia and artificial hydrogel cilia.

The left column shows mouse ventral nodal cilia[8] (with permission from the publisher, Elsevier) and the right column shows the hydrogel cilia developed in this work. a, Cilia arrays (Supplementary Video 1). b, 3D non-reciprocal motions (Supplementary Video 1). c, Reconfigurable motions (Supplementary Videos 2 and 3). d, Counter-rotating motions of two adjacent cilia (Supplementary Video 4). Scale bars, 8 µm (a (i)); 80 µm (a (ii)); 5 µm (b (i)); 40 µm (b (ii)); 5 µm (c (i)); 40 µm (c (ii)); 5 µm (c (iii)); 40 µm (c (iv)); 6 µm (d (i)); 50 µm (d (ii)).

Extended Data Fig. 5 Working mechanism comparison between millimetre hydrogel and micrometre hydrogel.

a, The bending mechanism of millimetre hydrogel in DI water. b, The bending mechanism of micrometre hydrogel in DI water. c, The bending mechanism of millimetre hydrogel in 0.15380 mol l−1 NaCl solution. d, The bending mechanism of micrometre hydrogel in 0.15380 mol l−1 NaCl solution. Scale bars, 10 mm (a); 100 µm (b); 15 mm (c); 100 µm (d).

Extended Data Fig. 6 Osmotic pressure comparison between millimetre hydrogel and micrometre hydrogel.

a, (i) Photo of the millimetre-scale hydrogel actuation setting. (ii) Schematic illustrating the millimetre-scale hydrogel actuation setting. b, Schematics to show the working environments of the micrometre-scale hydrogel actuator. (i) Cross-sectional view of the gel microcilia working bath. The bath measures 4 cm (length) × 4 cm (width) × 3 mm (height) and is filled with solution, with NaCl solution used here as a typical example. The gel microcilia array and microelectrode array are integrated at the bottom of the bath. The hydrogel working region occupies a small portion of the bath, highlighted by the dashed red box. (ii) Enlarged view of the hydrogel working region. Two brown dots represent microelectrodes positioned at 0 µm and 200 µm, respectively. A voltage of 1.5 V is applied to the 0-µm electrode and 0 V to the 200-µm electrode. During actuation, mobile ions from the surrounding solution freely enter the working region, as indicated by black arrows. c, 2D simulation settings without and with ion sources. (i) Simulation settings to mimic the millimetre-scale actuation; there are no ion sources applied at the boundaries. (ii) Simulation settings for micrometre-scale actuation; ion sources are applied at the boundaries to mimic the external buffer ions. d, Osmotic pressure simulation in 0.01920 mol l−1 NaCl. Here osmotic pressure refers to the interfacial osmotic-pressure difference that governs centimetre-scale and millimetre-scale hydrogel actuation (as defined in Extended Data Fig. 5c). (i) Osmotic pressure Δπ/RT data for the simulation without ion source. The anode-side osmotic pressure is much larger than the cathode-side pressure. This pressure difference bends the hydrogel towards the cathode and aligns with previous centimetre-scale hydrogel results[43]. (ii) Osmotic pressure data for the simulation with ion sources. The osmotic pressure difference between the anode and the cathode decreases substantially owing to the external buffer mobile ions.

Extended Data Fig. 7 Further characterizations of TPP-printed hydrogels and hydrogel microactuators.

a, Young’s modulus of hydrogels with varying AAc ratios. Data are presented as mean ± s.d. (n  =  20 tests). b, Sperm number of 30 wt% AAc hydrogel cilia at different actuation frequencies (diameter 10 µm, height 90 µm). c, Effect of electric-field intensity on the dynamic performance of the hydrogel microactuator. A 1.5-V potential is applied on electrodes with a distance between 75 and 230 µm to generate the corresponding electric-field intensity (30 wt% AAc, diameter 10 µm, height 90 µm, DI water). Data are presented as mean ± s.d. (n  =  6 samples). d, Effect of cilium height on dynamic performance. Cilia with diameters of 10 µm and heights of 90, 70 and 50 µm are tested (30 wt% AAc, DI water). Data are presented as mean ± s.d. (n  =  6 samples for 90 µm height, n  =  4 samples for 70 and 50 µm height). e, Effect of printing power on the dynamic performance of hydrogel actuators (45 wt% AAc, diameter 10 µm, height 90 µm, DI water). Data are presented as mean ± s.d. (n  =  4 samples). f, Effect of photoinitiator concentration on the dynamic performance of hydrogel actuators (15 wt% AAc, diameter 10 µm, height 90 µm, DI water). Data are presented as mean ± s.d. (n  =  4 samples for 7.5 wt% and 15 wt% photoinitiator, n = 6 samples for 22.5 wt% photoinitiator).

Extended Data Fig. 8 Further configurations and application scenarios of the TPP-printed hydrogel microactuators.

a,b, Hydrogel cilia patterned on a 3D curved surface. a, Right-to-left flow on 3D surface. Left, SEM image with arrows indicating cilia rotation. Right, PIV result with the flow region highlighted by a red dashed box. b, Top-to-bottom flow on 3D surface. Left, SEM image with arrows indicating rotation directions. Right, PIV result with the flow region marked by a red dashed box. c, Four hydrogel cilia mounted on a 3D pyramid frame structure rotate synchronously to generate vortex flows. Left, SEM image showing the pyramid frame and hydrogel pillars, with arrows marking rotation directions. Right, PIV result of the flow field. d, Anticlockwise and clockwise helical hydrogel actuators. Left, SEM images with arrows marking motion directions. Right, corresponding PIV flow fields. e, Rotary micromachine array integrated with hydrogel microactuators. The first row is the SEM image of the array, with arrows marking actuator and machine positions. Following are video frames showing rotary motion, with arrows indicating the bending direction of hydrogel actuators and the joint rotation of the mechanical structure under actuation. Scale bars, 240 µm (a); 240 µm (b); 40 µm (c); 50 µm (d); 200 µm (e).

Extended Data Table 1 Comparison of the proposed gel microcilia in this work with the previously reported ciliary actuation works in the literature

Extended Data Table 2 Material parameters of the Ogden model for hydrogels at different AAc concentrations

Supplementary information

Supplementary Information

This file contains Supplementary Notes, Supplementary Figures, legends for the Supplementary Videos and Supplementary References.

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Supplementary Videos

Supplementary Videos 1–31.

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Liu, Z., Wang, C., Ren, Z. et al. 3D-printed low-voltage-driven ciliary hydrogel microactuators. Nature 649, 885–893 (2026). https://doi.org/10.1038/s41586-025-09944-6

- Received: 21 March 2025

- Accepted: 19 November 2025

- Published: 14 January 2026

- Version of record: 14 January 2026

- Issue date: 22 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09944-6

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Global subsidence of river deltas

L. O. Ohenhen,

M. Shirzaei,

J. L. Davis,

A. Tiwari,

R. Nicholls,

O. Dasho,

N. Sadhasivam,

K. Seeger,

S. Werth,

A. J. Chadwick,

F. Onyike,

J. Lucy,

C. Atkins,

S. Daramola,

A. Ankamah,

P. S. J. Minderhoud,

J. Olsemann &

…

G. C. Yemele

Nature volume 649, pages 894–901 (2026)

Abstract

River deltas sustain dense human populations, major economic centres and vital ecosystems worldwide1,2. Rising sea levels and subsiding land threaten the sustainability of these valuable landscapes with relative sea-level rise and associated flood, land loss and salinization hazards1,2,3. Despite these risks, vulnerability assessments are impeded by the lack of contemporary, high-resolution, delta-wide subsidence observations[4]. Here we present spatially variable surface-elevation changes across 40 global deltas using interferometric synthetic aperture radar. Using this dataset, we quantify delta surface-elevation loss and show the prevalence and severity of subsidence in river deltas worldwide. Our analysis of three key anthropogenic drivers of delta elevation changes shows that groundwater storage has the strongest relative influence on vertical land motion in 10 of the 40 deltas. The other deltas are either influenced by multiple drivers or dominated by sediment flux or urban expansion. Furthermore, we find that contemporary subsidence surpasses absolute (geocentric) sea-level rise as the dominant driver of relative sea-level rise for most deltas over the twenty-first century. These findings suggest the need for targeted interventions addressing subsidence as an immediate and localized challenge, in parallel with broader efforts to mitigate and adapt to climate change-driven global sea-level rise.

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Main

River deltas, which occupy only 1% of land area, are among the most vital landforms on Earth[1]. Globally, deltas host an estimated 350–500 million people (representing 4–6% of the global population), including 10 of the 34 megacities of the world1,2,3. These dynamic landforms serve important socioeconomic, ecological and energy-related functions5,6. They sustain agricultural productivity and fisheries, their ecosystems sustain important biodiversity and their infrastructure, such as ports and transportation networks, anchors maritime trade vital to national, regional and global economies5,6,7.

This recognized importance, which makes deltas indispensable, also increases their exposure to compounding climatic, environmental and anthropogenic threats2,8,9,10. As low-lying landforms, with extensive areas less than 2 m above sea level[11], deltas are acutely susceptible to rising sea level, storm surge, land subsidence, shifting temperature and rainfall patterns, and other environmental pressures, which are amplified by climate change2,3,7,8,9,10,12,13. These pressures degrade agricultural land; disrupt freshwater availability; exacerbate coastal and fluvial flooding; promote wetland loss, saltwater intrusion and shoreline retreat; and threaten infrastructure in deltas2,5,6,14,15. Beyond direct physical impacts, the interplay of these hazards also creates potential cascading socioeconomic consequences. For example, land loss and freshwater scarcity may drive displacement and migration, heightening competition for dwindling resources and fuelling social tensions16,17. Together, these intersecting climatic, environmental, human-driven pressures and multi-hazards render deltas the most fragile landscapes on Earth, with their low elevation and high urban exposure placing them at the forefront of climate and environmental risks3,5,9 (Extended Data Fig. 1).

Among these threats, land subsidence often emerges as an important contributor to risks in global river deltas1,2,3,12,18,19. This predominantly human-driven process is just as, or more, influential than climate-induced sea-level rise (SLR) in the twenty-first century3,20,21, with subsidence control now providing an important component of future coastal adaptation strategies22,23. Despite its perceived importance, land subsidence remains underrepresented in global assessments of delta vulnerability9,24 largely because of the lack of modern, high-resolution subsidence observations4,13. Even with recent advances in space-based geodetic monitoring, high-resolution synoptic measurements of subsidence rates remain scarce, as most observations remain restricted to main urban centres within deltas, neglecting rural and ecologically critical zones[4]. Understanding delta-wide spatial characteristics of contemporary land elevation changes is important for informing their sustainable management.

Here, we present high-spatial-resolution datasets of surface-elevation change derived from Sentinel-1 synthetic aperture radar (SAR) interferometry across 40 deltas globally (Fig. 1). These datasets capture delta-wide temporal trends, subsidence rates and horizontal motion at 75 m resolution, spanning five continents and 29 countries. Our analysis encompasses all major river deltas with a population exceeding 3 million people[4], historically recognized sinking deltas[2] and representatives of less-populated, understudied deltas of regional ecological and economic importance (Methods).

Fig. 1: Land subsidence in global deltas.

Each circle represents the location of the 40 deltas evaluated in this study, colour-coded by the average land subsidence rate. The size of the circle represents the percentage of the delta area subsiding faster than geocentric SLR. For visualization purposes, the geocentric SLR rate is shown as a colour gradient over entire watersheds or basins, although this does not represent the actual extent of exposure. Global coastlines are based on public-domain data from the CIA World DataBank II (using GSHHG (Global Self-consistent, Hierarchical, High-resolution Geography Database)), distributed with MATLAB. The delta basin polygons were obtained along with the sediment flux dataset from ref. [29].

Source data

Global analysis of delta subsidence

We measured the spatial patterns and rates of subsidence in 40 deltas by analysing the complete archive of the Sentinel-1 SAR dataset between 2014 and 2023 using advanced multitemporal interferometric SAR (InSAR) analysis (Methods). InSAR measures surface-elevation changes, capturing vertical land motion (VLM), sediment deposition and erosional processes13,25. For consistency, to reflect both VLM and surface-elevation change in the deltas, we use the terms VLM or elevation gain or loss to describe net surface-elevation change across all delta environments, with positive values indicating uplift or elevation gain and negative values indicating subsidence or net elevation loss. Throughout this study, negative VLM is quoted with negative signs and references land subsidence rates, whereas only the absolute values are reported when presenting subsidence rates.

Our analysis shows that subsidence threatens deltas globally, with the delta-scale average rate of VLM on all deltas indicating subsidence (Fig. 1). In 12 out of 40 deltas, the average sinking rate is moderate, at less than 2 mm yr−1. By contrast, more than half of the deltas exhibit subsidence rates exceeding 3 mm yr−1, and in 13 of these deltas (Nile, Po, Vistula, Ceyhan, Brahmani, Mahanadi, Chao Phraya, Mekong, Red, Ciliwung, Brantas, Godavari and Yellow River), the average subsidence rates exceed the current estimates of global SLR (that is, about 4 mm yr−1). Among these, the Chao Phraya (Thailand), Brantas (Indonesia) and Yellow River (China) deltas show an average sinking rate of more than twice the current global SLR rate. To further highlight the severity of subsidence in deltas, we compared the subsidence with the regional geocentric SLR rates for the twenty-first century (2001–present). In 18 of the 40 deltas (the Nile, Po, Vistula, Ceyhan, Rioni, Brahmani, Mahanadi, Ganges–Brahmaputra, Godavari, Chao Phraya, Mekong, Red River, Ciliwung, Brantas, Amazon, Parana, Pearl and Yellow River), the average rate of local land subsidence is greater than the rate of regional geocentric SLR (Fig. 1 and Supplementary Table 1). However, in almost every delta (except Rio Grande) at least 1% of the delta area is subsiding faster than both global and geocentric sea levels (Fig. 1 and Supplementary Table 1).

Among all deltas, we find that at least 35% of the area is sinking, and in 38 deltas (excluding Neva and Fraser), more than 50% of the delta area is sinking (Fig. 2a). Of the 40 deltas, 19 show widespread subsidence patterns, with greater than 90% of the delta area affected by subsidence (for example, Mississippi, Niger, Nile, Rhine–Meuse, Po, Vistula, Brahmani, Mahanadi, Ganges–Brahmaputra, Chao Phraya, Mekong and Brantas deltas). Deltas with notable subsiding areas with greater than 50% of the delta area sinking faster than 5 mm yr−1 include the Chao Phraya (94% of delta area), Nile (80%), Brahmani (77%), Po (74%), Mahanadi (69%), Brantas (66%), Vistula (57%), Yellow River (53%) and Mekong (51%) deltas (Fig. 2a and Supplementary Table 1). In sum, we estimate that a total delta area of 460,370 km^2 is exposed to subsidence. If we consider a global habitable geomorphic area of 710,000–855,000 km^2 for deltas6,26, approximately 54–65% of global delta areas are sinking just from the analysis of the 40 deltas. By region, South Asia, East Asia and Southeast Asia, with 17 representative deltas, have the greatest exposure to subsidence, with 274,000 km^2 of delta area subsiding. Africa, South America, North America and Europe have total subsiding delta areas of 78,800 km^2, 39,800 km^2, 37,800 km^2, and 30,000 km^2, respectively. Seven large deltas—Ganges–Brahmaputra, Nile, Mekong, Yangtze, Amazon, Irrawaddy and Mississippi deltas—contribute about 57% of the total subsiding delta area, with a combined area of 265,000 km^2. Coastal cities such as Alexandria (Nile), Bangkok (Chao Phraya), Dhaka and Kolkata (Ganges–Brahmaputra), Shanghai (Yangtze), Yangon (Irrawaddy), Cần Thá (Mekong), Thái Bình (Red River), Niigata (Chikuma-gawa), Jakarta (Ciliwung), Surabaya (Brantas) and Dongying (Yellow River) are experiencing subsidence at rates equal to or exceeding the delta-wide averages, indicative of the intensity of subsidence and elevation loss processes in cities on deltas.

Fig. 2: Spatial pattern of VLM across global deltas.

a, Proportion of each delta exposed to different rates of subsidence. Note that only subsiding areas are represented in each bar, and areas of uplift within each delta are omitted to emphasize the extent of elevation loss. b–m, Spatial maps of VLM rates for the Fraser (Canada) (b), Mississippi (the USA) (c), Parana (Argentina) (d), Niger (Nigeria) (e), Nile (Egypt) (f), Po (Italy) (g), Ganges–Brahmaputra (India–Bangladesh) (h), Chao Phraya (Thailand) (i), Mekong (Vietnam) (j), Red River (Vietnam) (k), Pearl (China) (l), Yellow River (China) (m) deltas. Positive VLM (green–purple hues) suggests uplift or elevation gain, whereas negative VLM (yellow–orange–red hues) indicates land subsidence. The spatial VLM maps for the other 28 deltas are shown in Extended Data Figs. 2–4. Background image in b–m is Esri, streets-dark. Scale bars, 5 km (b); 50 km (c,f,h,j); 20 km (d,e,i,k,l,m); 10 km (g).

Source data

Furthermore, we observe non-uniform spatially variable VLM within individual deltas, reflecting the complex interplay of natural and anthropogenic processes2,5,13,27 (Fig. 2 and Extended Data Figs. 2–4). Although all deltas exhibit an overall trend of subsidence, localized and broad zones of uplift, which vary from 0 mm yr−1 to greater than 5 mm yr−1 are observed in some areas (Fig. 2b,d,k,m, and Extended Data Figs. 2e,f,i,j,l and 3c,f). In some deltas (for example, Wouri, Zambezi, Indus, Ciliwung and Yellow River), the observed uplift or elevation-gaining parts correlate with patterns of horizontal land motion (Extended Data Figs. 5–7). Possible mechanisms may include sediment redistribution processes potentially driven by river dynamics or growth faulting, either of which can cause localized zones of elevation gain even within a predominantly subsiding deltaic system28,29. This highlights the necessity of comprehensive assessments and models of delta vulnerability to consider not only overall absolute subsidence rates but also the spatial heterogeneity of elevation change dynamics.

Anthropogenic drivers of delta subsidence

All deltas, by their inherent nature, subside over time as recently deposited sediments or in situ organic material compact under their weight30,31,32, a process further influenced by isostatic adjustments and tectonic activity13,27. However, human interventions have accelerated subsidence rates in many of the major deltas of the world, transforming a gradual geological process into an urgent environmental crisis4,20,32. The primary anthropogenic drivers that dominate delta subsidence include excessive groundwater extraction, oil and gas exploitation, and land-use changes associated with urbanization and agriculture4,6,13,20,33,34.

To quantify the relative contributions of anthropogenic factors to delta subsidence and elevation loss, we analysed the relationship between three main anthropogenic drivers—groundwater storage change, sediment flux alteration and urban expansion—and non-glacial isostatic adjustment VLM/subsidence rates across the 40 deltas (Methods and Supplementary Table 2).

Figure 3a and Extended Data Fig. 8 show the interplay of anthropogenic factors and their correlation with subsidence rates across the 40 deltas. Deltas experiencing groundwater storage (GWS) loss (indicative of groundwater extraction), negative sediment flux change (red and yellow hues; reflecting sediment reduction due to upstream human activities) and higher urban population growth tend to have higher rates of subsidence (for example, the Yellow River, Po, Nile, Chao Phraya and Mekong deltas). Conversely, deltas with GWS stability or gain (net increase in groundwater storage), positive sediment flux change (blue colours; sediment surplus) and limited urban expansion show lower subsidence rates (for example, Saloum, Amazon and Ogooué deltas).

Fig. 3: Anthropogenic drivers of land subsidence and elevation loss in global deltas.

a, Bubble plot showing the relationship between VLM rates and anthropogenic drivers across deltas. Plot shows VLM rate (mm yr−1) against GWS rate (mm yr−1). Bubble colours represent sediment flux change (%), in which positive values (blue colours) indicate increased sediment supply due to human activities (and thereby increased potential to gain elevation and compensate subsidence-induced elevation loss), whereas negative values (yellow–orange–red colours) indicate a decline in sediment availability. Bubble size indicates urban fraction change (%), with larger circles representing a greater urban expansion over the twenty-first century. The dashed line represents the MLR fit. See Extended Data Fig. 8 for individual pairwise relationships between each anthropogenic driver and VLM. b, Ternary plot of subsidence rates with nLIME scores.

Source data

The initial multilinear regression (MLR) model, which included interaction terms between the different anthropogenic factors, poorly captured subsidence dynamics on the deltas (R^2 = 0.2 ± 0.1), as it failed to account for nonlinear interactions between the different processes (Fig. 3a). For instance, urban expansion not only directly increases infrastructure loading but also indirectly elevates groundwater demand, thereby compounding aquifer depletion and extraction-induced subsidence, which are synergistic effects that linear models cannot resolve.

To address these limitations, we used a random forest (RF) machine learning approach designed to capture nonlinear relationships and variable interactions. The RF model shows a moderate to strong relationship between the predictors (GWS, sediment flux and urban expansion) and VLM, achieving improved performance over the MLR model (R^2 = 0.6 ± 0.1; RMSE (root mean square error) = 1.9 ± 0.1 mm yr−1; MAE (mean absolute error) = 1.4 ± 0.2 mm yr−1), and capturing complex, non-additive relationships between anthropogenic stressors and subsidence rates (Fig. 3a and Supplementary Fig. 1). However, we observe some underestimation at high subsidence rates (>8.0 mm yr−1) (Supplementary Fig. 1), which probably suggests that natural processes or other anthropogenic predictors (not considered in our analysis) may contribute to subsidence in these highly dynamic deltaic environments.

Note that the primary objective in our analysis is not to predict subsidence rates across deltas, but rather to identify and extract key features that explain the dynamic relationships between the three anthropogenic drivers and subsidence across these deltas. Feature importance analysis from the RF model identifies GWS as the dominant anthropogenic predictor of delta subsidence (0.5 ± 0.2), whereas sediment flux change (0.3 ± 0.2) and urbanization (0.3 ± 0.1) have secondary roles as subsidence rate predictors across these deltas (Fig. 3a and Supplementary Fig. 1b). However, the large standard deviations in feature importance values reflect substantial variability in predictor dominance across subsampled delta subsets, suggesting that the primary contributors to subsidence differ locally depending on the anthropogenic or geomorphic context. To resolve delta-specific mechanisms, we applied local interpretable model-agnostic explanations (LIME), which interprets individual predictions by approximating the RF model locally with simpler, interpretable functions. Deltas with low LIME model fidelity (R^2 < 0.5) were excluded from this interpretative analysis, refining the dataset from 40 to 28 deltas (Methods). The low fidelity scores for some deltas could be due to unaccounted processes (natural and/or other anthropogenic) in our RF model. The retained 28 deltas show improved overall model performance (R^2 = 0.7 ± 0.1; RMSE = 0.4 ± 0.1 mm yr−1; MAE = 0.3 mm yr−1), ensuring reliable interpretation of local feature importance. Normalized LIME feature importance scores (nLIME) showed substantial heterogeneity in predictor dominance (Supplementary Table 2). GWS emerged as the most significant factor across the different deltas (0.6 ± 0.3), whereas sediment flux change (0.3 ± 0.1) and urbanization (0.1 ± 0.1) exhibited lower but context-dependent impacts (Supplementary Fig. 1b).

To assess the dominant influence on land motion across individual deltas, the nLIME for each delta was mapped onto a ternary diagram (Fig. 3b). Of the 28 deltas, 35%, including the Mekong, Ganges–Brahmaputra, Rhine–Meuse, Fraser, Cauvery, Irrawaddy and Red River systems, cluster within the GWS portion of the diagram (nLIMEGWS > 0.7), suggesting that observed GWS changes in these deltas are the primary driver of subsidence among the three anthropogenic variables examined (Fig. 3b and Supplementary Table 2). The Chao Phraya and Yellow River deltas, with the highest average subsidence rates, plot near the centre of the ternary diagram, reflecting relatively balanced contributions from GWS, sediment flux and urban expansion. Sediment flux correlates most closely with elevation changes in deltaic systems, such as the Saloum, Mississippi, Amazon and Rio Grande deltas, suggesting that reduced sediment delivery may exacerbate land elevation loss in these deltas. The Nile, Po, Chikuma-gawa, Mahanadi, Kabani, Niger and Volta deltas exhibit mixed contributions from GWS, sediment flux changes and population change, with GWS slightly outweighing sediment deficits as predictors in the Nile and Po deltas, possibly reflecting reliance on aquifer-dependent irrigation[35]. These findings are consistent with delta-specific studies that attribute accelerated subsidence in densely populated Asian deltas—Mekong, Ganges–Brahmaputra and Chao Phraya—to urbanization and unsustainable groundwater extraction for agriculture, industry and domestic use6,20,31,32,36. Moreover, the Nile, Po and Mississippi deltas, which were historically sustained by seasonal floods that deposited sediments, are now documented to experience severe sediment deficits due to dams and levees, accelerating elevation loss2,20,29.

We acknowledge several limitations. First, GRACE-derived GWS trends (spatial resolution of about 300–400 km) may introduce signal leakage from adjacent basins, particularly affecting smaller deltas. Second, the sediment flux dataset represents percentage changes between pristine and disturbed conditions rather than contemporary absolute rates, potentially masking recent trends. Third, other natural VLM processes (sediment compaction and tectonics) and anthropogenic drivers (hydrocarbon extraction and peat drainage) are not explicitly separated. Fourth, RF model results are inherently dependent on input variable distributions and should be interpreted within the context of these datasets. Last, although the 40 deltas represent a substantial portion of global delta area and population, they are not globally representative. Nevertheless, our analysis focuses on understanding the relative influence of three key anthropogenic variables across these diverse systems rather than providing delta-specific VLM budgets. Future studies incorporating spatially dense, delta-specific datasets will better resolve local-scale processes within individual deltas and enable rigorous partitioning of anthropogenic compared with natural contributions to land motion and elevation change.

Relative impacts of SLR and subsidence

Globally, deltas face a ‘double burden’ of climate-induced SLR and sinking land, which together drive relative sea-level rise (RSLR) at rates exceeding global averages2,3,7,8,18. Unlike SLR, which reflects global-scale processes and progresses at a relatively uniform rate globally7,37, subsidence operates at local to regional scales, is highly variable and reflects localized natural and human processes13,27,30. In many deltas, contemporary rates of subsidence may surpass the current SLR rates2,14 (see previous section), creating a compound hazard in which RSLR is dominated not by climate-induced changes in sea surface height but by VLM.

To quantify the contributions of SLR and land subsidence in deltas, we evaluated their relative impact on the exposed delta populations. Our analysis shows that current average subsidence rates exceed geocentric SLR in 18 of the 40 deltas, including the Nile, Mekong, Red River, Ganges–Brahmaputra, Brahmani, Mahanadi, Chao Phraya, Ciliwung, Brantas and Yellow River deltas, affecting approximately 236 million people—a population about 50% larger than those residing in deltas in which the current rates of geocentric SLR outpace the subsidence rates (156.9 million) (Fig. 4a). This disparity is particularly pronounced for vulnerable populations occupying land below 1 m elevation[11]. In these lowest elevation areas, subsidence dominates the contribution to RSLR in about two-thirds of the deltas, including Amazon, Fraser, Niger, Rhone, Vistula, Ganges–Brahmaputra, Mekong, Red River, Pearl, Yangtze and Godavari deltas (Fig. 4b). Of the 76 million people living in delta areas with an elevation below 1 m, 84% (63.7 million people) reside in rapidly sinking areas of the deltas (Fig. 4b). These observations are striking, revealing the current dominance of subsidence over geocentric SLR in global deltas. Moreover, the spatial heterogeneity of VLM creates localized extreme rates of subsidence within deltas, further exacerbating their vulnerability. Under the current trajectory, moderate emission scenarios (shared socioeconomic pathway 2-4.5 (SSP2-4.5)), current maximum subsidence rates in the deltas already surpass projected twenty-first-century SLR rates (no VLM)[38]. Through the end of the twenty-first century, current maximum subsidence rates in all 40 deltas exceed projected SLR rates (Fig. 4c). This disparity extends to the 95th percentile subsidence rates, representing widespread, high-magnitude sinking across the deltas. In 29 deltas, 95th percentile subsidence rates exceed the projected SLR rates by 2050, outpacing SLR by 1.1 (Niger delta) to 10.3 (Yellow River delta) times. By 2100, as the current maximum rate of SLR (SSP2-4.5) accelerates to 0.9 cm yr−1, current 95th percentile subsidence rates still dominate in 22 deltas, surpassing geocentric SLR by up to seven times. Even accounting for worst-case, high-emission scenarios (SSP5-8.5), subsidence will exceed projected SLR rates in all deltas (considering maximum subsidence) and in 23 deltas (considering 95th percentile subsidence) through 2050. By 2100, current maximum subsidence rates exceed projected SLR in 38 of 40 deltas, whereas 95th percentile subsidence rates remain dominant in seven deltas (Godavari, Chao Phraya, Mekong, Ciliwung, Brantas, Red River and Yellow River) (Supplementary Table 1).

Fig. 4: Relative contributions of land subsidence and SLR in global deltas.

a, Bubble plot comparing geocentric (absolute) SLR (mm yr−1) and land subsidence (mm yr−1) across 40 deltas. Deltas in which subsidence rates exceed geocentric SLR fall to the right of the 1:1 line, whereas those in which geocentric SLR exceeds subsidence fall to the left. Bubble colours indicate the total delta population, ranging from fewer than 100,000 (lighter colours) to more than 100 million (darker colours). Bubble size represents the percentage population living in delta land areas subsiding faster than geocentric SLR. b, Same as a, but considering only the population living at elevations below 1 m. Note that the Brantas and Yellow River deltas have values greater than 15 mm yr−1 and are not represented on the plot for visual clarity. c, Bar plots comparing the range of land subsidence rates, contemporary and projected SLR for 30 representative deltas. The maximum subsidence rate is calculated as the median of the 50 highest rates to avoid biases from single extreme values. The dashed vertical line shows the maximum 2100 projected SLR rate across all deltas.

Source data

These findings identify VLM as the principal hazard in deltaic systems and other subsidence-prone low-elevation coastal zones. Although global coastal zones face baseline threats from SLR[38], subsidence in many deltas often dominates RSLR, creating a distinct and more acute risk profile, which is amplified by the high populations in many of these deltas4,12. Yet, subsidence remains underprioritized in global coastal risk discourse, a tendency that stems from its perceived tractability. Unlike climate-induced SLR, which can be slowed but not stopped on human policy time scales, human-induced subsidence can theoretically be slowed or halted through targeted interventions22,23,31,32. Its responsiveness to human action, however, has paradoxically relegated it to the periphery of international policy3,31,39. This disconnect reflects a broader misalignment between the spatial scales of climate impacts and adaptation priorities. Thus, subsidence does not merely compound SLR; it undermines the foundational logic of incremental, SLR-centric adaptation[39]. Addressing this requires shifting adaptation from just a global climate challenge to a regional socio-technical imperative and an integrated approach that prioritizes subsidence mitigation (for example, groundwater regulation, managed aquifer recharge and sediment management) alongside RSLR adaptation.

Adaptive capacity in vulnerable deltas

From the Fraser delta in Canada to the Yellow River delta in China, global deltas are sinking, as climate change accelerates SLR, compounding the vulnerabilities of low-lying regions. These combined effects create a multifaceted threat, forcing delta communities to contend with land loss, more frequent flooding and saltwater intrusion6,7,8,9,20. Whereas the urgency of adaptation is immediate and worldwide, the capacity to act is not. For many deltas, especially those in low- and middle-income countries, adaptive capacity is limited by institutional, social and financial constraints[9]. These systemic barriers are quantified by the Notre Dame Global Adaptation Index (ND-GAIN), a framework that evaluates the vulnerability of countries to climate change and their readiness to deploy adaptation resources across economic, social and governance dimensions40,41. A higher ND-GAIN adaptation readiness score (>0.52) is an indication of the capacity of a country to absorb funds and translate these into actionable strategies[41].

To visualize disparities in adaptive capacity and risk, we mapped global deltas into a two-dimensional (2D) impact matrix defined by RSLR and ND-GAIN adaptation readiness scores (Fig. 5). This framework allows for a comparative assessment of deltas assuming that the adaptation readiness of the delta is reflected by the adaptation readiness of its country, categorizing them into four quadrants: (1) Unprepared Divers (high RSLR (>4 mm yr−1), low readiness (<0.52)); (2) Rising Ready (high RSLR, high readiness (>0.52)); (3) Latent Threats (low RSLR, low readiness); and (4) Safe Havens (low RSLR, high readiness). 65% of the deltas (26 out of 40 deltas), predominantly in low- and middle-income nations, fall into the Unprepared Divers group, in which nations have a diminished adaptive capacity and RSLR rates exceeding current global SLR (Fig. 5a). These challenges are compounded for indigenous communities, who primarily live in the lowest-lying delta areas; lack the resources needed to implement large-scale adaptation; and face relocation barriers due to cultural and subsistence ties despite escalating risks42,43.

Fig. 5: RSLR and adaptive capacity in global deltas.

a, Scatter plot showing the relationship between RSLR and ND-GAIN adaptation readiness score for 40 deltas in the twenty-first century. The horizontal dashed line represents the current global SLR (about 4 mm yr−1), whereas the vertical dashed line indicates the threshold between ‘good’ and ‘very good’ readiness categories (0.52), as defined in ref. [41]. b, Same as a but including both twentieth- and twenty-first-century data for 15 deltas. Arrows illustrate the trajectory of the readiness score of each delta from the twentieth century to the twenty-first century. The four quadrants represent Unprepared Divers (deltas with high RSLR, low adaptation readiness), Rising Ready (deltas with high RSLR, high adaptation readiness), Latent Threats (deltas with low RSLR, low adaptation readiness) and Safe Havens (deltas with low RSLR, high adaptation readiness).

Source data

Most deltas in high-income countries, including the Yellow River (China), Vistula (Poland), Po (Italy), Rhine–Meuse (the Netherlands) and Mississippi (the USA) deltas, cluster in the Rising Ready group, demonstrating robust governance (Fig. 5a). For example, the integrated flood management approach of the Dutch delta, which combines ecological restoration with infrastructural fortifications, has become a model for coastal hazard resilience[44]. However, some deltas even within this group face substantial gaps. For instance, the Mississippi delta has lost more than 5,000 km^2 of land (mainly wetlands) since 1932 because of a lack of adaptation (for example, sediment diversion projects)45,46, whereas the Po delta struggles with salinization driven by agricultural groundwater extraction, highlighting how economic priorities can undermine adaptation even in high-income regions[47]. Although RSLR exceeds global rates of SLR in most deltas, exceptions exist. The Latent Threats group includes the Saloum and Neva deltas, which exhibit relatively low RSLR and low adaptive capacity (Fig. 5a), indicating their unpreparedness and potential vulnerability to a future rise in sea level (Fig. 4c). The Rioni and the Fraser delta fall into the Safe Havens group, in which lower RSLR is coupled with higher adaptive capacity, indicative of low risk and preparedness for current and future sea-level changes. The Rioni Delta is the only delta in our sample exhibiting negative sea-level trends for the twenty-first century, in which long-term regional sea-level decline masks short-term fluctuations (Methods).

To examine the evolving risk landscape, we compared twentieth-century and present-day impact matrices (Fig. 5b). For our analysis, we used tide gauge data to estimate twentieth-century RSLR rates, which were available for only 15 of the 40 deltas. Our estimates show that 10 deltas previously classified as Latent Threats (low RSLR, low readiness) and Safe Havens (low RSLR, high readiness) groups during the twentieth century have transitioned to Unprepared Divers (high RSLR, low readiness) and Rising Ready (high RSLR, high readiness) groups in the twenty-first century (Fig. 5b). This shift highlights the accelerating contemporary RSLR trends, driven by land subsidence and SLR48,49. Deltas such as the Mississippi, Ganges–Brahmaputra and Mekong show sustained increases in long-term RSLR rates above 4.0 mm yr−1 since the twentieth century, exacerbating vulnerabilities in these densely populated regions. Conversely, the Chao Phraya and the Rioni deltas showed a decline in RSLR and improved adaptive capacity in the twentieth century. However, although the Rioni Delta exhibited a more than 200% decline in RSLR, the Chao Phraya Delta still experiences high RSLR rates (12.3 mm yr–1). The pronounced decrease in RSLR for the twentieth century in the Rioni Delta probably reflects localized subsidence at the tide gauge station rather than a delta-wide RSLR trend[50] (Methods). The greatest change in RSLR was observed in the Nile Delta, surging from 1 mm yr–1 in the twentieth century to more than 10 mm yr–1 in the twenty-first century (Fig. 5b). Moreover, we find that all deltas in low- and middle-income countries in the present-day Unprepared Divers groups, transitioned from the Latent Threats group, suggesting stagnant adaptive capacity despite worsening RSLR. By contrast, deltas such as the Yangtze (China), Pearl (China) and Vistula (Poland) shifted from Latent Threats to Rising Ready, demonstrating increased adaptation readiness due to economic growth, raising governance and institutional capacity to adapt, although RSLR has surged (Fig. 5b). Although deltas in the Rising Ready quadrant showed potential for robust adaptation policies, deltas in the Unprepared Divers remain trapped in cycles of reactive, underfunded responses.

These long-term trajectories reveal a challenging reality in which deltas with strong adaptive capacity still struggle to manage persistent subsidence and climate-driven SLR, whereas those with limited capacity face severe and escalating risks on both fronts. Ideally, the goal for sustained coastal resilience is a transition to Safe Havens, characterized by both low RSLR and high adaptation readiness. However, only two deltas (the Fraser and Rioni) currently occupy this quadrant. As the climate crisis and related threats intensify, the challenge for the up to 500 million people in deltas demands more than incremental adaptation; it requires global attention to subsidence and other key vulnerability drivers while advancing governance approaches that preserve land elevation and long-term habitability over short-term adaptation.

Methods

Selection of global river deltas

We selected 40 deltas globally, prioritizing 35 deltaic systems with the greatest exposed area and population currently below sea level, supplemented by five less-exposed deltas of local and regional significance and previously identified risks[9]. To assess the 35 deltas with the greatest exposure among global river deltas, we used 955 delineated delta boundaries in ref. [6] and identified coastal delta elevation below sea level using the DeltaDTM dataset v.1.1 (ref. [11]) resampled to 3 arcseconds (100 m) and referenced to mean sea level[51]. Global delta population was estimated by aggregating 100 m resolution WorldPop population count for each delta, which is calibrated to the 2020 national population estimates from the United Nations population data[52].

Our estimates show that globally, 42,000 km^2 of the delta area at present lies below sea level, containing a population of 10.2 million people (Extended Data Fig. 1). The 35 deltas with the greatest exposure included in this analysis are Nile, Mississippi, Rhine–Meuse, Mekong, Niger, Cauvery, Po, Red River, Vistula, Rhone, Amazon, Ganges–Brahmaputra, Chao Phraya, Kabani, Pearl, Rio Grande, Yangtze, Yellow River, Senegal, Indus, Saloum, Grijalva, Ceyhan/Seyhan, Rioni, Cross, Chikuma-gawa, Volta, Brantas, Neva, Wouri, Irrawaddy, Ogooué, Zambezi, Magdalena and Ciliwung (Extended Data Fig. 1). The cumulative delta area and population below sea level are 38,000 km^2 and 10.1 million people, respectively, reaching within rounding errors of the global total exposure. Deltas such as the Danube, Orinoco and Shatt-el-Arab met the selection criteria but were excluded due to challenges associated with the SAR imaging and interferometric analysis (including spatial coverage gaps, excessive temporal baselines, poor coherence and limited data availability). The five supplementary deltas are Brahmani, Mahanadi, Godavari, Parana and Fraser deltas.

The final selection of 40 deltas spans five continents (Asia, Africa, Europe, North America and South America) and 29 countries, encompassing deltas with noted and emerging environmental, geophysical and social vulnerabilities9,24, historically sinking river deltas[2] and densely populated coastal megacities3,4,53.

SAR dataset

We analysed 132 SAR frames from the Sentinel-1A/B C-band satellite, spanning September 2016 to May 2023. The SAR datasets include 3,300 images obtained in single-orbit geometry (ascending or descending) for 13 deltas and 10,700 images obtained in both ascending and descending orbits for 27 deltas. See Supplementary Table 3 for the complete inventory of SAR images used in each delta. For each SAR dataset, we applied a multi-looking factor of 32:6 (range:azimuth) to improve the signal-to-noise ratio, obtaining an average pixel resolution of about 75 m. To minimize decorrelation errors, we also constrained the interferometric pairs to a maximum temporal and perpendicular baselines of 300 days and 80 m, respectively. For deltas requiring multi-frame coverage (for example, Amazon, Mississippi, Mekong, Ganges–Brahmaputra, Nile, Red River and Niger), we arranged in a mosaic form the overlapping adjacent frames along a single path before processing or post-processed deltas with coverage spanning multiple paths to ensure full spatial continuity across expansive deltas.

SAR interferometric analysis

We processed each SAR frame or single-path multiple-frame coverage to generate high-spatial resolution maps of surface deformation for the 40 deltas using a multitemporal wavelet-based InSAR (WabInSAR) algorithm54,55,56,57. First, we generated 59,000 high-quality interferograms from the coregistered SAR images using GAMMA software58,59, with an interferogram pair selection algorithm[57] optimized through dyadic downsampling and Delaunay triangulation. To minimize phase errors and to maximize the pixel density associated with dynamic surface changes over deltas (for example, flooding, vegetation growth or soil saturation), we screened the initial set of interferograms based on their coherence stability to exclude interferograms with high coherence variability, while maintaining a 50% temporal baseline coverage. The final selection retained about 55,000 interferometric pairs (93%) for further analysis. Moreover, we implemented a statistical framework to discard noisy pixels with average coherence less than 0.7 for distributed scatterers and amplitude dispersion of greater than 0.35 for permanent scatterers[57]. Next, we used a minimum cost flow phase unwrapping algorithm optimized for sparse coherent pixels60,61 to estimate the absolute phase changes of the elite (less noisy) pixels in each interferogram. We corrected all unwrapped interferograms for the effects of residual orbital error[62] and minimized the effects of topography-correlated components of atmospheric phase delay and spatially uncorrelated DEM error by applying a suite of wavelet-based filters[54]. Last, we estimated the time series, velocities and standard deviation for each geocoded elite pixel along the line of sight (LOS) of the satellite using a reweighted least-squares optimization[55]. The standard deviation of the LOS velocity corresponds to the uncertainty of the regression slope derived from the least-squares fit. For each delta, the reference point was selected as the pixel corresponding to a global navigation satellite systems (GNSS) station within the processed SAR frame when available. In areas without GNSS stations, a preliminary reference point was randomly selected from pixels with average temporal coherence >0.85. Following initial processing, the reference point was refined by visually identifying stable ground features (for example, bedrock outcrops and deep-foundation structures) and low displacement variability (standard deviation <1 mm yr–1), then reprocessing with this final reference point. For large deltas requiring overlapping SAR frame coverage, the LOS velocities were arranged in a mosaic form to ensure seamless spatial representation across the entire delta.

In the 27 deltas with overlapping spatiotemporal SAR satellite coverage and different orbit geometries (ascending and descending), we estimate the horizontal (east–west) and VLM components of deformation by jointly inverting the LOS time series of the ascending and descending tracks63,64,65. To this end, we identified the co-located pixels of the LOS time series by resampling the pixels from the descending track onto the ascending track to obtain two co-located LOS displacement velocities \(\{{\mathrm{LOS}}_{\mathrm{ASC}},{\mathrm{LOS}}_{\mathrm{DES}}\}\). Given \(\{{\mathrm{LOS}}_{\mathrm{ASC}},\,{\mathrm{LOS}}_{\mathrm{DES}}\}\) and their associated variances \(\{{\sigma }_{\mathrm{ASC}}^{2},{\sigma }_{\mathrm{DES}}^{2}\}\) are the LOS displacement and variances for a given pixel, the model to combine the LOS velocities to generate a high-resolution map of the east–west (E) and VLM (U) displacements are given by

$$[\begin{array}{c}{{\rm{L}}{\rm{O}}{\rm{S}}}_{{\rm{A}}{\rm{S}}{\rm{C}}}\\ {{\rm{L}}{\rm{O}}{\rm{S}}}_{{\rm{D}}{\rm{E}}{\rm{S}}}\end{array}]=[\begin{array}{cc}{C}_{{\rm{A}}{\rm{S}}{\rm{C}}}^{E} & {C}_{{\rm{A}}{\rm{S}}{\rm{C}}}^{U}\\ {C}_{{\rm{D}}{\rm{E}}{\rm{S}}}^{E} & {C}_{{\rm{D}}{\rm{E}}{\rm{S}}}^{U}\end{array}]\,[\begin{array}{c}E\\ U\end{array}]$$

(1)

where, C represents the unit vectors for projecting (E) and (U) displacements onto the LOS, which is a function of the heading angle of the satellite and incidence angles of each pixel[66]. The solution to the model in equation (1) is given by

$$X={[{G}^{{\rm{T}}}\mathrm{PG}]}^{-1}{G}^{{\rm{T}}}\mathrm{PL}$$

(2)

where X represents the unknowns (E) and (U), G is the design matrix comprising the unit vectors for projecting the horizontal and vertical displacements onto the line of sight, L are the observations \(\{{\mathrm{LOS}}_{\mathrm{ASC}},{\mathrm{LOS}}_{\mathrm{DES}}\}\), and P is the weight matrix, which is inversely proportional to the observant variances \(\{{\sigma }_{\mathrm{ASC}}^{2},{\sigma }_{\mathrm{DES}}^{2}\}\). To obtain the parameter variance–covariance matrix (QXX), we use the concept of error propagation[67] to calculate the associated parameter uncertainties given the observation errors as follows:

$${Q}_{{\rm{XX}}}={[{G}^{{\rm{T}}}\mathrm{PG}]}^{-1}$$

(3)

For the 13 deltas imaged in single-orbit geometry (ascending or descending), we projected the LOS velocities to the vertical direction, assuming the principal deformation is vertical:

$${\mathrm{VLM}}_{i}=\frac{{\mathrm{LOS}}_{i}}{{\cos \theta }_{i}}$$

(4)

where, cosθi is the local incidence angle for each pixel. This assumption of zero gradients in the horizontal components of deformation is tenuous for most coastal areas, given the significant localized horizontal motion noted (up to 10 mm yr–1) across the 27 deltas with multiple orbit geometries. Nevertheless, the assumption is necessary given that overlapping ascending and descending orbit geometries are available for less than 50% of global land areas (for European Space Agency Sentinel-1 satellite), limiting the ability to resolve 2D deformation trends. However, under this assumption, it is necessary for the locally referenced VLM estimates to be transformed into a globally consistent reference frame, particularly for comparative studies across multiple regions13,27.

To transform the VLM rates from a local to a global reference frame, we used the available GNSS datasets for 17 deltas (the Fraser, Mississippi, Rio Grande, Rhine–Meuse, Rhone, Po, Vistula, Red River, Amazon, Parana, Ciliwung, Brantas, Ganges–Brahmaputra, Chao Phraya, Mekong, Pearl and Chikuma-gawa). The GNSS datasets across the 17 deltas were obtained from the Nevada Geodetic Laboratory[68] and previous regional studies[69]. For each delta with GNSS coverage, we calculated the offset between the InSAR-derived vertical velocity at the reference point and the corresponding GNSS vertical velocity, then applied this offset to transform all InSAR velocities in that delta to the IGS14 reference frame. The uncertainty in the final velocity was estimated by propagating both the InSAR velocity uncertainty (from the reweighted least-squares inversion) and the GNSS velocity uncertainty (reported by data sources) through standard error propagation. In deltas without GNSS stations, we used the global VLM model[70], which mainly includes long-wavelength deformation signals due to TWS changes, tectonics and glacial isostatic adjustment (GIA) referenced to the IGS14 global frame. We then applied an affine transformation to align the VLM rates from local to IGS14 global reference frame23,71. This approach ensures consistency in VLM rates across global deltas by correcting for local reference biases and should be the standard practice in coastal research using InSAR[27]. When comparing these measurements to other subsidence rate estimation techniques in deltas, such as RSET, marker horizons, sediment cores, repeat lidar or other InSAR measurements, careful consideration must be given to differences in both reference frames and temporal ranges. Reference frame incompatibility may require adjustments to align local or relative measurements with other datasets, whereas mismatches in monitoring periods introduce temporal biases that complicate direct quantitative comparisons.

The distribution of the standard deviations (precision of the results) for all pixels (20.5 million) across the 40 deltas is shown in Supplementary Fig. 2. The standard deviation distribution shows that 99% of the pixels have a value <0.5 mm yr–1. We evaluated the accuracy of the results by comparing the averaged VLM rates of pixels within a radius of 100 m with more than 100 independent GNSS data (that is, stations that were not used in the reference frame transformation). The validation included 122 GNSS stations across 23 deltas with historical long-term records (spanning various periods before and/or including the InSAR observation window) and 81 GNSS stations across 15 deltas with time series covering at least 70% of the InSAR observation period (2014–2023) (Supplementary Fig. 3). We found a strong correlation (0.7–0.8), between GNSS and InSAR velocities, with an RMSE of 1.4 mm yr–1 for long-term rates (Supplementary Fig. 3a) and 1.2 mm yr–1 for rates within the InSAR observation period (Supplementary Fig. 3b). The improved agreement for temporally coincident measurements suggests that nonlinear subsidence behaviour contributes to some scatter when comparing historical GNSS rates to contemporary InSAR measurements, although the overall correlation remains strong in both cases. Note that some GNSS stations used for validation, while within the broader processed SAR frame, are outside the clipped delta boundaries. Note that the final delta extents were delineated using a tiered approach. Primary boundaries were derived from ref. [9], supplemented by ref. [6] for deltas not covered in the former. For extensive deltas in which the entire delta surface is not analysed (for example, the Ganges–Brahmaputra), boundaries were defined using the SAR spatial extent.

GIA influence on VLM

We estimated VLM trends and the associated uncertainty due to GIA using the model in ref. [72], which was derived from a probabilistic ensemble of 128,000 GIA forward simulations. Each model solves the sea-level equation for a compressible, viscoelastic Maxwell Earth under late-Pleistocene ice-sheet loading, incorporating solid-Earth deformation, geoid change and rotational feedback. The ensemble samples a wide range of Earth rheological structures, including lithospheric thickness, upper and lower mantle viscosities, and scaling factors applied to regional deglaciation histories over the past 122,000 years. Likelihoods were assigned to each simulation based on fit to a global dataset of 11,451 relative sea-level records and 459 GNSS-derived uplift rates using a Bayesian framework that accounts for data uncertainties and spatial correlations. The resulting posterior distributions enable spatially resolved estimates of GIA-driven VLM with formal uncertainty.

For each delta, we extracted the ensemble mean and standard deviation in GIA vertical velocity to correct observed deformation rates and isolate contemporary, non-GIA contributions to VLM. Supplementary Table 1 shows the mean GIA-induced VLM, the associated standard deviation and the per cent contribution of GIA to the total observed VLM magnitude for each delta. GIA accounts for the largest proportion and exceeds (>100%) the total VLM in the Neva (540%) and Fraser (455%) deltas, in which low observed VLM rates are substantially influenced by strong GIA uplift. Moderate GIA contributions (25–55%) are observed in five deltas, including the Rio Grande, Mississippi, Volta, Rhine and Ogooué deltas. Most of the deltas (55%) exhibit minimal GIA influence, with contributions under 10%, indicating that observed VLM is primarily governed by contemporary anthropogenic and natural processes such as groundwater withdrawal, sediment compaction, or tectonics. In 28–67% (accounting for uncertainty) of the deltas, the sign and approximate magnitude of observed and GIA-corrected VLM are consistent, implying limited distortion from GIA and the sustained expression of contemporary processes on the average local subsidence. By contrast, the Fraser and Neva deltas illustrate how substantial GIA-induced uplift in high-latitude, post-glacial regions can obscure contemporary subsidence processes through opposing vertical trends. In both cases, modest observed subsidence rates (Fraser −0.4 mm yr−1 and Neva −0.2 mm yr–1) are counteracted by substantial GIA uplift of 1.8 ± 2.3 mm yr−1 and 1.0 ± 0.3 mm yr−1, respectively.

Anthropogenic drivers datasets

We analysed the relationship between major anthropogenic pressures on global deltas to subsidence and elevation loss by quantifying the contributions of groundwater storage change, sediment flux alteration and urban expansion to the residual rates of sinking (after GIA correction) across the 40 deltas. These globally consistent datasets provide insights into human-induced impacts on land subsidence and elevation change in river deltas (Supplementary Table 2).

Groundwater storage change

We derived twenty-first-century groundwater storage trends for all deltas by leveraging Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) satellite observations73,74. We used the JPL GRACE/GRACE-FO level 3 mascon solutions (RL06.3) (refs. 75,76), which provide monthly global estimates of total water storage (TWS) change relative to a 2004.9–2009.999 mean baseline. The final solutions span 2002–present and are derived from solving for monthly gravity field variations in terms of 4,551 equal-area 3° spherical cap mass concentration functions rather than global spherical harmonic coefficients. The mascon approach implements geophysical constraints during the level-2 processing step to filter out noise, applies improved accelerometer data and standard corrections, including several geophysical adjustments, such as gravity anomaly due to ocean (GAD), GIA, degree-1, C20 and C30 replacement and representation on ellipsoidal earth75,76,77. We extracted TWS values at 3° mascon resolution (about 300–400 km spatial resolution) covering each delta area to compute representative regional water storage estimates. TWS change from GRACE contains contributions from GWS, soil moisture storage (SMS), snow water equivalent (SWE) and surface water storage (SWS) represented by

$$\Delta \mathrm{TWS}=\Delta \mathrm{GWS}+\Delta \mathrm{SWS}+\Delta \mathrm{SMS}+\Delta \mathrm{SWE}$$

(5)

To isolate GWS change from TWS, we used the 1/4° global land data assimilation system Noah model[78] to remove changes in SMS and SWE contributions and used the WaterGAP Global Hydrology Model (WGHM v.2.2d) (refs. 79,80) to remove SWS contributions. The contribution from SWE was negligible in most deltas, given their prevailing arid and semi-arid climate (Fig. 1), although it was included to maintain consistency across all deltas. SWS components include contributions from rivers, lakes, wetlands and reservoir storage within the GRACE footprint for each delta. The residual signal following removal of SWS, SMS and SWE was interpreted as the GWS anomaly.

To estimate the temporal trend of groundwater storage changes, we applied harmonic analysis to account for annual and semiannual variations in the time series of the GWS anomalies. In standard practice, environmental variables (for example, GRACE data, GNSS data and sea-level anomalies) are modelled as time-invariant seasonal signals. However, the response of Earth to environmental changes represented as seasonal signals is not time-invariant81,82,83. To account for this variability, we adopted the stochastic-seasonal model in the following equation, in which the harmonic amplitudes evolve as random walks, allowing for time-dependent seasonal variations and the seasonal trends are modelled using a Kalman filter[83]:

$$\begin{array}{l}x(t)={x}_{0}+v(t)(t-{t}_{0})\\ \,\,\,+\mathop{\sum }\limits_{k=1}^{2}[{a}_{k}(t)\cos (2{\rm{\pi }}{kf}(t-{t}_{0}))+{b}_{k}(t)\sin (2{\rm{\pi }}{kf}(t-{t}_{0}))]\end{array}$$

(6)

where t_0 is the reference epoch, x_0 is the reference intercept at t_0, v(t) is the time-varying rates, k indexes the annual (k = 1) and semiannual (k = 2) components, ak and bk are the harmonic amplitudes. v(t), ak, and bk are modelled as random walk parameters. To estimate the long-term multi-year trend (vf) of GWS from the time-varying rates, we computed the weighted average of the time-varying rates v(ti) using

$${v}_{f}=\frac{{\sum }_{i=1}^{m}v({t}_{i})/{\sigma }_{v({t}_{i})}^{2}}{{\sum }_{i=1}^{m}1/{\sigma }_{v({t}_{i})}^{2}}$$

(7)

where m is the total number of epochs in the time series and \({\sigma }_{v({t}_{i})}^{2}\) is the variance of the rate at epoch ti, derived from the posterior covariance matrix of the Kalman filter. The uncertainty \({\sigma }_{v}^{2}\) in the rate is given by

$${\sigma }_{v}^{2}=\frac{1}{{\sum }_{i=1}^{m}1/{\sigma }_{v({t}_{i})}^{2}}$$

(8)

Supplementary Figs. 4 and 5 compare the time-invariant model (black curves) with the stochastic-seasonal model (red curves) for GRACE-derived GWS and RSLR from tide gauges in the Mississippi and Chao Phraya deltas. These plots show that a stochastic-seasonal process better represents the observed variability in the time series. The post-fit residuals of the time-invariant model show some systematic seasonal patterns, particularly during periods when seasonal amplitudes deviate from the assumed constant values (Supplementary Figs. 4b,d and 5b,d). By contrast, the stochastic model accommodates time-dependent variations in seasonal amplitudes, resulting in reduced (often near-zero) residuals (Supplementary Figs. 4b,d and 5b,d), demonstrating the advantage of the stochastic-seasonal model in capturing transient seasonal variations rather than fixed annual and semiannual cycles[83].

The GWS rates for each delta are summarized in Supplementary Table 2, and Fig. 3a and Extended Data Fig. 8a show the relationship with the subsidence rates. Negative GWS trends indicate mass depletion, primarily driven by groundwater extraction, whereas positive trends represent net groundwater accumulation due to recharge processes, reduced extraction or hydrological interventions. To evaluate the reliability of GRACE-derived GWS trends, we compared them with in situ groundwater level trends for 18 deltas (Supplementary Fig. 6). Groundwater levels were compiled from two publicly available sources: 13 deltas from ref. [84] and 5 deltas from the Global Groundwater Monitoring Network[85]. Given the spatial scale discrepancy between GRACE (basin-wide) and well observations (point-scale), we emphasized agreement in trend direction rather than absolute magnitudes. Each site was categorized based on the sign of the GRACE and well trends, and a confusion matrix was constructed to assess consistency. The analysis yielded an overall classification accuracy of 88.9%, with six sites exhibiting positive–positive trends (PPT) and 10 showing negative–negative trends (NNT). Only two sites showed mixed behaviour (NPT or PNT), and no site exhibited fully opposing trends. Moreover, a high correlation (R = 0.7) was observed between the GRACE-based GWS and well-derived trends, further supporting the consistency of GRACE estimates at the basin scale despite localized variability in in situ measurements. Although the coarse spatial resolution of GRACE/GRACE-FO may not capture localized variations[84], its basin-scale sensitivity is well-suited to characterizing basin-wide groundwater trends. Moreover, the dominance of groundwater extraction in many deltas2,20,31 probably ensures that GWS trends are the primary signal captured.

We find a modest linear correlation (R = 0.5) between GWS and subsidence rate; however, a cubic regression model (R = 0.6) provides a better fit (Extended Data Fig. 8a).

Sediment flux alteration

We obtained values for the sediment flux alteration for the 40 deltas from ref. [29]. This dataset provides a global assessment of fluvial sediment supply, distinguishing between pristine sediment fluxes (before substantial anthropogenic influences) and disturbed or contemporary sediment fluxes (reflecting human influences such as dam construction and land-use changes) within the contributing delta basins. We quantified the per cent change in sediment flux for each delta using the following equation, which expresses the relative alteration (increase or decrease) in sediment delivery due to human activities:

$$\Delta \mathrm{Sediment}\,\mathrm{flux}=\left(\frac{\mathrm{Disturbed}\,\mathrm{sediment}\,\mathrm{flux}}{\mathrm{Pristine}\,\mathrm{sediment}\,\mathrm{flux}}-1\right)\times 100 \% $$

(9)

The pristine and disturbed sediment flux, along with computed sediment flux changes for each delta, are summarized in Supplementary Table 2. A negative sediment flux change indicates a decline or loss in fluvial sediment supply (disturbed < pristine) due to human activities, whereas a positive sediment flux change reflects an increase or gain (disturbed > pristine). We acknowledge that this framework represents a simplified characterization of complex sediment delivery processes and may not capture all temporal variations in sediment supply. Furthermore, some concerns have been raised about potential errors in global sediment flux datasets[86], which we consider as a limitation in our analysis.

Figure 3a and Extended Data Fig. 8b show the relationship between sediment flux change and subsidence rates. Although a poor correlation (R < 0.4) is observed, we find that 62% of the deltas (25 out of 40) exhibit negative sediment flux change, indicating widespread human-induced reductions in sediment supply.

Urban expansion

Urban expansion is one of the most visible and rapid types of ongoing anthropogenic changes in river deltas[6]. To assess how population-driven land-use changes may affect subsidence rates across deltas, we used a global 1/8° (about 12.5 km) urban land fraction dataset, derived from high-spatial-resolution remote sensing observations[87]. This dataset tracks the conversion of natural landscapes (that is, wetlands and forests) into built environments and serves as a proxy for land-use changes that may exacerbate subsidence through increased infrastructure loading and increased groundwater demand. We quantified the urban fraction change in deltas in the twenty-first century by calculating the percentage change in the proportion of urban areas relative to total delta area between 2000 and 2020.

Supplementary Table 2 summarizes the urban fraction dataset (2000 and 2020) and the urban fraction change for each delta. Figure 3a and Extended Data Fig. 8c show the subsidence–urban expansion relationship across the 40 deltas. All deltas showed consistent urban expansion in the twenty-first century, ranging from relatively low increases (<1%) in the Ogooué river delta to significant increases (>400%) in the Indus delta. However, despite this rapid expansion, the Indus delta remains one of the least urbanized, with only 0.4% of its total area classified as urban in 2020. By contrast, the Ciliwung (Jakarta) and Neva (Saint Petersburg) deltas exhibit the highest urban fractions, exceeding 50%. A logarithmic fit best describes the full dataset and reveals a moderate but significant nonlinear inverse correlation (correlation, R = 0.38–0.51), indicating that deltas with significant urban land conversion tend to experience more pronounced land sinking (Extended Data Fig. 8c). Steadily urbanizing deltas, such as the Rio Grande and Rhine–Meuse, exhibit slower subsidence rates, whereas rapidly urbanizing deltas, such as the Brahmani and Yellow River deltas, show faster rates of land sinking. However, regional variability is evident, as some deltas deviate from the overall trend (for example, Indus and Cauvery deltas). When excluding outliers (the Indus and Cauvery deltas), subsidence and urban expansion exhibit a strong linear correlation across deltas (Extended Data Fig. 8c).

We also explored the relationship among the anthropogenic drivers (Extended Data Fig. 8d–f), finding a low (R = 0.1–0.3) correlation depending on the specific driver.

RF analysis for identifying anthropogenic drivers of subsidence and elevation loss

Given the nonlinear and interacting relationships among GWS, sediment flux alteration, urban expansion and residual land subsidence (after GIA correction) discussed above, a machine learning framework was implemented to model these complexities. First, we attempted a multilinear regression model, incorporating interaction terms between variables, formulated as

$$\mathrm{VLM}={x}_{0}+\mathop{\sum }\limits_{i=1}^{n}{x}_{i}{X}_{i}+\mathop{\sum }\limits_{j=1}^{m}\mathop{\sum }\limits_{k=j+1}^{m}{x}_{{jk}}({X}_{j}{X}_{k})+{\epsilon }$$

(10)

where VLM is the predicted VLM, x_0 is the intercept, Xi,j,k are the predictor variables (GWS, sediment flux alteration and urban expansion), xi,j,k are the regression coefficients for each predictor variable, xjk represents the interaction effects between predictor variables and ϵ is the residual error term. However, this multilinear regression model yielded poor performance (correlation R = 0.38; R^2 = 0.15; RMSE = 4.7 mm yr^1) (Fig. 3a), demonstrating the inefficiency of linear models to capture these complex dependencies and the need for a machine learning model.

Next, we used an RF machine learning model to better account for these complex nonlinear interactions between variables. RF has been widely applied in environmental and hydrological studies to model complex systems with nonlinear dependencies, outperforming traditional regression techniques in similar contexts88,89,90,91,92. The RF model is well-suited for this analysis due to its ability to handle small datasets (40 deltas), its simpler hyperparameter tuning, and its ability to compute feature importance. In this study, the primary objective for applying RF is not to predict the subsidence rates, but rather to extract key features that explain the dynamic relationships between anthropogenic drivers and subsidence across global deltas.

The RF algorithm is an ensemble learning method that uses the strength of multiple independent regressor decision trees {T}, in which each tree {Tt} is trained on a randomly sampled subset of the input features ({X = X_1, X_2, X_3}, representing GWS, sediment flux and urban expansion) through bootstrap aggregation (bagging). Key hyperparameters, including the number of trees, maximum tree depth, minimum samples per split and minimum samples per leaf, were optimized using grid search with five-fold cross-validation to minimize overfitting and maximize predictive accuracy[93]. This ensemble approach enhances predictive performance by creating a learning environment in which a large number of predictors work on various characteristics of the input features and learn to combat overfitting and generate predictions (VLM) by computing the average of all decision tree predictions:

$$\mathrm{VLM}=\frac{1}{T}\mathop{\sum }\limits_{t=1}^{T}{T}_{t}(X)$$

(11)

The RF regressor optimizes each decision tree using the mean square error (MSE) defined as a cost function to identify node splits and model performance during model training and testing:

$$\mathrm{MSE}=\frac{1}{N}\mathop{\sum }\limits_{i=1}^{N}{({\mathrm{VLM}}_{i}-\hat{{{\rm{V}}{\rm{L}}{\rm{M}}}_{{i}}})}^{2}$$

(12)

where VLMi is the observed VLM rate for individual delta i, \(\hat{{{\rm{V}}{\rm{L}}{\rm{M}}}_{{i}}}\) is the predicted VLM rate and N is the total number of observations. To assess uncertainty, we used Monte Carlo simulations to create multiple holdout fractions (0.1–0.5) across 100 iterations, randomly subsampling the 40 deltas for training and validation in each iteration. This random partitioning ensures that each delta is used in both training and validation phases across iterations, enhancing the robustness against overfitting and sampling bias. The final RF model predictions were obtained by averaging prediction estimates across all iterations. The final model performance was evaluated using the coefficient of determination (R^2), RMSE and mean absolute error (MAE):

$${R}^{2}=1-\frac{{\sum }_{i=1}^{N}{({\mathrm{VLM}}_{i}-\hat{{{\rm{V}}{\rm{L}}{\rm{M}}}_{{i}}})}^{2}}{{\sum }_{i=1}^{N}{({\mathrm{VLM}}_{i}-\bar{{{\rm{V}}{\rm{L}}{\rm{M}}}_{i}})}^{2}}$$

(13)

$$\mathrm{RMSE}=\sqrt{\frac{1}{N}\mathop{\sum }\limits_{i=1}^{N}{({\mathrm{VLM}}_{i}-\hat{{{\rm{V}}{\rm{L}}{\rm{M}}}_{{i}}})}^{2}}$$

(14)

$$\mathrm{MAE}=\frac{1}{N}\mathop{\sum }\limits_{i=1}^{N}\,|{\mathrm{VLM}}_{i}-\hat{{{\rm{V}}{\rm{L}}{\rm{M}}}_{{i}}}\,|$$

(15)

where \(\bar{{{\rm{V}}{\rm{L}}{\rm{M}}}_{i}}\) is the mean observed VLM rate, and the other variables are defined in equation (12). The feature importance If for input feature {X = X_1, X_2, X_3} was computed using the following equation, based on the cumulative reduction in node, j impurity among all the trees:

$${I}_{{\rm{f}}}=\sum _{j\in N}\frac{\Delta {I}_{j}}{N}$$

(16)

where N denotes the total number of trees and ΔIj denotes the change in impurity.

Although RF effectively captures nonlinear relationships, its ensemble structure limits delta-specific interpretability. To resolve local insights into delta-specific subsidence drivers, we applied LIME, a technique within the field of explainable artificial intelligence (XAI)[94]. LIME approximates black-box models such as RF by fitting interpretable models to perturbed samples of the input data, allowing for local feature importance estimation. For each delta Xi, LIME approximates the RF prediction locally by using a linear surrogate model trained on perturbed instances around Xi. The explanation function is obtained by solving the following minimization problem:

$$\xi ({X}_{i})=\arg \,\mathop{\min }\limits_{g\in G}[L(f\,,g,{{\rm{\pi }}}_{{X}_{i}})+\varOmega (g)]$$

(17)

where ξ(Xi) is the local interpretable model for each delta Xi, g is the interpretable model, f is the RF model, \({{\rm{\pi }}}_{{X}_{i}}\) is a proximity kernel, \(L(f\,,g,{{\rm{\pi }}}_{{X}_{i}})\) is the loss function measuring the differences between f and g, and Ω(g) penalizes complexity. This process was repeated for each delta, and deltas with low LIME model fidelity (R^2 < 0.5) were excluded to ensure reliable interpretation (Supplementary Table 2). The final dataset for interpretation consisted of 30 deltas, in which LIME produced more consistent feature importance estimates. The feature importance scores from LIME are normalized to obtain normalized LIME (nLIME) scores:

$${I}_{{\rm{f}}}^{\mathrm{LIME}}=\frac{|{\omega }_{{\rm{f}}}|}{{\sum }_{{{\rm{f}}}^{{\prime} }\in F}|\,{\omega }_{{{\rm{f}}}^{{\prime} }}\,|}$$

(18)

where ωf is the LIME-derived coefficient for feature f and F is set for all features. The nLIME scores provide an instance-specific (local) explanation rather than a global one to evaluate the relative contributions of GWS, sediment flux alteration and urban expansion in each delta. The nLIME values for each delta are summarized in Supplementary Table 2 and were analysed in a ternary diagram to visualize the heterogeneity in delta-specific subsidence and elevation-loss drivers (Fig. 3b).

It is important to emphasize that machine learning model predictions are inherently dependent on the input variables and their distributions. In this study, the predictor–response relationship implies that variations in predictor magnitudes (for example, subsidence rates and GWS rates), dataset composition (for example, inclusion or exclusion of specific deltas), and the selection of input features could influence the weighted feature importance across deltas. Moreover, localized policy interventions, such as groundwater extraction regulations or sediment management initiatives, may alter subsidence and elevation change trends over time, potentially affecting future predictions. Therefore, although our RF-based analysis provides valuable insights into the anthropogenic drivers of subsidence and elevation loss, these results should be interpreted with an awareness of dataset limitations and the potential for evolving land-use and hydrological management practices. Furthermore, the inclusion of additional deltas, particularly those representing undersampled geographic regions or differing geomorphic, socioeconomic or governance conditions, may shift model behaviour and feature rankings, as is typical in data-driven learning frameworks. Nonetheless, within the context of the current global delta sample and observed subsidence patterns, the RF-derived feature importance values provide a consistent and interpretable estimate of the relative influence of anthropogenic drivers under present conditions for these deltas.

Historical, current and projected SLR rates

We analysed historical (twentieth century), present-day (early twenty-first century) and projected (2050 and 2100) SLR rates to assess the relative and combined impacts of rising seas and sinking lands on global river deltas.

Historical relative sea-level changes were obtained from the Revised Local Reference database of the Permanent Service for Mean Sea Level[95] (https://psmsl.org), which provides monthly relative sea-level records from globally distributed tide gauge stations. These tide gauge records have undergone quality control procedures, including corrections for datum inconsistencies, jumps and spurious data points, and validation through comparisons with neighbouring tide gauge stations95,96. For this study, we selected 20 tide gauge stations across 15 deltas (the Mississippi, Rio Grande, Fraser, Amazon, Chao Phraya, Mekong, Red River, Nile, Ganges–Brahmaputra, Vistula, Rhine–Meuse, Chikuma-gawa, Yangtze, Pearl and Rioni deltas), considering only stations within 100 m of the delta boundary and at least 5 years (twentieth century) of valid record. The RSLR rates for each delta were estimated by applying the stochastic-seasonal model (equations (6–8)) over the full observational record for each tide gauge. For deltas with multiple stations (for example, the Mississippi, Ganges–Brahmaputra and Rhine–Meuse deltas), individual station rates were averaged to provide a delta-wide estimate of twentieth-century RSLR. Note that the representativeness of the derived RSLR may vary for each delta following individual tide gauge characteristics (for example, is the station founded on bedrock or ‘floating’ in unconsolidated sediments, is the station GNSS corrected). Supplementary Fig. 7 shows the time series of relative sea level over the twentieth century for six representative deltas. Supplementary Table 1 provides a complete summary of the RSLR rates for the 15 deltas. The median twentieth-century RSLR trend across all deltas is 2.9 mm yr–1, with measured rates ranging from –0.5 mm yr–1 in the Amazon delta (indicating declining twentieth-century sea level) to a maximum rate of 1.5 cm yr–1 in the Chao Phraya Delta (Fig. 5b).

To estimate present-day (early twenty-first century) absolute (geocentric) SLR rates, we used the multi-mission satellite altimetry data from 2001 to present, obtained from Copernicus Marine Environment Monitoring Service (CMEMS). This dataset provides 1/8° (about 12.5 km) gridded monthly sea level anomalies (SLA) referenced to a 20-year mean baseline (1993–2012). SLA estimates are derived from optimal interpolation, merging the level 3 along-track measurement from multiple contemporaneous altimeter missions (Jason-3, Sentinel-3A, HY-2A, Saral/AltiKa, Cryosat-2, Jason-2, Jason-1, TOPEX/Poseidon, ENVISAT, GFO and ERS1/2)[97] (https://marine.copernicus.eu/). Several necessary corrections have been applied to the raw altimetry data, including instrumental biases and drifts, geophysical, tidal and atmospheric corrections, to ensure accurate SLA estimates. Monthly mean sea-level anomalies were obtained for each delta by spatially averaging the altimetry grid points within a 100-m radius, culling outliers beyond the 95th percentile. Supplementary Fig. 8 shows the monthly SLA time series in six deltas. We estimated the twenty-first-century trends in sea-level anomalies, using equations (6–8). The altimetry-derived geocentric SLR rates for the twenty-first century show exacerbating regional SLR rates over global sea-level estimates (about 4 mm yr–1) for 45% of the deltas (18 out of 40) (Supplementary Table 1). Regional sea-level rates vary from 0.2 mm yr–1 in the Parana delta to 7.3 mm yr–1 over the Mississippi delta (Fig. 1 and Supplementary Table 1). However, a negative geocentric sea-level rate of −1.9 mm yr–1 was observed in the Rioni Delta (Black Sea) (Supplementary Table 1). This long-term sea-level decline in the twenty-first century persists in the background of short-term fluctuations (Supplementary Fig. 8d); a characteristic feature of Black Sea sea-level dynamics[50]. This twenty-first-century decline in geocentric sea level for the Rioni Delta represents more than a 100% reduction compared with historical (twentieth-century) rates, even when accounting for average VLM across the delta. To investigate this anomaly, we estimated VLM at the Poti tide gauge (Rioni Delta) by differencing twenty-first-century RSLR rates obtained from the Poti tide gauge station from geocentric SLR. The resulting VLM rate of −6.7 mm yr–1 matches the average InSAR-derived VLM rate (−5.9 ± 0.7 mm yr–1) within 100 m of the tide gauge. This rapid subsidence rate at the coast of Poti represents localized conditions and highlights the need for caution when extrapolating point-based tide gauge measurements to infer delta-wide or city-wide subsidence and exposure. Note that satellite altimetry data, although highly valuable for global sea-level monitoring, were primarily optimized for open ocean conditions. Coastal environments naturally exhibit additional complexity due to processes such as shelf circulation, freshwater discharge and tidal amplification, which contribute to the inherent variability in nearshore sea-level measurements compared with offshore altimetric observations.

We use projected sea-level rates from the Intergovernmental Panel on Climate Change Sixth Assessment Report (AR6)38,98 to assess future SLR rates across all deltas. The sea-level rate projections integrate process-based models that account for the key contributors to climate-induced sea-level change, such as thermal expansion, ocean dynamics, and glacier and ice sheet mass loss, and consider uncertainties in global temperature change and their influence on sea-level drivers[38]. We focus on the no-VLM 50th percentile (median) projected rates for 2050 (mid-twenty-first century) and 2100 (end of the twenty-first century) under shared socioeconomic pathway 2-4.5 (SSP2-4.5) and SSP5-8.5 scenario. SSP5-8.5 represents a high reference scenario associated with the highest emission levels (global atmospheric CO_2 concentrations exceeding 800–1,100 ppm by 2100) and associated warming of 3.3–5.7 °C (refs. 38,99). These projections provide an upper-bound reference scenario, capturing the potential worst-case outcome for future SLR. Figure 4c shows the comparison of projected SLR rates with observed land subsidence rates.

Data availability

The vertical land motion data for all deltas are available at Zenodo[100] (https://doi.org/10.5281/zenodo.15015923). GRACE data are available from https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_GRID_RL06.3_V4. The Sentinel-1 data used in this study are publicly available through the Alaska Satellite Facility and can be accessed at https://search.asf.alaska.edu. The satellite altimetry data for sea-level change are available from Copernicus Marine Environment Monitoring Service (CMEMS) and are available through http://marine.copernicus.eu/. The population for deltas was estimated using the WorldPop dataset available through https://www.worldpop.org/. Source data are provided with this paper.

Code availability

The WabInSAR algorithm v.5.6 used to perform the SAR analysis is available at https://www.eoivt.com/software. The code for the RF analysis using MATLAB 2024b is available at Zenodo[100] (https://doi.org/10.5281/zenodo.15015923).

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Acknowledgements

We thank T. Jarriel, J. Swartz and P. Passalacqua for their 2021 study ‘Global rates and patterns of channel migration in river deltas’, which inspired the design of Figs. 1 and 3b. The research is supported by funding from the National Science Foundation and the Department of Defense. S.W. and N.S. were partly funded by the National Aeronautics and Space Administration grant no. 80NSSC21K0061.

Author information

Authors and Affiliations

1. Department of Earth System Science, University of California, Irvine, Irvine, CA, USA

L. O. Ohenhen

2. Department of Geosciences, Virginia Tech, Blacksburg, VA, USA

M. Shirzaei, O. Dasho, N. Sadhasivam, S. Werth, F. Onyike, J. Lucy, C. Atkins & A. Ankamah

3. Institute for Water, Environment and Health, United Nations University, Richmond Hill, Ontario, Canada

M. Shirzaei, O. Dasho, N. Sadhasivam, S. Werth, F. Onyike, J. Lucy & C. Atkins

4. Lamont-Doherty Earth Observatory, Columbia University, New York, NY, USA

J. L. Davis & A. J. Chadwick

5. Texas A&M AgriLife Research Center, Corpus Christi, TX, USA

A. Tiwari

6. Tyndall Centre for Climate Change Research, University of East Anglia, Norwich, UK

R. Nicholls

7. School of Engineering, University of Southampton, Southampton, UK

R. Nicholls

8. Institute of Geography, University of Cologne, Cologne, Germany

K. Seeger

9. Soil Geography and Landscape Group, Wageningen University and Research, Wageningen, The Netherlands

K. Seeger & P. S. J. Minderhoud

10. Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA, USA

S. Daramola

11. Department of Civil, Environmental and Architectural Engineering, University of Padova, Padova, Italy

P. S. J. Minderhoud & G. C. Yemele

12. Department of Groundwater and Water Security, Deltares Research Institute, Utrecht, The Netherlands

P. S. J. Minderhoud

13. Department of River-Coastal Science and Engineering, Tulane University, New Orleans, LA, USA

J. Olsemann

Contributions

L.O.O. and M.S. conceptualized the study. L.O.O. created the figures and wrote the first draft of the paper with contributions from M.S., J.L.D. and A.T. The analysis was carried out by L.O.O., M.S., A.T., O.D., N.S., S.W., F.O., J.L., C.A., A.A. and J.O.; L.O.O., M.S., J.L.D., A.T., R.N., O.D., N.S., K.S., S.W., A.J.C., F.O., J.L., C.A., S.D., A.A., P.S.J.M., J.O. and G.C.Y. reviewed and edited the paper.

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Correspondence to L. O. Ohenhen.

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Extended data figures and tables

Extended Data Fig. 1 Global Distribution of Delta Area and Population Below Mean Sea Level.

Each circle represents one of the 955 global deltas from Edmonds et al.[6], with latitude constrained to below 60°N due to limitations in the digital elevation model dataset. The circle color indicates the land area below mean sea level (exposed area), while the circle sizes represent the population living in those areas (exposed population). The 40 deltas selected for this study are labelled. Global coastlines are based on public-domain data from the World Data Bank II (via GSHHG), distributed with MATLAB.

Extended Data Fig. 2 Spatial Pattern of Vertical Land Motion (VLM) in Deltas.

Spatial maps of VLM rates for the (a) Rio Grande (USA-Mexico), (b) Grijalva (Mexico), (c) Magdalena (Colombia), (d) Amazon (Brazil), (e) Senegal (Senegal), (f) Saloum (Senegal), (g) Volta (Ghana), (h) Cross (Nigeria), (i) Wouri (Cameroon), (j) Ogooué (Gabon), (k) Zambezi (Mozambique), and (l) Rhine-Meuse (the Netherlands) deltas. Positive VLM (green-purple hues) indicates elevation gain (uplift), while negative VLM (yellow-orange-red hues) indicates elevation loss (land subsidence). Background image is ESRI, streets-dark.

Extended Data Fig. 3 Spatial Pattern of Vertical Land Motion (VLM) in Deltas.

Spatial maps of VLM rates for the (a) Rhone (France), (b) Vistula (Poland), (c) Neva (Russia), (d) Ceyhan (Türkiye), (e) Rioni (Georgia), (f) Indus (Pakistan), (g) Kabani (India), (h) Cauvery (India), (i) Godavari (Cameroon), (j) Mahanadi (India), (k) Brahmani (India), and (l) Irrawaddy (Myanmar) deltas. Positive VLM (green-purple hues) indicates elevation gain (uplift), while negative VLM (yellow-orange-red hues) indicates elevation loss (land subsidence). Background image is ESRI, streets-dark.

Extended Data Fig. 4 Spatial Pattern of Vertical Land Motion (VLM) in Deltas.

Spatial maps of VLM rates for the (a) Ciliwung (Indonesia), (b) Brantas (Indonesia), (c) Yangtze (China), and (d) Chikuma-gawa (Japan) deltas. Positive VLM (green-purple hues) indicates elevation gain (uplift), while negative VLM (yellow-orange-red hues) indicates elevation loss (land subsidence). Background image is ESRI, streets-dark.

Extended Data Fig. 5 Spatial Pattern of Horizontal Land Motion (HLM) in Deltas.

Spatial map of HLM for the (a) Fraser (Canada), (b) Rio Grande (USA-Mexico), (c) Grijalva (Mexico), (d) Magdalena (Colombia), (e) Rhine (the Netherlands), (f) Rhone (France), (g) Po (Italy), (h) Vistula (Poland), and (i) Neva (Russia) deltas. Positive HLM (green-purple hues) indicates eastward motion, while negative HLM (yellow-orange-red hues) indicates westward motion. Near-zero HLM (yellow hues) represents areas with minimal horizontal displacement. Background image is ESRI, streets-dark.

Extended Data Fig. 6 Spatial Pattern of Horizontal Land Motion (HLM) in Deltas.

Spatial map of HLM for the (a) Rioni (Georgia), (b) Ceyhan (Türkiye), (c) Niger (Nigeria), (d) Cross (Nigeria), (e) Wouri (Cameroon), (f) Nile (Egypt), (g) Zambezi (Mozambique), (h) Indus (Pakistan), and (i) Mahanadi (India) deltas. Positive HLM (green-purple hues) indicates eastward motion, while negative HLM (yellow-orange-red hues) indicates westward motion. Near-zero HLM (yellow hues) represents areas with minimal horizontal displacement. Background image is ESRI, streets-dark.

Extended Data Fig. 7 Spatial Pattern of Horizontal Land Motion (HLM) in Deltas.

Spatial map of HLM for the (a) Brahmani (India), (b) Irrawaddy (Myanmar), (c) Chao Phraya (Thailand), (d) Mekong (Vietnam), (e) Red (Vietnam), (f) Ciliwung (Indonesia), (g) Brantas (Indonesia), (h) Yellow (China), and (i) Chikuma-gawa (Japan) deltas. Positive HLM (green-purple hues) indicates eastward motion, while negative HLM (yellow-orange-red hues) indicates westward motion. Near-zero HLM (yellow hues) represents areas with minimal horizontal displacement. Background image is ESRI, streets-dark.

Extended Data Fig. 8 Relationship between Vertical Land Motion (VLM) Rates and Anthropogenic Drivers.

Scatter plots of VLM (mm per year) versus (a) groundwater storage (GWS) rate (mm per year), (b) sediment flux change (%), and (c) urban fraction (UF) change (%) for the 40 deltas. Scatter plot of GWS rate (mm per year) versus (d) sediment flux change (%) and (e) UF change (%). (f) Scatter plot of sediment flux change (%) versus UF change (%). Each relationship is analyzed using linear regression as well as polynomial and logarithmic regression models to assess the best-fit representation. Multiple regression fits (linear, quadratic, logarithmic) are shown to illustrate the varied nature of relationships between individual predictors and VLM, demonstrating the need for a nonlinear modeling approach.

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Supplementary Figs. 1–8.

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Ohenhen, L.O., Shirzaei, M., Davis, J.L. et al. Global subsidence of river deltas. Nature 649, 894–901 (2026). https://doi.org/10.1038/s41586-025-09928-6

- Received: 13 March 2025

- Accepted: 18 November 2025

- Published: 14 January 2026

- Version of record: 14 January 2026

- Issue date: 22 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09928-6

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Early hominins from Morocco basal to the Homo sapiens lineage

Jean-Jacques Hublin,

David Lefèvre,

Serena Perini,

Giovanni Muttoni,

Matthew M. Skinner,

Shara E. Bailey,

Sarah Freidline,

Philipp Gunz,

Mathieu Rué,

Mohssine El Graoui,

Denis Geraads,

Camille Daujeard,

Thomas W. Davies,

Kornelius Kupczik,

Mykolas D. Imbrasas,

Alejandra Ortiz,

Christophe Falguères,

Qingfeng Shao,

Jean-Jacques Bahain,

Alain Queffelec,

Asier Gómez-Olivencia,

Stefano Benazzi,

Adeline Le Cabec,

Rita Sorrentino,

Inga Bergmann,

Fatima-Zohra Sbihi-Alaoui,

Rosalia Gallotti,

Jean-Paul Raynal &

…

Abderrahim Mohib

Nature volume 649, pages 902–908 (2026)

Abstract

Palaeogenetic evidence suggests that the last common ancestor of present-day humans, Neanderthals and Denisovans lived around 765–550 thousand years ago (ka)[1]. However, both the geographical distribution and the morphology of these ancestral humans remain uncertain. The Homo antecessor fossils from the TD6 layer of Gran Dolina at Atapuerca, Spain, dated between 950 ka and 770 ka (ref. [2]), have been proposed as potential candidates for this ancestral population[3]. However, all securely dated Homo sapiens fossils before 90 ka were found either in Africa or at the gateway to Asia, strongly suggesting an African rather than a Eurasian origin of our species. Here we describe new hominin fossils from the Grotte à Hominidés at Thomas Quarry I (ThI-GH) in Casablanca, Morocco, dated to around 773 ka. These fossils are similar in age to H. antecessor, yet are morphologically distinct, displaying a combination of primitive traits and of derived features reminiscent of later H. sapiens and Eurasian archaic hominins. The ThI-GH hominins provide insights into African populations predating the earliest H. sapiens individuals discovered at Jebel Irhoud in Morocco[4] and provide strong evidence for an African lineage ancestral to our species. These fossils offer clues about the last common ancestor shared with Neanderthals and Denisovans.

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Main

Our understanding of the evolutionary history of both Neanderthals (Homo neanderthalensis) and H. sapiens is firmly grounded in morphological, genetic and archaeological analyses of extensive fossil hypodigms and numerous prehistoric sites across Europe and Africa. However, identifying the last common ancestor of these two species remains challenging. At times, Homo heidelbergensis was proposed as this ancestor[5]. Yet, anatomical and chronological evidence suggests that fossils assigned to H. heidelbergensis may not represent a coherent species[6]. Most of the Eurasian specimens assigned to this species probably belong to the common ancestral form of the Neanderthals and their Asian sister group, the Denisovans, or belong to them, but are not ancestral to H. sapiens[6]. Some have considered a Eurasian origin of H. sapiens[7], but the morphological evidence for this is limited. By contrast, recent fossil evidence has pushed back the presence of H. sapiens in Africa to over 300 ka (ref. [4]), highlighting the need to understand hominin diversity in Africa during the late Early Pleistocene (EP) and the first half of the Middle Pleistocene (MP). MP African fossils—such as those from Kabwe (Zambia), Bodo (Ethiopia) and Saldanha (South Africa)—are generally considered close African relatives of H. heidelbergensis (or Homo rhodesiensis). Among MP African specimens, those from Ndutu (Tanzania) and Salé (Morocco) have been more closely associated with the ancestry of H. sapiens[8].

Thomas Quarry I (ThI), located in the southwest part of the city of Casablanca, Morocco (Fig. 1a), represents a key archaeological locality in northwest Africa. ThI is excavated in the Oulad Hamida Formation (OHF)9,10 and comprises two primary sites (Extended Data Figs. 1 and 2a).

Fig. 1: ThI-GH site.

a, Location map of ThI, modified according to ref. [13]. b, Magnetostratigraphy of members OH3A, OH3B, OH4, GH-CCC and OH5 of ref. [13] and this study. The black bars represent normal polarity, and the white bars represent reverse polarity. Further details are provided in Supplementary Fig. 2. Magnetochron ages are from ref. [22]. c, Photograph of the outcrop stratigraphy with indication of magnetic polarity from this study and a previous study[13] and lithologic members. Here we focused on sections A–E, of which only section A is reported here. d, Magnetostratigraphy of sections A–E comprised stratigraphic units OH4 SU6–5 and GH-CCC SU4–3. Context and details for lithostratigraphic units are provided in Extended Data Fig. 2. The red stars with labels represent hominin remains (the larger stars indicate mandibles) (Extended Data Table 1): ThI-GH-UA28-7 (femur, a); ThI-GH-OA23-24 (tooth, b); ThI-GH-SA26-88 (tooth, c); ThI-GH-SA26-90 (tooth, d); ThI-GH-PA24-107 (tooth, e); ThI-GH-10717 (mandible) and ThI-GH-10717/1-5 (vertebrae, f); ThI-GH-10725 and ThI-GH-10725/1 (vertebrae, g); ThI-GH-10726 (vertebra, h); and ThI-GH-10978 (mandible, i). Note that ThI-GH-UA28-7 (a) is located outside the section on the right. Close to the bottom wall of the cavity, its insertion into the stratigraphy is imprecise (SU4/5).

In the oldest member of the OHF, the ThI-L site has yielded one of the most extensive early Acheulean lithic assemblages in Africa, dating back to around 1.3 million years ago11,12,13. The second site is a cave opened in the northeastern wall of the quarry named in 1994 Grotte à Hominidés (hereafter, ThI-GH) by the research team. In 1969, Philippe Beriro, an amateur collector, found a partial hominin mandible (ThI-GH-1) (Fig. 2) on a slope below the northwestern part of this cave, along with other mammal fossils and lithics. This material probably originated from the filling of the ThI-GH cave, which had been partially disturbed by quarrying activities14,15. ThI-GH-1 was initially described as Atlanthropus mauritanicus[16]. Subsequent systematic investigations at ThI-GH, carried out between 1994 and 2015, yielded an Acheulean industry, a diverse faunal assemblage and several additional hominin fossils in an undisputable stratigraphic context thanks to modern controlled excavations17,18,19.

Fig. 2: Hominin specimens from ThI-GH.

Mandible ThI-GH-1: (1) lateral view; (2) occlusal view; (3) lingual view. Mandible ThI-GH-10717: (4) right lateral view; (5) occlusal view. Mandible ThI-GH-10978: (6) lateral view; (7) lingual view. UP4 ThI-GH-OA23-24: (8) distal view; (9) mesial view. UP3 ThI-GH-PA24-107: (10) distal view; (11) mesial view. UP3 ThI-GH-SA26-90: (12) mesial view; (13) distal view. UI1 ThI-GH-SA26-88: (14) buccal view; (15) lingual view. (16) Fused C2 and C3 vertebrae ThI-GH-10725 and ThI-GH-10725/1, caudal view. (17) C4 vertebra ThI-GH-10717/5, cranial view. (18) C6 vertebra ThI-GH-10717/1, cranial view. (19) C7 vertebra ThI-GH-10717/3, cranial view. (20) T1 vertebra ThI-GH10717/2, cranial view. (21) T2 vertebra ThI-GH-10717/4, cranial view. Scale, 5 cm.

ThI-GH is a cave that was carved during a marine high-stand into the older marine-aeolian OH1 and OH3 deposits of the OHF. It was filled by marine (OH4 stratigraphic unit 6, SU6) then supratidal (SU5) deposits and, without any discontinuity, by continental deposits (GH-CCC SU4 and SU3). Then, aeolian deposits (OH5) separated the latter from upper continental deposits (SU2 and SU1)10,18 (Fig. 1c, Extended Data Fig. 2, Supplementary Note 1 and Supplementary Fig. 3). A rich palaeontological assemblage has been recovered from OH4 SU5 and GH-CCC SU4, with hominin remains and lithic artifacts17,18,19,20 (Extended Data Fig. 3a,b and Supplementary Notes 2 and 3). The abundance of carnivores, numerous coprolites and carnivore-modified bone remains lacking evidence of cut or chop marks, combined with the scarcity of lithic artifacts, point to the presence of a carnivore den[21] (Supplementary Note 2). The most representative hominin specimens have been found in SU5, including an adult mandible (ThI-GH-10717), eight associated vertebrae (ThI-GH-10717/1 to 5, ThI-GH-10725, ThI-GH-10726 and ThI-GH-10725/1) and a fragmentary mandible (ThI-GH-10978) of a child who died aged at most 1.5 years (Fig. 2 and Supplementary Note 7). A portion of a hominin femoral shaft (ThI-GH-UA28-7) scavenged by a large carnivore, probably a hyena[21], was found at the back of the cavity in a layer belonging to SU4 or SU5. Although the precise stratigraphic origin of the ThI-GH-1 hemimandible remains uncertain, sedimentological analysis of the embedding sediment suggests that it also probably derives from either SU4 or SU5 (ref. [18]).

Dating

We proposed a chronostratigraphic and depositional model for the OHF within a sequence stratigraphy framework shaped by Pleistocene sea-level fluctuations and moderate regional uplift9,10,13. Sea-level transgressive phases mark calcarenite onlap and the carving of cliff and erosional notch at the base of previously lithified aeolian dunes, whereas regressive phases involve seaward progradation and the buildup of new dunes. Early cementation in semiarid, bioclastic-rich coastal settings allows rapid lithification[9], enabling successive transgressive erosion and cliff formation (Supplementary Note 1 and Supplementary Fig. 2). A previous study[13] placed the Matuyama–Brunhes transition (MBT, 773 ka)[22] close to the base of SU4 and recognized the Jaramillo subchron (1,070–990 ka) in member OH3. This interpretation excludes hiatus long enough to imply older subchrons like the Olduvai in place of the Jaramillo, which would also contradict the Acheulean lithics found at ThI-L. However, the preliminary sampling within the GH-CCC SU4 and OH4 SU5 deposits containing the human remains (five samples in section A) did not allow precise placement of the MBT in relation to these remains. We refined this model by adding 119 new magnetostratigraphic samples (Methods and Supplementary Note 5) from OH3, OH4 and GH-CCC to the 62 from ref. [13], improving the resolution of the Jaramillo and the MBT[22] (Fig. 1b,c).

Characteristic remanent magnetization (ChRM) component directions of samples from two different sections yielded virtual geomagnetic pole (VGP) latitudes indicating that the Jaramillo subchron lies within member OH3. Most of the samples from SU6 to SU3 (Fig. 1c,d (sections A–E)) provided VGP latitudes of reverse magnetic polarity or ChRM directions showing a tendency towards reverse polarity (Supplementary Note 5). This post-Jaramillo interval of dominant reverse polarity is punctuated by a thin normal polarity excursion in SU5. Above, a reverse-to-normal polarity transition occurs in GH-CCC close to the SU4–SU3 contact, with stable normal (Brunhes) polarity extending into GH-CCC-SU3 (Fig. 1d) and continuing into younger OH5 deposits (Fig. 1c).

These results reveal a detailed recording of the MBT occurring throughout SU6 to SU3. In records of high sediment accumulation rate (>15 cm per thousand years), the MBT is characterized by brief VGP excursions occurring between stable reverse (Matuyama) and stable normal (Brunhes) polarity23,24, with a mid-point at 773 ka and a transition duration of around 8 or 10.8 thousand years23,24. Our sampling probably captured one such excursion in OH4-SU5 (Fig. 1b,d and Extended Data Fig. 4). The intertidal biocalcarenites of SU6 and the littoral sands of SU5 are interpreted as representing the marine isotope stage (MIS) 20–MIS19 transgression of sea-level (starting at around 795 ka)[25] and the subsequent maximum flooding surface, respectively. The continental deposits of SU4–SU3 are interpreted as part of the ensuing regressive system tract associated with the MIS19 highstand (around 780 ka). This is consistent with a sedimentation rate of around 20 cm per thousand years, largely sufficient to capture the MBT variability. As in the Gran Dolina TD6 layer (Sierra de Atapuerca)[2], our analysis indicates hominin ages younger than 990 ka (top of Jaramillo) and close to the MBT at a nominal age of 773 ± 4 ka (ref. [23]) (Fig. 1d and Extended Data Fig. 4).

Biochronological data closely agree with the magnetostratigraphic ones (Supplementary Note 2). The fauna includes 37 species of mammals; it shares many species with that of Tighennif in Algeria, at least 1 million years old[26]. It documents the last known occurrences of the hare Trischizolagus and of the rhino Ceratotherium mauritanicum; Theropithecus oswaldi and Kolpochoerus are also indicative of an early age. Comparisons with other African sites are in good agreement with an age close to the EP–MP boundary20,27. Resemblances with East and South African faunas attest to easy latitudinal exchanges, demonstrating that the Sahara was not a permanent barrier in EP times owing to the recurrent expansion of savanna landscapes across North Africa in response to short-lived, astronomically driven periods of enhanced monsoon rainfall28,29.

Optically stimulated luminescence (OSL) dating, performed in unit SU4 on cemented sands provided age estimates of 420 ± 34 ka and 391 ± 32 ka (refs. 17,19), of the same order as the ages obtained from OH2 to OH5 (ref. [30]). OSL ages appear to be inconsistent with the evidence that these formations belong to at least three glacioeustatic cycles9,10 and, for this reason, can be disputed. Combined electron-spin resonance (ESR) and U-series dating methods applied directly to an isolated hominin tooth from SU4 resulted in an estimated age of 501 ka +94 ka/−76 ka (refs. 17,18,19,31). The same method yielded ages ranging from 591 ± 103 ka to 538 ± 52 ka on three well-preserved herbivore teeth from SU4 (Extended Data Fig. 3a,b and Supplementary Note 4). However, the ThI-GH ESR samples have high uranium content in the dental tissues and particularly in enamel. In this case, the internal dose rate is probably too high to efficiently generate ESR signals in hydroxyapatite, leading to varying degrees of equivalent dose underestimation. Thus, combined ESR and U-series results obtained at ThI-GH are considered to be minimum ages (Supplementary Note 4).

Hominin fossils

While the femoral shaft of ThI-GH-UA28-7 has already been analysed in detail[21], most of the hominin fossils of the ThI-GH have not been described, including two partial mandibles, a large number of teeth and several vertebrae, which provide invaluable phylogenetic information.

Mandibular morphology

ThI-GH-10717 is a gracile and nearly complete adult mandible preserving a full (although worn) dentition (Fig. 2, Extended Data Fig. 5 and Supplementary Note 6). Its corpus is long, low and narrow, with a slight pre-angular notch. In the lateral view, its symphysis is receding—an orientation similar to Homo erectus sensu lato. There is a small mentum osseum, conforming to category 2 (ref. [32]). Its superior portion forms a faint incurvatio mandibulae. This morphology is also present in Homo sp. ATE9-1 from Sima del Elefante, as well as several early Homo individuals[33] (such as Olduvai, Dmanisi, Malawi, Koobi Fora and Sangiran 9). Like ATE9-1, ThI-GH-10717 displays an archaic marked submental incisura. The anterior marginal tubercle is weak and located below the fourth mandibular premolar (P_4), in a similar position to that observed on the EP mandibles from Tighennif, Algeria. The internal morphology of the symphysis is relatively smooth in its topography. It lacks both a superior and inferior transverse torus and expresses a shallow genioglossal fossa. The planum alveolare is nearly vertically oriented and has slight alveolar prominence. These features are similar in H. antecessor and considered derived relative to H. erectus[33]. The mental foramen is located below P_4, intermediate between the archaic position at P3–4, found in Homo habilis, Homo ergaster, H. erectus and H. antecessor and the mandibles from Tighennif, and the derived position below the first mandibular molar (M_1), which is found in some MP hominins and Neanderthals[34]. The lateral prominence of the corpus is weak, with the maximum expression at the level of M_2. The M_3 is partially covered by the ramus in lateral view. In contrast to the common condition in EP and MP hominins[35], the masseteric fossa is shallow as in Neanderthals and H. sapiens[36]. The flat pterygoid fossa and the symmetrical mandibular notch are reminiscent of H. sapiens[37] and diverge from the Neanderthal pattern36,37,38.

ThI-GH-1 is a more robust, but less complete, left adult hemimandible missing both the coronoid process and the mandibular condyle and preserving P_4–M_3 in situ. Like ThI-GH-10717, the corpus is low but with a more pronounced pre-angular notch that is also found in some European MP hominins33,39. It differs from ThI-GH-10717 in having a more pronounced and posterior lateral prominence at the level of M_2–M_3, an M_3 that is not covered by the ramus in lateral view and an intermediate (between parallel and oblique) trajectory of the mylohyoid line in relation to the alveolar margin. These three features align it with some European MP hominins and Neanderthals34,37,40,41. It also has a deeper relief of the masseteric fossa than ThI-GH-10717, a frequent condition in EP and MP hominins[35]. However, like ThI-GH-10717, Tighennif and TD6 hominins, and unlike the archaic condition found in H. erectus[42], the internal corpus shows moderate hollowing of the subalveolar fossa.

In three-dimensional (3D) landmark-based geometric morphometric analysis, the size of ThI-GH-10717 is modest, with a centroid size at the low end of the H. erectus sensu lato range. In shape analyses, it plots within the H. erectus sensu lato range of variation, along with African EP and MP hominins. Its shape differs from both Neanderthals and H. sapiens by having a broad ramus, narrow mandibular breadth, long and low corpus, and receding symphysis. Compared with all three groups, it has a smaller coronoid process, a more-expanded gonial profile and a lower anterior corpus. ThI-GH-1 is considerably larger than ThI-GH-10717, and falls within the H. erectus range. It also plots closer than ThI-GH-10717 to the European MP and Neanderthal range of variation in shape space.

Dental morphology

The fossils of ThI-GH include a sizable series of well-preserved permanent and deciduous teeth (comprehensive descriptions are provided in Supplementary Note 8 and Supplementary Tables 20–22). The postcanine teeth of ThI-GH-1 are consistently larger than the corresponding teeth of ThI-GH-10717. In both individuals, the molar size pattern is M1 < M2 > M3 (Extended Data Fig. 6), with a strong reduction in M3 contrasting with the conditions usually observed in H. erectus. Molar size patterns are variable, especially in H. sapiens[43]; however, this pattern is more common in H. antecessor, H. sapiens and Neanderthals. The crown outlines are similar to those of other EP hominins but, for the deciduous molars, they are closer to those of early H. sapiens than to those of H. antecessor (Supplementary Note 9).

The shape of the enamel–dentine junction (EDJ) can be studied on four permanent and two deciduous post-canine tooth positions using 3D landmark-based geometric morphometrics (Fig. 3 and Extended Data Fig. 7). In the deciduous dentition, the EDJ ridge shape of the ThI-GH-10978 first lower deciduous molar (dm_1) falls outside H. sapiens and Neanderthals. This is also the case for the ThI-GH-10978 dm_2, which also falls close to the fossil H. sapiens specimen Skhul 10. The H. antecessor TD6-112 dm_2 is more similar in EDJ ridge shape to H. sapiens and Neanderthals, falling broadly between these two species. The P_4 EDJ ridge and cervix shape of ThI-GH-1 place it close to the H. sapiens and Neanderthal samples, while the same features of ATD6-4 H. antecessor place it closer to H. erectus. The P^4 EDJ ridge and cervix shape of ThI-GH-OA23-24 place it close to H. sapiens and Neanderthals, while ATD6-9 is close to Neanderthals. The EDJ ridge shape of the ThI-GH-10978 M_1 (the cervix is not preserved) falls close to Sidi Abderrahmane, H. erectus, and relatively close to OH 22 and H. antecessor specimens ATD6-94 and ATD6-112. Like the dm_2, H. antecessor M_1 specimens ATD6-94 and ATD6-112 are more similar in shape to Neanderthals and H. sapiens. Finally, the EDJ ridge and cervix shape of the ThI-GH-10717 M_3 fall just outside the H. sapiens sample and adjacent to the H. erectus sample, while the ThI-GH-1 M_3 falls close to both H. sapiens and Neanderthal samples. In the molar roots, there is a decreasing overlap between H. sapiens and Neanderthals from M_1 to M_3, with H. erectus clustering on its own (Extended Data Fig. 8). The ThI-GH-1 M_1 roots fall on the margin of recent H. sapiens and close to early H. sapiens from North Africa. The M_1 roots of ThI-GH-10717 are more derived in their small size, towards recent H. sapiens. ThI-GH-1 M_2 and M_3 roots fall close to early H. sapiens specimens and near H. erectus, but ThI-GH-10717 M_2 and M_3 roots are well within the recent H. sapiens cluster.

Fig. 3: EDJ morphology of teeth.

Principal component (PC) analyses of EDJ shape variation for the first (top left) and second (top right) deciduous molars, the mandibular fourth premolar (middle left), the maxillary fourth premolar (middle right), the first mandibular molar (bottom left) and the third mandibular molar (bottom right). Shape is captured using 3D landmarks on the primary cusps and marginal ridge of the EDJ and, in the case of the the fourth premolars and third mandibular molar, also the cervix (partial EDJ ridge means that only the unworn portion of the marginal ridge was analysed). For each tooth position, the ThI-GH teeth fall outside and adjacent to our samples of Neanderthals and H. sapiens. Fossil H. sapiens (H. sap), MP hominins and H. antecessor specimens are identified individually by accession number. H. erectus is identified by H. ere and H. neanderthalensis by H. nea.

The anterior dentition of ThI-GH-10717 is heavily worn (right canine) or broken (left canine and all incisors), but the roots are preserved although fragmented. The right canine is gracile (crown and root), similar to modern humans, and much smaller than in other EP and MP hominins such as Tighennif, Irhoud and Neanderthal individuals (Supplementary Note 10). The incisor ThI-GH-SA26-88 has a relatively small crown that is within the early and recent H. sapiens variations. By contrast, its root length falls in the upper end of the H. sapiens variation, in the lower range of Neanderthals, and is smaller than that of H. erectus (KNM-WT 15000).

From a non-metrical dental trait perspective (Supplementary Table 20), the mandibular molars from ThI-GH are comparable to other EP and MP teeth from North Africa (for example, Sidi Abderrahmane and Tighennif) and are similar to the TD6 H. antecessor molars in their variable expression of trigonid crests, cusp 7 and lack of cusp 6. However, in both deciduous and permanent postcanine teeth, the cusps of the TD6 specimens are more closely spaced than they are in the North African specimens. In this way, the TD6 H. antecessor specimens appear more derived towards Neanderthals. Moreover, ThI-GH-1 M_2 and M_3 (as well as Tighennif 1) differ from the TD6 material in the way they taper distally, which is an archaic feature seen in African H. erectus. Furthermore, neither the crown of the Tighennif nor the ThI-GH teeth show any lingual relief, especially shovel-shaped morphology for incisors.

Vertebrae

Directly underneath ThI-GH-10717, a series of eight vertebrae (six cervical and two thoracic) was discovered (Fig. 2 and Extended Data Fig. 9). Their small size and very close spatial proximity to the mandible suggest that they belonged to the same small-bodied adult. Although the fossil record allows limited comparisons, morphologically, the most complete vertebrae (C7, T1 and T2) are more similar to H. erectus than to recent Homo species. In particular, C7 displays a more lateral (ventral–lateral) orientation of the lower articular facets relative to the condition observed in H. sapiens and Neanderthals. The immature H. antecessor C7 (ATD6-75) is more H. sapiens-like in the orientation of the lower articular facets[44]. Moreover, the orientation of the transverse processes in the ThI-GH both T1 and T2 vertebrae is slightly more dorsal than in recent H. sapiens, while it is notably more dorsal in KNM-WT 15000 (Supplementary Table 23). The vertebral canal section area in the ThI-GH specimens is similar to the Dmanisi C3 and to the H. antecessor C7 vertebrae, and similar (T1) or larger than those of KNM-WT 15000 (C7, T2). These areas are below the mean but not significantly different from a recent H. sapiens sample (Supplementary Table 24). When standardized relative to the geometric mean of vertebral body linear dimensions, all of the values fall within the H. sapiens variation, except for the T2, where both KNM-WT 15000 and ThI-GH specimens show very low values (Extended Data Fig. 9).

Discussion and conclusions

In North Africa, the ThI-GH hominins are the only specimens unearthed within an indisputable stratigraphic context and securely dated to the MBT at a nominal age of 773 ± 4 ka. These hominins cannot be directly compared with later specimens, such as the Kabwe or Bodo skulls, which have been tentatively assigned to H. heidelbergensis. Not only do these specimens differ substantially in age, but they also lack preservation of comparable anatomical parts. Our analysis suggests that the ThI-GH hominins probably belong to an evolved form of H. erectus sensu lato in North Africa, much as H. antecessor does in Europe. However, the ThI-GH hominins offer an interesting contrast to both the Spanish fossils and the considerably older fossils from Tighennif (Algeria), which are likely to date to at least 1,000 ka (refs. 45,46,47,48). The fossil mandibles from Tighennif appear more primitive, larger and more robust than both the European H. antecessor and the northwest African ThI-GH fossils. The Spanish and Moroccan fossils share several features in their teeth and mandibles. Both groups display a combination of archaic and derived features reminiscent of later hominins (Supplementary Table 25). These similarities revive the question of possible exchanges across the Strait of Gibraltar during the EP. Nevertheless, the ThI-GH hominins are different from the TD6 hominins. The pattern of these differences suggests that regional differentiation between Europe and North Africa was already present by the late EP. Apparent Neanderthal-like features on the larger ThI-GH-1 mandible could reflect primitive retentions, allometric effects or convergent evolution but, when more phylogenetically informative dental characters are considered, the Spanish specimens appear more derived towards the Neanderthal morphology that later emerged in western Eurasia (see also refs. 49,50).

The origin of H. sapiens, and the precise timing of the divergence of its ancestral populations from the Neanderthal–Denisovan clade, remain subjects of debate. Anatomical evidence has at times been used to argue for a split predating 800 ka (ref. [51]) and even for an alternative Asian ancestry of our species[52]. In this context, the Maghreb fossils are key to understanding the diversification of MP hominins. The morphology of the ThI-GH hominins places them close to the split between the African and Eurasian lineages. Our findings not only align with the phylogenetic structure inferred from palaeogenetic data but also highlight the Maghreb as a pivotal region for understanding the emergence of our species, reinforcing the case for an African rather than a Eurasian ancestry of H. sapiens.

Methods

Excavation methods

ThI-GH SU4 and SU5 have been systematically excavated since 1994. A 0.5/1-m-deep sequence of an area of 48 m^2 was excavated (Fig. 3a and Extended Data Fig. 1b). Excavation was performed according to the stratigraphic sediment deposition, and stratigraphic units were subsequently numbered from 1 to 7 from top to bottom. We established an arbitrary excavation 1 m × 1 m grid, and spatial data (x, y, z) of all finds (worked and unworked lithics, as well as faunal and human remains) were recorded (Extended Data Table 1). From 1994 to 2005, single finds were assigned unique IDs consisting of the quarry acronym (ThI), the site acronym (GH), the name of the square and a progressive number (for example, ThI-GH-SA26-88). From 2006 onwards, spatial data measurement was carried out with the total station. The code for each find consists of the quarry acronym, the site acronym and a number from 10000 (for example, ThI-GH-10634). We documented layers, special features and profiles in 3D models using total station measurements, digital photographs and drawings. The 3D models were referenced with control points recorded with the total station to align them to the excavation grid. Sediments have been collected for every m^2, dissociated with diluted formic acid and wet-screened to recover lithic and faunal small fragments.

Stratigraphy of the OHF

The chronostratigraphic framework of the OHF exposed at ThI is based on the direct observations of sedimentary formations, stratigraphic boundaries and facies. The successions and associations of facies have been used to characterize the depositional environments, their evolution and to infer sea level changes. According to the sequence stratigraphy concept, an allostratigraphic unit is defined by a sedimentary sequence characterized by a succession of deposits attributed to intertidal, supratidal and aeolian/continental environments, bounded at its base and top by unconformities. This sequence is essentially deposited during phases of marine transgression and sea-level high stands. According to the international stratigraphic guide, the allostratigraphic units were formalized as members of the OHF. Microfacies analysis was carried out on large thin sections prepared from blocks of oriented sediments vacuum-impregnated with polyester resin. These analyses provided specific information about diagenetic processes occurring during and after deposition.

Sedimentology of ThI-GH infilling

Stratigraphic units SU5 to SU3 were studied using a geoarchaeological approach, integrating field observations (sedimentary structure, colour, discontinuities and so on), micromorphology and analyses (particle size distribution, magnetic susceptibility measurements and energy-dispersive X-ray fluorescence (ED-XRF) analyses). Micromorphology was based on the observation of large thin sections taken continuously in stratigraphic order. Particle size analyses were performed on bulk samples after decarbonatation. Volume magnetic susceptibility was measured along the section using a Bartington MS2K sensor, with a vertical resolution of 2 cm. Air-dried and crushed bulk samples (<2 mm) were analysed by ED-XRF using a calibrated portable spectrometer (SPECTRO X-SORT) (Supplementary Note 1).

Magnetostratigraphy and rock magnetic properties

Magnetostratigraphic data were obtained from a population of 119 oriented core-samples retrieved from members OH3, OH4 and GHCCC in 2022 and 2023 and integrated with 62 samples previously analysed in 2018–2019[13] from the same members plus member OH5. The sampling of ThI-GH infilling (from SU6 to SU3) was conducted along 5 sections, A and B (68 samples), C (14 samples), D (6 samples) and composite section E (19 samples) (Fig. 1d and Extended Data Fig. 2b), yielding a total of 107 oriented core-samples taken to better anchor the hominin bearing site ThI-GH to the MBT. Furthermore, 13 samples were retrieved from member OH3 to refine the record of the Jaramillo subchron previously observed[13].

Magnetostratigraphic samples were thermally demagnetized from room temperature up to a maximum of 690 °C with a TD48 ASC furnace. Alternating-field (AF) demagnetization up to 200 mT performed on two test samples with an LDA5 AF demagnetizer resulted inadequate to resolve the magnetic remanence of the samples. After each thermal demagnetization step, the initial magnetic susceptibility was measured using a Bartington susceptibility bridge. The natural remanent magnetization was measured on a 2G DC-SQUID cryogenic magnetometer located in a magnetically shielded room. Standard least-squares analysis was used to calculate ChRM component directions from vector end-point demagnetization diagrams, from which VGP latitudes were derived (positive VGP values for normal polarity, negative values for reverse polarity). Great circles were used to assess qualitatively the ChRM orientation in absence of stable end points. The magnetic mineralogy was investigated using hysteresis experiments from −1.5 T to +1.5 T, low-resolution first-order reversal curves (FORCs, n = 76) interpreted with FORCinel[53], stepwise acquisition of an isothermal remanent magnetization (IRM) up to 1.5 T, AF decay of a 1 T IRM in AF peak fields from 50 mT to 1.5 T and thermomagnetic decay of a 1 T magnetization performed in Ar atmosphere from room temperature to 680 °C. These experiments were performed using a MicroSense EZ7 Vibrating Sample Magnetometer with heating ability. Additional samples were also subjected to thermal demagnetization of a three-component IRM using orthogonal fields of 1.5 T, 0.4 T and 0.12 T imparted with an ASC pulse magnetizer. Details are provided in the main text and Supplementary Note 5.

Geometric morphometric analysis of ThI-GH-1 and ThI-GH-10717 mandibles

The fossil sample (Supplementary Table 17) comprises Early, Middle and Late Pleistocene hominins from Africa, Europe and Asia, including specimens attributed to H. erectus, Homo floresiensis, Homo naledi, H. neanderthalensis, H. sapiens and Denisovan (Xiahe mandible). As the taxonomy of the European MP hominins is contested, we have refrained from assigning specimens from this period to a taxon but refer to them as European MP hominins. Moreover, there are several EP fossils from Africa with ambiguous taxonomic attribution (that is, from Baringo Kapthurin, Kenya and Tighennif, Algeria) that we refer to as African EP hominins. We used the term early H. sapiens to refer to the oldest members of our species from around 300 to 100 ka found at sites in Africa and the Near East (such as Jebel Irhoud, Klasies River Mouth, Border Cave, Skhul and Qafzeh). All of the specimens are adults based on dental eruption and spheno-occipital fusion, except for KNM-WT 15000.

Micro-computed tomography (micro-CT) scans of ThI-GH-1 and ThI-GH-10717 were made with Diondo d3 at the Department of Anthropology, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany, with a scan resolution of 30 µm. 3D surface models were reconstructed from these CT scans using Avizo v.7.1 (Thermo Fisher Scientific). 3D surface models of the comparative sample were created from either CT scans using Avizo v.7.1 or photogrammetry. For the latter, between 40 and 90 2D photographs were taken using the Nikon D600 (4,512 × 3,008 pixels) and processed with Agisoft PhotoScan Professional v.1.2.0 (Agisoft)[53]. Error tests evaluating differences in imaging techniques are within the acceptable range of error in osteometry[54]. For most fossils, surface models were generated from the original specimen; however, when surface models from the original specimen were not available research quality casts were used54,55,56,57,58.

Minor virtual reconstruction was needed for most specimens in the comparative sample and was performed in either Geomagic Studio 2014 v.3.0 (3D Systems) or Avizo v.7.1. The type of reconstruction varied considerably depending on the specimen, but generally included the filling of cracks or holes, removal of sediments, smoothing of abraded areas and refitting of fragments. For some fossils in which one side was missing or deformed, bilateral symmetry was exploited by mirror-imaging. Specific details regarding the reconstruction techniques and error tests have been published previously54,55,56,57,58,59.

Geometric morphometric methods were used to analyse the shape and size of the ThI-GH fossils in a comparative context. Separate landmark datasets (Supplementary Figs. 32 and 33) were created according to the preserved anatomical elements of the ThI-GH mandibles: (1) a mandibular dataset, consisting of 301 (semi)landmarks, based on the preserved morphology of ThI-GH-10717; (2) a left mandibular dataset consisting of 87 (semi)landmarks, based on the preserved morphology of ThI-GH-1; and (3) and an anterior corpus dataset, consisting of 153 (semi)landmarks, which allowed for an expanded comparative sample. Three-dimensional coordinates of anatomical landmarks and curve semilandmarks were digitized on the surface models using Landmark Editor (v.3.0.0.6)[60]. Landmark and semilandmark data were processed and analysed previously[61] using the packages Morpho (v.2.9)[62] and geomorph (v.4.0.2)63,64. For each dataset, missing bilateral landmarks and semilandmarks were estimated by mirroring the preserved side. Missing landmarks and semilandmarks lacking a bilateral counterpart were estimated by deforming the sample average onto the deficient configuration using thin-plate spline interpolation56,57,58,65. Curve and surface semilandmarks were slid by minimizing the bending energy of a thin-plate spline deformation between each specimen and the sample mean shape66,67. After sliding, all landmarks and semilandmarks datasets were symmetrized and converted to shape variables using a generalized Procrustes analysis[68].

For each dataset, the Procrustes coordinates were analysed using principal component analyses (PCA) in shape space, and nearest neighbours were calculated according to interindividual Procrustes distances. The ThI-GH fossils were projected into this PCA space. Shape changes were visualized along PC 1 and PC 2 by warping the sample mean shape along the positive and negative ends of PC 1 and PC 2, ±2 s.d. from the sample mean. To evaluate the size of the ThI-GH mandibles, the natural logarithm of centroid size was calculated for each specimen and compared across groups.

Mandibular metric data are shown in Supplementary Note 6 and Supplementary Tables 7 and 8. Linear measurements were taken by I.B. on 3D surface models generated from micro-CT scans in Avizo and were complemented by measurements of the original specimens taken by E. Trinkaus and by comparative data taken from the literature4,14,34,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116. The H. antecessor data include ATD6-96, ATD6-5, ATD6-113; the H. habilis data include KNM-ER 1501, KNM-ER 1502, KNM-ER 1805, OH 7, OH 13, OH 37; the H. ergaster data include KNM-ER 730, KNM-ER 992A, OH 22, OH 23, and OH 51; the H. erectus data include Zhoukoudian Lower Cave (G1.6, G1/G2, H/1), Lantian, Sangiran (1b, 5, 9); the African EP sample includes KNM-BK 67, KNM-BK 8518, Sidi Abderrahmane 2, Tighennif (1, 2, 3); the European MP sample includes Arago (II, XIII), Mauer, Montmaurin 1, Sima de los Huesos (XIX, XXI, XXVIII), AT-1, AT-75, AT-300, AT-605, AT-607; the Neanderthal sample includes Amud 1, Arcy II, Banyoles, Chagyrskaya 6, El Sidrón (1, 2, 3), Guattari (2, 3), Hortus 4, Kebara 2, Krapina (57, 58, 59), Suard S 36, Bourgeois Delaunay 1, La Ferrassie 1, La Quina H5, La Naulette 1, Le Regourdou 1, Saint-Césaire 1, Shanidar (1, 2, 4), Sima de las Palomas (1, 6, 23, 59), Spy (1, 3), Subalyuk 1, Tabun C1, Vindija (206, 226, 231, 250, 11.39, 11.40, 11.45), Weimar-Ehringsdorf F1009 and Zafarraya; the early H. sapiens sample includes Contrebandiers 1, Dar es-Soltan II-H5, Dire Dawa, El Harhoura 1, Jebel Irhoud 11, Klasies River (KRM 13400, KRM 14695, KRM 16424, KRM 21776, KRM 41815), Qafzeh (9, 25), Skhul (IV, V) and Tabun C2. The Upper Palaeolithic and Epipalaeolithic sample includes individuals from Abri Pataud 1, Arene Candide (2, 18), Asselar, Barma del Caviglione, Chancelade, Cro Magnon (1, 3), Dar es-Soltan (II-H2, II-H3), Dolni Věstonice (3, 13, 14, 15, 16), El Mirón, Grotte des Enfants 4, Hayonim (8, 17, 19, 20, 25, 27, 29 and 29a), Isturitz (106 and 115), Le Roc (1, 2), Minat 1, Moh Khiew, Muierii 1, Nahal Oren (6, 8, 14, 18), Nazlet Khater 2, Oase 1, Oberkassel (1, 2), Ohalo II (1, 2), Pavlov 1, Předmostí (3, 21), Sunghir (1, 6), Villabruna 1 and Zhoukoudian Upper Cave (101, 104, 108).

3D EDJ shape analysis

The shape of the EDJ was examined for multiple tooth positions represented in the ThI-GH sample and compared to a sample of fossil hominins as well as early and recent modern humans. Details of the comparative sample are listed in Supplementary Table 22. TIFF stacks were filtered using only a mean of least variance filter (kernel size one), or a 3D median filter (kernel size of three) followed by a mean of least variance filter (kernel size of three) using MIA open source software[117]. Enamel and dentine tissues of each tooth were then segmented using the watershed module in Avizo 6.3 (Thermo Fisher Scientific). After segmentation, the EDJ was reconstructed as a triangle-based surface model. We then used Avizo 6.3 to digitize 3D landmarks and curve-semilandmarks on these EDJ surfaces. Anatomical landmarks were placed on the tip of the dentine horn of the protocone/protoconid and metacone/metaconid (premolars), as well as the entoconid and hypoconid (molars). A sequence of landmarks was also placed along the marginal ridge connecting the dentine horns beginning at the top of the protocone/protoconid moving in the lingual direction. In R[61], a smooth curve was fit through this set using a cubic spline function, before dividing the curve into sections using the dentine horn landmarks (four sections for molars, two for premolars). A fixed number of equidistant semilandmarks were then placed along each section of the curve (landmark numbers reflect the relative length of these sections; in premolars the sections have 20 and 25 landmarks, respectively; and, in molars, they have 18, 15, 22 and 12 landmarks). Likewise, we digitized and resampled a curve along the cemento–enamel junction (cervix) as a closed curve starting either on the mid-face of the base of the protocone/protoconid (premolars) or on the mesiobuccal corner below the protoconid (molars). Homologous landmarks were then derived in R using the packages Morpho[62] and princurve[118] using a freely available R-based software routine[119]. Anatomical landmarks were fixed while the resampled points along the curves were treated as semilandmarks and allowed to slide along their curves so as to reduce the bending energy of the thin-plate spline interpolation function calculated between each specimen and the Procrustes average for the sample66,67. Sliding was performed twice, with landmarks projected back onto the curve after each step, before landmarks were considered geometrically homologous. Slid landmarks were then converted into shape coordinates using generalized least squares Procrustes superimposition, which removes scale, location and orientation information from the coordinates68,120. PCA was used to summarize shape variation in the comparative sample and assess morphological affinities of the ThI-GH teeth.

Tooth size analysis

Tooth size was analysed for two sets of mandibular teeth: dm_1/dm_2/M_1 and P_3/P_4/M_1/M_2/M_3. The size of each tooth was represented by its centroid size, calculated using the cervix or EDJ landmark set used in the EDJ GM analysis (see above). Centroid size was calculated as the square root of the sum of squared distances of each landmark to the centroid of all landmarks. Associated teeth are represented in plots by lines between points of adjacent teeth. The sample used for the tooth size analysis is listed in Supplementary Table 22.

Tooth wear

Wear categories were scored according to Molnar[121].

Tooth descriptions and measurements

The outer enamel surface (OES) and EDJ of the ThI-GH specimens were scored using a combination of visual inspection of the originals, photographs (OES) and virtual 3D models (OES and EDJ).

Descriptions of the pulp cavity configurations of the ThI-GH specimens were based on virtual 3D models. Measurements of the buccolingual and mesiodistal lengths of the ThI-GH specimens were taken from virtual 3D models. No corrections were made for interproximal wear and teeth that were too damaged to measure were omitted. With a few exceptions (for example, Atapuerca material) morphological assessment and crown measurements of the comparative material were taken from the original specimens using Mitutoyo digital callipers.

Crown and root morphology

Scores for most non-metric traits were obtained using a combination of written descriptions and (where applicable) reference plaques of the Arizona State University Dental Anthropology System (ASUDAS[122]). Traits that are not part of the ASUDAS, or those that have been reassessed since its publication were scored using different methods. We scored shovelling for incisors and canine; labial convexity for incisors; Tomes’ root for lower third premolar, anterior fovea, deflecting wrinkle, fissure pattern, cusp 6; and protostylid and enamel extension for lower molars as described previously[122]. We scored lower molar cusp 7 variation according to ref. [123]. Trigonid cresting patterns and variation on the lower molars were assessed as described previously[124]. Premolar asymmetry and transverse crest on the lower fourth premolar were assessed based on ref. [125]. We referred to ref. [126] to make assessments of maxillary premolar accessory ridges or MxPAR on the upper premolars.

Crown outline

The 3D digital models of the teeth were aligned with the cervical plane parallel to the x–y plane of the Cartesian coordinate system and rotated around the z axis with the lingual side parallel to the x axis. For the ThI-GH specimens, the right dm_1 and dm_2 were first mirrored in Geomagic Design X (3D Systems Software) to be compared with the left-side comparative sample. The crown outlines of the digital models were then extracted as a .igs file and imported into Rhinoceros v.5 (Robert McNeel & Associates). The centroid of the crown outlines was calculated based on the area of the outlines. A total of 24 equiangularly spaced radial vectors (with the first radius directed buccally and parallel to the y axis of the Cartesian coordinate system) from the centroid determined 24 pseudolandmarks of the crown outlines. The crown outlines were centred by their centroid and scaled to unit centroid size, transforming them into Procrustes shape coordinates, which were then used for computing PCA127,128,129.

ThI-GH specimens were projected in the shape-space PCA computed on Neanderthals and H. sapiens from the published comparative sample of dm_1 and dm_2128,130,131, updated with unpublished specimens (Supplementary Table 22). The differences in crown outlines among Neanderthals, H. sapiens (combined recent and Upper Palaeolithic) and early H. sapiens were investigated using permutation tests on the first three PCs (n = 10,000). A leave-one-out cross-validation quadratic discriminant analysis was performed, as the assumption of normality of variance was violated, on the minimum number of PCs accounting for 70% to 90% of the total variation131,132, to discriminate Neanderthal and H. sapiens groups and to estimate the taxonomic attribution of ThI-GH specimens to one of these groups with posterior probabilities (Ppost). Statistical analyses were performed using R (v.4.2.3)[61].

Anterior tooth root morphology

From the micro-CT scans, the tooth tissues of the anterior teeth were segmented (that is, enamel, dentine, pulp cavity). Linear, surface and volume variables were measured on the anterior tooth roots after the protocol described previously[133]. Comparative samples involve Neanderthals, early and recent H. sapiens, as well as early and lower Pleistocene hominins, some from Northern Africa. Details are provided in Supplementary Note 10.

Age at death for the juvenile

Based on 2D virtual sections generated from the micro-CT scans, the calcification stage of the ThI-GH-10978 deciduous and forming permanent teeth was scored. These scores were compared with other juvenile fossil hominins (details are provided in Supplementary Note 7).

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

All data supporting the findings of this study are provided in the Article and its Supplementary Information. Additional raw data (3D scans) are available from the corresponding authors on reasonable request.

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Acknowledgements

The study of ThI-GH is currently part of the Préhistoire de Casablanca Moroccan-French programme led and supported by the Institut National des Sciences de l’Archéologie et du Patrimoine (INSAP) of the Ministère de la Jeunesse, de la Culture et de la Communication/Département de la Culture of the Kingdom of Morocco and the Ministère de l’Europe et des Affaires Étrangères of France within the framework of the Mission archéologique Casablanca. Excavations and studies before 2016 were co-funded by the University of Bordeaux (France), the Région Aquitaine (France) through the Origines projects, the Muséum National d’Histoire Naturelle in Paris (France) as well as by the Department of Human Evolution of the Max-Planck Institute Evolutionary Anthropology (Germany). Recent research after 2016 has been also supported by the Laboratoire d’Excellence Archimède—Programme I.A. ANR-11-LABX-0032-01, through the Origines project, and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 819960). Magnetostratigraphic analyses are supported by the Ministero dell’Istruzione, dell’Università e della Ricerca of Italy, through the project ‘Dipartimenti di Eccellenza 2018–2022’ awarded to the Dipartimento di Scienze della Terra ‘A. Desio’ of the Università di Milano. Research permit no. 01/2020-2021 granted on 22 December 2020 was issued by INSAP letter 458. G.M. and S.P. benefited from financial support provided by the 2020 PRIN (Progetti di Rilevante Interesse Nazionale) project ‘Dynasty’ (PRIN202022GMUTT_01), funded by the Italian Ministry of University and Research. A.G.O. was supported by a Ramon y Cajal fellowship (RYC-2017-22558), by the Spanish Ministerio de Ciencia, Innovacion y Universidades (project PID2021122355NB-C31, MCIN/AEI/10.13039/501100011033/FEDER, UE) and by the research group IT1485-22 (Eusko Jaurlaritza/Gobierno Vasco). The excavators thank the members of the Institut National des Sciences de l’Archéologie et du Patrimoine (INSAP) of the Ministère de la Jeunesse, de la Culture et de la Communication/Département de la Culture of the Kingdom of Morocco and the France Embassy in Morocco for their constant material support during excavations. We thank M. Martinón-Torres for providing access to teeth from Gran Dolina, L. Martín-Francés for collecting landmark data on these teeth; E. Trinkaus, M. R. Meyer and M. Haeusler for providing skeletal comparative data; the many curators and colleagues who, over the years, gave us access to recent and fossil hominin specimens for computed tomography scanning or analysis; E. Ben Arous, O. Tombret and B. Ghaleb for ESR analysis; and the excavators A. Khadouma, K. Nader, A. Nader, D. Kridil, R. Allouche and N. E. Meaach. A.L.C.’s research benefited from the scientific framework of the University of Bordeaux’s IdEx Investments for the Future program/GPR Human Past. G.M. thanks A. Hirt for granting access to the ETH palaeomagnetics facility.

Funding

Open access funding provided by Max Planck Society.

Author information

Author notes

1. Rosalia Gallotti

Present address: Université de Bordeaux, CNRS, Ministère de la Culture, PACEA, UMR 519, Pessac, France

Authors and Affiliations

1. Chaire de Paléoanthropologie, CIRB, Collège de France, Université PSL, CNRS, Paris, France

Jean-Jacques Hublin

2. Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany

Jean-Jacques Hublin, Matthew M. Skinner, Sarah Freidline & Inga Bergmann

3. LabEx Archimède and ASM-UMR 5140, Université de Montpellier Paul Valéry, CNRS, Campus Saint Charles, Montpellier, France

David Lefèvre & Rosalia Gallotti

4. Dipartimento di Scienze della Terra “A. Desio”, Università degli Studi di Milano, Milan, Italy

Serena Perini & Giovanni Muttoni

5. Centre for the Study of Human Origins, Dept. Anthropology, New York University, New York, NY, USA

Shara E. Bailey & Alejandra Ortiz

6. Department of Anthropology, University of Central Florida, Orlando, FL, USA

Sarah Freidline

7. Department of Human Origins, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany

Philipp Gunz & Mykolas D. Imbrasas

8. SARL Paléotime, Villard-de-Lans, France

Mathieu Rué

9. Université de Montpellier Paul Valéry, CNRS, ASM UMR 5140, Campus Saint Charles, Montpellier, France

Mathieu Rué

10. Institut National des Sciences de l’Archéologie et du Patrimoine, Rabat, Morocco

Mohssine El Graoui, Fatima-Zohra Sbihi-Alaoui, Jean-Paul Raynal & Abderrahim Mohib

11. CR2P-UMR 7207, CNRS, MNHN, Sorbonne Université, CP 38, Paris, France

Denis Geraads

12. HNHP-UMR 7194, CNRS, MNHN, UPVD, Paris, France

Camille Daujeard

13. Department of Evolutionary Biology, University of Vienna, Vienna, Austria

Thomas W. Davies

14. Departamento de Antropología, Facultad de Ciencias Sociales, Universidad de Chile, Santiago de Chile, Chile

Kornelius Kupczik

15. HNHP-UMR 7194, CNRS, MNHN, UPVD, Alliance Sorbonne Université, Institut de Paléontologie Humaine, Paris, France

Christophe Falguères & Jean-Jacques Bahain

16. College of Geography Science, Nanjing Normal University, Nanjing, China

Qingfeng Shao

17. Université de Bordeaux, CNRS, Ministère de la Culture, PACEA, UMR 519, Pessac, France

Alain Queffelec, Adeline Le Cabec & Jean-Paul Raynal

18. Departamento de Geología, Facultad de Ciencia y Tecnología, Universidad del País Vasco/Euskal Herriko Unibertsitatea (UPV/EHU), Leioa, Spain

Asier Gómez-Olivencia

19. Dipartimento di Beni Culturali, Università di Bologna, Ravenna, Italy

Stefano Benazzi

20. Department of Biological, Geological and Environmental Sciences, Università di Bologna, Bologna, Italy

Rita Sorrentino

21. Direction Provinciale de la Culture, Quartier Administratif, Kenitra, Morocco

Abderrahim Mohib

Contributions

J.-P.R., F.-Z.S.-A. and A.M., co-directors of the program, and R.G. supervised excavations at ThI-GH from 1994 to 2015. R.G., A.M. and M.R. performed data recording during excavation. D.L., J.-P.R., M.R., M.E.G. and A.Q. conducted stratigraphy and sedimentary process analysis. G.M. and S.P. carried out magnetochronology. D.G. and C.D. carried out palaeontology and taphonomy. R.G., A.M. and J.-P.R. carried out lithic artifact analysis. C.F., Q.S. and J.-J.B. conducted ESR dating. J.-J.H., M.M.S., S.E.B., T.W.D., S.F., P.G., K.K., M.D.I., A.O., A.G.-O., S.B., A.L.C., R.S. and I.B. analysed the fossil hominins. J.-J.H., D.L., S.P. and G.M. designed research and wrote the paper with contributions from all of the authors.

Corresponding authors

Correspondence to Jean-Jacques Hublin, Serena Perini or Abderrahim Mohib.

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Competing interests

The authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Location of the Hominin Cave in the Thomas Quarry I site.

A) General view of the quarry from the southwest (M. Rué). ThI-GH: Grotte à Hominidés. ThI-L: Unit L,early Acheulean site. See details in Extended Data Fig. 2a. B) Excavations plan in the local coordinate system (R. Gallotti, J.-P. Raynal, A. Mohib, M. Rué). Top left: Google Earth view (WGS84 coordinate system).

Extended Data Fig. 2 Stratigraphical context of the Grotte à Hominidés (ThI-GH).

A) Thomas Quarry I (ThI). The Oulad Hamida Formation: OH1 to OH5 Members and GH - Continental Cave Complex (CCC) (D. Lefèvre, M. Rué). ThI-GH: Grotte à Hominidés. ThI-L: Unit L, early Acheulan site. OH1: coarse calcirudite and calcarenites (Bed 1) overlain by bioclastic sands and limestones (Bed 2); OH2: coarse cross-bedded and finer planar-bedded intertidal biocalcarenites, then massive coarse bioclastic aeolianites; OH3A: massive coarse or coquinoid biocalcarenites, OH3B: clino-stratified aeolianites; OH4: planar-bedded intertidal calcarenites; OH5: cross-bedded coarse aeolian sands; GH-CCC: continental cave deposits. B) Sedimentary infilling of ThI-GH (D. Lefèvre, M. Rué, photogrammetric survey by S. Sanz). C. Synthetic lithostratigraphic log of ThI-GH (M. Rué, D. Lefèvre). Mb: Member, SU: Stratigraphic unit. ThI-GH infilling starts with OH4 marine deposits: calcirudites (SU7) preserved in a notch and plurimetric collapsed blocks of calcarenites and calcirudite with blunt-surfaced onlapped by fine, grey, planar-bedded intertidal biocalcarenites (SU6), and then well-sorted bioclastic and quartzose sands derived from reworked loose littoral deposits (SU5). Without any apparent disconformity, the sequence transitions into continental well-sorted bioclastic and quartzose reddish sands (GH-CCC-SU4 and 3). The upper continental deposits lie in discontinuity on SU3. SU2 consists of multilayer dripstone interbedded with loose red sands, and SU1 comprises massively bedded, rubified sands. At the entrance of the cavity (see A), cross-bedded grey aeolianite sandstones (OH5) are interposed between SU3 and the upper continental deposits SU2-SU1.

Extended Data Fig. 3 Excavations at ThI-GH.

A) Horizontal and B) vertical distribution of all coordinated remains including hominin fossils and OSL/ESR-U series dating (M. Rué, R. Gallotti, A. Mohib, J.-P. Raynal). Same location as Fig. 1d. The red stars with labels represent hominin remains: a: ThI-GH-UA28-7 (femur); b: ThI-GH-OA23-24 (tooth); c: ThI-GH-SA26-88 (tooth); d: ThI-GH-SA26-90 (tooth); e: ThI-GH-PA24-107 (tooth); f: ThI-GH-10717 (mandible), ThI-GH-10717/1-5 (vertebrae); g: ThI-GH-10725 and ThI-GH-10725/1 (vertebrae); h: ThI-GH-10726 (vertebra); i: ThI-GH-10978 (mandible). The stratigraphic insertion of femur ThI-GH-UA28-7 (a) located near the bottom wall of the cavity is uncertain (SU4/5). C) Photograph of the excavation (D. Lefèvre, M. Rué). SU: stratigraphic unit (see Extended Data Fig. 2). Section C, D and E: magnetostratigraphic sections (see Fig. 1d). Red stars: hominin remains: f (ThI-GH-10717) and i (ThI-GH-10978). D) and E) Photographs of hominin fossils ThI-GH-10978 and ThI-GH-10717 (Photographs J.-P. Raynal).

Extended Data Fig. 4 MBT record and MIS 19.

Comparison of magnetostratigraphic (VGP) data across the MBT in Members OH4 and GH-CCC of Thomas Quarry I and in North Atlantic IODP deep-sea cores[24], correlated to the δ^18O [‰] stack of 8 centred on Marine Isotope Stage (MIS) 19. Considering that the MBT (773 ± 4 ka) occurs during MIS 19c to MIS 18e[24] and that the sea-level transgression of MIS20/MIS19 began at ~795 ka[26], we interpreted the intertidal biocalcarenites of OH4-SU6 and the littoral sands of OH4-SU5 as part of this transgressive phase culminating whit a maximum flooding surface corresponding to the top of OH4-SU5. The following continental deposits of GH-CCC-SU4-3 are interpreted as the ensuing regressive phase associated to the MIS 19 high-stand beginning approximately at 780 ka. The transition between marine and continental sedimentation is gradual and substantially in stratigraphic continuity, implying a continuous and high-resolution recording of the MBT that is characterized by VGP swings between stable reverse (Matuyama) and stable normal (Brunhes) polarity. According to this interpretation, we attribute the thin normal magnetic polarity interval recorded in OH4-SU5 to the VGP variability of the MBT and exclude an attribution to the MB precursor dated to 794 ka[23] as this geomagnetic event occurred during MIS 20 and may have been recorded at or below the base of the sampled section.

Extended Data Fig. 5 Mandibular shape and size.

A) Principal Component Analysis Plots of the ThI-GH mandibular shape. Shape changes along PC 1 relate to the height and breadth of the corpus, PC 2 (left) the breadth of the ramus and the gonial profile, and PC 3 (right) the breadth of the ramus and shape of the anterior ramus. Both ThI-GH mandibles have a corpus height and breadth intermediate between H. sapiens and Neanderthals; whereas ThI-GH-10717 has a wider ramus and more everted gonial profile ploting with H. sapiens variation and near to the H. erectus hull and ThI-GH-1 has a taller and narrower ramus ploting within Neanderthal variation. (B) Box plot of mandibular size, measured as log centroid size (CS); ER n = 4, AEP n = 4, EMP n = 7, EHS n = 9, NEA n = 15, LP/EH n = 32. ThI-GH-10717 is considerably smaller than Th1-GH-1, and most fossils in the sample. Horizontal lines represent the median of each group. Boxes show the interquartile range (IQR, 25th to 75th percentile). Whiskers extend to 1.5 times IQR. Outliers are represented by circles.

Extended Data Fig. 6 Tooth size comparisons.

Plots of tooth size measured as centroid size comparing the three ThI-GH specimens (black solid lines) to H. erectus/H. antecessor (left), H. neanderthalensis (middle), and H. sapiens(right). The top row compares the first deciduous molar, second deciduous molar, and first permanent molar, and the bottom row the third mandibular premolar to the third mandibular molar. Shaded areas represent the range for H. erectus (red), H. neanderthalensis (blue) and H. sapiens (green). Fossil H. sapiens are marked separately and individually identfied by accession number. Continuous lines connect the measurements corresponding to different teeth of the same individual. The teeth of the ThI-GH-10978 immature mandible are larger than those of each comparative sample, whereas the permanent postcanine dentition of adult individuals displays smaller third premolars and third molars than H. erectus, and shows a similar pattern of relative tooth size to H. neanderthalensis and H. sapiens.

Extended Data Fig. 7 Molar morphology of ThI-GH-10978.

Principal component plots of enamel-dentine junction (EDJ) shape of the first deciduous molar (top left), second deciduous molar (top right), and first permanent molar (bottom) from the ThI-GH-10978 mandible. The EDJ shape of all molars is different, and approximately equidistant from H. neanderthalensis and modern H. sapiens. Note that the H. antecessor second deciduous and first permanent molar are more derived in shape and sit closer to these two taxa.

Extended Data Fig. 8 Mandibular molar root size and shape.

Principal component analyses of four molar root variables: root length, square root of cervix plane area, cube root of root cervix volume, and cube root of root branch volume. There is decreasing overlap between H. sapiens and H. neanderthalensis from the first to the third mandibular molar. ThI-GH-10717 falls within the variation of both taxa for the first and second molar, but with H. sapiens for the third molar. Thl-GH-1 falls on the edge of the H. sapiens distribution for each molar position and towards the H. erectus samples.

Extended Data Fig. 9 Vertebral morphology.

A) Articular pillar morphology and lower facet orientation. Comparison in dorsal and caudal views of the seventh cervical vertebra from ThI-GH-10717/3 compared to KNM-WT 15000 and to a recent Homo sapiens (figures not to scale). The arrow indicates the presence of a marked notch in the articular pillars between the upper and the lower articular facets. The black lines indicate the approximate orientation of the lower articular facets, which are slightly convex and display a ventral-lateral orientation in ThI-GH and KNM-WT 15000, while they are flatter with a ventral to ventral-medial orientation in recent Homo sapiens. B) Box plots of vertebral canal area relative to vertebral body geometric mean (following and modified from[134]). All the Casablanca specimens fall close to the H. sapiens mean, except for the T2, where both KNM-WT 15000 and ThI-GH specimens show very low value. In the boxplots, the whiskers represent the 1.5 times the interquartile range, the box represents the interquartile range, and the centre represents the median. The Pan troglodytes sample was compared to the Homo sapiens sample using a two-tailed Student’s T-tests. The different fossil specimens (Dmanisi, Sima de los Huesos, ThI-GH, KNM WT-15000 and Gran Dolina-TD6 and MH1) were compared to the Homo sapiens sample using a z-score analysis. * = significantly different from Homo sapiens (p < 0.05); ** = significantly different from Homo sapiens (p < 0.01). Comparative sample sizes: Pan troglodytes (C3: n = 18; C7: n = 13; T1: n = 10; T2: n = 19); Homo sapiens (C3: n = 62; C7: n = 37; T1: n = 25; T2: n = 52)[134].

Extended Data Table 1 List of hominin fossils

Supplementary information

Supplementary Information

Supplementary Notes 1–10, Supplementary Figs. 1–32, Supplementary Methods, Supplementary Tables S1–S25 and Supplementary References.

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Hublin, JJ., Lefèvre, D., Perini, S. et al. Early hominins from Morocco basal to the Homo sapiens lineage. Nature 649, 902–908 (2026). https://doi.org/10.1038/s41586-025-09914-y

- Received: 24 December 2024

- Accepted: 13 November 2025

- Published: 07 January 2026

- Version of record: 07 January 2026

- Issue date: 22 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09914-y

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Rare genetic variants confer a high risk of ADHD and implicate neuronal biology

Ditte Demontis,

Jinjie Duan,

Yu-Han H. Hsu,

Greta Pintacuda,

Jakob Grove,

Trine Tollerup Nielsen,

Janne Thirstrup,

Makayla Martorana,

Travis Botts,

F. Kyle Satterstrom,

Jonas Bybjerg-Grauholm,

Jason H. Y. Tsai,

Simon Glerup,

Martine Hoogman,

Jan Buitelaar,

Marieke Klein,

Georg C. Ziegler,

Christian Jacob,

Oliver Grimm,

Maximilian Bayas,

Nene F. Kobayashi,

Sarah Kittel-Schneider,

Klaus-Peter Lesch,

Barbara Franke,

Andreas Reif,

Esben Agerbo,

Thomas Werge,

Merete Nordentoft,

Ole Mors,

Preben Bo Mortensen,

Kasper Lage,

Mark J. Daly,

Benjamin M. Neale &

…

Anders D. Børglum

Nature volume 649, pages 909–917 (2026)

Abstract

Attention deficit hyperactivity disorder (ADHD) is a childhood-onset neurodevelopmental disorder with a large genetic component[1]. It affects around 5% of children and 2.5% of adults[2], and is associated with several severe outcomes3,4,5,6,7,8,9,10,11. Common genetic variants associated with the disorder have been identified12,13, but the role of rare variants in ADHD is mostly unknown. Here, by analysing rare coding variants in exome-sequencing data from 8,895 individuals with ADHD and 53,780 control individuals, we identify three genes (MAP1A, ANO8 and ANK2; P < 3.07 × 10−6; odds ratios 5.55–15.13) that are implicated in ADHD. The protein–protein interaction networks of these three genes were enriched for rare-variant risk genes of other neurodevelopmental disorders, and for genes involved in cytoskeleton organization, synapse function and RNA processing. Top associated rare-variant risk genes showed increased expression across pre- and postnatal brain developmental stages and in several neuronal cell types, including GABAergic (γ-aminobutyric-acid-producing) and dopaminergic neurons. Deleterious variants were associated with lower socioeconomic status and lower levels of education in individuals with ADHD, and a decrease of 2.25 intelligence quotient (IQ) points per rare deleterious variant in a sample of adults with ADHD (n = 962). Individuals with ADHD and intellectual disability showed an increased load of rare variants overall, whereas other psychiatric comorbidities had an increased load only for specific gene sets associated with those comorbidities. This suggests that psychiatric comorbidity in ADHD is driven mainly by rare variants in specific genes, rather than by a general increased load across constrained genes.

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Main

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that affects around 5% of children and 2.5% of adults worldwide[2]. The disorder is linked to a variety of serious outcomes, including higher risks of substance-use disorder3,4, accidents[5], premature death[6], unemployment[7], incarceration and crime[8], suicide[9] and metabolic conditions10,11. Gaining insight into the biological mechanisms that drive the disorder is crucial for understanding how it develops and how it could be treated in the future.

A large proportion of ADHD risk can be explained by genetics, with an estimated twin heritability of 77–88%[1]. Large genome-wide association studies (GWASs) have found that common genetic variants explain 14–22% of the overall variation in liability12,13. The most recent GWAS of ADHD identified 27 genome-wide significant loci and estimated that around 7,300 common variants explain 90% of the single-nucleotide polymorphism (SNP) heritability of ADHD[13]. ADHD is thus highly polygenic, with a considerable proportion of risk explained by common genetic variation; however, an investigation of rare variants is also necessary to explain more of the heritability. We have previously established that rare deleterious variants in evolutionary constrained genes have a role in ADHD, at a level comparable with what has been found in autism[14].

Although rare coding variants might explain only a minor part of the overall liability, they can confer substantial risk individually and, in contrast to common variants, they often directly pinpoint the causal gene affected and the probable functional consequence, providing clues to the underlying aetiology of ADHD.

Here we present results from a whole-exome-sequencing study of ADHD and identify three significant genes with an increased load of rare deleterious variants in individuals with ADHD, compared with control individuals. We provide insights into the genetic architecture and neurobiological mechanisms involved in ADHD, by linking identified rare-variant risk genes to gene-expression data from brain tissues and cell types, and through analyses of protein–protein interaction (PPI) networks of the identified rare-variant risk genes. We evaluate the load of rare deleterious variants across various comorbidities, and show that rare deleterious variants affect socioeconomic status (SES) and cognition in individuals with ADHD.

Sequencing of individuals from the iPSYCH cohort

We analysed whole-exome-sequencing data from 8,895 individuals with ADHD and 9,001 control individuals from the Danish iPSYCH15,16 (Methods, Supplementary Table 1 and Extended Data Fig. 1), doubling the sample size from our previous study[14].

We focused on rare variants with an allele count no higher than five across iPSYCH (17,896 individuals; Supplementary Figs. 1 and 2) and and a subset of individuals with European (non-Finnish) ancestry from the Genome Aggregation Database (gnomAD) who had not been diagnosed with a psychiatric disorder[17] (44,779 individuals). There was a high comorbidity with intellectual disability (ID) among the included individuals (18.4%; Supplementary Table 1) and, consequently, the effect of co-occurring ID was evaluated by doing analyses both with and without comorbid ID.

Effects across functional categories

Rare variants were grouped on the basis of their functional effect on the encoded protein, and their load in ADHD compared with that in control individuals was assessed for all autosomal genes (18,866 genes) and autosomal genes with a probability of being loss-of-function intolerant (pLI) ≥ 0.9 (2,811 genes)[18], hereafter referred to as constrained genes. We found a significantly increased burden of rare protein-truncating variants (rPTVs) in ADHD compared with control individuals in all genes (odds ratio (OR) = 1.06, 95% confidence interval (CI) = [1.04, 1.08], P = 2.41 × 10−7), and a further increased load in constrained genes (OR = 1.35, CI = [1.26, 1.45], P = 1.52 × 10−17; Fig. 1a and Supplementary Table 2). In line with observations in schizophrenia[19], the latter effect size was similar to what was observed for rare severe damaging missense variants (rSevereDMVs; defined as variants with a missense badness, PolyPhen-2 and constraint (MPC)[20] score > 3) in all genes (OR = 1.29, CI = [1.09, 1.54], P = 4.11 × 10−3; Fig. 1a and Supplementary Table 2). Consequently, rPTVs and rSevereDMVs were grouped together (referred to as class I variants) in the gene-discovery analysis. The burden of rare missense variants predicted to have a moderate effect on protein function (rModerateDMVs; 2 ≤ MPC score ≤ 3) was significantly increased in ADHD, but with a lower effect size (ORall_genes = 1.11, CI = [1.07, 1.16], P = 1.43 × 10−8; Fig. 1a and Supplementary Table 2) than was observed for class I variants; these were therefore analysed separately (referred to as class II variants). For comparison, there was no increased load of rare synonymous variants in ADHD in constrained genes (Fig. 1a).

Fig. 1: ADHD risk across rare-variant categories and mean load.

a,Odds ratio (OR) of rPTVs, rSevereDMVs, rModerateDMVs and rare synonymous variants (rSYNs) in all genes (marked in yellow) and in constrained genes (pLI ≥ 0.9; marked in red) in individuals with ADHD (n = 8,895) and in control individuals (n = 9,001). Dots represent OR point estimates, and error bars indicate the corresponding 95% confidence intervals (CIs). Owing to the similar effect sizes of rPTVs and rSevereDMVs in constrained genes (pLI ≥ 0.9) these variants were grouped into class I variants, and rModerateDMVs were categorized as class II variants. Note: the count of rSYNs in each individual is used as a covariate in the analyses, and thus it is not possible to test for differences in rSYN load across all autosomal genes. b, Number of class I variants (rPTVs + rSevereDMVs) in constrained genes (pLI ≥ 0.9) per person on average. ADHD, individuals diagnosed with ADHD regardless of any comorbidities; ADHDwoID, individuals diagnosed with ADHD but not ID; ADHDwID, individuals diagnosed with both ADHD and ID. ORs and two-sided P values were calculated using logistic regression in a,b.

Class I variants in constrained genes were identified in around one out of five individuals with ADHD (Fig. 1b, Supplementary Table 3 and Supplementary Fig. 3), indicating that highly deleterious variants did not contribute to disease risk in most individuals with ADHD.

No differences in variant load were observed between male and female individuals with ADHD (Supplementary Fig. 4), suggesting that there is a similar overall burden of rare variants in the two sexes, in line with what is observed for common variants21,22.

ADHD gene discovery

To increase the power to identify rare-variant risk genes for ADHD, the control group was expanded by combining iPSYCH controls with 44,779 individuals from gnomAD[17]. In total, we analysed 8,895 individuals with ADHD and 53,780 control individuals, assessing only genomic regions with high-quality data across iPSYCH and gnomAD samples. To ensure that a potential signal of rare deleterious variants in ADHD was not driven by a generally higher rate of variants in iPSYCH samples, we only included genes that had a higher rate of rare synonymous variants in control individuals than in ADHD (15,603 genes analysed; 3,263 genes excluded).

We performed a gene-based burden test to identify genes with an increased burden of class I or class II variants using a two-tailed Fisher’s exact test. Because we focused on variants with a deleterious effect on protein function, we expected these to be increased in ADHD compared with controls. Therefore, and owing to our variant filtering strategy (Methods), the analysis was restricted to include only genes with a higher rate of class I variants (3,698 out of 15,603 genes) or class II variants (1,026 genes out of 15,603) in ADHD than in controls. For genes with both class I and class II variants (347 genes), a higher number of both types of variants in ADHD compared to controls was required, and the combined effect was estimated in a meta-analysis (Methods).

We identified three significant genes: MAP1A (P = 1.02 × 10−6, OR = 13.31), ANO8 (P = 1.90 × 10−6, OR = 15. 31) and ANK2 (P = 2.72 × 10−6, OR = 5.55) (Fig. 2a and Supplementary Table 4). The results for MAP1A and ANO8 were driven entirely by class I variants (only rPTVs); for ANK2, the result stemmed from both class I and class II variants (rPTVs and rModerateDMVs). Details on the phenotypes of individuals with class I or class II variants in the three risk genes can be found in the Supplementary Information and Supplementary Fig. 5. The ORs implied that rare deleterious variants in these genes confer a risk that is much higher than that observed for common variants12,13, and higher than the risks observed for copy-number variants (CNVs) in iPSYCH23,24 and other studies[25] (Fig. 2b). Out of the top 20 genes (P < 1 × 10−3; Supplementary Table 4), 16 are constrained.

Fig. 2: Association of rare coding variants with ADHD.

a, Manhattan plot with −log_10(P) on the y axis for gene-based associations from a two-sided Fisher’s exact test of counts of rare class I and II variants in 8,895 individuals with ADHD and 53,780 controls. The chromosome position of genes is shown on the x axis. Genes that met the threshold for exome-wide significance (two-sided P < 3.07 × 10−6, implying significance correcting for 16,297 tests) are highlighted in dark red; genes with two-sided P < 0.001 are in orange. The red dashed line denotes exome-wide significance; the blue dashed line represents two-sided P ≤ 0.001. b, Genetic architecture plot. Dots represents ORs from this study and the most recent GWAS[13] and CNV study[25] of ADHD. The minor allele frequency in the control cohorts is shown on the x axis. Dot colour represents variant type (rare, common or CNV) and dot size reflects the strengths of the associations. The 20 genes from this study with a gene-based two-sided burden P < 0.001 are shown in red; the exome-wide-significant genes are solid red and the remainder are translucent red. Two genes (WNT1 and EIF3G) have an infinite OR, as rare deleterious variants were only observed in individuals with ADHD and none in control individuals.

We examined the generalizability of our findings in another European sample, consisting of 1,078 individuals who had been clinically diagnosed with persistent ADHD and 1,738 controls. Overall, class I variants were significantly enriched in ADHD, with further enrichment when restricting to constrained genes (OR = 1.24, CI = [1.07, 1.45], P = 0.005; Supplementary Fig. 6 and Supplementary Table 5). Owing to the small sample size, we had no power to discover significant genes, but the effect-size point estimate was higher for the top gene set from our discovery analysis than for constrained genes (OR = 1.42, CI = [1.08, 1.85], P = 0.012; Supplementary Table 6; see also Supplementary Information), suggesting that the identified top genes overall confer more ADHD risk than do constrained genes. It is noteworthy that the number of deleterious variants in MAP1A and ANK2 was higher in individuals with ADHD than it was in control individuals (no rare deleterious variants observed in ANO8; Supplementary Table 7).

X-chromosome analyses

Significant differences in the load of deleterious variants on the X chromosome were observed only for class II variants in ADHD with ID (Extended Data Fig. 2a–d and Supplementary Table 8), compared with controls, and in the sex-stratified analysis comparing male individuals with ADHD to control individuals (Supplementary Fig. 7), but not after excluding comorbid ID. No genes on the X chromosome were associated with ADHD after Bonferroni correction (Supplementary Table 9).

Rare burden heritability

The variability in the phenotype explained by rare variants revealed a burden heritability of 2.5% (s.e. = 0.7%) for class I variants and 0.1% (s.e. = 0.3%) for class II variants for ADHD on the liability scale, using a population prevalence of 5% (Supplementary Table 10). When excluding comorbid ID, the burden heritability decreased to 1.43% (s.e. = 0.74%) and 0.26% (s.e. = 0.27) for class I and class II variants, respectively. These estimates are in line with findings for schizophrenia (1.7% (s.e. = 0.3%)) and bipolar disorder (1.8% (s.e. = 0.3%))[26]. Rare synonymous variants showed no evidence of non-zero burden heritability for ADHD. The three significant genes (MAP1A, ANO8 and ANK2) explained 5.2% (s.e. = 3.4%) of the class I burden heritability, suggesting that other ADHD risk genes implicated by rare coding variants remain to be identified.

Linking ADHD risk genes to biology

The three identified risk genes might point to a larger set of genes and biological mechanisms involved in ADHD through their protein interaction partners, as reported in other disorders[27]. To examine this, we performed immunoprecipitation–mass spectrometry (IP–MS) for proteins encoded by the three genes (hereafter referred to as index proteins) in human induced pluripotent stem (iPS)-cell-derived neural progenitor cells (NPCs) and excitatory neurons (ExNs) to generate their PPI networks (Supplementary PPI Tables 1–5). Across the two cell types, we identified 184, 35 and 158 interaction partners for MAP1A, ANO8 and ANK2, respectively; 36 were linked to more than one index protein and thus could point to convergent biology (Fig. 3a–d). Of the interacting proteins, 48 have previously been implicated in neurodevelopmental disorders by genetic studies13,19,28 (Supplementary PPI Table 6). Furthermore, the MAP1A-, ANK2- and combined network of all three index proteins were significantly enriched (false discovery rate (FDR) < 0.05, one-tailed Kolmogorov–Smirnov tests) for rare-variant risk genes associated with autism spectrum disorder (ASD) and developmental disorders (DDs) in both NPCs and ExNs, compared with other protein-coding genes expressed in the neuronal cell model (Fig. 3e and Supplementary PPI Tables 7 and 8). The ANO8 network was enriched for rare-variant risk genes associated with DD and with schizophrenia in ExNs. The networks were not enriched for ADHD or other neurodevelopmental risk genes identified by common variants (Supplementary PPI Table 9).

Fig. 3: Enrichment of rare-variant risk genes in PPI networks.

a, PPI network derived from three IP–MS experiments. Nodes represent index proteins (MAP1A, ANK2 and ANO8) and their interactors (purple); colour intensity and size of the interactor nodes scale with interactor frequency (that is, number of linked index proteins). Line colour indicates cell type: NPCs in blue, ExNs in green and both cell types in orange. b, Distribution of interactor frequency in the network. c, Distribution of InWeb versus newly reported interactions in the network. d, Distribution of interactions across cell types. e, Results from one-tailed Kolmogorov–Smirnov tests of enrichment in the PPI networks of rare-variant risk genes associated with autism (ASD)[28], developmental disorders (DDs)[28] or schizophrenia (SCZ)[19]. *Kolmogorov–Smirnov test one-sided P < 0.05, **FDR < 0.05, minimum P was capped at 1 × 10−15 for visualization purposes. Results for all genes with available pLI score[48] (all), constrained genes (pLI ≥ 0.9) and non-constrained genes (pLI < 0.9) are shown separately.

In addition, most networks were strongly enriched for proteins encoded by constrained genes (Supplementary PPI Table 8). The constrained network genes were generally not further enriched for disease risks, compared with other constrained genes, whereas the non-constrained genes in some networks showed stronger enrichment, compared with the rest of the non-constrained genome (Fig. 3e and Supplementary PPI Table 10).

For each of the three index proteins, the union (NPCs + ExNs) PPI-network genes were significantly enriched among several gene sets. For MAP1A, top findings included genes encoding RNA binding (P = 2.32 × 10−99) and cytoplasmic ribosomal proteins (P = 2.09 × 10−85); for ANO8, genes expressed in cell junctions (P = 2.23 × 10−11) and synapses (P = 7.09 × 10−8); and for ANK2, genes encoding the actin cytoskeleton (P = 2.77 × 10−39) and cell junction proteins (P = 1.17 × 10−30) (Supplementary Table 11). For all three PPI networks, a high proportion of the genes mapped to genes with synaptic annotations in SynGO[29] (MAP1A 52.7%; ANK2 44.30%; and ANO8 57.14%) and showed significant enrichment among genes involved in several synaptic processes (Supplementary Table 11). This was especially the case for the MAP1A PPI network, in which 37 and 49 genes (out of 184) mapped to presynaptic and postsynaptic functions, respectively (Ppresynaptic ribosome = 3.02 × 10−51; Ppostsynaptic ribosome = 6.94 × 10−68).

In addition, the top 20 ADHD risk genes were enriched among genes expressed in the main axon (P = 4.8 × 10−7) and the initial segment of the axon (P = 1.4 × 10−6), and among genes involved in channelopathies (P = 2.4 × 10−7).

Because ADHD has a common-variant risk component that affects genes expressed in the brain[13], we evaluated the expression of the top associated rare-variant risk genes across neocortical brain developmental stages using BrainSpan data (see ‘Data availability’). ADHD risk genes showed significantly higher mean expression in 11 out of 12 brain developmental stages (prenatal to adult) when compared with the average gene expression, and in 10 out of 12 stages when compared with neuronally expressed genes (Supplementary Table 12, Fig. 4a and Supplementary Fig. 8). This contrasts with common-variant risk genes, which were enriched only among genes expressed prenatally[13].

Fig. 4: Expression of ADHD risk genes across brain developmental stages and cell types.

a, Mean expression, expressed as ln(reads per kilobase million (RPKM) + 1)), of the 17 ADHD risk genes and background genes across neocortical brain developmental stages in BrainSpan. Background genes (22,402 genes or transcripts) include genes expressed in BrainSpan, except for the 17 ADHD risk genes. Asterisks indicate a significant difference between the two gene sets at a given developmental stage using a two-sided paired t-test or Wilcoxon rank test; P = 4.17 × 10−3 was considered significant, correcting for 12 brain developmental stages. Vertical lines represent standard error. b, Expression of the three significant genes across brain developmental stages, and expression of background genes (22,416 genes or transcripts). Asterisks next to the gene names at the right side indicate significant differences in mean prenatal and postnatal expression using a two-sided two-sample t-test; P = 2.2 × 10−16 (MAP1A) and P = 2.2 × 10−16 (ANO8); two-sided P = 1.67 × 10−2 was considered significant, correcting for three comparisons. Vertical lines represent standard error. Sample sizes for each neocortical brain developmental stage are in Supplementary Table 12. c, Top, uniform manifold approximation and projection (UMAP) of scRNA-seq data[32], showing clustering of cell types from human iPS cell cultures developed towards midbrain neuronal cell types. Results are shown for cells developed for 11, 30 and 52 days: astrocyte-like (Astro), dopaminergic neurons (DA), ependymal-like 1 (Epen1), ependymal-like 2 (Epen2), floor-plate progenitors (FPP), neuroblasts (NB), proliferating floor-plate progenitors (P_FPP), proliferating serotonergic-like neurons (P_Sert), serotonergic-like neurons (Sert), unknown neuron 1 (U_Neur1), unknown neuron 2 (U_Neur2) and unknown neuron 3 (U_Neur3). Bottom, scDRS for each cell, with the strength indicated by the bar on the right. Red or blue indicates a positive or a negative score, respectively, reflecting increased or decreased expression of ADHD rare-variant risk genes compared with the distribution of expression of control gene sets in a cell.

Overall, there was no difference in the mean expression of the top genes prenatally and postnatally (P = 0.76), but MAP1A was expressed significantly more highly postnatally than prenatally (P = 2.2 × 10−16) and the opposite was observed for ANO8 (P = 2.2 × 10−16; Fig. 4b).

Linking ADHD risk genes to cell types

Single-cell disease relevance scores (scDRSs)[30] were used to link risk genes to cell types. A higher scDRS indicates a stronger deviation of the expression of risk genes from the expression of control gene sets in a cell. Common risk variants have previously been linked to genes with high expression in dopaminergic neurons in prenatal human midbrain[13]. In analyses of the same prenatal human midbrain single-cell RNA sequencing (scRNA-seq) dataset[31], we identified a significant association (that is, an increased scDRS across a cell type) between rare-variant risk genes and the dopaminergic neurons classified as type 1 in the previous study (P = 9.99 × 10−4), consistent with the common-variant findings[13]. Furthermore, we found a significant signal for GABAergic neurons (P = 9.99 × 10−4) and medial neuroblasts (P = 2 × 10−3) (Supplementary Fig. 9 and Supplementary Table 13). The results could be influenced by variables that we were not able to correct for in this older data (Methods), and thus we validated our findings in a more recent, larger dataset of developing midbrain neuronal cell types, derived from human iPS cells[32] generated from 215 healthy donors. Here we identified a significantly increased scDRS in both 30-day-old (P = 9.99 × 10−4) and 52-day-old (P = 9.99 × 10−4) dopaminergic neurons (Fig. 4c and Supplementary Table 13).

We also investigated the scDRS across 382 cell-type clusters representing a spectrum of neurons from the entire human brain[33], with no significant findings (Supplementary Table 13). In this dataset, the dopaminergic neuronal cluster (scDRS P = 0.054), did not reflect the strong signal observed for the iPS-cell-derived dopaminergic neurons, which could be due to differences in neuronal age across datasets (neurons from post-mortem brains[33] versus iPS cells[32]).

Our results add to the emerging evidence that genetic risk variants, both common[13] and rare, influence ADHD by their effects on genes expressed in neurons, specifically GABAergic neurons and younger stages of dopaminergic neurons.

Impact on socioeconomic outcomes

In common-variant analyses, a strong negative genetic correlation has been found between ADHD and cognition-related phenotypes, including educational attainment[13]. Here we examined the association of rPTVs in constrained genes with education level and SES among individuals with ADHD, by linking rare-variant data to data in the Danish registers. rPTVs had a negative effect on education level in individuals with ADHD; rPTVs in constrained genes were significantly associated with finishing only primary school (OR = 1.24, s.d. = 5.23 × 10−2, P = 3.68 × 10−5; Fig. 5a and Supplementary Table 14). Epidemiological studies have also consistently linked ADHD to decreased SES34,35. We found that rPTVs in constrained genes were significantly associated with low SES, defined by social security payment, early retirement benefit and/or unemployment for more than six months (OR = 1.28, s.d. = 0.07, P = 9.09 × 10−5). When excluding individuals with ID, the results remained significant for education and nominally significant for SES (Fig. 5a).

Fig. 5: Association of rare variants with education, socioeconomic status and IQ.

a, OR (from logistic regression) of lower education and lower SES in individuals with ADHD who have one or more rPTVs in constrained genes (pLI ≥ 0.9), compared with individuals with ADHD who do not have rPTVs in constrained genes. The analysis was also performed for individuals with ADHD without intellectual disability (ADHDwoID) who have one or more rPTVs in constrained genes, compared with individuals with ADHD without intellectual disability (ADHDwoID) who do not have rPTVs in constrained genes. ADHD with lower education, n = 6,488; ADHD with higher education, n = 1,436; ADHDwoID with lower education, n = 5,297; ADHDwoID with higher education, n = 1,253; ADHD with lower SES, n = 3,110; ADHD with higher SES, n = 3,223; ADHDwoID with lower SES, n = 2,509; ADHDwoID with higher SES, n = 2,778). Dots represent OR point estimates, and error bars indicate the corresponding 95% CIs. b, Association of IQ and the number of ultra-rare class I variants in the German ADHD clinical sample (n = 962). Results for all autosomal genes and constrained genes (pLI ≥ 0.9). The effect on IQ is the β coefficient from linear regression. Dots represent β coefficient point estimates, and error bars indicate the corresponding 95% CIs. The dotted line represents an OR of 1 in a, and a β of 0 in b.

Overall, individuals with ADHD without ID with one or more rPTVs in constrained genes were around five to seven times more likely to have finished only primary school and to have lower SES, compared with controls in the iPSYCH cohort. This increased to six to eight times when including individuals with ID (Supplementary Fig. 10a,b).

We also evaluated the impact of ultra-rare class I variants on cognition in the clinical ADHD sample, in which we identified a decrease of 2.25 intelligence quotient (IQ) points per rare deleterious variant in constrained genes (β = −2.25, P = 0.02; Fig. 5b and Supplementary Table 15). Our results add to researchers’ understanding of ADHD, showing that rare deleterious variants, in line with common ADHD risk variants, have a negative effect on cognitive performance and life outcomes.

Joint effect of common and rare variants

Whenwe analysed GWAS data on common variants in ADHD, we found that 4 out of the top 20 rare-variant risk genes were nominally significantly associated with ADHD, including MAP1A (P = 0.005, Supplementary Table 16), but that overall the gene set of the 20 genes was not enriched in common-variant associations.

The joint effect of rare and common variants in ADHD was evaluated by combining data on class I variants with ADHD polygenic risk scores (ADHD-PGSs) capturing the common-variant risk load. When considering individuals without class I variants in constrained genes, the ADHD-PGS was significantly associated with an increased risk of ADHD (ORbin5_vs_bin1 = 6.52; s.e. = 0.06; Extended Data Fig. 3 and Supplementary Table 17), and the risk increased across PGS bins following a linear pattern (Extended Data Fig. 3). When considering individuals with one or more class I variants in constrained genes, the risk for ADHD was significantly higher within all PGS bins, when compared with individuals without class I variants of the same bin (except pentile 4; Extended Data Fig. 3 and Supplementary Table 17). The risk increased following a linear pattern almost parallel to what was observed for individuals without class I variants, indicating that common and rare variants act additively on ADHD risk (Extended Data Fig. 3), in line with observations in other complex traits[26]. As shown in Extended Data Fig. 3, having one or more class I variants in constrained genes increased the risk of ADHD at a level comparable to a 20% increase in the common-variant polygenic risk load.

Rare-variant load across comorbidities

We evaluated the burden of class I and II variants in individuals with ADHD who have also been diagnosed with ID (n = 1,654), autism (n = 2,730), schizophrenia (n = 410), substance-use disorders (SUDs; n = 1,200), disruptive behaviour disorders (DBDs; n = 1,036) or other psychiatric disorders combined (n = 5,420), referred to as multimorbidities, and compared this with the burden in individuals with ADHD who do not have these comorbid conditions (sample sizes in Supplementary Table 18). Class I and class II variants were significantly increased in constrained genes in ADHD with ID, compared with ADHD without ID (Extended Data Fig. 4a and Supplementary Table 19). ADHD without ID still showed a significantly higher load than controls, consistent with previous findings[14] (Supplementary Table 19 and Extended Data Fig. 4e). On the basis of these results, individuals with ID were excluded in analyses of other comorbidities. In analyses of comorbid autism, schizophrenia, SUD, DBDs and multimorbidities, there were no differences between individuals with comorbidities and individuals without comorbidities in the load of class I and class II variants across constrained genes (pLI ≥ 0.9; Extended Data Fig. 4b–d,g,h and Supplementary Table 19). In addition, we evaluated the load of class I and class II variants across comorbid subgroups in seven gene sets (listed in Supplementary Table 20) representing genes involved in autism, schizophrenia and neurodevelopmental disorders, which were identified in previous studies of rare variants (Supplementary Information). All gene sets, except one, showed an increased load of class I variants in ADHD with ID, as compared with ADHD without ID (Extended Data Fig. 5a and Supplementary Table 21). When compared with controls, ADHD without ID showed an increased load of class I variants across four of the gene sets, and two gene sets remained nominally significant for ADHD without multimorbidities (Extended Data Fig. 5b,c and Supplementary Table 21). These results suggest that rare deleterious variants in risk gene sets defined on the basis of constrained genes or disorders have a role in ADHD, even in individuals who are less severely affected; that is, with no ID or psychiatric comorbidities.

The seven rare-variant risk gene sets showed an increased load with some specificity towards comorbidity for the disorders to which they were most related to, but not for other co-occurring conditions. To specify, there were no significant findings in analyses of comorbidity with DBDs or SUD (Extended Data Fig. 5g,h), but when comparing ADHD comorbid with schizophrenia to ADHD without schizophrenia, we found a nominally significant increased load of class I variants in rare-variant schizophrenia risk genes[19] (OR = 7.27, CI = [1.67, 31.67], P = 0.0083; Extended Data Fig. 5e and Supplementary Table 21). ADHD comorbid with autism also showed a nominally significant increased load of class I variants in the three gene sets previously identified in rare-variant studies of autism, with the highest OR observed for the ‘ASD_FDR0.001’ gene set[28] (OR = 1.94, CI = [1.15, 3.30], P = 0.014; Extended Data Fig. 5d and Supplementary Table 21).

No strong impact of class II variants on comorbidity risk was identified—at least, not in the investigated gene sets (Extended Data Figs. 4b–d,g,h and 5d–h and Supplementary Tables 19 and 21).

Overall, our results suggest that the contribution from class I variants to the risk of psychiatric comorbidities (other than ID) in ADHD is, to some extent, driven by variants in sets of specific risk genes related to the comorbidity being considered, rather than being a result of a more general increased load across highly constrained genes.

Rare variants across ADHD and autism

In our previous study of rare variants in ADHD and autism[14], we did not find any differences in the distributions of the genes affected by rPTVs across the two disorders. We have now substantially increased the sample size for both ADHD and autism, and re-examined whether the distributions of constrained genes with rare class I, class II and synonymous variants in ADHD only (n = 5,536) and autism only (n = 7,554) still have the same underlying distribution, by applying a C-alpha[36] test as used previously[14]. We found no significant differences between ADHD and autism risk genes for any of the variant groups analysed (Supplementary Table 22), when considering both individuals with ID and those without ID. By contrast, when comparing ADHD-only with controls, significant differences in the distributions of genes affected by both class I (P = 8.09 × 10−9) and class II (P = 0.003) variants were observed, and the same was observed when comparing autism-only to controls for class I variants (P = 0.014; Supplementary Table 22). These results suggest that there is a substantial sharing of rare-variant risk genes across ADHD and autism, and that deleterious variants might affect shared neurodevelopmental processes.

Discussion

In this study, we have advanced researchers’ understanding of the role of rare coding variants in ADHD risk and its comorbidities, revealed their association with major life outcomes and implicated pathophysiological components in ADHD rare-variant risk.

We identified three significant rare-variant risk genes for ADHD (MAP1A, ANO8 and ANK2). MAP1A encodes a protein involved in the assembly of microtubules[37], and thus it could be hypothesized that disruption of this gene confers ADHD risk through dysfunction of the cytoskeleton, affecting synapse formation and function in neurons38,39. Notably, rPTVs in MAP1A’s sister gene MAP1B have been reported to cause ID, autism and extensive brain-wide deficits in white matter[40]. Both ANK2 and ANO8 encode proteins involved in calcium-ion transport across the plasma membrane41,42, suggesting that neuronal synaptic channelopathies are involved in ADHD. This idea was also supported by our gene-set enrichment results, and is in line with what has been proposed for autism[43].

Around 50% and around 30% of the association signal, for MAP1A and for ANO8, respectively, was driven by rare deleterious variants in individuals diagnosed with ADHD only (without comorbid schizophrenia, ID or autism), whereas the ANK2 signal was driven mainly by ADHD with co-occurring autism or ID. This is consistent with ANK2 being a known rare-variant risk gene in autism[28]. MAP1A and ANO8 have not been associated with ASD or other neurodevelopmental conditions in other studies19,28, although MAP1A was significantly associated when combining rare deleterious variants in both ADHD and ASD, but not significant when we examined the disorders separately in our previous study[14]. A discussion on the potential effect of comorbid autism on the results can be found in the Supplementary Information.

When evaluating the top 20 ADHD risk genes identified here, 9 have been implicated with autism and/or other neurodevelopmental disorders (FDR < 5%)[28], which supports the validity of our findings. Sixteen of the genes are evolutionarily constrained (Supplementary Table 4), which suggests that many of the genes we identify in rare-variant studies are genes involved in fundamental neurodevelopmental processes that are likely to be shared across disorders. That said, it is noteworthy that among our top genes are also genes that have not previously been linked to psychiatric disorders by rare variants and might be more specific to ADHD.

A previous study[44] identified KDM5B as a potential rare-variant risk gene for ADHD (FDR = 0.04). Although KDM5B was not significantly associated with ADHD in this larger dataset and was not among our top 20 genes, it did show a moderate association signal, with an OR of 2.93 (P = 1.23 × 10−3).

Rare deleterious variants in constrained genes had the highest effect on ADHD risk, as reported previously for ADHD[14], autism28,45 and schizophrenia[19]. Of note, we did not observe an increased load of rare deleterious variants in constrained genes in individuals with comorbid conditions, compared with those who had only ADHD (when excluding ID); likewise, we were not able to identify differences in the distribution of genes affected by rare deleterious variants in ADHD and autism. However, we did identify an increased load in ADHD with comorbid conditions in specific gene sets representing neurodevelopmental risk genes. This suggests that comorbid conditions—other than ID—are not associated with a general increased rare-variant risk load in constrained genes, but rather, with an increased load in a smaller set of specific risk genes. So, when sample size increases, we might be able to detect rare-variant risk genes that are more disorder specific.

Exploring the biological implications of the identified rare-variant risk genes, the broader PPI networks of the three significant genes were enriched for autism, DD and schizophrenia rare-variant risk genes, which supports the validity of our findings and reinforces the conclusion that rare-variant risk genes and their interacting proteins are most likely to affect gene networks involved in fundamental neurodevelopmental processes. Enrichment pointed towards several biological mechanisms or cell components that could potentially be affected in individuals with ADHD, including both presynaptic and postsynaptic functions, which were identified for all three networks. Our analyses also implicated dopaminergic neurons in ADHD. It has been hypothesized that dysregulation of dopamine in the brain has a role in ADHD, owing to the observation that stimulant medications, such as methylphenidate, often treat the symptoms of ADHD successfully[46]. Methylphenidate blocks the dopamine transporter, which increases neurotransmitter concentrations in the synaptic cleft[47].

In addition, we found that the top 20 risk genes had a high mean expression across all stages of brain development, suggesting that the genes have key roles in both the development and the function of the brain across lifespan. In accordance with this, we identified both immature neurons (medial neuroblasts) and mature GABAergic and dopaminergic neurons as enriched for the expression of rare-variant risk genes (as mentioned above). This suggests that a diverse set of neuronal dysfunctions could be involved in ADHD. When combining the effects of rare and common variants, rare variants were found to act additively with the common-variant PGS. Neuronal cell types, including dopaminergic neurons, have also been highlighted in analyses of common ADHD risk variants[13], and the biological mechanisms affected might be (partly) shared across common and rare variants. The convergence in affected biological mechanisms by variants across the allele spectrum might be a common feature of neurodevelopmental psychiatric disorders, given that this has also been observed for schizophrenia[19].

Assessing pertinent socioeconomic outcomes, we found that individuals with ADHD who carry one or more rare deleterious variants have significantly poorer educational attainment and lower SES than do people with ADHD who do not carry these variants (also when excluding comorbid ID). Compared with the general Danish population, individuals with ADHD who carry one or more deleterious variants were more than five to seven times as likely to experience these lower educational or socioeconomic outcomes. Likewise, ultra-rare deleterious variants had a significantly negative effect on IQ in adults with ADHD. Thus, rare deleterious variants have an impact not only on ADHD diagnosis but also on important life outcomes, which is consistent with the observation that an increased load of common ADHD risk variants is associated with decreased performance across a range of cognitive measures[13]. These findings show that ADHD is a polygenic disorder with a genetic component that influences certain cognitive domains, and this can present challenges for some individuals, particularly in academic settings. This insight could promote the development of supportive measures and innovative approaches designed to create better learning environments for individuals with ADHD.

In summary, we show that genes carrying rare deleterious risk variants have a considerable effect on ADHD, its comorbid conditions and key outcomes. We reveal the biological implications of rare-variant risk, and show that rare- and common-variant risk act additively in ADHD. While the risk genes described here explain only a fraction of the overall rare-variant risk, our study provides a path forward for identifying the many other risk genes that have yet to be discovered to further our understanding of ADHD pathophysiology.

Methods

The iPSYCH study was approved by the Scientific Ethics Committee in the Central Denmark Region (case number 1-10-72-287-12) and the Danish Data Protection Agency. In accordance with Danish legislation, the above-mentioned ethics committee waived the need for specific informed consent in biomedical research based on existing biobanks. iPSYCH was approved by the ethics committee in 2012, with subsequent amendments in 2013, 2015 and 2018. More details can be found at https://ipsych.dk/en/data-security/health-research-and-ethical-approval. New Danish legislation (effective from January 2024) introduces the possibility for participants to opt out of studies that are exempt from active informed consent. After consulting with the Ethics Committees and patient organizations, iPSYCH contacted all participants (around 140,000) in the iPSYCH cohort in June 2025 and offered the possibility of opting out of new genetic studies initiated henceforth. Overall, 1.8% of the iPSYCH participants chose to opt out, and their data will be deleted from the active research database. Data included in finalized and ongoing studies will not be removed.

The clinical data were approved by the ethics committee at the University of Würzburg in Germany. In the Netherlands, they were approved by the regional ethics committee (Commissie Mensgebonden Onderzoek: CMO Regio Arnhem—Nijmegen; protocol III.04.0403 and 2014/290; ABR: NL47721.091.14) and the Institutional Review Board of the Radboud University Medical Center. Participants were included at the Department of Psychiatry at the Radboud University Nijmegen Medical Centre. All participants in the German and Dutch samples provided signed informed consent in accordance with the Declaration of Helsinki.

Samples

iPSYCH

The individuals selected for exome sequencing were part of the iPSYCH cohort, which has been described in detail elsewhere15,16. In short, the study base includes all singleton births to mothers who were living in Denmark between 1 May 1981 and 31 December 2008, where the child was alive and resided in Denmark at their one-year birthday (n = 1,657,449). All individuals diagnosed with major psychiatric disorders by the end of 2016 according to the ICD10 criteria (ADHD (1.8% in the study base), autism, bipolar disorder, schizophrenia, major depressive disorder or post-partum depression (n = 93,608)) were identified in the study base using information in the Danish Psychiatric Central Research Registry[49] (and the Danish Patient Registry[50] for some disorders). In addition, 50,000 randomly selected population-based controls from the study base were selected. Subsequently, biological material for genotyping was obtained from the Danish Neonatal Screening Biobank (DNSB)[51]. The DNSB has stored residual biological material from screening of newborns for rare metabolic disorders since May 1981, and includes material from practically all births in Denmark since then. A subsample of 34,544 individuals was selected for whole-exome sequencing, and from these, we included individuals with an ICD10 diagnosis of ADHD (F90) in the Danish Psychiatric Central Research Registry[49] and the Danish Patient Registry[50] given before or during 2016; individuals with no diagnosis of the major psychiatric disorders (autism (ICD10: F84.0, F84.1, F84.5, F84.8 and F84.9), bipolar disorder (ICD10: F30–F31), schizophrenia (ICD10: F20) or major depressive disorder (ICD10: F32–F33)) were included as controls. The samples were included in iPSYCH in 2012–2016 and the sequencing was performed in 2012–2018.

For the comorbidity analyses, we identified individuals with the following ICD10 diagnosis codes in the Danish Psychiatric Central Research Registry: ID (ICD10: F70, F71, F72, F73, F78 and F79); ASD (as above); schizophrenia (as above); DBDs (including conduct disorder and oppositional defiant disorder (ICD10: F91 and F90.1); SUDs (ICD10: F10.1–9, F11.1–9, F12.1–9, F13.1–9, F14.1–9, F15.1–9, F16.1–9, F17.1–9, F18.1–9 and F19.1–9) and multi-comorbidities, which, besides the comorbidities already listed, included comorbid anxiety (ICD10: F40.0–F40.2, F41.0–F41.1, F42 and F43.0–F43.1), tic disorder (ICD10: F95), bipolar disorder (ICD10: F30–F31), major depressive disorder (ICD10: F32–F33), anorexia nervosa (ICD10: F50.0), DDs (ICD10: F80–F83) and antisocial personality disorder (ICD10: F60.2).

Clinical samples

The clinical samples consisted of adults (over 18 years old) with persistent ADHD diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders IV (DSM-IV) criteria. None of the individuals was diagnosed with ID. They were recruited as part of the International Multicenter Persistent ADHD Collaboration (IMpACT) at two sites: Radboud University Medical Center, the Netherlands, and University Hospital Würzburg, Germany. In analyses of these clinical samples, we used control samples from individuals recruited together with the clinical cases at the IMpACT site at Radboud University Medical Center (ADHD-screened controls), and from 1,766 German control individuals who were recruited from the German MI Family Study[52] and the Angio-Lub study; the latter samples were whole-exome-sequenced by the MIGen Exome Sequencing Consortium: Lubeck Heart Study (dbGaP accession number phs000990/DS-CVD, https://dbgap.ncbi.nlm.nih.gov/beta/study/phs000990.v1.p1/). The German controls consisted of 870 individuals with cardiovascular disease and 896 without. The total set of samples described in this section is referred to as the ‘clinical sample’ in the remainder of this manuscript.

GnomAD

All references to gnomAD refer to release 2.1.1 exomes from a subset of gnomAD consisting of individuals with non-Finnish European ancestry, and no diagnosis of psychiatric and neurological disorders (n = 44,779) (see ‘Data availability’).

Exome sequencing and quality control

iPSYCH

In this study, we applied the same methods for quality control (QC) described in our previous study[14] to an updated dataset including new exome-sequenced individuals. To recap, the sequencing of 34,544 individuals from the iPSYCH cohort was performed using the Illumina Nextera Capture kit and the Illumina HiSeq sequencer. The sequencing process was performed in waves, including a pilot wave and three more substantial production waves. After sequencing, raw data were processed using the Genome Analysis Toolkit (GATK) v.3.4 to generate a variant call format (VCF) v.4.1 file. As per Danish regulations, American College of Medical Genetics (ACMG) v3.2 genes[53] were removed from the dataset. Next, we did thorough, multiple-round quality checks on the samples and the genetic variations using Hail 0.1. Samples were removed if they lacked complete phenotype information; if the imputed sex was inconsistent with the reported sex in the registries; if duplicates or genetic outliers were identified by principal component analysis (PCA); if they had an estimated level of contamination greater than 5%; or if they had an estimated level of chimeric reads higher than 5%. In addition, one of each pair of related samples was removed if the pairwise pi-hat value was 0.2 or higher.

Genotypes were removed if they did not pass GATK variant quality score recalibration (VQSR) or had a read depth lower than 10 or higher than 1,000. QC was done in Hail 0.1. Homozygous alleles were removed if they had reference calls with a genotype quality lower than 25, homozygous alternate alleles with PL(HomRef) (that is, the phred-scaled likelihood of being homozygous reference) < 25 or less than 90% of reads supporting the alternate allele. Heterozygous alleles were removed if they had PL(HomRef) < 25 or less than 25% of reads supporting the alternate allele, less than 90% informative reads (that is, number of reads supporting the reference allele plus number of reads supporting the alternate allele < 90% of the read depth) or a probability of the allele balance (calculated from a binomial distribution centred on 0.5) less than 1 × 10−9. After these filters were applied, variants with a call rate lower than 90% were removed, then samples with a call rate lower than 95%, and then variants with a call rate lower than 95% were removed. After QC, 28,448 individuals and 1,362,971 variants remained for further analysis.

Subsequently, we selected for this study the individuals diagnosed with ADHD and controls as described above, resulting in 8,895 cases and 9,001 controls (see Supplementary Table 23 for sample of individuals with other ancestries not included in this study). Finally, we defined rare variants (n = 565,053) as those with an allele count no higher than five across our dataset (n = 17,896) and the exome subset of gnomAD used in this study (n = 44,779).

Clinical samples

Biological samples collected at the Dutch and German IMpACT sites were sequenced at BGI, Shenzhen, China. The coding regions of the DNA were targeted using BGI´s exome-capture kit (developed by Beijing Genomics Institute targeting 58.8 Mb) and paired-end sequenced on the Illumina HiSeq 2000 platform, with an average sequencing depth of 50×. The exome-sequencing data obtained from dbGaP were generated at the Broad Institute of Harvard and MIT using Illumina’s ICE Capture reagent kit, and sequencing was performed on an Illumina HiSeq 2000 or 2500. Bam files for all samples (clinical cases and dbGaP controls) were reprocessed together, adhering to the same QC approach described above for the iPSYCH samples. After this QC process, 2,816 samples, consisting of 1,078 individuals with ADHD and 1,738 controls, remained for further analysis. We were not able to obtain a group of completely homogenous individuals after the exclusion of genetic outliers based on PCA (Supplementary Fig. 11), and thus, to minimize the effect of population stratification in subsequent analyses, we aimed at getting as close to de novo mutations as possible; we therefore included only ultra-rare variants, defined as singletons not in the non-psychiatric subsample of individuals in the ExAC[18] database. We could not filter based on presence in gnomAD because the dbGaP data (that is, the controls) are included in this database.

Effects of variant categories on ADHD

Quality controlled variants were functionally annotated using SnpEff v.4.354,55, and SnpSift[54] was used to annotate information derived from dbNSFP[56]. If a variant had more than one annotation, only the most severe annotation was considered. PTVs were defined by being annotated as frameshift, splice-site or stop-gained, and predicted with a loss of function (LOF) flag by SnpEff. For missense variants, we annotated the potential effect of the variant on protein function using the MPC score[20].

In iPSYCH data, the burden of different rare-variant categories (rPTVs; rSevereDMVs, rModerateDMVs and rSYNs) in ADHD cases compared with controls was tested using logistic regression. Following the same approach, the load of rare variants across variant categories in female individuals with ADHD (n = 2,265) was compared with male individuals with ADHD (n = 6,630), both with and without individuals with ID (1,827 female individuals without ID; 5,414 male individuals without ID). The burdens in all genes and genes stratified by their pLI score were evaluated. Covariates included in analyses of iPSYCH samples were: birth year, sex, the first ten principal components (PCs) from PCA (performed after excluding non-European samples), total number of variants, number of rare synonymous variants, percentage of exome target covered at a read depth of at least 20, mean read depth at sites within the exome target passing VQSR, and sequencing wave (one-hot encoded).

The burden of different types of variant categories in gene sets was also tested for the clinical samples following the approach described above but restricting to ultra-rare variants and including the following covariates: sex, total number of variants, number of ultra-rare synonymous variants, number of all variants and PC1–PC10 from PCA (performed after excluding individuals with non-European genetic ancestry).

Gene-based burden tests

To increase power for gene discovery, we combined iPSYCH data with a subset of 44,779 individuals from gnomAD[17] (as defined above). Only variants in high-confidence regions for the two datasets were included, defined as regions in which at least 80% of the samples in both datasets had at least 10× sequencing coverage (based on analysis of bam files for the Danish samples and on coverage summary tables for gnomAD). To avoid biases caused by variations in call rates between cases (entirely iPSYCH) and controls (83.3% gnomAD), all autosomal genes with higher rates of rare synonymous variants in cases than controls were excluded (15,603 out of 18,866 autosomal genes remained; 3,263 excluded).

Variants were grouped depending on their impact on ADHD: class I variants include rPTVs and rare missense variants with MPC > 3 (rSevereDMVs); class II variants include missense variants with 2 ≤ MPC ≤ 3 (rModerateDMVs). Gene-based burden analysis was performed with a two-tailed Fisher’s exact test. Only genes with an increased load of class I (3,698 genes) and/or class II (1,026 genes) variants in cases compared with controls were considered. We did not consider genes with higher numbers of class I and II variants in controls, because such observations could be caused by a generally higher number of variants in controls, owing to the previous filtering step in which only genes with a higher number of rare synonymous variants in controls than in cases were retained. A small number of genes (347) had an increased load of both class I and class II variants in cases, and for these genes we also estimated the combined impact of class I and class II variants in a meta-analysis using the weighted z-score method. Weights were the ratio of the standardized effect sizes observed for the classes in enrichment analysis of constrained genes. For the 347 genes, we used the minimum P value across the three analyses done for these genes. In total, we tested 15,603 genes (347 genes were tested three times) resulting in 16,297 tests. We considered a gene significantly associated with ADHD if the P value was lower than 0.05/16,297 = 3.07 × 10−6.

As described above, genes with an increased rate of rare synonymous variants in individuals with ADHD relative to controls were excluded to avoid biases when combining iPSYCH and gnomAD data. When inspecting the QQ-plot of rare synonymous variants (Supplementary Fig. 12), the plot revealed the anticipated pattern of inflation that corresponds to our gene selection strategy.

In the clinical sample, we performed a gene-based burden test of class I variants using emmaxCMC57,58 implemented in EPACTS v.3.3.0 (https://genome.sph.umich.edu/wiki/EPACTS). This method allows for the incorporation of a kinship matrix to account for potential remaining population stratification among individuals. We generated the kinship matrix using ‘epacts make-kin --min-maf 0.01 --remove-complex’. In addition, we included sex, the number of ultra-rare synonymous variants, the number of all variants and PC1–PC10 from PCA (performed after excluding non-European samples) as covariates.

Gene-set analyses in the clinical sample

We tested for increased load of ultra-rare class I and class II variants in ADHD compared with controls in the clinical sample. We used different P-value thresholds from the gene-based burden test in iPSYCH + gnomAD samples to define three ADHD rare-variant risk gene sets: P < 1 × 10−3 (20 genes), P < 5 × 10−2 (316 genes) and P < 1 × 10−1 (583 genes) (gene sets are listed in Supplementary Table 20). The control group included 870 with cardiovascular disease and 896 without, so we also tested for the potential difference in ultra-rare class I variants across the two groups in the three ADHD gene sets. Only class I variants were tested, because these were the only type of variants with a tendency to be overrepresented in clinical ADHD cases versus controls (Supplementary Information, Supplementary Fig. 13 and Supplementary Table 24). The gene sets were tested using logistic regression with sex, number of ultra-rare synonymous variants, number of all variants, and PC1–PC10 from PCA (performed after excluding non-European samples) as covariates.

X-chromosome analyses

Given the distinct regions of the X chromosome, we applied region-specific thresholds to define rare variants: in the pseudoautosomal regions (PARs), rare variants were defined as those with an allele count of five or less following what was done for the autosomes. In non-pseudoautosomal regions (nonPARs), the rare-variant threshold was adjusted to an allele count of three of less to account for the hemizygosity in male individuals. This adjustment was derived using a scaling factor based on the relative contributions of male individuals and female individuals in iPSYCH (6,002 female and 11,894 male individuals) and subset of gnomAD used in this study (19,916 female and 24,863 male individuals):

$$5\times \frac{({{\rm{n}}{\rm{u}}{\rm{m}}}_{{\rm{m}}{\rm{a}}{\rm{l}}{\rm{e}}{\rm{s}}}+2\times {{\rm{n}}{\rm{u}}{\rm{m}}}_{{\rm{f}}{\rm{e}}{\rm{m}}{\rm{a}}{\rm{l}}{\rm{e}}{\rm{s}}})}{2\times ({{\rm{n}}{\rm{u}}{\rm{m}}}_{{\rm{m}}{\rm{a}}{\rm{l}}{\rm{e}}{\rm{s}}}+{{\rm{n}}{\rm{u}}{\rm{m}}}_{{\rm{f}}{\rm{e}}{\rm{m}}{\rm{a}}{\rm{l}}{\rm{e}}{\rm{s}}})}=3.53$$

Variants were categorized into class I, class II and synonymous variants, using the same criteria applied for autosomal variants. In the burden test of male individuals, the number of alleles for nonPAR variants was counted as two to account for hemizygosity.

Logistic regression was performed on three gene sets stratified by their pLI score, comparing the following groups of individuals: ADHD versus controls; ADHD without ID versus controls; ADHD with ID versus controls; and ADHD with ID versus ADHD without ID. Covariates included birth year, sex, the first ten PCs from PCA (non-European samples excluded), total variant count, rare synonymous variant count, exome target coverage (percentage ≥ 20×), mean read depth at target sites and sequencing batch (one-hot encoded). In addition, sex-stratified analyses were performed using the same approach.

For gene discovery on the X chromosome, we applied the same approach as for autosomes when combining iPSYCH data with gnomAD. We excluded genes with higher numbers of synonymous variants in individuals with ADHD than in controls and retained genes with a higher rate of class I variants in cases than in controls (78 genes included) or a higher number of class II variants (44 genes). We performed gene-based burden analysis separately for male individuals and female individuals, in which we, for each gene, compared the number of individuals with ADHD carrying at least one class I or class II variant to the number found for control individuals, using a two-tailed Fisher’s exact test. Notably, six genes had both increased class I and II variants in cases compared with controls; for these genes, a meta-analysis was used to combine the effect of class I and class II variants using the weighted z-score method. The weight was determined as the ratio of the standardized effect sizes observed for class I and class II variants in the enrichment analysis of constrained genes on chromosome X (weight = 1.03/1.86 = 0.55). In total, we performed 78 × 3 tests for class I variants (female, male and combined), 44 × 3 tests for class II variants and 6 tests for combining class I and II, resulting in a total of 372 tests. The Bonferroni correction threshold for statistical significance was set at P < 1.34 × 10−4.

Rare burden heritability

We used BHR[26] to estimate the burden heritability and the contribution of a gene set to the burden heritability (namely, the burden heritability enrichment of a gene set). We used BHR to estimate the heritability of ADHD explained by the load of rare class I, class II and synonymous variants.

Variant-level summary statistics associated with ADHD, including allele count and allele frequency from the iPSYCH exome data, were used as input for BHR. The method regresses gene burden test statistics (based on the variant category being evaluated) against burden scores that correspond to the combined allele frequency. The slope of the regression represents the burden heritability and confounding factors such as population stratification, are controlled through the intercept.

PPI-network analyses

The ANK2 PPI networks were derived from published IP–MS datasets included in Table S2 of a previous study[27]. All significant proteins with log_2-transformed fold change (FC) > 0 and FDR ≤ 0.1 in the ANK2_WH, ANK2_CNCR1 and ANK2_CNCR2 datasets were defined as the ANK2 interactors in ExNs; the significant proteins in the ANK2_WT dataset were defined as the ANK2 interactors in NPCs.

The MAP1A and ANO8 PPI networks in NPCs and ExNs were derived from IP–MS experiments performed in this study. We evaluated the expression of cell-type marker genes using single-nucleus RNA-seq (snRNA-seq) and performed immunofluorescence staining on NPCs and fully differentiated ExNs to confirm their identities as immature neural progenitors and upper-layer prefrontal cortex neurons, respectively (results shown in Supplementary Fig. 14). Details on cell culture and differentiation, snRNA-seq, immunofluorescence, protein extraction, immunoprecipitation, immunoblotting, mass spectrometry and IP–MS data analysis can be found in the Supplementary Information. Consistent with the ANK2 networks, we defined significant proteins with log_2-transformed FC > 0 and FDR ≤ 0.1 in each IP–MS experiment to be the interactors of the index protein. The resulting IP–MS datasets are provided in Supplementary PPI Tables 1–4.

We parsed the interactors identified across all IP–MS datasets into 12 PPI networks grouped by index proteins and cell-type specificity (Supplementary PPI Table 5). For each index protein, we merged all interactors identified in the same cell type into an ‘NPC’ or ‘ExN’ network, or in either cell type, into a ‘Union’ network. We also merged interactors for all three index proteins into combined NPC, ExN and Union networks accordingly.

We also annotated unique interactors identified across all IP–MS datasets with the following information (Supplementary PPI Table 6): (1) name and number of associated baits (index proteins) in NPCs, ExNs or either cell type; (2) whether the interactor had been implicated in genetic association studies of ADHD (P < 0.001 in this study, or by common-variant risk genes listed in Supplementary Table 7 of Demontis et al.[13]), autism, DDs, neurodevelopmental disorders (Supplementary Table 11 of Fu et al.[28]) or schizophrenia (Supplementary Table 5 of Singh et al.[19] at various significance thresholds).

For subsequent network enrichment analyses, we compared the network genes against a background set of protein-coding genes expressed in neurons (hereafter, ‘neuronal background’). To define the neuronal background, we re-analysed RNA-seq data derived from the same ExN cellular model used in this study (day-21 and day-51 data from the Gene Expression Omnibus (GEO) GSE178896 dataset)[27]. We first performed transcript quantification from FASTQ files using Salmon (v.1.10.2)[59] and GENCODE (v.43)[60] reference files. We summarized the quantification results to gene-level counts using tximport (v.1.26.1), then removed non-protein-coding genes and low-count genes using the filterByExpr function in edgeR (v.3.40.2). This resulted in a list of 13,018 neuronal background genes (Supplementary PPI Table 7) to be used in downstream analyses.

To perform rare-variant enrichment analysis for the PPI networks, gene-based association scores were obtained from exome-sequencing studies of autism and DDs (FDR_TADA_ASD and FDR_TADA_DD columns in Supplementary Table 11 of Fu et al.[28]) and schizophrenia (‘P meta’ column in Supplementary Table 5 of Singh et al.[19]). Loss-of-function constraint scores (pLI scores) were obtained from gnomAD v.2.1.1[17]. For each phenotype and each PPI network, we performed a one-tailed Kolmogorov–Smirnov test to assess whether the network genes had more significant scores than the neuronal background genes. Because the PPI networks were significantly enriched for loss-of-function constrained genes, we also repeated the analysis for constrained (pLI ≥ 0.9) and non-constrained (pLI < 0.9) genes separately. That is, we used one-tailed Kolmogorov–Smirnov tests to compare the network genes with pLI ≥ 0.9 to other neuronal background genes with pLI ≥ 0.9, and vice versa for the network genes with pLI < 0.9.

A description of the common-variant enrichment analysis can be found in the Supplementary Information.

Enrichment analyses

We tested for enrichment of top associated rare-variant risk genes (20 genes with P < 1×10−3 in ADHD iPSYCH cases versus iPSYCH + gnomAD controls) among genes in the following gene sets: (1) gene sets related to gene ontology analysed using the Gene Ontology (GO) knowledgebase61,62 (GO biological processes v.2022-07-01, 9,290 gene sets; GO cellular component ontology v.2022-07-01, 1,581 gene sets; GO molecular function ontology v.2022-07-01, 2,997 gene sets); (2) genes related to synapse function using synaptic annotations based on published, expert-curated evidence for 1,602 genes in SynGO[29] (v.20231201 release 1.2); the list of brain-expressed genes provided by SynGO was used as background; (3) gene sets related to biological pathways (BioCarta 2016, 237 gene sets; KEGG 2021, 320 gene sets; Reactome 2022, 1,818 gene sets; canonical pathway gene sets derived from the WikiPathways pathway database 2021, 622 gene sets)[29]; and (4) gene sets related to diseases (PheWeb v.2019, 1,161 gene sets; PhenGenI Association v.2021, 950 gene sets; GWAS catalogue v.2021, 17,37 gene sets; DisGeNET v.6.0, 9,828 gene sets; OMIM Disease, 90 gene sets). Enrichment analyses of the latter two (that is, pathway- and disease-related gene sets) were performed using Enrichr[63]. All of the enrichment analyses were done using a one-sided Fisher´s exact test and the within-database correction for multiple testing was done using the Benjamini–Hochberg method. A gene set was considered significant if the within-database corrected P value was less than P = 0.0038 (0.05 divided by the number of databases [0.05/13 = 0.0038]).

In addition, the genes encoding proteins in the union PPI networks (NPCs + ExNs) for each of the three index proteins (MAP1A, ANO8 and ANK2) were tested for enrichment among gene sets related to (1) gene ontology, (2) synapse function and (3) biological pathways, as described above.

Expression of risk genes in the brain

Expression of the top 20 rare-variant risk genes (P < 0.001) across neocortex developmental stages was evaluated using bulk RNA-seq data v.10 (gene-level RPKMs) obtained from BrainSpan (www.brainspan.org). The samples represent an age span from post-conceptual week 8 to 40 years of age, and were grouped into brain developmental stages as defined previously[64]. Following a previous study[45], we analysed neocortical regions. Samples with poor quality (RIN ≤ 7) were removed. Genes were defined as expressed if the RPKM was at least one in at least 80% of the samples for at least one neocortical region in one major temporal epoch.

After filtering, the BrainSpan dataset contained expression data from 324 samples (information about the number of samples analysed for each developmental stage can be found in Supplementary Table 12). We focused on the 20 ADHD risk genes, of which 17 had expression data in BrainSpan. Gene expression was natural logarithm-transformed (log(RPKM + 1)) and a two-sided paired t-test was used to test for differential expression of the set of the 17 ADHD risk genes against a background gene set (22,402 genes or transcripts) for all developmental stages merged and across developmental stages (P = 4.17×10−3 was considered significant correcting for 12 brain developmental stages). A two-sided paired t-test was also performed to determine differential expression of the 17 genes prenatally and postnatally. Likewise, each of the three exome-wide-significant genes (MAP1A, ANK2 and ANO8) were tested for differences in pre- and postnatal expression. Because the sample sizes were small for some developmental stages, we tested to see whether the data were normally distributed. If this assumption was violated, a non-parametric Wilcoxon signed-rank test was performed instead.

Linking risk genes to cell types

We used scDRS[30] to link rare-variant risk genes to cell types. First, we used a hypothesis-based approach that focused on midbrain neurons, motivated by findings linking common variants[13] to midbrain dopaminergic neurons. We used four datasets from two published snRNA-seq and scRNA-seq studies (see ‘Data availability’): one scRNA-seq dataset from a study (study I) of gene expression in prenatal human brain cells[31] derived from ten human embryos 6–11 weeks old (1,695 cells analysed); and three snRNA-seq datasets from a study (study II) of gene expression in developing midbrain neuronal cell types[32]. These data were generated from 215 pluripotent stem cell (iPS cell) lines, each derived from a single healthy donor (88 male and 127 female individuals), for differentiation towards midbrain neuronal cell types. The three datasets represent cells developed for 11 days (253,381 cells analysed), 30 days (250,923 cells analysed) and 52 days (303,856 cells analysed). We used processed scRNA-seq and snRNA-seq count data and cell-type annotations generated as described in the two papers31,32; for study I, the data were downloaded as a collapsible expression format (CEF) file and converted into a hierarchical data format version 5 annotated data (h5ad) file format using the Python package AnnData (see ‘Data availability’); for study II, h5ad file formats were available. For study II, the 11-day and 30-day datasets (downloaded) were used without any modifications, whereas the 52-day dataset was filtered to remove cells that were treated with rotenone (following what was done in the published study[32]). In addition, the raw read counts from study I (ref. [31]) and the 52-day data from study II (ref. [32]) were filtered so that only genes represented in a minimum of 30 cells were included.

Furthermore, we used a hypothesis-free approach, in which we evaluated the scDRS in 382 cell-type clusters (2,480,956 cells analysed) representing a spectrum of neurons from the entire human brain (study III; ref. [33]). This dataset consists of snRNA-seq data of neurons desiccated from four adult post-mortem human brains (three male individuals and one female individual) from 105 locations across the forebrain (cerebral cortex, hippocampus, cerebral nuclei, hypothalamus and thalamus), midbrain and hindbrain (pons, medulla and cerebellum). We used a h5ad file with processed data (see ‘Data availability’) and cell cluster definitions reported previously[33].

For the dataset from study I (ref. [31]) and the 52-day dataset from study II (ref. [32]), there were no generated UMAPs for visualizing cell-type clusters. To generate UMAPs for these data, we did the following, using Python and the single-cell analysis libraries Scanpy and AnnData: for each of the four scRNA-seq datasets, the raw counts were filtered to contain genes represented in a minimum of 30 cells. Afterwards, the raw counts were normalized and log-transformed, and highly variable genes were identified using ‘highly_variable_genes’ in Seurat[65] implemented in Scanpy with default settings. The expression data for the highly variable genes were scaled and used as input in PCA generated using the singular value decomposition (SVD) solver method in the ‘arpack’ algorithm implemented in Scanpy. PCs from the PCAs were then used to compute a neighbourhood graph generated using the Scanpy function ‘pp.neighbors’, which afterwards was embedded and visualized as a UMAP constructed with the Scanpy function tl.umap. For study I, the neighbourhood graph was constructed with the first 20 PCs and ‘n_neighbors’ set to 100. For study II, the neighbourhood graph was constructed with the first 40 PCs and ‘n_neighbors’ set to 30 for each of the 3 datasets. The parameters were set based on visual inspections.

We used the P values from the top 100 most associated genes from the gene-discovery analysis of iPSYCH + gnomAD samples as input for scDRS analysis, which was done separately for the above-described data from studies I, II and III. The number of genes was set to 100, which is the recommended minimum number of genes for the method. The method computes, on the basis of the expression of these 100 most significant genes, one raw disease score per cell. In addition, 1,000 raw control scores were computed for each cell. The control scores were generated using Monte Carlo sampling, in which 1,000 gene sets were produced, each consisting of 100 genes with similar gene size, mean gene expression and expression variance to that observed for the disease gene set. The computation of scores was corrected for relevant covariates (see below) and the number of genes in each of the cells. The normalized disease score was compared with the empirical normalized control score distribution to estimate a P value that quantifies the association between the disease genes and their expression in individual cells. These scores were used in the downstream scDRS cell-type-level analysis to test the association between the scores and predefined cell types using a t-test and the top 5% quantile of the disease score of cells from the given cell type. In addition, a heterogeneity test using the Geary’s C statistic[66] was performed to determine heterogeneity in disease score within cell types. Both the compute-score and the perform-downstream functions were run with default settings on raw counts.

We included donor ID as a covariate in analyses of studies II and III. Information on donor ID was not available for study I, and we were therefore not able to correct for the potential effects of factors captured by the donor ID covariate; that is, differences in biological variance between individuals, potential technical variation linked to donor samples or unequal donor representation among cells. The day of cell collection (collected from week 6 to week 11) was also used as a covariate in analyses of data from study I. Sex was used as a covariate in the analyses of data from study II. Information about the sex of the donors was obtained from the Human Induced Pluripotent Stem Cell Initiative (HipSci) data browser (see ‘Data availability’) and merged with the downloaded data. Results were considered significant after within-study Bonferroni correction, correcting for the number of cell types analysed.

Gene-set analyses across comorbidities

The loads of class I and class II variants in individuals with ADHD comorbid with other disorders (ID, autism, schizophrenia, DBDs, SUD and multi-comorbidities; see diagnosis codes above), compared with ADHD without comorbidities, were evaluated in iPSYCH samples for sets of autosomal genes grouped by their pLI score: pLI ≥ 0.9 (2,811 genes), 0.5 < pLI < 0.9 (1,332 genes), pLI ≤ 0.5 (14,267 genes), and for 7 gene sets related to other psychiatric disorders and ID: (1) ‘SCZ_Pval2.14e-6’ includes the 10 genes significantly associated with schizophrenia at P < 2.14 × 10−6, identified from exome-sequencing data[19]; (2) ‘SCZ_Qval0.05’ includes a broader set of 32 genes with a nominal association with schizophrenia at q < 0.05, identified from exome-sequencing data[19]; (3) ‘ASD_Pval2.5e-6’ includes 60 genes significantly associated with ASD at P < 2.5 × 10−6, identified from sequencing data[67]; (4) ‘ASD_FDR0.001’ includes 72 genes significantly associated with ASD at FDR < 0.001, identified from exome-sequencing data[28]; (5) ‘ASD_FDR0.05’ includes 183 genes with a nominal association with ASD at FDR < 0.05 based on exome-sequencing data[28]; (6) ‘NDD_FDR0.001’ includes 373 genes significantly associated with neurodevelopmental disorder (NDD) at FDR < 0.001, identified from exome-sequencing data[28]; and (7) ‘NDD_FDR0.05’ includes 662 genes with a nominal association with NDD at FDR < 0.05, identified from exome-sequencing data[28] (gene sets are listed in Supplementary Table 20). The analyses were done using logistic regression and the same covariates as were used in analyses of the effects of variant categories on ADHD in iPSYCH samples (described above). The load of synonymous variants was also evaluated as a sanity check, because we expected no different load for this variant category. We considered tests with P < 0.05/7 = 7.14×10−3 as significant (seven gene sets tested).

Effects of rare variants on education and SES

A copy of the whole-exome-sequencing data (after QC and functional annotation) on iPSYCH ADHD cases was transferred to Statistics Denmark (no controls due to restriction on file size), to link rare variants to variables only available at the secured servers at Statistics Denmark. In these analyses, rare variants were defined as singletons in iPSYCH ADHD cases. Individuals with ADHD with at least one rPTV (nonsense, frameshift and essential splice-site variants) in constrained genes (pLI ≥ 0.9) were compared with individuals with ADHD without rPTVs in constrained genes; rPTVs were used as exposure, and high versus low SES or education were used as outcomes in logistic regression analysis using the glm() function in R and for two-tailed Fisher’s exact test using the fisher.test() function in R[68]. We corrected for gender, birth year, first ten PCs generated from ancestry PCA, number of rare synonymous variants, percentage of target with coverage greater than 20×, mean read depth at sites within the exome target passing VQSR, total number of variants, and sequencing wave. As control tests, we analysed rare synonymous variants in constrained genes.

SES information was obtained from the Income Statistics Register[69]; data were available for 5,297 individuals with ADHD who were over 16 years old. The ‘low SES’ group was defined as individuals receiving social security payment, receiving early retirement benefit and/or having been unemployed for more than six months (n = 3,110); the ‘high SES’ group consisted of the remaining individuals (n = 3,223). Education level was obtained from the Danish Population Education Register[70]. Low education was defined as finishing only primary school, which is nine years of schooling in Denmark (n = 6,488); high education was defined as an education beyond primary school (n = 1,463).

We used two-tailed Fisher´s exact test to estimate the risk of low SES or low education among individuals with ‘ADHD with one or more rPTVs on constrained genes’ or individuals with ‘ADHD without rPTVs in constrained genes’ against 21,413 population-based controls (over 16 years old).

We tested for associations of ultra-rare deleterious variants with measures of IQ in adults with ADHD in the German clinical sample (n = 962 individuals). Intellectual function was assessed with the Mehrfachwahl Wortschatz Intelligenztest (MWT-B)[71] test, and none of the recruited individuals had an IQ lower than 80. We used linear regression with the ‘lm‘ function in R to assess the correlation between IQ and the number of ultra-rare class I variants in all autosomal genes and constrained genes (pLI ≥ 0.9). The analysis was adjusted for sex, first ten PCs from PCA, number of ultra-rare synonymous variants and total number of variants.

Overlap with common-variant risk loci

Common-variant gene-based associations were calculated using MAGMA[72] and summary statistics from our previous GWAS meta-analysis of ADHD[13]. Association was tested using the SNP-wise mean model and linkage disequilibrium correction was based on estimates from samples with European ancestry from phase 3 of the 1000 Genomes Project[73]. No window around genes was used. Gene-based results were subsequently used in a MAGMA competitive gene-set analysis to test for the enrichment of common-variant associations in two gene sets: (1) rare-variant risk genes with P < 0.001 (20 genes); and (2) rare-variant risk genes with P < 0.005 (62 genes).

Joint effect of common and rare variants

For the individuals in iPSYCH, common-variant data are also available. These data were included in our previous GWAS meta-analysis of ADHD, which contains detailed information on data generation[13]. In short, the samples were genotyped using Illumina’s PsychChip (iPSYCH1 samples) or Illumina’s Global Screening Array (iPSYCH2 samples). QC, imputation and association analysis were done using the bioinformatics pipeline Ricopili[74]. After stringent QC, individuals and variants were included according to the following parameters: subject call rate > 0.95, autosomal heterozygosity deviation (|Fhet| < 0.2), variant call rate > 0.98, difference in variant missingness between cases and controls < 0.02 and SNP Hardy–Weinberg equilibrium (HWE) (P > 10−6 in controls and P > 10−10 in cases). Imputation was done separately for iPSYCH1 and iPSYCH2 samples using EAGLE v.2.3.5[75] and Minimac3[76], and the Haplotype Reference Consortium[77] panel v.1.0 was used as reference.

The PGS was constructed by splitting the relatedness pruned GWAS dataset (25,895 with ADHD; 37,148 controls) in 50 random subsets of roughly even size. For each of these, a GWAS was run on the complimentary 49 subsets using 10 PCs as covariates, and the results were meta-analysed with PGC and deCODE ADHD GWAS summary statistics described elsewhere[13]. The resulting summary statistics were then used to generate PGSs in the index set using the SBayesR algorithm implemented in LDAK[78]. Finally, the scores from the 50 subsets, which together cover the full dataset, were assembled into one dataset.

We binned the PGSs into pentiles, and ten dummy variables were generated identifying the individuals in each pentile bin who have at least one class I variant or none, respectively. Logistic regression of ADHD status on the nine dummy variables was performed to estimate the impact on ADHD risk in each PGS pentile using individuals in the first pentile with no class I variants in constrained genes as reference. This was done separately for individuals with no class I variants in constrained genes and for individuals with at least one class I variant in constrained genes. The regression was adjusted for individual birth year, total number of variants, number of rare synonymous variants, percentage of exome target covered at a read depth of at least 20, mean read depth at sites within the exome target passing VQSR, sequencing wave, and the first ten PCs.

C-alpha test

To assess whether the rare variants identified in ADHD and ASD come from the same underlying gene distribution, we performed C-alpha tests on the iPSYCH exome data, including both ADHD and ASD. This comparison was designed to be simultaneous. To start with, we identified individuals diagnosed with either ADHD or ASD by the end of 2016, along with a control group in the quality-controlled iPSYCH data (n = 28,448). Subsequently, we defined and classified the rare variants into class I, class II and synonymous categories.

We performed a C-alpha test between ADHD without ASD (ADHD only) and ASD without ADHD (ASD only), regardless of ID comorbidity. In addition, we stratified the samples by the presence or absence of ID to perform C-alpha tests between ADHD only and ASD only. A separate set of C-alpha tests compared the single disorders with the control group.

The C-alpha test[36], implemented in the AssotesteR R package (http://cran.r-project.org/web/packages/AssotesteR/index.html), was used to evaluate the similarities between ADHD and ASD, and between controls and either ADHD or ASD. Each pairwise comparison for class I, class II and synonymous variants underwent 10,000 permutations, allowing us to verify the asymptotic P value against the permutation-based P value.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

iPSYCH data, including gene-based summary statistics data, are available from the authors after approval by the iPSYCH Data Access Committee and can only be accessed on the secured Danish server (GenomeDK; https://genome.au.dk) because the data are protected by Danish legislation. With regard to the clinical samples, which include samples from the International Multicenter persistent ADHD Collaboration (IMpACT) at Radboud University Medical Center, the Netherlands, and University Hospital Würzburg, Germany, the ethical permissions do not allow sharing of the data outside the secured Danish server and can only be used to study ADHD. For data access and correspondence, please contact one of the corresponding authors. The response time will be within two weeks. The IP–MS data for MAP1A and ANO8 have been deposited to MassIVE with identifier MSV000098548. Other data sources are as follows: BrainSpan: www.brainspan.org; gnomad v.2.1.1: https://gnomad.broadinstitute.org/downloads#v2-lof-curation-results; gnomAD release 2.1.1: https://gnomad.broadinstitute.org/downloads#v2; La Manno et al.[31] scRNA-seq data: GSE76381 (GSE76381_EmbryoMoleculeCounts.cef.txt.gz); Jerber et al.[32] snRNA-seq datasets: https://zenodo.org/record/4651413#.ZAcbxXbMJEZ; Siletti et al.[33] snRNA-seq data: CZ CELLxGENE platform at https://datasets.cellxgene.cziscience.com/f9ecb4ba-b033-4a93-b794-05e262dc1f59.h5ad; Pintacuda et al.[27] RNA-seq data: GSE178896; HipSci data browser: https://www.hipsci.org/#/lines; Python package AnnData: https://anndata.readthedocs.io/en/latest/generated/anndata.AnnData.html; and EPACTS: https://genome.sph.umich.edu/wiki/EPACTS.

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Acknowledgements

D.D. was supported by the Novo Nordisk Foundation (NNF20OC0065561, NNF21SA0072102, NNF23OC0083657), the Lundbeck Foundation (R344-2020-1060) and the European Union (EU)’s Horizon 2020 research and innovation programme under grant agreement 965381(TIMESPAN). The iPSYCH team was supported by grants from the Lundbeck Foundation (R102-A9118, R155-2014-1724 and R248-2017-2003), the National Institute of Mental Health (NIMH) of the National Institutes of Health (NIH) (1R01MH124851-01 to A.D.B.), the EU’s Horizon Europe programme under grant agreement 101057385 (R2D2-MH to A.D.B.) and the Universities and University Hospitals of Aarhus and Copenhagen. The Danish National Biobank resource was supported by the Novo Nordisk Foundation. High-performance computer capacity for handling and statistical analysis of iPSYCH data on the GenomeDK HPC facility was provided by the Center for Genomics and Personalized Medicine and the Centre for Integrative Sequencing (iSEQ), Aarhus University, Denmark (grant to A.D.B.). K.-P.L. was supported by ERA-NET NEURON under grant 01EW1902 (DECODE!), the EU’s Horizon 2020 research and innovation programme under grants 728018 (Eat2beNICE), 101086453 (Aqua-Synapse) and 953327, the Deutsche Forschungsgemeinschaft (DFG KFO 125, SFB 581, SFB TRR 58 and GRK 1253) and the Bundesministerium für Bildung und Forschung (BMBF 01GV0605). For the Dutch IMpACT sample, we acknowledge support from the European College of Neuropsychopharmacology network ‘ADHD Across the Lifespan’. M.H. was supported by the Netherlands Organisation for Scientific Research (NWO; grants Veni 91619115 and Vidi 09150172210069). B.F. was supported by funding from the EU’s Horizon 2020 programme (H2020/2014–2020) under grant agreement 847879 (PRIME) and the NIMH/NIH under award number R01MH124851. M.K. was supported by personal grants from the Dutch Research Council (NWO/ZonMW; Rubicon grant 45219212 and Veni grant 09150162010073). K.L. was supported by the Stanley Center for Psychiatric Research, the US NIMH (U01MH121499), the Simons Foundation Autism Research Initiative (award 735604), the Lundbeck Foundation (R350-2020-963) and the Novo Nordisk Foundation (NNF21SA0072102). S.G. was supported by the Lundbeck Foundation ‘ADHD of Mice and Men’ (R108-2012-10957). J.G. was supported by NIH (1R01NS131433-01). G.P. was supported by the Simons Foundation Autism Research Initiative (SFARI) Bridge to Independence (BTI) award (00002804). We thank the Broad Institute for generating high-quality sequence data supported by funds from the National Human Genome Research Institute (NHGRI) (grant U54 HG003067) with E. Lander as PI. One of the datasets used in this manuscript was obtained from dbGaP at http://www.ncbi.nlm.nih.gov/gap through dbGaP accession number phs000990. We thank the Center for the Development of Therapeutics at the Broad Institute for assisting with immunofluorescence imaging.

Author information

Author notes

1. These authors contributed equally: Ditte Demontis, Jinjie Duan

Authors and Affiliations

1. Department of Biomedicine–Human Genetics, Aarhus University, Aarhus, Denmark

Ditte Demontis, Jinjie Duan, Jakob Grove, Trine Tollerup Nielsen, Janne Thirstrup, Jason H. Y. Tsai, Simon Glerup & Anders D. Børglum

2. Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark

Ditte Demontis, Jinjie Duan, Jakob Grove, Trine Tollerup Nielsen, Janne Thirstrup, Jonas Bybjerg-Grauholm, Jason H. Y. Tsai, Esben Agerbo, Thomas Werge, Merete Nordentoft, Ole Mors, Preben Bo Mortensen & Anders D. Børglum

3. Center for Genomics and Personalized Medicine, Aarhus, Denmark

Ditte Demontis, Jinjie Duan, Jakob Grove, Trine Tollerup Nielsen, Janne Thirstrup, Jason H. Y. Tsai & Anders D. Børglum

4. Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA

Ditte Demontis, Yu-Han H. Hsu, Greta Pintacuda, Makayla Martorana, Travis Botts & Kasper Lage

5. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA

Yu-Han H. Hsu, Greta Pintacuda, Makayla Martorana, Travis Botts, F. Kyle Satterstrom, Kasper Lage, Mark J. Daly & Benjamin M. Neale

6. Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

F. Kyle Satterstrom, Mark J. Daly & Benjamin M. Neale

7. Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark

Jonas Bybjerg-Grauholm

8. Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands

Martine Hoogman, Jan Buitelaar, Marieke Klein & Barbara Franke

9. Department of Medical Neuroscience, Radboud University Medical Center, Nijmegen, The Netherlands

Martine Hoogman, Jan Buitelaar, Marieke Klein & Barbara Franke

10. Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands

Martine Hoogman

11. Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital Würzburg, Würzburg, Germany

Georg C. Ziegler

12. Department of Psychiatry and Psychotherapy, Medius Hospital of Kirchheim, Kirchheim unter Teck, Germany

Christian Jacob

13. Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany

Oliver Grimm, Maximilian Bayas, Nene F. Kobayashi & Andreas Reif

14. Department of Psychiatry and Neurobehavioural Science, University College Cork, Cork, Ireland

Sarah Kittel-Schneider

15. APC Microbiome Ireland, University College Cork, Cork, Ireland

Sarah Kittel-Schneider

16. Division of Molecular Psychiatry, Center of Mental Health, University Hospital Würzburg, Würzburg, Germany

Klaus-Peter Lesch

17. Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital Würzburg, Würzburg, Germany

Klaus-Peter Lesch

18. Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, The Netherlands

Klaus-Peter Lesch

19. Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands

Barbara Franke

20. Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), Frankfurt am Main, Germany

Andreas Reif

21. National Centre for Register-Based Research (NCRR), Business and Social Sciences, Aarhus University, Aarhus, Denmark

Esben Agerbo & Preben Bo Mortensen

22. Centre for Integrated Register-based Research at Aarhus University (CIRRAU), Aarhus University, Aarhus, Denmark

Esben Agerbo & Preben Bo Mortensen

23. Mental Health Centre Sct. Hans, Capital Region of Denmark, Institute of Biological Psychiatry, Copenhagen University Hospital, Copenhagen, Denmark

Thomas Werge & Kasper Lage

24. Mental Health Centre Copenhagen, Capital Region of Denmark, Copenhagen University Hospital, Copenhagen, Denmark

Merete Nordentoft

25. Psychosis Research Unit, Aarhus University Hospital, Aarhus, Denmark

Ole Mors

26. Department of Medicine, Harvard Medical School, Boston, MA, USA

Mark J. Daly

27. Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland

Mark J. Daly

Contributions

Analysis: D.D., J.D., Y.-H.H.H., G.P., J.G., T.T.N., J.T., M.M., T.B., F.K.S. and J.H.Y.T. Providing and/or processing samples and/or data: J.B.-G., S.G., M.H., J.B., M.K., G.C.Z., C.J., O.G., M.B., N.F.K., S.K.-S., K.-P.L., B.F., A.R., E.A., T.W., M.N., O.M., P.B.M., K.L., M.J.D., B.M.N. and A.D.B. Writing: D.D., J.D., Y.-H.H.H., G.P., J.G., T.T.N. and A.D.B. Study supervision: D.D., K.L., B.M.N. and A.D.B. Study direction: D.D. and A.D.B. All authors contributed to critical revision of the manuscript.

Corresponding authors

Correspondence to Ditte Demontis or Anders D. Børglum.

Ethics declarations

Competing interests

D.D. has received speaker fees from Takeda and Medice Nordic. A.D.B. has received speaker fees from Lundbeck. M.H. has received a speaker fee from Medice. S.K.-S. has received speaker honoraria from Medice, Takeda and Janssen. B.F. has received educational speaking fees and travel support from Medice. The remaining authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Overview of study design.

Study design and analytical approach for gene discovery. a, Sample sizes used in the gene-discovery analysis (NFE = non-Finnish European). b, Definition of rare-variant classes. c, Overview of the analytical approach for gene discovery. The numbers of genes illustrated correspond to autosomal genes. The same approach was applied to genes on the X chromosome, and the corresponding results are described in the main text.

Extended Data Fig. 2 ADHD risk across variant categories on the X chromosome.

Burden (odds ratio; OR) of class I, class II and rare synonymous variants (SYN) in chromosome X genes stratified by their pLI score. Results are from logistic regression (results in Supplementary Table 8). a, ADHD compared to controls. b, ADHD with no intellectual disability (ID) compared to controls. c, ADHD with ID compared to controls. d, ADHD with ID compared to ADHD without ID. *Indicates nominal significant association P < 0.05, **Indicates significant association after Bonferroni correction correcting for three gene sets (P values less than P = 0.0167 are considered significant).

Extended Data Fig. 3 Combined effect of common and rare variants on ADHD risk.

Risk for ADHD is given on the y-axis (Dots represent beta coefficient point estimates from logistic regression, and error bars indicate the corresponding 95% confidence intervals [CI]) across ADHD-PGS pentiles on the x-axis, in individuals with no rare class I variants in constrained autosomal genes (pLI ≥ 0.9; marked in green) (n = 14,634 individuals) and in individuals with 1 or more class I variants in constrained autosomal genes (pLI ≥ 0.9; marked in orange) (n = 3,262 individuals). The beta (β) score across groups is relative to the risk in ADHD-PGS pentile one of individuals with no rare class I variants in constrained autosomal genes.

Extended Data Fig. 4 Effect of rare variants on comorbidity in ADHD across pLI bins.

Burden (odds ratio; OR) of rare class I, class II and rare synonymous variants (SYN) in individuals with ADHD and comorbidities compared to individuals without the comorbid condition being analysed across pLI bins. Results are from logistic regression (detailed results in Supplementary Table 19). a, ADHD with intellectual disability (ID) compared to ADHD with no ID. b, ADHD comorbid with autism spectrum disorder (ASD) compared to ADHD without ASD. c, ADHD comorbid with schizophrenia (SZ) compared to ADHD without SZ. d, ADHD with multimorbidities compared to ADHD without multimorbidities. e, ADHD with no ID compared to controls. f, ADHD without multimorbidities compared to controls without multimorbidities. *Indicates nominal significant association P < 0.05, **Indicates significant association after Bonferroni correction (correcting for three gene sets; P values less than P = 0.0167 are considered significant).

Extended Data Fig. 5 Effect of rare variants on comorbidity in ADHD across brain-disorder gene sets.

Burden (odds ratio; OR) of rare class I, class II and rare synonymous variants in individuals with ADHD and comorbidities compared to individuals without the comorbid condition being analysed. a, ADHD with intellectual disability (ID) compared to ADHD with no ID. b, ADHD comorbid with ASD compared to ADHD without ASD. c, ADHD comorbid with schizophrenia (SZ) compared to ADHD without SZ. d, ADHD with multimorbidities compared to ADHD without multimorbidities. e, ADHD with no ID compared to controls. f, ADHD without multimorbidities compared to controls without multimorbidities. g, ADHD comorbid with disruptive behaviour disorders (DBS) compared to ADHD without DBS. h, ADHD comorbid with substance use disorders (SUD) compared to ADHD without SUD. The loads in gene sets related to autism, schizophrenia and developmental disorders have been evaluated. Results are from logistic regression (Sample sizes can be found in Supplementary Table 18, detailed results in Supplementary Table 21). *Indicates nominal significant association P < 0.05, **Indicates significant association after Bonferroni correction correcting for seven gene sets. P values less than P = 7.14 × 10−3 are considered significant.

Supplementary information

Supplementary Information

Supplementary Notes, Supplementary Figs. 1–14 and Supplementary References.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–24.

Supplementary PPI Tables

Supplementary PPI Tables 1–10.

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Demontis, D., Duan, J., Hsu, YH.H. et al. Rare genetic variants confer a high risk of ADHD and implicate neuronal biology. Nature 649, 909–917 (2026). https://doi.org/10.1038/s41586-025-09702-8

- Received: 02 July 2024

- Accepted: 02 October 2025

- Published: 12 November 2025

- Version of record: 12 November 2025

- Issue date: 22 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09702-8

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Specificity, length and luck drive gene rankings in association studies

Jeffrey P. Spence,

Hakhamanesh Mostafavi,

Mineto Ota,

Nikhil Milind,

Tamara Gjorgjieva,

Courtney J. Smith,

Yuval B. Simons,

Guy Sella &

…

Jonathan K. Pritchard

Nature volume 649, pages 918–925 (2026)

Abstract

Standard genome-wide association studies (GWAS) and rare variant burden tests are essential tools for identifying trait-relevant genes[1]. Although these methods are conceptually similar, by analysing association studies of 209 quantitative traits in the UK Biobank2,3,4, we show that they systematically prioritize different genes. This raises the question of how genes should ideally be prioritized. We propose two prioritization criteria: (1) trait importance — how much a gene quantitatively affects a trait; and (2) trait specificity — the importance of a gene for the trait under study relative to its importance across all traits. We find that GWAS prioritize genes near trait-specific variants, whereas burden tests prioritize trait-specific genes. Because non-coding variants can be context specific, GWAS can prioritize highly pleiotropic genes, whereas burden tests generally cannot. Both study designs are also affected by distinct trait-irrelevant factors, complicating their interpretation. Our results illustrate that burden tests and GWAS reveal different aspects of trait biology and suggest ways to improve their interpretation and usage.

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Main

A central goal of human genetics is to identify which genes affect traits and disease risk and to what extent. This is essential for addressing fundamental questions such as what biological processes underlie trait variation, which genes and pathways are most critical for understanding those processes, and which genes could serve as potential therapeutic targets.

Although many techniques exist to study gene function in model systems or in vitro (for example, see refs. 5,6,7), the study of organism-level traits in humans largely relies on naturally occurring genetic variation, primarily through GWAS[1].

GWAS have been deeply informative about the genetic basis of complex traits, from uncovering actionable drug targets[8] to identifying trait-relevant cell types and programs9,10,11,12. However, it remains unclear how best to extract biological insight from GWAS. First, GWAS do not directly pinpoint relevant genes, as most associated variants are non-coding[13]. Moreover, a surprisingly large fraction of the genome contributes to the heritability of many traits14,15,16, and trait-associated variants often cannot be mapped to genes with clear phenotypic relevance.

Recently, large whole-exome and whole-genome sequencing datasets have enabled the direct study of genes through rare protein-coding variants, which are excluded or underpowered in GWAS[3]. To boost statistical power, these variants are analysed using burden tests4,17. Burden tests aggregate variants — typically loss-of-function (LoF) variants — within a gene to create a ‘burden genotype’, which is then tested gene by gene for association with phenotypes. This is similar to common-variant GWAS but focused on rare variants combined at the gene level.

Despite this conceptual similarity, recent work has found anecdotally that for many traits, LoF burden tests and GWAS discover distinct genes, although with some overlap18,19. In a systematic analysis, Weiner et al. found that for many traits, burden heritability is explained by fewer genes than are required to explain single-nucleotide polymorphism (SNP) heritability, and burden tests tend to prioritize genes that are seemingly more closely related to trait biology[20].

To better understand these differences, we analysed the results of GWAS and LoF burden tests for 209 quantitative traits in the UK Biobank2,3,4. We showed that burden tests and GWAS prioritize different genes, and these differences persist even when conservatively addressing power differences and issues linking variants to genes.

The discrepancy between GWAS and LoF burden tests raises thorny questions, such as what criteria each method uses to prioritize genes, and how these criteria relate to the underlying biology; which method is more relevant for understanding trait biology; and which method is better suited for downstream applications, such as drug target discovery.

We analysed association study results and used population genetics models to address these questions. Our results show that burden tests tend to prioritize trait-specific genes — those primarily affecting the studied trait with little effect on other traits — whereas GWAS also capture more pleiotropic genes often missed by burden tests. In addition, we highlight the effect of trait-irrelevant factors on discovery, particularly gene length and random genetic drift. Ultimately, GWAS and LoF burden tests reveal distinct but complementary aspects of trait biology, with important implications for interpreting and using association studies.

Burden test and GWAS gene ranks differ

GWAS and LoF burden tests are conceptually similar (Fig. 1a,b), but previous studies have highlighted key differences in their findings[20]. To more thoroughly quantify how concordantly these methods prioritize genes and genomic loci using P values, we systematically compared GWAS and LoF burden test results for 209 quantitative traits from the UK Biobank (Methods)[4].

Fig. 1: GWAS and LoF burden tests prioritize different loci.

a,b, Schematics of GWAS (a) and LoF burden tests (b). c, Each cell is a genome-wide significant gene according to LoF burden tests, ordered by significance (most significant (top) to least significant (bottom)). Genes are coloured by the rank of the locus that contains the gene according to GWAS or are coloured black if they are not contained in any genome-wide significant locus. Across 151 traits with at least one burden hit and one GWAS hit, 74.6% (1,382 out of 1,852) of genome-wide significant burden test hits fall within a GWAS locus. Trait abbreviations are defined in Supplementary Table 1. d, Minimum LoF burden test P values for any gene overlapping a genome-wide significant GWAS locus plotted against the minimum GWAS P value within that locus (n = 382 loci). e, The genomic region surrounding NPR2. GWAS P values of approximately independent genome-wide significant GWAS hits (top), and the location of genes coloured by LoF burden test P values (bottom) are shown. f, Similar to panel e but for the genomic region surrounding HHIP.

In principle, technical artefacts could drive discordance between GWAS and LoF burden test results. The causal genes driving GWAS hits are usually unknown, and errors in linking hits to genes could reduce the overlap between genes prioritized by the two study designs.

To minimize these technical effects, we maximized concordance whenever possible. We conservatively defined GWAS loci by taking a 1-Mb window around each genome-wide significant GWAS hit and merging overlapping windows. We ranked these loci by the minimum GWAS P value within each locus and, for each genome-wide significant gene in the burden test, compared its rank based on burden P value to the rank of the GWAS locus (if any) that contained it.

Consistent with previous reports19,21, we found that the majority of burden hits fall within a GWAS locus (Fig. 1c). Yet, the two association study designs rank genes very differently. Some of the top burden hits fall outside the defined GWAS loci or are within GWAS loci that are ranked lower than hundreds of other loci (Fig. 1c). We quantified this difference in ranking by calculating how many burden hits fall within ‘top’ GWAS loci (Methods), finding that only 26% (480 out of 1,852) of genes with burden support fall in the top GWAS loci (Supplementary Fig. 1).

To gain intuition about the source of these discordant rankings, we considered height as an example trait, for which there are 382 genome-wide significant GWAS loci (Fig. 1d). The rankings from the two study designs are somewhat concordant (Spearman’s \(\rho =0.46\)), suggesting that they are not uncovering totally disparate axes of biology. Yet, there is little overlap in the top hits, with many significant GWAS loci not containing a single significant burden gene. This pattern is not unique to height (Supplementary Figs. 2 and 3), and these results are robust to the details of the analysis choices (Supplementary Appendix A and Supplementary Figs. 4–31).

We can illustrate these differences with two examples of discordantly ranked loci. Figure 1e shows the NPR2 locus. NPR2 is the second most significant gene in the LoF burden tests, but it is contained in the 243rd most significant GWAS locus. It is unsurprising that this locus is significant in both association tests: mutations in NPR2 have been linked to short stature in humans and mice22,23,24,25,26,27. Yet, hundreds of loci are more strongly prioritized by GWAS, including the HHIP locus (Fig. 1f). The HHIP locus is the third most significant locus and has numerous uncorrelated GWAS hits (r^2 < 0.1) with P values as small as 10−185. HHIP is a biologically sensible hit for height[28] as HHIP has been implicated in osteogenesis[29] and interacts with three different Hedgehog proteins30,31, which are involved in body patterning and limb formation[32]. Nonetheless, there is essentially no burden signal for HHIP or any of the other genes in the locus. These differences motivated us to explore why GWAS and LoF burden tests might rank loci so differently.

How should genes be prioritized?

Given the extensive differences in how GWAS and LoF burden tests rank genes, we are faced with an underexplored question: if we could precisely measure any quantity of interest for each gene, what properties would make us want to rank one gene higher than another for a given trait. That is, how should genes ideally be prioritized?

We propose two distinct ranking criteria: trait importance and trait specificity. Imagine a gene that is only expressed in developing bones and whose disruption results in shorter stature but has minimal effects on other traits (Fig. 2a). In some sense, this is a quintessential ‘height gene’, and we might want this gene to be highly ranked in association studies. Conversely, consider a broadly expressed transcription factor whose disruption results in an even greater reduction of height, but also disrupts the normal functioning of numerous organ systems. This is less obviously a height gene, but it has a larger effect on height than the first gene. We have defined trait specificity and trait importance such that the first gene has higher trait specificity, but the second gene has higher trait importance.

Fig. 2: How should genes be prioritized?

a, A cartoon of two genes that affect a trait under study. The widths of the arrows represent the relative effect sizes. Gene 1 is more trait specific, but gene 2 is more trait important. b, Formal definitions of trait importance and trait specificity for genes in the context of LoF burden tests. The effect of an LoF in the gene on trait \(t\) is \({\gamma }_{t}\), with trait 1 being the study trait. We have defined trait importance as \({\gamma }_{1}^{2}\) and trait specificity as \({\gamma }_{1}^{2}\,/\,{\sum }_{t}{\gamma }_{t}^{2}\).

Formally, we have defined the trait importance of a variant as its squared effect on the trait of interest, considering high-impact variants important regardless of their direction of effect. We have defined the trait importance of a gene as the trait importance of LoF variants in that gene. Throughout, we used \({\alpha }_{t}\) to refer to the effect size of a variant on trait \(t\), and \({\gamma }_{t}\) to refer to the LoF burden effect size of a gene; that is, trait importance for trait 1 is denoted as \({\alpha }_{1}^{2}\) and \({\gamma }_{1}^{2}\) for variants and genes, respectively. Throughout, we took the trait under study to be trait 1.

We have defined trait specificity as the importance for the trait of interest relative to the importance across all fitness-relevant traits measured in appropriate units (Fig. 2b). We have denoted trait specificity by \({{\rm{\Psi }}}_{V}:= {\alpha }_{1}^{2}\,/\,{\sum }_{t}{\alpha }_{t}^{2}\) for variants and \({{\rm{\Psi }}}_{G}:= {\gamma }_{1}^{2}\,/\,{\sum }_{t}{\gamma }_{t}^{2}\) for genes. See Supplementary Appendix B for more details. Ideally, association studies would prioritize genes based on trait importance, trait specificity or some combination thereof.

Burden tests favour trait-specific genes

To determine how LoF burden tests prioritize genes, we analysed population genetics models of complex traits developed by Simons et al.[33] (Supplementary Appendix B). Our analysis revealed that LoF burden tests prioritize genes in part by their trait specificity, and not by importance (Fig. 3a). We briefly outline the argument here.

Fig. 3: Burden tests prioritize trait-specific genes, not large-effect genes.

a, Burden tests prioritize genes by trait specificity. \(\mu \) is the per-site mutation rate, \(L\) is the number of potential LoF positions, and \({s}_{\mathrm{het}}\) is the strength of selection against heterozygous LoF carriers. b, Genes were binned by estimated shet (ref. 35) with approximately 184 genes per bin. Aggregate LoF frequencies were averaged across genes within each bin. The trend line was fit using LOESS. Spearman’s \(\rho \) between posterior mean \({s}_{\mathrm{het}}\) and pLOF = −0.547; P < 10−15; n = 18,154 genes. c, Similar to panel b but averaging over an unbiased estimate of the mean of \({\gamma }_{t}^{2}\) across traits. Pearson’s \(r\) between posterior mean \({s}_{\mathrm{het}}\) and unbiased estimate of \({\sum }_{t}{\gamma }_{t}^{2}=0.078\); P < 10−15; n = 18,154 genes. d, Genes were binned as in panel b, and the mean of squared z-scores, \({z}^{2}\), across traits was plotted against the average of an unbiased estimate of the mean of \({\gamma }_{t}^{2}\) across traits. Points are coloured by the mean \({s}_{\mathrm{het}}\) within the bin and the trend line was fit using LOESS. Pearson’s \(r\) between mean importance and mean \({z}^{2}\) across the 25 highest \({s}_{\mathrm{het}}\) bins = 0.188, P = 0.368. Note that this correlation is probably overestimated as the noise in estimated importance and \({z}^{2}\) is correlated. e, Quantile–quantile plot of LoF burden test P values across nine trait–tissue pairs. Genes were stratified for each trait–tissue pair based on the specificity of their expression to the trait-relevant tissue. The y axis has been non-linearly transformed. One-sided Wilcoxon test P < 10−5 for all comparisons of the distribution of P values in the most expression-specific bin to each other bin. The five bins contained n = 11,596, n = 11,441, n = 11,185, n = 11,477 and n = 11,470 P values from the least-specific to most-specific bins, respectively.

In burden tests, the strength of association, \({z}^{2}\), for a gene depends on both its trait importance, \({\gamma }_{1}^{2}\), and the aggregate frequency of LoFs, \({p}_{\mathrm{LoF}}\), with the expected strength of association being proportional to \({\gamma }_{1}^{2}{p}_{\mathrm{LoF}}(1-{p}_{\mathrm{LoF}})\).

Natural selection acts to keep LoFs rare: for sufficiently strong selection, \({p}_{\mathrm{LoF}}(1-{p}_{\mathrm{LoF}})\) is proportional to \(\mu L/{s}_{\mathrm{het}}\), where \(\mu \) is the per-base mutation rate, \(L\) is the number of sites where an LoF could occur, and \({s}_{\mathrm{het}}\) is the strength of selection in heterozygotes[34]. As expected, there is a strong negative relationship between estimates of shet[35] and the average of \({p}_{\mathrm{LoF}}\) across genes within \({s}_{\mathrm{het}}\) bins (Fig. 3b and Supplementary Fig. 32).

Furthermore, many complex traits are thought to be under stabilizing selection36,37,38,39. Crucially, this predicts a connection between \({s}_{\mathrm{het}}\) and total trait effects. Specifically, \({s}_{\mathrm{het}}\approx {\sum }_{t}{\gamma }_{t}^{2}\) where \({\sum }_{t}{\gamma }_{t}^{2}\) is the sum of trait importances across all fitness-relevant traits measured in appropriate units (Supplementary Appendix B). To test this, we computed unbiased estimates of trait importance from LoF burden test results for 27 genetically uncorrelated traits (Methods). The average trait importance across these traits shows a strongly significant positive relationship with \({s}_{\mathrm{het}}\) as predicted by our model (Fig. 3c).

Combining these results, the strength of association in LoF burden tests is proportional to \({\gamma }_{1}^{2}\,/\,{\sum }_{t}{\gamma }_{t}^{2}\) (Fig. 3a), exactly our definition of \({{\rm{\Psi }}}_{G}\), the trait specificity of the gene.

A key implication is that LoF burden tests do not prioritize genes based on trait importance. The most trait-important genes will often be the most constrained and have the smallest frequencies and hence largest standard errors, an effect previously referred to as flattening33,40. Indeed, Fig. 3d shows that, for genes with sufficiently large effects, the strength of association (\({z}^{2}:= {(\hat{{\gamma }_{1}}/{\rm{SE}}(\hat{{\gamma }_{1}}))}^{2}\)) is completely decoupled from trait importance in the UK Biobank LoF burden tests.

Instead, our theory predicts that LoF burden tests prioritize genes by their trait specificity, \({{\rm{\Psi }}}_{G}\). To confirm this prediction, we would ideally compare strength of association to an independent measure of \({{\rm{\Psi }}}_{G}\). It is difficult to directly estimate \({{\rm{\Psi }}}_{G}\) independently of our theory as \({{\rm{\Psi }}}_{G}\) depends on the unknown true trait importances. Instead, we used how specifically expressed a gene is as an imperfect proxy.

We focused on nine traits that we could confidently assign to a single causal tissue or cell type, and binned genes based on their expression in the causal tissue relative to their average expression across considered tissues (Methods).

Using results from the LoF burden tests for these nine trait–tissue pairs, we constructed a quantile–quantile plot (Fig. 3e). Consistent with our prediction, we observed significantly stronger signals in the most specific expression bins. We also observed that many of the top hits are plausibly trait specific. Our theory predicts that this relationship between specificity and power should hold regardless of \({s}_{\mathrm{het}}\), which we confirmed empirically (Supplementary Fig. 34).

We found qualitatively similar results when using burden tests that include both LoFs and probably deleterious missense variants (Supplementary Figs. 33 and 35), although the relationship between \({s}_{\mathrm{het}}\) and \({\gamma }^{2}\) is less pronounced, presumably due to a known artefact from using variants with different effect sizes[20]. We also obtained similar results using a regression model to predict LoF burden \({z}^{2}\) from expression specificity while controlling for effect sizes (Supplementary Fig. 36).

GWAS prioritize trait-specific variants

We next turned to GWAS. In contrast to LoF burden tests, GWAS are performed at the variant level, and so we considered what drives the rankings of variants. Following the same argument as above reveals that the expected strength of association is proportional to \({\alpha }_{1}^{2}\,/\,{\sum }_{t}{\alpha }_{t}^{2}\), the trait importance of the variant for the trait under study relative to the total trait importance of the variant across all fitness-relevant traits. This is exactly \({{\rm{\Psi }}}_{V}\), the trait specificity of the variant.

The fact that GWAS prioritizes trait-specific variants rather than genes has profound implications for understanding the differences between GWAS and LoF burden tests. In particular, variants can be trait specific in two ways (Fig. 4a): they can either affect a trait-specific gene (variant 3 in Fig. 4a) or have context-specific effects on a pleiotropic gene (variant 1 in Fig. 4a). For example, context-specific variants might regulate expression only in trait-relevant cell types or developmental time points, resulting in trait-specific effects even when acting on pleiotropic genes. In Supplementary Appendix C, we developed a model formalizing the relationship between \({{\rm{\Psi }}}_{V}\), \({{\rm{\Psi }}}_{G}\) and context-specific expression.

Fig. 4: GWAS prioritize trait-specific variants.

a, Schematic of what determines trait specificity for variants, \({{\rm{\Psi }}}_{V}\). \({{\rm{\Psi }}}_{V}\) is determined by two components: the trait specificity of the gene that the variant acts through, and the trait specificity of the variant relative to that gene. Three representative types of variants are highlighted with gene models. The green variant is non-coding, whereas the red variants are coding. Shaded contexts represent cellular contexts or cell types in which the gene affects traits. b, Heritability enrichment for coding variants as measured by S-LDSC \(\tau \) as a function of expression specificity for nine trait–tissue pairs. The inverse variance-weighted average of the results for the individual traits is in black. Black dashed line represents no effect on heritability. The y axis has been non-linearly transformed. One-sided Z-test P < 0.009 for all comparisons between \(\tau \) in the most specifically expressed bin and each other bin. c, Heritability enrichment for non-coding variants in ATAC peaks as measured by S-LDSC \(\tau \) as a function of ATAC peak tissue specificity for nine trait–tissue pairs. The inverse variance-weighted average of the results for the individual traits is in black. Black dashed line represents no effect on heritability. The y axis has been non-linearly transformed. One-sided Z-test P < 0.004 for all comparisons between \(\tau \) in the most specifically expressed bin and each other bin.

To test our predictions, we considered these two ways that variants can be trait specific and used stratified linkage disequilibrium (LD) score regression (S-LDSC)9,41 to quantify how heritability changes along these axes. The average heritability contributed by a set of variants is a proxy for how highly those variants would be prioritized by GWAS on average. We quantified effects on heritability by \(\tau \) as reported by S-LDSC normalized by heritability, which can be interpreted as how much a given annotation increases the per-variant proportion of heritability explained conditioned on all other annotations (Methods).

First, we looked into whether the trait specificity of the gene on which a variant acts affects GWAS prioritization for variants with a given context specificity (moving along the horizontal axis of Fig. 4a). To this end, we restricted our analyses to coding variants and used our measure of expression specificity as a proxy for \({{\rm{\Psi }}}_{G}\). Overall, variants acting on specifically expressed genes are prioritized higher by GWAS (Fig. 4b and Supplementary Figs. 37 and 38).

We next examined the effect of context specificity (moving along the vertical axis of Fig. 4a). We used non-coding variants, and used the tissue specificity of assay for transposase-accessible chromatin using sequencing (ATAC-seq) peaks as an imperfect proxy for trait specificity. We estimated the effect of ATAC specificity on heritability using S-LDSC while controlling for the strength of the ATAC peaks (Methods).

Across all nine traits, we saw a significant trend of increasing contribution to heritability in more tissue-specific ATAC peaks (Fig. 4c and Supplementary Figs. 39 and 40), even when conditioning on \({s}_{\mathrm{het}}\) (Supplementary Figs. 41 and 42; total contributions to heritability instead of enrichment in Supplementary Fig. 43).

Overall, our results show that LoF burden tests and GWAS both prioritize trait specificity, but prioritize different loci because LoF burden tests rank genes, whereas GWAS rank variants. Variants can be specific either by acting through trait-specific genes or by being context specific, and both of these axes contribute to GWAS prioritization. The difference between LoF burden tests and GWAS is not driven by differences in the frequencies of the variants they consider, but rather by GWAS including non-coding variants, which can be context specific, and LoF burden tests including only coding variants. This explains why LoF burden tests and GWAS prioritize different loci even when restricting to lower-frequency variants in GWAS (Supplementary Figs. 26–28).

LoF burden tests prioritize long genes

Our modelling also revealed factors beyond trait specificity that affect how GWAS and LoF burden tests prioritize genes. These factors have nothing to do with any aspect of trait biology: LoF burden tests prioritize genes in part by the length of their coding sequence, and GWAS prioritize variants in part due to randomness in their frequencies caused by genetic drift.

LoF burden tests aggregate all LoF variants within a gene (Fig. 1b). As we derived above, this results in an expected strength of association that increases with \(\mu L\), the average mutation rate multiplied by the number of potential LoF positions within a gene. Intuitively, if a gene has more potential LoFs, then the proportion of individuals that are LoF carriers will be larger, resulting in greater power, all else being equal.

We confirmed these predictions using the UK Biobank LoF burden tests. In particular, longer genes generally do not have substantially larger effect sizes (Extended Data Fig. 1a), but do have considerably smaller standard errors (Extended Data Fig. 1b), resulting in a significant effect of gene length on burden signal, \({z}^{2}\) (Extended Data Fig. 1c). This also causes longer genes to be hits for more traits, causing them to appear more pleiotropic, despite not having larger effects across more traits. See Supplementary Appendix D for details.

Random genetic drift affects GWAS

We showed above that the expected strength of association in GWAS is proportional to the trait specificity of a variant, \({{\rm{\Psi }}}_{V}\). This is true on average, but there is considerable variation around this expectation. In well-powered GWAS, variants are ranked by \(2{\alpha }_{1}^{2}p(1-p)\), where \(p\) is the variant allele frequency (Supplementary Appendix B). We refer to \(2{\alpha }_{1}^{2}p(1-p)\) as the realized heritability of a variant. Under our modelling assumptions, the expected value of \(p(1-p)\) is proportional to \(1/\,{\sum }_{t}{\alpha }_{t}^{2}\) (Supplementary Appendix G), resulting in trait-specific variants being ranked more highly on average. Yet, random genetic drift causes variant frequencies to be spread widely around their expected values (Extended Data Fig. 2a).

In LoF burden tests, this effect is largely ameliorated by the aggregation of variants, which averages out the stochasticity in the frequencies of individual LoFs (Supplementary Appendix B). However, GWAS consider variants one at a time, causing this stochasticity to have a large role in gene prioritization.

Indeed, in simulated GWAS, the ranking of variants in terms of realized heritability is largely random with respect to trait importance for sufficiently trait-important variants (Extended Data Fig. 2b), driven by differences in minor allele frequency (MAF) due to genetic drift.

This randomness in MAF drives in a counterintuitive result: variants that are the strongest hits for one trait are more likely to be hits for other traits, even though they are, on average, more trait specific (Extended Data Fig. 3; see Methods and Supplementary Appendix E for details). This reconciles our findings on the importance of trait specificity with previous studies that report GWAS hits appearing to be surprisingly pleiotropic42,43,44: the seemingly high pleiotropy at GWAS hits is a statistical artefact of the increased power at high-frequency variants.

Estimating trait importance

We began by proposing that it could be desirable to prioritize genes either by trait importance or trait specificity. Yet, we have shown that when ranking by P value, neither LoF burden tests nor GWAS rank genes by importance. We wanted to see whether there was some way to use GWAS or LoF burden test results to prioritize genes in a way that is correlated with trait importance.

Throughout, we have focused on prioritizing genes based on P value or strength of association. It is natural to ask whether ranking genes on some other summary of the association tests, such as unbiased estimates of trait importance, would better prioritize important genes. In Supplementary Appendix H, we discuss why these simple approaches fail at ranking by trait importance.

To see whether more-complex uses of association test results might result in ranking genes by importance, we considered a simplified model where a variant has an effect \(\beta \) on a gene that has effect \(\gamma \) on the trait. This results in the overall effect of the variant on the trait being \(\alpha =\beta \gamma \) (similar to the models in ref. [45]). Extensions of our results to the case where \(\alpha \) depends non-linearly on \(\beta \) (as in ref. [46]) and other caveats are discussed in Supplementary Appendix I.

As discussed above, estimating trait importance is most difficult for the most important genes due to flattening20,33,40. Flattening refers to the expected strength of association (equivalently, the expected contribution to heritability) first increasing as \({(\beta \gamma )}^{2}\) increases, but then becoming uncoupled from \({(\beta \gamma )}^{2}\) for sufficiently large \({(\beta \gamma )}^{2}\) (Fig. 5a and Supplementary Appendix F). This decoupling causes association studies to be incapable of prioritizing by trait importance, which we saw in LoF burden tests (Fig. 5b), in which contributions to heritability are poor predictors of \({s}_{\mathrm{het}}\), which we used there as a proxy for trait importance.

Fig. 5: Estimating trait importance by combining different variant types.

a, Theoretical expected contributions to heritability, \({h}^{2}\), as a function of the total effect of a variant on a trait \({\alpha }^{2}={(\beta \gamma )}^{2}\). The coloured lines are variants with different trait specificities. These functions can be approximately divided into a regime where variants contribute very little to heritability (black) or their contribution depends very little on \({(\beta \gamma )}^{2}\) (red). We denote the dividing line between these regimes by \(t\). b, Enrichment of LoF burden test heritability for genes binned by \({s}_{\mathrm{het}}\). The plotted heritability enrichment is a normalized inverse variance-weighted average of heritability enrichments across 27 genetically uncorrelated traits (Methods). Each bin contains approximately 184 genes. The trend line was fit using LOESS. Pearson’s \(r\) between heritability enrichment and \({s}_{\mathrm{het}}\) across the 25 highest shet bins = −0.337, P = 0.099. c, Schematic of how the contribution of a variant to heritability depends on the \({\gamma }^{2}\) of the gene through which it acts. The green boxes represent cis-regulatory regions. d, Similar to panel b but estimated using GWAS results instead of LoF burden test results. Per-trait estimates were obtained using AMM[47], and we plotted a normalized inverse variance-weighted average across the same traits as in panel b (Methods). Pearson’s \(r\) between heritability enrichment and \({s}_{\mathrm{het}}\) across the 25 highest \({s}_{\mathrm{het}}\) bins = 0.832, P = 2.57 × 10−7.

Yet, flattening does not affect all variants equally. For simplicity, imagine that variants either contribute minimally to heritability, or if their effect is greater than some threshold, \(t\), then they contribute an amount independent of their importance (Fig. 5a). Now, imagine two genes: one has a large effect on the trait (large \(\gamma \)), and one has a small effect (small \(\gamma \)). Even variants that weakly perturb the large \(\gamma \) gene will contribute to heritability, whereas for the small \(\gamma \) gene, only variants with very large \(\beta \) will contribute to heritability (Fig. 5c). Each individual variant experiences flattening, but collectively there will be more variants that contribute to heritability for more trait-important genes, all else being equal. As a result, the total heritability contributed by variants acting on a given gene should correlate with its trait imporance.

To test this, we used AMM[47], which estimates the total heritability of variants acting via a given set of genes using GWAS data (Methods). We found that compared with LoF burden heritability, this measure of total heritability better tracks \({s}_{\mathrm{het}}\) and hence trait importance (Fig. 5d). The results of this analysis do not rely on using AMM: similar results hold whenever aggregating signals across variants with different β (for example, Supplementary Figs. 42, 49 and 50; Methods; see ref. [48] for an example using missense variants). Furthermore, this argument does not depend on the exact details of flattening. Our argument only requires that contributions to heritability generally increase with effect size but eventually plateau, which is sensible under any model where selection acts more strongly on larger effect variants[49].

Discussion

It is often stated that GWAS and burden tests converge on similar gene sets19,21,50,51. Indeed, some genes are implicated by both approaches, such as LDLR for low-density lipoprotein levels20,52. Generally, GWAS loci are enriched near burden genes and, conversely, burden genes — as well as genes identified in familial studies of Mendelian counterparts of the same traits — are enriched within GWAS loci4,53.

Here, we found that, despite this overall concordance, LoF burden tests and GWAS rank genes differently. Our analysis shows that LoF burden tests prioritize long, trait-specific genes, whereas GWAS prioritize genes near trait-specific variants that have drifted to unexpectedly high frequencies. Because context-specific variants can be trait specific even if they act on pleiotropic genes, GWAS can prioritize trait-relevant, pleiotropic genes, unlike LoF burden tests. This explains why burden tests often appear less polygenic than GWAS and tend to prioritize genes that are seemingly more directly related to trait biology[20].

We found numerous GWAS loci with essentially no LoF burden signal, suggesting that context-specific variants acting on highly pleiotropic genes are major drivers of complex traits. We hypothesize that some of these genes have developmental roles, and these context-specific variants perturb developmental trajectories in a trait-specific manner[54].

Although both study designs can only discover sufficiently trait-important genes, neither directly ranks genes by trait importance. LoF burden tests estimate trait importance, but selection causes estimation noise to increase with gene effect size, making rankings by significance nearly independent of trait importance. Gene length is also a major confounder. Although larger sample sizes will help to reduce noise, we anticipate that Bayesian frameworks using priors based on gene features35,55 could be particularly effective for improving the accuracy of burden tests.

In GWAS, genetic drift makes the P values of individual variants essentially arbitrary as long as the variants are sufficiently trait specific and important. This makes variant-level ranking of GWAS loci inefficient for identifying top genes. Instead, genes can be prioritized by trait importance using non-standard GWAS approaches that aggregate signals across multiple variants47,48,56,57, motivating further development of such methods.

Our findings also explain why GWAS results are highly effective for identifying trait-relevant tissues and cell types using approaches such as S-LDSC[9]. Variants that are only active in trait-relevant cell types are much more likely to be trait specific, and thus contribute more to heritability. This is not necessarily because such variants have larger effect sizes, but rather because all else being equal they are less constrained.

The question of how genes should ideally be prioritized is surprisingly understudied. Here we propose ranking genes based on either trait importance or trait specificity. Both concepts capture different aspects of what it means for a gene to be ‘relevant’ for a trait.

Which criterion should be used depends on the situation. For example, trait-specific genes may be better drug targets due to reduced side effects, perhaps explaining why LoF burden evidence is more predictive of drug trial success than GWAS evidence[58]. Yet, if pleiotropic genes can be targeted in a context-specific way, targeting the most trait-important genes may be more clinically impactful. In addition, the effects of pleiotropic genes in knockout experimental systems may differ fundamentally from the phenotypic consequences of regulatory variants identified in GWAS.

The fact that LoF burden tests and GWAS prioritize different genes is a blessing: both are useful, and both reveal different aspects of trait biology. However, it is important to understand what genes they prioritize and why. Our results make clear that both association study designs will be important in future efforts to map the genetic underpinnings of complex traits.

Methods

GWAS summary statistics

GWAS summary statistics for 305 continuous traits were downloaded from the Neale Lab (http://www.nealelab.is/uk-biobank/; v3). These regressions were run on inverse rank normal-transformed phenotypes in a subset of the UK Biobank consisting of approximately 360,000 individuals and included age, age^2, inferred sex, age × inferred sex, age^2 × inferred sex and principal components 1–20 as covariates. We used 5 × 10−8 as the threshold for genome-wide significance unless otherwise stated.

LoF burden test summary statistics

Summary statistics for 292 LoF burden tests were downloaded from Backman et al.[4]. Two-hundred and nine traits overlapped with traits for which we had GWAS summary data (Supplementary Table 1). Burden genotypes were calculated by calling individuals homozygous for the non-LoF variant at all sites as being homozygous non-LoF, calling individuals homozygous for the LoF allele at any site as being homozygous LoF, and calling all other individuals heterozygotes. Burden tests were run using REGENIE[59] on inverse rank normal-transformed phenotypes. For our primary analyses, we used the result of the burden test with mask M1, which only includes variants that are predicted as being LoFs using the most stringent filtering criteria and an allele frequency upper bound of 1%. For analyses including missense variants, we used mask M3, which also includes ‘likely damaging’ missense variants, again upper bounding the frequency of included variants at 1% (see ref. [4] for more details). We used a per-trait genome-wide significance threshold of 2.7 × 10−6, derived by applying a Bonferroni correction to a significance threshold of 0.05 for testing approximately 18,000 genes per trait.

A subset of genetically uncorrelated traits

The set of 209 quantitative traits included some that were highly correlated, such as sitting height and standing height. For certain analyses, we selected a subset of 27 traits that were not highly correlated by intersecting the 209 traits with those analysed by Mostafavi et al.[45] (Supplementary Table 1). In brief, the trait list was pruned to ensure that all pairwise genetic correlations, as reported by the Neale laboratory, were below 0.5, prioritizing traits with higher heritability. Biomarkers were excluded from this subset because their genetic correlations with other traits were not provided by the Neale laboratory. Genetic and phenotypic correlations between these 27 traits as reported by the Neale laboratory are listed in Supplementary Table 2. Genetic correlations ranged between −0.3096 and 0.2742. Phenotypic correlations for eight trait pairs were missing from the Neale laboratory (all including the trait ‘heel quantitative ultrasound index, direct entry’). The remaining phenotypic correlations ranged between −0.2117 and 0.1972.

We used this subset of traits to ensure that our results (Figs. 3b–d and 4b,c and Extended Data Figs. 1a–c and 3a–c) were not driven by many correlated phenotypes all sharing the same underlying biology. As such, slight correlations between these phenotypes should not substantively affect our results or interpretations.

Defining GWAS loci

For a systematic comparison of discoveries between GWAS and burden tests (shown in Fig. 1c,d), we grouped GWAS variants into large, non-overlapping genomic loci. This approach avoids multiple counting of the same GWAS genes, as nearby hits within a locus may map to the same gene, and it provides a conservative estimate of the overlap between GWAS and burden test results as described below.

We focused on 151 quantitative traits with at least one burden test hit and one GWAS hit. For each trait, we analysed the set of LD-clumped hits (\(P < 5\times {10}^{-8}\), clumping \({r}^{2} < 0.1\)) from 8,136,100 filtered SNPs provided by Mostafavi et al.[45]. A secondary analysis (Supplementary Figs. 11–13) used the same LD-clumping pipeline but with a stricter threshold of \({r}^{2} < 0.01\).

For each trait, we began the grouping procedure with the most significant hit and iteratively processed all hits until they were assigned to a locus. For each hit, we included all independent hits with larger P values (lower significance) within 1 Mb to form a locus. The locus size was then expanded to ensure that no other hit was within 1 Mb of any variant already included in the locus. After completing one locus, we moved on to the next most significant hit that had not yet been assigned to any locus. Finally, we assigned overlapping genes to each locus, focusing on the 18,524 protein-coding genes analysed in the LoF burden test.

In Fig. 1c, we show the ranking of genome-wide significant genes from the burden test and the ranking of their corresponding GWAS loci, based on the P value of the most significant GWAS variant within each locus. In Fig. 1d, we plot the P value of the most significant GWAS variant within each locus on the x axis and the P value of the most significant gene from the burden test within the same locus on the y axis.

In a subset of analyses, we included only the top GWAS loci to match the statistical power of the burden test for gene discovery. We illustrate our procedure with the example of standing height. The LoF burden test for standing height identified 82 significant genes (\(P < 2.7\times {10}^{-6}\), to account for the 18,524 genes tested). The GWAS analysis identified 3,374 nearly independent hits. Following the grouping procedure outlined above, these hits were consolidated into 382 loci (median size of 3.2 Mb). We ranked these loci by the minimum P value within each locus. Starting with the top-ranked locus, we iteratively added GWAS loci until we selected 82 genes. From each locus, we selected all genes that were significant in the LoF burden test. If no such genes existed, we selected the gene with the smallest burden test P value.

This procedure ensures that our analysis of the overlap between burden test and GWAS discoveries is conservative. The overestimation arises first from prioritizing genes based on burden test P values and second from using large GWAS loci, which may contain more than one causal gene, thereby increasing the likelihood of overlap with burden test results.

Comparing GWAS and LoF burden tests in LD blocks

To avoid exacerbating dissimilarities between LoF burden tests and GWAS caused by mislocalization of GWAS signals, we also performed analyses at the LD block level. We downloaded bed files containing the coordinates of approximately independent LD blocks from ref. [60]. For each trait, we computed the minimum GWAS P value of variants within each block and compared that with the minimum LoF burden test P value for all genes that overlapped any part of that block. In a small number of cases, the smallest LoF burden test P value in two adjacent blocks would be the same because a single highly significant gene overlapped both blocks. This generally reduced the correlation between the minimum P values of GWAS and LoF burden tests, and so we dropped all such blocks to be conservative.

Ranking loci with conditionally independent hits

To evaluate the robustness of GWAS locus ranking, we also performed SNP selection using COJO[61] instead of LD clumping. Starting from the set of 8,136,100 filtered SNPs (described above), we ran COJO for each trait using the --cojo-p 5e-8 and --cojo-slct options. The output is a set of conditionally independent SNPs along with their co-estimated effect sizes, which we used to define GWAS loci as described above for LD-clumped SNPs. Results based on this alternative approach are presented in Supplementary Figs. 8–10.

Ranking genes in GWAS by MAGMA P value

We used MAGMA[57] to obtain gene-level P values from GWAS data. We generally followed the suggestions from ref. [62]. In brief, as recommended by PoPS[62], we assigned SNPs to genes using magma --annotate with the default settings, which only uses SNPs inside of gene bodies. These were then combined using the 1000 Genomes EUR[63] LD panel distributed with MAGMA to obtain gene-level P values using the --gene-model snp-wise=mean option.

Ranking genes in GWAS by PoPS score

We also obtained gene-level scores using PoPS[62]. PoPS uses gene features to predict the gene-level scores produced by MAGMA. We used the MAGMA results as described above, and then downloaded approximately 40,000 features derived from gene expression datasets from GitHub (https://github.com/FinucaneLab/gene_features) as listed as the source of data in ref. [62]. Additional features from protein–protein interaction networks and pathway membership are described in ref. [62], but only the expression features were available in the GitHub repository. Furthermore, there was a bug where features from different datasets had the same name causing PoPS to crash. We used a custom script to provide a unique name to each feature provided with PoPS. We then used PoPS with the default setting and used the output PoPS_Score from the resulting *.pred files to rank genes.

Simulating GWAS in smaller samples

To simulate the results of performing smaller GWAS, we sampled summary statistics in a subsample of size \(n\), \({\hat{\alpha }}^{\mathrm{sub}},\) conditioned on summary statistics in the full sample of size \(N\), \({\hat{\alpha }}^{{\rm{full}}}\), as

$${\hat{\alpha }}^{\mathrm{sub}}{\rm{| }}{\hat{\alpha }}^{\mathrm{full}}\sim \mathrm{Normal}\,\left({\hat{\alpha }}^{\mathrm{full}},\frac{N-n}{n}{SE}{({\hat{\alpha }}^{\mathrm{full}})}^{2}\right)$$

(1)

and inflated the standard errors by a factor of \(\sqrt{N/n}\). For a derivation see ref. [64], supplementary material section 3.2. We performed the sampling independently across 8,136,100 filtered SNPs (described above) that passed our filtering. This independent sampling alters the LD structure between linked SNPs, but as we use these statistics in analyses that depend only on the most significant SNP within a locus, this effect on LD should be inconsequential for our qualitative conclusions.

Ranking GWAS loci using MAF thresholds

For Supplementary Figs. 26–28, we considered all 8,136,100 filtered SNPs (described above). We then filtered to only those SNPs equal to or below a given MAF threshold (0.01, 0.1 or 0.5, which includes all SNPs). We then constructed GWAS loci as described above and ranked loci by the minimum P value in that locus.

Ranking loci by largest significant effect size

For Supplementary Figs. 29–31, we again considered all 8,136,100 filtered SNPs (described above). We then constructed GWAS loci as described above but ranked loci by taking the largest absolute effect size among the genome-wide significant SNPs in each locus. We ranked loci for burden tests by taking the largest absolute effect size among all genome-wide significant genes that overlapped the locus.

Association study model

We combined population genetics and statistical genetics models to understand how natural selection affects variants based on their trait specificity and trait importance. Our model assumes that traits are under stabilizing selection based on prevailing hypotheses36,38,39 and uses standard population genetics theory33,65,66,67,68. The details of our model are outlined in the Supplementary Appendices.

Unbiased estimates of trait importance

In several analyses we require estimates of trait importance, either \({\alpha }^{2}\) from GWAS or \({\gamma }^{2}\) from LoF burden tests. The details in both cases are identical, so here we describe \({\gamma }^{2}\). The naive estimator of squaring the LoF burden test estimated effect size, \({(\hat{\gamma })}^{2}\), is biased. Worse, this bias is anticorrelated with the frequency of the variant, which results in spurious correlations between the biased estimates and various gene properties such as \({s}_{\mathrm{het}}\).

To derive an unbiased estimator, we appealed to standard statistical genetics theory[69] to assume that LoF burden estimates are approximately normally distributed about their true values with noise dependent on their standard errors. In particular, for a gene with standard error \(s\) and effect size estimate \(\hat{\gamma }\), we have that \(\hat{\gamma }\sim \mathrm{Normal}(\gamma ,{s}^{2})\) approximately. This approximation is widely used for GWAS and was recently confirmed to be accurate for LoF burden tests[46]. It is then a routine calculation to check that \({(\hat{\gamma })}^{2}-{s}^{2}\) is an unbiased estimator of \({\gamma }^{2}\).

Although we focus exclusively on quantitative phenotypes in this study, we note that this approximation may not be valid for effect size estimates from logistic regression applied to case–control data.

LoF burden summary statistics binned by \({{\boldsymbol{s}}}_{{\bf{het}}}\)

When comparing LoF burden summary statistics (standard errors, \({z}^{2}\), and unbiased estimates of \({\gamma }^{2}\)) to \({s}_{\mathrm{het}}\), we used \({s}_{\mathrm{het}}\) values inferred in ref. [35] and downloaded from ref. [70]. We binned genes by \({s}_{\mathrm{het}}\) into 100 bins, each with approximately 184 genes. Within each bin, we averaged the respective summary statistics (for example, unbiased estimate of \({\gamma }^{2}\)) across traits and genes. To make sure that our results were not driven by redundant traits, we used our 27 genetically uncorrelated traits for these analyses. For heritability enrichment (Fig. 5b), we used the fact that heritability should be proportional to \({z}^{2}-1\) (Supplementary Appendix B). Within each bin of genes, we then computed the average \({z}^{2}-1\) in that bin relative to the average of \({z}^{2}-1\) across all genes for each trait. This produced a trait-level enrichment for each bin, and by using the empirical standard deviation of the relative \({z}^{2}-1\) within the bin, we could also obtain an empirical standard error for the enrichment. We then obtained an overall enrichment for each bin, by taking an inverse-variance weighted average across traits. After this averaging, the mean heritability enrichment across genes need not be one. As such, we renormalized the estimates to average to one.

ATAC peak specificity

We downloaded all ATAC-seq files from ChIP-Atlas[71] that contained more than 5,000,000 mapped reads and identified at least 5,000 peaks. Across all files, overlapping peaks were combined using bedtools merge[72]. This yielded a total of 2,131,526 peaks. Samples other than blood samples were grouped into 17 tissues based on their annotations in ChIP-Atlas: adipocyte (146 samples), bone (190 samples), breast (815 samples), cardiovascular (559 samples), digestive (417 samples), epidermis (661 samples), gonad (138 samples), kidney (375 samples), liver (191 samples), lung (1,679 samples), muscle (118 samples), neural (1,349 samples), pancreas (322 samples), placenta (48 samples), pluripotent (1,895 samples), prostate (312 samples) and uterus (255 samples). In addition, samples with any of the following annotations were categorized as T cell (1,356 samples): CD4+ T lymphocytes, CD4+ T cells, CD8+ T lymphocytes, CD8+ T cells, fetal naive T cells, γδ T cells, naive T cells, T cells, chimeric antigen receptor T cells, follicular helper T cells, helper 0 T (TH0) cells, TH17 cells, TH1 cells, TH2 cells, TH9 cells or T lymphocytes. Samples with any of the following annotations were categorized as erythroid (102 samples): erythroid progenitors, erythroid cells or erythroblasts. Ultimately, this resulted in 19 tissues or cell-type categories.

A peak was considered to be present in a tissue if more than 5% of samples contained the peak. In downstream analyses, we used both the ‘number of shared tissues’ and ‘peak intensity’. We calculated the number of shared tissues by considering all peaks in the relevant tissue for a given trait (for example, bone for height) and then counting the number of tissues in which that peak was present. In particular, we only considered peaks that are present in the relevant tissue. We calculated peak intensity as the fraction of samples within the focal tissue that contain the peak.

Gene expression specificity

We compiled estimates of gene expression in 17 tissue or cell types, which were intended to overlap with the categorization of ATAC-seq peaks when possible. All tissues that were ultimately matched to traits (see below) were included in both our ATAC-seq tissues and our expression tissues, but there are some differences between the remaining tissues. Average gene expression transcripts per million (TPM) of the following tissues were downloaded and extracted from the Human Protein Atlas[73] tissue gene data (rna_tissue_hpa.tsv.zip): adipose tissue, breast, heart muscle, colon, skin, ovary, kidney, liver, lung, skeletal muscle, amygdala, pancreas, placenta and prostate. Average gene expression TPM of the following cell types were downloaded and extracted from the Human Protein Atlas single-cell type data (rna_single_cell_type.tsv.zip): erythroid cells and T cells. Average gene expression TPM of human bone samples was downloaded from the Gene Expression Omnibus[74] accession GSE106292 (refs. 75,76).

In each tissue, genes with more than 10 TPM were considered to be ‘expressed’. We then restricted our analyses to genes expressed in the trait-relevant tissue. We computed an expression specificity score by taking the expression level in TPM in the trait-relevant tissue divided by the sum of expression levels across all 17 tissues. This provided an expression specificity score for every gene expressed in the trait-relevant cell type. For analyses involving expression specificity bins, we took all of these expression specificity scores across all nine trait–tissue pairs, computed quintiles and then assigned each gene for a given trait–tissue pair to its quintile.

Linking traits to tissues

To identify which tissue (or cell type) is predominantly associated with a given trait, we ran S-LDSC9,41 to partition the heritability of all of our traits that had an estimated heritability of more than 0.04. We used annotations for 19 tissues and cell types constructed from our ATAC-seq analysis described above, along with the LDSC baseline v1.1 covariates. Our aim was to identify trait–tissue pairs in which heritability could clearly be explained by one tissue as opposed to multiple tissues. As such, we only retained traits that had a tissue with an LDSC τ with a z-score of more than 4.5 and had more than 40% of their heritability explained by variants in ATAC-seq peaks of the corresponding tissue. If more than one trait was assigned to the same tissue, we only kept genetically uncorrelated traits (\({r}^{2} < 0.04\)). This resulted in nine trait–tissue pairs (Supplementary Table 1): mean corpuscular volume (30040_irnt) → erythroid, reticulocyte percentage (30240_irnt) → erythroid, eosinophil percentage (30210_irnt) → T cell, lymphocyte count (30120_irnt) → T cell, standing height (50_irnt) → bone, heel bone mineral density (3148_irnt) → bone, glucose (30740_irnt) → pancreas, creatinine (30700_irnt) → liver, and alanine aminotransferase (30620_irnt) → liver.

Regression of burden z 2 on expression specificity

For each of the nine trait–tissue pairs described above, we performed a linear regression of the burden \({z}^{2}\) for all genes expressed in the top tissue on the expression specificity of genes, binned into quintiles as described earlier. We included the unbiased estimates of the trait importance of genes (defined above) as a covariate. For each specificity bin, we calculated an inverse-variance weighted average of the regression coefficients across all nine traits, with standard errors computed as the square root of the reciprocal of the total weight. The results, shown in Supplementary Fig. 36, demonstrate that the burden test prioritization of specifically expressed genes in Fig. 3e is not driven by differences in the importance of genes across specificity bins.

S-LDSC analysis using ATAC-seq peaks

For each trait–tissue pair, we ran S-LDSC9,41 to estimate the heritability enrichment of tissue-specific ATAC-seq peaks. To this end, we categorized ATAC-seq peaks present in each tissue into five bins based on their presence in other tissues: present in 1–2 tissues, present in 3–8 tissues, present in 9–15 tissues, present in 16–18 tissues and present in all 19 tissues. Also, we categorized ATAC-seq peaks present in each tissue into five bins based on their intensity. The size of these bins were set to match the sizes of the tissue-specificity-based bins. We included the annotations based on ATAC peak tissue specificity and peak intensity bins with the LDSC baseline v1.1 model and used S-LDSC v.1.0.1 on HapMap3 SNPs[77]. In all analyses, we report \(\tau /{h}^{2}\), which represents the change in the proportion of heritability explained by a single variant caused by toggling the annotation of that variant from 0 to 1 while keeping all other covariates included in the regression fixed.

S-LDSC analysis using coding variants

We downloaded the variant annotation file (variants.tsv.bgz) from the Neale laboratory website (http://www.nealelab.is/uk-biobank/). We used the consequence information in the file, which corresponds to Ensembl Variant Effect Predictor (v85)[78], for annotating variants. Specifically, we classified variants as being coding if their most severe consequence was any of:

- splice_donor_5th_base_variant

- missense_variant

- splice_region_variant

- splice_acceptor_variant

- splice_donor_variant

- splice_donor_region_variant

- stop_gained

- start_lost

- stop_lost

- frameshift_variant

- inframe_insertion

- protein_altering_variant

For each trait–tissue pair, we ran S-LDSC9,41 to estimate the heritability enrichment of coding variants as a function of expression specificity. We included the expression specificity bin (as defined above) as an annotation in the S-LDSC model. We also categorized genes into five equally sized bins based on their expression level in the tissue of interest, and all the coding variants were categorized into one of these five bins based on the expression level of the corresponding genes. These annotations were also included in the S-LDSC model. In addition, we used the covariates in the baseline v1.1 model and restricted our analysis to HapMap3 SNPs[77]. All analyses were run with S-LDSC v.1.0.1. As described above, we always report \(\tau /{h}^{2}\).

LoF burden summary statistics binned by \({\boldsymbol{\mu }}{\boldsymbol{L}}\)

Analyses comparing LoF burden summary statistics to \(\mu L\) were performed analogously to the analyses comparing the summary statistics to \({s}_{\mathrm{het}}\). As a proxy for μL, we downloaded the expected number of segregating LoFs for each gene as calculated in gnomAD (v2)[79] from ref. [70]. To show that \(\mu L\) is essentially driven by coding DNA sequence (CDS) length, we downloaded CDS lengths for MANE select canonical transcripts (genome build GRCh38) from Ensmbl[80] and correlated them with the expected number of segregating LoFs from gnomAD[79] (Supplementary Fig. 44).

Computing frequency spectra given \({{\boldsymbol{s}}}_{{\bf{het}}}\)

To simulate under our model, we required the distribution of allele frequencies for a given selection coefficient. We assumed a stabilizing selection model, which is approximately equivalent to homozygotes having a relative fitness of 1 and heterozygotes having a fitness of 1-shet33,65,66,67. We used fastDTWF[81] to compute likelihoods under this model. We assumed an equilibrium population of 20,000 diploids, and computed allele frequency distributions along a grid of 50 shet values from 10−7 to 0.05 evenly spaced on the log scale. We used 1.25 × 10−8 as the per-generation mutation rate. We considered a model where the ancestral allele is known by using the no_fix=True option in fastDTWF. In addition, fastDTWF has two parameters that control the accuracy of its approximation. On the basis of the recommendations of ref. [81], we set dtwf_tv_sd to 0.1 and dtwf_row_eps to 10−8.

Simulating realized heritability

To generate Extended Data Fig. 2b, we simulated 50,000 unlinked variants from our stabilizing selection model. We considered 1,000 values of \({s}_{\mathrm{het}}\) log-uniformly spaced between 10−7 and 2.3 × 10−4. For each value of \({s}_{\mathrm{het}}\), we then simulated 50 variants by drawing 50 allele frequencies from the allele frequency distributions that we computed as described above. To model the slight differences between population and GWAS sample allele frequencies, we then drew a GWAS sample allele count for each variant as a \(\mathrm{Binomial}(\mathrm{600,000},f)\) random variable, where \(f\) was the population frequency, and 600,000 was chosen to match the roughly 300,000 diploids in the UK Biobank. These allele counts were then normalized to obtain GWAS sample allele frequencies, \(\widetilde{f}\). For this simulation, we assumed that all variants have the same trait specificity. This makes \({\alpha }^{2}\) on the focal trait proportional to \({s}_{\mathrm{het}}\), so we set the realized heritability to \(2{s}_{\mathrm{het}}\,\widetilde{f}\,(1-\widetilde{f})\), and normalized all results relative to the maximum simulated realized heritability. Likewise, effect sizes were reported relative to the maximum simulated effect size.

Computing pleiotropy of GWAS hits

To investigate the pleiotropy of top versus weak GWAS hits, we considered all of the 27 uncorrelated traits that had at least 100 GWAS hits, leaving 18 traits. For each trait, we grouped the hits into four quartiles based on variant P values, with quartile 1 containing the most statistically significant hits and quartile 4 containing the least. For each hit, we calculated the number of traits (out of 18) in which the variant was a hit and computed the mean values within each quartile.

Simulating pleiotropy of GWAS hits

To simulate the effects of genetic drift on the apparent pleiotropy of GWAS hits, we simulated GWAS summary statistics. To match the real data described above, we considered 18 traits and simulated effect sizes for 10 million not necessarily segregating positions. We simulated the effect sizes independently for each position, and drew the vector of squared effect sizes for variant j,\({\vec{{\alpha }^{2}}}_{j}\in {{\mathbb{R}}}^{18}\) as

$${\vec{{\alpha }^{2}}}_{j}\sim \frac{{10}^{-7}}{f}\times \exp \{3f\times \mathrm{Normal}({\bf{0}},p{\bf{I}}+(1-p){{\bf{11}}}^{T})\}$$

where the exponentiation is performed element-wise, and \(f\) and \(p\) are parameters that affect the range of different total effect sizes, \({{||}{\vec{{\alpha }^{2}}}_{j}{||}}_{1}\), and distribution of trait specificities.

We then assumed that the strength of selection against the variant was \({{||}{\vec{{\alpha }^{2}}}_{j}{||}}_{1}\). We obtained the MAF for each variant by drawing from the variant frequency distribution with the closest \({s}_{\mathrm{het}}\), computed as described above.

Finally, we simulated a GWAS by assuming that the observed association statistic for each trait was independently normally distributed about its true value. For example, for trait \(k\) and the variant at position \(j\) we have:

$${\hat{\alpha }}_{{jk}}\sim \mathrm{Normal}\,\left(\sqrt{{\vec{{\alpha }^{2}}}_{{jk}}},\frac{1}{\sqrt{2{N}_{\mathrm{eff}}\times \mathrm{MA}{{\rm{F}}}_{j}(1-\mathrm{MA}{{\rm{F}}}_{j}))}}\right)$$

where \({N}_{{\rm{eff}}}\) is a scaling factor that captures both the amount of environmental noise contributing to the trait as well as the sample size. We converted these to P values by taking \(2{N}_{{\rm{eff}}}{{\rm{MAF}}}_{j}(1-{{\rm{MAF}}}_{j}){\hat{\alpha }}_{{jk}}^{2}\) as a squared z-score, which is chi-squared distributed with 1 degree of freedom under the null. We considered a variant to be a genome-wide significant hit if its P value was smaller than a parameter, \(t\).

This simulation approach has four free parameters. In the main text, we used \(f=0.33\), \(p=0.5\), \({N}_{\mathrm{eff}}=10000000\) and \(t={10}^{-5}\). Although these parameters are related to standard GWAS parameters (for example, the GWAS sample size or genome-wide significance threshold), the exact quantitative relationship should not be overanalysed. For example, we assumed that the strength of selection is exactly \({{||}{\vec{{\alpha }^{2}}}_{j}{||}}_{1}\). If instead there was some scaling factor, that could be absorbed into \({N}_{{\rm{eff}}}\). Similarly, there is a qualitative inverse relationship between the effects of \(t\) and \({N}_{{\rm{eff}}}\) (for example, lower \(t\) has a similar effect to increasing \({N}_{{\rm{eff}}}\)), making the exact setting of either parameter somewhat arbitrary. We chose the values that we used here to roughly match the distribution of selection coefficients inferred from real GWAS data[82], as well as the observed patterns of MAF and pleiotropy in the UK Biobank GWAS results. In Supplementary Figs. 45–48, we vary each of \({N}_{{\rm{eff}}}\), \(t\), \(p\) and \(f\), respectively, while holding the others fixed to show that our qualitative results are not sensitive to the particular simulation parameters that we chose.

AMM analysis

We ran AMM[47] to estimate heritability enrichments for gene sets, following the workflow previously described (https://github.com/danjweiner/AMM21; commit 524c620). We binned genes into 100 approximately equally sized bins based on \({s}_{\mathrm{het}}\) as described above and used these bins as our gene sets. AMM requires an estimate of the probability that a SNP is acting via the closest gene, second closest gene, and so on. For more distant genes, there is insufficient power to estimate these probabilities so AMM recommends combining these into bins. We followed the recommended binning, and then used the probabilities estimated in the original AMM paper[47] (supplementary table 5 of ref. [47]). AMM recommends using LDSC baseline covariates in all models, for which we used v2.3. We restricted our analysis to HapMap3 variants. The results in Fig. 5d are the inverse-variance weighted average of the heritability enrichment estimates across our 27 genetically uncorrelated traits. These inverse-variance weighted estimates of the average enrichments do not necessarily need to average to one in contrast to the true enrichments. As such, we renormalized the estimated enrichments so that they sum to one.

Correlation of GWAS hit probability and \({{\boldsymbol{s}}}_{{\bf{het}}}\)

We analysed the GWAS hits curated in our previous study[45], filtered to a set of 6,971,256 SNPs that passed quality control procedures. This set excluded lead GWAS SNPs in LD (\({r}^{2} > 0.8\)) with variants predicted to have protein-altering consequences, to condition on putatively non-coding trait associations. We focused on 15,591 approximately independent GWAS hits associated with our 27 uncorrelated traits, for which estimates of shet for the nearest gene were available. We performed logistic regression to differentiate GWAS hits from 100,000 SNPs randomly sampled from the same 6,971,256 SNP set. The shet values of the nearest genes were used as the predictor, categorized into 100 percentile bins. As in our previous work, the regression model included additional covariates: MAF, LD score, gene density and the absolute distance to the nearest transcription start site. We also incorporated dummy variables representing 20 quantiles of each of these covariates (MAF, LD score, gene density and distance to the transcription start site). Results of this analysis are presented in Supplementary Fig. 49. The covariate data were obtained from Mostafavi et al.[45].

Correlation of \({\hat{{\boldsymbol{\gamma }}}}^{{\bf{2}}}\) and number of GWAS hits

To avoid double counting GWAS hits due to LD, we restricted our analysis to approximately independent hits. For each trait, we analysed the set of LD-clumped hits (\(P < 5\times {10}^{-8}\), clumping \({r}^{2} < 0.1\)) from 8,136,100 filtered SNPs provided in ref. [45]. We then assigned each GWAS hit to the closest gene (using the midpoint of genes as released with AMM[47]). For each trait, we then correlated the number of GWAS hits assigned to each gene with our unbiased estimate of the trait importance of that gene, \({\hat{\gamma }}^{2}\), based on the LoF burden test results. To make certain that our results were not driven by differences between genes with no GWAS signal versus genes with any GWAS signal, we also computed correlations between the number of GWAS hits assigned to each gene and \({\hat{\gamma }}^{2}\) restricting to genes with at least one GWAS hit.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

All data for reproducing the figures are provided on GitHub (https://github.com/jeffspence/specificity_length_luck).

Code availability

The scripts for reproducing the figures and simulations are provided on GitHub (https://github.com/jeffspence/specificity_length_luck).

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Acknowledgements

We thank T. Capellini, E. Dann, M. Przeworski, D. Richard, C. Tcheandjieu, H. Zhu and the members of the Pritchard laboratory for feedback. This work was supported by the US National Institutes of Health (grants R01HG011432, R01HG008140, R01HG014005 and U01HG012069 to J.K.P., R01GM115889 to G.S. and R35GM157134 to H.M.).

Author information

Author notes

1. These authors contributed equally: Jeffrey P. Spence, Hakhamanesh Mostafavi

Authors and Affiliations

1. Department of Genetics, Stanford University, Stanford, CA, USA

Jeffrey P. Spence, Hakhamanesh Mostafavi, Mineto Ota, Nikhil Milind, Tamara Gjorgjieva, Courtney J. Smith, Yuval B. Simons & Jonathan K. Pritchard

2. Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA

Jeffrey P. Spence

3. Department of Epidemiology & Biostatistics, University of California, San Francisco, San Francisco, CA, USA

Jeffrey P. Spence

4. Center for Human Genetics and Genomics, New York University School of Medicine, New York, NY, USA

Hakhamanesh Mostafavi

5. Department of Population Health, New York University School of Medicine, New York, NY, USA

Hakhamanesh Mostafavi

6. Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan

Mineto Ota

7. Section of Genetic Medicine, University of Chicago, Chicago, IL, USA

Yuval B. Simons

8. Department of Human Genetics, University of Chicago, Chicago, IL, USA

Yuval B. Simons

9. Department of Biological Sciences, Columbia University, New York, NY, USA

Guy Sella

10. Program for Mathematical Genomics, Columbia University, New York, NY, USA

Guy Sella

11. Department of Biology, Stanford University, Stanford, CA, USA

Jonathan K. Pritchard

Contributions

J.P.S. conceptualized the study, undertook the methodology, performed the analysis, and wrote the original draft of and reviewed and edited the manuscript. H.M. conceptualized the study, undertook the methodology, performed the analysis, and wrote the original draft of and reviewed and edited the manuscript. M.O. undertook the methodology, performed the analysis, and reviewed and edited the manuscript. N.M. performed the analysis, and reviewed and edited the manuscript. T.G. performed the analysis, and reviewed and edited the manuscript. C.J.S. reviewed and edited the manuscript. Y.B.S. conceptualized the study, and reviewed and edited the manuscript. G.S. conceptualized and supervised the study, and reviewed and edited the manuscript. J.K.P. conceptualized and supervised the study, and wrote the original draft of and reviewed and edited the manuscript.

Corresponding authors

Correspondence to Jeffrey P. Spence, Hakhamanesh Mostafavi, Mineto Ota or Jonathan K. Pritchard.

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Extended data figures and tables

Extended Data Fig. 1 Coding sequence length drives prioritization in LoF burden tests.

A) Average of an unbiased estimate of the squared trait importance, \({\gamma }^{2}\), across 27 genetically uncorrelated traits, averaged within bins of approximately 184 genes binned by expected number of unique LoFs (Methods). The trend line was fit using LOESS. Pearson’s \(r\) between expected number of unique LoFs and an unbiased estimate of mean squared effect size across traits \(=\,0.017\), p-value \(=\,0.023\), \(N=\mathrm{18,344}\) genes. This analysis was repeated for B) the average of the squared LoF burden test standard errors within each bin (Spearman’s \(\rho \) between expected number of unique LoFs and mean standard error across traits \(=-\,0.255\), p-value \( < {10}^{-15}\), \(N=\mathrm{18,344}\) genes), and C) the average LoF burden test \({z}^{2}\) across traits within each bin (Pearson’s \(r\) between expected number of unique LoFs and mean \({z}^{2}\) across traits \(=\,0.112\), p-value \( < {10}^{-16}\), \(N=\mathrm{18,344}\) genes).

Extended Data Fig. 2 GWAS variant rankings are driven largely by genetic drift.

A) \(\mathrm{10,000}\) frequency trajectories of identical mutations simulated under the Discrete-Time Wright-Fisher model. Trajectories were simulated assuming no recurrent mutation, an \({s}_{\mathrm{het}}\) of \({10}^{-3}\), no fitness consequences in homozygotes, and a population size of \({N}_{e}=\mathrm{10,000}\). All mutations were assumed to arise \(\mathrm{1,000}\) generations before present. B) Simulations of realized heritability for individual variants with varying trait importances, scaled by the maximum simulated realized heritability. Spearman’s \(\rho =0.052\), p-value \(=\,0.348\) for \(N=330\) variants with a scaled squared effect >0.25.

Extended Data Fig. 3 Genetic drift makes GWAS hits appear more pleiotropic.

A) Schematic of the effects of minor allele frequency (MAF) and trait specificity on GWAS p-values. B) The relationship between MAF and p-value rank for \(N=100\) simulations and real data from \(N=\mathrm{18,879}\) uncorrelated genome-wide significant GWAS hits across genetically uncorrelated traits (Methods). Genome-wide significant hits were binned by p-value, and the mean MAF within each bin was compared to the overall mean MAF across all hits. Black points are results from UKB GWAS, and orange lines are simulations. This analysis was repeated for C) the mean trait specificity within each bin and D) the mean number of traits for which each hit was genome-wide significant. Panel C contains only simulations as the trait specificities for the UKB GWAS results are unknown.

Supplementary information

Supplementary Information

This file contains all Supplementary Figures, descriptions of the Supplementary Tables, and the Appendices. The document begins with a table of contents providing the location of each figure, table description, and appendix in the document. The Appendices contain mathematical details and secondary analyses that were performed as robustness checks.

Reporting Summary

Supplementary Table 1

List of traits and abbreviations used in the study. A table in CSV format of the 209 traits used in this study with the UKB trait IDs, trait names, abbreviations used, tissue to which each trait was linked (if applicable), and an indication of whether or not the trait was included in our subset of 27 genetically uncorrelated traits (Methods)

Supplementary Table 2

Genetic and phenotypic correlations between 27 “genetically uncorrelated traits”. A table in CSV format of the genetic correlation \({r}_{g}\) and phenotypic correlation between each pair of traits among the 27 genetically uncorrelated traits used throughout the paper. Phenotype IDs match those listed in Supplementary Table 1. Estimates were obtained from the Neale Lab (Methods), and phenotypic correlations are missing for 8 of 351 trait pairs

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Spence, J.P., Mostafavi, H., Ota, M. et al. Specificity, length and luck drive gene rankings in association studies. Nature 649, 918–925 (2026). https://doi.org/10.1038/s41586-025-09703-7

- Received: 20 December 2024

- Accepted: 03 October 2025

- Published: 05 November 2025

- Version of record: 05 November 2025

- Issue date: 22 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09703-7

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Mimicking opioid analgesia in cortical pain circuits

Corinna S. Oswell,

Sophie A. Rogers,

Justin G. James,

Nora M. McCall,

Alex I. Hsu,

Gregory J. Salimando,

Malaika Mahmood,

Lisa M. Wooldridge,

Meghan Wachira,

Adrienne Y. Jo,

Raquel Adaia Sandoval Ortega,

Jessica A. Wojick,

Katherine Beattie,

Sofia A. Farinas,

Samar N. Chehimi,

Amrith Rodrigues,

Jacqueline W. K. Wu,

Lindsay L. Ejoh,

Blake A. Kimmey,

Emily Lo,

Ghalia Azouz,

Jose J. Vasquez,

Matthew R. Banghart,

Kevin T. Beier,

Kate Townsend Creasy,

Richard C. Crist,

Charu Ramakrishnan,

Benjamin C. Reiner,

Karl Deisseroth,

Eric A. Yttri &

…

Gregory Corder

Nature volume 649, pages 938–947 (2026)

Abstract

The anterior cingulate cortex is a key brain region involved in the affective and motivational dimensions of pain, but how opioid analgesics modulate this cortical circuit remains unclear[1]. Uncovering how opioids alter nociceptive neural dynamics to produce pain relief is essential for developing safer and more targeted treatments for chronic pain. Here we show that a population of cingulate neurons encodes spontaneous pain-related behaviours and is selectively modulated by morphine. Using deep learning behavioural analyses combined with longitudinal neural recordings in mice, we identified a persistent shift in cortical activity patterns following nerve injury that reflects the emergence of an unpleasant, affective chronic pain state. Morphine reversed these neuropathic neural dynamics and reduced affective–motivational behaviours without altering sensory detection or reflexive responses, mirroring how opioids alleviate pain unpleasantness in humans. Leveraging these findings, we built a biologically inspired chemogenetic gene therapy that targets opioid-sensitive neurons in the cingulate using a synthetic μ-opioid receptor promoter to drive inhibition[2]. This opioid-mimetic chemogenetic gene therapy recapitulated the analgesic effects of morphine during chronic neuropathic pain, thereby offering a new strategy for precision pain management that targets a key nociceptive cortical opioid circuit with safe, on-demand analgesia.

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Main

Pain is a complex, aversive perception, fundamental to adaptive survival[1]. Understanding the neural circuits and cell types underlying the affective and motivational features of pain experiences is crucial for advancing precision therapeutics for individuals with chronic pain conditions3,4. Current analgesic drugs, such as opioids, act on widely expressed molecular targets that reduce pain unpleasantness but also promote serious and fatal side effects. By pinpointing specific neural circuits for pain-associated aversion, which intersect with the expression of the μ-opioid receptor (MOR)[5], the molecular target of morphine, a new class of effective analgesics can be developed that interfere with the unpleasantness of pain rather than pain sensation, with reduced addiction and respiratory depression side effects6,7.

Coordinated neural activity in the anterior cingulate cortex (ACC) is essential for encoding the emotional and motivational dimensions of pain8,9,10,11,12,13,14, guiding behavioural choices in real time and promoting future avoidance of harmful stimuli15,16. According to the gate control theory[17] and central control models15,18, pain perception is not a passive relay of nociceptive input but is actively shaped by spinal and brain circuits that integrate sensory, cognitive and emotional information to amplify or inhibit pain signals. Within this framework, the ACC has a central role in evaluating nociceptive input in relation to valence, context and internal state. This processing supports the selection of adaptive responses, such as escape and recuperative behaviours[19], which act as negative feedback to reduce further injury and promote healing. By tracking spontaneous pain-related behaviours and activity recordings in cortical neurons, we can infer the latent dynamic computations underlying pain. This approach provides a window into identifying single neurons and distributed ensembles in the ACC that represent the affective–motivational components of pain, offering candidate cellular targets for therapeutic strategies.

Perceived pain unpleasantness strongly correlates with functional magnetic resonance imaging activity in the human ACC9,19. Patients with intractable chronic pain treated with surgical cingulotomy lesions do not report changes in pain perception, intensity discrimination or reactions to momentary harmful stimuli20,21,22. Rather, their attitude towards pain is modified, dissociating the negative valence from the experience of pain. In these cases, pain becomes a sensation rather than a threat[23]. Similarly, in preclinical models, lesions24,25, opioids26,27,28,29 and optogenetic manipulation30,31,32,33,34,35,36 of ACC neural circuits attenuate aspects of the affective–motivational component of pain. Thus, ACC neural circuits that drive adaptive behaviour to acute noxious stimuli might also be maladaptive during chronic pain37,38. Acute pain engages MORs in the dorsal anterior cingulate and lateral prefrontal cortex, and the affective perception of pain is correlated with MOR availability in the ACC and other brain regions26,27,39,40,41. This evidence supports the role of MOR signalling within the ACC in altering the perception of pain[13]. Leveraging personalized deep-brain stimulation42,43,44 or cell-type-specific approaches45,46 to modulate neural activity in MOR-expressing neurons[2] may mimic the prefrontal cortical actions of opioid analgesia without the associated risks of off-target pharmacotherapies[1].

Mapping nociceptive and µ-opioidergic ACC neurons

Using nociceptive activity-dependent tagging (painTRAP) and immediate early gene (IEG) mapping (painFOS), we identified a ‘nociceptive hotspot’ approximately 700 μm in length located in the anterior ACC dorsal Cg1 and ventral Cg2 (Extended Data Fig. 1, Supplementary Table 1 (rows 48–49) and Supplementary Note 1). In situ hybridization revealed that approximately 30–50% of painFOS neurons and approximately 70% of Slc17a7+ glutamatergic neurons co-express Oprm1, indicating that a substantial fraction of the nociceptive hotspot expresses MORs (Extended Data Fig. 2 and Supplementary Note 1). Single-nucleus RNA sequencing of this ACC nociceptive hotspot, before and after the development of chronic neuropathic pain using the spared nerve injury (SNI) model, resolved 23 cell types. Only three glutamatergic neuronal clusters (L2/3 IT-3, L5 IT-1 and L6 CT-2) showed persistent nociceptive and neuropathic IEG signatures, all of which expressed Oprm1 (Extended Data Fig. 3 and Supplementary Note 2). Oprm1 transcript levels and distribution were unchanged during chronic neuropathic pain (Extended Data Fig. 3 and Supplementary Note 2), implying that morphine can access molecularly distinct ACC neurons that encode nociception throughout chronic pain.

ACC MORs mediate morphine analgesia

To determine the role of cortical MORs in morphine-mediated analgesia, we genetically deleted MORs selectively in ACC neurons (Extended Data Fig. 4, Supplementary Table 1 (row 50) and Supplementary Note 3). In Oprm1fl/fl mice given AAV9–hSyn–Cre in the ACC versus controls, systemic morphine (0.5 mg kg−1) no longer reduced affective–motivational pain responses to noxious stimuli despite intact sensory thresholds (Extended Data Fig. 4f–j and Supplementary Table 1 (rows 51–57)). Conversely, AAV9–hSyn–FLEx–OPRM1 re-expression of MORs exclusively in the ACC of global MOR knockout mice restored morphine analgesia (Extended Data Fig. 4k–r and Supplementary Table 1 (rows 58–65)). Together, these loss-of-function and gain-of-function experiments demonstrate that ACC MORs are both necessary and sufficient for the affective pain relief produced by clinically relevant morphine doses, identifying this cortical ensemble as a key target for μ-opioid-mediated analgesia.

A deep learning system for pain behaviour analysis

The complexity of pain cannot be fully captured by reflexive withdrawal responses (the current standard for preclinical analgesic evaluation), which assess only evoked responses and fail to reflect the continuing affective experience most relevant to patients with chronic pain47,48,49. Although assays such as conditioned place preference or aversion measure memory-based responses to prior pain or relief24,50,51, they do not capture the dynamic, moment-to-moment motivational behaviours driven by spontaneous or continuing pain1,52.

To address these limitations, we developed Light Automated Pain Evaluator (LUPE; Fig. 1), a behavioural analysis platform designed to resolve fine-scale, naturalistic pain-related behaviours across several timescales. LUPE enables a nuanced and translationally relevant assessment of affective–motivational pain states in freely moving mice. Named after the Greek daemon of pain and suffering (Lýpē; λῡ́πη), LUPE provides a standardized, dark environment optimized for the behaviour of nocturnal prey animals. Critically, it eliminates the presence of the human experimenter both as a looming threat and as a subjective observer, allowing for objective quantification of spontaneous nocifensive behaviours.

Fig. 1: Deep learning analysis of natural behaviour reveals how pain and opioids shape internal affective–motivational states.

a, Schematic of the standardized LUPE chamber. b, A 20-body point DLC pose-tracking model was built from male and female mouse pain behaviour. c, Behaviour segmentation models trained iteratively on supervised annotations of behaviours with A-SOiD, followed by unsupervised sub-clustering with B-SOiD. d, Motion energy heat maps illustrating spatial trajectories and intensity distributions for the six primary behavioural repertoires. e, Temporal probability plots for the six primary behaviour repertoires in 1-min bins, comparing uninjured mice to mice with left hindpaw injections of 1% formalin, 5% formalin or capsaicin. f, Raster plots of behaviour transitions within a 30-s window. g, Procedure for behavioural state inference from statistical structure of spontaneous behaviour. h, Left, model centroid transition matrices characterizing each of six inferred states. Right, comparing the fraction occupancy of mice in each state between uninjured (grey), formalin (magenta) and capsaicin (cyan) pain models (n = 20 per group; one-way analysis of variance (ANOVA); Tukey correction: Pstate1 = 0.0007, Pstate3 = 0.0078 and Pstate4 < 0.0001). i, Two-dimensional (2D) visualization of PCA of state occupancies across pain models. j, Magnitude of coefficients of each state in each PCA. k, Scores of each animal along PC1 (top) and PC2 (bottom) across pain models (n = 20 per group; one-way ANOVA; Tukey correction: PPC1 = 0.0027 and PPC2 = 0.0082). l, Dose–response of morphine on PC1 (top) and PC2 (bottom) scores in uninjured, formalin-administered and capsaicin-administered mice (n = 20 per group and dose; one-way ANOVA; Tukey correction: PPC1 uninjured < 0.0001, PPC1 formalin < 0.0001, PPC1 capsaicin < 0.0001, PPC2 uninjured < 0.0001, PPC2 formalin < 0.0001 and PPC2 capsaicin < 0.0001). ⋆P < 0.05. Bars are mean; dots are individual animals; vertical lines and shaded areas are s.e.m. See Supplementary Table 1 (rows 1–14) for statistics. NS, non-significant. Scale bar, 2.5 s (f).

Source data

In addition to the standardized chamber and high-speed infrared videography recorded from below a glass floor (Fig. 1a and Supplementary Fig. 1), behavioural classification was driven by a multilayered analysis pipeline. Using DeepLabCut (DLC)[53] to track 20 body key points, LUPE extracted detailed posture dynamics that were processed through both semi-supervised (A-SOiD[54]) and unsupervised (B-SOiD[55]) algorithms to identify six holistic behavioural repertoires: still, walk, rear, groom, lick left hindpaw and lick right hindpaw (Fig. 1b,c and Supplementary Fig. 1). These repertoires were assembled from sub-second behavioural syllables and allowed quantitative analysis of transitions across time. Motion energy plots visualized the displacement of tracked body points, defining each behaviour and distinguishing similar actions such as grooming and paw-directed licking (Fig. 1d).

To evaluate the sensitivity of LUPE to dynamic changes in pain-related behaviour, we applied the formalin and capsaicin models of acute pain[56]. Male and female C57Bl/6J mice were habituated to the LUPE chamber for two consecutive days and then injected in the left hindpaw with 1% or 5% formalin or 2% capsaicin, or left uninjured as controls (Fig. 1e). LUPE computed the behavioural probabilities for all six repertoires over 30-min sessions for all 60 mice in under 2 h, compared with 50–150 min for manual[57] scoring of one behaviour in one mouse (with the upper bound equivalent to 54,000 min for full dataset scoring; Supplementary Fig. 1k). By automating behaviour classification, LUPE increases the speed, rigor and reproducibility of preclinical pain behaviour analysis. It also generates archival-quality datasets that include video logs and computer-scored results, facilitating transparent cross-laboratory comparison, long-term record keeping and future reanalysis.

LUPE identified a low dose of morphine (0.5 mg kg−1) that reduced licking of the injured hindpaw following both formalin-induced and capsaicin-induced injury that did not affect walking (Extended Data Fig. 6a–g and Supplementary Table 1 (rows 75–87)). Therefore, LUPE provides a sensitive measure of ethologically relevant affective–motivational pain behaviour that can identify translationally relevant analgesic doses.

Discovery of morphine-sensitive latent pain states

Inferring internal states, such as pain, from sparse spontaneous behaviour is a central challenge in ethological neuroscience. An injury to the left hindpaw may or may not elicit licking at a given moment, although the animal may still be experiencing pain. We therefore tested whether latent cognitive–affective pain states could be determined from LUPE-scored behaviour. From 58 mice (formalin, n = 19; capsaicin, n = 20; and SNI, n = 19; Fig. 1g and Extended Data Fig. 5), we modelled behavioural transitions as Markov processes using 30-s sliding windows to produce per-animal transition matrices (Fig. 1g and Extended Data Fig. 5a). Matrices were clustered by k-means (k = 6; 100-fold cross-validation; Fig. 1g and Extended Data Fig. 5b). Classification of animals to these six clusters exceeded chance (Euclidean distance between real versus shuffled cluster centroids; Extended Data Fig. 5c–h and Supplementary Table 1 (rows 66–72)). No single behaviour drove the clustering; systematic removal of individual behaviours disrupted classification less than expected by chance (Extended Data Fig. 5c).

Cluster centroids define the mean transition matrices of six distinct behaviour states (Fig. 1h): (1) stillness, walking, rearing and grooming; (2) stillness, walking and rearing; (3) state 1 plus licking the injured paw; (4) all behaviours except licking the uninjured paw; (5) all behaviours except stillness; and (6) stillness, walking, rearing and licking the injured paw. States evolve over seconds to minutes and show conserved dynamics across pain models (Extended Data Fig. 5i–k and Supplementary Table 1 (rows 73–74)). Behaviour states distinguished injured from uninjured animals but did not separate injury types (Fig. 1h and Supplementary Table 1 (rows 1–6)). Uninjured mice predominantly occupied states 1 and 2; capsaicin and formalin increased occupancy of states 3 and 4 and reduced time in state 1. Notably, state 4 was uniquely and dose-dependently suppressed by morphine, indicating a selectively opioid-sensitive spontaneous-pain dimension (Extended Data Fig. 6g and Supplementary Table 1 (rows 97–99)), although morphine modulated all states dose-dependently across conditions (Extended Data Fig. 6g and Supplementary Table 1 (rows 88–105)). Thus, latent affective–motivational states inferred from spontaneous behaviour track pain and analgesia.

Numeric pain index tracks injury and analgesia

To compress the diverse effects of pain and morphine across all six states, we applied principal component analysis (PCA) to the fraction of time each mouse spent in each state across pain conditions (Fig. 1i). This revealed two principal axes of variation in behaviour. Both capsaicin and formalin shifted scores along these axes, reducing the first component and increasing the second, regardless of injury model (Fig. 1k and Supplementary Table 1 (rows 7–8)). The first principal component (PC1), driven primarily by states 1 and 2, reflects a baseline behavioural structure disrupted by both injury and high-dose morphine and is termed the general behaviour scale (Fig. 1j,l (top) and Supplementary Table 1 (rows 9–11)). The second component (PC2) was weighted by states 2 and 4, selectively increased by injury and dose-dependently suppressed by morphine (Fig. 1l (bottom) and Supplementary Table 1 (rows 12–14)), capturing the presence and relief of affective pain. Because PC2 responds bidirectionally to injury and analgesia, we define it as the affective–motivational pain scale (AMPS), a data-driven, continuous index of pain-related behavioural states.

Licking as a structured motivated response to pain

The gate control theory asserts that volitional behaviours, such as rubbing or licking injured tissue, act as antinociceptive responses by recruiting touch afferents that inhibit spinal nociceptive signalling (Extended Data Fig. 7a). Consequently, the unpleasantness of pain drives motivated licking, which then reduces pain, forming a negative feedback loop. Thus, motivated licking is expected to increase with affective pain and decline as analgesia—or recuperation—is achieved.

Our analysis treated latent behavioural states as Markovian processes, in which behavioural probabilities are stable within a state (Extended Data Fig. 7b (top)). We therefore tested whether licking dynamics followed theoretical predictions by measuring the probability of each behaviour as a function of elapsed time within pain state 4, a latent state consistently enhanced by injury and dose-dependently suppressed by morphine (Fig. 1h).

Across injury models, injured-paw licking showed a reproducible temporal profile within pain state 4: near zero at state onset, accumulating in the latter half and declining just before the state transition (Extended Data Fig. 7b,c and Supplementary Table 1 (rows 106–109)). This temporal structure was not seen for other behaviours in pain state 4 or for licking pooled across all states (Extended Data Fig. 7d–f and Supplementary Table 1 (row 110)). These results indicate that paw licking is not merely a reflexive nocifensive action but an innate affective–motivational response engaged to negatively modulate pain, consistent with the gate control theory and its role as a motivated antinociceptive behaviour.

ACC dynamics reflect nociception and behaviour

To link ACC activity to morphine-sensitive pain behaviour, we performed single-cell calcium imaging in freely behaving mice inside LUPE. We expressed AAV9–hSyn–jGCaMP8m and implanted 1.0-mm GRIN lenses at ACC nociceptive hotspot coordinates (n = 5 male mice; Fig. 2a,b and Extended Data Fig. 8a,b). With a head-mounted one-photon miniscope, we recorded neural activity during acute inflammatory pain (left hindpaw intraplantar injection of 2% capsaicin (10 μl)) and after morphine analgesia (0.5 mg kg−1; Fig. 2c,d). A Fisher linear decoder (100-fold cross-validation) reliably decoded spontaneous behaviours from ACC population activity across mice and sessions, independent of injury or opioid treatment (Fig. 2e, Extended Data Fig. 8g,h and Supplementary Table 1 (row 116)).

Fig. 2: Neural dynamics in ACC track acute pain and analgesia.

a, Microendoscope calcium imaging synced with LUPE behaviour tracking. b, GRIN lens implant and hSyn–GCaMP8m expression in ACC Cg1. c, From top to bottom, average and single-cell neural activity (z-score) from a representative mouse, LUPE behaviours, states inferred by our behavioural state model and probability of behaviours given states and behaviour history (binomial GLM). d, Capsaicin imaging protocol injury (intraplantar; 2%; left hindpaw) and morphine (intraperitoneal (i.p.); 0.5 mg kg−1; n = 5). e, Fisher decoder accuracies predicting behaviours from neural activity, averaged over mice (permutation test; Extended Data Fig. 8g). f, Area under the receiver operating characteristic curve (auROC) of GLMs predicting Plick from e in each animal (n = 5). g, Calcium events per second of neurons in all sessions (two-way ANOVA; Tukey correction: Pinteraction = 0.0009). h, Mean ± s.e.m. fraction of positive and negative Plick neurons during capsaicin (red outline) and capsaicin + morphine (purple outline) sessions. i,j, Calcium events per second of positive (i) and negative (j) Plick neurons in capsaicin and capsaicin + morphine sessions (two-way ANOVA; Tukey correction: positive Plick neurons, Pinteraction = 0.0004; negative Plick neurons, Pinteraction = 0.026). k, Average lick probability around lick bout onset (two-tailed unpaired t-test: Pnegative, 1–2 s = 0.0003). Light grey, 0–1 s; dark grey, 1–2 s. l,m, Left, average activity in positive (l) and negative (m) Plick neurons around lick bout onset, pooled across animals. Right, area under the curve (AUC) of lick probability from 0 to 1 s and from 1 to 2 s post- initiation (two-tailed unpaired t-test: Pnegative, 0–1 s = 0.0003). Light orange, 0–1 s; dark orange, 1–2 s (l). Light blue, 0–1 s; dark blue, 1–2 s (m). n, Behavioural probability as a function of fraction time in pain state 4 (n = 19–20 per group). o, Cumulative lick probability over fraction of time remaining in pain state 4 (two-sided Kolmogorov–Smirnov test: P = 1.1 × 10−23). p, Summary of results. ⋆P < 0.05. Bars, lines or dots are mean; error bars and shaded areas are s.e.m. (see Supplementary Table 1 (rows 15–24) for statistics). Scale bars, 1.0 mm (b (yellow bar)), 1 zS (c).

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We next investigated whether ACC ensembles encode nociceptive signals by modality or valence. In uninjured mice, we delivered mechanical and thermal stimuli to the left hindpaw (0.16-g filament; pin prick; 30 °C water drop; 55 °C hot-water drop; 6 °C acetone) and compared these to orally consumed stimuli of opposing valence (10% sucrose versus quinine) and a 55 °C hot-water drop. ACC neurons showed greater overlap in responses to noxious stimuli across modalities (approximately 11–20% overlap among excited cells and approximately 25% among inhibited cells) than between stimuli of opposite valence. Among excited cells, only 6% responded to both 55 °C heat and sucrose versus 13% for heat and quinine; the overlap among inhibited cells was approximately 11% for both pairings (Extended Data Figs. 9a,c and 10e). Activity patterns and cross-decoding performance further separated heat-activated versus sucrose-activated neurons (Extended Data Fig. 10a–e), consistent with valence-specific encoding in ACC neural populations.

Morphine inhibits pain-tracking ACC neurons

To test whether neurons encoding lick probability are morphine-sensitive, we first estimated lick probability by aligning neural activity to behaviour and latent states and fitting a binomial generalized linear model (GLM; current state and behaviour history using two previous time steps; Fig. 2c). These state-based GLMs outperformed chance and yielded a pseudo-continuous lick-probability trace (Fig. 2c). We then trained GLMs to predict behavioural probability from the PCs capturing 80% of neural variance per animal per session (Extended Data Fig. 8c). These models exceeded performance on shuffled data (Fig. 2f, Extended Data Fig. 8d and Supplementary Table 1 (rows 111–115)). Neurons with the largest absolute weights in the top 3 PCs (P < 0.001; |z-score of coefficient | > 1.5) were labelled Plick neurons (Extended Data Fig. 8e,f).

The ACC population activity was elevated during capsaicin sessions versus baseline and was selectively suppressed by 0.5 mg kg−1 of morphine only with injury (Fig. 2g and Supplementary Table 1 (row 15)). Plick neurons split into positive (activity increased at lick onset; 16.5 ± 3.4% of cells) and negative (activity decreased at lick onset; 16.6 ± 3.4% of cells) subpopulations (Fig. 2h). Morphine inhibited these subpopulations differently; positive Plick cells were suppressed selectively during pain state 4, whereas negative Plick cells showed broader state-independent inhibition (Fig. 2i,j and Supplementary Table 1 (rows 16–17)). Together, this indicates population-dependent and state-dependent modulation of Plick neurons by morphine.

We next examined the dynamics surrounding the onset of a lick bout. Behaviourally, morphine reduced lick probability 1–2 s after lick onset, indicating impaired lick maintenance (Fig. 2k and Supplementary Table 1 (rows 18–19)). Morphine did not alter positive Plick neurons but increased inhibition of negative Plick neurons 0–1 s after lick offset, preceding the behavioural change (Fig. 2l,m and Supplementary Table 1 (rows 20–23)). These effects sharpened Plick selectivity for licking versus other behaviours (Extended Data Fig. 8i–l and Supplementary Table 1 (rows 117–119)).

Over longer pain state 4 bouts, morphine narrowed the lick-probability profile in capsaicin-treated mice by reducing early-state licking and causing greater accumulation near the state end (Fig. 2n,o and Supplementary Table 1 (row 24)). Thus, morphine relieves the affective–motivational drive to lick by suppressing spontaneous activity in positive Plick neurons and enhancing behaviour-locked inhibition of negative Plick neurons, producing delayed initiation and reduced maintenance of licking during pain state 4 (Fig. 2p).

Morphine restores chronic pain-disrupted dynamics

We performed longitudinal miniscope calcium imaging in mice expressing hSyn–GCaMP8m with GRIN lenses in ACC Cg1, recorded in LUPE 1 day before and 1 day, 7 days, 14 days and 21 days after SNI (n = 9) or in uninjured controls (n = 9; Fig. 3a). SNI produced an immediate, sustained increase in spontaneous left-hindpaw lick rate, pain-state occupancy and AMPS scores versus controls (Fig. 3b–e and Supplementary Table 1 (rows 25–27)). As in acute injury, a single 0.5 mg kg−1 of morphine dose at 3 weeks post-SNI reduced AMPS scores in mice with SNI (Fig. 3f and Supplementary Table 1 (row 28)), indicating that opioids remain effective for affective–motivational features of chronic neuropathic pain.

Fig. 3: Morphine targets functionally compromised ACC neurons to relieve chronic pain.

a, SNI protocol for chronic neuropathic pain. b, Log-transformed rate of licking at the injured limb in SNI (red; n = 9) or uninjured controls (grey; n = 9; two-way repeated measures ANOVA; Tukey correction: Pinteraction = 0.0036). c, Heat map of average state occupancy. d, Occupancy of pain and non-pain states in SNI and uninjured mice (two-way repeated measures ANOVA; Tukey correction: Pinteraction < 0.0001). e, AMPS score in SNI and uninjured mice (two-way repeated measures ANOVA; Tukey correction: Pinteraction = 0.0089). f, AMPS score in SNI and uninjured mice 3 weeks post-SNI and morphine (0.5 mg kg−1; intraperitoneal; two-way repeated measures ANOVA; Tukey correction: Pinjury = 0.0085; Ptreatment = 0.0041). g,h, Left: lick-evoked activity in positive (g) and negative (h) Plick neurons before (black) and after SNI (warm colour gradient; yellow = 1 day and red = 3 weeks post-SNI). Right: area under the curve of lick-evoked activity (0–1 s post-onset) in SNI and uninjured mice (two-way repeated measures ANOVA; Tukey correction: Ppositive,interaction < 0.0001; Pnegative,interaction = 0.021). i,j, Lick-evoked activity in positive (i) and negative (j) Plick neurons. Left, lick-evoked activity at baseline (black), 3 weeks post-SNI (red) and 3 weeks post-SNI + morphine (blue). Right, lick-evoked activity 3 weeks post-SNI versus uninjured mice (two-way repeated measures ANOVA; Tukey correction: Ppositive,interaction = 0.035; Pnegative,treatment < 0.0001; Pnegative,injury < 0.0001). k,l, Calcium event rate in positive (k) and negative (l) Plick neurons before and after morphine treatment (two-way repeated measures ANOVA; Tukey correction: Ppositive,treatment = 0.0023; Pnegative,treatment = 0.0124). m, Linear regression predicting lick rate (purple) and pain state occupancy (grey) from the average magnitude of lick-evoked activity 3 weeks post-SNI (top), after morphine (middle) and change between sessions (bottom; Bonferroni-corrected P values displayed). ⋆P < 0.05. Bars, lines or dots are mean; error bars and shaded areas are s.e.m. (see Supplementary Table 1 (rows 25–37) for statistics).

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Using the same Plick identification shown in Fig. 2, SNI impaired decoding accuracy for sensory stimuli, behaviours and latent states relative to baseline and controls (Extended Data Fig. 11a–h and Supplementary Table 1 (rows 121–128)) and persistently blunted lick-evoked responses in both positive and negative Plick neurons (Fig. 3g,h and Supplementary Table 1 (rows 29–30)). Morphine reversed these SNI deficits at lick onset and further enhanced Plick responses in uninjured mice (Fig. 3i,j and Supplementary Table 1 (rows 31–32)). SNI reduced single-cell lick selectivity in both Plick subtypes, which morphine restored (Extended Data Fig. 11i,j and Supplementary Table 1 (rows 129–132)). At 3 weeks, morphine suppressed positive Plick activity in mice with SNI and inhibited negative Plick cells across groups, reproducing the state-dependent and state-independent effects seen in acute pain (Fig. 3k,l and Supplementary Table 1 (rows 33–34)) and shifted the proportions of negative and positive Plick neurons regardless of injury (Extended Data Fig. 11k,l and Supplementary Table 1 (rows 133–136)). The magnitude of lick-evoked responses predicted within-session lick rate and pain-state occupancy, and morphine-induced increases in these responses predicted reductions in both behaviour and time spent in pain states (Fig. 3m and Supplementary Table 1 (rows 35–37)).

Together, these results show that morphine produces analgesia in chronic pain by inhibiting spontaneous activity in positive and negative Plick neurons in an injury-dependent manner, thereby rescuing the behaviour-discriminating and state-discriminating dynamics of ACC pain-tracking ensembles.

Chemogenetic gene therapy targets ACC MOR neurons

Given that morphine relieves chronic pain by modulating defined populations of ACC neurons, including injury-dependent inhibition of positive Plick neurons and widespread suppression of negative Plick neurons, we next sought to mimic these effects through a targeted chemogenetic gene therapy approach. Rather than systemic delivery of opioids, which carries substantialt risk of addiction and off-target effects, we aimed to develop a circuit-specific and cell-type-specific strategy for precision pain management[1]. Specifically, we designed a chemogenetic gene therapy to silence MOR-expressing neurons in ACC. We engineered an adeno-associated virus (AAV)-packaged synthetic mouse MOR promoter (MORp) to drive the expression of Gi-coupled inhibitory hM4–DREADD, which leverages endogenous transcription factors and molecular machinery to express transgene cargo in MOR+ cell types. The MORp sequence was derived from a 1.5-kb region upstream of the Oprm1 transcription start site on the basis of conserved regulatory elements in both mouse and human promoter regions previously shown to drive selective expression in MOR+ cells[2]. This allowed us to restrict hM4–DREADD expression to MOR+ neurons within ACC, enabling remote and reversible inhibition through the ligand deschloroclozapine (DCZ)[58].

We tested two AAV-delivered strategies for targeting these cells: direct expression of hM4Di or a control fluorescent protein (eYFP) under the control of MORp (constructs: MORp–hM4Di and MORp–eYFP), and an intersectional approach combining painTRAP labelling of noxious stimulus-responsive neurons with MORp-dependent expression using a CreON/FlpON switch to restrict expression to MOR+ pain-activated neurons (Fig. 4a). Following viral incubation, fluorescence in situ hybridization quantification confirmed that more than 97% of ACC neurons expressing endogenous Oprm1 mRNA also expressed MORp-driven eYFP mRNA, indicating high specificity of the synthetic promoter (Fig. 4b). All viral constructs yielded robust expression throughout ACC layers (Fig. 4c and Extended Data Fig. 12), validating this strategy as a viable tool for selective neuromodulation of opioid-sensitive cortical ensembles.

Fig. 4: Precision neuromodulation of chronic pain through an ACC opioid cell-type-specific chemogenetic gene therapy.

a, MORp-driven viruses deliver actuators to ACC MOR+ neurons. b, Co-expression of MORp–eYFP and endogenous Oprm1 mRNA. c, MORp-driven fluorophore or hM4 expression across ACC layers. d, Timeline of SNI and DCZ exposure (0.3 mg kg−1; intraperitoneal) with LUPE and sensory testing. e–h, Licking of the injured hindpaw (e; Pinteraction = 0.0012), occupancy of behavioural states (f), fraction of time spent in pain versus non-pain states (g; pain states Pinteraction < 0.0001, non-pain states Pinteraction < 0.0001) and AMPS scores (h; Pinteraction = 0.0003) in MORp–hM4 versus MORp–eYFP mice. Two-way repeated measures ANOVA; Tukey correction for all panels (n = 19 MORp–eYFP mice; n = 30 MORp–hM4 mice). i,j, Brain-wide projections of ACC MOR+ axons expressing hM4 or eYFP (i) to assess neuropathic activity (touchFOS) with or without chemogenetic inhibition (j). Unpaired t-test, two-tailed per region (n = 6, MORp–eYFP; n = 6, MORp–hM4). AIC, anterior insular cortex; BLA, basolateral amygdala; CeA, central nucleus of the amygdala; CL, central lateral nucleus; CLA, claustrum; dPAG, dorsal periaqueductal grey region; DRN, dorsal raphe nucleus; IL, infralimbic cortex; lHab, lateral habenula; mORB, medial orbitofrontal cortex; NAc, nucleus accumbens; NAcC, nucleus accumbens core; PBN, parabrachial nucleus; PIC, posterior insular cortex; PL, prelimbic cortex; PVT, paraventricular thalamus; RSP, retrosplenial cortex; vlPAG, ventrolateral periaqueductal grey; ZI, zona incerta. k, Maximum possible analgesia for morphine (0.5 mg kg−1; intraperitoneal) versus MORp–eYFP, MORp–hM4 or nociceptive MORp–hM4 + DCZ in uninjured mice and mice with SNI (n = 11 uninjured + morphine; n = 10 injured; n = 15 MORp–eYFP uninjured; n = 10 injured; n = 15 MORp–hM4 uninjured; n = 20 injured; n = 15 Noci/MORp–hM4). Maximum possible analgesia of evoked mechanical thresholds (left, Pinteraction = 0.0061), affective–motivational behaviours to acetone (middle, Pinteraction < 0.0001) and 55 °C hot water (right, Pinteraction < 0.0001). One-way ANOVA; Tukey correction for all panels. l, AMPS of uninjured mice or mice with SNI treated with morphine versus acute or chronic MORp–hM4 ACC (neuropathic, left, one-way ANOVA; Tukey correction; Ptreatment = 0.0085. Neuropathic, right, two-way ANOVA; Tukey correction; Pinteraction = 0.0003). m, Using a deep learning behaviour tracking platform to classify behaviour pain states, paired with single-neuron calcium imaging, we uncovered cortical mechanisms of opioid analgesia, which informed the creation of an opioid cell-type chemogenetic gene therapy for chronic pain that mimics morphine analgesia with circuit-targeted precision. ⋆P < 0.05. Bars, lines or dots are mean; error bars and shaded areas are s.e.m. (see Supplementary Table 1 (rows 38–47) for statistics). Scale bars, 50 μm (b,c), 200 μm (i).

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Chemogenetic therapy reduces chronic pain

Building on the successful targeting of ACC MOR+ neurons with AAV–MORp, we next investigated whether chemogenetic inhibition of this population could provide sustained relief from chronic neuropathic pain without inducing tolerance. In mice 3 weeks after SNI, we began daily administration of the DREADD agonist DCZ (0.3 mg kg−1; intraperitoneal) for 1 week to assess whether DCZ activation of hM4Di-expressing MOR+ neurons reduced spontaneous and evoked pain behaviours and whether repeated inhibition would produce analgesic tolerance (Fig. 4d). Inhibition of ACC MOR+ neurons significantly reduced several measures of evoked affective–motivational pain behaviour to 6 °C acetone and 55 °C water, with no evidence of tolerance. Spontaneous pain-related licking, occupancy of high-pain latent states and AMPS scores were all reduced following a single dose of DCZ (SNI day 23) and remained suppressed after 1 week of daily dosing (SNI day 29; Fig. 4e–h, Extended Data Fig. 14 and Supplementary Table 1 (rows 38–41 and 146–155)).

Given the anatomical connectivity of ACC with key pain-processing regions, including the prelimbic and orbitofrontal cortices37,59, nucleus accumbens core35,60, insula and calustrum61,62, medial thalamus36,63, basolateral amygdala64,65,66, periaqueductal gray[67] and other regions, we suggest that inhibition of ACC MOR+ neurons may influence activity across broader nociceptive circuits. To test this, we collected brain tissue from the same chronic pain cohort after DCZ treatment and exposure to a standardized light touch stimulus (0.16-g filament to the injured hindpaw). Using FOS expression as a proxy for neuropathic neural activation, we quantified the number of light touch-responsive neurons (touchFOS+ cells) across brain-wide projection targets of ACC MOR+ axons (Fig. 4i). We observed reductions in touchFOS+ cell counts in 14 of 19 brain regions downstream of ACC MOR+ projections, indicating that inhibition of this cortical ensemble can suppress widespread nociceptive responses throughout the brain (Fig. 4j and Supplementary Table 1 (row 42)).

Inhibition of ACC MOR neurons is not reinforcing

We tested potential side effects of our MORp chemogenetic approach using a modified real-time place preference (RTPP) assay (Extended Data Fig. 15 and Supplementary Table 1 (rows 156–157)). In painTRAP mice, we injected a Cre-dependent, MORp-driven, light-sensitive inhibitory channel (iC++) bilaterally into ACC and implanted fibre-optic cannulae and then assigned mice to uninjured or SNI groups. At 3 weeks post-SNI, the mice completed a 9-day RTPP: a pretest for baseline preference, seven daily 20-min closed-loop sessions in which entry to one chamber triggered blue-light (5 mW) inhibition and a final free-exploration recall test. Optogenetic inhibition of ACC MOR+ neurons did not change chamber preference in uninjured mice during conditioning or recall. By contrast, mice with SNI developed a preference for the light-emitting diode (LED)-paired chamber and a robust conditioned recall, spending significantly more time in the chamber previously paired with inhibition. Thus, silencing nociceptive ACC MOR+ neurons relieves continuing spontaneous pain and is reinforcing only in the injured state, indicating state-dependent negative reinforcement with minimal addiction-like liability in uninjured animals. Together with our chemogenetic data, these results show that selective, chronic inhibition of ACC MOR+ neurons yields stable, state-dependent analgesia without tolerance or reinforcement in normal conditions.

Chemogenetic therapy mimics morphine analgesia

To assess translational potential, we compared our MORp–hM4Di chemogenetic therapy to systemic morphine across models and stimuli, quantifying per cent maximum analgesia (Fig. 4k, Extended Data Fig. 13 and Supplementary Table 1 (rows 44–45 and 137–145)). Mice expressing MORp–hM4Di, painTRAP/MORp–hM4Di or MORp–YFP controls were tested with or without DCZ (0.3 mg kg−1; intraperitoneal) in uninjured and SNI conditions. These results were compared with mice receiving morphine (0.5 mg kg−1; intraperitoneal) in the same behavioural tests. Consistent with previous reports25,28,29, neither morphine nor our interventions altered mechanical von Frey thresholds (Fig. 4k (left)). However, both morphine and DCZ (in hM4Di or painTRAP/MORp–hM4Di mice) produced significant analgesia to acetone and 55 °C water in uninjured animals versus MORp–YFP controls (Fig. 4k (middle and right)). Critically, in mice with SNI, DCZ in MORp–hM4Di animals significantly reduced responses to noxious heat and cold, whereas morphine failed (Fig. 4k). Acute or 1-week chronic DCZ matched morphine in lowering AMPS scores during chronic neuropathic pain (Fig. 4l and Supplementary Table 1 (rows 46–47)), demonstrating that MORp-based chemogenetic therapy mimics morphine analgesia and, in some cases, may provide superior efficacy against the affective–motivational features of chronic pain.

Discussion

The affective mechanisms of morphine mirror our preclinical results with the genetic tuning of MOR expression in the ACC and our AAV–MORp-driven therapy. We conclude that opioids and our opioid mimicry therapy inhibit cortical nociceptive neural functions to reduce the integration of negative valence information within the ACC, resulting in reduced aversive arousal and attentional processes that bias the selection of nocifensive behaviours (Supplementary Note 5).

Our study demonstrates the therapeutic potential of targeting defined cortical ensembles for precision pain relief. By integrating LUPE’s unbiased tracking of sub-second, spontaneous pain states with single-cell calcium imaging, alongside neural activity-tagging and single-nucleus RNA sequencing ACC cell-type profiling, we pinpointed a μ-opioidergic ACC ensemble with an activity that scales with the affective burden of acute to chronic pain and is uniquely dampened by morphine (Supplementary Note 6). By leveraging the MORp promoter to drive chemogenetic inhibition in opioid-sensitive cortical neurons, we silenced this circuit, successfully reproducing the analgesic effects of morphine while avoiding the sensory, tolerance and reinforcement effects. This cell-type-specific strategy offers a new direction for pain management, one that could bypass the systemic risks of traditional opioids by modulating unique dimensions of pain perception at its cortical origin. For translational neuromodulation, our human opioid receptor promoter-based chemogenetic gene therapy[2] (hMORp–hM4) could be adapted for non-invasive delivery using focused ultrasound blood–brain barrier opening[68] to access and control multiple pain-encoding opioidergic neurons simultaneously in cortical and subcortical circuits (Supplementary Note 7). Thus, by integrating behavioural state modelling, neural ensemble identification and circuit-targeted intervention, our study provides a framework (Fig. 4m) for biologically informed, precision-based pain therapeutics. Selectively targeting the ACC opioidergic circuits through MORp-based gene therapies holds the promise of offering safer and more effective alternatives to conventional pain treatments, ultimately advancing precision medicine in pain management.

Methods

Sample sizes

The sample sizes were not statistically determined before the experiments. Rather, the group sizes were on the basis of published literature for the type of manipulation (chemogenetics, site-specific genetic knockout and pharmacology) and measured outcome (such as pain behaviours) published in the field and/or by the authors involved2,28,64,66. The sample sizes for all experiments are included in the figure legends.

Data exclusions

For all imaging and behavioural studies, virus-injected animals with either little or no evidence of viral transduction and/or incorrect viral targeting were excluded from any final analyses. No other mice or data points were excluded across analyses.

Replication

For many of the behavioural studies, several cohorts were used owing to the large number of animals in the final group sizes. All behaviour results were consistent and replicated across cohorts. Individual data points or lines were included, indicating consistent trends across many mice in each behavioural study.

Blinding

Mice were randomly assigned into control or experimental groups to the best of the experimenter’s abilities, with counterbalancing for age and sex as needed. In most of the included studies, the experimental and control groups differed only in the type of virus infused intracranially. The surgical protocol for all mice was identical in the amount, wait time and location of the intracranial injection. Each surgery day was randomly assigned as a control or experimental surgery date, and the corresponding mice from the predetermined groups underwent surgery on that day. GRIN lens and fibre placements and viral spread maps were included in the supplement to demonstrate the similarity of the injection protocol and outcome. Once the experimental and control groups were formed to comprise the study cohort of mice, the cohort underwent all behavioural testing concurrently, and experimenters were blinded. After the analyses were completed, the experimenters were unblinded.

Animals

All experimental procedures were approved by the Institutional Animal Care and Use Committee of the University of Pennsylvania and performed in accordance with the US National Institutes of Health guidelines. Male and female mice aged 2–5 months were housed two to five per cage and maintained on a 12-h reverse light–dark cycle in a temperature-controlled and humidity-controlled environment. All experiments were performed during the dark cycle. The mice had ad libitum food and water access throughout the experiments. For behavioural, anatomical and transcriptomic experiments, we used Fos–FOS–2A–iCreERT2 or ‘TRAP2’ mice (Fostm2.1(icre/ERT2)Luo)Luo; The Jackson Laboratory; stock no. 030323)[69] bred to homozygosity, C57BL/6J mice (The Jackson Laboratory; stock no. 000664), Oprm1Cre/Cre mice (B6.Cg–Oprm1tm1.1(cre/GFP)Rpa/J; The Jackson Laboratory; stock no. 035574) and Oprm1fl/fl mice (B6;129–Oprm1tm1.1Cgrf/KffJ; The Jackson Laboratory; stock no. 030074). Further anatomical experiments used TRAP2 mice crossed with Ai9 (B6.Cg–Gt(ROSA)26Sortm9(CAG-tdTomato)Hze/J; The Jackson Laboratory; stock no. 007909) reporter mice that express a tdTomato fluorophore in a Cre-dependent manner.

Mouse μ-opioid receptor promoter

MORp[2] is a 1.5-kb segment selected and amplified from mouse genomic DNA using cgcacgcgtgagaacatatggttggacaaaattc and ggcaccggtggaagggagggagcatgggctgtgag as the 5′ and 3′ end primers, respectively. All MORp plasmids were constructed on an AAV backbone by inserting either the MORp ahead of the gene of interest (iC++–eYFP) using M1uI and AgeI restriction sites. Every plasmid was sequence verified. Next, all AAVs were produced at the Stanford Neuroscience Gene Vector and Virus Core. Genomic titre was determined by quantitative polymerase chain reaction (qPCR) of the WPRE element. All viruses were tested in cultured neurons for fluorescence expression before use in vivo.

Viral vectors

All viral vectors were either purchased from Addgene or custom designed and packaged by the authors as indicated. All AAVs were aliquoted and stored at −80 °C until use. The following viral vectors were used (titre in viral genomes (vg) per millilitre; volume in nanolitre; total dose in viral genomes; source): AAV9–hSyn–HI–eGFP–Cre–wpre–SV40 (5.44 × 10^11 vg ml−1; 400 nl; 2.18 × 10^8 vg; Addgene 105540-AAV9), AAV9–hSyn–GFP (1.90 × 10^11 vg ml−1; 400 nl; 7.60 × 10^7 vg; Addgene 50465-AAV9), AAVDJ–hSyn1–DIO–mCh–2A–MOR (1.13 × 10^12 vg ml−1; 500 nl; 5.65 × 10^8 vg; custom from Banghart Lab), AAV5–hSyn–DIO–EGFP (1.30 × 10^12 vg ml−1; 500 nl; 6.50 × 10^8 vg; Addgene 50457-AAV5), AAV9–hSyn–jGCaMP8m–WPRE (1.90 × 10^12 vg ml−1; 800 nl; 1.52 × 10^9 vg; Addgene 162375-AAV9), AAV1–mMORp–hM4Di–mCherry (1.17 × 10^12 vg ml−1; 500 nl; 5.85 × 10^8 vg; custom from Deisseroth Lab), AAV1–mMORp–eYFP (1.00 × 10^12 vg ml−1; 500 nl; 5.00 × 10^8 vg; custom from Deisseroth Lab), AAV1–mMORp–Flpo (9.21 × 10^11 vg ml−1; 400 nl; 3.68 × 10^8 vg (co-injected with Con/Fon–hM4D(Gi)); custom from Deisseroth Lab), AAV5–nEF–Con/Fon–hM4D(Gi)–mCherry (7.40 × 10^12 vg ml−1; 400 nl; 2.96 × 10^9 vg (co-injected with mMORp–FlpO); custom from Deisseroth Lab) and AAV1–mMORp–DIO–iC++–eYFP (1.35 × 10^12 vg ml−1; 400 nl; 5.40 × 10^8 vg; custom from Deisseroth Lab).

Stereotaxic surgery

Adult mice (approximately 8 weeks of age) were anaesthetized with isoflurane gas in oxygen (initial dose, 5%; maintenance dose, 1.5%) and fitted into World Precision Instruments or Kopf stereotaxic frames for all surgical procedures. NanoFil Hamilton syringes (10 µl; World Precision Instruments) with 33G beveled needles were used to intracranially infuse AAVs into the ACC. The following coordinates were used on the basis of the Paxinos mouse brain atlas to target these regions of interest (ROI): ACC (bregma: anterior–posterior, +1.50 mm; medial–lateral, ±0.3 mm; dorsal–ventral, −1.5 mm). The mice were given a 3-week to 8-week recovery period to allow ample time for viral diffusion and transduction to occur. For all surgical procedures in mice, meloxicam (5 mg kg−1) was administered subcutaneously at the start of the surgery, and a single 0.25-ml injection of sterile saline was provided upon completion. All mice were monitored and given meloxicam for up to 3 days following surgical procedures.

Chronic neuropathic pain model

As described previously[65], to induce a chronic pain state, we used a modified version of the SNI model of neuropathic pain. This model entails surgical section of two of the sciatic nerve branches (common peroneal and tibial branches) while sparing the third (sural branch). Following SNI, the receptive field of the lateral aspect of the hindpaw skin (innervated by the sural nerve) displays hypersensitivity to tactile and cool stimuli, eliciting pathological reflexive and affective–motivational behaviours (allodynia). To perform this peripheral nerve injury procedure, anaesthesia was induced and maintained throughout surgery with isoflurane (4% induction; 1.5% maintenance in oxygen). The left leg and/or hindleg was shaved and wiped clean with alcohol and Betadine. We made a 1-cm incision in the skin of the mid-dorsal thigh, approximately where the sciatic nerve trifurcates. The biceps femoris and semimembranosus muscles were gently separated from one another with blunt scissors, thereby creating a less than 1-cm opening between the muscle groups to expose the common peroneal, tibial and sural branches of the sciatic nerve. Next, approximately 2 mm of both the common peroneal and tibial nerves were transected and removed, without suturing and with care not to distend the sural nerve. The leg muscles were left unsutured, and the skin was closed with tissue adhesive (3M Vetbond), followed by a Betadine application. During recovery from surgery, the mice were placed under a heat lamp until awake and achieved normal balanced movement. The mice were then returned to their home cages and closely monitored for well-being over the following 3 days.

Targeting Recombination in Active Populations protocols

PainTRAP

PainTRAP induction was performed as previously described[65]. We habituated mice to a testing room for two to three consecutive days. During these habituation days, no nociceptive stimuli were delivered, and no baseline thresholds were measured (the mice were naive to pain experience before the Targeting Recombination in Active Populations (TRAP) procedure). We placed individual mice in red plastic cylinders (approximately 9 cm in diameter), with a red lid, positioned on a raised perforated and flat metal platform (61 cm × 26 cm). The experimenters remained in the testing room for 30 min to allow habituation. This was done to mitigate potential alterations to the animals’ stress and endogenous antinociception levels. To execute the TRAP procedure, we placed the mice in their habituated cylinder for 30 min, after which a 55 °C water droplet was applied to the central–lateral plantar pad of the left hindpaw once every 30 s for 10 min. Following the water stimulations, the mice remained in the cylinder for an extra 60 min before subcutaneous injection of 4-hydroxytamoxifen (40 mg kg−1 in vehicle). After the injection, the mice remained in the cylinder for an extra 4 h to match the temporal profile for c-FOS expression, at which time the mice were returned to their home cages.

Home-cageTRAP

Home-cageTRAP induction was performed without habituation. At least 2 h into the dark cycle, mice were gently removed from their home cages. The mice were then injected with 4-hydroxytamoxifen (40 mg kg−1 in vehicle; subcutaneous) and returned to their home cages.

Immunohistochemistry

Animals were anaesthetized using Fatal-Plus (Vortech) and transcardially perfused with 0.1 M PBS, followed by 10% normal buffered formalin (NBF) solution (Sigma; HT501128). Brains were quickly removed and post-fixed in 10% NBF for 24 h at 4 °C and then cryo-protected in a 30% sucrose solution prepared in 0.1 M PBS until sinking to the bottom of their storage tube (approximately 48 h). The brains were then frozen in Tissue-Tek O.C.T. Compound (Thermo Fisher Scientific), coronally sectioned on a cryostat (CM3050S; Leica Biosystems) at 30 μm or 50 μm, and the sections were stored in 0.1 M PBS. Floating sections were permeabilized in a solution of 0.1 M PBS containing 0.3% Triton X-100 (PBS-T) for 30 min at room temperature and then blocked in a solution of 0.3% PBS-T and 5% normal donkey serum (NDS) for 2 h before being incubated with primary antibodies (chicken anti-GFP (1:1,000; Abcam; ab13970), guinea pig anti-FOS (1:1,000; Synaptic Systems; 226308), rabbit anti-FOS (1:1,000; Synaptic Systems; 226008) and rabbit anti-DsRed (1:1,000; Takara Bio; 632496)), prepared in a 0.3% PBS-T and 5% NDS solution for approximately 16 h at room temperature. Following washing three times for 10 min in PBS-T, secondary antibodies (Alexa Fluor 647 donkey anti-rabbit (1:500; Thermo Fisher Scientific; A31573), Alexa Fluor 488 donkey anti-chicken (1:500; Jackson ImmunoResearch; 703-545-155), Alexa Fluor 555 donkey anti-rabbit (1:500; Thermo Fisher Scientific; A31572) and Alexa Fluor 647 donkey anti-guinea pig (1:500; Jackson ImmunoResearch; 706-605-148), prepared in a solution of 0.3% PBS-T and 5% NDS, were applied for approximately 2 h at room temperature, after which the sections were washed again three times for 5 min in PBS-T and then again three times for 10 min in PBS-T, and then counterstained in a solution of 0.1 M PBS containing DAPI (1:10,000; Sigma; D9542). Fully stained sections were mounted onto Superfrost Plus microscope slides (Fisher Scientific) and allowed to dry and adhere to the slides before being mounted with Fluoromount-G Mounting Medium (Invitrogen; 00-4958-02) and coverslipped.

Fluorescence in situ hybridization

Animals were anaesthetized using isoflurane gas in oxygen, and the brains were quickly removed and fresh frozen in O.C.T. using Super Friendly Freeze-It Spray (Thermo Fisher Scientific). The brains were stored at −80 °C until cut on a cryostat to produce 16-μm coronal sections of the ACC. Sections were adhered to Superfrost Plus microscope slides and immediately refrozen before being stored at −80 °C. Following the manufacturer’s protocol for fresh frozen tissue for the RNAscope v.2 manual assay (Advanced Cell Diagnostics), slides were fixed for 15 min in ice-cold 10% NBF and then dehydrated in a sequence of ethanol serial dilutions (50%, 70% and 100%). The slides were briefly air-dried, and then a hydrophobic barrier was drawn around the tissue sections using a PAP Pen (Vector Labs). The slides were then incubated with hydrogen peroxide solution for 10 min, washed in distilled water and then treated with the Protease IV solution for 30 min at room temperature in a humidified chamber. Following protease treatment, C1 and C2 complementary DNA (cDNA) probe mixtures specific for mouse tissue were prepared at a dilution of 50:1, respectively, using the following probes from Advanced Cell Diagnostics: Oprm1 (C1; 315841), Slc17a7 (C3; 416631) and Fos (C4; 316921). Sections were incubated with cDNA probes (2 h) and then underwent a series of signal amplification steps using FL v.2 Amp 1 (30 min), FL v.2 Amp 2 (30 min) and FL v.2 Amp 3 (15 min). A 2-min wash in 1x RNAscope wash buffer was performed between each step, and all incubation steps with probes and amplification reagents were performed using a HybEZ oven (ACD Bio) at 40 °C. The sections then underwent fluorophore staining through treatment with a series of TSA Plus HRP solutions and Opal 520, 570 and 620 fluorescent dyes (1:5,000; Akoya Biosciences; FP1487001KT and FP1495001KT). All HRP solutions (C1 and C2) were applied for 15 min and Opal dyes for 30 min at 40 °C, with an extra HRP blocker solution added between each iteration of this process (15 min at 40 °C) and rinsing of sections between all steps with the wash buffer. Finally, the sections were stained for DAPI using the reagent provided in the Fluorescent Multiplex Kit. Following DAPI staining, the sections were mounted and coverslipped using Fluoromount-G mounting medium and left to dry overnight in a cool, dark place. The sections from all mice were collected in pairs using one section for incubation with the cDNA probes and another for incubation with a probe for bacterial mRNA (dapB; ACD Bio; 310043) to serve as a negative control.

Imaging and quantification

All tissue was imaged on a KEYENCE BZ-X all-in-one fluorescence microscope at 48-bit resolution using the following objectives: PlanApo-λ ×4, PlanApo-λ ×20 and PlanApo-λ ×40. All image processing before quantification was performed with the KEYENCE BZ-X analyzer software (v.1.4.0.1). Quantification of neurons expressing fluorophores was performed through manual counting of TIFF images in Photoshop (Adobe, 2021) using the Count function or HALO software (Indica Labs), which is a validated tool for automatic quantification of fluorescently labelled neurons in brain tissue70,71,72. Counts were made using ×20 magnified z-stack images of designated ROI. For axon density quantification, immunohistochemistry was performed to amplify the signal and visualize ACC axons throughout the brain in 50-μm tissue free-floating slices as described above. Areas with dense axon innervation were identified using ×4 imaging. Areas implicated in emotion and nociception were selected for further ×20 imaging with z stacks. These ROI were initially visualized at ×20 to determine the region with the highest fluorescence. Exposures for FITC and CY3 were adjusted to avoid overexposed pixels for the brightest area. This exposure was kept consistent for all slices for an individual mouse. For an individual ROI, one slice per mouse was included.

We used HALO software for all quantifications. One representative 16-μm slice containing ACC (selected from 1.1–1.3 mm anterior of bregma) was quantified per mouse using HALO Image Analysis software (Indica Labs). The borders for left and right ACC, Cg1, Cg2, L1, L2/3, L5, L6a and L6b were hand-drawn as individual annotation layers using the Allen Brain Reference Atlas as a guide. Slices were visually inspected for damage, dust or other debris and bound probe, and these areas were manually excluded from their respective annotation layers. Co-localization of nuclei (DAPI) with Oprm1, Fos and Vglut mRNA puncta was automatically quantified using the fluorescence in situ hybridization module (v.3.2.3) and traditional nuclear segmentation. Setting parameters were optimized by comparing performance across six slices, randomly selected across experimental groups, and confirming proper detection by visual inspection. Identical parameters were applied across all slices in the dataset.

Drugs and delivery

For chemogenetic studies, water-soluble DCZ dihydrochloride (Hello Bio; HB9126) was delivered intraperitoneally at a dose of 0.3 mg kg−1 body weight. For Oprm1 knockout, re-expression and miniscope testing, morphine sulfate (Hikma) was delivered acutely through intraperitoneal injection at a dose of 0.5 mg kg−1 body weight.

Human-scored behavioural tests

All experiments were performed during the dark phase of the cycle (0930 hours to 1830 hours). Group-housed and singly housed mice were allowed a 1-week to 2-week acclimation period to housing conditions in the vivarium before starting any behavioural testing. Additionally, 3–5 days before the start of testing, the mice were handled daily to help reduce experimenter-induced stress. On test days, the mice were brought into procedure rooms approximately 1 h before the start of any experiment to allow for acclimatization to the environment. They were provided food and water ad libitum during this period. For multi-day testing conducted in the same procedure rooms, the animals were transferred into individual ‘home away from home’ secondary cages approximately 1 h before the start of testing and were only returned to their home cages at the end of the test day. Testing and acclimatization procedures were conducted under red light conditions (less than 10 lux), with minimal exposure to bright light to avoid disruption of the reverse light cycle schedule. Equipment used during testing was cleaned with 70% ethanol solution before starting and between each behavioural trial to eliminate odors and scents.

Sensory testing for pain affective–motivational and nociceptive reflex behavioural assays

To evaluate responses to acute stimuli, animals were placed in transparent red cylinders placed on top of a metal hexagonal-mesh floored platform. Stimuli were applied to the underside of the left plantar hindpaw. This process was repeated for a total of ten applications, with each droplet applied at a 1-min interval. The animals were continuously recorded using a web camera positioned to face the front of the cylinder in which the animal was housed, and the time spent attending to the affected paw was quantified for up to 30 s after the stimulation.

To evaluate mechanical reflexive sensitivity, we used a logarithmically increasing set of eight von Frey filaments (Stoelting), ranging in gram force from 0.07 g to 6.0 g. These filaments were applied perpendicular to the plantar hindpaw with sufficient force to cause a slight bending of the filament. A positive response was characterized as a rapid withdrawal of the paw away from the stimulus within 4 s. Using the up-and-down statistical method, 50% withdrawal mechanical threshold scores were calculated for each mouse and then averaged across the experimental groups[65]. To evaluate affective–motivational responses evoked by thermal stimulation[65], we applied either a single unilateral 55 °C drop of water or acetone (evaporative cooling) to the left hindpaw, and the duration of attending behaviour was collected for up to 30 s after the stimulation. Response to the noxious stimulus was also tested following acute intraperitoneal administration of morphine (0.5 mg kg−1 body weight) or DCZ (0.3 mg kg−1 body weight). After injection, the animals were returned to their home away from home cages for 30 min to allow complete absorption of the drug. Hot-water hindpaw stimulation testing was conducted in the naive condition as described above.

Additionally, we used an inescapable hotplate set to 50 °C. The computer-controlled hotplate (6.5 in. × 6.5 in. floor; Bioseb) was surrounded by a 15-in.-high clear plastic chamber, and two web cameras were positioned at the front or side of the chamber to continuously record animals to use for post hoc behavioural analysis. For the tests conducted for chemogenetic or pharmacology studies, mice were administered morphine or DCZ 30 min before behavioural testing to allow for complete absorption of the drug and previous sensory testing[73]. The mice were gently placed in the centre of the hotplate floor and removed after 60 s.

Maximum possible analgesia effect calculation

The maximum possible analgesia (%MPA) metric quantifies how much a pain-related behaviour is reduced following drug administration, relative to both the animal’s baseline response and the maximum behavioural response for that assay. This normalization enables meaningful comparisons across animals with different baseline sensitivity levels. It is calculated as

$$100\,\times \,\frac{\mathrm{post}-\mathrm{drug}\,\mathrm{behaviour}-\mathrm{baseline}\,\mathrm{behaviour}\,}{\mathrm{maximum}\,\mathrm{behaviour}-\mathrm{baseline}\,\mathrm{behaviour}\,}$$

This normalization allows for comparisons across animals with different baseline response levels. For example, in the von Frey up-and-down test, the maximum behaviour for withdrawal threshold is 6.0 g (the highest filament force that would indicate the maximal amount of analgesia post-drug), and the minimum threshold is 0.007 g. For affective–motivational responses to thermal stimuli, behavioural responses such as attending or escape are measured over a 60-s window. Here the minimum behavioural response time is 0.0 s, and the maximum behaviour is capped at 30 s (trial duration). In this case, maximum analgesia corresponds to no response to the noxious stimulus; thus, the maximum behaviour in the %MPA formula is 0.0 s. This approach ensures consistent scaling of behavioural change across experiments and conditions. This formula provides a normalized score ranging from 0% (no analgesia) to 100% (complete analgesia). The definition of ‘maximum behaviour’ depends on the behavioural test and reflects the highest measurable response in the absence of any analgesia, whereas the ‘baseline behaviour’ is typically the pre-drug measurement for that animal. This approach ensures consistent and interpretable scaling of drug-induced behavioural changes across assays and experimental conditions.

LUPE acquisition and analysis software

Video acquisition

Behavioural videos were recorded using a Basler ace UacA2040-120um camera at a fixed resolution of 768 (width) by 770 (height) pixels. Imaging parameters were standardized, with gain set to 10.0 dB and gamma at 2.0. Exposure mode was timed with an exposure duration of 1,550 ms per frame, triggered at the start of each frame. Videos were captured at a consistent frame rate of 60 frames per second at maximum quality, with a recording buffer size of 128 frames. Frames were stored every 16 ms to ensure high temporal resolution of behavioural sequences.

Pose estimation through DeepLabCut

Assigning 2D markerless pose estimation of mice within LUPE was achieved through the DLC program (v.2.3.5-8). DLC was favoured for this purpose because of its ability to track body points at high confidence when animals perform diverse behaviours and to accurately report if a body part is visible in a given frame. Its extensive toolkit, documentation and forums allow flexible user input and manipulation when creating models.

The body points considered for assigning pose in the LUPE–DLC model were based on the clarity and frequency of appearance, involvement in behaviour sequences and prospective analyses performed. As such, 20 body points were included in the LUPE pose estimation network built in DLC: snout, upper mouth, middle forepaw digit and palm of left and right forepaws, all digits and palm of left and right hindpaws, genital region and tail base.

The model was trained iteratively 17 times for up to 350,000 iterations per training when loss and learning rate plateaued. The network architecture and augmentation method chosen were ResNet-50 and imgaug, respectively. The model was trained on 95% of the dataset, with the remaining 5% reserved for testing and evaluation. In the final training iteration, the mean average Euclidean errors between the manual labels and those predicted by the model were 2.2 pixels (0.073 cm) for the training dataset error and 2.33 pixels (0.077 cm) for the test dataset error.

Frames for labelling were manually extracted, targeting specific behavioural sequences and individual frames not accurately or confidently labelled by the model. After each training, frames for data input were added as needed for accuracy and confidence to label videos trained on and new videos analysed through LUPE not trained in the model. The total number of frames labelled was 14,554, with 10,825 (74.38%) and 3,729 (25.62%) frames coming from male and female video data files, respectively. Frames were extracted from 169 unique mouse video files comprising 133 males (78.69%) and 36 females (21.31%). The behavioural assays chosen for recording and model input captured different experimental paradigms and chemically evoked manipulations. From this, the model was able to assign pose data points with high accuracy and confidence for both male and female mouse video data from a variety of behavioural data.

The male video dataset included subcutaneous saline injection response (210; 1.94%), subcutaneous morphine 10 mg kg−1 response (1,105; 10.21%), left hindpaw intraplantar capsaicin response (666; 6.15%), left hindpaw intraplantar 1% formalin (960; 8.87%), left hindpaw intraplantar 5% formalin (1,271; 11.74%), habituation to LUPE chamber (720; 6.65%), formalin left hindpaw intraplantar injection (829; 7.66%), formalin right hindpaw intraplantar injection (719; 6.64%), formalin cheek injection (472; 4.36%), SNI left hindpaw injury day 0 (170; 1.57%), SNI left hindpaw injury day 3 (508; 4.69%), SNI left hindpaw injury day 7 (380; 3.51%), SNI left hindpaw injury day 21 (255; 2.36%), SNI right hindpaw injury day 0 (652; 6.02%), SNI right hindpaw injury day 3 (508; 4.69%), SNI right hindpaw injury day 7 (510; 4.71%), SNI right hindpaw injury day 21 (254; 2.35%) and naloxone precipitated morphine withdrawal (636; 5.88%).

The female video dataset included subcutaneous morphine 10 mg kg−1 response (133; 3.57%), habituation to LUPE chamber (320; 8.59%), formalin left hindpaw intraplantar injection (148; 3.97), formalin right hindpaw intraplantar injection (150; 4.02%), SNI left hindpaw injury day 0 (1,050; 28.17%), SNI left hindpaw injury day 3 (450; 12.07%), SNI left hindpaw injury day 7 (350; 9.39%), SNI right hindpaw injury day 0 (826; 22.16%), SNI right hindpaw injury day 3 (150; 4.02%) and SNI right hindpaw injury day 7 (150; 4.02%).

Behaviour classification through A-SOiD/B-SOiD

We trained a random forest classifier to predict five different behaviours (still, walking, rearing, grooming and licking hindpaw) given the pose estimation of the previously described 20 body parts. This supervised classifier was refined using an active learning approach over 27 iterations, with a total of 51,377 frames: still (11,599), walking (12,809), rearing (7,270), grooming (12,719) and licking hindpaw (6,971). Upon reaching an average f1 score of 93.5% across the five classes, we predicted all existing pose files and segmented licking hindpaw into licking left hindpaw and licking right hindpaw because they were clearly dissociable. After splitting the laterality of licking hindpaw, we retrained the random forest classifier to expand its classification from five classes to six classes. The final average f1 score across the six classes was 94.3%.

LUPE analyses

Once the model was trained, we predicted all the behavioural data in this study using the same random forest classifier model. Owing to the nature of intermittent pose estimation noise, we decided to smooth the output behaviour, considering only continuous bouts of 200 ms or longer.

To analyse behaviour ratio over time (Fig. 1), we calculated per-minute counts for each behaviour and normalized them by the total number of frames. This quantification allowed us to track when a particular behaviour occurred during each session. To explore variability across animals, we plotted the mean ± s.e.m.

To analyse the distance travelled, one body point high in confidence for pose detection and always present in behavioural sequences was chosen as the tail base to calculate the Euclidean distance between consecutive frames of the tail base position. This was calculated by subtracting the x and y coordinates of the tail base between consecutive frames and then calculating the Euclidean norm of the resulting vector. The distance calculated in pixels was then converted to centimetres using a conversion factor of 0.0330828 cm per pixel. This conversion factor is unique to the aspect ratio of our frames and resolution of the video data. To explore the variability across animals, we plotted the mean ± s.e.m.

Heat maps of the distance travelled were generated by constructing a 2D histogram of the tail base x and y coordinates. The code functions by binning the pose data points into a specified number of bins (50 in this case) along each x-coordinate and y-coordinate range. The ‘counts’ collected represent the frequency of occurrences in the 2D histogram that fall in each bin representing a range of x and y coordinates.

Identification of behaviour states and AMPS

Behavioural state identification

LUPE behaviour scores recorded at 60 Hz from all male and female animals across all pain models used in this study (uninjured mice, 2% capsaicin, 1% and 5% formalin and SNI) were downsampled to 20 Hz by taking the mode of every three frames. Transition matrices were generated between behaviours, with values expressed as the percentage of all frames, in which behaviour bx at time t was followed by behaviour by at time t + 1.The transition matrices were taken over 30-s windows sliding by 10-s increments within animals to avoid missing transitions. The window size was chosen in line with empirical findings that showed spontaneous bouts of intense subjective pain under chronic pain conditions lasting 22.5 ± 22.1 s (mean; s.d.)[74]. The transition matrices were transformed into single rows such that each transition matrix became a single vector of probabilities with 36 possible transitions: P(stillt+1|stillt), P(walkt+1|stillt), P(reart+1|stillt)…P(right lickt+1|right lickt). These probability vectors were then stacked to create a matrix of 215,760 observations (3,596 observations for 60 animals) by 36 transitions. These observations were clustered using 100-fold cross-validated k-means, in which the silhouette and elbow methods robustly converged at six clusters over 100 iterations. Each of six centroids thus defined a single behavioural state that could be expressed as reconstructed transition matrices.

Behavioural state classification

To classify each time point as one of six behavioural states, the same process as described above was repeated to generate smoothed transition matrices over time for each animal. At each time point, the Euclidean distance was calculated between its given transition matrix and each model centroid. The state at that time point was chosen to minimize the distance from the true transition matrix and the model centroid. Model fit for each animal in each session is thus expressed as the mean distance from the nearest centroid to its real transition matrix over the session. As distance approaches 0, the model approaches perfect fit. Transition matrices randomly shuffled over probabilities show that, at chance, model fit converges between 2 arbitrary units (a.u.) and 3 a.u., whereas true model fit ranges between 0 a.u. and 1.5 a.u., indicating genuine discovery of behavioural structure at a timescale of seconds to minutes.

Behaviour state model validation

To ensure that states were not trivially dependent on the occurrence or absence of a single behaviour, states were classified after systematically removing each behaviour from the dataset. Model fit and the percentage of observations matching the original classifications were compared with those of the shuffled dataset.

Pain scale

To distill behavioural states into a single index of pain, PCA was performed on the state distributions of each animal in the uninjured, capsaicin and formalin experiments that received 0 mg kg−1 of morphine. Each animal was described by the fraction time spent in each state, yielding a six-dimensional dataset. The scores of each animal along the first two PCs were considered. To yield scores for animals in every other experiment and condition, state distributions were projected into this PC state by subtracting the mean of the original dataset and matrix multiplying by the coefficients defining the PC space. We predetermined that the PC that scales oppositely with pain condition and analgesia would be designated the AMPS ‘pain scale’. PC2 met this requirement.

Within-state behaviour dynamics

To assess temporal dynamics of behaviours over states, the binarized behaviour classification vectors (behaviour of interest, 1; all others, 0) over the course of a given bout of a state were resampled to be 100 steps long. These vectors were pooled across bouts and animals in a given condition and averaged to yield behavioural probability as a fraction of time completed in state. The fraction of time remaining in state with respect to a behaviour occurrence was calculated by subtracting the absolute time of the behaviour from the absolute end time of that state bout and dividing by the duration.

For simulated behaviour dynamics, 100 Markov simulations of behaviours on the basis of the state 4 centroid transition matrix were produced over the course of a state for each possible initial condition (stillness, walking, rearing, grooming and licking) and allowed to proceed for the empirically determined average number of time steps state 4 lasts (600) before undergoing the same procedure.

In vivo calcium imaging

Miniscope surgery

For miniscope studies, all mice underwent an initial intracranial injection using previously described methods, followed 2 weeks later by a GRIN lens implant surgery. During the intracranial injection surgery, 800 nl of AAV9–hSyn–jGCaMP8m at a titre of 1.9 × 10^12 (Addgene virus no. 162375) was infused into the right ACC (anterior–posterior, +1.5; medial–lateral, 0.3; dorsal–ventral, −1.5 mm). Two weeks later, GRIN lens implantation surgeries were performed following the same protocol up to the craniotomy step. A 1-mm craniotomy was made by slowly widening the craniotomy with a drill. The dura was peeled back using microscissors, sharp forceps and curved forceps. The craniotomy was regularly flushed with saline, and gel foam was applied to absorb blood. An Inscopix Pro-View Integrated GRIN lens and baseplate system was attached to the miniscope and stereotax. Using the Inscopix stereotax attachment, the lens was slowly lowered into a position over the injection site. The final dorsal–ventral coordinate was determined by assessing the view through the miniscope stream. If tissue architecture could be observed in full focus with light fluctuations suggesting the presence of GCaMP-expressing cells, the lens was implanted at that coordinate (−0.6 mm to −0.3 mm dorsal–ventral). The GRIN lens/baseplate system was secured to the skull with Metabond, followed by dental cement. After surgery, the mice were singly housed and injected with meloxicam for three consecutive days during recovery.

Miniscope data collection for acute capsaicin

Miniscope neural activity and associated behaviour data were collected over 2 days (baseline/capsaicin (test day 1) and morphine/capsaicin (test day 2)), with 2 weeks between test days. On each day, the Inscopix nVista3.0 miniscope was first affixed on the mouse, and ideal focus was determined on the basis of the field of view. Imaging parameters (power, 0.7 mW mm−2; gain, 2) were held consistent across all mice and test days. The mice were injected with saline (test day 1) or 0.5 mg kg−1 of morphine (test day 2) and placed in LUPE. Five minutes later, miniscope and LUPE recordings were started and continued uninterrupted for 20 min. The recording was then stopped for 5 min to reduce photobleaching risk. Next, the mice were injected with 2-μg capsaicin (Hello Bio; HB1179) in the left hindpaw (both test days) using a Hamilton syringe affixed with a 30G needle and placed in LUPE. Both miniscope and LUPE recordings were immediately restarted after and continued for 30 min.

Miniscope data collection for chronic neuropathic pain

The behaviour and neural activity of mice were recorded eight times before, during and after the onset of SNI. The mice were tested at baseline (1 day before SNI) and then 1 h, 1 day, 3 days, 1 week, 2 weeks and 3 weeks post-SNI. One day after the 3-week testing session, the mice underwent another test day in which they were injected with 0.5 mg kg−1 of morphine 30 min before recording. Each testing day consisted of a 30-min LUPE recording. Ideal imaging parameters were determined on each day, and neural activity was aligned to LUPE behaviour tracking through a transistor–transistor logic pulse at the start of the recording session.

Miniscope data collection for pain and valence panels

Within 1 week after completion of the chronic neuropathic pain LUPE testing, mice underwent exposure to a panel of acute stimuli while neural activity was recorded. The animals were placed in transparent containers placed on top of a metal hexagonal-mesh floored platform. Stimuli were applied to the underside of the left plantar hindpaw. The animals were continuously recorded using two web cameras.

The stimuli were light touch (0.16 g von Frey filament), 30 °C water, pin prick (25G syringe needle), acetone and hot water (55 °C). Water and acetone stimuli were delivered using a needleless syringe, and a droplet of the liquid was applied. Five presentations of each stimulus were administered to the left hindpaw with 90 s between each presentation.

After the pain panel, the mice underwent 3 days of 20-min training for the valence panel, in which they learned to lick a 10% sucrose (Sigma-Aldrich S7903; diluted in water) droplet from a needleless syringe poked through the mesh of the metal rack. On the day of the panel, neural activity was recorded while they licked sucrose (approximately seven presentations), with at least 45 s between each presentation, and then the liquid in the syringe was switched to 0.06 mM quinine (Sigma-Aldrich Q1125; diluted in water). Quinine was presented until the mice licked at least five times. Finally, seven presentations of 55 °C water were applied to the left hindpaw. Both sucrose and quinine concentrations were adapted from ref. [65].

Calcium imaging preprocessing

Videos were downloaded from the Inscopix Data Acquisition Box and uploaded to the Inscopix Data Processing Software. Videos were spatially downsampled by a factor of 4 and spatial bandpass filtered between 0.005 and 0.500. The videos were then motion corrected with respect to their mean frame. Cells were identified and extracted using constrained non-negative matrix factorization for microendoscopic data (default parameters in the Inscopix implementation, except the minimum in-line pixel correlation is 0.7 and the minimum signal-to-noise ratio is 7.0) and deconvolved using second-order constrained spike deconvolution.

Spontaneous activity in neurons

Deconvolved activity in each neuron was z-scored. Peaks were identified using the findpeaks function in MATLAB with the argument ‘MinPeakProminence’ set to 1.

Neural encoding of behavioural probabilities

Categorical frame-by-frame vectors of behavioural values (still, walking, rearing, grooming, licking left hindpaw and licking right hindpaw) were downsampled from 60 Hz to 20 Hz to match sampling rates of neural recordings by taking the floor of the mode for within a sliding window of three frames. The probability of engaging in a behaviour as generated by this k-means–GLM model thus takes into account recent behaviour history and slowly evolving state of the animal. These higher-order measurements of behavioural state have been linked to neural activity previously75,76,77. To identify neurons encoding this higher-order index of behavioural state, a binomial GLM was trained to predict binarized probabilities of a given behaviour (thresholded at Pbehaviour = 0.5) from the PCs of the ACC activity explaining 80% of the variance in individual animals. Neurons with a coefficient greater than 1.5 z-score in the three most highly weighted PCs were classified as Pbehaviour-encoding neurons. Neurons that, on average, increased or decreased their activity within 500 ms following the onset of behaviour were classified as behaviour+ or behaviour− neurons, respectively.

Behaviour-evoked activity

To assess modulation of activity in these neurons by behaviour, peri-behavioural time histograms of neural activity were generated from 2 s before to 2 s after the start of each behaviour bout. To quantify, peri-behavioural time histograms were z-scored to the 1 s before behaviour onset, and areas under the curve were taken for each of the 2 s after the bout start. If a neuron was suppressed after behaviour onset, it was designated a behaviour-off neuron and vice versa for a behaviour-on neuron. Behavioural tuning curves were generated by taking the z-score of the activity of each population of encoding neurons during each scored behaviour. Selectivity of these neurons for a given behaviour was calculated by taking the d′ between their encoded behaviour and each other behaviour:

$${d}^{{\prime} }=\frac{|\mu 1-\mu 2|}{\sqrt{\frac{\sigma 1+\sigma 2}{2}}}$$

Behaviour, sensory and state decoding

For behaviour and sensory decoding, a Fisher linear decoder was trained to predict each behaviour or sensory stimulus in a given session from the activity of all neurons. For state decoding, a support vector machine decoder was trained to predict whether animals were in a pain or non-pain state using the activity of lick-probability-encoding neurons. Each decoder for each animal and session underwent 100-fold cross-validation, training on a random 80% of the data each time and testing on 20%. Data were randomly subsampled to ensure an equal number of samples per class, thereby eliminating training bias. Decoders were also trained on randomly shuffled data as a control. Confusion matrices were generated, averaged over cross-validations and normalized by the true frequency of each behaviour in the test set. Significance was determined using a permutation test.

Identifying stimulus-active neurons

For pain and valence panels, the activity of each neuron from 0 s to 3 s after stimulus was compared with the activity from −3 s to −1 s before stimulus with a permutation test (false discovery rate (FDR) threshold P < 0.01).

Single-nucleus RNA sequencing

Nuclei preparation

A single punch of the right side of the basolateral amygdala measuring 2 mm in width and 1 mm in depth was used to prepare nuclei suspensions. Nuclei isolation was performed using the Minute Single Nucleus Isolation Kit designed for neuronal tissue/cells (BN-020; Invent Biotechnologies). In brief, the tissue was homogenized using a pestle in a 1.5-ml LoBind Eppendorf tube. Subsequently, the cells were resuspended in 700 µl of cold lysis buffer and RNase inhibitor and incubated on ice for 5 min. The homogenate was then transferred to a filter within a collection tube and incubated at −20 °C for 8 min. Following this, the tubes were centrifuged at 13,000g for 30 s, the filter was discarded and the samples were centrifuged at 600g for 5 min. The resulting pellet underwent one wash with 200 µl of PBS + 5% bovine serum albumin and then resuspended in 60 µl of PBS + 1% bovine serum albumin. The concentration of nuclei in the final suspension was assessed by staining with Trypan Blue and counted using a haemocytometer. The suspension was diluted to an optimal concentration of 500–1,000 nuclei per microlitre.

Single-nucleus gene expression assay

Nuclei suspensions were used as input for the 10x Genomics 3′ gene expression assay (v.3.1), following manufacturerʼs protocols. A total of 20,000 nuclei were loaded into the 10x Genomics Chromium Controller, with the aim of recovering approximately 10,000–12,000 nuclei per sample. Subsequently, sequencing libraries were constructed, and unique dual-indexed libraries were pooled together at equimolar concentrations of 1.75 nM and sequenced on the Illumina NovaSeq 6000 using 28 cycles for read 1, 10 cycles for the i7 index, 10 cycles for the i5 index and 90 cycles for read 2.

Data analysis

Preprocessing of single-nucleus RNA sequencing data

Paired end sequencing reads were processed using 10x Genomics Cell Ranger v.5.0.1. Reads were aligned to the mm10 genome optimized for single-cell sequencing through a hybrid intronic read recovery approach[78]. In short, reads with valid barcodes were trimmed by template switching oligonucleotide sequence and aligned using STAR v.2.7.1 with MAPQ adjustment. Intronic reads were removed, and high-confidence mapped reads were filtered for multimapping and unique molecular identifier correction. Empty gel beads in emulsion were also removed as part of the pipeline. DESeq2 was used to compare expression at the 3-day, 3-week and 3-month time points to control animals for each cluster. Pseudobulked expression differences were assessed by performing Wald test, and a FDR of 0.05 was used to correct for multiple testing.

Clustering and comparison

Count matrices for each individual sample were converted to Seurat objects using Seurat 4.3, and nuclei were filtered with thresholds of greater than 200 minimum features and less than 5% mitochondrial reads. Initial dimensionality reduction and clustering were performed to enable removal of cell-free mRNA using SoupX[79]. SCTransform was used to normalize and scale expression data, and all samples were combined using the Seurat integration method. Putative doublets identified by DoubletFinder, as well as residual clusters with mixed cell-type markers or high mean unique molecular identifier, were removed. The cleaned dataset was clustered using the first 20 PCs at a resolution of 0.3. Cluster identity was determined by expression of known marker genes.

Modular activity scoring

Modular activity scores were calculated for all clusters using AddModuleScore with the list of the 25 putative IEGs (Arc, Bdnf, Cdkn1a, Dnajb5, Egr1, Egr2, Egr4, Fos, Fosb, Fosl2, Homer1, Junb, Nefm, Npas4, Nr4a1, Nr4a2, Nr4a3, Nrn1, Ntrk2, Rheb, Sgsm1, Syt4 and Vgf) against a control feature score of 5 (ref. [80]).

Gene ontology analysis

Gene Ontology analysis was performed on differentially expressed genes (DEGs) identified between uninjured and SNI conditions. To investigate the functional enrichment of genes upregulated in the SNI condition, we used SynGO (https://www.syngoportal.org/), a synapse-specific, evidence-based Gene Ontology annotation platform that provides curated information about synaptic genes and their roles in biological processes, molecular function and cellular localization. Gene symbols corresponding to upregulated DEGs were submitted to the SynGO web-based analysis tool. Enrichment analysis was performed against the background of all protein-coding human genes mapped to orthologous mouse genes using SynGO’s default statistical settings. We focused on enrichment within high-confidence, expert-curated Gene Ontology categories on the basis of experimental evidence. Overrepresentation testing was conducted with FDR correction, and enriched Gene Ontology terms were considered significant at FDR < 0.05. The analysis enabled functional interpretation of synaptic gene expression changes in chronic pain conditions, with particular attention to processes related to synaptic signalling, neurotransmitter transport and pre-synaptic or post-synaptic structural components.

Quantitative PCR

Cellular RNA was extracted with RNAZol (Sigma-Aldrich; R4533) according to the manufacturer’s protocol, and cDNA was synthesized from 1-μg RNA (Applied Biosystems; 4374966). cDNA was diluted 1:10 and assessed for mRNA transcript levels by qPCR with SYBR Green Mix (Applied Biosystems; A25741) on a QuantStudio 7 Flex Real-Time PCR System (Thermo Fisher Scientific). The oligonucleotide primer sequences for target and reference genes are as follows: mouse_GAPDH (forward: AACGACCCCTTCATTGACCT; reverse: TGGAAGATGGTGATGGGCTT), mouse_L30 (forward: ATGGTGGCCGCAAAGAAGACGAA; reverse: CCTCAAAGCTGGACAGTTGTTGGCA) and mouse_OPRM1 (forward: CTGCAAGAGTTGCATGGACAG; reverse: TCAGATGACATTCACCTGCCAA). Fold change in the target mRNA abundance was normalized by the reference gene GAPDH and calculated using the 2−ΔΔCT method[81].

Closed-loop, optogenetic real-time place preference and conditioned place preference

First, painTRAP2 mice injected with AAV1–MORp–DIO–iC++ and bilateral fibre-optic implants (200-µm diameter; 0.66 numerical aperture; 700-µm pitch between each fibre centre; Thorlabs), with SNI or no injury, were habituated for 30 min a day for 5 days in a holding cage. For basal place-preference measurements (pretest session), mice with attached patch cables were placed in a two-chamber acrylic box (60 × 25 × 30 cm^3), with each side of the chamber measuring 30 × 25 cm^2), at room temperature (approximately 23 °C) and under approximately 10 lux red light. Each chamber had different contextual pattern cues on the wall: one side had a black-and-white striped pattern and the other a dotted pattern. Mouse movements were recorded in real time using an overhead Basler top-view camera connected to EthoVision tracking software (Noldus), connected through a mini-I/O box to a 450-nm LED and pulse-wave generator (Prizmatix), for 30 min. Chamber preference times were quantified using EthoVision for the amount of time spent in each chamber. Using a biased design, on the basis of the quantified basal preference, the mice received LED stimulation in the non-preferred chamber because our hypothesis was that activation of iC++ would reduce spontaneous pain and drive increased dwell time in the LED-paired chamber. The following day, to assess optogenetic real-time place preference, the mice were placed in the centre of the apparatus for a 30-min session. During this session, EthoVision body contour tracking of the mouse centre point activated the blue LED when the centre point was detected in the originally non-preferred chamber, which in turn delivered 10-mW continuous light through the bifurcating fibre-optic implants for the entire duration that the mouse centre point was detected in the chamber; the LED would turn off when the mouse centre point was detected in the other chamber (closed-loop protocol). This procedure was repeated daily for seven consecutive sessions to optogenetic real-time place preference-induced learning rates over time. After the pretest and seven closed-loop optogenetic sessions, we performed one post-test session to assess if a conditioned place preference had developed. Here, the mice remained connected to the patch cables, but no light was delivered, and the time spent in each chamber was calculated. Increased time in the formally LED-paired chamber indicates a learned preference for iC++-mediated inhibition of ACC nociceptive MOR+ cell types.

Statistics and reproducibility

The MORp viruses used in Fig. 4 are available from the Gene Vector and Virus Core at Stanford University (https://neuroscience.stanford.edu/shared-resources/gvvc). In our laboratory, MORp–eYFP, MORp–hM4Di and MORp–FlpO have each been used successfully at least three times in independent experiments.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

All μ-opioid receptor promoter viruses (AAV–MORp) are available for academic use from Stanford Universityʼs Gene Vector and Virus Core (https://neuroscience.stanford.edu/research/neuroscience-community-labs/gene-vector-and-virus-core) and/or by contacting the lead authors. The RNA sequencing data are available in the Gene Expression Omnibus (accession no. GSE308301). Source data are provided with this paper.

Code availability

All codes for LUPE analyses of behaviour and paired calcium imaging can be found at https://github.com/justin05423/LUPE-2.0-NotebookAnalysisPackage.

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Acknowledgements

This study was supported by the US National Institutes of Health awards NIGMS DP2GM140923 (G.C.), NIDA R00DA043609 (G.C.), NIDA R01DA054374 (G.C. and K.B.), NINDS R01NS130044 (G.C. and K.B.), NIDA R01DA056599 (G.C. and K.B.), NIDA R21DA055846 (B.C.R.), NIDA F31DA062445 (C.S.O.), NINDS F31NS143421 (S.A.R.), NIDA F32DA053099 (N.M.M.), NIDA F32DA055458 (B.A.K.), NIDA F31DA057795 (L.M.W.), NINDS F31NS125927 (J.A.W.), NIDA T32DA028874 (G.J.S.), NINDS RF1NS126073 (M.R.B.), Howard Hughes Medical Institute (K.D.), Whitehall Foundation (G.C.) and Tito’s Love Research Fund (G.C.). We thank J. Blendy, A. Heyeler, L. Marconi, L. Hastings and the Penn University Laboratory Animal Resources veterinarians and husbandry staff for shared reagents and technical assistance. We thank Stanford Universityʼs ‘Cracking the Neural Code’ Program and the Gene Vector and Virus Core for providing custom viruses.

Author information

Author notes

1. These authors contributed equally: Corinna S. Oswell, Sophie A. Rogers, Justin G. James

2. These authors jointly supervised this work: Benjamin C. Reiner, Karl Deisseroth, Eric A. Yttri, Gregory Corder

Authors and Affiliations

1. Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Corinna S. Oswell, Sophie A. Rogers, Justin G. James, Nora M. McCall, Gregory J. Salimando, Malaika Mahmood, Lisa M. Wooldridge, Meghan Wachira, Adrienne Y. Jo, Raquel Adaia Sandoval Ortega, Jessica A. Wojick, Sofia A. Farinas, Samar N. Chehimi, Jacqueline W. K. Wu, Lindsay L. Ejoh, Blake A. Kimmey, Emily Lo, Richard C. Crist, Benjamin C. Reiner & Gregory Corder

2. Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Corinna S. Oswell, Sophie A. Rogers, Nora M. McCall, Gregory J. Salimando, Malaika Mahmood, Lisa M. Wooldridge, Meghan Wachira, Adrienne Y. Jo, Raquel Adaia Sandoval Ortega, Jessica A. Wojick, Katherine Beattie, Jacqueline W. K. Wu, Lindsay L. Ejoh, Blake A. Kimmey & Gregory Corder

3. Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Corinna S. Oswell, Sophie A. Rogers, Nora M. McCall, Gregory J. Salimando, Malaika Mahmood, Lisa M. Wooldridge, Meghan Wachira, Adrienne Y. Jo, Raquel Adaia Sandoval Ortega, Jessica A. Wojick, Jacqueline W. K. Wu, Lindsay L. Ejoh, Blake A. Kimmey & Gregory Corder

4. Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA

Justin G. James, Alex I. Hsu & Eric A. Yttri

5. Department of Biobehavioral Health Sciences, School of Nursing, and Translational Medicine and Human Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Amrith Rodrigues & Kate Townsend Creasy

6. Department of Physiology and Biophysics, University of California, Irvine, Irvine, CA, USA

Ghalia Azouz, Jose J. Vasquez & Kevin T. Beier

7. Department of Neurobiology, School of Biological Sciences, University of California, San Diego, San Diego, CA, USA

Matthew R. Banghart

8. CNC Program, Stanford University, Stanford, CA, USA

Charu Ramakrishnan & Karl Deisseroth

9. Department of Bioengineering, Stanford University, Stanford, CA, USA

Karl Deisseroth

10. Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA

Karl Deisseroth

11. Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA

Karl Deisseroth

12. Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA

Justin G. James & Eric A. Yttri

Contributions

Conception and design were carried out by C.S.O., S.A.R., J.G.J., N.M.M., A.I.H., C.R., B.C.R., K.D., E.A.Y. and G.C. Acquisition of data was performed by C.S.O., S.A.R., J.G.J., N.M.M., G.J.S., M.M., L.M.W., M.W., A.Y.J., R.A.S.O., J.A.W., K.B., S.A.F., S.N.C., A.R., J.W.K.W., L.L.E., B.A.K., E.L., G.A., J.J.V. and G.C. Analysis of data was conducted by C.S.O., S.A.R., J.G.J., N.M.M., A.I.H., M.R.B., K.T.B., K.T.C., C.R., B.C.R., E.A.Y. and G.C. Creation of new software was undertaken by S.A.R., J.G.J., A.I.H., E.A.Y. and G.C. Drafting and revision of the paper were completed by C.S.O., S.A.R., J.G.J., E.A.Y. and G.C. Oversight was provided by R.C.C., B.C.R., E.A.Y. and G.C.

Corresponding authors

Correspondence to Eric A. Yttri or Gregory Corder.

Ethics declarations

Competing interests

G.C., K.D., C.R. and G.J.S. are listed as inventors on a provisional patent application filed through the University of Pennsylvania and Stanford University about the custom sequences used to develop and the applications of synthetic opioid promoters (patent application no. 63/383,462 462 ‘Human and Murine Oprm1 Promotes and Uses Thereof’). The other authors declare no competing interests.

Peer review

Peer review information

Nature thanks Megan Keiser, Monique Smith and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Genetic capture and tagging of noxious stimulus-responsive neurons in the anterior cingulate cortex (ACC).

(a) Timing strategy of 4-hydroxytamoxifen-mediated recombination in TRAP2 mice for activity-dependent genetic capture and permanent tagging of stimulus-active neurons, paired with stimulus-driven FOS expression and timed tissue collection for visualization of multiple stimulus-activity pairings. (b) Utilizing TRAP2 mice for permanent fluorescent tagging of nociceptive cell-types (painTRAP), and noxious re-engagement visualized with the immediate early gene, FOS (painFOS). (c) ACC painTRAP tagging and painFOS co-expression. (d) Quantification of painTRAP cells across all layers of the dorsal Cg1 and ventral Cg2, displayed across the entire anterior-posterior extent of the ACC. (e) Utilizing TRAP2 mice to permanently tag active neurons in a no-stimulus control condition (home-cageTRAP) paired with timed tissue collection of neurons active to a noxious stimulus via expression of FOS (painFOS). (f) Expression and overlap of home-cageTRAP tagged cells and painFOS expression. (g) Quantification of home-cageTRAP cells across layers and anterior-posterior coordinates of ACC. (h) Comparison of TRAP cells in painTRAP and home-cageTRAP conditions across the entire ACC and within the peak range that displayed the highest values (unpaired t-test, p = 0.0402). ⋆ = p < 0.05. Bars are mean; error bars are SEM. See Table S1 Rows 48–49 for statistics.

Extended Data Fig. 2 FISH quantification of nociceptive and opioidergic ACC neurons in chronic neuropathic pain.

(a) Representative 20X stitched images for Oprm1, noxious-evoked Fos (painFos) and Slc17a7 mRNA using fluorescence in situ hybridization (FISH). (b) Expression of Oprm1, and its overlap with painFos and Slc17a7 mRNA across the layers of ACC in uninjured male and female mice. (c) Oprm1 mRNA quantification across the left hemisphere (ipsilateral to the left hindlimb Spared Nerve Injury, SNI) of ACC, broken down by subregion and layers, in male and female mice with or without neuropathic pain. (d) Same as (C) but for the right hemisphere, contralateral to the injury. (n = 8 neuropathic (5 male, 3 female) and 9 uninured (4 male, 5 female)).

Extended Data Fig. 3 Transcriptomic classification of nociceptive and opioidergic cell-types in the cingulate.

(a) Design of single-nucleus RNA sequencing experiments from ACC punches of mice with bilateral sciatic nerve transections collected after different timepoints of chronic pain development and light-touch-evoked immediate early genes (IEGs); n = 4 male mice per condition. (b) Dimensionality reduction plot of all nuclei from all groups in 23 cell-type clusters. (c) Broad classification of all clusters into neuronal and non-neuronal classes with the percentage of nuclei per condition in each cluster. (d) Specific cell-type identities and dot-plot for selected marker genes. (e) Dot plot for all opioid receptors and peptides. (f) Nociceptive activity score per cluster based on a panel of n = 25 IEGs. (g) Nebulosa density plot of the IEG panel overlaying the UMAP of cell-type clusters. (h) Feature plot for Oprm1 expression. (i) Intermixed nuclei with UMAP space from Uninjured mice and the three post-Spared Nerve Injury (SNI) timepoints: 3-days, 3-weeks and 3-months. (j) The number of DEGs per cell-type for each injury condition compared to the Uninjured condition. (k) Synaptic Gene Ontology (SynGO) analysis for differential expressed genes (DEGs) in ACC nociceptive cell-types (L2/3 IT-3 (light blue), L5 IT-1 (dark blue), L6 CT-2 (maroon) 3-days after SNI compared to Uninjured. Left: DEG gene count and logQ10 enrichment related to Cellular Components, such as presynapse. Right: DEG gene counts and logQ10 enrichment related to Biological Processes, such as vesicle recycling.

Extended Data Fig. 4 Morphine analgesia is mediated by action on ACC MOR+ cell types.

(a) Schematic of the timing of common behavioural responses to noxious stimuli over seconds to weeks. (b) Classification and timing of reflexive and affective-motivational pain behaviours. (c) Design to virally-delete MORs from ACC neurons in Oprm1Flox/Fox mice (n = 11 hSyn-eGFP mice, n = 14 hSyn-eGFP-Cre mice; 9 males and 5 females). (d) ACC Oprm1 qPCR (n = 3 males per group, two-tailed Student’s t-test, p = 0.002). (e) Expression of hsyn-eGFP-Cre injected into ACC of Oprm1Flox/Fox mice. (f) Reflexive withdrawal threshold and time spent engaging in affective-motivational pain behaviours before and after administration of morphine (0.5 mg/kg, intraperitoneal) in mice with virally deleted ACC MORs (hsyn-eGFP-cre) or controls (hsyn-eGFP). Two-Way ANOVA, Tukey correction, Von Frey, interaction p = 0.2; 55 °C, interaction p = 0.007; Acetone, interaction p = 0.003. (g) Morphine analgesia for mechanical and thermal noxious stimuli. Unpaired t-test, two-tailed: von Frey p = 0.66; 55° p = 0.015; acetone p = 0.001. (h) Behaviour metrics for thermal thresholds and affective-motivational engagement on an inescapable hotplate after morphine (unpaired t-test, two-tailed: Latency withdrawal, p = 0.089; total withdrawal, p = 0.617; latency to attend, p = 0.131; total attending, p = 0.0123). (i) Time between the first reflexive withdrawal and first attending bout on the inescapable hotplate after morphine. (unpaired t-test, two-tailed, p = 0.0015) (j) Proportion of time spent engaging in attending behaviours after the first bout of such attending behaviours on the inescapable hotplate after morphine (unpaired t-test, two-tailed, p = 0.012). (k) Design to virally re-express MORs in an embryonic MOR knockout mouse (n = 10 null expression mice, hsyn-FLEx-eGFP; n = 16 re-expression mice, hSyn-FLEx-mCherry-2A-hMOR; 8 males and 8 females). (l) ACC OPRM1 qPCR (n = 3 males per group; unpaired t-test, two-tailed, p = 0.0006). (m) Expression of hSyn-FLEx-mCherry-2A-hMOR injected into ACC of MOR KO mice. (n) Reflexive withdrawal threshold and time spent engaging in affective-motivational pain behaviours before and after administration of morphine (0.5 mg/kg, intraperitoneal) in mice with a global knockout of MORs or with re-expression of ACC MORs. Two-Way ANOVA, Tukey correction, Von Frey, interaction p = 0.66; 55 °C, interaction p = 0.005; Acetone, interaction p = 0.008 (o) Morphine analgesia for mechanical and thermal noxious stimuli (unpaired t-test, two-tailed: von Frey p = 0.84; 55° p = 0.003; acetone p = 0.001). (p) Behaviour metrics for thermal thresholds and affective-motivational engagement on an inescapable hotplate after morphine (unpaired t-test, two-tailed: Latency withdrawal, p = 0.12; total withdrawal, p = 0.22; latency to attend, p = 0.013; total attending, p = 0.019). (q) Time between the first withdrawal and first attending bout. (unpaired t-test, two-tailed, p = 0.003). (r) Proportion of time of attending behaviours after the first attending bout (unpaired t-test, two-tailed, p = 0.021). ⋆ = p < 0.05. Bars or dots are mean; small dots are individual animals; error bars are SEM. See Table S1 Rows 50–65 for statistics.

Extended Data Fig. 5 Behaviour state model fits animals across models and treatments.

(a) Cartoon of unfolded transition matrices between six spontaneous behaviours (top), which are k-means clustered in a 36-dimensional space. Model centroids are described by 36 dimensional points (unfolded transition matrices) in that state space (middle). Behavioural trajectories can be plotted in this state space, and portions of this trajectory are classified as belonging to a state by calculating the nearest centroid (Euclidean distance, bottom). (b) 100-fold cross-validated models with k = 6 clusters maximize silhouette score and represent the “elbow” of the sum of squared distances. (c) Fit across full model and control models (left; permutation tests: p < 0.0001 for each condition compared to n = 100 shuffles) and fraction of matching classification across control models (right; permutation tests: p < 0.0001 for each condition compared to n = 100 shuffles) in the pain model animals used as training data. (d-f) Fit across full model and control models in uninjured (e), capsaicin (f), and formalin (g) mice across morphine doses (Permutation tests: p < 0.0001 for each condition and treatment compared to n = 100 shuffles). (g,h) Fit across full model and control models in mice expressing either MORp-YFP (left) or MORp-hM4Di (right) before and after spared nerve injury (SNI) with DCZ administration (Permutation tests: p < 0.0001 for each condition compared to n = 100 shuffles). (i) Log-transformed transition matrices between states. (j,k) Bouts (j) or mean duration (k) per session of each state in capsaicin (cyan), formalin (magenta), and SNI (dark red) animals (One-way RM ANOVA, Tukey correction, pState, bout <0.0001, pState, duration <0.0001). ⋆ = p < 0.05. Dots are mean; error bars are SEM. See Table S1 66–74 for statistics.

Extended Data Fig. 6 Behaviours and states are dose-dependently modulated by morphine.

(a-c) Average behaviour probabilities over 30 min in uninjured (a), 1% formalin- (b), and 2% capsaicin-injured (c) mice (n = 20/group). (d) Top: Average probability of licking injured hindpaw in uninjured, 1% formalin-, 5% formalin, and 2% capsaicin injured mice over 30 min. Bottom: Comparison of total seconds licking between groups in the first 10 min (left) and second 20 min (right; One-way ANOVA, Tukey correction). (e) Dose response of morphine on average probability of walking (top) and licking (bottom) in 1% formalin-injured mice during Phase 1 (left) and Phase 2 (right; One-way ANOVA, Tukey correction). (f) Dose response of morphine on average probability of walking (top) and licking (bottom) in capsaicin-injured mice (One-way ANOVA, Tukey correction). (g) Dose response of morphine on fraction occupancy in all behavioural states in uninjured, 1% formalin, and capsaicin (One-way ANOVA, Tukey correction: pAll conditions, states 1–3 < 0.0001, pCapsaicin and formalin, state 4 < 0.0001, pUninjured, State 5 = 0.019, pFormalin, State 5 < 0.0001, pCapsaicin, State 5 = 0.0001, pUninjured, State 6 = 0.046, pFormalin, State 6 = 0.0018, pCapsaicin, State 6 = 0.001). ⋆ = p < 0.05. Bars or lines are mean; small dots are individual animals; error bars and shaded areas are SEM. See Table S1 Rows 75–105 for statistics.

Extended Data Fig. 7 Licking is an affective-motivational pain behaviour as predicted by Gate Control Theory.

(a) Schematic of behavioural negative feedback loop of pain and recuperation and its physiological basis as posited by gate control theory. Alternative hypotheses as to how licking could be organized during pain if each of four hypotheses (pain-driven and analgesic; pain-driven but not analgesic; analgesic but not pain-driven; and neither pain-driven nor analgesic) were true. (b) Top row: Markov simulated probabilities of each behaviour (left to right: still, walk, rear, groom, lick) averaged over initial conditions (n = 100 simulations per initial condition). Bottom rows: real probabilities of each behaviour in each injury condition as a function of fraction time in Pain State-4 (n = 19–20 animals per injury group, bouts pooled over animals). (c) Left: Cumulative distributions of each behaviour as fraction time remaining in Pain State-4. Right: Kolmogorov–Smirnov test log_10 p-values comparing cumulative distributions to each other. (d) Distribution of licks in each state pooled across all injured animals. (e) Cumulative distribution of each behaviour as a fraction time remaining in each state. (f) Kolmogorov–Smirnov test p-values comparing the distributions of licks between each state. Lines are mean, shaded areas are SEM. See Table S1 Rows 106–110 for statistics.

Extended Data Fig. 8 Characterizing behaviour probability-encoding ACC neurons.

(a) GRIN lens implant locations in each mouse. (b) Maps of registered ROIs in each mouse. (c) Diagram of procedure to identify behaviour probability-encoding neurons from GLM over representative trace of a lick probability-encoding PC aligned to licks. (d) auROC of GLMs predicting each behaviour across sessions (paired t-tests, FDR < 0.01: pAll comparisons <0.0001). (e) Average PSTHs of lick-encoding neurons over onset of each behaviour, sorted by Lick-evoked activity and z-scored to 1-s prior to behaviour initiation. (f) Average activity across pooled behaviour probability-encoding neurons around their preferred behaviour. From left to right: neurons activated vs. inhibited by that behaviour. From top to bottom: behaviour-evoked activity in still, walk, rear, groom, and lick-probability encoding neurons. (g) Permutation test p-values within each animal testing if Fisher decoding accuracy for each behaviour is significantly higher than chance in each session (n = 1000 shuffles). (h) auROC of Fisher decoder in each animal and session in real and shuffled data. (i) Average Fisher decoding accuracy of pain vs. non-pain states in capsaicin (left) and capsaicin + morphine (right). (j) Permutation test p-values within each animal testing if Fisher decoding accuracy for states is significantly higher than chance in each session (n = 1000 shuffles). (k) auROC of Fisher decoder in each animal and session in real and shuffled data. (l) Selectivity of positive (left) and negative (right) pLick neurons for lick compared each other behaviour in capsaicin and capsaicin+morphine sessions measured by d-prime (Two-Way ANOVA, Tukey correction: positive pLick neurons pTreatment = 0.037, pBehaviour <0.0001, negative pLick neurons, pTreatment = 0.0007, pBehaviour <0.0001). ⋆ = p < 0.05. Bars, lines, or dots are mean; small dots are individual animals; error bars and shaded areas are SEM. See Table S1 Rows 111–119 for statistics.

Extended Data Fig. 9 ACC neurons discriminate sensory stimuli of varying modalities, valence.

(a) Top: Activity of hot water-activated neurons during all sensory stimuli in sorted to hot water-responses in uninjured mice (z-scored to 1 s before onset; n = 9 mice). Bottom: Same as top for hot water-inhibited neurons (n = 9 mice). (b) Same as (a) in SNI mice (top: n = 9 mice; bottom: n = 9 mice). (c,d) Average fraction overlap of significantly stimulus-activated (top) or -suppressed (bottom) in uninjured (c) and SNI (d) mice. (e) Average Fisher decoding accuracy of sensory stimuli in uninjured (left) and SNI (right) mice. (f) Permutation test p-values within each animal testing if Fisher decoding accuracy for stimuli is significantly higher than chance in each session (n = 1000 shuffles). (g) auROC of Fisher decoder in each animal in real and shuffled data (paired t-tests, pSNI = 0.001, pUninjured <0.0001). ⋆ = p < 0.05. Dots are individual animals. See Table S1 Row 120 for statistics.

Extended Data Fig. 10 ACC neurons are selective for valence, nociception.

(a,c) Activity of all recorded neurons across stimuli, sorted to hot water- (a) or sucrose- (c) evoked activity (n = 4 mice). (b,d) Average activity of significantly hot water- (b) or sucrose- (d) suppressed (top) and -activated (bottom) neurons during each stimulus. (e) Average fraction overlap of stimulus-activated (left) and -suppressed (right) neurons. (f) Average Fisher decoding accuracy of sensory stimuli. (g) Permutation test p-values within each animal testing if Fisher decoding accuracy for stimuli is significantly higher than chance in each session (n = 1000 shuffles). (h) auROC of Fisher decoder in each animal in real and shuffled data. Dots are individual animals.

Extended Data Fig. 11 Chronic pain impairs encoding of behaviours and states in ACC.

(a,e) Average Fisher decoding accuracy of behaviours (a) and pain states (e) in SNI mice in each session. (b,f) Left: Average Fisher decoding accuracy of behaviours (b) and pain states (f) in SNI (n = 9) and uninjured mice (n = 9) before and after SNI or anesthesia (Mixed effects model, Tukey correction: pLick, interaction = 0.0089, pState, interaction = 0.067). Right: Same as left at three weeks post-SNI or anesthesia before and after morphine (Mixed effects model, Tukey correction: pLick, interaction = 0.041, pState, injury = 0.025). (c,g) auROC of behaviour (c) or state (g) Fisher decoder in each SNI (left) or uninjured (right) animal and session in real and shuffled data (Mixed effects model, Tukey correction: pSNI and control, lick and state, real vs shuffle <0.0001). (d,h) Permutation test p-values within each animal testing if Fisher decoding accuracy for behaviour (d) or state (h) is significantly higher than chance in each session (n = 1000 shuffles). (i) Left: Single cell discriminability for lick in positive pLick neurons before and after SNI or anesthesia (Two-Way ANOVA, Tukey correction: pInteraction = 0.0044). Right: Same as left at three weeks post-SNI or anesthesia before and after morphine (Two-Way ANOVA, Tukey correction: pInteraction = 0.048). (j) Same as (g) for negative pLick neurons (Two-Way ANOVA, Tukey correction: pLeft, interaction = 0.0031, pRight, injury = 0.0016, pRight, treatment = 0.029). (k) Left: Fraction of positive pLick neurons before and after SNI or anesthesia (Mixed effects model, Tukey correction). Right: Same as left at three weeks post-SNI or anesthesia before and after morphine (Mixed effects model, Tukey correction: pInteraction = 0.0024). (l) Same as (k) for negative pLick neurons (Mixed effects model, Tukey correction: pRight, treatment = 0.046). (m,n) Average behaviour-evoked activity in neurons encoding that behaviour which increase (m) or decrease (n) their activity upon bout onset (z-score from −2 to −1 s before onset). From top to bottom: Still, walk, rear, groom, and lick-encoding neurons at still, walk, rear, groom, and lick onsets, respectively. ⋆ = p < 0.05. Dots in panels b, f, and i-l are mean; dots in panels c and g are individual animals; error bars are SEM. See Table S1 Rows 121–136 for statistics.

Extended Data Fig. 12 AAV-mMORp transfection in ACC Oprm1+ cell-types.

(a) AAV-mMORp-hM4-mCherry DREADD expression in Cg1 ACC of Oprm1-2A-Cre; Sun1sf-GFP mice co-stained for anti-MOR immunoreactivity. (b) RNAscope FISH for eYfp (AAV-mMORp-eYFP), Slc17a7 (Vesicular glutamate transporter 1, VGLUT1) and Gad2 (Glutamic acid decarboxylase 2, GAD2). (c) 20X images of panel B. (d) HALO quantification of eYfp mRNA overlaps with Slac17a7+ and Gad2+ cells. n = 6 separate hemisphere injection sites from n = 3 mice.

Extended Data Fig. 13 Opioid analgesia mimicry via chemogenetic inhibition of nociceptive MOR + ACC neurons in acute pain.

Chemogenetic inhibition of ACC MOR+ cells (Red: mMORp-hM4, n = 15), ACC nociceptive MOR+ cells (Blue: mMORp-FlpO + CreON/FlpON-hM4 in painTRAP mice, n = 15), or control/non-inhibited (Gray: mMORp-eYFP, n = 15). (a) Reflexive withdrawal thresholds and affective-motivational response duration to noxious hot (55 °C hot water) and cold (acetone) stimuli at baseline and after administration of DCZ. (Two-way ANOVA,Tukey correction: von Frey pInteraction = 0.6451, acetone pInteraction = 0.003, hot water pInteraction <0.0001) (b) Latency to withdraw (p < 0.0001), total withdrawals (p < 0.0001), latency to attend (p < 0.0001), and total duration of attending (p < 0.0001) induced in 60 s on a 50 °C inescapable hot plate after DCZ. (One-way ANOVA, Tukey correction for all panels) (c) Duration of time between the first reflexive withdrawal and the first bout of attending behaviours on the inescapable hot plate after DCZ. (One-way ANOVA, Tukey correction, p < 0.0001) (d) The proportion of the trial engaging in attending behaviours after the first attending bout on the inescapable hot plate after DCZ. (One-way ANOVA, Tukey correction, p < 0.0001). ⋆ = p < 0.05. Barsor dots are mean; lines are individual animals; error bars are SEM. See Table S1 Rows 137–145 for statistics.

Extended Data Fig. 14 Opioid analgesia mimicry via chemogenetic inhibition of MOR + ACC neurons in chronic pain.

(a) Design for chemogenetic inhibition of ACC MOR+ cell-types in neuropathic pain mice (n = 19 mMORp-eYFP mice, n = 30 mMORp-hM4; equal sexes). (b) Effects of acute and chronic DCZ to engage mMORp-hM4 signaling on mechanical hypersensitivity (left, pInteraction = 0.9873), cold allodynia (middle, pInteraction <0.0001), and heat hyperalgesia (right, pInteraction <0.0001). (Two-way ANOVA,Tukey correction for all panels) (c) Effects of mMORp-hM4 inhibition on LUPE-scored neuropathic pain spontaneous behaviour repertoires pre and 23/29 days post-SNI. (Two-way ANOVA, Tukey correction for all panels: lick pInteraction = 0.0012, groom pInteraction = 0.3668, rear pInteraction = 0.4428, walk pInteraction = 0.5024, still pInteraction = 0.6690). (d) General Behaviour Scale and AMPS scores. (Two-Way RM ANOVA, Tukey correction: top pDay = <0.0001, bottom pInteraction = 0.0003. (e) Viral spread maps for AAV-mMORp-eYFP vs. AAV-mMORp-hM4-mCherry. ⋆ = p < 0.05. Bars, lines, or dots are mean; transparent lines and dots are individual animals; error bars and shaded areas are SEM. See Table S1 Rows 146–155 for statistics.

Extended Data Fig. 15 Optogenetic inhibition of ACC MOR+ cells induces a conditioned place preference in injured mice.

(a) Real-time place preference setup with Ethovision triggering an external LED system to administer blue light to the ACC of painTRAP2 mice transfected with the inhibitory chloride channel opsin, AAV-mMORp-DIO-iC + +. (b) Bilateral fiber optic design for LED light penetrance to the ACC (left), with a representative image of the cannula tracks over the dorsal Cg1 ACC expressing iC + + in painTRAP neurons. (c) Map of optic fiber placements across mice. (d) Design for optogenetic inhibition of ACC chronic nociceptive-MOR+ cell-types in a real-time place preference assay. (e) Cross-day changes in real-time, within-session self-administered inhibition via volitional selection to remain in an LED-triggered chamber, and subsequent conditioned place preference assessment with no LED exposure (n = 12 Uninjured mice, n = 14 SNI mice). (left Two-way ANOVA, Tukey correction: pVirus = 0.0066. right unpaired t-test: p = 0.0466). ⋆ = p < 0.05. Bars or dots are mean; lines and small dots/triangles are individual animals; error bars are SEM. See Table S1 Rows 156–157 for statistics.

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Oswell, C.S., Rogers, S.A., James, J.G. et al. Mimicking opioid analgesia in cortical pain circuits. Nature 649, 938–947 (2026). https://doi.org/10.1038/s41586-025-09908-w

- Received: 19 August 2024

- Accepted: 12 November 2025

- Published: 07 January 2026

- Version of record: 07 January 2026

- Issue date: 22 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09908-w

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Article Open access Published: 17 December 2025

Astrocyte CCN1 stabilizes neural circuits in the adult brain

Laura Sancho,

Matthew M. Boisvert,

Trinity Eddy,

Jillybeth Burgado,

Minerva Contreras,

Lara Labarta-Bajo,

Ellen Wang,

Lisa Tatsumi &

…

Nicola J. Allen

Nature volume 649, pages 948–958 (2026)

Abstract

Neural circuits in many brain regions are refined by experience. Sensory circuits support higher plasticity at younger ages during critical periods—times of circuit refinement and maturation—and limit plasticity in adulthood for circuit stability1,2. How astrocytes, a glial subtype, maintain these differing plasticity levels, and whether they stabilize the properties of sensory circuits in adulthood, remain largely unclear. Here we take a comprehensive approach to address these questions and establish astrocytes as key orchestrators of circuit stability. Combining a transcriptomic approach with ex vivo electrophysiology and in vivo imaging, we identify that astrocytes release CCN1 (refs. 3,4) to maintain synapse and circuit stability in the adult visual cortex. Overexpressing CCN1 in astrocytes during the critical period promotes the maturation of inhibitory neurons, limits ocular dominance plasticity and promotes oligodendrocyte differentiation and maturation. Conversely, knocking out astrocyte CCN1 in adults destabilizes binocular circuits and reduces myelination. This establishes CCN1 as an astrocyte-secreted factor that stabilizes neuronal circuits by coordinating the maturation state of multiple cell types, and demonstrates that the composition and properties of sensory circuits require ongoing maintenance in adulthood, and that these maintenance cues are provided by astrocytes.

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Main

Mammalian neural circuits are highly labile at an early age as the organism learns to adapt to its environment. This is evident in sensory circuits, which typically have higher plasticity at younger ages, a period of circuit refinement, and increased stability and reduced plasticity in adulthood[1]. This stability in adulthood is necessary to maintain functional neuronal connectivity[2]. Notably, however, circuit stability in adulthood is reversible. Digesting the extracellular matrix with enzymes, or transplanting juvenile inhibitory neurons or astrocytes, can re-open the window for circuit plasticity5,6,7, demonstrating a requirement for cues to actively stabilize and maintain neural circuits in the adult. Astrocytes, a major glial subtype with important roles in neuronal function[8], are poised to have a key role in maintaining circuit stability owing to their stage-specific roles in regulating developmental synapse formation, function, stabilization and plasticity during the critical period7,9,10,11. Whether and how astrocytes restrict plasticity and maintain sensory circuit stability in the adult brain remains unclear.

In this study we establish the astrocyte-secreted protein CCN1 as a factor that promotes circuit stability in mouse visual cortex. CCN1 is a four-domain secreted protein that can bind with many components of the plasma membrane and the extracellular matrix, including heparan sulfate proteoglycans and integrins[12]. The role of CCN1 in the periphery has been investigated in the context of tumorigenesis, angiogenesis and inflammation3,13,14, although its role in the central nervous system remains largely unknown. Here we show that astrocyte-secreted CCN1 regulates circuit stability by exerting its effect on multiple cell types, including excitatory neurons, inhibitory neurons, oligodendrocytes and microglia.

Astrocyte genes that support stability

To examine how astrocytes regulate circuit stability, we used the mouse visual cortex as our model. The mouse visual cortex has a well-characterized critical period for binocular circuits, which begins at approximately the end of the third postnatal week and lasts for two weeks. By the end of the critical period, essential properties of the visual system are established15,16 and the binocular circuitry is well established and fine-tuned[2].

To identify astrocyte factors involved in restricting visual cortex plasticity and maintaining stability, we performed transcriptional profiling of astrocytes during periods of high stability versus high plasticity and after the induction of plasticity, using bulk mRNA sequencing from astrocyte-Ribotag (Rpl22fl/fl; Gfap-cre) mice17,18 (Fig. 1a and Supplementary Table 1). We examined astrocyte transcriptomes during the critical period (postnatal day 28 (P28)), a time of high plasticity, and in adulthood (P120), a time of high stability17,18 (Fig. 1b,c). To induce plasticity, we dark-reared mice from birth until P45 and compared them with normally reared mice at P45 (Fig. 1b). Specifically, dark rearing delays astrocyte maturation and the closure of the critical period, prolonging plasticity19,20,21. Of note, 17 out of 44 genes that are upregulated in dark rearing are also upregulated at P7 versus P28, suggesting delayed maturation of astrocytes (P7 from ref. [18]; Fig. 1c and Supplementary Table 1); these genes include Vim, which is known to be upregulated in early development in astrocytes[18]. As a second model for plasticity induction, we performed two days of monocular deprivation (MD; lid suturing) at P28 to induce ocular dominance plasticity[2] and compared the hemisphere contralateral to the deprived eye (most deprived) to the hemisphere ipsilateral to the deprived eye (least deprived; Fig. 1b,c)

Fig. 1: Identifying CCN1 as an astrocyte pro-stability factor in the mouse visual cortex.

a, Experimental setup for bulk transcriptomics of visual cortex using RiboTag (Rpl22fl/fl; Gfap-cre) mice. Created in BioRender. Allen Lab (2025) https://BioRender.com/j3g9rj1. HA, haemagglutinin. b, Experimental conditions. Contra, contralateral to MD; CP, critical period; DR, dark rearing; ipsi, ipsilateral to MD; NR, normal rearing. c, Volcano plots of astrocytic DEGs in different experimental comparisons. Two mice pooled per biological replicate. P7: n = 3; P14: n = 4; P28: n = 5; P120: n = 6; MD contra: n = 3; MD ipsi: n = 3; P45 DR: n = 4; P45 NR: n = 3. Statistics from DESeq2 using HOMER on biological replicates, Benjamini–Hochberg corrections for multiple comparisons; NS, not significant (P ≥ 0.05). d, Proportional Venn diagram of DEGs from c showing overlapping DEGs. e, Left, heat map of fragments per kilobase million (FPKM) of the astrocyte-expressed overlapping genes from d. Right, heat map of log_2-transformed fold change (log_2FC) of overlapping genes. *, adjusted P value < 0.05. f, FPKM for Ccn1 in different experimental conditions. Developmental data from refs. 17,18. Data are mean ± s.e.m. Symbols indicate biological replicates; *, significant P value. g, Representative z-projection of smFISH in a tiled image of the P120 visual cortex (VC) (left and middle; scale bars, 100 µm) and in different cortical layers (right; scale bars, 5 µm). h, Thresholded Ccn1-positive area within Slc1a3 regions of interest (astrocytes) in different cortical layers in the visual cortex at P7, P14, P28 and P120. Small dots show data for individual astrocytes; circles show mouse averages; and error bars represent s.e.m. n = 3 mice per age. Two-way ANOVA with post hoc Tukey tests on mice (statistics in Supplementary Table 7). *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Source Data

To identify ‘pro-stability’ genes in astrocytes, we examined differentially expressed genes (DEGs) that are upregulated in astrocytes in adulthood compared with the critical period, and downregulated after the plasticity-inducing paradigms (Extended Data Fig. 1a). Pathway analysis identified the involvement of genes in synapses, membrane organization and cell adhesion (Extended Data Fig. 1b–d and Supplementary Table 2). Some DEGs encode known astrocyte synapse-modifying and synapse-associated extracellular matrix factors, including Thbs4, Chrdl1, Ncan and Hapln2 (Extended Data Fig. 2a). To limit the number of potential candidates, we focused on DEGs that were altered in more than one comparison (14 genes; Fig. 1d,e). We identified Ccn1 (also known as Cyr61) as a putative pro-stability factor that is high in adulthood and downregulated after dark rearing and MD (Fig. 1f). Ccn1 encodes the secreted protein CCN1, which contains integrin and heparan sulfate proteoglycan binding sites, and whose predicted functional interaction network highlights its association with integrins (integrin αV, encoded by Itgav), the extracellular matrix and components of the YAP/TAZ Hippo pathway (Extended Data Fig. 2b). We also detected ‘pro-plasticity’ gene candidates, such as Fbln2, that are upregulated during the critical period, downregulated in adulthood, and upregulated after plasticity induction (Fig. 1e).

To confirm that Ccn1 is expressed in astrocytes and is upregulated in adulthood, we used single-molecule fluorescent in situ hybridization (smFISH) to characterize Ccn1 in visual cortex astrocytes across different ages and plasticity paradigms. Ccn1 is expressed by astrocytes in all layers of the visual cortex, and expression increases across development and stays high into adulthood (Fig. 1g,h and Extended Data Fig. 2c). Ccn1 is also expressed by neurons, although at a lower level (Extended Data Fig. 2d). We validated that dark rearing decreases Ccn1 in astrocytes (Extended Data Fig. 2e). Thus, the expression pattern of Ccn1, which is high in adulthood and low after plasticity induction, suggests that CCN1 produced by astrocytes may act a pro-stability factor.

CCN1 restricts remodelling

To test the role of CCN1 as a pro-stability factor, we manipulated astrocyte expression of Ccn1 in vivo in juvenile and adult mice. To test if we could prematurely restrict plasticity, we injected the visual cortex of P14 wild-type mice with AAV2/5-tdTomato (tdT) as a control for viral transduction or AAV2/5-CCN1-HA (CCN1) driven by the minimal Gfap promoter (Fig. 2a). These constructs were specific for astrocytes and had high penetrance (Extended Data Fig. 3a–c). CCN1 overexpression led to production of CCN1 protein, validated in culture and in whole visual cortex lysates, and did not induce astrocyte reactivity in vivo (Extended Data Fig. 3d–i). To probe for large-scale plasticity, we performed monocular enucleation (ME) to induce remodelling of inputs to the visual cortex and measured outcomes with an Arc induction assay. This assay probes for Arc-positive neurons in the binocular zone contralateral to the deprived eye activated by visual stimulation and is thus a proxy for the width of the binocular zone (Fig. 2a,b and Methods). We found that four days of ME during the critical period in tdT-overexpressing (tdT-OE) mice was sufficient to induce binocular zone remodelling, whereas CCN1-overexpressing (CCN1-OE) mice showed much less remodelling (Fig. 2b,c and Extended Data Fig. 4a). These results indicate that CCN1 overexpression restricts plasticity at a time when plasticity is normally high. We then explored whether CCN1 overexpression in adulthood, when plasticity is low and CCN1 expression in astrocytes is already high, would be sufficient to further repress plasticity. We found that CCN1 overexpression in adulthood did not change the size of the binocular zone relative to tdT-OE mice (Extended Data Fig. 4d–f).

Fig. 2: CCN1 regulates binocular zone remodelling and excitation.

a, Top, experimental setup for overexpression of CCN1 or tdTomato (tdT) in critical period astrocytes (grey). Bottom, Arc induction assay. BZ, binocular zone; WT, wild type. b, Representative image of Arc smFISH. Left, 12 h after ME. Right, 4 days after ME (4 days ME). Scale bars, 1 mm. Dashed yellow lines indicate Arc binocular zone width. c, Quantification of Arc activation width. n = 4 mice per group. d, cKO of Ccn1 in adult mouse astrocytes by intraperitoneal tamoxifen injection. Created in BioRender. Allen Lab (2025) https://BioRender.com/cp7gqpp. e, Representative images of Arc smFISH in Ccn1-cKO visual cortex. Scale bars, 1 mm. f, Arc activation width. n = 6 mice per group. c,f, Two-way ANOVA with post hoc Tukey tests on mouse averages. *, significant P value. Data are mean ± s.e.m. Symbols indicate mouse averages. g, Whole-cell patch clamping of layer 2/3 pyramidal neurons. h, Representative sEPSCs. i, Average inter-event interval. j, Average amplitude. tdT no MD: n = 16 cells, 6 mice; tdT MD: n = 17 cells, 5 mice; CCN1 no MD: n = 17 cells, 7 mice; CCN1 MD: n = 18 cells, 7 mice. k, Representative sIPSCs. l, Average inter-event interval. m, Average amplitude. tdT no MD: n = 17 cells, 7 mice; tdT MD: n = 18 cells, 7 mice; CCN1 no MD: n = 15 cells, 7 mice; CCN1 MD: n = 18 cells, 8 mice. i,j,l,m, Unfilled symbols represent cells and filled symbols represent mouse averages. Data are mean ± s.e.m. Nested one-way ANOVA with post hoc Sidak tests on mouse averages. *, significant P value. n, Representative z-projection of WFA around parvalbumin (PV)-positive cells in critical period visual cortex. Scale bar, 20 µm. o, Integrated WFA density around parvalbumin-expressing cells. Dots represent cells. tdT no MD, n = 204 cells; tdT MD, n = 197 cells; CCN1 no MD, n = 228 cells; CCN1 MD, n = 193 cells. Five mice per group. Kruskal–Wallis test with post hoc Dunn’s tests on cells. *, significant P value. Data are median ± 95% confidence interval. a.u., arbitrary units.

Source Data

Having established that increasing CCN1 level during the critical period is sufficient to restrict plasticity, we hypothesized that decreasing CCN1 in astrocytes in adulthood would result in increased plasticity. To knock down CCN1 specifically in adult astrocytes, we crossed a Ccn1-floxed mouse with a tamoxifen-inducible Aldh1l1-creERT2 mouse, with tamoxifen treatment at one month of age, to produce wild-type (Ccn1fl/fl;cre−) and astrocyte-specific CCN1 conditional knockout (Ccn1-cKO mice; Ccn1fl/fl;cre+; Fig. 2d and Extended Data Figs. 3j–n and 4c). We performed ME and the Arc induction assay in four-month-old wild-type and Ccn1-cKO mice, a timepoint when plasticity is low (Fig. 2d,e). This revealed increased plasticity after four days of ME in Ccn1-cKO mice (Fig. 2e,f and Extended Data Fig. 4b), demonstrating that CCN1 is actively maintaining visual cortex stability in adulthood. To assess whether reducing astrocyte CCN1 would promote more plasticity during the critical period, a time when plasticity is high, we removed CCN1 from astrocytes in juvenile mice (Extended Data Fig. 4g–k). The Arc induction assay at P28 revealed no differences in the width of the binocular zone after 4 days of ME in cKO mice relative to wild-type mice (Extended Data Fig. 4l,m). Thus, during the critical period when CCN1 level is low, it is not a major regulator of plasticity. Overall, these findings identify a role for astrocyte-secreted CCN1 in restricting plasticity in visual circuits in an age-dependent manner.

CCN1 regulates excitatory synapses

To understand whether CCN1 is affecting circuit stability by altering synapses, we performed ex vivo whole-cell patch clamp recordings of spontaneous excitatory postsynaptic currents (sEPSCs) and spontaneous inhibitory postsynaptic currents (sIPSCs) from layer 2/3 pyramidal neurons in the visual cortex during the critical period (Fig. 2g–m). Previous studies have shown long-term depression of sEPSCs after MD[22] and enhancement of inhibition after MD[23]. We performed recordings in tdT-OE or CCN1-OE P28 mice that underwent 5 days of MD from P23 or no manipulation (Fig. 2g). For sEPSCs, we found an increase in the basal inter-event interval (a decrease in frequency) in CCN1-OE mice relative to tdT-OE mice (Fig. 2h,i and Extended Data Fig. 5a,c,d). We found a trend for a decrease in sEPSC amplitude in tdT-OE mice after MD (Fig. 2j), indicating long-term depression as expected, which was absent in CCN1-OE mice. For sIPSCs (Fig. 2k), we found no differences in inter-event interval or amplitude between tdT-OE or CCN1-OE mice with or without MD (Fig. 2l,m and Extended Data Fig. 5b,e).

We also performed sEPSC recordings from L2/3 pyramidal neurons at a later time point to parallel the experiments in Fig. 2b, with MD being performed at P28 and recordings being done at P33. Similar to the results in Fig. 2i, we observed an increase in the basal sEPSC inter-event interval (a decrease in frequency) in CCN1-OE mice relative to tdT-OE mice, which was reversed with MD at this later age (Extended Data Fig. 5f–j,p). Since we did not detect basal differences at the 12 h timepoint in the Arc induction assay, this suggests that the electrophysiology and Arc induction assays are probing different populations of neurons. These data demonstrate that CCN1 decreases excitatory drive onto pyramidal neurons and that MD reverses some of these changes.

CCN1 promotes maturation of inhibition

Because the maturation of inhibitory neurons occurs during the critical period and is a proposed mechanism underlying its closure, we next examined the role of astrocyte CCN1 in regulating this maturation. Parvalbumin-positive interneurons (PV interneurons) show an increase in EPSC frequency after the critical period[24], so we tested whether CCN1-OE could change EPSC frequency in these neurons. We recorded sEPSCs from morphologically and electrophysiologically identified[25] fast-spiking interneurons (putative parvalbumin-positive neurons (PV neurons); Extended Data Fig. 5k–o,q) at the end of the critical period, P33, after 5 days of MD or no MD. We found a significant decrease in inter-event interval in CCN1-OE mice relative to tdT-OE mice at baseline, which was reversed with MD (Extended Data Fig. 5l). We also observed an increase in the decay and risetime of sEPSCs in tdT-OE mice, but not CCN1-OE mice, after MD (Extended Data Fig. 5n,o), suggesting an MD-induced change in receptor composition with MD that is absent in CCN1-OE. This demonstrates that the overall effect of CCN1-OE on synaptic networks is to decrease excitatory drive onto excitatory neurons, while increasing excitatory drive onto inhibitory neurons.

The deposition of perineuronal nets (PNNs)—dense extracellular matrices that form predominantly around PV interneurons—also correlates with closure of the critical period and can regulate ocular dominance plasticity5,26. To test whether CCN1-OE mice exhibit premature maturation of PNNs, we performed immunohistochemistry against parvalbumin and labelled PNNs using Wisteria floribunda agglutinin (WFA) in the visual cortex of tdT-OE and CCN1-OE mice at P33, at baseline and after 5 days of MD. CCN1-OE mice had a pronounced increase in WFA signal around PV neurons compared with tdT at baseline (Fig. 2n,o and Extended Data Fig. 6a,b). This effect was reversed with MD, paralleling the reversal of the effects of CCN1 on excitatory drive by MD. Thus, CCN1 promotes the maturity of inhibitory neurons and their PNNs.

PNNs can be remodelled by microglia in the central nervous system27,28; additionally, CCN1 can regulate macrophage and microglial reactivity29,30. To assess whether the observed CCN1-dependent changes in PNN density are mediated by microglia, we examined microglial engulfment of PNNs, finding no basal or MD-induced effects of CCN1 on engulfment at P33 (Extended Data Fig. 6c,d and Methods). We found that CCN1-OE mice, relative to tdT-OE mice, had an increase in IBA1 volume and a decrease in CD68 volume within microglia (Extended Data Fig. 6e–g), suggesting altered microglia morphology, which we examined further by classifying microglia on the basis of morphotypes (Extended Data Fig. 6h,i and Methods). This showed that after MD, CCN1-OE mice have a higher proportion of ramified, resting microglia and a decrease in amoeboid microglia (Extended Data Fig. 6j–n). Thus, we find that CCN1 can regulate microglial state, as we observe a reduction in phagocytotic lysosomes and associated microglial morphotypes in CCN1-OE mice.

CCN1 maintains binocular circuits

The preceding experiments demonstrate sufficiency of CCN1 in inducing maturation of cells in the visual cortex; we next explored whether CCN1 in astrocytes is necessary to maintain visual circuit function in adulthood. To do this we used two-photon in vivo microscopy to examine binocular circuits in adult wild-type and Ccn1-cKO mice. We longitudinally imaged gCaMP6f calcium responses in layer 2/3 pyramidal neurons in the binocular zone of awake mice in response to monocular visual stimulation of both eyes (Fig. 3a,b, Extended Data Fig. 7a–f and Methods).

Fig. 3: Astrocyte CCN1 maintains binocular circuits.

a, Timeline of experiments. Created in BioRender. Allen Lab (2025) https://BioRender.com/cp7gqpp. b, Two-photon imaging of layer 2/3 pyramidal neurons in the binocular zone. Treadmill drawing from https://doi.org/10.25378/janelia.24691311 (CC BY 4.0). c, Representative image of longitudinal tracking of cell identity. Arrows, cell with changing identity. Scale bar, 100 µm. d–i, Proportions of contralateral-responsive (d), binocular-responsive (f) and ipsilateral-responsive (h) cell types in longitudinally tracked cells, and changes in the number of contralateral-responsive (e), binocular-responsive (g) and ipsilateral-responsive (i) cells following MD. Wild-type responsive cells pre MD: n = 324, wild-type responsive cells post MD: n = 267; Ccn1-cKO responsive cells pre MD: n = 319;Ccn1-cKO responsive cells post MD: n = 203. Four mice per genotype. Chi-square tests on cells with corrections (4 comparisons; α = 0.0125; *, significant P value). j, Alluvial plot of cell-type proportions. B, binocular-responsive; C, contralateral-responsive; I, ipsilateral-responsive; U, unresponsive. k, Histogram of ODI. Inset, hierarchically bootstrapped means. Longitudinally tracked responsive cells. Wild type: n = 229 cells; cKO: n = 163 cells. l, Median ODI by mouse. n = 4 per genotype. Paired t-tests on mice with α = 0.025; *, significant P value. m, Median changes in contralateral and ipsilateral eye responses per pre-MD ODI bin. *P < 0.00625 versus 0% change: wild-type contra, P = 0.002216; wild-type ipsi, P = 8.8 × 10−6; cKO contra, P = 0.000903. Two-sided Wilcoxon rank signed tests with correction (α = 0.00625). Circles, median response changes (%). Inset, bootstrapped mean ± 95% confidence interval; values along axes match those along axes of main plot. n, Summary schematic. o, Top, visual cliff assay. Bottom, representative trajectories. p, Time in cliff across 1 min bins. q, Number of zone transitions. p,q, Two-way ANOVA on mice with post hoc Sidak’s tests against first-minute bin; *, significant P value. Wild type: n = 8 mice; Ccn1-cKO: n = 13 mice. Data are mean ± s.e.m. r, Representative z-projection of WFA around parvalbumin-positive somas. Scale bars, 20 µm. s, Integrated WFA density around parvalbumin-positive cells. Wild type no MD: n = 235 cells; wild type MD: n = 212 cells; cKO no MD: n = 220 cells; cKO MD: n = 198 cells. Dots represent individual cells. Five mice per group. Lines show median ± 95% confidence interval. Kruskal–Wallis tests with post hoc Dunn’s tests on cells; *, significant P value.

Source Data

To characterize changes in binocular circuitry induced by MD or decreased CCN1 in astrocytes, we performed longitudinal tracking of layer 2/3 pyramidal neurons before and after five days of MD of the eye contralateral to the imaged hemisphere in wild-type and Ccn1-cKO mice (Extended Data Fig. 7d–f and Methods). Imaged neurons were classified on the basis of whether they were contralateral eye-responsive (contralateral-responsive), ipsilateral eye-responsive (ipsilateral-responsive), binocular or unresponsive (Fig. 3c,j and Methods). We found that Ccn1-cKO mice had a higher proportion of contralateral-responsive neurons relative to wild type before MD, and that this proportion decreased with MD of the contralateral eye (Fig. 3d,e and Extended Data Fig. 7i). We also found that Ccn1-cKO mice had a smaller proportion of binocular neurons relative to wild type and an increased binocular cell turnover (Fig. 3f,g and Extended Data Fig. 7j). Paralleling our results with the Arc induction assay, we found that Ccn1-cKO mice showed an increase in the number of ipsilateral-responsive neurons after MD (Fig. 3h,i and Extended Data Fig. 7k). After MD, both wild-type and Ccn1-cKO showed an increase in unresponsive neurons, though this increase was markedly greater in cKO mice (Extended Data Fig. 7g,h,l). This increase in unresponsiveness is similar to what is reported after MD during the critical period in wild-type mice[31]. Additionally, immature visual circuits have higher proportions of contralateral-responsive cells and lower proportions of binocularly responsive cells[32], paralleling the results in adult Ccn1-cKO mice. We performed the same experiment, longitudinally tracking neurons, in naive mice that did not undergo MD. At baseline, we found the alterations in contralateral and binocular proportions in naive Ccn1-cKO versus wild-type mice, with no longitudinal changes (Extended Data Fig. 8a–k). In naive Ccn1-cKO mice, we did not see an increased binocular cell turnover relative to wild-type mice[32] (Extended Data Fig. 8h). Together, our findings show that astrocyte CCN1 is necessary for the maturity and stability of binocular visual circuits.

Since ocular dominance of neurons is affected by MD[33], we tested whether MD had a differential effect in Ccn1-cKO versus wild-type mice. We calculated ocular dominance index (ODI) of all longitudinally tracked responsive neurons (Methods). We found a robust effect of MD in wild-type mice, with the ODI shifting towards the ipsilateral, non-deprived eye, but no change in Ccn1-cKO mice in either the raw or hierarchically bootstrapped data (Fig. 3k and Extended Data Fig. 7m). The median ODI per mouse also shows a significant shift towards the ipsilateral eye in wild-type but not in Ccn1-cKO mice (Fig. 3l). We plotted the changes in contralateral and ipsilateral eye responses for each longitudinally tracked responsive neuron as a function of its pre-MD ODI, and found that neurons with initially stronger contralateral eye biases experienced a significant decrease in the magnitude of their contralateral eye responses after MD (Fig. 3m). However, only wild-type mice experienced a concomitant increase in ipsilateral response magnitude (Fig. 3m,n). When we examined average response magnitude for all longitudinally responsive cells, regardless of initial ODI, we found no difference at the mouse level between wild-type and cKO mice before or after MD (Extended Data Fig. 7o).

We next explored whether CCN1 could have an effect on additional functional properties of binocular circuits. We found that contralateral neurons and contralateral responses of binocular neurons in Ccn1-cKO mice, compared with wild-type mice, had different preferred orientations and circular variances, but similar tuning to cardinal orientations (Extended Data Fig. 8l,m,p and Methods). Relative to wild-type mice, Ccn1-cKO mice also showed decreased pairwise cellular spiking correlations and cell response reliability during stimulus presentation to the contralateral eye (Extended Data Fig. 8n,o). We did not find any significant modulation of spiking activity by locomotion in either wild-type or Ccn1-cKO mice (Extended Data Fig. 8q,r). We did not find any differences in binocular matching between wild-type and Ccn1-cKO mice (Extended Data Fig. 7n). However, to address whether the reduction in binocular neurons in Ccn1-cKO mice had an effect on binocular vision, we performed the visual cliff assay (Fig. 3o), which probes for depth perception[34]. We found that Ccn1-cKO mice, relative to wild-type mice, spent more time in the cliff zone (Fig. 3p), less time in the ground (Extended Data Fig. 9a and Methods), and had an increased number of zone transitions (Fig. 3q) with no difference in total distance travelled (Extended Data Fig. 9b). These findings reveal that underlying changes in the binocular circuit in Ccn1-cKO mice have a functional effect on binocular vision.

Together, these data indicate that in the absence of astrocyte CCN1 in adulthood, there is a shift in binocular circuitry, with reduced binocular cells and increased contralateral cells. Additionally, MD in Ccn1-cKO mice leads to a change in the binocular circuitry composition in adults that is absent in the wild type. Whereas we find changes in ODI—as previously reported—in the adult in wild-type mice, namely an increase in the magnitude of non-deprived eye responses[35], we do not see these changes in Ccn1-cKO mice, suggesting that wild-type and cKO circuits have different strategies for regulating responsivity after MD (Fig. 3n).

CCN1 regulates PNNs and microglia

Since we found that removal of CCN1 from astrocytes results in immature binocular circuits in the adult, we predicted that reduction of CCN1 in adult astrocytes would result in decreased maturity of inhibitory neurons. To address this, we stained for PNNs with WFA and found that in adult mice, loss of CCN1 in astrocytes results in decreased PNN density around PV interneurons at baseline. PNN density in Ccn1-cKO mice was further reduced after five days of MD, with no change in wild-type mice (Fig. 3r,s and Extended Data Fig. 9c,d). These data suggest that inhibitory neurons are less mature in Ccn1-cKO mice. We then assayed microglial interaction with PNNs in adult wild-type and Ccn1-cKO mice (Extended Data Fig. 9e). We found no differences in PNN engulfment or in CD68 volume across groups (Extended Data Fig. 9f–i). Morphological examination of microglia revealed modest differences between wild-type and cKO mice (Extended Data Fig. 9j–o). Therefore, astrocyte CCN1 may have a larger role in regulating microglial state in younger mice.

ITGAV as a target of CCN1

Having established the role of astrocyte CCN1 in maintaining PNN, binocular circuit and inhibitory neuron maturity, we next set out to identify how CCN1 mediates these effects. In the periphery, CCN1 signals through multiple mechanisms, including by binding to various integrins[36]. A single point mutation, D125A, renders CCN1 unable to bind to αVβ5 or αVβ3 integrins[4] (Fig. 4a). To test the role of these integrins in the effects of CCN1 on plasticity, we overexpressed CCN1(D125A) in juvenile mice using local viral injections into the visual cortex (Fig. 4a–c). We performed the Arc induction assay during the critical period and found no differences between tdT-OE mice and mice overexpressing CCN1(D125A) (CCN1(D125A)-OE mice) after ME (Fig. 4d,e), in contrast to wild-type CCN1, which is repressive of remodelling (Fig. 2). These data indicate that CCN1 binding to αVβ5 or αVβ3 is required for its effect on large-scale remodelling after visual deprivation.

Fig. 4: Mechanisms of CCN1 action.

a, Top, CCN1 structure and binding sites. The position of the D125A mutation is indicated. Bottom, timeline of viral injections and experiments. CT, cysteine-knot-containing domain; IGFBP, insulin-like growth factor binding protein domain; HSPGs, heparan sulfate proteoglycans; IHC, immunohistochemistry; TSP-1, thrombospondin type 1 repeat homology domain; VWC, von Willebrand factor type C repeat. b, Representative z-projection of HA tag, SOX9 and NEUN immunofluorescence in a CCN1(D125A)-OE mouse. Scale bar, 100 µm. c, Penetrance and specificity of CCN1(D125A) overexpression. n = 4 mice. d, Representative images of Arc smFISH. Scale bars, 1 mm. Dashed yellow lines indicate Arc binocular zone width. e, Quantification of Arc activation width. n = 4 mice per group. Unpaired t-test on mouse averages. c,e, Symbols show mouse averages. Data are mean ± s.e.m. f, Representative z-projection of WFA and parvalbumin immunofluorescence. Scale bars, 20 µm. g, Integrated WFA density around parvalbumin-positive cells. tdT: n = 147 cells; CCN1: n = 200 cells; CCN1(D125A): n = 284 cells. Four mice per group. Kruskal–Wallis test with post hoc Dunn’s tests on cells; *, significant P value. Dots represent cells. Lines show median ± 95% confidence interval. h, Representative z-projection of aggrecan (ACAN) immunofluorescence. Scale bars, 50 µm. i, Number of ACAN+ cells. n = 4 mice per group. One-way ANOVA with post hoc Tukey’s tests on mouse averages; *, significant P value. Symbols show mouse averages. Data are mean ± s.e.m. j, snRNA-seq experiment. See Supplementary Fig. 2 for fluorescence-activated cell sorting (FACS) gating. Created in BioRender. Allen Lab (2025) https://BioRender.com/o2k2lbh. k, Uniform manifold approximation and projection (UMAP) of MapMyCells cell types from four samples (two replicates for tdT and CCN1). BAM, border-associated macrophage; COP, committed oligodendrocyte precursor; CT, corticothalamic; ET, extratelencephalic; MFOL, myelin-forming oligodendrocyte; MOL, mature oligodendrocyte; NFOL, newly formed oligodendrocyte. Interneurons include Lamp5-, Pvalb-, Sst- and Vip-expressing subtypes. l. Integrin gene expression. Normalized counts. m, AUGUR cell-type prioritization. AUC, area under the receiver operating characteristic curve. n, Excitatory neuron overrepresentation analysis (ORA) Gene Ontology (GO) terms. o, Mean UCell gene signature scores. Kruskal–Wallis test with pairwise Wilcoxon signed rank tests with Holm’s corrections; *, adjusted P value < 0.05.

Source Data

We then assessed whether mutant CCN1 was able to regulate the density of PNNs using WFA, finding no difference between CCN1-OE and CCN1(D125A)-OE, with the mutant form still increasing PNN density relative to tdT-OE (Fig. 4f,g). Given our finding that CCN1(D125A) was unable to restrict large-scale remodelling, and the link between PNNs and plasticity repression, we investigated this further by specifically analysing aggrecan, a key component of the PNN that regulates plasticity37,38. Aggrecan immunostaining revealed that CCN1-OE mice have an increased number of aggrecan-positive cells in the visual cortex relative to tdT-OE mice, whereas this effect is absent in CCN1(D125A)-OE mice (Fig. 4h,i and Extended Data Fig. 10a). These findings demonstrate that CCN1 regulates multiple components of PNNs through distinct mechanisms, and that integrin binding is probably required for the effects of CCN1 on aggrecan and repression of plasticity.

snRNA-seq identifies cell targets of CCN1

Integrins are ubiquitously expressed among cell types in the brain[39]; we therefore utilized an unbiased approach to determine which cell types are most affected by CCN1 overexpression in astrocytes. We conducted single-nucleus RNA sequencing (snRNA-seq) of all cell types in the visual cortex of tdT-OE or CCN1-OE mice during the critical period (Fig. 4j, Extended Data Fig. 10b–d and Supplementary Table 3). We used the MapMyCells tool from the Allen Brain Atlas to cluster cells (Fig. 4k and Extended Data Fig. 10e–j), verifying that integrin subunits αV, β5 and β3 are expressed in many different cell types (Fig. 4l). DEG analysis revealed that Ddx5 is a commonly downregulated gene across multiple cell types in the visual cortex of CCN1-OE mice (Extended Data Fig. 11a,b and Supplementary Tables 3 and 4). Ddx5 is a negative regulator of the YAP/TAZ Hippo pathway[40], an important pathway in nervous system development that can be regulated by CCN1[41], indicating direct signalling of astrocyte CCN1 to multiple cell types (Extended Data Fig. 2b).

We then performed AUGUR cell-type prioritization to determine the cell type that is most affected by astrocyte CCN1-OE, and identified excitatory neurons and oligodendrocytes as candidates (Fig. 4m). Many of the top DEGs in excitatory neuron subtypes (L2/3 intra-telencephalic (IT), L4/5 IT and L6 IT) are synapse-associated structural and functional genes (for example, Nrg2, Ctnna3) (Fig. 4n, Extended Data Fig. 11d–f and Supplementary Tables 4 and 5). These findings are in line with the electrophysiological changes that we observed in excitatory synapses onto pyramidal cells. We next examined oligodendrocyte lineage cells, which also express high levels of the αV integrin subunit (Fig. 4l). We performed UCell module signature scoring for myelin and oligodendrocyte-associated pathways and found an increase in the score for myelin sheath formation and cholesterol synthesis in oligodendrocytes in CCN1-OE mice (Fig. 4o and Supplementary Table 6). We also identified myelin-associated DEGs (Mag and Mbp; Extended Data Fig. 11c,g and Supplementary Tables 3 and 4). These data suggest that astrocyte CCN1 may be promoting maturation of oligodendrocytes.

CCN1 promotes oligodendrocyte maturation

To more closely examine the role of astrocyte CCN1 in regulating oligodendrocyte differentiation, maturation and myelination, we labelled different stages of oligodendrocyte development in tdT-OE, CCN1-OE or CCN1(D125A)-OE mice during the critical period. We found that CCN1 overexpression decreases the number of oligodendrocyte precursor cells (OPCs), whereas overexpression of CCN1(D125A) does not (Fig. 5a,b). When we examined newly formed oligodendrocytes and new myelin-forming oligodendrocytes, we observed an increase in their numbers in the CCN1-OE mice relative to tdT-OE mice, but not in the CCN1(D125A)-OE mice (Fig. 5c,d). We found no change in the number of mature oligodendrocytes or myelin expression (Fig. 5e–h). Together, these findings demonstrate that CCN1 induces OPC differentiation into myelin-forming oligodendrocytes and that this effect is likely to require CCN1 binding to αVβ5 or αVβ3 integrins.

Fig. 5: Astrocyte CCN1 promotes oligodendrocyte differentiation and maturation.

a,c,e,g, Representative z-projections of oligodendrocyte-associated immunostaining in critical period visual cortex overexpressing tdT, CCN1 or CCN1(D125A). a, NG2 staining of OPCs. Scale bars, 50 µm. b, NG2+ cell density. tdT: n = 5 mice; CCN1: n = 6 mice; CCN1(D125A): n = 6 mice. c, Left, BCAS1 staining shows newly formed oligodendrocytes. Right, BCAS1 and MBP colocalization shows myelin-forming oligodendrocytes in the same sections. Scale bars, 50 µm. d, BCAS1+ (left) and BCAS1+MBP+ cell densities. tdT: n = 5 mice; CCN1: n = 6 mice; CCN1(D125A): n = 6 mice. e, CC1 and OLIG2 staining, showing mature oligodendrocytes. Scale bars, 50 µm. f, CC1+OLIG2+ cell density. tdT: n = 5 mice; CCN1: n = 6 mice; CCN1(D125A): n = 4 mice. b,d,f, One-way ANOVA with post hoc Tukey’s tests on mouse averages; *, significant P value. g, Bottom, myelin basic protein (MBP) staining. Top, illustration of myelin around an axon. Created in BioRender. Allen Lab (2025) https://BioRender.com/1acx0gt. Scale bars, 200 µm. h, MBP integrated density across visual cortex layers. tdT: n = 5 mice; CCN1: n = 4 mice; CCN1(D125A): n = 5 mice. Two-way ANOVA with post hoc Tukey’s tests on mouse averages; *, significant P value. See Supplementary Table 7 for post hoc statistics. i–p, As in a–h but in adult wild-type mice versus Ccn1-cKO mice. j, WT: n = 4 mice; cKO: n = 4 mice. l, WT: n = 5 mice; cKO: n = 5 mice. n, WT: n = 8 mice; cKO: n = 7 mice. p, WT: n = 5 mice; cKO: n = 5 mice. j,l,n, Unpaired t-tests on mouse averages. p, Two-way ANOVA with post hoc Sidak’s tests on mouse averages; *, significant P value. Data are mean ± s.e.m. Symbols represent mouse averages. q, Schematic summarizing mechanisms of CCN1 action.

Source Data

We next examined whether CCN1 loss from astrocytes in adult mice affects OPCs and oligodendrocytes, and found no differences in the numbers of OPCs or differentiated oligodendrocytes (Fig. 5i–n). We found a significant reduction in myelin as labelled by myelin basic protein in Ccn1-cKO mice, most prominently in the deeper layers of visual cortex (Fig. 5o,p). We did not observe changes in myelin between wild-type and Ccn1-cKO mice in the hippocampus, entorhinal cortex or corpus callosum (Extended Data Fig. 12a,b). Thus, our data identify a role for astrocyte CCN1 in regulating oligodendrocyte differentiation and myelination in the visual cortex.

Discussion

Astrocytes have previously been described as producing stage-specific cues that instruct synapse formation, maturation and elimination8,18. Astrocytes can also modulate synaptic plasticity through secreted factors such as chordin-like 1 and Hevin9,42, clearance of ions and neurotransmitters from the synaptic cleft43,44, and through connexin 30 (ref. [10]). In this study, we find that astrocytes also actively induce circuit stability in the adult visual cortex through CCN1, a secreted protein whose expression increases in adulthood and is downregulated when plasticity is induced. Most of the changes that arise after manipulating CCN1 expression in vivo do not require plasticity induction to be revealed, highlighting the role of CCN1 in regulating circuit stability during normal visual experience. We find that CCN1 acts on multiple cell types—excitatory neurons, inhibitory neurons, oligodendrocytes and microglia—underscoring the complex regulation of plasticity and stability by astrocytes (Fig. 5q). Overall, we have identified that CCN1 coordinates the maturational changes that occur in multiple cell types underlying the loss of plasticity in adulthood, highlighting the role of astrocytes as a hub for these effects.

We find that a point mutation that renders CCN1 unable to bind to αVβ5 or αVβ3 integrins occludes the effects of CCN1 on aggrecan, binocular zone remodelling and oligodendrocyte differentiation and maturation. Although this mutation has been characterized to specifically affect signalling through αVβ5 and αVβ3 integrins, and not other integrins or known receptors, we cannot rule out that other unidentified binding partners are also affected by this mutation. We also find that CCN1 reduction in adult astrocytes results in profound deficits in myelination in the visual cortex. This reduction in myelination is likely to be independent of a broad reduction in neuronal activity[45], as calcium imaging did not show any reductions in response magnitude in Ccn1-cKO mice. Recent studies have pinpointed oligodendrocyte maturation and myelination as important regulators of critical period plasticity[46], and our findings link CCN1 to these results. Together, our findings identify αV integrins as one of the putative signalling partners of astrocyte CCN1 in regulating binocular zone remodelling, aggrecan, and oligodendrocyte differentiation and maturation.

Our study also demonstrates that adult visual circuits, even after the establishment of binocularity[47], require ongoing maintenance cues to maintain functional connectivity and visual behaviour, and that astrocytes provide these cues via the secretion of CCN1. Of note, these changes to the binocular visual circuit have an effect on a depth perception-based visual behaviour. As we used the Camk2a promoter to express gCaMP6f in predominantly excitatory neurons, this does not exclude the possibility that some of the imaged neurons were inhibitory cells[48]. Indeed, future studies could explore effects of CCN1 reduction in astrocytes on inhibitory neuron visual responses.

This study establishes the crucial role of astrocytes in actively stabilizing the connectivity of neuronal circuits. The effect of astrocyte CCN1 on the oligodendrocyte lineage and microglia, in addition to neurons, demonstrates that astrocytes are poised to orchestrate plasticity and circuit stability in the visual cortex by regulating the maturation of many cell types simultaneously. In turn, these findings deepen our understanding of how stability of sensory circuits is actively maintained in the adult brain, through a complex orchestration of multiple cell types by astrocytes. Identifying factors that make the neural environment permissive to plasticity in youth and restrictive in adulthood opens new avenues for therapies for recovery after brain injury. Both Ccn1 (ref. [49]) and its transcriptional regulator Srf[50] are upregulated after stroke, suggesting that restricting the induction of Ccn1 expression, or its actions, are targets to promote remodelling after injury.

Methods

Animals

All animal experiments were approved by the Salk Institute Institutional Animal Care and Use Committee (IACUC). Rats and mice were typically housed with a standard 12 h:12 h light:dark cycle in the Salk Institute animal facilities, with lights on at 06:00 and lights off at 18:00. Dark-reared mice were housed with a 24 h dark cycle since birth. Mice and rats were provided access to food and water ad libitum. Humidity ranged from 38–62% and temperature from 20 to 22 °C.

Mice

For bulk RNA=sequencing experiments, astrocyte-Ribotag mice were generated by crossing Gfap-cre hemizygous females (B6.Cg-Tg (GFAP-cre)73.12Mvs/J, Jax 012886) to homozygous flox-Rpl22-HA males (B6N.129-Rpl22tm1.1Psam/J, Jax 011029). Male mice hemizygous for cre and heterozygous for flox-Rpl22-HA (Rpl22-HA+;Gfap-cre+) were used for all experiments. Wild-type C57Bl6/J mice were used (Jax 000664) for experiments. For snRNA-seq, male wild-type mice were used. For smFISH experiments (Fig. 1h) validating the bulk RNA sequencing, male mice were used. For adult knockout experiments, Ccn1fl/fl mice were a gift from L. Lau[51] and were maintained on a C57Bl6/J background. These mice were crossed to mice expressing tamoxifen-inducible Cre recombinase under an astrocytic promoter for temporal elimination (Aldh1;creERT2, Jax 029655 (ref. [52])). Experimental mice were homozygous for the Ccn1 floxed allele and either cre− or cre+ (wild type or Ccn1-cKO). For adult cKO experiments, mice were injected intraperitoneally with 75 mg kg−1 of tamoxifen (MP Biomedicals 156738) for 5 consecutive days at 1 month of age. For juvenile cKO experiments, mice were injected intraperitoneally once at P3–4 with 100 mg kg−1 of tamoxifen. Mice of both sexes were used and sexes were noted. Sample sizes were chosen on the basis of power analyses (80% power) and literature review. Experimenter was blinded to genotype or manipulation when analysing data.

Rats

Sprague-Dawley rats (Charles Rivers) were used at P1–2 for the preparation of primary cortical astrocyte cultures.

Surgical procedures

Juvenile viral injections

For adeno-associated virus (AAV) injections at P14–15, P11–12, or 3 months of age, C57Bl6/J mice were used. In brief, mice were administered pre-operative carprofen (5 mg kg−1) subcutaneously and anaesthetized using isoflurane. Stereotaxic coordinates for the binocular zone were 2.25 mm lateral and 0.5 mm anterior from lambda. Virus was injected at 3 sites at a depth of 500–600 µm from just below the skull surface. The pipette was kept in the brain for 3 min after each injection to allow the virus to diffuse. TdT, CCN1 and CCN1(D125A) viruses were injected for a total titre of ~2 × 10^8 viral genomes (vg) per ml. For snRNA-seq and western blotting, bilateral injections of both binocular zones were performed. After injection, mice were sutured and placed back with the dam if pre-weaning.

Monocular enucleation and monocular deprivation

For ME at P28 or 4 months, mice were anaesthetized using isoflurane. The eye was removed using curved forceps and pressure was applied to stop any bleeding. GelFoam was inserted into the eye socket and 2 box sutures (Henry Schein 5616446) were used to close the eyelid. Lidocaine jelly (2.0%, glydo) and erythromycin (0.5%, Bausch + Lomb) was applied to the eyelid. Mice were monitored daily and administered ibuprofen water (0.15 mg ml−1) to minimize any swelling.

For MD at P23, P28, or 4 months, mice were anaesthetized using isoflurane. Eyelashes were trimmed down to the eyelid margins and 4 box sutures using nylon sutures (Ethilon 1647 G) were used to close the eyelid. Lidocaine jelly (2.0%, glydo) and erythromycin (0.5%, Bausch + Lomb) was applied to the eyelid. Mice were monitored daily and administered ibuprofen water (0.15 mg ml−1) to minimize any swelling. Mice were removed from the experiment if the eyelids opened. For suture removal, mice were again placed under isoflurane and sutures were removed. Mice were removed from the experiment if the eye looked damaged or cloudy.

Cranial window implantation and viral injections

For in vivo imaging of ocular dominance plasticity and neuronal response properties, cranial windows were implanted on ~3-month-old Ccn1 wild-type or Ccn1-cKO mice. Mice were injected with buprenorphine SR (1 mg kg−1, subcutaneously), Baytril (10 mg kg−1, intramuscularly) and dexamethasone (2 mg kg−1, intraperitoneally) prior to surgery for anaesthesia, infection prevention and inflammation prevention. Mice were anaesthetized with isoflurane inhalant (3%) and maintained at 1.5–2.5% during the surgery. Mice were mounted on a stereotaxic surgical stage via ear bars and a bite bar. Their body temperature was maintained at 37 °C using a heating pad. The scalp was shaved and the skin was removed and the skull surface was allowed to dry. The skull and scalp margins were covered with a thin layer of Vetbond (Fisher Scientific NC0304169). Avoiding the area above visual cortex, a layer of dental cement (Tetric evoflow A1, Henry Schein 9458634) was applied and a metal head plate was glued to skull. A 3 mm circular piece of skull over the binocular zone (coordinates from lambda: 3 mm lateral, 1.0 mm anterior) was removed with a high speed microdrill with a 0.5 mm burr. Care was taken not to damage the dura. Before window implantation, viral injections of AAV2/1-CAMK1a-gCaMP6f (Addgene #100834-AAV1) were made into the binocular zone. A volume of 150 nl was injected into each of 5 sites at a depth of 300 µm from the pia for a total titre of ~1.2 × 10^10 vg. The pipette was kept in the brain after each injection for 3 min to ensure diffusion of the virus before being retracted. A 4 mm diameter coverslip was placed on the dura and sealed to the edges of the skull using Vetbond. Dental cement was then used to further seal the coverslip. Mice were injected subcutaneously with 10 ml kg−1 physiological saline and carprofen (5 mg kg−1,subcutaneously) and placed on a heating pad to recover. Carprofen was administered daily for three days post-surgery. Mice were maintained with Baytril (8.5 mg kg−1 day−1) in their water to prevent infection for 3 days.

Bulk RNA sequencing

Data for experiments from P28 (critical period) and P120 (adult) mice were obtained from Farhy-Tselnicker et al.[18] (GEO GSE161398) and Boisvert et al.[17] (GEO GSE99791). The samples presented in this study were collected, processed and run at the same time as the samples from Farhy-Tselnicker et al.[18]. Three P120 biological replicates from Boisvert et al.[17] were included and mapped together with the other samples onto the genome. All samples were processed and collected in the same way.

Conditions for analysis

Developmental time course

For experiments comparing mice P28 (critical period) to P120 (adult), collection was performed as described17,18. The visual cortices from two mice (Rpl22-HA+;Gfap-cre+) were pooled for RNA isolation and RNA-sequencing library preparation (P28: n = 5 biological replicates (10 mice, 2 × 5); P120: n = 6 biological replicates (12 mice, 2 × 6)).

Dark rearing

Dark rearing was performed by housing mice in ventilated telemetry cabinets, in complete darkness, and all husbandry and cage changes were done under red light. Mice for dark rearing were born in the dark and remained there until P45, anaesthetized under red light, and perfused with a hood over their head to prevent light from reaching the eyes. For age-matched comparison mice were raised under 12 h light:12 h dark cycle until P45. The visual cortices from 2 mice (Rpl22-HA+; Gfap-cre+) were pooled for RNA isolation and RNA-sequencing library preparation (P45 DR: n = 4 biological replicates (8 mice, 2 × 4); P45: n = 3 biological replicates (6 mice, 2 × 3)).

Monocular deprivation

MD was performed at P26, for 2 days until P28, during the peak of the critical period. The visual cortex contralateral to the deprived eye (major loss of visual input) and ipsilateral to the deprived eye (minor loss of visual input) were collected separately for analysis and comparison. The visual cortices from 2 mice (Rpl22-HA+; Gfap-cre+) were pooled for RNA isolation and RNA-sequencing library preparation (MD contra: n = 3 biological replicates, (contralateral visual cortex from 6 mice, 2 × 3); MD ipsi: n = 3 biological replicates, (ipsilateral visual cortex from 6 mice, 2 × 3)).

Ribotag pulldown and RNA sequencing

Male mice heterozygous for flox-Rpl22-HA (Jax 011029) and hemizygous for Gfap-cre (Jax 012886) (astrocyte-Ribotag) were used to isolated astrocyte mRNA on the basis of a modified Ribotag protocol as described[18].

Dissection

All mice were collected between 09:30 and 12:30 on the day of experiment. Dissection was performed as described17,18. In brief, mice were anaesthetized with an intraperitoneal injection of 100 mg kg−1 ketamine (Victor Medical Company) plus 20 mg kg−1 xylazine (Anased) and then transcardially perfused with 10 ml phosphate-buffered saline (PBS) and then 10 ml 1% paraformaldehyde (PFA). Visual cortices were dissected out in 2.5 mM HEPES-KOH (pH 7.4), 4 mM NaHCO_3 in Hank’s balanced salt solution with 100 μg ml−1 cycloheximide added day of the dissection. Visual cortices were dissected by cutting at approximately −2.4 mm posterior from Bregma, lateral cuts were made at 1 mm and 3 mm from the midline, and the white matter and any subcortical structures were removed. For each time point or plasticity group, the visual cortices from two mice were pooled.

Ribotag pulldown

A modified Ribotag protocol was performed as described17,18. In brief, brains were homogenized, centrifuged and incubated with HA antibody-conjugated magnetic IgG beads. RNA was purified using the RNeasy plus micro kit (Qiagen 74034) and eluted into water and stored at −80 °C.

RNA-sequencing library generation and sequencing

Library preparation was performed as described17,18. In brief, RNA quality was measured with a TapeStation (Agilent) and Qubit Fluorimeter (ThermoFisher). More than 100 ng of RNA was used to make libraries, and mRNA was extracted with oligo-dT beads to capture polyA tails. cDNA libraries were made with Illumina TruSeq Stranded mRNA Library Preparation Kit (RS-122-2101) by the Salk Institute Razavi Newman Integrative Genomics and Bioinformatics core. Samples were sequenced on an Illumina HiSeq 2500 with single-end 50 base-pairs reads, at 12–60 millions reads per sample.

Processing and analysis

RNA-sequencing mapping, analysis and statistics

Sequencing data mapping, analysis and statistics were done as described17,18. All samples were processed and aligned to the genome at the same time. In brief, raw sequencing data was converted into FASTQ files using CASAVA (v.1.8.2). Alignment to the mm10 genome was performed using STAR aligner (v.2.5.1b). Mapping was performed using the default parameters, and >75% uniquely mapped reads were confirmed with exonic alignment. Raw and normalized (FPKM) gene expression was quantified across all genes using the top-expressed isoform using HOMER (v.4.10). This resulted in 10–55 million uniquely mapped reads in exons. Differential gene expression was carried using DESeq2 (v.1.14.1) using the HOMER getDiffExpression.pl script with default normalization and replicates used to compute within-group dispersion. Significance for differential expression was set using adjusted P <0.05, using Benjamini–Hochberg correction for multiple comparisons adjustments.

Selection of DEGs

DEG analysis was run in the following comparisons: (1) P28 (critical period) versus P120 (adult); (2) P45 DR versus P45 NR; and (3) MD contralateral hemisphere versus ipsilateral hemisphere. Selection of DEGs for subsequent analysis was performed as follows: (1) FPKM >1 in mean of samples of at least one group per comparison; (2) Ribotag pulldown FPKM (astrocyte)/input FPKM (all cells) >0.75 in at least one group per comparison; (3) adjusted P value <0.05; and (4) fold change >|1.25| or log_2FC >|0.3219|.

Venn diagrams of overlapping DEGs in different experimental comparisons were generated using R. Heat maps showing the LFC of DEGs were generated in R using the Pheatmap package. The predicted functional interaction network of CCN1 was generated using the STRING database[53] (Extended Data Fig. 2b).

Pathway analysis

Gene-set enrichment analysis (GSEA) was performed to determine which predefined sets of genes were significantly enriched across the plasticity paradigms[54]. Enrichment of gene sets and pathways from the GO, Reactome and KEGG databases was carried out. Selection of DEGs for pathway analysis was performed as described above, but without a log_2FC cut-off. Genes were ranked on the basis of descending log_2FC in each comparison. A cut-off of adjusted P < 0.05 was used to determine significantly enriched pathways and terms. Simplifying GSEA results for visualization was conducted by only selecting pathways that were differentially expressed in at least two comparisons. Furthermore, we selected the top three downregulated and upregulated (negative and positive normalized enrichment score, respectively) Reactome pathways in each comparison (Extended Data Fig. 1b). ORA was performed to determine GO terms that were enriched in the significant DEGs across the plasticity paradigms (Extended Data Fig. 1c,d). Lists of upregulated and downregulated DEGs based on the same criteria as for the heat maps and Venn diagrams were included and the background list of genes were those that had FPKM >1 in the sample mean of at least one group per comparison. The following criteria were used to select significantly enriched GO terms: adjusted P value <0.05 (Benjamini–Hochberg correction) and q value < 0.01. The clusterProfiler package (v.4.10.0) was used to perform GSEA and ORA[55].

Single-nucleus RNA sequencing

Tissue collection

P14 wild-type male mice were bilaterally injected with AAV-tdT or AAV-CCN1-HA. Mice were collected at P28. Mice were anaesthetized with intraperitoneal injection of 100 mg kg−1 ketamine/20 mg kg−1 xylazine mix and then decapitated. The brains were extracted and the injected visual cortices were dissected in ice-cold Dulbecco’s PBS (dPBS). To dissect the entire area expressing AAV, brains were cut from 2.4 mm posterior from bregma; lateral cuts from 1 to 3 mm from the midline were made to dissect the visual cortex. Two mice (four cortices) were pooled for each biological replicate. Two biological replicates were collected per viral group (four samples total).

Nuclei preparation and flow cytometry

Nuclei were extracted from the dissected visual cortices using the protocol in Farhy-Tselnicker et al.[18] and nuclei were labelled with Hoechst 33342 solution. Fifty thousand single nuclei were purified using FACS using a BD FACS Aria Fusion with a 70-µm nozzle. Single Hoechst-positive nuclei were gated using fluorescence measured in the BV421 channel, and debris was excluded using forward and side scatter area and width parameters (FSC-A versus FSC-W, and SSC-A versus SSC-W). Nuclei were kept on ice for all the steps. TdT and CCN1 injected samples were processed in parallel on the same day for each repeat.

10X Chromium barcoding, library preparation and sequencing

Single nuclei separation, barcoding and cDNA generation were performed using the Chromium single cell 3′ kit following the manufacturer’s protocol (v.3.1 HT, 10X Genomics, 1000494 Kit and 1000371 Chip). cDNA concentration and quality measurements were performed using an Agilent Tape Station. Library preparation was carried out immediately after cDNA quality control. Libraries were generated as per the 10X instructions using the V3.1 HT kit. Quality and concentration were measured using an Agilent Tape Station and a Qubit Fluorimeter. Sequencing was performed at the University of Calfornia San Diego IGM Genomics Center using a NovaSeqX (Illumina) at 300 million reads per sample, or ~52 K average reads per cell.

Data preprocessing

Sequencing reads were mapped onto the mouse genome (mm10) using CellRanger count v.8.0.1. Median unique molecular identifier (UMI) counts per cell were as follows: tdt_replicate1: 6,428, ccn1_replicate1: 7,626, tdt_replicate2: 2,496, ccn1_replicate2: 2,810. Total genes detected were as follows: tdt_replicate1: 26,053, ccn1_replicate1: 25,318, tdt_replicate2: 24,940, ccn1_replicate2: 24,752. Median genes per cell: tdt_replicate1: 2,502, ccn1_replicate1: 2,720, tdt_replicate2: 1,272, ccn1_replicate2: 1,382. Mean reads per cell: tdt_replicate1: 67,863, ccn1_replicate1: 74,455, tdt_replicate2: 26,638, ccn1_replicate2: 41,400. Biological replicates were aggregated separately as they were sequenced in separate runs (replicate 1 and replicate 2) using CellRanger Aggr v.8.0.1 using normalization default parameters. Subsequent analyses were run in R Studio (R v.4.4.1) using Seurat (v.5.1.0). We discarded nuclei with less than 200 and more than 8,000 detected unique genes, with over 20% of sequencing reads mapped to mitochondrial genes and with over 10,000 detected RNA molecules per nucleus (UMI). Raw counts were normalized and scaled using the SCTransform function from Seurat and the default parameters, and log-normalized using NormalizeData. SCT data (normalized and scaled) were only used for visualization purposes for clustering, whereas normalized counts were used in differential expression analysis and all other analyses and visualizations.

To integrate the samples from the two different sequencing runs, the IntegrateLayers function from Seurat was used, with the Harmony integration method run using the SCT assay. Clustering was performed using the integrated data, with 30 principal component dimensions for reduction. UMAP embedding was used to visualize the data.

Clustering using MapMyCells

A count matrix as an AnnData object was extracted and uploaded to the Allen Brain Atlas MapMyCells website. The reference taxonomy was the 10x Whole Mouse Brain (CCN20230722), with Hierarchical mapping as the mapping algorithm. Classes, subclasses, and supertypes were mapped onto the Seurat object metadata and used to exclude non-visual cortex neuronal cell types.

DEG analyses

DEG analyses were performed using the normalized RNA counts. The Seurat FindMarkers function with Wilcoxon ranked sum tests with Bonferroni corrections for multiple comparisons were used. A min.pct = 0.01 and a log fold change cut-off of 0.0 was used. log_2FC and min.pct cut-offs were adjusted for subsequent data visualization in the extended data figures as described in the legends.

Overrepresentation analysis

ORA was carried out using gene lists filtered out for up or downregulated significant DEGs (adjusted P <0.05). The clusterProfiler package (v.4.12.6) was used to perform ORA using GO terms (enrichGO)[55].

Gene signatures

UCell Module scores were calculated for each gene using the UCell package (v.2.8.0)[56]. UCell module scoring is robust to dataset size and composition[56], an advantage for examining the heterogeneous oligodendrocyte lineage. Lipid metabolism and myelin-associated gene lists were obtained from Gene Ontology. Wilcoxon signed rank tests were run between the tdT and CCN1 samples for each signature with Holm’s corrections for multiple comparisons.

AUGUR analysis

AUGUR cell-type prioritization analysis[57] (v.1.0.3) was performed using the random forest classifier and setting the minimum number of cells to 100 and the subsample size to 100. This excluded some cell types with low abundance in the dataset, such as border-associated macrophages. In brief, AUGUR withholds a subset of labelled cell-type data (‘test’ data) and trains random forest classifiers for each cell type. Predictions are made on the unlabelled test data and accuracy is calculated on the basis of the area under the AUC of the classifier predictions. To minimize the confound introduced by differing number of cells in each cell type, AUGUR selects small subsamples from the dataset and calculates the mean AUC across subsamples.

Cloning

For the overexpression of HA-tagged CCN1, cDNA for the coding sequence of mouse CCN1 (Origene MR221828) was used. This cDNA was MYC-Flag tagged, so we amplified the cDNA with PCR primers (forward: GCGATCGCCATGAGCTCC, reverse: ttaaccggttgcataatccggaacatcatacggataGAGCGGCCGCGTACG) to insert an HA tag at the end of the C terminus in lieu of MYC-Flag. The fragment was then amplified again with primers optimized for InFusion cloning (Takara Bio 638909) (forward: cgactcactataggctagcgccaccATGAGC; reverse: tgtctgctcgaagcggccgcttaaccggttgcataatccggaacatcatacg). The linearized product was run out on a 0.8% agarose TAE gel and extracted. We used a pZac2.1 AAV2 backbone for overexpression. pZac2.1-gfaABC1D-tdTomato (GFAP-tdTomato) from Addgene (#44332) was digested with NheI and NotI to remove the tdTomato. The linearized product was run out on a 0.8% agarose TAE gel and extracted. An InFusion reaction was performed with the linearized PCR-amplified CCN1-HA and the linearized vector. Clones were selected using carbenicillin and sequenced to confirm presence of the inserted CCN1-HA. The cloned CCN1 plasmid was validated in vitro using astrocyte and HEK 293T/Cre cell cultures.

For synthesis of the CCN1(D125A) plasmid, the cloned CCN1 plasmid was sent to Genscript for point mutagenesis. The aspartic acid at position 125 was mutated into an alanine[4]. The mutated sequence was confirmed by next-generation sequencing.

The GFAP-CCN1-HA and GFAP-CCN1(D125A)-HA plasmids were sequenced and sent to the Salk Institute Gene Transfer, Targeting, and Therapeutics Core and were packaged into an AAV2/5 virus. Obtained titre ranged from 2–4 × 10^12 vg ml−1. For the control virus, we obtained the AAV2/5 virus of the GFAP -tdTomato vector from Addgene (#44332-AAV2/5). Titres ranged from 1–4 × 10^13 vg ml−1.

Cell culture

For validation of the plasmids, in vitro overexpression was performed in astrocytes purified from Sprague-Dawley P1–2 rats and in human embryonic kidney (HEK) 293T/Cre cells (ATCC CRL-3216, authenticated by ATCC and by morphology). The astrocytes were purified using the McCarthy–de Vellis method[58], and were grown in culture media containing DMEM (Life Technologies 11960044), 10% Fetal bovine serum (Life Technologies 10437028), 1% penicillin-streptomycin (Life Technologies 15140122), 1% Glutamax (Life Technologies 35050061), 1% sodium pyruvate (Life Technologies 11360070), 5 μg ml−1 NAC (Sigma A8199), 5 μg ml−1 insulin (Sigma 11882) and 10 µM hydrocortisone (filtered with 0.22-µm filter). Cell culture dishes were coated with poly-D-lysine (Sigma P6407) before splitting cultured astrocytes onto them. Cultured astrocytes were passaged 2–3 times before being transfected.

HEK cells were grown in HEK cell growth media containing DMEM, 10% fetal bovine serum, 1% penicillin-streptomycin, 1% Glutamax, and 1% sodium pyruvate. HEK cells were passaged 4–5 times before being transfected. Cells were not routinely tested for mycoplasma contamination.

Plasmid validation

To validate plasmids via immunocytochemistry (Extended Data Fig. 3d,e), cultured astrocytes and HEK cells were plated in 24-well plates containing glass coverslips. For HEK cells, coverslips were coated with 1:50 CELLstart (ThermoFisher A1014201) diluted in water. For astrocytes, coverslips were coated with poly-D-lysine as described above. HEK cells and astrocytes were transfected the day after plating with 500 ng of plasmid DNA using Lipofectamine 2000 (Invitrogen 11668019) and OptiMEM (Life Technologies 31985-070). After transfection, astrocytes were maintained in astrocyte growth media, while HEK cells were maintained in HEK cell growth media. After 5 days of expression, cells were fixed with 4% PFA. Cells were then permeabilized with 1% BSA and 0.2% Triton X-100. Coverslips were incubated overnight at 4 °C in primary antibodies diluted in 1% BSA. For HEK cells, rabbit anti-HA antibodies (CST 3724), and sheep anti-CCN1 (R&D Systems AF4055) were used at 1:500. Secondaries were used at 1:1,000 for 2 h at room temperature. For astrocytes, mouse anti-GFAP (Millipore 360) and rabbit anti-HA were used. SlowFade Gold with DAPI mounting media (LifeTech S36939) was used. Coverslips were imaged using an Axio Imager.Z2 fluorescent microscope (Zeiss) with an AxioCam HR3 camera (Zeiss) at 20x magnification.

Mouse tissue collection

Tissue for single-molecule fluorescent in situ hybridization (smFISH) in Fig. 1 and Extended Data Fig. 2 was collected at P7, P14, P28, P45 and P120. Tissue for Ccn1-cKO validation was collected at two months of age. Tissue for smFISH against Arc was collected at P33 or 4 months of age. Tissue for immunohistochemistry was collected at approximately 1 month of age and 4 months of age.

smFISH

All mice for smFISH were collected between 13:00 and 17:00. Mice were anaesthetized by intraperitoneal injection of 100 mg kg−1 ketamine (Victor Medical Company)/20 mg kg−1 xylazine (Anased) mix and transcardially perfused with PBS. Brains were removed and embedded in OCT media (Sakura 4583), frozen in dry ice–ethanol slurry solution, and stored at –80 °C until use. Sagittal sections were obtained using a cryostat (Hacker Industries OTF5000) at a slice thickness of 18–20 µm. Sections were mounted on Superfrost Plus slides (Fisher 1255015). smFISH was performed on the same day as sectioning. Three to six mice were used for each experimental group. For each mouse (biological replicate), two or three sections (technical replicates) were imaged and analysed.

Arc induction

All mice for Arc induction were collected from the vivarium right before the end of the dark cycle (lights on at 06:00). Mice were brought back to the laboratory space and exposed to bright light for 30 min. Mice were then anaesthetized by intraperitoneal injection of 100 mg kg−1 ketamine (Victor Medical Company)/20 mg kg−1 xylazine (Anased) mix. Mice were decapitated, brains were extracted, embedded in OCT and flash frozen in dry ice–ethanol slurry mix. Brains were stored at −80 °C until use. Coronal sections were obtained using a cryostat at a slice thickness of 18–20 µm. Sections were mounted on Superfrost Plus slides (Fisher 1255015). smFISH was performed on the same day as sectioning. 3–6 mice were used for each experimental group. For each mouse (biological replicate), 3–6 sections (technical replicates) were imaged and analysed.

Immunohistochemistry

Mice were collected from the vivarium between 13:00 and 17:00 and were anaesthetized by intraperitoneal injection of 100 mg kg−1 ketamine/20 mg kg−1 xylazine mix and transcardially perfused with PBS, then 4% PFA at room temperature. Brains were removed and incubated in 4% PFA overnight at 4 °C, then washed 3 times for  10 min with PBS, and cryoprotected in 30% sucrose for 2–3 days. Brains were then embedded in tissue freezing media (TFM; General Data Healthcare TFM-5), frozen in dry ice–ethanol slurry solution, and stored at –80 °C until use. Brains were sectioned using a cryostat (Hacker Industries OTF5000) in sagittal or coronal orientations depending on experimental needs at a slice thickness of 18–20 µm. Sections were mounted on Superfrost Plus slides (Fisher 1255015). Three to eight mice were used for each experimental group. For each mouse (biological replicate), two or three sections (technical replicates) were imaged and analysed.

Western blot

Mice were collected between 13:00 and 17:00 and were anaesthetized by intraperitoneal injection of 100 mg kg−1 ketamine/20 mg kg−1 xylazine mix and transcardially perfused with dPBS. Brains were removed and bilaterally injected visual cortices were dissected out in ice-cold dPBS. To dissect the entire area expressing AAV, brains were cut from 2.4 mm posterior from bregma; lateral cuts from 1 to 3 mm from the midline were made to dissect the visual cortex. Both cortices were pooled per mouse. RIPA buffer with 1:100 Halt protease and phosphatase inhibitors (ThermoFisher 78430, 78420) was added (300 µl per sample) and homogenates were placed on a rotator for 1 h at 4 °C. Samples were spun down for 20 min at 13,000 RPM at 4 °C and supernatant was collected and frozen for subsequent analysis. Three mice for tdT and six mice for CCN1 were used. For each mouse (biological replicate), two immunoblots (technical replicates) were run and analysed.

Histology

Immunohistochemistry on mouse brain tissue

Slides containing the sections were blocked for 1 h at room temperature in blocking buffer consisting of 1% BSA and 0.2% Triton X-100 diluted in PBS. Primary antibodies were diluted in this blocking buffer and incubated overnight at 4 °C. The next day, slides were washed 3 times for 10 min with PBS and secondary antibodies conjugated to Alexa Fluor were diluted in blocking buffer and applied for 2 h at room temperature. Slides were mounted with the SlowFade Gold with DAPI mounting media, covered with 1.5 glass coverslip (Fisher 12544E), and sealed with clear nail polish. All secondary antibodies were applied at 1:500 dilution.

For validation of the viral vectors, the following antibodies were used: goat anti-SOX9 (R&D Systems af3075, 1:250), rabbit anti-HA (CST 3724, 1:500), mouse anti-NEUN (Millipore MAB377 1:100), mouse anti-GFAP (Millipore 360, 1:500). Secondary antibodies were donkey anti-goat Alexa Fluor 488 (Jackson ImmunoResearch 705-545-147), donkey anti-rabbit Alexa Fluor 568 (ThermoFisher A-10042), donkey anti-mouse Alexa Fluor 647 (Jackson ImmunoResearch 715-605-150), and goat anti-mouse Alexa Fluor 647 (ThermoFisher A-21235).

For PNN deposition experiments, the following antibodies were used: rabbit anti-HA (CST 3724, 1:500), mouse anti-parvalbumin (Millipore Sigma p3088, 1:500). Biotinylated WFA (Vector Laboratories B-1355-2, 1:500) was used at the same time as the primary antibodies to stain for the PNNs. Secondaries were used at 1:500 and included goat anti-rabbit Alexa Fluor 647 (ThermoFisher A-21245), goat anti-mouse Alexa Fluor 488 (ThermoFisher A-11001) and streptavidin conjugated Alexa Fluor 568 (adult mice, ThermoFisher S-11226) or 647 (critical period mice, ThermoFisher S-21374) to stain for PNNs.

For aggrecan staining, the following antibodies were used: rabbit anti-aggrecan (Millipore Sigma AB1031, 1:500), rat anti-HA (Sigma 11867423001, 1:100), and mouse anti-parvalbumin (Millipore Sigma p3088, 1:500). Secondaries were used at 1:500 and were goat anti-mouse Alexa Fluor 647 (ThermoFisher A-21236), goat anti-rat Alexa Fluor Plus 555 (ThermoFisher A48263), and goat anti-rabbit Alexa Fluor 488 (ThermoFisher A-11034).

For microglia morphology and phagocytosis experiments, the following antibodies were used: rabbit anti-IBA1 (Fuji Film Wako 162001, 1:500), rat anti-CD68 (Bio-Rad MCA1957GA, 1:100), and biotinylated WFA (Vector Laboratories B-1355-2, 1:500). Secondaries were used at 1:500 and were goat anti-rabbit Alexa Fluor 488 (ThermoFisher A-11008), goat anti-rat Alexa Fluor 594 (ThermoFisher A-11007), and streptavidin conjugated Alexa Fluor 647 (ThermoFisher S-21374).

For oligodendrocyte experiments, sections were counterstained with DAPI at 1:10,000 to enable easy cell counting. For all juvenile experiments, to confirm the presence of CCN1 or CCN1(D125A), an antibody against HA was used. To label oligodendrocyte progenitor cells (OPCs), rabbit anti-NG2 (1:200, Sigma Aldrich AB5320) and rat anti-HA (1:100, Sigma 11867423001) for juvenile experiments were used. Secondary antibodies were goat anti-rabbit Alexa Fluor 488 (ThermoFisher A-11034) and goat anti-rat Alexa Fluor Plus 555 (ThermoFisher A48263).

To label newly differentiated oligodendrocytes and myelin, mouse anti-BCAS1 (1:300, Santa Cruz SC-136342), rat anti-myelin basic protein (MBP, 1:200, Millipore MAB386), and rabbit anti-HA (1:500, CST 3724S) for juvenile experiments were used. For juvenile experiments, secondaries were goat anti-mouse Alexa Fluor Plus 488 (ThermoFisher A32723), goat anti-rat Alexa Fluor Plus 647 (ThermoFisher A48265), and goat anti-rabbit Alexa Fluor 555 (ThermoFisher A21429). For adult experiments, secondaries were goat anti-mouse Alexa Fluor 555 (ThermoFisher A21424) and goat anti-rat Alexa Fluor Plus 647 (ThermoFisher A48265).

To label mature oligodendrocytes, rabbit anti-OLIG2 (1:400, Millipore AB9610), mouse anti-APC (1:100, Millipore OP80), and rat anti-HA for juvenile experiments were used. For juvenile experiments, secondaries were goat anti-mouse Alexa Fluor Plus 488 (ThermoFisher A32723), goat anti-rat Alexa Fluor Plus 555 (ThermoFisher A48263), and goat anti-rabbit Alexa Fluor 647 (ThermoFisher A21245). For adult experiments, secondaries were goat anti-mouse Alexa Fluor 488 (ThermoFisher A-11001) and goat anti-rabbit Alexa Fluor 555 (ThermoFisher A21429).

For all immunohistochemistry, two or three sections (technical replicates) per mouse were imaged and averaged.

smFISH

All smFISH experiments were done as described[18] except on fresh- frozen tissue as described in ‘Mouse tissue collection’. For tissue from P7 mice, slides were incubated with protease plus for 15 min; for P14–P120, protease 3 or 4 10–20 min. Probes used were: Arc (Biotechne 316911-C1), Ccn1 (Biotechne 429001-C1), Tubb3 (Biotechne 423391-C3) and Slc1a3 (Biotechne 430781-C2).

For experiments done in Fig. 2 and Extended Data Fig. 3k,m, the RNAScope V1 Multiplex assay was performed (Biotechne 320851). For the experiments done in Extended Data Fig. 4d–m, the RNAScope V2 Multiplex assay was run (Biotechne 323100) according to manufacturer instructions. The tissue baking step was eliminated and the hydrogen peroxide step was performed at room temperature for 10 min.

For Arc smFISH, three to six sections (technical replicates) were imaged and averaged per mouse (biological replicate). For Ccn1 smFISH, two to three sections (technical replicates) were imaged and averaged per mouse (biological replicate).

Western blot

Protein concentrations of the whole visual cortex lysates were quantified using the Bradford Protein Assay (Bio-Rad 5000006) on a plate reader (Tecan Infinite 200 PRO). Samples were diluted at least 40× to ensure accurate protein measurements and BSA was used as a standard. Reducing sample buffer (ThermoFisher 39000) was added to 20 µg of total proteins and samples were denatured at 55 °C for 45 min. Samples were then loaded into a 12-well Bolt 4–12% Bis-Tris gradient gel (ThermoFisher NW04122), using a PageRuler Prestained Protein Ladder (ThermoFisher 26626). Gels were run at 100 V for 1.5 h using a Mini gel tank (ThermoFisher A25977) and Bio-Rad PowerPac using MOPS running buffer (ThermoFisher NP000102). Transfer was done in Mini Trans-Blot Gel tank (Bio-Rad 1703930), using Tris-glycine transfer buffer (ThermoFisher 28363) with 20% methanol onto a PVDF membrane (Millipore Immobilin-FL IPFL00010). Membranes were blocked at room temperature for 1 h using 1% casein in Tris buffered saline (TBS; Bio-Rad 1610782). Primary antibodies were incubated overnight at 4 °C. Following primary antibody incubation, membranes were washed 3 times for 10 min in TBS (ChemCruz SC-362186) with 1% Tween (TBS-T). Secondary antibodies were used at 1:10,000 for 1 h at room temperature. Membranes were washed with 3 times for 10 min TBS-T and then placed in TBS until they were imaged.

For CCN1 overexpression validation, sheep anti-CCN1 (1:650, R&D AF4055) and mouse anti-pan actin (1:5000, Sigma Aldrich A1978) were used. Secondaries were donkey anti- sheep Alexa Fluor 680 (ThermoFisher A21102) and donkey anti-mouse Alexa Fluor 800 (ThermoFisher A32789). CCN1 and actin were probed for in the same blot, with CCN1 imaged in the 700 nm channel and actin in the 800 nm channel. The expected molecular mass of CCN1 is 37 kDa and the expected molecular mass of actin is 42 kDa.

Imaging and analysis

Membranes were imaged on a Licor Odyssey Imager Clx at a resolution of 84 μm (300 dpi). Automatic exposure was used. Analysis was conducted using ImageStudio and background lane fluorescence was used for normalization. CCN1 band intensities were additionally normalized using the actin loading control within each blot. The CCN1 group was also normalized to the tdT group for each membrane.

Histology imaging and analysis

Epifluorescence microscopy

Imaging was performed using an Axio Imager.Z2 fluorescent microscope (Zeiss) with the apotome module (Apotome2) and AxioCam HR3 camera (Zeiss) at 10× or 20× magnification, depending on the experiment. Tile images that contained the entire primary visual cortex (from pial surface to white matter tract) were acquired. Number of tiles were maintained consistent within each experiment. Images were 14 bit. All tiles had 10% overlap. For critical period experiments looking at WFA expression, all sections were confirmed to have viral vector expression.

For the Arc smFISH (Figs. 2b,e and 4d and Extended Data Fig. 4e,l), images were acquired at a single plane at 10× magnification, and the entire section was tiled. Arc activation width was measured along layer 4 (ref. [59]) using the distance tool in Zen Blue.

For smFISH in Extended Data Figs. 3k and 4h for the Ccn1-cKO validation, images were acquired at 20×, with 3 tiles of the entire visual cortex taken, and a z-stack width of 10–12 μm.

For the immunohistochemistry for viral vector validation in Fig. 5 and Extended Data Fig. 2, images were acquired at 20× with 6 tiles taken of the visual cortex at 10% overlap and a z-stack width of 10–12 μm. For WFA and parvalbumin staining, images were acquired at 20× with 3 tiles taken of the visual cortex at 10% overlap and a z-stack width of 10–12 μm. For NG2, CC1 and OLIG2 staining in Fig. 5, images were also acquired at 20× with 3 tiles taken of the cortex at 10% overlap and a z-stack width of 10–12 μm.

In all cases, when comparing wild type and cKO or different viruses per given experiment, slides were imaged on the same day using the same acquisition settings, most notably camera exposure time was kept the same.

Confocal and super-resolution microscopy

smFISH images used for layer-specific developmental profile of Ccn1 expression were acquired using a Zeiss LSM 700 confocal scanning microscope. Images were acquired at 8-bit depth, 1,024 × 1,024 resolution using a 20× objective with a pixel dwell time of 0.79 µs. The scaling per pixel was 0.31 µm × 0.31 µm and 2 frames were averaged per plane. z-stacks at 1 µm steps were taken and the visual cortex was tiled at 10% overlap. Number of tiles remained consistent between experiments.

For microglia engulfment analysis, a Zeiss LSM 880 with Airyscan module was used. The images were acquired in super-resolution mode with a Fast Airyscan module using an oil-immersion 63× objective with a numerical aperture of 1.46 at a pixel dwell time of 0.60 µs. The scaling per pixel was 0.041 µm × 0.041 µm. z-stacks with 0.110 µm steps were acquired using a piezo, typically 150–200 planes per image. For the critical period mice expressing tdTomato, the 563 laser was used to photobleach the tdTomato signal prior to imaging the engulfment. To confirm that critical period mice injected with CCN1–HA, we confirmed viral vector expression with half the sections on each slide that were stained with rabbit anti-HA. The WFA channel (647) was imaged with an excitation beamsplitter of 488/561/633 and an emission filter set of 570–620 bandpass and 645 longpass. The CD68 channel (594) was imaged with an excitation beamsplitter of 488/594 and an emission filter set of 420–480 bandpass and 495–620 bandpass. The IBA1 channel (488) was imaged with an excitation beamsplitter of 488/561/633 and an emission filter set of 420–480 bandpass and 495–550 bandpass. Laser power for acquisition was kept the same across experiments. Images were processed by Airyscan in automatic mode.

For microglia morphology analysis, the same microscope was used as for the engulfment assay but in normal confocal mode. Images were acquired at 16-bit depth using a 20× objective with a pixel dwell time of 2.05 µs. The scaling per pixel was 0.21 µm × 0.21 µm. z-stacks at 2 µm steps were taken and the visual cortex was tiled at 15% overlap. Number of tiles remained consistent between experiments.

For analysis of newly differentiated oligodendrocytes (BCAS1-labelled oligodendrocytes) and myelin (MBP levels), a Zeiss LSM 900 was used. Images were acquired at 16-bit depth, 2,392 × 2,456 resolution using a 20× objective with a pixel dwell time of 0.41 µs. The scaling per pixel was 0.124 µm × 0.124 µm. z-stacks at 1 µm steps were taken and the visual cortex was tiled at 10% overlap. Four times two tiles were acquired (total eight tiles).

Image analysis

All image analysis of the smFISH in Fig. 1 and the Ccn1-cKO validation in Extended Data Figs. 3k and 4h was performed in ImageJ using a custom macro developed in the laboratory. The Slc1a3 signal was thresholded and used to segment astrocyte regions of interest (ROI). The Ccn1 probe signal was also thresholded and the total area within each astrocyte ROI was quantified. Thresholds were kept the same within experiments (for example, images acquired on the same day).

For quantification of the immunohistochemistry validating the viral vectors, the cell counter plug-in in ImageJ was used to count the number of transduced cells. For quantifying astrocyte GFAP level in Extended Data Fig. 3h,i, GFAP signal was thresholded equally in each pair of mice (tdT versus CCN1) and total area was quantified and compared.

To quantify the PNN around parvalbumin-expressing (PV) cells, a custom ROI detection pipeline was developed in CellProfiler. The PV channel intensity was rescaled in order to use the full intensity range to increase the brightness of the image for ROI selection. Both channels had a gaussian filter with a sigma of 1 applied. In order to reduce image heterogeneity and optimize ROI detection, the lower quartile of pixel intensity values were subtracted from the WFA and PV channels. ROIs were manually curated and added or removed. Finalized WFA and PV ROIs were then overlaid with the original, unprocessed images and the integrated density of WFA or PV per ROI was obtained. To separately quantify only WFA that surrounded PV cells, ROIs were overlaid and excluded if overlap was absent. The overlapping ROIs were overlaid with the original, unprocessed images and the integrated density per ROI was obtained. Values were exported to a csv file.

For oligodendrocyte and oligodendrocyte progenitor cells (OPCs), the cell counter plug-in in ImageJ was used to count the number of positive cells labelled by the different markers.

For analysis of microglia engulfment, images were analysed using pyclesperanto in the Napari viewer (https://github.com/clEsperanto/napari_pyclesperanto_assistant). In brief, the WFA, IBA1 and CD68 channels were independently segmented. A gaussian filter followed by a gamma correction at 1.5 were applied to the WFA signal prior to thresholding using the Otsu method. The IBA1 and CD68 signals were thresholded using the Otsu method. However, for critical period mice, for the CD68 signal the manual thresholding value was set to 900 instead of using the Otsu method due to differences in the CD68 signal in critical period aged mice. The overlapping regions (WFA + CD68 + IBA1 and CD68 + IBA1) were generated using the segmentations and saved as tiff files. To extract volumes of the segmentations, the tiff files were analysed in ImageJ using the 3D Object Counter plug-in and using the same thresholding for all images (Extended Data Figs. 6 and 9).

Microglia morphological classification

For microglia morphology analysis, images were processed in a custom ImageJ macro and analysed using a custom CellProfiler pipeline. In ImageJ, the contrast for each image was normalized so that at least 70% of the pixels are saturated, and the image was despeckled to remove noise. The pia and white matter were excluded from analysis to standardize the microglia morphology in each image. Then, in CellProfiler, the EnhanceOrSuppressFeatures module was used to suppress feature sizes of ten to segment soma from processes. The soma was identified as the primary object via the adaptive two-class Otsu thresholding method (threshold smoothing scale: 1, threshold correction factor: 1, size of adaptive window: 100). Soma area was measured using the MeasureObjectSizeShape module. The non-suppressed image underwent a gaussian filter (sigma 1) and thresholded using the global robust background method (averaging method: mean, variance method: standard deviation, no. of deviations: 0.8, threshold smoothing scale: 1, threshold correction factor: 0.9). From the thresholded image, secondary objects (processes) were identified via propagation from the overlaid primary objects (soma) using the adaptive two-class Otsu thresholding method (threshold smoothing scale: 0.3, threshold correction factor: 1, size of adaptive window: 10, regularization factor: 1). Microglia that had processes touching the borders of the image were excluded from analysis to remove artificial decreases in measurements. The MorphologicalSkeleton module was used to generate skeletons within the processes, and the MeasureObjectSkeleton module quantified number of trunks, non-truck branches, and total skeleton length of each microglia. Average branch length was calculated by dividing the total skeleton length by the sum of the number of trunk and non-trunk branches. All measurements were converted to microns.

For microglial morphology clustering analysis, Python sci-kit learn was used to scale and perform dimensionality reduction on all the extracted morphology features using principal component analysis (PCA). The number of components was chosen as the minimum number that explained 80% of the variance. Then, k-means clustering was performed and the elbow method was used to minimize the within cluster sum of squares. The number of chosen clusters was four for both critical period and adult mice, which is in line with the literature60,61,62,63. To plot the cluster heat maps, we used the scaled mean of each feature; we identified each cluster as amoeboid, rod-like, ramified (resting) or hyper-ramified on the basis of descriptions from the literature. The mean proportion per cluster for each mouse was calculated and a linear mixed effects model with a beta distribution was built in R using the glmmTMB and DHARMa libraries[64]. Post hoc tests looking at pairwise comparisons of clusters across viral groups or genotypes and manipulations were performed with P value adjustments for multiple comparisons.

Electrophysiology

Acute slice preparation

Coronal slices of the binocular zone of the visual cortex were prepared from P27–28 or P33–34 wild-type mice, that had been injected at P14 with GFAP–TdTomato or CCN1–HA, following described protocols65,66. Animals were deeply anaesthetized by injection with Avertin and decapitated. The brain was removed, hemi-sected, and cut into 300 µm coronal sections using a Leica VT1000s vibratome. The brain dissection was performed in cold, sucrose-based dissection solution consisting of (in mM): 2.5 KCl, 7.0 MgCl_2, 1.25 Na_2HPO_4, 11 glucose, 234 sucrose, 0.50 CaCl_2, and 24 NaHCO_3 and equilibrated with carbogen (95% O_2/5% CO_2). Slices were then placed in a recovery chamber containing artificial cerebrospinal fluid (aCSF) consisting of (in mM): 126 NaCl, 26 NaHCO_3, 1.25 Na_2HPO_4, 2.5 KCl, 2 CaCl_2, 1 MgCl_2, 25 glucose, and saturated with carbogen. Slices recovered for 30 min at 34 °C and were then maintained at room temperature until recordings were performed for 4–6 h after slicing.

Electrophysiology

Slices were placed in a recording chamber and perfused with a recirculating bath of carbogen-saturated aCSF maintained at 31 °C. Whole-cell patch clamp recordings were obtained from neurons in layer 2/3 of the injected binocular zone that were visualized using IR-DIC on a Scientifica microscope. Open pipette resistances were 2–5 MΩ (borosilicate glass pipette; Harvard Apparatus 30-0057). Recordings were performed using a Multiclamp 700B amplifier (Molecular Devices). All recordings were sampled at 10 kHz. For measuring the cell membrane properties, data were filtered at 10 kHz and measurements for analysis were taken 5 min after patching onto the cell. Recordings were discarded if the series resistances were >25 MΩ or changed >25% during the entire recording. For each mouse, 1–5 cells were recorded.

For experiments recording sEPSCs and sIPSCs at P27–28, sEPSCs were recorded in voltage clamp holding the cell at −60 mV and sIPSCs were recorded at +0 mV. Five minutes after breaking into the cell, cell membrane properties were measured as described above at −60 mV. sEPSCs were then recorded for 5 min with a filter of 2 kHz and a gain of 10 to allow the detection of small events and then the cell membrane properties were recorded again as described above. The holding voltage was then set to +0 mV to record sIPSCs for 5 min, filtered at 2 kHz and with a gain of 10. After recording sIPSCs, the holding voltage was set back to −60 mV to measure the membrane properties again. For each cell, sEPSCs or sIPSCs were excluded if series resistances changed >25% before and after recording or if cell seal was inadequate. Thus, for some cells, both sEPSCs and sIPSCs were analysed but for others only sEPSCs or sIPSCs were analysed. A caesium methanesulfonate internal was used (in mM): 100 caesium methanesulfonate, 20 KCl, 10 HEPES, 4 Mg-ATP, 0.3 Na-GTP, 10 Na-phosphocreatine, 3 QX 314 Chloride (Tocris 2313). Osmolarity and pH of the internal solutions were adjusted to 290–310 mOsm and pH 7.3–7.4 with double-distilled water and with CsOH.

For experiments recording sEPSCs from pyramidal and fast-spiking cells at P33–34, aCSF containing 40 µM bicuculline methochloride (Tocris 0131) was washed in. sEPSCs were recorded at −70 mV for 5 min and were filtered at 2 kHz and acquired at a gain of 10 to allow the detection of small events. Fast-spiking cells were confirmed as fast-spiking with little-to-no spike-frequency adaptation by injecting a 100 ms current step and noting the frequency and AP width in current-clamp mode. Additionally, fast-spiking neurons had a low input resistance (<150 MΩ) and multipolar morphology[25]. In these experiments, a potassium gluconate internal consisting of (in mM) was used: 115.0 potassium gluconate, 20.0 KCl, 10.0 phosphocreatine disodium salt, 10.0 HEPES acid, 0.2 EGTA, 4.0 Mg-ATP, and 0.3 Na-GTP. Osmolarity and pH of the internal solutions were adjusted to 290–310 mOsm and pH 7.3-7.4 with double-distilled water and with KOH.

Data analysis

Off-line data analyses were performed using MiniAnalysis (Synaptosoft) and MATLAB. sEPSC and sIPSC recordings were filtered post hoc with a 1 kHz low-pass Bessel 8-pole filter in Clampfit (Molecular Devices). For all experiments, 1–5 cells were analysed per mouse.

Visual cliff assay

The visual cliff experiments34,67,68 were conducted in a quiet room during the 12-h light cycle. The apparatus consisted of an open field behaviour box with clear walls and bottom. The arena was 40 cm × 40 cm and placed on the edge of a 1-m-tall table with half the box extended out over the table. A black and white checkboard mat was placed on the bench top and dropped down to the floor and extended out to create the ‘cliff’. A lamp was placed directly over the box to provide 30 lux of light but oriented to prevent any reflections on the plexiglass bottom. A camera was placed directly overhead pointed down at the arena and connected to AnyMaze tracking software. Mice were brought to the room 1 h prior to testing for habituation. The mouse’s movements were tracked during the duration of the entire 5 min trial. Measurements were binned by 1 min intervals. The arena was cleaned with Virkon followed by RO water between each mouse.

In vivo two-photon imaging

Two-photon calcium imaging

Recordings and habituation were performed during the 12 h light cycle. After recovery from viral injection and cranial window implantation, mice were habituated to handling and head fixation on a linear treadmill (LabMaker) over the course of one week. Imaging was performed during the day cycle of the mouse. We used a custom-built two-photon laser scanning microscope from Neurolabware equipped with a pulsed femtosecond Ti:Sapphire laser (Chameleon Vision II, Coherent) controlled by Scanbox acquisition software. The laser was tuned to 920 nm for imaging gCaMP6f and focused through a 16× water-immersion objective (Nikon, 0.8 numerical aperture). Images were acquired at a frequency of 15.5 Hz at a depth of 150–300 µm below the pial surface for layer 2/3. Images were 512 × 796 pixels (500 × 600 µm). A rotary encoder was used to track the running speed of the mice on the linear treadmill. A camera fitted with a 740 nm longpass filter was used to track pupil diameter during imaging (Mako U-015B). Both the encoder and the pupil camera were triggered at the scanning frame rate. To measure the responses of neurons to presentation of visual stimuli to either eye independently, an opaque eye path was placed in front of the other, non-imaged eye.

Binocular zone confirmation

Imaged areas that were stereotaxically identified during cranial window implantation (see above) as the binocular zone were confirmed as such by performing retinotopic mapping. A BENQ LCD 27-inch monitor (60 Hz refresh rate) was used for visual stimulus presentation, was placed 20 cm from the eyes, and covered approximately 113° in azimuth and 60° in elevation. Stimuli were designed in PsychoPy 2.22 and custom code was written to enable communication between the stimulus computer and the imaging computer. Stimulus presentation onset was tracked using a photodiode (Thorlabs, FDS1010) that was fed to an Arduino UNO rev. 3 and generated a transistor-transistor logic (TTL) pulse that was sampled by the imaging computer and time-stamped with the imaged frame. To confirm that the imaged area was indeed binocular, small checkerboard stimuli were presented pseudo-randomly at 15 neighbouring positions at a rate of 3 Hz. Stimuli were presented to either eye independently and randomly and regions were considered binocular if the peak responses in both eyes were evoked by stimuli presented in the central, upper visual field (−20° to +20° azimuth relative to the midline)[33] (Extended Data Fig. 7a–c). Regions with no or weak ipsilateral responses were also not considered binocular.

Visual stimulus presentation for ocular dominance mapping

Drifting sinusoidal gratings were presented to each eye independently and randomly. The stimuli consisted of 16 directions (from 0° to 337.5°) and 2 spatial frequencies (0.03 and 0.13 cycles per degree) at 80% contrast. They were generated in PsychoPy 2.22. The full stimulus set was presented 5 times in one experiment in pseudo-random sequence. Gratings drifted at 1 Hz for 2 s, followed by a 4 s grey screen. Stimulus presentation onset was tracked using a photodiode and TTL pulses were generated and sampled by the imaging microscope to synchronize stimulation and imaging data.

Analysis

Image processing

Scanbox.sbx files were converted to tiff format and motion-corrected and segmented using Suite2p in Python (https://github.com/MouseLand/suite2p). The files from the contralateral and ipsilateral eye stimulation were aligned together to ensure the same cells were segmented. Files from different days were aligned and segmented separately. Rigid and non-rigid registration were run. After automated cell detection, the registered binary was manually checked and additional ROIs were drawn if necessary.

Identification of visually responsive cells

The F for the entire fluorescent trace of each cell was calculated by subtracting 0.7 × Fneuropil from Fcell. ΔF/F was calculated as (F − F_0)/F_0, where F_0 is the baseline fluorescence. F_0 was calculated as the 25th percentile of the fluorescence signal in a 30 s sliding window69,70. ΔFstimulus/F was calculated as the ΔF/F during the stimulus window and ΔFoff/F was calculated as the ΔF/F during the grey screen presentation. To compute whether a neuron was significantly visually responsive, we performed a Wilcoxon signed ranks test comparing ΔFstimulus/F and ΔFoff/F with a Bonferroni-corrected α = 0.05/number of unique trials (32 trials; α = 0.00156) to correct for multiple comparisons. Cells were considered visually responsive if they passed significance threshold for at least 25% of the trials of the single stimulus condition35,71. Cells were considered monocular if they had significant visual response to one or more stimulus conditions presented to either the contralateral or ipsilateral eye. Cells were considered binocular if they had a significant visual response to one or more stimulus conditions presented to each eye.

Ocular dominance index

The ODI of individual neurons was calculated as the ratio between the difference and the sum of the mean ΔFstimulus-peak/F in response to the ipsilateral or contralateral eye experimentally determined preferred drifting direction: ODI = (Rc − Ri)/(Rc + Ri), where Rc is the contralateral eye response to its preferred direction, and Ri is the ipsilateral eye response to its preferred direction. An ODI of −1 or +1 indicates ipsilateral or contralateral eye dominance, respectively. The stimulus-triggered response, ΔFstimulus-peak/F, was calculated by subtracting the mean ΔF/F for the 16 frames prior to stimulus presentation from the mean ΔF/F 7 to 31 frames (0.5 s to 2 s) after stimulus presentation (averaged 4 frames around the peak). Only neurons that were longitudinally tracked and responsive across imaging sessions were used for ODI calculations. For Fig. 3m, we used data only from longitudinally tracked neurons and binned the cells on the basis of their pre-MD ODI. We made 8 bins and calculated the median change in the binned cells’ contralateral eye responses and ipsilateral eye responses.

Orientation tuning

The preferred orientation of a neuron was calculated as:

$$\mathrm{Orientation}=\arctan (({\Sigma }_{n}{{\rm{O}}}_{n}\times {{\rm{e}}}^{2i{\rm{\pi }}\theta }/180)/2)$$

On is the peak neuronal response to the 18 different orientations (0° to 170° spaced every 18°). θ is the orientation.

Global orientation selectivity was calculated as:

$$\mathrm{Circular}\,\mathrm{variance}=1-|({\Sigma }_{n}{{\rm{O}}}_{n}\times {{\rm{e}}}^{2i{\rm{\pi }}\theta }/180)/{\Sigma }_{n}{{\rm{O}}}_{n}|.$$

Cardinal proportions were calculated as the proportion of neurons having an orientation preference to 0° or 90°, ± 11.25°.

Binocular matching for binocular neurons was quantified as the absolute difference in preferred orientation of the contralateral and the ipsilateral eyes. \(\Delta {\rm{Orientation}}=|{{\rm{Ori}}}_{{\rm{contra}}}-{{\rm{Ori}}}_{{\rm{ipsi}}}|\).

If ΔOrientation > 90°, then the actual value is 180°- ΔOrientation.

Spiking correlations

Using Suite2p, the deconvolved spike train was extracted from each cell. An unconstrained non-negative deconvolution using exponential kernels was used. The kernel decay timescale was set to 1.0 and a gaussian filter with a smoothing constant of 10 was applied to the neuropil subtracted fluorescence trace. The neuropil subtraction coefficient was set to 0.7. The spiking activity of each pair of significantly responsive, longitudinally tracked neurons was calculated by concatenating the spiking activity for all trial-on periods and performing a pairwise Pearson’s correlation. This was done independently for contralateral and ipsilateral eye responses.

Response reliability

For all responsive, longitudinally tracked neurons, the response reliability was calculated by the coefficient of variation (cv = σ/μ) of the neuron across all trials of each unique stimulus condition. σ is the standard deviation of the neuron’s responses to all trials of a specific stimulus and μ is the mean of the trial responses.

Longitudinal imaging

To locate the same imaging plane for longitudinal imaging, we used the images acquired on previous days as reference, such as the vascular map of the brain surface acquired with the PCO camera and epifluorescence and the mean motion-corrected two-photon fluorescence images. The angle of the objective was kept the same. Fine adjustment of imaging depth and x,y location was performed using the Scanbox built-in plug-in searchref. In brief, a z-stack was automatically acquired and projected onto the mean motion-correct image from the prior imaging. The plug-in computes (using fast Fourier transform) the optimal translation of the microscope and moves the scope to best align the images (https://scanbox.org/2019/07/18/scanbox-searches-for-a-population/).

Longitudinal cell tracking

To track cells across multiple imaging sessions across different days, roiMatchPub.m, a MATLAB package written by A. Ranson was used (https://github.com/ransona/ROIMatchPub). In brief, the package uses a control point-based affine geometric transformation to correct for the plane rotation. The transformation is then applied to the image from the second imaging time point (‘post MD’). The overlap of ROIs between the mask file from the reference image and the transformed mask file from the second imaging time point is calculated. Overlapping ROIs were considered as longitudinally tracked after visual inspection and verification (Extended Data Fig. 7d,e). The average percentage of cells that were segmented before MD that were longitudinally tracked and also segmented post MD was 46 ± 6% (Extended Data Fig. 7f). For naive mice, the average percentage of cells that were longitudinally tracked from day 1 to day 6 of imaging was 26 ± 3.0% for wild-type mice and 35.9 ± 9.2% for Ccn1-cKO mice (Extended Data Fig. 8j). The difference in number of neurons that were longitudinally tracked between the two genotypes, combined with a lower number of mice in the wild-type naive condition, could account for the difference in the unresponsive number of neurons for all segmented cells versus longitudinally tracked unresponsive neurons (Extended Data Fig. 8d,j,k).

For experiments looking at cell proportions (Fig. 3d–j and Extended Data Figs. 7g–l and 8a–h), we used cells that were longitudinally tracked but not necessarily responsive, as we were also interested in cells that were unresponsive and became responsive after MD or vice versa. Contralateral, ipsilateral and binocular proportions were reported as a proportion of those cells that were responsive either pre MD or post MD32,47,72. For experiments looking at ODI, spiking correlations, locomotion, or tuning properties (Fig. 3k–n and Extended Data Figs. 7m,n and 8l–q), we looked at cells that were longitudinally tracked as responsive pre and post MD.

Bootstrapping

For bootstrapping analysis in Fig. 3k, we performed a hierarchical bootstrapping method[73] with two levels: the animal level and the cells level. Sampling was performed 10,000 times for the dataset. A sample was taken with replacement at the first level (mice) with the sample size being equal to the number of mice. Then, a sample was taken with replacement at the second level, the neurons imaged. We chose a sample size of 100 which was slightly larger than the maximum number of neurons imaged per mouse. We reported the median across the 10,000 samples and directly calculated a P value (Pboot) that represents the probability that the mean ODI of the pre-MD group is larger than that of the post-MD group. The Pboot was used to determine statistically significant differences for the pre-MD and post-MD samples. For the bootstrapping analysis in Fig. 3m, inset, we used the bootci function in MATLAB for 1,000 samples for each of the pre-MD ODI bins. We calculated the mean for these 1,000 samples and a 95% confidence interval.

Locomotion analysis

Spiking data for each trial were extracted as described above. Locomotion data was obtained from the Scanbox quadrature file. Locomotion was converted to speed during each trial. Correlations between speed and spiking for all trials per responsive, longitudinally tracked cell were calculated using pairwise Spearman correlations to account for any non-normality in the locomotion data.

Statistical analysis and reproducibility

For most experiments, analyses were performed in GraphPad Prism (v.8.4.3 and v.10), with P values calculated to four decimal points. For WFA and PV quantifications, two-photon in vivo imaging data, and microglial morphology analyses were run in MATLAB (MathWorks) and Python. Significance was set at α = 0.05. Data were tested for normality, and two-tailed parametric or non-parametric tests were run as appropriate. Tests were corrected for multiple comparisons using either the Tukey or Sidak method for two-way ANOVA. For nested ANOVA for the electrophysiology, Tukey’s correction for multiple comparisons was used. For non-parametric multiple comparisons, the Dunn method was applied. Adjusted P values are shown. For the majority of statistical tests presented, the adjusted P values corrected for multiple comparisons are shown. For chi-square tests of proportions done in MATLAB, a Bonferroni correction for multiple comparisons was used to calculate the corrected α threshold (corrected α = 0.05/number of comparisons). For chi-square tests in Fig. 3 and Extended Data Figs. 7g and 8a–h, unadjusted P values are shown, with significance indicated based on Bonferroni-corrected α. For Wilcoxon Signed Rank tests of locomotion modulation the unadjusted P value is shown, with significance indicated based on Bonferroni-corrected α. For Kolmogorov–Smirnov tests, the unadjusted P value is shown, with significance indicated based on Bonferroni-corrected α. For microglia morphology, bulk RNA-sequencing and snRNA-seq analyses, R studio was also used. All statistical analyses excluding the sequencing are in Supplementary Table 7.

For data comparing mouse averages, biological replicates are an average of technical replicates, with the exception of the visual cliff assay, which was run once per mouse. Each experiment was repeated a minimum of two times. No data were excluded from analyses unless viral injection was off-target or if electrophysiology recordings had series resistances that were >25 MΩ or changed > 25% during the entire recording.

Data presentation

Figure legends indicate whether mean and s.e.m. or median and the 95% confidence interval are shown. P values are shown to 2 significant figures, except in the case of P < 0.0001 or P > 0.9999. Box plots show median and upper and lower quartiles. Violin plots represent all the data points, with medium smoothing. Bar graphs represent mean, with error bars showing s.e.m. Dot plots show all data points, with line at median and error bars showing 95% confidence intervals.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Bulk RNA-sequencing data have been deposited at GEO: GSE161398 (P7, P14, P28, P120 Ribo-seq), GSE99791 (P120 Ribo-seq), and GSE259341 (P45 NR, P45 DR, P28 contra and ipsi MD Ribo-seq). snRNA-seq data are deposited at GSE298814. Two-photon data processed with suite2p is available at https://doi.org/10.5281/zenodo.15785197 (ref. [74]). Other data available on reasonable request from the authors. Source data are provided with this paper.

Code availability

All code and analysis pipelines, including the in vivo calcium imaging analysis, transcriptomics, microglia morphology and classification, CellProfiler WFA quantification and spiking correlations analysis, are available at https://doi.org/10.5281/zenodo.15785196 (ref. [75]).

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Acknowledgements

This work has been supported by US National Institutes of Health (NIH) National Institute of Neurological Disorders and Stroke (NINDS) 1R01NS105742, NIH NINDS DP11NS142587, the L.I.F.E. Foundation, a Pew Scholars Award, the Chan Zuckerberg Initiative, and the Roger Guillemin Chair to N.J.A. L.S. was supported by NIH National Eye Institute National Research Service Award 5F32EY033629 and a Salk Pioneer Fund Award. J.B. was supported by NINDS 1F99NS134205. M.C. was supported by a National Academies of Sciences, Engineering, and Medicine Ford Foundation Predoctoral Fellowship and NINDS 1F99NS1139511. L.L.-B. was supported by a Hewitt-Eckhart Postdoctoral Fellowship. L.T. was supported by the Shurl and Kay Curci Foundation. This work was supported by the following Salk Institute core facilities: Waitt Advanced Biophotonics Core, GT3 Core, Razavi Newman Integrative Genomics and Bioinformatics Core, Flow Cytometry Core, and In Vivo Scientific Services (NIH National Cancer Institute Cancer Center Support Grant P30 CA01495, NIH National Institute on Aging San Diego Nathan Shock Center P30 AG068635, NIH National Eye Institute R24NS092943, NIH National Institute on Aging Liver Cancer P01 AG073084-04, the Howard and Maryam Newman Family Foundation and the Helmsley Trust). Single-nucleus sequencing was conducted at the IGM Genomics Center at University of California, San Diego, La Jolla, CA. We thank the members of the Allen laboratory, E. Callaway, B. Emery and M. V. Sofroniew for discussion and feedback on the manuscript.

Author information

Authors and Affiliations

1. Molecular Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA, USA

Laura Sancho, Matthew M. Boisvert, Trinity Eddy, Jillybeth Burgado, Minerva Contreras, Lara Labarta-Bajo & Nicola J. Allen

2. Neurosciences Graduate Program, University of California San Diego, La Jolla, CA, USA

Jillybeth Burgado, Minerva Contreras & Ellen Wang

3. Biological Sciences Graduate Program, University of California San Diego, La Jolla, CA, USA

Lisa Tatsumi

Contributions

N.J.A. and L.S. conceived of the project and designed the experiments. L.S. performed the experiments, designed the analysis pipelines and conducted the data analysis. M.M.B. performed the bulk RNA-sequencing experiments. J.B. performed the analysis and generated plots for the bulk RNA sequencing. M.C. and L.L.-B. performed the snRNA-seq. T.E., E.W. and L.T. contributed to immunostaining and image analysis for PNNs, microglia and oligodendrocytes, respectively. L.S. and N.J.A. interpreted the results and wrote the paper with comments from all authors.

Corresponding author

Correspondence to Nicola J. Allen.

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Competing interests

The authors declare no competing interests.

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Nature thanks Marc Freeman and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Bulk ribo-Seq of visual cortex astrocytes.

a. Upset plot showing up- and down-regulated genes across the different comparisons (MD con vs ipsi, DR vs NR, CP vs Adult). MD, monocular deprivation. Con, contralateral eye to deprivation. Ipsi, ipsilateral eye to deprivation. CP, critical period. Adult, P120. DR, dark rearing. NR, normal rearing. 2 mice pooled per biological replicate. P28, n = 5. P120, n = 6. MD con/ ipsi, n = 3. P45 DR, n = 4. P45 NR, n = 3. Statistics done with DESeq2 using HOMER on biological replicates, Benjamini-Hochberg’s corrections for multiple comparisons. p-adjusted <0.05. b. Gene-set enrichment analysis (GSEA) results using the Reactome Database. NES is normalized enrichment score. Cutoff of adjusted p < 0.05 with a Benjamini-Hochberg correction. Pathways were significant in at least two comparisons. c, d. Overrepresentation analysis (ORA) to determine Gene Ontology (GO) terms enriched in DEGs across the comparisons. BP: Biological Process, CC: Cellular Component, MF: Molecular Function. Cutoff of adjusted p < 0.05 with a Benjamini-Hochberg correction.

Extended Data Fig. 2 Identifying CCN1 as a pro-stability astrocyte factor.

a. Heatmaps showing FPKM values and Log2FC of astrocyte-expressed synaptogenic, synapse elimination, and extracellular matrix genes. Stars, significance. P-adjusted <0.05. b. Predicted functional protein network of CCN1 using STRING database. c. Tiled sagittal brain section of an adult mouse smFISH for Ccn1. Scale bar, 1 mm. d. Representative z-projection of P120 smFISH in a tile image of the visual cortex (VC). smFISH against Slc1a3, Tubb3, and Ccn1. Same image from Fig. 1g. e. Ccn1 relative area per astrocyte after dark-rearing to P45 (DR) or normal rearing to P45 (NR) in the VC. Two-way ANOVA with post-hoc Tukey tests on mouse averages. Small dots, individual astrocytes. Large circles and bars, mouse averages ± SEM. N = 3 mice/ group.

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Extended Data Fig. 3 In vivo manipulation of Ccn1 expression in astrocytes.

a. Experimental set-up for critical period overexpression (o/e) of CCN1 or tdTomato (tdT) viral vectors in astrocytes. BZ, binocular zone. b. Representative z-projection of P28 VC expressing tdT (left) or CCN1 (right). Scale bar, 50 µm. c. Left, penetrance of viral vectors quantified as % SOX9+ cells also expressing tdT/HA-tag. Right, specificity quantified as % total cells expressing tdT or CCN1 that are co-stained with SOX9, NEUN or no co-staining (other). tdT, n = 6 mice. CCN1, n = 5 mice. Unpaired T-test on mouse averages. d. Immunocytochemistry (ICC) of cultured astrocytes transfected with pAAV-GFAP-CCN1-HA. Cyan, HA-tag. Scale bar, 100 µm. e. ICC of HEK cells transfected with pAAV-GFAP-CCN1-HA and stained for CCN1 protein and the HA-tag. Scale bar, 100 µm. f. Cropped representative image of immunoblot against CCN1 (top) and β-actin (bottom). All uncropped immunoblots are shown in Supplementary Fig. 1. β-actin loading control and CCN1 immunoblotted on the same gel. g. Quantification of immunoblotted CCN1 band at ~42 kDa. tdT, n = 3 mice. CCN1, n = 6 mice. Unpaired T-test on mouse averages. h. Representative z-projection of VC GFAP in tdT and CCN1 o/e P28 mice. Scale bar, 200 µm. i. GFAP quantified as % total area of section. N = 5 mice/ group. Unpaired T-test on mouse averages. j. Schematic of adult conditional knockout validation experiments. Created in BioRender. Allen Lab (2025) https://BioRender.com/cp7gqpp. k. Representative z-projection of 2 month VC smFISH against Slc1a3 and Ccn1. Scale bar, 100 µm. l. Left, quantification of Ccn1 threshold area per astrocyte. Unpaired T-test done on mouse averages. Right, Ccn1 threshold area per neuron (labeled with Tubb3, bottom). Mann-Whitney U-test on mouse averages. m. Representative image of Ccn1 smFISH in layer 6 VC of adult WT and CCN1 cKO mice. Scale bar, 50 µm. Z-projection. n. Ccn1 threshold area per Tubb3+ cell in layer 6 of VC. Unpaired T-test on mouse averages. l,n. N = 6 mice/ genotype. For all bar graphs, data shown as mean ± SEM with symbols denoting mouse averages.

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Extended Data Fig. 4 Context-dependent effects of manipulating Ccn1 expression in astrocytes.

a. Cumulative probability distribution of all sections Arc induction assay in critical period mice after 12 h of monocular enucleation (12 h) or 4 days of monocular enucleation (4D ME). N = 24 sections per group. b. As in a but in 4 month WT and cKO mice. WT 12 h, n = 30 sections. WT 4D ME, n = 34 sections. cKO 12 h, n = 27 sections. cKO 4D ME, n = 32 sections. a,b. Kolmogorov-Smirnov tests (α = 0. 0125) on sections. c. Arc induction assay in 4 month old Aldh1l1-creERT2 mice, not crossed to Ccn1fl/fl mice. Two-way ANOVA with post-hoc Tukey tests on mouse averages. Symbols, mouse averages. Cre- 12 h, n = 4 mice. Cre+ 12 h, n = 4 mice. Cre- 4D ME, n = 3 mice. Cre+ 4D ME, n = 4 mice. d. Experimental timeline for tdT or CCN1 o/e in adult WT mice. e. Representative image of Arc smFISH in the BZ after 4 days of monocular enucleation (ME). Left, tdT. Right, CCN1. Scale bar, 1 mm. Dashed yellow lines, Arc BZ width. f. Quantification of Arc activation width in layer 4 of hemisphere contralateral to enucleated eye. Unpaired T-tests on mouse averages. N = 3 mice/ group. Symbols, mouse averages. g. Schematic of experimental set-up for juvenile cKO of CCN1 in astrocytes. Created in BioRender. Allen Lab (2025) https://BioRender.com/cp7gqpp. h. Representative z-projection of 1 month VC smFISH against Slc1a3 and Ccn1. Scale bar, 50 µm. i. Quantification of Ccn1 threshold area per astrocyte. WT n = 4 mice, cKO n = 3 mice. j. Representative z-projection of 1 month VC smFISH against Tubb3 and Ccn1. Scale bar, 50 µm. k. Ccn1 threshold area per neuron (labeled with Tubb3, bottom). WT n = 4 mice, cKO n = 3 mice. l. Representative image of Arc smFISH in the juvenile BZ after 4 days of monocular enucleation (ME). Left, WT. Right, CCN1 cKO. Scale bar, 1 mm. Dashed yellow lines, Arc BZ width. m. Quantification of Arc activation width. N = 3 mice/ genotype. i,k,m. Unpaired T-tests on mouse averages. Symbols, mouse averages. For all bar graphs, data shown as mean ± SEM with symbols denoting mouse averages.

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Extended Data Fig. 5 Astrocyte CCN1 regulates excitation onto pyramidal and fast-spiking neurons.

a. Decay (left) and 10–90% risetime (right) for sEPSC recordings in Fig. 2h at P28. tdT no MD: n = 16 cells, 6 mice. tdT MD: n = 17 cells, 5 mice. CCN1 no MD: n = 17 cells, 7 mice. CCN1 MD: n = 18 cells, 7 mice. b. As in a. but for sIPSC. tdT no MD: n = 17 cells, 7 mice. tdT MD: n = 18 cells, 7 mice. CCN1 no MD: n = 15 cells, 7 mice. CCN1 MD: n = 18 cells, 8 mice. c. Membrane resistance (left, MΩ) and capacitance (right, pF) at P28. tdT no MD: n = 16 cells, 6 mice. tdT MD: n = 16 cells, 5 mice. CCN1 no MD: n = 17 cells, 7 mice. CCN1 MD: n = 18 cells, 7 mice. d. Cumulative probability distributions of sEPSC amplitudes (left) and IEIs (right). e. Cumulative probability distributions of sIPSC amplitudes (left) and IEIs (right). d,e. Kolmogorov-Smirnov tests (α = 0.0125). f-q. Whole-cell patch clamp recordings done at P33 after no MD or 5 days of MD starting at P28. f. Representative spontaneous excitatory postsynaptic currents (sEPSCs) from each of the four conditions. g. Interevent interval (ms). h. Amplitude (pA). i. Decay (ms). j. 10- 90% risetime (ms). tdT no MD: n = 17 cells, 8 mice. tdT MD: n = 16 cells, 7 mice. CCN1 no MD: n = 13 cells, 7 mice. CCN1 MD: n = 16 cells, 8 mice. k. Representative sEPSCs as in f from fast-spiking neurons. l. Interevent interval (ms). m. Amplitude (pA). n. Decay. o. 10- 90% risetime. tdT no MD: n = 16 cells, 10 mice. tdT MD: n = 14 cells, 11 mice. CCN1 no MD: n = 12 cells, 7 mice. CCN1 MD: n = 10 cells, 6 mice. p. Pyramidal cells. Left, membrane resistance (MΩ). Right, capacitance (pF). q. Fast-spiking cells. Left, membrane resistance. Right, capacitance. Symbols, cells. Larger symbols, mouse averages. Thick black line, mean ± SEM. For all tests except in d and e, nested one-way ANOVAs with post-hoc Sidak tests on mouse averages.

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Extended Data Fig. 6 Astrocyte CCN1 regulates microglial reactivity.

a. Representative single channel z-projections of WFA and PV staining. Same images as in Fig. 2n. Scale bar, 10 µm. b. WFA integrated density in all WFA+ cells, not just PV cells. N = 5 mice/ group. tdT no MD n = 577 cells. tdT MD n = 572 cells. CCN1 no MD n = 491 cells. CCN1 MD n = 538 cells. c. Representative z-projection of IBA1, WFA, and CD68 staining in critical period VC. Scale bar, 10 µm. d. Volume of WFA within CD68+ puncta within IBA1 volume. e. IBA1 volume per imaged microglia. f. Representative z-projection of IBA1and CD68 staining in critical period VC. Scale bar, 10 µm. g. Volume of CD68+ puncta within IBA1 volume. N = 39-40 microglia/group from 4 mice/group. b-g. All statistics are Kruskal Wallis with post-hoc Dunn’s tests on cells. h. Representative z-projection and skeletons of amoeboid microglia, rod-like, ramified, and hyper-ramified, left to right. i. Representative z-projection of IBA1 staining from critical period VC, top. Representative cell body and process tracing (skeleton), bottom. Scale bar, 10 µm. tdT no MD, n = 747 microglia from 4 mice. tdT MD, n = 876 microglia from 4 mice. CCN1 no MD, n = 496 microglia from 3 mice. CCN1 MD, n = 379 microglia from 3 mice. j. Proportion of microglial morphotypes- amoeboid, rod-like, ramified, and hyper-ramified. Two-way ANOVA with post-hoc Tukey tests on mouse averages. Only significant tests are shown. Symbols, mouse averages. Box plots, median with upper and lower quartiles. Whiskers, full range of data. k. Form factor (4*π*Area/Perimeter^2). l. Total skeleton length. m. Mean branch length. n. Soma eccentricity. k-n. All statistics are Kruskal Wallis with post-hoc Dunn’s tests on cells. Violin plots show median, upper and lower quartiles. For all dot plots, each dot is an individual cell, line at median ± 95% CI.

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Extended Data Fig. 7 Validation of in vivo two-photon calcium imaging of binocular zone neurons.

a. Retinotopic mapping of the BZ with small checkerboard boxes. b. Representative retinotopy in degrees of visual field in example mouse “L022”. Top, horizontal retinotopy, “azimuth”. Bottom, vertical retinotopy, “elevation”. Left, contralateral eye responses. Right, ipsilateral eye responses. Scale bar, 100 µm. c. Example mouse “L022”. Histograms of number of cells with peak responses to a particular azimuth (horizontal) or elevation (vertical). d. Validation of longitudinal tracking. Example mouse “L022”. Mean images pre, left, and post, right, MD. e. Left, fused image. Right, white denotes overlapping ROIs. f. Percent of longitudinally tracked cells across WT and CCN1 cKO mice. g. Unresponsive cells as a proportion of total cells. Corrected chi-square tests (4 comparisons; α = 0.0125). Stars, significance. h. Change in unresponsive cell proportion after MD, data and statistics from g. Stars, significant change relative to pre MD. i. C2I, contralateral to ipsilateral. C2B, contralateral to binocular. C2U, contralateral to unresponsive. j. B2M, binocular to monocular. M2B, monocular to binocular. Stable B, stable binocular. k. C2I, contralateral to ipsilateral. B2I, binocular to ipsilateral. U2I, unresponsive to ipsilateral. l. I2U, ipsilateral to unresponsive. C2U, contralateral to unresponsive. B2U, binocular to unresponsive. i-l. Corrected chi-square tests (3 comparisons; α = 0.01667). Stars, significance. WT total longitudinally tracked cells n = 453. CCN1 cKO total longitudinally tracked cells n = 511. m. ODI of longitudinally imaged responsive neurons only. WT n = 229 cells. cKO. n = 163 cells. n. Binocular matching as difference in preferred orientation for contralateral and ipsilateral responses for longitudinally tracked binocular neurons. WT n = 73 cells. cKO n = 37 cells. 4 mice/ genotype. m and n. Kruskal-Wallis tests with post-hoc Dunn’s tests on cells. For all dot plots, dots are individual cells. Lines and bars, median ± 95% CI. o. Mouse average peak ΔF/F for contralateral and ipsilateral. Two-way ANOVAs with paired uncorrected Fisher’s LSD tests on mouse averages. 4 comparisons; α = 0.0125. Starred p-values, significance. Bar graphs, mean ± SEM.

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Extended Data Fig. 8 Binocular zone responses are altered in adult CCN1 cKO mice.

a-i. Longitudinally tracked neurons in naïve WT and CCN1 cKO mice. a-c. Contralateral, binocular, ipsilateral cells as a percentage of responsive cells. WT responsive cells day 1, n = 102. WT responsive cells day 6, n = 74. CCN1 cKO responsive cells day 1 n = 75. CCN1 cKO responsive cells day 6 n = 71. d. Unresponsive cells as a proportion of total cells. WT total cells n = 134. CCN1 cKO total cells n = 155. e. C2I, contralateral to ipsilateral. C2B, contralateral to binocular. C2U, contralateral to unresponsive. f. C2I, contralateral to ipsilateral. B2I, binocular to ipsilateral. U2I, unresponsive to ipsilateral. g. I2U, ipsilateral to unresponsive. C2U, contralateral to unresponsive. B2U, binocular to unresponsive. h. B2M, binocular to monocular. M2B, monocular to binocular. Stable B, stable binocular. a-h. Corrected chi-square tests of proportions. Stars, significance (a-d, α = 0.0125. e-h, α = 0.01667). i. Ocular dominance index for longitudinally tracked responsive cells. WT, n = 64 cells. cKO, n = 52 cells. Kruskal-Wallis test with post-hoc Dunn’s tests on cells. j. Percent of longitudinally tracked neurons in WT and cKO naïve mice. k. Pie chart of cell proportions for all cells, not just longitudinally tracked. a-k. Data from 2 WT mice, 3 CCN1 cKO mice. l-p. Longitudinally tracked responsive neurons from mice that underwent MD. l-m. Longitudinally tracked contralateral and binocular neurons in WT and CCN1 cKO adult mice. l. Orientation preferences. Kolmogorov-Smirnov tests on cells (α = 0.0125). Brackets, p-values. m. Global orientation tuning measured by circular variance. l-m. Contralateral neurons: WT n = 60, cKO n = 51. Binocular neurons: WT n = 73, cKO n = 37. n. Response reliability of all responsive, longitudinally tracked neurons. Left, contralateral eye responses. Right, ipsilateral eye responses. WT n = 229 neurons, cKO n = 163 neurons. o. Spiking correlations of all responsive, longitudinally tracked neurons. WT n = 9736 pairwise correlations, cKO n = 4241 pairwise correlations. m-o. Kruskal-Wallis tests with post-hoc Dunn’s tests on cells or correlation coefficients. p. Relative proportions of contralateral, ipsilateral, and binocular neurons with cardinal orientation preferences. Chi-square tests on cells. For binocular neurons, contralateral and ipsilateral response cardinal proportions were averaged. WT mice: contralateral neurons n = 60, binocular neurons n = 73, ipsilateral neurons n = 4. CCN1 cKO mice: contralateral neurons n = 51, binocular neurons n = 37, ipsilateral neurons n = 3. q. Histogram of locomotion during trial presentation in mice. r. Modulation by locomotion. Pair-wise Spearman’s coefficient of correlation between spiking activity and speed on treadmill. One sample Wilcoxon signed rank test on cells done against Spearman’s coefficient = 0. Corrected α = 0.0125 (4 comparisons). For all dot plots, dots represent individual cells. Lines and bars, median ± 95% CI.

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Extended Data Fig. 9 Impaired depth perception and decreased PNN density in adult CCN1 cKO mice.

a. Exploration index in 1 min bins. Two-way ANOVA with post-hoc Tukey tests. b. Total distance traveled by mice during full 5 min of trial. Unpaired T-test. Symbols, mice. a,b. WT, n = 8. CCN1 cKO, n = 13. c. Representative single channel z-projections of WFA and PV staining. Same images as in Fig. 3r. Scale bar, 10 µm. d. WFA integrated density quantified per WFA+ cell. WT no MD n = 493 cells. WT MD n = 506 cells. cKO no MD n = 474 cells. cKO MD n = 446 cells. 5 mice/group. e. Representative z-projection of IBA1, WFA, and CD68 staining in adult VC. Scale bar, 10 µm. f. WFA colocalized to CD68+ puncta within IBA1 volumes. g. IBA1 volume within each field of view (FOV). h. Representative z-projection of IBA1and CD68 staining in adult VC. Scale bar, 10 µm. i. Volume of CD68+ puncta within IBA1 volume. N = 39-40 microglia/group from 4 mice/group. d-i. Kruskal Wallis tests with post-hoc Dunn’s tests on cells. j. Representative z-projection of IBA1 staining from adult VC, top. Representative cell body and process tracing (skeleton), bottom. Scale bar, 10 µm. WT no MD, n = 883 microglia, 4 mice. WT MD, n = 612 microglia, 4 mice. cKO no MD, n = 667 microglia, 4 mice. cKO MD, n = 543 microglia, 3 mice. k. Proportion of microglial morphotypes. Black symbols, mouse averages. Box plots, median with upper/lower quartiles. Whiskers, full range of data. Two-way ANOVA with post-hoc Tukey’s tests on mouse averages. l. Form factor. m. Total skeleton length. n. Mean branch length. o. Soma eccentricity. l-o. Kruskal Wallis test on cells with post-hoc Dunn’s tests. Bar graphs, mean ± SEM. Violin plots, median, upper and lower quartiles. For all dot plots, dots represent individual cells. Lines and bars, median ± 95% CI.

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Extended Data Fig. 10 Validation of single-cell transcriptomics of visual cortex cell types.

a. Left, aggrecan (ACAN) integrated density per cell. Right, Proportion of PV+ cells expressing ACAN. One-way ANOVA with Tukey’s post-hoc tests on mouse averages. N = 4 mice/ group. Symbols, mouse averages. Bar graphs, mean ± SEM. b. Post-filtering genes per cell for all cell types. c. Post-filtering UMIs per cell across cell types. d. Post-filtering mitochondrial gene % per cell. b-d. dots, individual cells. e. Violin plots of canonical cell markers in different cell types. Normalized counts. f. UMAP after harmony integration of two biological replicates. g. UMAP of cell types with counts in different biological replicates, rep1 and rep2, and experimental groups, tdT and CCN1 o/e. h. UMAP of MapMyCells Subclass Annotations. i. Number of DEGs in each cell type for CCN1 vs tdT. min_pct > 0.01 and p-adjusted <0.05 using Seurat FindMarkers function with Wilcoxon Ranked Sum tests and Bonferroni corrections for multiple comparisons. j. Relative differences in cell proportions for CCN1 vs tdT using scProportion. Dot, mean of bootstrapped differences. Line, 95% CI. FDR set at 0.05.

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Extended Data Fig. 11 Identifying targets of CCN1 using single-cell transcriptomics.

a. Violin plot of expression of Ddx5 across cell types. P-adjust shown from DEG analysis. Seurat FindMarkers function with Wilcoxon Ranked Sum tests and Bonferroni corrections for multiple comparisons. |Fold change| > 1.25 in all significant comparisons in CCN1 vs tdT. Dots, cells. b. Violin plot of Ddx5 expression in selected cell types with biological replicates separated. c. As in b. but for Mbp and Mag. b,c. Boxes inside violin plots, median and upper/lower quartiles. Dots, outliers (>1.5*inter-quartile distance). d. Over-representation analysis (ORA). GO terms (cellular components) of upregulated genes in excitatory neurons. e. ORA of Reactome pathway of upregulated genes in excitatory neurons. d,e. P-adjusted <0.05. f. Top DEGs in L2/3, L4/5, and L6 IT. IT is Allen Brain Atlas nomenclature for intra-telencephalic. min.pct >0.10. P-adjusted <0.05. Log2FC > |0.10 |. g. Top DEGs in oligodendrocytes. Seurat FindMarkers function with Wilcoxon Ranked Sum tests and Bonferroni corrections for multiple comparisons. min.pct >0.10. P-adjusted <0.05. Log2FC > |0.10|. Normalized count data.

Extended Data Fig. 12 Myelin basic protein in different brain regions of adult CCN1 cKO mice.

a. Representative z-projections of MBP in different brain regions. Left, hippocampus. Scale bar 200 µm. Middle, entorhinal cortex (EC). Scale bar 100 µm. Right, corpus callosum (CC) below VC. Scale bar 50 µm. b. Quantification of MBP integrated density. Unpaired T-tests on mouse averages. N = 5 mice/genotype. Symbols, mouse averages. Bar graphs, mean ± SEM.

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Supplementary information

Supplementary Figures

Supplementary Figs. 1 and 2. Supplementary Fig. 1 includes the uncropped western blots from Extended Data Fig. 3 and Supplementary Fig. 2 includes an example plot for the single-nuclei gating strategy for FACS for snRNA-seq.

Reporting Summary

Supplementary Table 1

Data from the bulk RNA (Ribo-seq) sequencing experiments. Developmental data from Farhy-Tselnicker et al. 2021 and Boisvert et al. 2018. Ccn1 is named Cyr61. Related to Fig. 1 and Extended Data Fig. 2a.

Supplementary Table 2

Gene-set enrichment analysis (GSEA) data and overrepresentation analysis (ORA) for Ribo-seq experiment. Related to Extended Data Fig. 1.

Supplementary Table 3

DEGs for different cell types from the snRNA-seq. Related to Fig. 4j and Extended Data Figs. 10b–j and 11a–c.

Supplementary Table 4

DEGs in excitatory neuron and oligodendrocyte lineage subclusters from snRNA-seq data. Each tab is a different cluster. Related to Extended Data Fig. 11f,g.

Supplementary Table 5

ORA for snRNA-seq using GO terms that were enriched in the significant DEGs across the plasticity paradigms. Related to Fig. 4n and Extended Data Fig. 11d,e.

Supplementary Table 6

UCell module signature summary scores with statistics for snRNA-seq. Related to Fig. 4o.

Supplementary Table 7

Statistical tests and summary statistics from studies.

Source data

Source Data Fig. 1 and Extended Data Fig. 2

Source Data Fig. 2 and Extended Data Figs. 3–6

Source Data Fig. 3 and Extended Data Figs. 7–9

Source Data Fig. 4 and Extended Data Fig. 10

Source Data Fig. 5 and Extended Data Fig. 12

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Sancho, L., Boisvert, M.M., Eddy, T. et al. Astrocyte CCN1 stabilizes neural circuits in the adult brain. Nature 649, 948–958 (2026). https://doi.org/10.1038/s41586-025-09770-w

- Received: 21 February 2024

- Accepted: 16 October 2025

- Published: 17 December 2025

- Version of record: 17 December 2025

- Issue date: 22 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09770-w

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Lesion-remote astrocytes govern microglia-mediated white matter repair

- Sarah McCallum

- Keshav B. Suresh

- Joshua E. Burda

Nature Article Open Access 17 Dec 2025

Article Open access Published: 17 December 2025

Lesion-remote astrocytes govern microglia-mediated white matter repair

Sarah McCallum,

Keshav B. Suresh,

Timothy S. Islam,

Manish K. Tripathi,

Ann W. Saustad,

Oksana Shelest,

Aditya Patil,

David Lee,

Brandon Kwon,

Katherine Leitholf,

Inga Yenokian,

Sophia E. Shaka,

Connor H. Beveridge,

Palak Manchandra,

Caitlin E. Randolph,

Gordon P. Meares,

Ranjan Dutta,

Jasmine Plummer,

Vinicius F. Calsavara,

Riki Kawaguchi,

Simon R. V. Knott,

Gaurav Chopra &

…

Joshua E. Burda

Nature volume 649, pages 959–970 (2026)

Abstract

Spared regions of the damaged central nervous system undergo dynamic remodelling and exhibit a remarkable potential for therapeutic exploitation[1]. Lesion-remote astrocytes (LRAs), which interact with viable neurons and glia, undergo reactive transformations whose molecular and functional properties are poorly understood[2]. Here, using multiple transcriptional profiling methods, we investigated LRAs from spared regions of mouse spinal cord following traumatic spinal cord injury. We show that LRAs acquire a spectrum of molecularly distinct, neuroanatomically restricted reactivity states that evolve after spinal cord injury. We identify transcriptionally unique reactive LRAs in degenerating white matter that direct the specification and function of local microglia that clear lipid-rich myelin debris to promote tissue repair. Fuelling this LRA functional adaptation is the secreted matricellular protein CCN1. Loss of astrocyte-derived CCN1 results in excessive, aberrant activation of local microglia, characterized by abnormal molecular specification, impaired debris processing reflected by the intracellular accumulation of myelin and axon debris, and dysregulated lipid metabolism with distinctive attenuation in lipid droplet accumulation. Mechanistically, we find that CCN1 binds microglial SDC4 to augment lipid storage, linking this signalling axis to a vital repair-associated lipid buffering response in debris-clearing microglia. Accordingly, microglial deficits resulting from astrocyte CCN1 depletion culminate in blunted clearance of white matter debris and impaired neurological recovery from spinal cord injury. Ccn1-expressing white matter astrocytes are induced by local myelin damage and are generated in diverse demyelinating disorders in mice and humans, pointing to their fundamental, evolutionarily conserved role in white matter repair. Our findings show that context-specific cues shape regionally distinct LRA reactivity states with functional adaptations that orchestrate multicellular processes underlying neural repair and influence disease outcome.

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Main

Astrocytes tile the central nervous system (CNS), where they serve vital roles that uphold healthy nervous system function, including regulation of synapse development, buffering of neurotransmitters and ions, and provision of metabolic substrates[3]. In response to diverse CNS insults, astrocytes exhibit context-specific transformations that are collectively referred to as reactivity4,5,6,7,8,9. The characteristics of regionally and molecularly distinct reactivity states are incompletely understood. The mechanisms through which different reactivity states arise, how they evolve or resolve over time, and their consequences for local cell function and CNS disorder progression remain unknown.

Immediately adjacent to CNS lesions, border-forming astrocytes (BFAs) undergo transcriptional reprogramming and proliferation to form a neuroprotective barrier that restricts inflammation and supports axon regeneration10,11. Beyond the lesion, spared but dynamic regions of the injured CNS exhibit varying degrees of synaptic circuit remodelling and progressive cellular responses to secondary damage that have profound consequences for neural repair and recovery2,12. Throughout these cytoarchitecturally intact, but injury-reactive regions, LRAs intermingle with neurons and glia, undergo little to no proliferation, and exhibit varying degrees of cellular hypertrophy13,14. The molecular and functional properties of LRAs remain undefined, and whether regionally restricted microenvironments impose discrete astrocyte reactivity states is not understood. Therapeutically harnessing spared regions of the injured CNS will require a clearer understanding of the accompanying cellular and molecular landscape.

Here, we leveraged integrative transcriptional profiling methodologies to identify multiple spatiotemporally resolved, molecularly distinct states of LRA reactivity within the injured spinal cord. We show that LRAs exhibit a transcriptional identity that is distinct from that of BFAs and astrocytes associated with non-traumatic spinal cord insults and disorders. We used computational modelling of LRA-mediated heterotypic cell interactions, astrocyte-specific gene deletion, and multiple mouse models of acute and chronic CNS white matter degeneration to reveal and probe a previously unrecognized white matter LRA reactivity state. We show that this state (1) is induced by local myelin damage; (2) serves to govern the molecular, metabolic and functional specification of debris-clearing microglia; and (3) supports neurological recovery after injury. Our findings further suggest that CCN1 secreted by white matter LRAs may bind SDC4 on microglia to augment their lipid storage activity. Astrocyte-specific CCN1 depletion attenuates white matter debris clearance and impairs neurological recovery after spinal cord injury (SCI), linking LRA-mediated CCN1 signalling to vital repair-associated lipid buffering responses in debris-clearing microglia. These findings position LRAs as key orchestrators of multicellular neural repair processes that promote functional recovery after CNS injury.

Molecular dissection of LRAs after SCI

LRAs exhibit varying degrees of hypertrophy and intermingle with viable neurons, glia and neural circuitry throughout cytoarchitecturally intact regions of the injured CNS[13] (Fig. 1a). Whether LRA reactivity evolves or resolves over time and how this form of reactivity differs from BFAs is unclear. We addressed these questions first by broadly examining injury-reactive gene expression dynamics of LRAs in a mouse model of anatomically and functionally incomplete SCI (iSCI). After iSCI, spared regions of the injured spinal cord rostral and caudal to the lesion undergo synaptic circuit reorganization that re-establishes brain–cord communication and gives rise to recovery of locomotor behaviour1,12,15 (Extended Data Fig. 1a). Concurrently, discrete spared white matter regions undergo widespread Wallerian axon degeneration, which gives rise to chronic gliosis and inflammation[16]. We performed bulk RNA sequencing (RNA-seq) of astrocyte-specific ribosome-associated mRNA (RiboTag) and whole-tissue mRNA from spared tissue regions rostral and caudal to the lesion epicentre at multiple post-injury time points. These time points reflect distinct phases of functionally meaningful neuroplasticity and locomotor recovery after iSCI[1] (Fig. 1b and Extended Data Fig. 1a). Thus, we could investigate LRA transcriptional dynamics associated with post-traumatic neuroplasticity, inflammation and neurological recovery.

Fig. 1: Spatiotemporal molecular decoding of SCI LRAs.

a, Comparison of BFAs and hypertrophic reactive LRAs after iSCI. b, RNA-seq of LRAs. HA, haemagglutinin. c, DEGS in bulk RNA-seq of LRAs (false discovery rate (FDR) P ≤ 0.05) at 3 (n = 5), 7 (n = 4), 14 (n = 6), and 28 (n = 6) dpi versus healthy LRAs (n = 5). Rostral and caudal data are combined. d, BFA expression of LRA DEGs across acute (3 dpi), subacute (7 and 14 dpi), and chronic (28 dpi) post-injury phases. On average, 46% of LRA DEGs are either not regulated in BFAs or are regulated in the opposite direction across these phases. e, Principal component analysis of astrocyte DEGs from SCI LRAs and BFAs, EAE, amyotrophic lateral sclerosis (ALS) and lipopolysaccharide (LPS)-reactive spinal cord astrocytes relative to healthy spinal cord astrocytes. LRAs acquire a separable reactivity state with a distinct trajectory relative to SCI and non-traumatic CNS insults. f, Schematic of combined snRNA-seq and spatial transcriptomics approach for investigating SCI-reactive LRAs and neighbouring cell types. g, Uniform manifold approximation and projection (UMAP) of cell types identified by snRNA-seq in healthy and all post-injury (all time points) spinal cord (230,570 cells). Tissue collected from uninjured thoracic (n = 5), uninjured lumbar (n = 4), 3 dpi thoracic (n = 4), 3 dpi lumbar (n = 4), 7 dpi thoracic (n = 4), 7 dpi lumbar (n = 4), 28 dpi thoracic (n = 6) and 28 dpi lumbar (n = 3) spinal cords. h, Diagram of intraspinal regions used in deconvolution of spatial transcriptomic data (n = 4 mice per time point per region). GM, grey matter; WM, white matter. i, UMAP of healthy and iSCI astrocytes, illustrating neuroanatomically restricted LRA subtypes and their distinct molecular states. Ast, astrocytes; cGM, central grey matter; dGM, dorsal grey matter; vGM, ventral grey matter. j, Spatial transcriptomic characterization of vGM3 astrocytes illustrates restricted positioning in ventral horn grey matter. k, High-magnification image of vGM3 markers Arex and Thrsp in Gfap+/Aldoc+ ventral horn grey matter astrocytes. Scale bars, 10 µm. l, Spatial transcriptomic characterization of WM4 astrocytes illustrates unilaterally restricted white matter expression. m, High-magnification image of WM4 markers shows expression of Glipr2 and Ccn1 in Gfap+/Aldoc+ white matter astrocytes. Scale bars, 10 µm. n, Heat map showing relative expression of functionally related genes across regionally restricted LRA molecular states. ECM, extracellular matrix; MHC, major histocompatibility complex. Exact P values are provided in the Source Data.

Source Data

Analysis of differentially expressed genes (DEGs) uncovered large and persistent alterations in astrocyte molecular profile that progress over time following injury, many of which were significantly astrocyte enriched (Fig. 1c, Extended Data Fig. 1b–d and Supplementary Data 1). LRA DEG profiles were largely distinct from BFAs[17], with an average of 46% (range: 34.4–52.2%) of LRA DEGs being either not regulated in BFA or altered in the opposite direction across acute, subacute and chronic time periods post-injury (Fig. 1d). Temporal transcriptomic profiles of LRAs and BFAs occupied discrete domains in principal component space (Fig. 1e). Further, we compared LRAs to reactive spinal cord astrocyte subtypes from across multiple divergent, non-traumatic CNS insults. DEG profiles of astrocytes from each of these conditions were heterogenous and exhibited divergent positioning in principal component space relative to LRAs (Fig. 1e and Extended Data Fig. 1b). Thus, LRAs undergo a reactive transformation in molecular phenotype that is unique relative to BFAs of the injured spinal cord and constitutes a subtype of reactivity that is distinct from that observed in spinal cord astrocytes under the context of divergent non-traumatic neurological disorders. Analysis of DEGs in LRAs revealed divergent patterns of temporal regulation and distinct functional pathway enrichment (Extended Data Fig. 1e–g). We also found that genetic deletion of astrocyte Stat3 (Stat3-cKO), a reactivity master regulator[9], markedly attenuates injury-reactive LRA gene expression (Extended Data Fig. 1h and Supplementary Data 2). However, the effects of Stat3-cKO on LRA reactivity due to altered BFA function cannot be ruled out.

Together, these findings show that LRAs in spared regions of the injured spinal cord undergo a profound degree of transcriptional reprogramming that evolves over time after iSCI. The majority of injury-reactive alterations in LRA gene expression are: (1) divergent from SCI BFAs; (2) prominently distinct from other reactive subtypes observed in the spinal cord under diverse neurological insults and disorders; and (3) may be dependent on phosphorylation-dependent STAT3 signalling.

LRAs exhibit spatiotemporal heterogeneity

In contrast to BFAs, LRAs tile anatomically and functionally discrete white and grey matter domains of grossly intact spinal cord regions[13]. We reasoned that since transcriptional profiles of reactive astrocytes are highly context-dependent, LRAs from different neuroanatomical microenvironments of the injured cord may manifest divergent reactivity states. To test this hypothesis, we performed single-nuclei RNA-seq (snRNA-seq) and spatial transcriptomics on equivalent spared regions of the injured adult mouse spinal cord rostral and caudal to an iSCI lesion at 3, 7 and 28 days post injury (dpi) (Fig. 1f–h and Extended Data Fig. 2).

We identified 12 distinct astrocyte molecular states, the relative proportions of which varied from healthy to injured, and across post-injury time points (Fig. 1i). snRNA-seq profiles were used to deconvolve spatial transcriptomic data and map astrocyte molecular states to their native intraspinal anatomical location (Fig. 1h,i). This approach revealed that distinct snRNA-seq astrocyte subtypes mapped to discrete anatomical regions within the white matter and grey matter of the healthy and injured spinal cord (Fig. 1i–m and Extended Data Fig. 3a–i). Multiple grey matter astrocyte subtypes with unique spatiomolecular profiles along the dorsoventral axis of the spinal cord were also defined (Fig. 1i–k and Extended Data Fig. 3a–h). This separation of spinal cord astrocytes in functional neuroanatomical space paralleled marked transcriptional divergence, suggesting that region-specific alterations in LRA molecular state may differentially influence local cellular function, with consequences for neural tissue remodelling, inflammation and repair (Fig. 1n and Supplementary Data 3).

Decoding LRA reactivity states after SCI

We found multiple hypertrophic LRA reactivity states with characteristically increased expression of intermediate filament genes (Gfap and Vim) and that exhibit unique spatiotemporal profiles (Fig. 1i and Extended Data Fig. 3j). LRAs in the sensory laminae of the dorsal horn grey matter (dGM1 and dGM2) demonstrated a notable shift in molecular profile from dGM1 towards a dGM2 molecular state at 28 dpi (Fig. 1i and Extended Data Fig. 3c–g). Relative to astrocytes in the healthy dorsal horn grey matter (dGM1), the chronic dGM2 LRA reactivity state is characterized by increased expression of Glt1, Grm5 and multiple ionotropic AMPA glutamate receptor genes (Gria4, Grid1 and Grid2). dGM2 astrocytes also upregulate synaptogenic glypicans (Gpc5 and Gpc6) and genes implicated in debris and synapse phagocytosis (Megf10, Mertk and Dock1) (Fig. 1n and Supplementary Data 3). LRAs from the ventral grey matter (vGM1, vGM2 and vGM3) displayed a robust, but transient shift from vGM1 to vGM2 and vGM3 molecular state representation during acute and subacute post-injury time points (Fig. 1i–k and Extended Data Fig. 3a,h). Relative to astrocytes in the healthy ventral grey matter (vGM1), vGM2 and vGM3 LRAs dynamically upregulate metabotropic glutamate receptors Grm3 and Grm5. C1qa, C1qb, C1qc and C4b were also upregulated, consistent with potential involvement in complement-mediated post-traumatic synaptic circuit remodelling. Intra-regional reactive LRA heterogeneity is underscored by vGM2 versus vGM3 transcriptional differences. For example, LRAs acquiring a vGM2 reactivity state downregulate GABA (γ-aminobutyric acid) transporters (Gat1 and Gat3) and the primary astrocyte inward rectifying K+ channel Kir4.1 (Kcnj10), by contrast, vGM3 LRAs upregulate expression of these genes. vGM3 astrocytes also exhibited distinctly higher expression of genes encoding GABA receptor subunits (Gabbr1, Gabbr2 and Gabrg1), glutamate uptake and metabolism genes (Slc1a2, Slc1a3, Glul and Glud1) and key sterol metabolism genes (Hmgcs1, Dhcr24 and Sqle) (Fig. 1n and Supplementary Data 3). Thus, grey matter LRAs acquire region-specific hypertrophic reactivity states (dGM2, vGM2 and vGM3) with potential circuit-specific and functional consequences.

Hypertrophic white matter LRAs exhibiting WM2, WM3 or WM4 molecular states are restricted to the injured spinal cord and exhibit a lesion ipsilateral regional identity (Fig. 1i,l,m and Extended Data Figs. 2e and 3a,b,i). White matter LRA transcriptomic profiles evolve over time, with a greater proportion WM2 and WM3 LRAs at acute and subacute time points, transitioning to a WM4 molecular state in the chronic post-injury phase (Fig.1i). Relative to healthy white matter astrocytes (WM1), WM2, WM3 and WM4 LRAs displayed indicators of metabolic plasticity, namely widespread upregulation of lactate metabolism and transport genes (such as Mct1, Mct2 and Ldha) and glycolysis genes (such as Pgam1, Pgk1 and Pkm), which may underlie astrocyte-mediated alterations in axonal energy metabolism in lesion-remote white matter (Fig. 1n and Supplementary Data 3). Of note, we determined that WM2, WM3 and WM4 LRAs showed persistently increased expression of immune and inflammamodulatory genes, potentially implicating them in chronic white matter inflammation and repair (for example, Lcn2, Cxcl12, Ptx3, Tgfb1 and Tgfb2).

White matter degeneration-associated microglia

Deconvolution of our spatial transcriptomics data was used to define co-occurring cell states across space and time (Fig. 2a). This analysis identified multiple region-specific grey matter LRAs and co-occurring neurons, glia and endothelia (Extended Data Fig. 4). We further determined an ipsilesional white matter niche comprised of reactive white matter LRAs (WM3 and WM4 (hereafter WM3/4)) and two co-occurring white matter-restricted microglia subtypes (Mg2 and Mg5 (Mg2/5) with largely overlapping transcriptomic identities (Fig. 2b–d and Extended Data Fig. 5).

Fig. 2: White matter astrocyte CCN1 restricts the spatiotemporal dynamics of microglia-mediated white matter inflammation after SCI.

a, Non-negative matrix factorization (NMF) on deconvolved spatial transcriptomics data was used to identify co-occurring cell states across space and time. NMF loading weights plotted across healthy and post-injury time points predicts injury-reactive alterations in regionally co-occurring cell states. b, Spatial plotting of NMF 4 reveals enrichment in lesion ipsilateral white matter. NMF 4 is primarily composed of white matter LRAs (WM3/4) and Mg2/Mg5 microglia. c, Spatial transcriptomic localization of Mg2/Mg5 microglia and WM3/4 astrocytes illustrate overlapping unilateral white matter enrichment. d, Volcano plot of DEGs in Mg2/5 microglia versus homeostatic microglia (Mg1 and Mg3 (Mg1/3) (FDR P ≤ 0.05). FC, fold change. e,f, Dot plot of mean normalized expression of metabolism and function (e) and previously published microglia state-associated molecular signatures18,19,20,21,22,23,24,25,26 (f) in iSCI lesion-remote microglia clusters. g, WDM assemble into multicellular nodules that phagocytose myelin debris. Scale bars, 10 µm. h, NicheNet analysis of reactive WM3/4 LRAs (senders) and WDM (Mg2/5) (receivers) identified several putative ligands secreted by white matter astrocytes. i, Ccn1 is selectively expressed by LRAs neighbouring WDM nodules in degenerating white matter. Scale bar, 10 µm. j, Left, aligned average density plots of Ccn1-expressing astrocytes, illustrating regional and intraspinal relationships between Ccn1+ LRAs and anatomically defined zones of Wallerian degeneration. Right, quantification of Ccn1+ astrocytes per time point in each region. Cells were counted from cervical healthy (n = 5), thoracic healthy (n = 6), lumbar healthy (n = 4), cervical 3 dpi (n = 3), thoracic 3 dpi (n = 5), lumbar 3 dpi (n = 4), cervical 7 dpi (n = 5), thoracic 7 dpi (n = 4), lumbar 7 dpi (n = 5), cervical 14 dpi (n = 4), thoracic 14 dpi (n = 4), lumbar 14 dpi (n = 4), cervical 28 dpi (n = 4), thoracic 28 dpi (n = 8), lumbar 28 dpi (n = 6), cervical 90 dpi (n = 4), thoracic 90 dpi (n = 5) and lumbar 90 dpi (n = 4); 2–11 sections per mouse, region or time point. Scale bars, 250 µm. k, Schematic of iSCI lesion-remote spinal cord regions and expected patterns of Wallerian degeneration from which Ccn1+ astrocytes were evaluated. l, WDM nodules in Wallerian degenerating dorsal white matter (mid-thoracic) from wild-type (WT) and Ccn1-cKO spinal cord following iSCI. Yellow arrowheads indicate WDM in Wallerian degenerating regions; white arrowheads indicate spatially aberrant WDM nodules. Scale bars, 250 µm. m, Left, aligned average density plots of WDM nodules in Ccn1-cKO spinal cord after iSCI. Right, quantification of WDM nodules from lumbar, thoracic and cervical spinal cord of wild-type and Ccn1-cKO mice following iSCI. Statistical comparison between wild-type and Ccn1-cKO nodule counts by Conway–Maxwell–Poisson method and Tukey’s post hoc test. Nodules were counted from WT cervical 7 dpi (n = 5), Ccn1-cKO cervical 7 dpi (n = 5), WT thoracic 7 dpi (n = 4), Ccn1-cKO thoracic 7 dpi (n = 4), WT lumbar 7 dpi (n = 4), Ccn1-cKO lumbar 7 dpi (n = 4), WT cervical 28 dpi (n = 4), Ccn1-cKO cervical 28 dpi (n = 4), WT thoracic 28 dpi (n = 6), Ccn1-cKO thoracic 28 dpi (n = 5), WT lumbar 28 dpi (n = 6), Ccn1-cKO lumbar 28 dpi (n = 4), WT cervical 90 dpi (n = 5), Ccn1-cKO cervical 90 dpi (n = 4), WT thoracic 90 dpi (n = 5), Ccn1-cKO thoracic 90 dpi (n = 5), WT lumbar 90 dpi (n = 4), Ccn1-cKO lumbar 90 dpi (n = 5), 2–11 sections per mouse, genotype region or time point. Graphs show mean ± s.e.m. In graphs of histological counts or continuous data, coloured data points represent the mean value for each biological replicate (individual mouse); grey data points indicate replicate measurements from individual tissue sections. Unless stated otherwise, statistical analyses were performed using two-way ANOVA on mean values from biological replicates with Holm–Sidak’s post hoc test. Scale bars, 250 µm. *P ≤ 0.05, **P ≤ 0.002, ***P ≤ 0.0002, ****P ≤ 0.0001; NS, not significant. Exact P values are provided in the Source Data.

Source Data

Relative to homeostatic microglia (Mg1 and Mg3) and Mg2/5 microglia showed coordinated upregulation of genes involved in phagocytosis, lipid metabolism and inflammatory regulation, accompanied by suppression of homeostatic genes (Fig. 2d,e and Extended Data Fig. 5d–f). Their molecular profile resembled that of phagocytic microglia that emerge in ageing, injury or in developmental white matter, including white matter-associated (WAM) and disease-associated (DAM) states18,19,20,21,22,23,24,25,26 (Fig. 2f). These data define Mg2/5 microglia as a conserved white matter inflammation- and repair-associated state across diverse CNS disorders. Remarkably, we that find molecular markers of the Mg2/5 snRNA-seq profile correspond to microglia within Wallerian degenerating white matter that gradually assemble into multicellular nodules and phagocytose myelin debris (Fig. 2d,g and Extended Data Fig. 5g). We refer to these cells collectively as white matter degeneration-associated microglia (WDM).

WDM intermingle with Ccn1+ LRAs

Although some fundamental properties of debris-clearing microglia are characterized in white matter damage and disease20,25,27,28,29, the cellular interactions that shape microglia responses, and how these affect debris clearance efficacy, inflammation regulation, repair or recovery after CNS injury are not well defined. We carried out NicheNet analysis[30] to identify ligand-mediated pathways of communication from reactive WM3/4 LRAs and Mg2/5 WDM (Fig. 2h). Expression validation of putative WM3/4 LRA-derived ligands determined that cellular communication network factor 1 (Ccn1), which encodes a secreted matricellular protein, is prominently and specifically expressed by LRAs within degenerating spinal cord white matter (Fig. 2h–k). Ccn1-expressing LRAs intimately associate with myelin debris-clearing WDM nodules (Fig. 2i and Extended Data Fig. 6a). In the healthy spinal cord, Ccn1 expression is mainly restricted to ependymal cells, with astrocyte expression being exceptionally rare (Fig. 2j and Extended Data Fig. 6b–d). As early as 3 days post-SCI, abundant Ccn1-expressing astrocytes are found throughout degenerating white matter and persist for at least 90 dpi (Fig. 2j and Extended Data Fig. 6d,e). Notably, Ccn1+ LRAs contained significantly increased nuclear levels of YAP1, a canonical activator of Ccn1 transcription (Extended Data Fig. 6f). Consistently, we found that CCN1 is actively secreted by adult mouse astrocytes (Extended Data Fig. 6g and Supplementary Fig. 1). In contrast to LRAs, the number of Ccn1-expressing ependymal cells was unaltered by SCI (Extended Data Fig. 6b,c). Correspondingly, increased levels of CCN1 protein were detected in spared regions of the injured spinal cord, which can be attributed to Ccn1+ white matter LRAs (Fig. 2j, Extended Data Fig. 6b–d,h and Supplementary Fig. 1). A spinal cord-wide, spatiotemporally resolved analysis of Ccn1-expressing astrocytes revealed an unequivocal intraspinal regional relationship between Ccn1+ astrocytes and anatomically defined zones of Wallerian axon degeneration (Fig. 2j,k and Extended Data Fig. 6i,j).

Together, these data demonstrate that after SCI, a subset of reactive white matter LRAs (1) rapidly and persistently upregulate CCN1; (2) are restricted to Wallerian degenerating white matter; and (3) neighbour debris-clearing WDM nodules.

Astrocyte CCN1 restricts white matter inflammation

To determine the function of astrocyte-secreted CCN1, we examined WDM specification and function throughout lesion-remote Wallerian degenerating white matter after iSCI in young adult wild-type mice, and mice with conditional astrocyte-specific[31] Ccn1 gene deletion[32] (Ccn1-cKO mice, Aldh1l1-CreERT2::Ccn1fl/fl) (Extended Data Fig. 7a–f). Given the sustained intimate spatial relationship between Ccn1+ astrocytes and WDM (Fig. 2i), we quantified nodule formation dynamics across the rostrocaudal axis of the injured spinal cord (Fig. 2l,m and Extended Data Fig. 7g–k). WDM nodule accumulation was markedly accelerated in Ccn1-cKO mice, with significantly increased nodule formation evident by 7 dpi (Fig. 2l,m and Extended Data Fig. 7i). This amplified microglial response was observed across all spinal cord regions examined and persisted to 90 dpi, reaching up to a fourfold increase in WDM nodules relative to wild type (Fig. 2l,m and Extended Data Fig. 7i). Lesion-remote regions of the spinal cord of Ccn1-cKO mice also exhibited ectopic inflammation, including the increased presence of WDM nodules in the rostral corticospinal tract, contralateral white matter and grey matter (Fig. 2l,m and Extended Data Fig. 7i–k). Therefore, loss of astrocyte-derived CCN1 results in accelerated, chronically excessive and spatially aberrant activation of phagocytic microglia in degenerating white matter.

Astrocyte CCN1 aids white matter repair

We investigated whether loss of astrocyte CCN1 signalling affects WDM-mediated phagocytosis of lipid-rich myelin and axon debris (Fig. 3a–i and Extended Data Fig. 7l). By 90 dpi, Ccn1-cKO microglia contained an approximately 40% greater volume of internalized myelin debris, and around 23% greater volume of axon debris than their wild-type equivalents (Fig. 3b–e and Extended Data Fig. 7l). Loss of astrocyte-secreted CCN1 resulted in a higher overall proportion of microglia containing myelin or axon debris at 90 dpi (Fig. 3f,g). However, we observed that loss of astrocyte CCN1 also led to significantly attenuated debris clearance (Fig. 3a,h,i). Thus, although loss of astrocyte CCN1 signalling results in the amplified accumulation of phagocytic microglia, these cells become engorged with undigested debris and do not mount an efficient debris clearance programme—a process that is critical for white matter repair.

Fig. 3: Astrocyte CCN1 enables microglial clearance and intracellular digestion of white matter debris and is required for sensory recovery after SCI.

a, Low-magnification image showing FluoroMyelin-labelled myelin debris and IBA1+ WDM nodules in Wallerian degenerating dorsal white matter of iSCI lesion-remote spinal cord (mid-thoracic) of wild-type and Ccn1-cKO mice. Scale bars, 50 μm. b, High-magnification 3D reconstructions showing IBA1+ WDM nodules with internalized FluoroMyelin-labelled myelin. Scale bars, 10 μm. c, Quantification of internalized myelin in WDM from uninjured control (healthy) and iSCI wild-type and Ccn1-cKO mice. WT healthy (n = 3), Ccn1-cKO healthy (n = 3), WT 7 dpi (n = 2), Ccn1-cKO 7 dpi (n = 4), WT 28 dpi (n = 6), Ccn1-cKO 28 dpi (n = 5), WT 90 dpi (n = 6), Ccn1-cKO 90 dpi (n = 6). FM, FluoroMyelin. d, High-magnification 3D reconstructions showing IBA1+ WDM nodules with internalized axon debris labelled with the neurofilament monoclonal antibody SMI32. Scale bars, 10 μm. e, Quantification of internalized axon debris in WDM from WT healthy (n = 4), Ccn1-cKO healthy (n = 3), WT 7 dpi (n = 3), Ccn1-cKO 7 dpi (n = 4), WT 28 dpi (n = 6), Ccn1-cKO 28 dpi (n = 5), WT 90 dpi (n = 6) and Ccn1-cKO 90 dpi (n = 6) mice. f, Proportion of WDM containing internalized myelin debris in WT healthy (n = 3), Ccn1-cKO healthy (n = 3), WT 7 dpi (n = 3), Ccn1-cKO 7 dpi (n = 4), WT 28 dpi (n = 6), Ccn1-cKO 28 dpi (n = 5), WT 90 dpi (n = 6) and Ccn1-cKO 90 dpi (n = 6) mice. g, Proportion of WDM containing internalized axon debris. WT healthy (n = 4), Ccn1-cKO healthy (n = 3), WT 7 dpi (n = 3), Ccn1-cKO 7 dpi (n = 4), WT 28 dpi (n = 6), Ccn1-cKO 28 dpi (n = 5), WT 90 dpi (n = 6), Ccn1-cKO 90 dpi (n = 6). h, Quantification of extracellular myelin debris accumulation in Wallerian degenerating dorsal white matter from WT healthy (n = 4), Ccn1-cKO healthy (n = 3), WT 7 dpi (n = 3), Ccn1-cKO 7 dpi (n = 4), WT 28 dpi (n = 6), Ccn1-cKO 28 dpi (n = 5), WT 90 dpi (n = 6) and Ccn1-cKO 90 dpi (n = 6) mice. i, Quantification of extracellular axon debris accumulation in Wallerian degenerating dorsal white matter from WT healthy (n = 4), Ccn1-cKO healthy (n = 3), WT 7 dpi (n = 3), Ccn1-cKO 7 dpi (n = 4), WT 28 dpi (n = 6), Ccn1-cKO 28 dpi (n = 5), WT 90 dpi (n = 6) and Ccn1-cKO 90 dpi (n = 6) mice. j, Quantification of cold thermoception behaviour recovery in left (lesion ipsilesional) hindpaw after iSCI (n = 8 WT mice, n = 7 Ccn1-cKO mice). k, Quantification of mechanosensation behaviour recovery in left (lesion ipsilesional) hindpaw after iSCI as assessed by Von Frey testing (n = 8 wild-type mice, n = 7 Ccn1-cKO mice). Graphs show mean ± s.e.m. In graphs of histological continuous data, coloured data points represent the mean value for each biological replicate (individual mouse); grey data points indicate replicate measurements from individual tissue sections. Unless stated otherwise, statistical analyses were performed using two-way ANOVA on mean values from biological replicates with Holm–Sidak post hoc test. Exact P values are provided in the Source Data.

Source Data

Impaired clearance of myelin debris can restrict white matter repair[33]. We examined whether deficient astrocyte Ccn1 expression also affects spontaneous recovery of sensorimotor function after iSCI. Remarkably, recovery of locomotor function after iSCI was equivalent across wild-type and Ccn1-cKO mice (Extended Data Fig. 7m). iSCI severs the spinothalamic tract, which transmits information about pain, temperature and mechanosensation. We evaluated spinothalamic function after iSCI by measuring hindpaw sensitivity to non-noxious cold stimuli. In contrast to wild-type iSCI mice, who exhibited a full recovery of cold sensitivity, Ccn1-cKO iSCI mice did not recover cold thermoception (Fig. 3j and Extended Data Fig. 7n). Spinothalamic tract function was further assessed by Von Frey assay, which revealed a divergence in the trajectory of mechanosensory recovery, with Ccn1-cKO mice, but not wild-type mice, returning to baseline sensitivity by 84 dpi (Fig. 3k and Extended Data Fig. 7o). Therefore, astrocyte-secreted CCN1 regulates neurological recovery after SCI.

CCN1 reprogrammes microglial lipid metabolism

Next, we sought to dissect the direct effects of CCN1 on microglia by investigating molecular mechanisms underlying their apparent dysfunction in metabolizing phagocytosed debris from the degenerating Ccn1-cKO white matter. Transcriptomic analysis of CCN1-stimulated naïve microglia revealed the prominent upregulation of TREM2-dependent WAM[25] and stage 2 DAM[26] gene signatures previously linked to white matter repair, debris clearance and neuroprotection, which include genes related to phagocytosis and lipid metabolism (Cst7, Cd63, Clec7a, Lpl, Lgals3, Spp1 and Tspo), (Fig. 4a,b and Supplementary Data 4). In microglia, TREM2 signalling mediates the alterations in molecular profile that are necessary for metabolizing lipid-rich myelin debris25,27. TREM2 levels in WDM were markedly attenuated in the absence of astrocyte CCN1, which could curb their molecular and functional specification (Fig. 4c,d and Extended Data Fig. 8a,b). Indeed, microglia nodules from the injured Ccn1-cKO spinal cord white matter exhibit aberrant expression of Gpnmb and Igf1, which are central molecular indicators of the WDM transcriptional signature (Fig. 4e–h and Extended Data Fig. 8c,d). CCN1 also mediated broad enhancement of lipid uptake and expression of lipid storage genes (Plin3, Acat1, Pparg, Fabp4, Fabp5, Olr1 and Marco), while suppressing the expression of genes required for lipid efflux (Abca1, Abca3, Abca5, Abca7 and Apoe) (Fig. 4a,b and Supplementary Data 4). Congruently, treatment of wild-type microglia with recombinant CCN1 significantly reduced cholesterol efflux (Fig. 4i and Extended Data Fig. 8e). Thus, CCN1 signals directly to microglia to instruct transcriptomic alterations for lipid metabolism reprogramming implicated in lipid uptake, intracellular processing of lipid-rich cellular debris, and associated buffering of excess lipids.

Fig. 4: Astrocyte CCN1 mediates lipid metabolism reprogramming in debris-clearing microglia.

a,b, Volcano plot of all DEGs (a) and associated functional pathway modulation (b) in CCN1-stimulated primary microglia, as determined by RNA-seq (log_2-transformed fold change versus vehicle (BSA), FDR P ≤ 0.05; vehicle, n = 3, and CCN1, n = 4). c,d, High-magnification 3D reconstruction (c) and quantification (d) of TREM2 in wild-type and Ccn1-cKO WDM. WT healthy (n = 4), Ccn1-cKO healthy (n = 3), WT 7 dpi (n = 6), Ccn1-cKO 7 dpi (n = 6), WT 28 dpi (n = 6), Ccn1-cKO 28 dpi (n = 5), WT 90 dpi (n = 6), Ccn1-cKO 90 dpi (n = 6). e,f, High-magnification 3D reconstruction (e) and quantification (f) of Gpnmb expression in WDM, data from WT healthy (n = 3), Ccn1-cKO healthy (n = 3), WT 28 dpi (n = 4), Ccn1-cKO 28 dpi (n = 4), WT 90 dpi (n = 4), Ccn1-cKO 90 dpi (n = 4). g,h, High-magnification 3D reconstruction (g) and quantification (h) of Igf1 expression in WDM from WT healthy (n = 3), Ccn1-cKO healthy (n = 4), WT 28 dpi (n = 3), Ccn1-cKO 28 dpi (n = 4), WT 90 dpi (n = 3) and Ccn1-cKO 90 dpi (n = 4) mice. i, Cholesterol efflux from cultured primary mouse microglia (n = 5 replicates from independent cultures run in triplicate; Students t-test, **P ≤ 0.002). j, schematic for SCI lesion-remote microglia lipidomics. MRM, multiple reaction monitoring. k, Principal component analysis of lipidomic profiles of healthy and iSCI microglia from wild-type and Ccn1-cKO spinal cord. Microglia were isolated from WT healthy (n = 5), Ccn1-cKO healthy (n = 6), WT 28 dpi (n = 6) and Ccn1-cKO 28 dpi (n = 5) mice. l, Comparison of wild-type and Ccn1-cKO injury-reactive alterations in microglia lipid profile (log_2-transformed fold change, iSCI versus healthy, FDR P ≤ 0.05). White represents non-significantly altered lipid species. CAR, carnitines; CE, cholesterol esters; CER, ceramides; FA, fatty acids; PC, phosphatidylcholines; PE, phosphatidylethanolamines; PG, phosphatidylglycerols; PI, phosphatidylinositols; PS, phosphatidylserines; SM, sphingomyelins; TAG, triacylglycerols. m,n, High-magnification 3D reconstructions (m) and quantification (n) of BODIPY+ lipid droplets in WDM from WT healthy (n = 4), Ccn1-cKO healthy (n = 3), WT 7 dpi (n = 4), Ccn1-cKO 7 dpi (n = 4), WT 28 dpi (n = 6), WT Ccn1-cKO (n = 5), WT 90 dpi (n = 6) and Ccn1-cKO 90 dpi (n = 6) mice. o,p, High-magnification 3D reconstructions (o) and quantification (p) of Abca1 expression in WDM from WT healthy (n = 3), Ccn1-cKO healthy (n = 3), WT 28 dpi (n = 4), Ccn1-cKO 28 dpi (n = 4), WT 90 dpi (n = 4) and Ccn1-cKO 90 dpi (n = 4) mice. q, Left, schematic for CCN1 receptor identification assay. Right, volcano plot of CCN1 binding partners in microglia from proteomic analysis of CCN1-directed co-immunoprecipitation (co-IP) (n = 4 experimental replicates from independent cultures; log_2-transformed fold change ≥2, CCN1 co-immunoprecipitation versus negative control antibody co-immunoprecipitation; t-test −log_10P > 1.3). Labels indicate microglial candidate CCN1 receptors. r,s, High-magnification images (r) and quantification (s) of Sdc4 in healthy IBA1+ microglia and IBA1+/Gpnmb+ WDM nodules from Wallerian degenerating regions in wild-type mice (n = 5 mice per group; two-sided Student’s t-test, *P ≤ 0.05). t,u, Quantification of lipid storage in microglia by flow cytometric analysis of lipid droplet-associated neutral lipid content in microglia with CCN1 and antibody treatments. Ctrl Ab, isotype control antibodies; fbAb, function-blocking antibodies. t, Median intensity of neutral lipid staining. u, Representative distribution of cell counts and intensity of neutral lipid staining (n = 4 replicates from independent cultures; one-way ANOVA with Holm–Sidak post hoc test, ****P ≤ 0.0001). AU, arbitrary units. Unless stated otherwise, graphs show mean ± s.e.m. In graphs of histological counts or continuous data, coloured data points represent the mean value for each biological replicate (individual mouse); grey data points indicate replicate measurements from individual tissue sections. Unless stated otherwise, statistical analyses were performed using two-way ANOVA on mean values from biological replicates with Holm–Sidak post hoc test. Exact P values are provided in the Source Data. Scale bars, 10 μm.

Source Data

CCN1 directs microglial lipid buffering

CCN1 directly modulates microglial lipid metabolism gene expression and loss of astrocyte CCN1 leads to amplified activation of debris-laden microglia nodules, but impairs debris clearance. Intracellular accumulation of myelin debris-derived lipids and impaired debris clearance have been linked to maladaptive shifts in microglial lipid metabolism, including disruptions in essential lipid buffering mechanisms (lipid efflux and storage) that are required for white matter repair20,25,27,33,34. Therefore, we determined whether astrocyte-secreted CCN1 regulates microglia lipid metabolism in vivo by performing an unbiased lipidomics analysis on whole-cell extracts of microglia from lesion-remote spinal cord regions undergoing Wallerian degeneration in wild-type and Ccn1-cKO mice (Fig. 4j and Extended Data Fig. 8f). Although microglia lipidomes from the healthy wild-type or Ccn1-cKO spinal cord were grossly similar (Fig. 4k and Extended Data Fig. 8g), we observed highly divergent injury-induced alterations in lipidomic profile across multiple lipid classes (Fig. 4k,l and Extended Data Fig. 8g–l). Microglia from the injured Ccn1-cKO spinal cord contained significantly increased levels of multiple lipid classes found in myelin and axonal debris, including phosphatidylethanolamine, sphingomyelin and phosphatidylcholine (Fig. 4l), echoing the increased levels of internalized myelin debris observed in WDM of the Ccn1-cKO spinal cord white matter (Fig. 3b–e) and revealing a profound disruption in microglial debris processing and associated lipid metabolism.

Following phagocytosis, excess myelin-derived cholesterol is converted into cholesterol esters and stored with triacylglycerols in lipid droplets[35]. In microglia, cholesterol esterification and lipid droplet biogenesis are adaptive lipid buffering responses to myelin debris uptake that enable efficient debris clearance and white matter repair[33]. Though our analysis did not permit for detection of free cholesterol, we found that microglia from the injured Ccn1-cKO cord contained a significantly lower proportion of cholesterol esters and triacylglycerols, relative to wild type, indicative of impaired lipid storage (Fig. 4l). Validating this result and interpretation, WDM from the Ccn1-cKO cord contained significantly fewer lipid droplets than their wild-type-derived equivalents (Fig. 4m,n and Extended Data Fig. 8m–r). The proportion of WDM containing lipid droplets was also significantly attenuated (Extended Data Fig. 8n). Thus, astrocyte CCN1 is critical for injury-induced accumulation of storage-associated lipids and lipid droplets in phagocytic white matter microglia.

In addition to being esterified and stored in lipid droplets, intracellular cholesterol can exist in a free state, which is actively effluxed via ATP-binding cassette transporters. We hypothesized that the accumulation of myelin debris-derived lipids, including free cholesterol in WDM of the injured Ccn1-cKO spinal cord, could result in amplified Abca1 expression, favouring cholesterol efflux over storage. We observed that Ccn1-cKO-derived WDM indeed express significantly higher levels of Abca1 than those from the wild-type spinal cord (Fig. 4o,p and Extended Data Fig. 8s). These data further verify microglia transcriptomic profiling and cholesterol efflux assay results demonstrating the direct CCN1-mediated suppression of microglia lipid efflux-related gene expression and efflux activity (Fig. 4a,b,i).

Together, these data show that coordination of injury-induced lipid metabolic reprogramming in debris-clearing microglia relies on astrocyte-derived CCN1. These CCN1-dependent alterations hinge on an adaptive lipid buffering response characterized by lipid droplet accumulation and reduced cholesterol efflux capacity that supports efficient uptake and intracellular digestion of lipid-rich debris.

A CCN1–SDC4 axis regulates microglial lipid storage

We sought next to investigate the receptor-dependent mechanism of action through which CCN1 regulates microglia lipid metabolism. We carried out CCN1-directed co-immunoprecipitation in microglia and identified binding partners by mass spectrometry proteomics. (Fig. 4q and Supplementary Data 5). Analysis of binding partners for known CCN1 receptors identified SDC4 (ref. [36]), a transmembrane heparan sulfate proteoglycan (Fig. 4q). Notably, Sdc4 expression was increased more than twofold in WDM of the injured spinal cord relative to healthy spinal cord microglia, as determined by snRNA-seq (Sdc4 log_2-transformed fold change 1.09, adjusted P value = 6.4 × 10−6), which was verified histologically in the degenerating spinal cord white matter (Fig. 4r,s).

Given prior work linking SDC4 to regulation of lipid droplet dynamics[37], we investigated whether CCN1 directly controls microglial lipid storage and whether this is dependent on SDC4. Stimulation of microglia with CCN1 led to an increase of about 400% in microglial lipid droplet accumulation (Fig. 4t,u and Supplementary Data 6), thus demonstrating a direct role for CCN1 in facilitating microglia lipid storage, as initially suggested by our in vivo microglia lipidomics and histological analyses (Fig. 4l–n and Extended Data Fig. 8m–r). This also provides functional validation of microglia transcriptomic profiling results demonstrating CCN1-mediated upregulation of key lipid storage enzymes and effectors (Fig. 4a,b). Remarkably, in alignment with CCN1 co-immunoprecipitation proteomics, the capacity of CCN1 to augment microglial lipid storage was abolished by SDC4 function-blocking antibodies (Fig. 4t,u and Supplementary Data 6).

Cumulatively, these findings suggest a CCN1–SDC4 signalling axis that regulates adaptive lipid buffering mechanisms in microglia. These results further establish astrocyte CCN1 as a direct and critical regulator of microglial lipid metabolic mechanisms that are necessary for the digestion of engulfed cellular debris and thereby the efficient clearance of debris from the degenerating white matter in the CNS.

Myelin degeneration induces astrocyte Ccn1

The nature of astrocyte-extrinsic mechanisms that trigger discrete reactivity states are not well understood. Using Ccn1 expression as a biomarker of a molecularly distinct white matter LRA reactivity state (Fig. 1l,m), we next explored the mechanism of its induction. Given that Ccn1-expressing LRAs localize to degenerating white matter, we investigated whether myelin debris is sufficient to induce astrocytic Ccn1 expression. We observed that intraspinal injection of CNS myelin triggered robust astrocytic Ccn1 expression (Fig. 5a,b and Extended Data Fig. 9a,b).

Fig. 5: Ccn1+ astrocytes are induced by myelin damage and are generated in diverse CNS white matter disorders in mouse and human.

a,b, Ccn1+ astrocytes in mouse spinal cord lateral white matter following microinjection of carboxyfluorescein succinimidyl ester (CFSE)-conjugated myelin (a; scale bars, 250 μm (left) or 50 μm (right)) and quantification relative to vehicle control (PBS) (b; n = 4 mice per group, 3–5 sections per mouse; two-sided Student’s t-test). c, Schematic of saporin injection experiment to determine the mechanism of astrocyte Ccn1 induction. d, FluoroMyelin (yellow arrowheads) and SMI32 (white arrowheads) staining to detect myelin and axon degeneration following saporin (Sap) injections. Scale bars, 50 μm. e–g, Imaging (e) and quantification (f,g) of Ccn1+ astrocytes (e,f) and IBA1+ microglia (e,g) in spinal cord dorsal white matter (dWM) following saporin-mediated neurodegeneration. Ccn1+ astrocytes and WDM-like nodules are largely restricted to degenerated myelinated tracts in the CTB–saporin condition (n = 3 mice per treatment for 14 dpi and 4 mice per treatment for 28 dpi, 6–11 sections per mouse). Scale bars, 250 μm (main image) or 10 μm (inset). h,i, Ccn1+ astrocytes (white arrowheads) and nodules with IBA1+ microglia/macrophages (yellow arrowheads) in mouse spinal cord lateral white matter at 3 days (n = 6 mice per condition), 10 days (n = 6 mice per condition) and 25 days (n = 5 mice per condition) following microinjection of lysphophosphatidyl choline (LPC) (h), and quantification of Ccn1+ astrocytes relative to vehicle (PBS) (i). Scale bars, 50 μm. j, Spatial transcriptomics demonstrates enrichment of Ccn1+ astrocytes and WDM-like microglia within LPC-demyelinated corpus (data from ref. [42]). k, Ccn1+ astrocytes (white arrowheads) intermingle with nodules containing IBA1+ microglia/macrophages (yellow arrowheads) neighbouring spinal cord white matter lesions in the mouse myelin oligodendrocyte glycoprotein (MOG35–55)-induced model of EAE. dpim, days post-immunization. Scale bars, 50 μm. l,m, Quantification of Ccn1+ astrocytes (l) and IBA1+ microglia (m) nodules per lesion at EAE onset (n = 4), peak EAE (n = 4) and in chronic EAE (n = 6) (6–32 sections per mouse). n, CCN1+ astrocytes are absent from white matter in healthy human spinal cord, but are present throughout the white matter in multiple sclerosis lesions (WML) and in lesion-remote normal-appearing white matter (WM). Scale bars, 250 μm. o, CCN1+ astrocytes in lesion-remote Wallerian degenerating corticospinal tract white matter in human SCI. Scale bars, 250 μm (main image) or 10 μm (inset). Graphs show mean ± s.e.m. In graphs of histological counts or continuous data, coloured data points represent the mean value for each biological replicate (individual mouse); grey data points indicate replicate measurements from individual tissue sections. Unless stated otherwise, statistical analyses were performed using two-way ANOVA on mean values from biological replicates with Holm–Sidak post hoc test. Exact P values are provided in the Source Data.

Source Data

Injections into white matter inherently damage axons and their associated myelin, obscuring whether astrocyte Ccn1 expression is initiated by the degeneration of axons, myelin or both. To isolate this central mechanistic detail, we carried out intraplantar injection of saporin conjugated to cholera toxin subunit B (CTB) or isolectin B4 (IB4) to selectively degenerate myelinated or non-myelinated sensory afferents, respectively (Fig. 5c,d). We then quantitatively assessed astrocyte Ccn1 expression along degenerating afferent fibres innervating the cord. We observed that the degeneration of myelinated, but not non-myelinated axons resulted in significant astrocytic Ccn1 expression (Fig. 5e,f), which also preceded the arrival of WDM-like multicellular microglia nodules (Fig. 5e,g and Extended Data Fig. 9c,d).

A conserved white matter reactivity state

We next examined astrocyte Ccn1 expression in the context of demyelinating diseases and insults in mice and humans. Ccn1+ astrocytes were found throughout mouse spinal cord white matter lesions at 3 days following lysolecithin-mediated demyelination and persisted for at least 25 days thereafter (Fig. 5h,i and Extended Data Fig. 9e–g). Remarkably, Ccn1+ astrocytes were found to associate intimately with WDM-like microglia around lysolecithin demyelinated lesions (Fig. 5h,j). We also assessed astrocyte Ccn1 expression in the spinal cords of mice with experimental autoimmune encephalomyelitis (EAE) inflammatory demyelinating disease (Fig. 5k,l and Extended Data Fig. 9h). Few Ccn1+ astrocytes were present at disease onset, and these localized mainly to normal-appearing ventrolateral white matter (Fig. 5k,l). In peak and chronic disease, Ccn1+ astrocytes were prevalent, concentrated around inflammatory white matter lesions, and adjacent to microglia nodules (Fig. 5k–m and Extended Data Fig. 9i).

We also evaluated astrocyte CCN1 expression in archival human spinal cord tissue from individuals with multiple sclerosis or SCI and neurologically healthy controls (Extended Data Fig. 9j). CCN1-expressing astrocytes were frequent in white matter associated with multiple sclerosis lesions and lesion-remote normal-appearing white matter, but rare in multiple sclerosis-associated grey matter and healthy spinal cord (Fig. 5n and Extended Data Fig. 9k,l). Corroborating findings from our mouse iSCI model, we observed CCN1+ astrocytes throughout human SCI lesion-remote Wallerian degenerating white matter, but not in neighbouring grey matter (Fig. 5o and Extended Data Fig. 9m).

Together, these results demonstrate that astrocyte CCN1 expression is: (1) an evolutionarily conserved response of white matter damage-reactive astrocytes; (2) induced by degeneration of myelin but not of axons; and (3) implicated in the regulation of debris-clearing phagocytes across divergent forms of CNS white matter damage and disease in mice and humans.

Discussion

The present work resolves multiple fundamental attributes of LRAs, including (1) their unique transcriptional identity; (2) their neuroanatomical regional heterogeneity; (3) the temporal evolution of transcriptional LRA reactivity states; (4) the roles of LRAs in local multicellular responses to CNS injury and in repair; and (5) the mechanisms that drive LRA reactivity states. Astrocytes exhibit regional heterogeneity across the spinal cord dorsoventral divide in development and adulthood38,39. Correspondingly, our results indicate that after SCI, LRAs acquire intraspinal region-specific reactivity states that are likely to have distinct functions and consequences. According to transcriptomic profiling, LRAs do not exhibit a simplified or dampened BFA-like response to SCI. Rather, LRAs acquire discrete reactivity states, altering expression of many hundreds of genes that are not detected in BFAs, underscoring that these intraspinal reactivity programmes are fundamentally distinct. Our findings suggest that divergent LRA reactivity states are linked to selective modulation of gene expression implicated in neurotransmitter sensing and buffering, as well as structural and functional synapse development. Overall, the exploration of how regionally restricted LRA reactivity states affect local circuit structure and function is essential to shaping next-generation treatments that manipulate spared regions of the injured CNS to promote neural repair.

White matter astrocyte reactivity is relatively understudied. We identified a type of regionally restricted, white matter degeneration-reactive LRA that exhibits distinctive expression of Ccn1. We find that astrocyte CCN1 regulates local microglia nodule formation, molecular phenotype, supports debris clearance ability and is critical for neurological recovery from SCI (Extended Data Fig. 10). Notably, the selective impact of astrocyte CCN1 depletion on sensory recovery, without affecting locomotor function, may reflect the spatial distribution of CCN1 expression and its association with degenerating tracts, which are unlikely to support compensatory locomotor processes. Defining mechanisms that coordinate debris clearance after white matter damage is essential for developing therapies that promote repair. Efficient intracellular processing of lipid-rich cellular debris by microglia is crucial for debris clearance and white matter repair25,33. We found that microglial metabolism of lipid-rich debris is dependent on molecular cues from local astrocytes. Our results support a working model in which astrocyte-derived CCN1 aids microglia-mediated debris clearance by enabling microglial metabolism of ingested cell debris via the SDC4-dependent modulation of mechanisms that buffer excess debris-derived lipids, specifically augmenting their sequestration into lipid droplets and restricting efflux (Extended Data Fig. 10). Beyond buffering excess lipids and preventing lipotoxicity[40], lipid droplets serve as microglial energy stores[35]. Disruption of this pathway may impair debris clearance[41], consistent with the stalled phagocytic phenotype in microglia from the injured Ccn1-cKO spinal cord. Collectively, our findings across multiple mouse models and human disorders show that CCN1 expression is an evolutionarily conserved astrocyte-derived cue induced by local myelin degeneration. This suggests the possibility that a pathological trigger that is common to multiple CNS disorders (myelin damage) may drive an astrocyte response with conserved consequences. Indeed, an important question raised by our findings is whether astrocyte CCN1-mediated signalling can be therapeutically harnessed to enhance clearance of white matter debris, restrict inflammation and promote white matter repair across a spectrum of CNS disorders and insults.

Our results indicate that after CNS injury, LRAs acquire heterogenous, evolving and spatially restricted reactivity states that are mediated by microenvironmental context-specific cues. We show that LRAs retain, but modify, their interactions with local cell types, and potently govern multicellular processes underlying degeneration-associated inflammation and tissue repair. This work strongly suggests that the manipulation of LRA reactivity states may be a viable path for limiting chronic neuroinflammation, enhancing functionally meaningful regenerative plasticity, and promoting neurological recovery after CNS injury and in disease.

Methods

Mice

Young adult male and female mice were used between 2 and 4 months of age at the time of experimental procedures. C57BL/6 J mice (JAX: 000664) were used for experiments requiring a wild-type background. For RNA-seq of astrocyte-specific ribosome-associated mRNA mice expressing RiboTag[43] (JAX: 029977) were crossed to the well-characterized, astrocyte-specific Cre-driver line, mGfap-cre 73.1237 to generate mGfap-cre-RiboTag mice. mGfap-cre-RiboTag mice were crossed to Stat3-loxP mice[10] to generate mGfap-cre-Ribotag-Stat3-loxP mice (Stat3-cKO). Astrocyte-conditional Ccn1-knockout mice were obtained by crossing the well-characterized, astrocyte-specific Cre-driver line Aldh1l1-CreERT2 (ref. [31]) JAX: 031008 to the Ccn1-LoxP line[32] (a gift from K. Lyons) to generate Ccn1-cKO mice. Aldh1l1-CreERT2 mice were crossed to the floxed-STOP-tdT (Ai9) reporter line to generate Aldh1l1-CreERT2::floxed-STOP-tdT mice. Cre recombinase expression was activated in young adult mice (6–8 weeks old) by administering tamoxifen (Sigma, T5648-1G, 20 mg ml−1 in corn oil) by subcutaneous injection (100 mg kg−1, once a day) for 5 days followed by clearance for 3 weeks so that no residual tamoxifen remained at the time of experiment initiation. All mice were housed in a facility with a 12 h:12 h light:dark cycle and controlled temperature and humidity, and were allowed free access to food and water. Experiments were conducted according to protocols approved by the Institutional Animal Care and Use Committee at Cedars-Sinai medical centre.

Surgical procedures

All surgeries were performed on male and female young adult mice (8–12 weeks old) under general anaesthesia with isoflurane in oxygen-enriched air using an operating microscope (Zeiss), and rodent stereotaxic apparatus (David Kopf).

Spinal cord injury

Laminectomy of a single vertebra was performed at spinal cord level T12. Incomplete iSCI by unilateral T12 hemisection was performed on the left side of the spinal cord using a microknife (Fine Science Tools). To be included in the study, mice exhibited complete unilateral hindlimb paralysis for the first three days following surgery. A T12 crush SCI was made using no. 5 Dumont forceps (Fine Science Tools) with a 0.4 mm spacer and with a tip width of 0.5 mm. T12 crush mice exhibited paralysis in both hind limbs. In each case, mice received the opiate analgesic buprenorphine subcutaneously before surgery and every 12 h for 48 h after injury. Mice were evaluated thereafter blind to genotype and experimental condition. Daily bladder expression was performed for the duration of the study or until voluntary voiding returned.

Injections of lysolecithin or myelin into the spinal cord

Five-hundred nanolitres of 1% lysolecithin or 1 mg ml−1 CFSE-myelin in PBS was delivered by intervertebral microinjection to the lateral spinal cord white matter at spinal cord level T12 (coordinates: 200 μm medial–lateral, 300 μm dorsal–ventral). Injections were carried out at 150 nl min−1 using finely bevelled glass micropipettes connected via high-pressure tubing (Kopf) to 10 μl gastight syringes under the control of microinfusion pumps (Harvard Apparatus). Needles were left in place for 6 min prior to being slowly retracted. An equal volume of PBS was injected into the contralateral white matter as vehicle control. Mice were euthanized at 3 days post myelin injection and at 3, 10 and 25 days post-lysolethicin.

Sciatic nerve injury

A small incision was made on the left hindlimb and the two heads of the bicep femoris muscle were gently separated to reveal the sciatic nerve. The sciatic nerve was released from the muscle and elevated using forceps. The isolated nerve was then clamped with haemostats for 10 s and then replaced under the muscle. Mice were euthanized seven days following sciatic nerve crush.

Saporin injection

Conjugated saponins were used to degenerate myelinated and unmyelinated fibres as previously described[44]. In brief, mice were anaesthetised and 8 μg (10μl of 0.8 μg μl−1 in PBS) of saporin (non-conjugated control), IB4-conjugated saporin (targets unmyelinated fibres) or CTB-conjugated saporin (targets myelinated fibres) was injected subcutaneously into the plantar surface of the left hindpaw foot pad using a 30G insulin syringe. Injections of IB4–saporin and CTB–saporin were considered successful if there was local swelling in the treated hindpaw for 24–48 h following injection. Mice were euthanized at 14 and 28 days after injection.

EAE induction and assessment

Active EAE was induced as described[45] with modifications. Nine-week-old C57BL/6 mice were immunized subcutaneously in both hind flanks with 100 μg of myelin oligodendrocyte glycoprotein peptide (MOG35–55) emulsified in Complete Freund’s adjuvant containing 200 μg of killed mycobacterium tuberculosis H37Ra (Hooke labs) and injected intraperitoneally on days 0 and 2 with 110 ng pertussis toxin. Assessment of EAE was as follows: 0, no disease; 1, decreased tail tone; 2, hind limb weakness; 2.5, partial hindlimb paralysis; 3, complete hind limb paralysis; 4, front and hind limb paralysis; and 5, moribund state. Mice were collected at different stages of disease on the basis of the following pre-defined criteria: onset, partial or completely limp tail (score 0.5–1) at day 10 ± 2 days; peak, near or complete paralysis of hindlimbs with or without forelimb weakness (score 2.5–3.5) at day 14 ± 2 days; chronic, mice that reached a score of at least 2.5 (limp tail and incomplete paralysis of hindlimbs) no later than day 16 and collected at day 56.

Myelin purification and conjugation

Myelin was purified from adult C57BL/6 mice brains by sequential ultracentrifugation on discontinuous sucrose gradient and hypo-osmotic shock as previously described[25]. Brains were homogenized with a glass Dounce in 10 mM HEPES, 5 mM EDTA and 0.32 M sucrose. This was layered on 0.85 M sucrose in HEPES/EDTA buffer and centrifuged in a SW41 Ti rotor at 24,600 rpm for 30 min with acceleration and deceleration set to 1. The crude myelin fraction was removed from interface, resuspended in ice-cold distilled water, and centrifuged at 9,500 rpm for 15 min. This step was repeated two more times. The pellet was then dissolved in 0.3 M sucrose in HEPES/EDTA buffer and placed on top of 0.85 M sucrose in HEPES/EDTA. All centrifugation/resuspension steps were then repeated. The final pure myelin pellet was resuspended in PBS, quantified using a BCA assay, and resuspended to 1 mg ml−1 and then conjugated to CFSE as previously described[46]. Myelin (1 mg ml−1) was incubated with 50 μM CFSE at 37 °C for 15 min and then washed with 100 mM glycine in PBS at 14,000 rpm for 15 min, washed twice with PBS at 14,000 rpm for 15 min each and pellets were then resuspended to 1 mg ml−1 in PBS.

Hindlimb locomotor evaluation

A modified Basso mouse scale (BMS) was developed to evaluate the gradual functional recovery of distinct hind limb muscle groups after iSCI, over time, in freely moving mice. We converted the original BMS protocol[47] of 5 locomotor categories with a maximal score of 9 into 12 locomotor categories (ankle movement, toe movement, knee movement, weight support, paw placement, dorsal stepping, missing steps, paw position on lift-off, paw position on initial contact, coordination, trunk instability and tail tone) with a maximal score of 37. Analysis was performed at days −5, 0, 1, 2, 3, 7, 14, 28, 42, 56, 70 and 84 dpi.

Cold thermoception behavioural evaluation

Hindpaw sensitivity to cold stimuli was evaluated using the acetone test[48]. Spontaneous thermoceptive behaviours were monitored for 1 min after a drop of acetone (~25 μl) was applied to the plantar surface of left or right hindpaw with the aid of a 22G flexible gavage needle attached to a 1 ml syringe. The total duration of acetone-evoked behaviours (paw withdrawal, biting, licking or scratching) was measured from videos reviewed in slow motion. Analysis was performed at days −5, 7, 28 and 84 dpi.

Von Frey testing

Mechanical sensitivity was assessed using the von Frey filament test. Mice were placed in individual elevated chambers each measuring 3.75 × 3.75 × 5 inches with a mesh floor. Mice were acclimated to the testing chamber for 5 days before beginning any measurements and allowed to acclimate for 15 min prior to the start of testing on data recording days. A set of 20 nylon Semmes Weinstein monofilaments was used for testing. In brief, a fibre was gently pushed against the surface of the skin from below. Filaments of increasing stiffness (0.02–2.0 g) were applied perpendicularly to the plantar surface of the hind paw with sufficient force to cause slight bending and held for 2–3 s. A withdrawal response is categorized as an indicator of nociception and is defined as paw withdrawal, paw lifting, paw rotation, sniffing, licking, scratching, shaking or rapid movement, was recorded, and subsequent filament selection followed the up-down paradigm. If a withdrawal response was observed, the next lower force filament was applied; if no response occurred, the next higher force filament was tested. This process continued until six responses had been recorded in a series bracketing the threshold. Data were analysed using the up-down Reader algorithm to determine the 50% withdrawal threshold, which was calculated using Dixon’s formula.

Tissue processing, immunohistochemistry and mRNA in situ hybridization

Mice were euthanized by barbiturate overdose followed by cardiac perfusion with 4% paraformaldehyde. Spinal cords were removed, post-fixed for 4–8 h, and cryoprotected in buffered 30% sucrose. Spinal cords were blocked into 5 mm segments centred around the lesion epicentre, embedded in optimal cutting temperature (OTC) medium and stored at −80 °C until sectioning. Serial frozen sections of cervical (C8-T4), thoracic (T3-T12) and lumbar (T9–L3) segments (40 μm, transverse) were prepared using a cryostat microtome (Leica) and stored in antifreeze solution (glycerol, sucrose and TBS) at −20 °C until processed for evaluation by immunofluorescence and/or mRNA in situ hybridization as described[9]. Primary antibodies include: Rat-CD18 (1:100, Invitrogen), Rat-GFAP (1:1,000, Thermofisher), Rabbit-GFAP (1:1,000, Dako), Goat-IBA1 (1:1,000, Abcam), Rabbit-IBA1 (1:1,000, Wako), Rabbit-LPL (1:50, Abcam), Rabbit-PLIN2 (1:500, Progen), Goat-SOX9 (1:200, R&D system), Mouse- (1:3,000, Biolegend), Sheep-TREM2 (1:250, R&D systems), Rabbit-YAP1 (1:200, Protintech). Mouse primary antibodies were visualized using the M.O.M. (Mouse on Mouse) Immunodetection Kit (Vector Laboratories). Primary antibodies were selected on the basis of validation for fluorescence immunohistochemistry analysis in mouse tissue by the manufacturer, and/or by other investigators on the basis of peer-reviewed publications. Fluorescence secondary antibodies were conjugated to Alexa 488, Cy3 or Cy5 (all from Jackson Immunoresearch Laboratories). Nuclear staining was performed using DAPI (2 ng ml−1; Molecular Probes). Sections were cover-slipped using ProLong Glass mounting agent (ThermoFisher). When applicable, tissue sections were incubated in FluoroMyelin Green to label myelin and myelin debris (1:300) or the neutral lipid dye BODIPY to label lipid droplets (1:1,000) (ThermoScientific) prior to DAPI incubation.

Florescent in situ hybridization on fixed-frozen mouse spinal cord sections was performed using RNAscope probes and the Multiplex Fluorescent Detection Kit v.2 per manufacturer’s instructions (Advanced Cell Diagnostics). Mouse spinal cord sections were permeabilized with Protease IV. Probes used on mouse spinal cord tissue were as follows: Abca1 (522251), AldoC (429531-c2, 429531-c3), Arex (541871), Ak3 (454791), Boc (876211), Ccn1 (429001), Gfap (313211-c2, 313211-c3), Glipr2 (467171), Gpnmb (489511), Igf1 (443901-c2), Lair (509151), Prdm16 (584281) Scl1a3 (430781) and Thrsp (1090411). mRNAs of interest were labelled with the following fluorophores (Akoya): Opal 520 (FP1487001KT), Opal 570 (FP1488001KT), Opal 620 (FP1495001KT) and Opal 690 (FP1497001KT). Slides were then processed for immunohistochemistry or stained with DAPI before mounting. Human spinal cord tissue was permeabilized with target retrieval reagent and protease plus. Probes used in human tissue were as follows: CCN1 (4452081), GFAP (311801-C2) and SLC1AA3 (461081-C2). Sections were stained with DAPI and mounted with ProLong Glass or Vectashield mounting medium.

Reference to protein or gene names follow standardized guidelines for mouse and human as established by the Human Gene Nomenclature Committee (HGNC) and the Mouse Genome Informatics (MGI) database. Here, human and mouse proteins are referred to in all upper case, non-italicized font (for example, CCN1, IBA1, TREM2); genes and mRNA is referred in all upper case, italicized for human (for example, CCN1) and first letter capitalized and otherwise lower case, italicized for mouse (for example, Ccn1, Gpnmb, Igf1).

Imaging

Images of tissue sections used for quantitative analyses were collected using an Apotome epifluorescence microscope with structured illumination hardware and deconvolution software (Zeiss). For whole spinal cord Ccn1 and microglial analysis, we generated 10× tiles of the entire spinal cord at a single z-plane. Microglial quantification was imaged at 20× (Trem2, LPL) with a z-thickness of 1 μm or 40× (FluroMyelin, SMI32, PLIN2, BODIPY, Gpnmb and Abca1) with a z-thickness of 0.5 μm. Similarly, images of astrocytes with subtype markers and were imaged at 40× with a 0.5 μm z-stack. Representative images for illustrative purposes were imaged on a Leica SP7 Confocal microscope at 20× or 63×.

Image analysis

Imaris image analysis software (v.10) was used to generate 3D volumes of surfaces of IBA1+ microglial and a marker of interest (for example, FluroMyelin, SMI32, PLIN2, BODIPY, TREM2). Overlap between IBA1 and marker surfaces (≤0.5 μm distance) was used to determine the proportion of microglia that were marker-positive. Similarly, overlap of marker-positive surfaces that were within an IBA1 surface (≤0.5 μm: TREM2, PLIN2, BODIPY, FluroMyelin, SMI32) determined the volume of marker present within microglia. Measurements were normalized to the total volume to IBA1 microglia and measurements were restricted to the spinal cord dorsal white matter unless stated otherwise. For YAP1 analysis, 3D surfaces were generated for all DAPI+ nuclei, YAP1, Gfap/Slc1a3 mRNA and Ccn1 mRNA. Astrocyte nucelli were determined by setting the overlap volume of DAPI and Gfap/Slc1a3 to 15. Astrocytes nuclei expressing Ccn1 were those containing an overlap volume of Ccn1 greater than 0.16. Finally, the YAP1 expression within the Ccn1+ and Ccn1− astrocytes was the volume of YAP1 (<0 μm) within these Ccn1+ or Ccn1− astrocyte nuclei.

Spatiotemporal analysis of Ccn1+ astrocytes and microglial nodules

Regional quantification of Ccn1+ astrocytes and IBA1+ microglial nodules was performed on 10× image tiles of transverse spinal cord sections using the cell counter plugin (Fiji). Transverse sections were only evaluated if they appeared cytoarchitecturally intact with normal-appearing white and grey matter anatomy. Initially, 8 anatomical reference points were used to align transverse spinal cord images: central canal; top of the dorsal white matter; bottom of the dorsal white matter; left and right lateral white matter; top and bottom of the central grey matter; the left and right sides of the central grey matter; the top of the dorsal horn grey matter on left and right sides, and the bottom of the ventral horns on left and right sides. For injured samples, the side containing the majority of Ccn1+ astrocytes or microglial nodules was labelled as left (ipsilesional). Next, Ccn1+ astrocytes were quantified as Gfap/Slc1a3 containing nuclei that contained at least 3 Ccn1 mRNA puncta (RNAscope). Similarly, microglial nodules were quantified as closely associated clusters of microglia containing more than three microglial nuclei[25]. At least two sections were quantified per mouse. Ccn1+ astrocytes and microglia nodule counts from different tissue sections were aligned to a common coordinate system using a custom python script. First, all reference and cell coordinates were linearly shifted such that the central canal was set at (0,0). The average of each reference point across all sections per spinal region were used to define a template spinal section which was then used to perform non-rigid transformation (ThinPlateSplineShapeTransformer from the OpenCV2 library) of all cell coordinates. For visualization, Ccn1 astrocyte/microglia counts were spatially binned per section using a 2D histogram (bin area 19.35 μm^2) and counts per bin were averaged per mouse and then per condition. The resulting cell count per bin was then plotted.

For statistical comparison of time-dependent differences in WDM nodule formation between wild-type and Ccn1-cKO mice, we employed a Conway–Maxwell–Poisson (COM–Poisson) generalized linear mixed model with a log link and fixed effects for group and time[49]. This model was selected to enable robust assessment of how nodule counts change over time in wild-type and Ccn1-cKO mice, and whether these changes differ by genotype. The COM–Poisson distribution accommodates both overdispersion and underdispersion in count data and accounts for the repeated-measures structure of the dataset, wherein multiple tissue sections were analysed per biological replicate at each post-injury time point. The model included fixed effects for group (Ccn1-cKO versus wild type), time (7, 28 and 90 dpi), and a random intercept for each mouse to account for within-subject clustering. The group-by-time interaction was formally tested using a likelihood ratio test comparing nested models with and without the interaction term. Post hoc pairwise comparisons of group (genotype) and time levels were conducted on the basis of model-derived estimated marginal means, with multiple testing adjustment using Tukey’s method. All hypothesis tests were two-sided with a significance level set at 5%. These statistical analyses were performed using R software (v.4.4.1). Mixed models were fitted using the glmmTMB package[50], and marginal means were estimated using the emmeans package[51].

Quantitative analysis of in situ mRNA hybridization

Quantification of RNAscope probe signal (mRNA) in astrocytes and microglia was carried as described[52]. In brief, thresholding of RNAscope probe signal was first carried out (Otsu method: Ccn1 and Gfap/Slc1a3; triangle method: Gpnmb and Abca1) and the area of pixels was then quantified within the soma of Gfap+/Slc1a3+ astrocytes, or IBA1+ microglia or microglial nodules, respectively. The area of Gfap/Slc1a3 and Ccn1 were analysed from the same astrocyte somas, whereas the area of Gpnmb, Abca1 and Sdc4 mRNA was then normalized to the size of the microglia or nodule.

Fresh spinal cord tissue collection for astrocyte RiboTag RNA-seq

Spinal cord tissue was isolated for astrocyte RiboTag RNA-seq as described[9]. In brief, wild-type (mGfap-cre-RiboTag) and Stat3-cKO (mGfap-cre-Ribotag-Stat3-loxP) mice were perfused with ice-cold PBS with heparin and spinal cords were dissected out. Three millimetres of spinal cord rostral (T9–T11) and caudal (L1–L3) to the lesion epicentre were then rapidly removed, snap-frozen in dry ice and stored at −80 °C until processing for RiboTag RNA-seq. Spinal cords were collected at 3, 7, 14 and 28 dpi and anatomically equivalent regions of spinal cord were isolated from age- and genotype-matched healthy controls.

Astrocyte ribosome-associated mRNA isolation, RNA-seq and analysis

Astrocyte ribosome-associated mRNA was isolated using our previously established methods[9]. In brief, fresh frozen spinal cord tissue was homogenized and haemagglutinin (HA) immunoprecipitation was carried out to purify of astrocyte ribosome-associated mRNA. Astrocyte RNA integrity was analysed using the 2100 Bioanalyzer (Agilent) with the RNA Pico chip, with RNA integrity number (RIN)  ≥  8 for all samples. RNA concentration was determined using the RiboGreen RNA Assay kit (Life Technologies). cDNA was generated from 10 ng of RNA using the Universal plus mRNA-seq Kit (Nugen). The workflow consisted of poly(A) RNA selection, RNA fragmentation and double-stranded cDNA synthesis using a mixture of random and oligo(dT) priming, followed by end repair to generate blunt ends, adaptor ligation, strand selection and PCR amplification to produce the final library. Multiplexed sequencing was performed using the NovaSeq 6000 sequencer (Illumina) on a NovaSeq S2 flow cell to produce 50 bp paired-end reads. Data quality was assessed using Illumina SAV and demultiplexing was performed using Illumina Bcl2fastq2 v.2.17. Sequences were aligned to the mouse mm10 genome using STAR aligner (v.2.4.0j). Average percent of uniquely mapped reads was 79 (±8.7)%. Read counts were determined using HT-seq (v.0.6.0). At least 4, and in most cases 6 samples were evaluated per experimental condition. Genes not expressed in minimum of 10 samples (5 counts or more) or average fragments per kilobase per million mapped fragments (FPKM) below 0.75 were filtered out from further analysis. Differential expression analysis (DEA) was conducted using the Bioconductor EdgeR package (v.3.6). DEGs were determined using FDR at 5%. To identify co-regulated astrocyte-enriched genes across time after injury, a gene-gene correlation matrix was constructed using genes that were significantly enriched in astrocytes with a logFC >1 and FDR P ≤ 0.05 at any time point. Astrocyte-enriched gene expression was identified by comparing astrocyte HA immunoprecipitation-derived ribosome-associated mRNA to whole-tissue mRNA (HA immunoprecipitation input-derived mRNA). Astrocyte versus whole-tissue DEA identified 1249 astrocyte-enriched DEGs, which were used as input for a spearman correlation using log_2FC changes values from iSCI versus healthy DEA and kmeans clustered into 11 gene modules. Genes in each module were used as input into gene ontology (GO) using Enrichr (GO_Biological Process_2018 database).

LRA gene expression data were compared to multiple other spinal cord astrocyte transcriptomics data sets from SCI and other non-traumatic CNS insults and disorders: BFAs[17], ALS[53], LPS[9] and EAE[54]. A composite list of DEGs (log_2 fold change versus healthy) across all datasets was compiled (9,558 DEGs) and used for all downstream analyses. Principal component analysis was performed as a descriptive visualization of global relationships across datasets, in conjunction with gene-level analyses of DEG overlaps, directionality, and functional groups. Together, these complementary approaches allowed us to assess both overall transcriptomic relationships and specific gene-level distinctions. For comparison between LRAs and BFAs, LRA 3 and 7 dpi were compared to BFA 2 and 5 dpi, respectively. DEG information was available for 14 and 28 dpi in both datasets. These data were used to tabulate the proportion of LRA DEGs that were either also significantly upregulated, downregulated, oppositely regulated, or not regulated in BFAs at each time point.

Nuclei isolation

iSCI mice were perfused with ice-cold PBS with heparin at 3, 7 or 28 dpi, spinal cords dissected out and 3 mm of spinal cord rostral (T9–T11) and caudal (L1–L3) to the lesion epicentre were then rapidly removed, snap-frozen in dry ice and stored at −80 °C. An anatomically equivalent region of spinal cord (T11–L1) was isolated from age- and genotype-matched healthy controls. Frozen tissue was homogenized in homogenization buffer (320 mM sucrose, 0.1 mM EDTA, 0.1% IGEPAL CA-630, 5 mM CaCl_2, 3 mM magnesium acetate, 10 mM Tris, Roche Protector RNAse Inhibitor, Complete Roche Protease Inhibitor v.12, 0.016 mM PMSF, 0.166 mM β-mercaptoethanol; pH=7.8). Nuclei were isolated from the homogenate by iodixanol gradient and resuspended in 1% BSA solution before proceeding immediately to 10x snRNA-seq.

snRNA-seq

snRNA-seq was performed using 10x Chromium Next GEMSingle Cell 3 (v.3.1) per manufacturer’s instructions. Samples were loaded to capture 10,000 nuclei per sample. During library preparation, the initial cDNA amplification was run for 13 cycles, which was found to be optimal for 10,000 nuclei. Following library preparation, quantitative PCR was run to quantify library concentration and samples were pooled to equivalent concentrations. Initially, a shallow sequencing run of the pooled libraries at ~20% sequencing saturation, the results of which informed library re-pooling in order to normalize nuclei number within the libraries to obtain ~40,000 reads per cell. Sequencing was performed by NovaSeq (Illumina) at 2 × 150 base pair reads at 150 pM (average reads per sample: mean: 2.9 × 10^8 ± 1.1 × 10^8).

snRNA-seq data analysis

Output FASTQ files for each sample were aligned with CellRanger v.6.0.2 using the mm10-2020-A reference genome for each sample. Cells matching the following criteria were removed from further analysis: >5% mitochondrial counts, >25,000 counts or <500 counts. Genes expressed in fewer than 50 cells were removed from downstream analysis. Scrublet[55] was used to remove predicted doublets from each sample. Individual sample data were then concatenated, normalized to 10^4 total counts per cell, log-transformed, and batch corrected using Harmony[56]. Quality control thresholding resulted in 230,620 cells from 35 samples for downstream analysis. Cell types were identified on the basis of putative marker genes12,57,58,59. DEG testing utilized sc.tl.rank_genes_groups with method=‘wilcoxon’ and corr_method= ‘benjamini-hochberg’ for all comparisons. Molecular markers of regionally restricted LRA subtypes (such as vGM3: Thrsp/Arex; WM4: Glipr2/Ccn1; see Fig. 1) were identified by screening our snRNA-seq data for genes that were (1) astrocyte-enriched relative to other cell types; and (2) significantly enriched for in a spatially restricted LRA subtypes. Nichenet[30] was performed on astrocytes (‘sender’) and ligands were identified by filtering Nichenet candidates for astrocyte subcluster enrichment relative to all other cell types. The relevant receiver cell type was selected on the basis of NMF cell subtype enrichment. Genes enriched in receiver cell subtype were used as gene set of interest. All expressed genes in the receiver cell subtype were used as the background gene set.

Spatial transcriptomics

Mouse spinal cord spatial transcriptomics was performed by Visium (10x Genomics). iSCI mice were perfused with ice-cold PBS with heparin at 3, 7 or 28 dpi, spinal cords were dissected out and rostral and caudal blocks were rapidly embedded in OTC, snap-frozen on dry ice and stored at −80 °C until sectioning. Visium slides were pre-chilled in a cryostat (Leica) for 30 min at the time of sectioning. Two 10-μm sections were taken from lesion-remote rostral (T9–T11) and caudal (L1–L3) blocks, equivalent to samples analysed by snRNA-seq. Samples were processed using the Visium Spatial Gene Expression Reagent Kit (10X Genomics) per the manufacturer’s established protocol. cDNA libraries were pooled in a NovaSeq6000 SP v.1.0 flowcell and paired-end sequencing was performed on an Illumina NovaSeq6000 sequencer.

Spatial transcriptomics analysis

Spots overlaying tissue sections were manually annotated in the Loupe (10X Genomics) and processed by spaceranger-v.1.3.0 and aligned against the mm10 reference genome mm10-2020-A. Haematoxylin and eosin staining of transverse spinal cord sections was used to manually annotate lateral white, ventral white, dorsal white, central grey, dorsal horn and ventral horn. Additionally, gene expression of inflammation and gliosis-associated genes was used to distinguish lesion ipsilateral and contralateral sides. Quality control thresholding resulted in 14,566 spots across 16 biological replicates (n = 4 mice per group; 2 sections per rostral and caudal block). Data were normalized to 10^4 counts and log-transformed before running principal component analysis and UMAP projection. To accommodate for morphological variation, a non-rigid transformation was applied (ThinPlateSplineShapeTransformer from the OpenCV2 library) using manually placed neuroanatomical reference points, in a manner equivalent to aligned average density plot construction for Ccn1+ astrocyte and WDM nodules counts. Tissue alignment was validated by examination of known spatially restricted gene expression (for example, Mbp and Syp). Cell2location was used to spatially integrate snRNA-seq subclusters and spatial transcriptomics data[60]. The top 30 highest expressed genes from each dataset, mitochondrial genes, and genes expressed in <5% of cells or spots and with mean <1.12 were filtered out to generate snRNA-seq input. Cell2location was run with the following parameters: (batch_key= “Date library prep”, continuous_covariates = “total_counts”, categorical_covariates = “User”, N_cells_per_location=12, detection_alpha=200) and trained for 40,000 epochs. The cell2location matrix was used as input for NMF to identify spatially co-occurring cell types. NMF from sklearn was run with the following parameters: (n_components=8, alpha=0.9,max_iter=1000, shuffle=True, init = “nndsvda”,l1_ratio=0.9).

Microglial isolation (lipidomic and culture)

Mice were perfused with ice-cold PBS with heparin and the brain and spinal cord were freshly dissected following. For iSCI mice, 1 mm rostral and caudal to the lesion epicentre was removed and discarded, and the injured lateral side of the spinal cord (rostral and caudal to the lesion) was collected for microglial isolation. Dissected tissue was minced with a sterile razor blade and then dissociated using the Neural Tissue Dissociation Kit (P) (Miltenyi Biotech) and the GentelMACS dissociator with heaters per the manufacturer’s instructions. Following dissociation, samples were filtered through a 70-μm strainer, and myelin was depleted using Myelin removal beads II (Miltenyi Biotech) and the AutoMACS separator. Finally, microglia were isolated by incubating samples with CD11b microbeads (Milteyni) and isolating with the AutoMACS per the manufacturer’s instructions. Cell number was then determined before proceeding to downstream applications (lipidomics and culture).

Microglial bulk RNA-seq and analysis

A 50 μl spot drop containing 50,000 microglia cells was seeded into the desired wells of a 24-well plate. Cells were cultured for 7 days, with a full media change given on day 1 followed by half media change on day 4. On day 7, cells were either left untreated or treated with CCN1 (Peprotech: 120-25) at 50 ng ml−1 or vehicle (BSA) for 24 h. Each condition was carried out in triplicate for each experiment. Cells were then removed using a cell scraper, replicates pooled, and then processed for RNA isolation using RNeasy Plus Micro Kit (Qiagen, 74034). Microglia RNA samples were prepared for analysis by RNA-seq. RNA integrity was analysed using the 2100 Bioanalyzer (Agilent) with the RNA Pico chip, with RIN  ≥  9.6 for all samples. RNA concentration was determined using the RiboGreen RNA Assay. Multiplexed paired-end sequencing was performed using the NovaSeq X Plus sequencer (Illumina). Data quality was assessed using Illumina SAV and demultiplexing was performed using Illumina Bcl2fastq2. Sequences were aligned to the mouse mm39 genome using STAR aligner (v.2.7.11b). DEA was conducted using EdgeR with a FDA of 5%. DEGs were defined with a log_2-transformed fold change threshold of 0.25 and adjusted P value < 0.05 (CCN1 stimulation versus vehicle).

Microglia lipid efflux and lipid droplet content evaluation

Primary microglia isolated from male and female mice 8–12 weeks of age were seeded at 1 × 10^5 cells per well in a flat-bottom 96-well plate coated with poly-L-lysine (0.01%, Sigma-Aldrich). Cells were grown in microglia media (10% fetal bovine serum, 1× penicillin-streptomycin, 10 ng ml−1 carrier-free (CF) recombinant mouse GM-CSF, 10 ng ml−1 CF recombinant mouse M-CSF, 10 ng ml−1 CF recombinant human TGFβ1, in DMEM/F-12 Ham) for 4 days at 37 °C, 5% CO_2 with full media change after 24 h. Microglia cholesterol efflux was assessed using a Fluorometric Cholesterol Efflux Assay Kit (Abcam; ab196985) following the manufacturer’s instructions. In brief, on day 4 microglia were loaded with fluorescent cholesterol for one hour, then placed into equilibration media containing CCN1 (50 ng ml−1 in 0.1%BSA) or vehicle (0.1% BSA) for 16 h. Following incubation, cells were washed with phenol red-free DMEM/F-12 Ham and incubated with cholesterol acceptor solution (2% (2-Hydroxypropyl)-β-cyclodextrin) for 6 h. Plates were then centrifuged for 2 min at 1,000g and the cell supernatant was collected for fluorometric analysis of cholesterol content. Meanwhile, adherent cells were lysed and processed for fluorometric cholesterol content within the cell. Fluorometric measurements were performed using Varioskan LUX (ThermoFisher) (excitation/emission, 485/523 nm). Per cent cholesterol efflux was then calculated for each sample by dividing relative fluorescence unit (RFU) of the supernatant by the total cholesterol content (RFU of supernatant plus cell lysate). The effect of treatment was then calculated by subtracting the percent cholesterol efflux of the negative control (no cholesterol acceptor) from the percent cholesterol efflux of treatment (CCN1 or vehicle), followed by normalization with the percent cholesterol efflux of vehicle.

For microglial lipid storage analysis, primary microglia were seeded at 500,000 cells per well in 6-well plates and cultured for 7 days, with a full media change on day 1 and a half media change on day 4. On day 7, cells were treated with purified recombinant CCN1 (50 ng ml−1; Peprotech) or vehicle control (BSA) for 22 h. To assess the involvement of SDC4, cells were pre-treated with either SDC4 function-blocking antibodies (Rat Anti-Mouse SDC4 Clone KY/8.2, BD Pharmingen, 550350) or serotype-matched control antibodies (Rat IgG2a kappa Isotype Control, eBioscience, 14-4321-82) (25 µg ml−1) for 1 h. Media containing antibodies was then washed out and replaced with media containing CCN1. After 22 h, culture media was removed and cells were labelled with LipidTOX Deep Red Neutral Lipid stain (Thermo Scientific, H34477; 1:500 in PBS) for 30 min. Cells were washed with PBS-EDTA, gently dissociated by pipetting, and collected by centrifugation (300g, 10 min, 4 °C). Cell pellets were resuspended in 500 µl of flow cytometry buffer (PBS + 5% FBS), stained with DAPI, and analysed on a SONY ID7000 Spectrum Cell Analyzer at the Cedars-Sinai Medical Center Flow Cytometry Core. FlowJo software was used for downstream analysis. The gating strategy used to identify and quantify microglia populations is provided in Supplementary Data 6.

Sample preparation for lipid extraction

Lipid extraction from the frozen microglial cell pellets was done following Bligh and Dyer protocol with slight modifications61,62. In brief, the pellets were thawed at 4 °C for 10 min, after which, cold methanol, and HPLC-grade chloroform were added in a ratio of 2:1. The samples were vortexed for 10 s, resulting in a one-phase solution which was then incubated for 15 min at 4 °C. A biphasic solution was then obtained by adding ultrapure water and chloroform in a 1:1 ratio. Next, the samples were centrifuged at 16,000g for 10 min, giving rise to 3 phases in each tube. The bottom phase in the tube is the organic phase that contains lipids. Next, the solvents from the organic phase were evaporated using SpeedVac vacuum concentrator for 1 h resulting in dried lipid extracts.

Unbiased Lipidomics using MRM profiling

Dried lipid extracts from microglial cells were reconstituted in 200 µl of a 50:50 methanol:chloroform solution containing 10 mM ammonium formate. Prior to analysis, lipid extracts were further diluted in acetonitrile: methanol 70:30, with 10 mM ammonium formate. The quality control sample was the injection solvent containing 0.02 µg ml−1 of the quantitative mass spectrometry internal standard EquiSPLASH (Avanti Polar Lipids, 330731), which was monitored over time to ensure the instrument’s appropriate operation. All MRM profiling experiments were conducted on an Agilent 6495C triple quadrupole mass spectrometer outfitted with an Agilent 1290 Infinity II LC system and G7167B autosampler. A volume of 8 μl of diluted lipid extract was introduced into the Agilent Jet Stream (AJS) ion source of the mass spectrometer by flow injection for each MRM method. In brief, MRM methods were established for 10 lipid classes and spanned 1,497 individual lipid species62,63. Lipid classes of interest were phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylglycerol (PG), phosphatidylinositol (PI), phosphatidylserine (PS), acyl carnitine (CAR), cholesterol ester (CE), diacylglycerol (DG), triacylglycerol (TG), and sphingomyelin (SM).

Lipidomics data analysis

Statistical analysis of lipid MRM transitions for all sample comparisons was performed using the CLAW in-house MRM processing software[64], followed by differential analysis with the edgeR software package[65], as described in our previous study62,63. The ion count of each lipid was denoted by l for a given sample s. An intercept sample, representing the experimental blank (injection medium), was included to ensure that all comparisons are meaningful relative to the blank. The expected count for lipid l in sample s was written as μls. The design matrix entry \({X}_{s}^{T}\) encoded the group or condition for sample s, and βl represented the set of regression coefficients for lipid l. The edgeR package fits a generalized linear model to a log-linear formula for mean variance relationship as follows:

$$\log {\mu }_{{l}{s}}={X}_{s}^{T}{\beta }_{l}+\log {N}_{s}$$

This formula calculates the total ion intensity for each sample s, summing to Ns. This approach enables the determination of the coefficient of variation (CV) for the ion count of each lipid in a sample (yls). The dispersion (Φl) of each lipid and is calculated using the common dispersion method[5]. On the basis of these values, the log_2 fold change (log_2FC) between samples is calculated, and the corresponding P values are derived using the likelihood ratio test. P values less than 0.05 were considered significant. Microglia lipidomics data are available online at https://github.com/chopralab/Lesion-remote_astrocytes_govern_microglia-mediated_white_matter_repair.

$${\rm{C}}{{\rm{V}}}^{2}({y}_{{ls}})=1/{\mu }_{{ls}}+{\varPhi }_{l}$$

CCN1 co-immunoprecipitation

To identify candidate CCN1 receptors on microglia, a CCN1-directed bait-and-prey approach was used. Co-immunoprecipitation was performed using the Pierce Crosslink Magnetic IP/Co-IP Kit (Thermo Fisher Scientific, 88805) following the manufacturer’s protocol with minor modifications. In brief, 25 µl of protein A/G magnetic beads were resuspended, washed on a magnetic stand, and incubated with 5 µg of anti-DDK for 2 h at 4 °C for conjugation. Beads were incubated with 5 µg IgG isotype control antibodies for negative control. Bead–antibody conjugates were then washed twice with 500 µl of wash buffer.

HEK-293T cells expressing Mouse CCN1 with a DDK–MYC molecular tag (Origene MR221828) were lysed in buffer supplemented with protease inhibitors, followed by centrifugation at 12,000g for 10 min to remove debris. The cleared lysate was incubated with bead–antibody conjugates overnight at 4 °C on a rotator to allow CCN1 binding. The following day, the beads were washed three times with 500 µl of wash buffer before incubation with microglial lysates. Next, primary microglia were cultured for 7 days then lysed by sonication in sample buffer (50 mM Tris, 150 mM NaCl, 1% NP-40 (v/v), 0.5% CHAPS (w/v), protease and phosphatase inhibitors, pH 7.4). The whole-cell lysate was then incubated with bead–antibody–CCN1 complexes overnight at 4 °C on a rotator. Bead–antibody–CCN1 complexes were then washed three times with wash buffer and sent to the Cedars-Sinai Proteomics and Metabolomics Core for on-bead digestion and proteomic analysis by liquid chromatography–mass spectrometry (LC–MS/MS).

Sample preparation for proteomics

Samples were lysed in 6 M urea, 1 M ammonium bicarbonate, 5% SDS lysis buffer and sonicated for 10 min at 70% power using a QSonica Q800 sonicator. Samples were cleared by centrifugation at 20,000g for 10 min and protein concentration measured by BCA. Samples were digested by an automated SP3 protocol adapted to a Beckman i7 workstation. Bead aliquoting, reduction, alkylation, digestion, and elution were all performed on-deck with a 96-well plate format. In brief, 50 μg of protein in 40 μl of the previously mentioned lysis buffer was reduced with the addition of 10 μl of 200 mM dithiothreitol and incubated 30 min at 37 °C with shaking at 300 rpm, then alkylated with 10 μl of 400 mM iodoacetamide at room temperature for 15 min in the dark. The volume was brought to 70 μl with Tris-HCl pH 8, then 5 μl of bead suspension (10:1 mass ratio of beads to protein), 1:1 mixture of hydrophilic/hydrophobic beads (Cytiva) was aliquoted into the samples using the span-8 pipetting head with constant agitation of the bead reservoir between transfers. Samples were brought to 50% acetonitrile (ACN) and incubated for 18 min, and then the solvent was removed on-magnet, and samples were rinsed with 2× 80% ethanol then 2× ACN with 200 μl volumes each. After the solvent was completely removed, the samples were resuspended in 50 mM Tris-HCl pH 8 and 10 mM CaCl_2 with trypsin at a 1:20 ratio. Samples were bath sonicated for 5 min then incubated 18 h at 37 °C and 1,200 rpm overnight. After digestion, the samples were then removed from beads and brought to 0.1% formic acid and 2% DMSO for injection into the instrument.

LC–MS/MS analysis

Approximately 500 ng of peptides from digested samples were analysed on a Thermo Orbitrap Astral coupled to a NeoVanquish LC. To assess carryover, a blank injection was included after every three injections. Samples were separated using 24 min gradient. The compositions for solvent A and B were 0.1% formic acid in water and 80% ACN with 0.1% formic acid, respectively. The gradient used was as follows: 1.2 μl min−1 flow, 0 min 4% B, 2 min 9% B, 13 min 25% B, 17 min 35% B. NeoVanquish LC was operated in direct injection mode, using a 150 μm internal diameter × 15 cm, 1.5 μm PepSep C18 (Bruker) column coupled to a nano source (Thermo EasySpray) on the Orbitrap Astral MS platform (ThermoFisher). All sample runs were acquired in data independent acquisition (DIA) mode from 380 to 980 Da with 240k Orbitrap resolution and 5 ms maximum injection time for MS1. All DIA scans were set to a 7 ms maximum injection time with varying window schemes between 2 and 5 Th depending on gradient length.

Proteomic data analysis

Mass spectrometry raw data files were searched against UniProt mouse reviewed protein sequence entries (accessed April 2023) using DIA-NN (v.1.8.1) (PMID: 31768060) in library-free mode with default parameters. On the basis of recent comparisons with library-based approaches, DIA-NN in library-free mode has been found to produce results that are comparable or better than those of experimental library-based searches while being freely available and was hence chosen for the analysis of all data. (PMID: 36609502). The output protein group matrix from DIA-NN was used to perform downstream analysis using MetaboAnalyst 6.0 (PMID: 38587201). Pairwise comparisons between CCN1 co-immunoprecipitation and negative control antibody co-immunoprecipitation were performed using uncorrected two-sided t-tests, with a significance threshold of −log_10(P) > 1.3 (P < 0.05). No multiple hypothesis correction was applied given the exploratory nature of the analysis.

Adult mouse cortical astrocyte culture and conditioned media

Adult mice were perfused with ice-cold PBS with heparin and mouse brains freshly dissected. Cortices were dissociated using Neural Tissue Dissociation Kit (P) (Miltenyi Biotech) and the GentleMACS dissociator with heaters per the manufacturer’s instructions. Following dissociation, samples were filtered through a 70-μm strainer, and myelin was depleted using 120 μl of Myelin removal beads II (Miltenyi Biotech) in 1,000 μl of MACS Buffer (0.5% BSA, 2 mM EDTA in PBS) using LS columns (modified from PMID: 26919701). The myelin-depleted sample was then treated with Debris Removal Solution (Miltenyi Biotech) per the manufacturer’s instructions to remove any further cellular debris. Finally, astrocytes were isolated using ACSA-2 beads with LS columns per the manufacturer’s instructions (Miltenyi Biotech). Astrocytes were resuspended in AstroMACS media (Miltenyi Biotech) and plated at 100,000 cells per well on laminin and poly-l-lysine coated coverslips and incubated at 37 °C with 5% CO_2. A half media change was performed every two days per the manufacturer instructions. On day 8 astrocyte conditioned media was collected, centrifuged (300g, 10 min, 4 °C) and the supernatant concentrated using 10 kDa Amcon Ultra Spin Columns (4,000g, 30 min, 4 °C). Concentrated astrocyte conditioned media samples were stored at −80 °C until analysis.

Western blot

Spinal cords were isolated from healthy and 3 dpi adult mice (n = 4, male and female) to evaluate CCN1 protein levels. Lesion-remote spinal cord tissue rostral and caudal to the SCI lesion and equivalent regions from the healthy core were homogenized by Dounce homogenizer using ice-cold RIPA lysis buffer (Thermo Scientific 89900) with cOmplete, Mini, EDTA-free Protease Inhibitor Cocktail (Roche, 04693159001). Homogenates were centrifuged at 17,000g for 30 min at 4 °C, and the resulting supernatant was collected. Protein concentration was estimated by Pierce BCA Protein Assay Kits and Reagents, (Thermo Scientific 23225). Total protein from astrocyte conditioned media was precipitated with acetone (−20 °C for 2 h) and centrifuged. The precipitated proteins were then centrifuged at 13,000 rpm for 10 min, and subsequently dissolved in RIPA buffer. Finally, samples were mixed with Laemmli sample buffer, heated, and resolved via SDS–PAGE for subsequent western blotting.

In each case, equal amounts of protein were resolved by 10% polyacrylamide gel electrophoresis (Tgx FastCast Acrylamide Kit, 10%, 1610173, Bio-Rad laboratories) with Precision Plus Protein Blue-Stained Protein Standards, 10–250 kDa (Bio-Rad, 1610373) and transferred onto a polyvinylidene fluoride (PVDF) membrane Trans-Blot Transfer Kit, (Bio-Rad, 1704272). To block non-specific binding, membranes were incubated for 2 h at room temperature in Tris-buffered saline with 0.1% Tween 20 (TBST, pH 7.4), supplemented with 5% dried skimmed milk. Following blocking, the membranes were incubated overnight at 4 °C under gentle shaking with CYR61 (E5W3H) Rabbit monoclonal primary antibodies (1:1,000; Cell Signaling, 39382S). After 24 h of incubation, membranes were washed with TBST and subsequently incubated with peroxidase (HRP) Anti-rabbit IgG goat secondary antibodies (Cell Signaling, 7074P) for 1 h at room temperature. Specific protein bands were visualized using Clarity Max Western ECL Substrate, (1705062, Bio-Rad laboratories). Membranes were stripped with Restore Western Blot Stripping Buffer, (Thermo Scientific, 21059), and reprobed for β-actin (13E5) rabbit monoclonal antibody (1:1,000; Cell Signaling, 4970S0). For quantitative analysis, protein band density was measured using ImageJ, with target signal normalization performed using the corresponding β-actin loading control. In the case of astrocyte conditioned media blots, protein loading was determined by Ponceau S staining.

Human multiple sclerosis and SCI spinal cord tissue

Human formalin-fixed paraffin-embedded (FFPE) spinal cord tissues from individuals with multiple sclerosis and neurologically healthy controls were prepared from autopsy-derived tissues collected by the rapid autopsy protocol approved by the Cleveland Clinic Institutional Review Board. Transverse spinal cord sections (7 μm) were prepared and the demyelinated lesions were identified by loss of proteolipid protein immunoreactivity. FFPE spinal cord tissues from individuals with SCI and associated clinical and neuropathological information were obtained from the International Spinal Cord Injury Biobank. The Clinical Research Ethics Board of the University of Columbia (Vancouver, Canada) granted permission for post-mortem spinal cord acquisition and for sharing biospecimens. Spinal cord biospecimens were collected from consented participants or their next of kin and provided as FFPE tissue sections at a thickness of 5 μm. SCI tissue sections evaluated herein derive from lesion-remote regions of the injured cord that exhibit white matter damage and/or Wallerian degeneration as determined by an experienced International Spinal Cord Injury Biobank neuropathologist on the basis of combined LFB with H&E staining, and results from 7 T magnetic resonance imaging of these spinal cord tissue blocks prior to sectioning. Deidentified information for healthy, multiple sclerosis and SCI patients is provided in Extended Data Fig. 9j.

Statistics, transparency and reproducibility

Statistical evaluations of repeated measures were performed using one-way or two-way ANOVA with post hoc independent pairwise analysis using Holm–Sidak test, Wilcoxon rank sum test, or t-tests using Prism 8 (GraphPad) unless indicated otherwise. In all cases, statistical measurements derive from means of biological replicates and error bars illustrate s.e.m. across biological replicates. P values are reported in the figures or figure legends. Differences with P  <  0.05 were considered to be statistically significant. Power calculations were performed using G*Power Software v.3.1.9.2. All immunohistochemistry and in situ hybridization analyses shown were repeated at least three times with similar results. In graphs of histological continuous or count data, coloured data points represent the mean value for each biological replicate (individual mouse), while grey data points indicate replicate measurements from individual tissue sections. Mice were assigned numerical codes and randomized into experimental groups. In vitro culture experiments were repeated at least three times using independent cultures. Experimental procedures and quantitative analyses were conducted by individuals blinded to experimental group assignments.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Raw and normalized genomic data have been deposited at the NCBI Gene Expression Omnibus under the SuperSeries accession number GSE312911. Source data are provided with this paper.

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Acknowledgements

This work was supported by US National Institutes of Health grants 5R01NS128094, R00NS105915, K99NS105915 (J.E.B.), F31NS129372 (K.B.S.), K99AG084864 (S.M.), R35NS097303 (R.D.), R01NS123532 (R.D.), R01MH128866, U18TR004146 and P30 CA023168; an ASPIRE Challenge and Reduction-to-Practice award (G.C.); the Paralyzed Veterans Research Foundation of America (J.E.B.); Wings for Life (J.E.B.); Cedars-Sinai Center for Neuroscience and Medicine Postdoctoral Fellowship (S.M.); American Academy of Neurology Neuroscience Research Fellowship (S.M.); California Institute for Regenerative Medicine Postdoctoral Scholarship (S.M.); The United States Department of Defense USAMRAA award W81XWH2010665 through the Peer Reviewed Alzheimer’s Research Program (G.C.); The Arnold O. Beckman Postdoctoral Fellowship (C.E.R.); and The Purdue University Center for Cancer Research funded by US National Institutes of Health grant P30 CA023168. G.C. is the James Tarpo Jr. and Margaret Tarpo Professor. The authors thank Agilent Technologies for donation of the Triple Quadrupole LC/MS instrument to the Chopra Laboratory; K. Lyons for providing the Ccn1 floxed mouse line; D. Lall for sharing expertise in microglia isolation and culture; and K. Tang for assistance with flow cytometry data analysis. Select illustrations were generated with BioRender under a publication license (BioRender.com/sne5gt6, BioRender.com/m3wsy6m, BioRender.com/2qndh5q, BioRender.com/en2za0v, BioRender.com/m9pw8ye, BioRender.com/58rye8y, BioRender.com/ddjmh6q, BioRender.com/qnzc1vw, BioRender.com/akvtyte, BioRender.com/bgqg4yo, BioRender.com/t6jc1y0 and BioRender.com/8mwglup). The authors acknowledge the International Spinal Cord Injury Biobank for providing the human SCI specimens used in this project; the Cedars-Sinai Applied Genomics, Computation and Translational Core for RNA-seq; A. Stotland and the Cedars-Sinai Proteomics and Metabolomics Core for assistance with proteomics analyses; and J. Suda and the Cedars-Sinai Flow Cytometry Core and the Cedars-Sinai Biobehavioral Research Core for support in conducting behavioural tests.

Author information

Author notes

1. These authors contributed equally: Sarah McCallum, Keshav B. Suresh

Authors and Affiliations

1. Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA

Sarah McCallum, Keshav B. Suresh, Timothy S. Islam, Manish K. Tripathi, Ann W. Saustad, Aditya Patil, David Lee, Brandon Kwon, Katherine Leitholf, Inga Yenokian, Sophia E. Shaka, Jasmine Plummer, Simon R. V. Knott & Joshua E. Burda

2. Center for Neural Science and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA

Sarah McCallum, Keshav B. Suresh, Timothy S. Islam, Manish K. Tripathi, Ann W. Saustad, Sophia E. Shaka & Joshua E. Burda

3. Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA

Oksana Shelest & Joshua E. Burda

4. Department of Chemistry, Purdue University, West Lafayette, IN, USA

Connor H. Beveridge, Palak Manchandra, Caitlin E. Randolph & Gaurav Chopra

5. Department of Neurology, The Neuroscience Research Institute, College of Medicine, The Ohio State University Wexner Medical Center, Columbus, OH, USA

Gordon P. Meares

6. Department of Neuroscience, Cleveland Clinic, Cleveland, OH, USA

Ranjan Dutta

7. The Center for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, Los Angeles, CA, USA

Jasmine Plummer

8. Department of Computational Biomedicine Cedars-Sinai Medical Center, Los Angeles, CA, USA

Vinicius F. Calsavara

9. Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

Riki Kawaguchi

10. Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA

Riki Kawaguchi

11. Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA

Simon R. V. Knott

12. Department of Computer Science, Purdue University, West Lafayette, IN, USA

Gaurav Chopra

13. Purdue Institute for Drug Discovery, Regenstrief Center for Healthcare Engineering, Purdue Institute for Cancer Research, Purdue Institute for Inflammation, Immunology and Infectious Disease, Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA

Gaurav Chopra

14. Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA

Joshua E. Burda

Contributions

S.M., K.B.S. and J.E.B. carried out SCI procedures. J.E.B. performed SCI astrocyte RiboTag sample collection and preparation for RNA-seq. R.K. carried our initial RNA-seq data processing, including read alignment and quality control. Differential expression analysis for SCI astrocyte RiboTag RNA-seq data was carried out by R.K., K.B.S. and J.E.B. S.M. and K.B.S. performed spinal cord single-nuclei isolation and carried out Ccn1+ astrocyte and WDM nodule time course analysis with assistance from A.W.S, A.P., D.L., B.K. and T.S.I. S.M. carried out human SCI histological evaluations. S.M. carried out mouse sciatic nerve injury studies and performed SCI histological evaluations with assistance from T.S.I, A.W.S., I.Y. and S.E.S. S.M. prepared single nuceli RNA-seq libraries, established adult mouse primary astrocyte culture method and carried out conditioned media studies with help from M.K.T. S.M. isolated spinal cord microglia for lipidomics studies and carried out saporin experiments. S.M. and O.S. performed SCI behavioural testing with assistance from A.W.S. K.B.S. carried out spinal cord tissue processing for spatial transcriptomics and J.P. performed downstream sample preparation for RNA-seq. K.B.S. carried out initial single-nuclei and spatial transcriptomics data processing, including read alignment, and quality control. K.B.S. performed all single nuclei and spatial transcriptomics analyses and data integration analyses with intellectual guidance from S.M., S.R.V.K. and J.E.B. K.B.S. also performed lysolecithin and myelin intraspinal injection studies with assistance from S.M. and T.S.I. K.B.S, S.M. and K.L. carried out microglia bulk RNA-seq analysis with help from J.E.B. M.K.T. performed CCN1 co-immunoprecipitation studies, prepared samples for LC–MS/MS and carried out all western blot analyses. M.K.T. also carried out microglia flow cytometry experiments with help from T.S.I. and technical guidance from P.M. T.S.I. performed cholesterol efflux assay experiments and microglia CCN1 stimulation studies for bulk RNA-seq, including primary microglia isolations and culture, with assistance from S.M. G.P.M. carried out mouse EAE, including behavioural assessment and provided tissue for histological analysis. S.M. carried out histological evaluations of EAE tissue with assistance from A.W.S. Lipidomics sample processing was carried out by C.H.B., P.M., C.E.R. and G.C. C.H.B. performed lipidomics data analysis with intellectual guidance from P.M., G.C., S.M. and J.E.B. V.F.C. assisted with statistical analyses and performed COM–Poisson testing for statistical comparison of time-dependent differences in WDM nodule formation, with help from S.M. and J.E.B. R.D. provided human multiple sclerosis tissue samples and carried out RNAscope staining and imaging of these samples. J.E.B. conceived and directed the project, planned experiments and guided analyses. J.E.B. prepared the manuscript with help from S.M., K.B.S. and G.C.

Corresponding author

Correspondence to Joshua E. Burda.

Ethics declarations

Competing interests

G.C. is the director of the Merck–Purdue Center funded by Merck Sharp & Dohme, a subsidiary of Merck, and is a co-founder of Meditati, BrainGnosis and LIPOS BIO. The other authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Temporal dynamics of lesion-remote astrocyte gene expression after SCI, their divergence from BFAs and other forms of astrocyte reactivity.

a, Spontaneous recovery of locomotor behavior in the left hindlimb after iSCI as scored by a modified BMS protocol. b, Heat map showing differential expression of BFA reactivity-associated functional gene sets across in LRAs and BFAs over time after SCI, and astrocytes from non-traumatic insults and disorders (lipopolysaccharide, LPS; experimental autoimmune encephalomyelitis, EAE; amyotrophic lateral sclerosis, ALS). (FDR P ≤ 0.05 vs Healthy). Due to differences in experimental design across LRA and BFA gene expression analysis, the closest available timepoints were used for comparison, enabling alignment of early, subacute, and chronic phases of post-injury astrocyte responses. c, UpSet plot illustrating overlap between LRA RiboTag RNA-Seq DEGs over time after iSCI, vs Healthy (FDR P ≤ 0.05) (n = 4-6 mice/genotype/group). d, Bar graphs of rostral and caudal LRA RiboTag RNA-Seq DEGs over time after iSCI, vs Healthy (FDR P ≤ 0.05) illustrate only minimal transcriptomic differences across regions. e, Identification of temporally co-regulated LRA-enriched gene modules by Spearman correlation. f, Line graphs illustrating temporally regulated gene expression of LRA co-regulated gene modules rostral and caudal to the injury. Temporal regulation patterns for rostral (red) and caudal (blue) are illustrated independently. Unless stated otherwise, statistical analyses were performed using two-way ANOVA with Tukeys, *P ≤ 0.05, **P ≤ 0.002, ***P ≤ 0.0002, ****P ≤ 0.0001. Lines represent average expression across all co-regulated genes. g, Heat map of gene ontology terms enriched in each LRA gene module. h, Heatmaps illustrating Stat3cKO effects on WT LRA RiboTag RNA-Seq DEGs over time after iSCI, (vs Healthy; rostral and caudal combined; FDR P ≤ 0.05, Log2FC ≥ 0.5). Abbreviations: DEG: differentially expressed gene; EMT: epithelial-to-mesenchymal transition; dARGs: delayed astrocyte reactivity genes; pARGs: persisting astrocyte reactivity genes; cAEGs: consensus healthy astrocyte expressed genes. Exact P values can be found in source data.

Source Data

Extended Data Fig. 2 Integrated snRNA-seq and spatial transcriptomic profiling of lesion-remote regions of the injured mouse spinal cord.

a, UMAP of the final snRNAseq dataset after quality control (230,570 nuclei) colored by timepoint (healthy, 3dpi, 7dpi, 28dpi) and b, spinal region (Rostral vs Caudal). c, Heatmap plotting expression of putative marker genes used to identify different cell types. d, Violin plots showing the distribution of total counts and genes detected, and a stacked bar plot showing the proportions of cell types for each individual snRNAseq sample. All samples showed similar quality control statistics and cell type contribution. e, Aligned Visium data shown for the rostral and caudal regions in space and colored by timepoint (healthy, 3dpi, 7dpi, 28dpi). f, Mean expression for Synaptophysin (Syp) and g, Myelin Basic Protein (Mbp) confirming localization to the grey and white matter respectively. h, Violin plots showing the distribution of total counts and genes detected, and a stacked barplot showing the proportions of spinal region for each individual Visium experimental replicate. All replicates showed similar quality control metrics and tissue region contribution. Exact P values can be found in source data. i, Schematic showing pipeline for intraspinal regional spatial transcriptomics. For iSCI tissue sections, unbiased clustering identifies the lesion ipsilateral spinal cord white matter, which exhibits elevated expression of inflammation and gliosis genes relative to contra-lesional spinal cord regions.

Source Data

Extended Data Fig. 3 Identification of spatially distinct LRA reactivity states.

a, Quantification of LRA subtype markers for vGM3 (Thrsp, Arex) and WM4 (Ccn1, Glipr2) demonstrates region-enriched expression. LRA data from 7 and 28 dpi was combined for analysis. Statistical analysis was performed using students Two-sided t-test, *P ≤ 0.05, **P ≤ 0.002, ***P ≤ 0.0002, ****P ≤ 0.0001 (Thrsp/Arex: n = 3 uninjured,n = 6 injured, Ccn1: n = 3 uninjured, n = 4 injured, Glipr: n = 3 uninjured, n = 5 injured). b, Cell2Location abundance plots of regionally-restricted astrocyte molecular states across lesion-remote rostral spinal cord. Astrocyte cluster 11 (not shown) was not used for this analysis due to the small number of cells in this cluster. C, Cell2Location abundance plots of dGM2 astrocytes in lesion-remote rostral and caudal spinal cord. d-g, High magnification image of dGM2 marker Ak4 and Lair shows elevated expression in astrocytes (Gfap+/Aldoc+) in dorsal horn grey matter, but not white matter. h, High magnification image of vGM3 markers Thrsp and Arex shows lack of injury-reactive astrocyte expression in white matter. i, High magnification image of WM4 markers Glipr2 and Ccn1 show lack of injury-reactive astrocyte expression in grey matter. j, Mean expression of Gfap and Vim is used to identify hypertrophy-associated astrocyte molecular states (red bars) detected by snRNA-Seq expression. Differential expression testing for Gfap and Vim relative to regional healthy cluster by Wilcoxon rank sum test with Bonferri correction for multiple corrections. Graphs show mean ± SEM. In graphs of histological count or continuous data, colored data points represent the mean value for each biological replicate (individual mouse), while grey data points indicate replicate measurements from individual tissue sections. *P ≤ 0.05, ****P ≤ 0.0001. Exact P values can be found in source data. Scale bars: 10 µm.

Source Data

Extended Data Fig. 4 Spatial transcriptomics integration reveals region-specific interactions between LRAs and neighboring cell types after SCI.

a, Spatial profiles of additional NMF factors. b, UMAP of spinal cord neuron subtypes identified by snRNA-Seq for healthy and all post-injury time points, rostral and caudal. c, Neuron subtype proportions across healthy and iSCI reveals little injury-reactive alterations in neuron subtype representation after iSCI. d, UMAP of spinal cord neurons colored by expression of established subtype markers. e-h, Spatial and cell identity loading profiles of NMF3 revealed that dorsal horn astrocytes (dGM1,dGM2) intermingle with multiple subtypes of sensory neurons of the superficial laminae (Neuron 8, 9). i, j, Volcano plots of DEGs in Neuron 8 Gal+-expressing inhibitory interneurons or Neuron 9 Tac2+ excitatory interneurons, relative to other neuron subtypes (FDR P ≤ 0.05, LogFC>0.25). k, l, NicheNet analysis of dGM2 LRAs (senders) and cluster 8 or 9 neurons (receivers) identified several putative dorsal grey matter LRA-secreted ligands. m, UMAP of spinal cord endothelial cell subtypes identified by snRNA-Seq for all time points, rostral and caudal. n, Endothelia subtype proportions across healthy and iSCI groups reveals multiple injury-reactive alterations in endothelia subtype representation after iSCI. o-q Spatial and cell identity loading profiles of NMF6 revealed that ventral grey matter reactive LRAs (vGM2) intermingle with local endothelia 14. r, Volcano plot of Endothelia 14 DEGs, relative to other endothelia subtypes (FDR P ≤ 0.05, LogFC>0.25). s, NicheNet analysis of vGM2 LRAs (senders) and cluster 14 endothelia (receivers) identified several putative ventral grey matter LRA-secreted ligands.

Source Data

Extended Data Fig. 5 Injury-reactive microglia subtypes Mg2 and Mg5 (WDM) exhibit overlapping transcriptional profiles and are distinct from homeostatic microglia.

a, UMAP of spinal cord microglia subtypes identified by snRNA-Seq for healthy and all post-injury time points, rostral and caudal. b, Microglia subtype proportions across healthy and iSCI groups reveals multiple injury-reactive alterations in subtype representation after iSCI. c, Upset plot of Mg2 and Mg5 microglia DEGs relative to all microglia. d, Venn diagram comparing Mg2 and Mg5 microglia DEGs relative to homeostatic microglia (Mg1/3). In addition to their shared spatial pattern, Mg2 and Mg5 microglia also exhibited largely overlapping transcriptomic identities and were combined for subsequent analyses. e, f, Volcano plots of Mg2 and Mg5 microglia DEGs relative to homeostatic microglia (Mg1/3). DEG FDR P ≤ 0.05 by Wilcoxon rank sum test with Bonferri correction for multiple corrections. DEG: differentially expressed gene. g, Expression of Mg2/5 marker genes (Gpnmb, Abca1) designate ‘WDM’ microglia that gradually assemble into multi-cellular nodules within Wallerian degenerating white matter regions of the injured spinal cord. Notably, appreciable numbers of multi-cellular microglia nodules are not apparent until after 7 dpi and increase in density thereafter. Mg2/5 microglia rostral to the iSCI lesion are restricted to Wallerian degenerating sensory tracts of the dorsal column white matter (DC), but mostly absent from the descending motor fibers of the corticospinal tract (CST), which are severed after iSCI, but do not undergo Wallerian degeneration in this region. Abbreviations: DC: dorsal column; CST: corticospinal tract. Exact P values can be found in source data. Scale bars h, low magnification: 250 µm; h inset and j: 10 µm.

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Extended Data Fig. 6 Ccn1 is selectively upregulated by WDM-adjacent white matter LRAs after SCI and is actively secreted by adult mouse astrocytes in vitro.

a, Plotting the distribution of distances of Ccn1+ astrocytes to WDM nodules within Wallerian degenerating dorsal column white matter relative to a randomly shuffled distribution illustrates that WDM nodules are more likely to be proximate to Ccn1+ astrocytes (mean = 78 μm) than would be expected by random chance (mean = 150 µm). **** = P < 0.001, Wilcoxon test. b, Low magnification of Ccn1 expression (RNAscope) at spinal cord central canal illustrates constitutive Ccn1 expression by ependymal cells. In contrast to white matter LRA, ependymal Ccn1 expression is unaltered by SCI. c, Quantification Ccn1 expression by white matter astrocyte, grey matter astrocytes, ependymal cells of the central canal and non-astrocyte/non-ependymal cell types. Ccn1 expression in the healthy spinal cord is mainly restricted to ependymal cells. Following SCI, Ccn1+ white matter LRAs are numerous, while expression by other cell types is unchanged. Statistical analysis was performed using two-way ANOVA, post-hoc Holm-Sidak, *P ≤ 0.05, **P ≤ 0.002, ***P ≤ 0.0002, ****P ≤ 0.0001. Data from Healthy (n = 4) and 28dpi (n = 3) mice., 2–7 tissue sections/mouse. d, Low magnification of Ccn1+ astrocytes (white arrowheads) in healthy vs lesion-remote spinal cord (rostral). e, Low magnification of Ccn1 expression (RNAscope) in SCI lesion core demonstrates expression by Gfap−/Slc1a3− cells (non-astrocytes). f, Comparison of nuclear YAP levels in Ccn1+ and Ccn1− astrocytes (Gfap+/Slc1a3+) (n = 6 mice, 2–4 sections/mouse **P ≤ 0.005, Nested t-test). g, Immunoblot for CCN1 from adult mouse astrocyte conditioned media demonstrates robust secretion of CCN1. Blot contains protein from n = 4 independent astrocyte cultures from adult mouse brain. Ponceau S is provided to show protein loading. h, CCN1 immunoblot with densitometry of lysates from WT healthy spinal cord and lesion-remote regions of the injured spinal cord at 3 dpi (n = 4 mice per condition). Statistical analysis was performed by Two-sided Student’s t-test, *P ≤ 0.05. i, Density plots of Ccn1+ astrocyte counts within the dorsal white matter at different spinal cord levels illustrates that their spatial distribution closely follows that of Wallerian Degeneration after iSCI. Rostral to the lesion (cervical, thoracic), Ccn1-expressing astrocytes localize to the Wallerian degenerating dorsal column white matter. Caudal to the lesion, Ccn1-expressing astrocytes localize mainly to the Wallerian degenerating corticospinal tract. j, Ccn1+ astrocytes (white arrowheads) are observed bilaterally after crush SCI that damages both sides of the spinal cord. Graph shows mean ± SEM. In graphs of histological count or continuous data, colored data points represent the mean value for each biological replicate (individual mouse), while grey data points indicate replicate measurements from individual tissue sections. Exact P values can be found in source data. Scale bar b: 50 µm; d: 250 µm; e: 50 µm; f: 10 µm; j: 250 µm.

Source Data

Extended Data Fig. 7 Astrocyte-specific Ccn1 deletion is efficient and selective and leads to aberrant white and grey matter inflammation after SCI.

a, Low magnification of Ccn1+ and Gfap+/Slc1a3+ in iSCI WT and Ccn1-cKO spinal cord. b, Percentage Ccn1 knockout in Ccn1-cKO spinal cord as proportion of WT Ccn1+ astrocytes (n = 6 mice/genotype at 7dpi, 5 mice/genotype at 28dpi and 6 mice/genotype at 90dpi, 5–25 sections per mouse). c, High magnification of Ccn1 expression in Gfap+/Slc1a3+ astrocytes from degenerating white matter of the 28 dpi WT and Ccn1-cKO spinal cord. d, Quantification of Ccn1 and Gfap/Slc1a3 expression in individual astrocytes shows reduction in Ccn1 expression per astrocyte in Ccn1-cKO spinal cord, while Gfap/Slc1a3 remain equivalent to WT astrocytes (n = 3 mice/genotype, 23–53 astrocytes analyzed/mouse; *P ≤ 0.05, Two-sided Student’s t-test). e, Ccn1 expression in Gfap+/Slc1a3+ ependymal cells of the central canal of injured WT and Ccn1-cKO spinal cord illustrates no effect of astrocyte Ccn1 gene targeting on ependymal Ccn1 expression. f, Quantification of tdT+ cells from Aldh1l1-CreERT2::floxed-STOP-tdT mice demonstrating ≥96% of tdT+ cells are SOX9+ astrocytes within the healthy and lesion-remote injured spinal cord. n = 3 mice/genotype. g, Spatial characterization of WDM nodules relative to Wallerian degenerating dorsal column white matter (DC; cervical, thoracic) and corticospinal tract white matter (CST; lumbar). h, Quantification of WDM nodules in the healthy cord in WT and Ccn1-cKO animals. (n = 3 WT mice, 8 cKO mice, 4–35 sections per mousen.s. = non-significant, Two-sided Students t-test). i, Quantification of WDM nodules in the lesion ipsilateral and contralateral white matter after iSCI in WT vs Ccn1-cKO (data from WT 7dpi (n = 8), cKO 7dpi (n = 6), WT 28dpi (n = 9), cKO 28dpi (n = 5), WT 90dpi (n = 5), cKO 90dpi (n = 6), 4–35 sections per mouse.***P ≤ 0.0002, ****P ≤ 1 × 10−4, two-way ANOVA with Holm-Sidak). j, Low and high magnification images of IBA1+ WDM nodules in Ccn1-cKO grey matter at 28 dpi. k, Comparison of grey matter WDM counts per spinal cord section in iSCI WT vs Ccn1-cKO (n = 16 WT mice, 29 cKO mice, 346–411 sections analyzed/genotype; *P ≤ 2 × 10−3, Student’s t-test). l, High magnification 3D images of FM+ and SMI32+ within IBA1+ WDM nodules from WT and Ccn1-cKO Wallerian degenerating dorsal column white matter. m, Bar graph illustrating spontaneous recovery of locomotor behavior in the left hindlimb after iSCI in WT vs Ccn1-cKO mice by modified Basso mouse scale scoring. n.s = non significant, two-way ANOVA with Holm-Sidak) n, Quantification of right hind-paw (lesion contralateral) cold thermoception after iSCI. n.s = non signficant, two-way ANOVA with Holm-Sidak) o, Quantification of the right hind-paw mechanosensation after iSCI. n.s = non signficant, two-way ANOVA with Holm-Sidak). (n = 8 WT mice, 7 cKO mice) Graphs show mean ± SEM. In graphs of histological count or continuous data, colored data points represent the mean value for each biological replicate (individual mouse), while grey data points indicate replicate measurements from individual tissue sections. Exact P values can be found in source data. Scale bar a: 250 µm; c: 10 µm; e: 50 µm; j: 150 µm, inset:10 µm; l: 10 µm.

Source Data

Extended Data Fig. 8 Astrocyte Ccn1 regulates the molecular specification and lipid metabolism of ‘WDM’ microglia after SCI.

a, High magnification image of IBA1+/TREM2+ WDM used for 3D reconstruction in main Fig. 5b. b, Relative proportions of IBA1+ microglia that are TREM2+ from WT and Ccn1c-KO Wallerian degenerating dorsal column white matter (Data from WT Healthy (n = 4), cKO Healthy (n = 3), WT 7dpi (n = 6), cKO 7dpi (n = 6), WT 28dpi (n = 6), cKO 28dpi (n = 5), WT 90dpi (n = 6), cKO 90dpi (n = 6), 2–5 sections per mouse. c, d, High magnification image of IBA1+/Gpnmb+ or Igf1+ WDM used for 3D reconstruction in main Fig. 5e and g. e, Cholesterol efflux measured from cultured primary mouse microglia following stimulation with positive control (Abcam 196985) or vehicle (BSA) (n = 6 experimental replicates from independent cultures; Students t-test, **P ≤ 0.05). f, Microglia lipid species distribution across treatment groups. Note that in both WT and Ccn1-cKO animals, the total microglia lipidome is reduced at 28 dpi. g, Heat map showing relative (Z-scored) levels of all significant lipid subtypes detected by unbiased microglia lipidomic analysis. h, i, Lipid pathway enrichment of the WT and Ccn1-cKO injury response highlighting a shift in the predominant lipid pathways employed after iSCI. j, Direct pairwise comparison of WT and Ccn1-cKO microglia lipid profile for healthy and iSCI, including lipid droplet- and myelin-associated lipid subtypes (Log_2 fold-change FDR P ≤ 0.01). k, Schematic of ceramide to sphingomyelin conversion mediated by sphingomyelin synthase predicted by Biopan lipid pathway analysis comparing the iSCI and healthy Ccn1cKO animals (z-score 1.921). l, Plots show percentage maximum summed intensity for sphingomyelin (SM) and ceramide (CER) in both WT and Ccn1-cKO mice. m, High magnification image of IBA1+/BODIPY+ WDM used for 3D reconstruction in in main Fig. 5m. n, Quantification of the proportion of BODIPY containing microglia from WT or Ccn1-cKO Wallerian degenerating dorsal column white matter (Data from WT Healthy (n = 4), cKO Healthy (n = 3), WT 7dpi (n = 4), cKO 7dpi (n = 4), WT 28dpi (n = 6), cKO 28dpi (n = 5), WT 90dpi (n = 6), cKO 90 dpi (n = 6), 1–6 sections per mouse). o, p, High magnification 3D image reconstruction and quantification of PLIN2+ lipid droplets within IBA1+ WDM nodules from WT and Ccn1cKO Wallerian degenerating dorsal column white matter (Data from WT Healthy (n = 4), cKO Healthy (n = 3), WT 7dpi (n = 4), cKO 7dpi (n = 4), WT 28dpi (n = 6), cKO 28dpi (n = 5), WT 90dpi (n = 6), cKO 90 dpi (n = 6), 4–6 sections/mouse). q, High magnification image of IBA1+/PLIN2+ WDM used for 3D reconstruction in panel o. r, Proportions of IBA1+ microglia that are PLIN2+ from WT and Ccn1-cKO Wallerian degenerating dorsal column white matter(Data from WT Healthy (n = 4), cKO Healthy (n = 3), WT 7dpi (n = 4), cKO 7dpi (n = 4), WT 28dpi (n = 6), cKO 28dpi (n = 5), WT 90dpi (n = 6), cKO 90 dpi (n = 6),; 1–6 sections per mouse). s, High magnification image of IBA1+/Abca1+ WDM used for 3D reconstruction in main Fig. 5n. Bar graphs show mean ± SEM. In graphs of histological count or continuous data, colored data points represent the mean value for each biological replicate (individual mouse), while grey data points indicate replicate measurements from individual tissue sections/cells. Unless stated otherwise, statistical analyses were performed using two-way ANOVA with Holm-Sidak, *P ≤ 0.05, **P ≤ 0.002, ***P ≤ 0.0002, ****P ≤ 0.0001. Exact P values can be found in source data. Scale bars, 10 µm.

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Extended Data Fig. 9 Ccn1-expressing astrocytes are selectively induced by CNS white matter degeneration, and observed in acute and chronic demyelinating disorders in mouse and human spinal cord.

a, High magnification of Ccn1 expression labeling and astrocytes (Gfap+/Slac1a3+ cells) in spinal cord lateral white matter 72 hrs following injection of PBS (vehicle control for CFSE-myelin). b, High magnification of Ccn1+ astrocytes (Gfap+/Slac1a3+ cells, white arrowheads) in spinal cord grey matter following injection of CFSE-conjugated myelin into dorsal horn grey matter. c, Low magnification and quantification of Ccn1+ astrocytes (Gfap+/Slac1a3+ cells) in dorsal half of lumbar spinal cord following unilateral (left) sciatic nerve crush (n = 3 mice). We did not observe Ccn1+ astrocytes following sciatic nerve crush, indicating that astrocyte Ccn1 expression depends on CNS myelin breakdown and not a generalized neuronal stress response. d, Evaluation of myelin and axon degeneration in spinal cord dorsal white matter following sciatic nerve crush. e, Ccn1 expression and astrocytes (Gfap+/Slac1a3+ cells) in spinal cord lateral white matter following injection of PBS (vehicle control for LPC). f-g, Assessment of myelin degeneration (Fluoromyelin, yellow arrowheads) and damaged axons (SMI32, white arrowheads) in spinal cord lateral white matter following demyelination by LPC or injection of. h, EAE disability severity scores assessing locomotor disability. Onset (n = 4), peak (n = 4), and chronic (n = 6) EAE timepoints. Graph shows mean ± SEM across experimental replicates. i, Astrocyte RiboTag RNA-Seq from mouse chronic EAE spinal cord shows significantly elevated Ccn1 expression[54] (n = 3 Healthy, n = 5 Chronic EAE, FDR P ≤ 0.05). Graph show mean ± SEM. Data points illustrate biological replicates. j, Anonymized pathology notes for human tissue used in this study. k, CCN1 expression and GFAP+/SLC1A3+ astrocytes in healthy human spinal cord grey matter; MS grey matter lesion, MS lesion-remote normal appearing grey matter. l, Comparison of astrocyte CCN1 expression in healthy and MS human brains by single cell RNA-seq[66]. Top plot: control: 35 cells, MS samples: 70 cells; ***P ≤ 0.0002, Two-sided Student t-test). m, CCN1 expression and GFAP+/SLC1A3+ astrocytes in human SCI lesion-remote spinal cord grey matter. Abbreviations: CFSE: Carboxyfluorescein Succinimidyl Ester; LPC: lysphophosphatidyl choline; dpl: days post-lesion. Exact P values can be found in source data. Scale bar a,b,d,e,f,g: 50 µm, c,j,k,l: 250 µm.

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Extended Data Fig. 10 CCN1 from myelin degeneration-reactive LRAs orchestrates molecular and lipid metabolic reprogramming of local phagocytic microglia to mediate white matter repair after SCI.

Following SCI, LRAs in spared but reactive tissue regions exhibit molecularly distinct, neuroanatomically restricted reactivity states that evolve over time. Wallerian degeneration of severed axons extends white matter pathology into lesion-remote regions of the injured spinal cord. In response to local myelin breakdown, reactive white matter LRAs rapidly and persistently upregulate expression of the matricellular protein CCN1. Astrocyte-secreted CCN1 directs the molecular and lipid metabolic specification and function of local white matter degeneration-associated microglia (WDM), which acquire a repair-associated molecular profile and phagocytose myelin and axon debris. Specifically, astrocyte CCN1 can engage SDC4 receptors on microglia to aid the intracellular digestion of lipid-rich cellular debris via modulation of mechanisms that buffer excess debris-derived lipids – processes known to be essential for debris clearance and white matter repair. Accordingly, astrocyte CCN1 signaling to local microglia facilitates efficient white matter repair and aids neurological recovery. In the absence of astrocyte CCN1 (Ccn1-cKO), phagocytic microglia in degenerating white matter are dysfunctional and exhibit intracellular accumulation of undigested cellular debris and impaired buffering of excess lipids, characterized by disrupted lipid storage. Consequently, phagocytic microglia recruitment is amplified, resulting in chronically excessive and spatially aberrant white matter inflammation. This compensatory mechanism ultimately fails to effectuate debris clearance, culminating in attenuated white matter repair and impaired neurological recovery.

Supplementary information

Supplementary Figure 1

Uncropped immunoblots corresponding to Extended Data Fig. 6g,h. a, Uncropped CCN1 immunoblot and corresponding Ponceau S membrane staining from astrocyte conditioned media (corresponding to Extended Data Fig. 6g). All lanes shown in the CCN1 blot and Ponceau S stain were run on the same gel and transferred to a single membrane. b, Uncropped CCN1 and β-actin immunoblots from spinal cord lysates (corresponding to Extended Data Fig. 6h). CCN1 and β-actin were probed sequentially on the same membrane. Molecular mass markers are visible at left in each panel. Boxed regions indicate the areas shown in the final extended data figure panels. No other lanes were cropped or removed.

Reporting Summary

Supplementary Data 1–6

Supplementary Data 1: Differential gene expression data from bulk RNA-seq of wild type spinal cord LRAs (RiboTag) and paired whole tissue. Supplemental Data 2: Differential gene expression data from bulk RNA-seq of wild type and Stat3-cKO spinal cord LRAs after SCI (RiboTag). Supplementary Data 3: LRA snRNA-seq genome-wide expression. Supplementary Data 4: Differential gene expression data from bulk RNA-seq of purified adult mouse cortical microglia stimulated with CCN1. Supplementary Data 5: Proteomic analysis of CCN1 co-immunoprecipitation eluent. Supplementary Data 6: Flow cytometry gating strategy for microglia neutral lipid load (LipidTox staining).

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McCallum, S., Suresh, K.B., Islam, T.S. et al. Lesion-remote astrocytes govern microglia-mediated white matter repair. Nature 649, 959–970 (2026). https://doi.org/10.1038/s41586-025-09887-y

- Received: 20 February 2024

- Accepted: 07 November 2025

- Published: 17 December 2025

- Version of record: 17 December 2025

- Issue date: 22 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09887-y

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Article Open access Published: 03 December 2025

The Microflora Danica atlas of Danish environmental microbiomes

C. M. Singleton,

T. B. N. Jensen,

F. Delogu,

K. S. Knudsen,

E. A. Sørensen,

V. R. Jørgensen,

S. M. Karst,

Y. Yang,

M. Sereika,

F. Petriglieri,

S. Knutsson,

S. M. Dall,

R. H. Kirkegaard,

J. M. Kristensen,

C. K. Overgaard,

B. J. Woodcroft,

D. R. Speth,

S. T. N. Aroney,

The Microflora Danica Consortium,

M. Wagner,

M. K. D. Dueholm,

P. H. Nielsen &

…

M. Albertsen

Nature volume 649, pages 971–981 (2026)

Abstract

Over the past 20 years, there have been considerable advances in revealing the microbiomes that underpin processes in natural and human-associated environments. Recent large-scale metagenome surveys have recorded the variety of microbial life in the oceans[1], in the human gut[2] and on Earth[3], with compilations encompassing thousands of public datasets4,5. However, despite their broad scope, these studies often lack functional information, and their sample locations are frequently sparsely distributed, limited in resolution or lacking metadata. Here we present Microflora Danica—an atlas of Danish environmental microbiomes encompassing 10,683 shotgun metagenomes and 450 nearly full-length 16S and 18S rRNA datasets, linked to a five-level habitat classification scheme. We show that although human-disturbed habitats have high alpha diversity, species reoccur, revealing hidden homogeneity. This underlines the role of natural systems in maintaining total species (gamma) diversity and emphasizes the need for national baselines for tracking microbial responses to land-use and climate change. Consequently, we focused our dataset exploration on nitrifiers, a functional group closely linked to climate change and of major importance for Denmark’s primary land use: agriculture. We identify several lineages encoding nitrifier key genes and reveal the effects of land disturbance on the abundance of well-studied, as well as uncharacterized, nitrifier groups, with potential implications for N_2O emissions. Microflora Danica offers an unparalleled resource for addressing fundamental questions in microbial ecology about what drives microbial diversity, distribution and function.

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In 1752, King Frederik V of Denmark, known for his “generous attitude […] towards natural science and applied art” commissioned the Flora Danica project, initiating an ‘Opus Incomparibile’ that took 122 years and produced one of the world’s most unique works in natural history[6]. Over 3,000 botanic engravings and 54 booklets were completed of flowers and plants, with which, “according to the unanimous contention of all connoisseurs”, “the whole world can eventually reap all the fruits that follow the extension of a science which, with regard to the benefit of mankind, is one of the most useful and without which medicine and economics would lack important advantages”[6]. In 2019, we initiated the Microflora Danica (MFD) project with the aim of cataloguing the microbiome of Denmark, in the hope that the microflora of Denmark can be similarly studied, and their riches contribute to the extension of science.

The MFD dataset

The MFD dataset comprises 10,683 samples, chosen to capture the diversity and geographical coverage of Danish microorganisms, associated with Illumina shotgun metagenomic DNA sequencing (average 4.5 Gb per sample, total 48.2 Tb) (Fig. 1a). Moreover, the dataset incorporates 14.9 million bacterial (median 4,528 bp) and 13.4 million eukaryotic rRNA operon sequences (median, 4,035 bp), as well as 6.4 million nearly full-length bacterial 16S rRNA gene sequences (median 1,355 bp). These data originate from a subset of samples (450 and 412, respectively) reflecting sample diversity while maintaining geographical coverage of the wider dataset (Fig. 1a). The samples are associated with GPS coordinates (Fig. 1b) and a highly curated five-level ontology (MFDO) (that is, habitat classification system) (Fig. 1c) that can be linked to other ontologies (EMPO3,7, Natura 2000 (ref. [8]) and EUNIS[9]). The habitat ontology comprises sample type, area type and up to three levels of increasingly specific habitat description: MFDO1, MFDO2 and MFDO3 (MFD ontology levels 1, 2 and 3) (Fig. 1a,c). The area type ‘natural’ describes habitats not directly managed or located in urban areas. The Danish landscape is made up mainly of agriculture (63.0%), buildings and infrastructure (13.9%), forest (13.3%) and natural areas (9.2%), as well as streams and lakes (2.8%)[10]. The MFDO1 habitat ontology level represents 28 different categories (Fig. 1c) and reflects the primary land uses of fields (that is, croplands; 3,003 samples), grassland formations (1,393 samples), forests (1,328 samples) and greenspaces (that is, urban parks; 711 samples). The breadth of sampling is exemplified by our coverage of 27% of the 986 registered lakes in Denmark. Combined, the datasets, ontology, associated metadata and spatial resolution provide an extraordinary resource to investigate research questions related to diversity and function in microbial ecology.

Fig. 1: MFD sampling campaign and ontology.

a, The mean ± s.d. metagenome and rRNA amplicon data sequencing depths. The unit of measurement for depth is reads, except for metagenomes, for which the depth is reported as bp. M, million. b, The MFD samples cover the land of Denmark and its surrounding waters. The map depicts the locations of the samples used for metagenomics, and the colours represent the three different sample types. The top right cutouts show the island of Bornholm, which is east of Copenhagen and south of Sweden. The base map was retrieved from the Eurostat countries portal EuroGeographics for the administrative boundaries, © EuroGeographics 2025. c, Sample counts in the first three levels of the habitat ontology. The MFD habitat ontology accounts for a variable number of samples per category/branch. The Sankey diagram reports the first three levels of the ontology, and the thickness of the branches is proportional to the number of samples in each category. Only classes with n > 20 samples and non-empty MFDO1 classification are reported. Each habitat category is followed by the number of samples for that category in parentheses. The Sankey plot, including all five levels of the ontology, is provided in high resolution at Zenodo (https://doi.org/10.5281/zenodo.17162544).

Establishing Denmark’s microbiome

To facilitate sequence diversity analysis of bacteria, we used nearly full-length 16S rRNA genes extracted from the rRNA operon data, sequenced on the PacBio platform, and nearly full-length 16S rRNA gene amplicon data generated using unique molecular identifiers (UMIs). The UMI approach relies on the use of molecular nucleotide template tagging to achieve high-accuracy single-molecule consensus calling on the Oxford Nanopore sequencing platform. The addition of UMIs to both ends of the template enables the bioinformatic identification and removal of chimeras formed during PCR[11]. We investigated the bacterial sequence diversity and novelty using the combination of these data (Methods and Extended Data Fig. 1). The combined nearly full-length 16S (V1–V8) rRNA gene dataset included 458 habitat-representative samples and 21.3 million sequences, with 605,861 amplicon sequence variants (ASVs) representing 141,252 bacterial species (98.7% operational taxonomic units (OTUs))[12] (Fig. 2a). Comparison of the species-level OTUs (clustered at 98.7% identity) against the SILVA v.138.1 database revealed that 82.5% were from new species (<98.7% identity) (Fig. 2a). However, the discovery rate of novelty quickly decreased at the higher taxonomic levels, with only 1.9% of the OTUs belonging to new families (<86.5% identity) (Fig. 2a). This suggests that while 16S rRNA gene sequences from bacteria originating from temperate northern European habitats are well represented in public databases at the higher taxonomic levels, the species-level diversity remains predominantly uncaptured.

Fig. 2: Novelty, diversity and read classification based on nearly full-length 16S and 18S rRNA gene sequences and the MFG 16S reference database.

a, Sequence novelty of species-level clustered bacterial 16S rRNA gene OTUs (98.7%) against SILVA[19] v.138.1 NR99 and eukaryotic 18S rRNA gene OTUs (99.0%) against PR2 (ref. [16]) v.5.0.0. Taxonomic thresholds for bacteria were adapted from ref. [12], whereas those for eukaryotes were calculated using a similar approach based on sequences from the PR2 v.5.0.0 database (Supplementary Note 1). Where indicated by an asterisk, thresholds were proposed on the basis of the sequence identity between species-level classified 18S rRNA gene sequences in the PR2 database and their closest relatives within and across ranks; meaningful thresholds above the family level could not be determined. b, Species-level rarefaction curves of UMI-based bacterial 16S rRNA and eukaryotic 18S rRNA gene OTUs from terrestrial samples. Insets: MFDO1 habitat-specific rarefaction curves for habitats represented by at least nine samples. c,d, Database evaluation based on 16S rRNA gene reads extracted from selected MFD metagenomes (c) and V4 OTUs clustered at 99% identity from the GPC[23] dataset (d). Classification of metagenomic reads or OTUs was done using the SINTAX[63] classifier. The following databases were used in addition to the MFG database created here: GreenGenes2_2022_10 taxonomy backbone[22], GTDB_ssu_all_r220 (ref. [34] and SILVA_138.1_SSURef_NR99 (ref. [19]). All databases were clustered at 98.7% sequence identity to enable direct comparison.

We used the nearly full-length 16S rRNA UMI dataset to estimate the Danish terrestrial bacterial richness (species count). The dataset encompasses 5.8 million 16S rRNA gene reads and 101,423 species (98.7% OTUs)[12] across 309 habitat-representative samples (Fig. 2b). Rarefaction analysis showed underlying variation in the detection of species among MFDO1-level habitats, but approached saturation across the combined dataset, indicating that most species were captured by the sequencing effort (Fig. 2b). To support this, we calculated the habitat and pan-habitat community coverage to estimate how well our dataset captures the total terrestrial diversity of bacterial species[13]. We found that the community coverage at the MFDO1-level habitat ranged from 0.46 to 0.90, showing a strong correlation with sampling effort r_7 = 0.95 (t = 7.96, 95% confidence interval (CI): 0.77–0.99, P = 9.4 × 10−5), but that the overall terrestrial community coverage amounted to 0.98, again indicating near complete species detection. Hill diversity estimates place the lower bound of the bacterial species count (Hill richness) in terrestrial MFD at a minimum of 114,400 species (95% CI = 113,897–114,902), with 43,447 common (intermediate to high frequency, Hill–Shannon[14]) and 22,036 dominant (most frequent, Hill–Simpson[14]) species based on their observation frequency in the dataset13,15. The community coverage estimates and rarefaction analysis indicate that the nearly full-length 16S rRNA gene dataset captures the collective Danish bacterial species pool across the investigated habitats and sets a conservative minimum estimate of the total environmental bacterial richness of terrestrial Denmark to be 114,400 species.

To investigate the diversity of eukaryotes, we used the eukaryotic rRNA operon sequences. These sequences exhibited a strong phylogenetic signal as they include both the ITS1 and ITS2 regions. However, the absence of a comprehensive rRNA operon reference database prompted us to focus our analysis on extracted nearly full-length (V4–V9) 18S rRNA genes that can be directly compared to the PR2 database[16]. The 13.4 million eukaryotic nearly full-length 18S rRNA gene sequences resolved into 28,575 ASVs representing 12,447 species (99% OTUs; Supplementary Note 1). Mapping of the species-representative sequences against PR2 revealed that most species (77%) are novel (Fig. 2a). Furthermore, 32% of the sequences had less than 93% similarity to a sequence in PR2, indicating high novelty at approximately the family level (Supplementary Note 1). Eukaryotic diversity varied between habitats but, based on Hill diversity estimates, the eukaryotic species count (Hill richness) is estimated to be a minimum of 19,295 species (Supplementary Note 2 and Extended Data Fig. 2). These findings show that vast microeukaryotic diversity remains undocumented.

MFG 16S rRNA gene database

Confident taxonomic assignment of 16S rRNA gene sequences relies on representative databases with clear taxonomic frameworks that include uncultured taxa17,18. As current universal reference databases lack the specificity we required, we used our extensive nearly full-length 16S rRNA gene dataset to create a comprehensive reference database for taxonomic classification of 16S rRNA gene reads extracted from our metagenomes. To enhance classification accuracy, we supplemented our sequences with high-quality sequences from SILVA v.138.1 SSURef NR99 (ref. [19]), EMP500 (ref. [3]), AGP70 (ref. [11]), MiDAS[20] and ref. [21] (Methods). This resulted in a total of 30.2 million sequences, which were processed using Autotax[18] to create the Microflora Global (MFG) 16S rRNA gene reference database. The 1,034,840 unique ASVs were clustered at 98.7% nucleotide identity, representing 342,673 bacterial or archaeal species-level OTUs with a complete seven-level taxonomic string.

To evaluate the MFG 16S reference database, we first compared classification of metagenome-derived 16S rRNA gene fragments from a subset (n = 2,348; Methods) of our samples using both the MFG 16S reference database and publicly available databases clustered at the species level (98.7% identity) (Fig. 2c). We classified 46.1% (4.79 million out of 10.40 million) of all extracted 16S rRNA gene reads to genus level using the MFG 16S reference database, compared with 32.2% (3.35 million out of 10.40 million) classified by the second-best-performing database GreenGenes2 (ref. [22]) (Fig. 2c). We next evaluated our database’s ability to classify data beyond Denmark’s temperate Northern Hemisphere habitats, using the Global Prokaryotic Census (GPC) V4 OTU dataset[23] (Fig. 2d). The MFG 16S reference database was able to classify 47.7% (1.05 million out of 2.20 million) of the GPC OTUs at the genus level, compared with 32.7% (0.72 million out of 2.20 million) classified by GreenGenes2 (ref. [22]). The combined results confirm that the MFG 16S reference database greatly improves classification not only for our samples, but for microbial profiling in general.

Diversity for habitat management

The level of microbial diversity in a habitat is often characterized by the alpha diversity, the richness in a single sample or average sample of a habitat and by the gamma diversity, the total observed richness of all samples within a habitat[24]. In contrast to the aboveground macro biodiversity, disturbed (that is, managed or directly affected by human activities) soils have been shown to have higher richness than undisturbed natural areas, both at continental and global scales24,25. Our detailed habitat ontology and the number of samples in each habitat type enabled us to re-evaluate these observations using both the metagenome-derived 16S rRNA gene fragments and the nearly full-length 16S UMI rRNA gene dataset taxonomically classified against the MFG 16S reference database.

To ensure that our data enabled valid comparisons between samples, we investigated biases introduced from sample treatment and location. Most of the agricultural samples were treated differently compared with the other soil samples (Methods), but this treatment had no observable effect on alpha and beta diversity and amounted to only around 2% of the community variation (Supplementary Note 3). We accounted for spatial bias resulting from more densely sampled locations by estimating the spatial autocorrelation using distance–decay analysis on the metagenome-derived 16S rRNA gene fragments (Extended Data Fig. 3). On the basis of the results, we identified representative samples of the MFDO1 habitats within the 10 km reference grid of Denmark (2,348 samples; Methods and Extended Data Fig. 1). Hierarchical clustering based on the average between-habitat Bray–Curtis dissimilarity of these samples (beta diversity) largely captured the expected relationships based on similar aboveground characteristics (for example, grass cover, monocultures, exposure) between the habitats. These relationships are exemplified by the clustering of fields, greenspaces and grassland formations (Fig. 3a).

Fig. 3: Microbial diversity of the Danish terrestrial habitats.

a, Diversity overview of the selected habitats. Each facet addresses a different measure of diversity. The nine MFDO1 habitats are represented in the rows of the multifacet plot. The dendrogram shows the between-group (branches) and within-group (nodes) Hellinger-transformed Bray–Curtis (BC) dissimilarity using the genus-level-classified 16S rRNA gene fragments from the spatially thinned dataset. Bootstrap values were calculated using 100 iterations. The heat map shows the relative abundances of the 20 most abundant phyla. The box plots of alpha diversity and bar charts of gamma diversity are based on the UMI 16S rRNA gene data. The number of biologically independent samples used for the diversity measurements per habitat are indicated. The hinges of the box plots correspond to 25th, 50th and 75th percentiles of the distributions, and the whiskers extend to 1.5× the distance between the 25th and 75th percentile. All individual samples are shown as points (with jitter for visualization). Gamma diversity (Hill–Shannon diversity) reflects a single value per habitat (that is, bar) based on rarefaction and extrapolation of n samples, and the error bars report the associated 95% CIs. b, Ordination of the metagenome (MG) dataset. PCoA of the 9,643 metagenome samples and coloured according to MFDO1 habitat description together with the results from the ANOSIM and PERMANOVA; P values were derived from 999 permutations in both cases. The visualization depicts the first two components. The contour plot was added to show the density of points. c, Subpanels of the individual 18 selected MFDO1 habitats coloured and presented with the results of the contrasts analysis. d, The sample distribution in the ordination space for MFDO1 ‘soil, natural, bogs, mires and fens’, coloured by classifications at the MFDO2 ontology level.

We calculated the alpha diversity from the nearly full-length UMI 16S rRNA gene data. In contrast to previous studies at the continental-Europe[25] and global[24] scale, which found the highest alpha diversity in samples from disturbed habitats, we found that the median bacterial diversity was highest in bogs, mires and fens (1,705 species) and lowest in temperate heath and scrub (1,274), with the diversity of disturbed habitats ranging in between (Fig. 3a and Supplementary Note 4). We found no significant difference in alpha diversity between fields, forests or grassland formations, contradicting the previous results from continental Europe while agreeing with global findings (Fig. 3a, Extended Data Table 1 and Supplementary Note 4). Additional large studies on other continents will be vital to resolving the effect of human disturbance on alpha diversity.

In contrast to the alpha diversity results, gamma diversity revealed key differences between disturbed and natural habitats (Supplementary Note 4). Fields had the lowest gamma diversity (12,797 common species), and along with greenspaces (20,336), was considerably less diverse than the more natural environment grassland formations (26,721). This trend was mirrored by sediments, with urban sediments (21,609) encompassing lower gamma diversity than natural sediments (27,126). Human disturbance reduces ecological breadth by creating more uniform environmental conditions, leading to lower gamma diversity[26]. This was supported by our comparison between urban and natural environments, in which greater environmental heterogeneity is encompassed by the natural habitats, reflecting greater habitat breadth and, consequently, higher gamma diversity. Overall, these data suggest that there is a gamma diversity gradient impacted by the level of perturbation, from highly disturbed fields to moderately disturbed greenspaces and relatively undisturbed grassland formations. These findings support an apparent homogenization (that is, low gamma diversity considering the high alpha diversity) of species in disturbed habitats—a pattern that was recently identified in other studies[27]. Habitat species homogenization was also supported by the Bray–Curtis analysis, which revealed low within-habitat dissimilarity of the prokaryotic communities (Fig. 3a).

Low gamma diversity in fields was most pronounced among bacterial communities (Fig. 3a), but also visible in the eukaryotic data (Supplementary Note 2), and reflected the aboveground macro biodiversity. Notably, temperate heath and scrub had similarly low gamma diversity to fields, but also low alpha diversity. However, this habitat is selective, defined by dry, infertile and acidic conditions (EUNIS habitat classification[9]), in contrast to the irrigated, nutrient- and pH-adjusted agricultural land.

These results show that the same bacterial species are found in the disturbed habitats, and that disturbed habitats are under selective pressures comparable to natural habitats with defined abiotic constraints. This highlights the need to incorporate gamma diversity when assessing microbial diversity. Including this broader perspective is particularly important when monitoring the impacts of land use and climate change, where community homogenization could lead to reduced ecosystem resilience and have implications for ecosystem functions[28].

Modelling for habitat classification

After revealing the importance of gamma diversity for biodiversity assessments, we investigated how the microbial community could be used to classify habitats and its potential for tracking future habitat changes. Exploratory principal coordinates analysis (PCoA) performed on the eukaryotic 18S rRNA gene dataset revealed some separation between MFDO1 habitat categories (n = 363, analysis of similarities (ANOSIM), R = 0.46, P = 0.001, permutational analysis of variance (PERMANOVA), R^2 = 0.07, P = 0.001; Supplementary Note 2). However, for the prokaryotic community, PCoA revealed good separation between MFDO1 habitats based on the metagenome-derived 16S rRNA gene fragment microbial community composition (n = 9,643, ANOSIM, R = 0.69, P = 0.001, PERMANOVA, R^2 = 0.27, P = 0.001) (Fig. 3b,c). An exception was MFDO1 ‘bogs, mires and fens’, which showed large dispersion in ordination space. At the MFDO2 level, this habitat consists of both calcareous fens and sphagnum acid bogs, which have large differences in pH that impact microbial communities[29] (Fig. 3d).

To determine the potential for microbial community DNA to be used in habitat classification, we investigated whether the 16S rRNA gene fragments could predict the habitat ontology (Fig. 4 and Supplementary Note 5). We evaluated habitat classifications using the precision recall area under the curve (PR-AUC; Fig. 4). Some habitats were difficult to model, for example, they had a lower PR-AUC (Fig. 4), such as the various types of fields, where the level of shared taxa was large. Conversely, other habitats—such as saltwater and wastewater, with higher PR-AUC—are associated with more specialized microbiomes. In general, low model scores reflected habitats in which samples would be misclassified to a few other selected habitats, for example, samples from grassland formations, greenspaces and fields were often misclassified as each other (Supplementary Note 5).

Fig. 4: Random-forest classification of habitat ontology levels using prokaryotic data.

The genus-level models were used to compile the per-class PR-AUC of every node of the ontology. The metric spans from 0 to 1, where 0 and 1 mean that none and all, respectively, of the samples of the given class were classified correctly. The mean results over iterations (n = 25 independent iterations) are reported in the tree labels and coloured accordingly, with brighter nodes carrying higher values. Moreover, the top 20 genera, according to variable importance (box plot at the bottom, computed using the MFDO3 models), are reported with their median relative abundance for each of the terminal nodes of the ontology. The three hinges of the box plots correspond to the 25th, 50th and 75th percentiles of the distributions, and the whiskers extend to a maximum of 1.5× the distance between the 25th and 75th percentile hinges. All of the individual samples are shown as points (with jitter to improve visualization). The sum of the variable importance across all variables was scaled to 100 for each model. The ranking of the variables indicates which genera have a greater discriminant power in the models. Notably, the models were reliable in classifying samples from agricultural soils (PR-AUC =0.95) but not at classifying individual crop types.

Considering which prokaryotic genera were the most important in discriminating among habitats (that is, highest variable importance) (Fig. 4), the strongest signal was provided by Paenibacillus, whose species have been found to be associated with crops, promoting plant growth and protection from pathogens, as well as fixation of nitrogen[30]. Paenibacillus was distributed across soils and sediments with higher counts in field habitats, perhaps functioning as a predictor for sample type and land use. Our findings support low-resolution discrete habitat classification (that is, MFDO1) using microorganisms, but not higher-resolution classifications (that is, MFDO2). This agrees with previous studies proposing the redefinition of habitats using continuous gradients[31]. We believe microbiome data could provide a scalable solution to future classification efforts, enabling gradients to be compared to measure or monitor changes related to climate, sustainable farming choices or restoration progress. Identifying the core microorganisms belonging to specific habitats, or habitat gradients, may help to simplify the use of microbiome data.

Core genera across Danish habitats

Core microorganisms are abundant and widespread within habitats, potentially reflecting populations with habitat-specific adaptations, functions and ecological importance[32]. We identified abundant core community genera in the habitats across all five habitat ontology levels (genera with more than 50% habitat-specific prevalence, as well as at least 0.1% relative abundance; Supplementary Data 1, Extended Data Fig. 4 and Supplementary Note 6).

Habitat-specific core genera were more numerous in habitats with strong selective environmental gradients (for example, halotolerance), or constrained habitats, such as biogas systems (Supplementary Data 2 and Extended Data Fig. 4). Conversely, we observed fewer habitat-specific core species if no habitat-specific selective pressure was present. For example, the median of core genera unique to the soil MFDO1 habitats was two, showing that many of the genera were shared among two or more MFDO1 habitats (such as fields and greenspaces). Combined with the observed model misclassification of ecologically similar environments, these findings suggest that despite the vast dispersal capabilities of microorganisms, the prokaryotic community follows a continuous gradient of change and is thus more influenced by specific environmental factors as opposed to geographical location, in accordance with the Baas Becking hypothesis: everything is everywhere, but the environment selects[33].

The alpha, beta and gamma diversity patterns, and high model score for fields among the terrestrial environments (Fig. 4), showed that land disturbance and management lead to similar microbial communities (Fig. 3a). Land-management practices, such as nutrient amendment and soil structure degradation, probably drive environmental filtering of the prokaryotic communities[27]. The disturbed soil habitats (fields, roadside and greenspaces) and the natural soil habitats (bogs, mires and fens; coastal; dunes; forests; grassland formations; rocky habitats and caves; sclerophyllous scrub; and temperate heath and scrub), encompassed 107 and 98 core genera (that is, a core genus in at least one of the habitats under the disturbed or natural categories), respectively (Supplementary Note 6). Comparing the natural and disturbed habitats revealed differences in core genera associated with nitrogen cycling (for example, Nitrospira, and genera within the Nitrososphaeraceae and Nitrosomonadaceae; Extended Data Fig. 4 and Supplementary Note 6), leading us to investigate this functional group more closely.

To provide genome-level resolution, recover potential functional group members and improve the representativeness of public genome databases, such as the Genome Taxonomy Database[34] (GTDB), we performed de novo assembly of the 10,683 metagenomes (Supplementary Note 7). We recovered 19,253 bacterial and archaeal metagenome assembled genomes (MAGs) of at least medium quality (Methods and Extended Data Fig. 5). These MAGs represented 5,518 species (95% average nucleotide identity clustering) with broad phylogenetic coverage of which 4,604 were novel compared with GTDB[34] R220 (Supplementary Note 7). This MFD genome database provides the foundation for functional analysis linked to species identity and habitat distribution and enabled us to examine key participants in the biogeochemical nitrogen cycle, the nitrifiers, across Denmark.

Distribution of Danish nitrifiers

Our investigations into microbial diversity indicated that bacteria and archaea involved in the nitrogen cycle were abundant, and form part of the core community differences in disturbed versus natural habitats (Supplementary Notes 6 and 8). This microbiome fingerprint reflects that Denmark is one of the most intensively cultivated countries in the world (63% of the land[10]), with much of its land impacted by management regimes involving fertilization with reactive nitrogen[35]. As Denmark has a large livestock sector, manure is a major nitrogen source, alongside synthetic fertilizers. Conversion of nitrogen fertilizers by nitrifying microorganisms leads to fertilizer loss, groundwater nitrate contamination, eutrophication of aquatic water bodies, and production of the potent ozone-depleting and greenhouse gas nitrous oxide35,36. Consequently, nitrification inhibition with synthetic or biological inhibitors is gaining importance to limit nitrate leaching, nitrous oxide emissions, and to increase nitrogen-use efficiency[36]. The use of two commercial nitrification inhibitors has risen fivefold in the past five years, now covering around 3% (78,129 ha in 2025) of Danish agricultural land[37]. Notably, the different groups of nitrifiers, comprising ammonia-oxidizing bacteria (AOB) and archaea (AOA), complete ammonia-oxidizing bacteria (CMX) and nitrite-oxidizing bacteria (NOB), vary in their sensitivities to nitrification inhibitors and in their nitrous oxide production rates36,38. To build knowledge needed to move towards sustainable agriculture, we performed an in-depth analysis of nitrifiers in the MFD datasets. On the basis of an analysis of functional genes (GraftM[39]), single-copy marker genes (SingleM[40]) and genome-level quantification (sylph[41]), we describe the diversity and distribution of Danish nitrifiers and identify new uncharacterized AOAs and NOBs.

Initially, curated gene-based search models of the nitrification marker genes amoA (encoding a subunit of the ammonia monooxygenase of AOB, AOA and CMX) and nxrA (encoding the active-site subunit of nitrite oxidoreductase of NOB and CMX) were created, accompanied by detailed classification of protein phylogeny from the translated genes, to separate nitrifier sequences from homologous sequences in other microorganisms, such as PmoA (particulate methane monooxygenase) and NarG (nitrate reductase)[42]. Furthermore, we included translated amoA and nxrA sequences from the recovered MFD MAGs in the search models, which markedly improved the resolution within groups of nitrifiers with few representative sequences (Fig. 5a and Supplementary Note 9).

Fig. 5: Nitrifier distribution in Danish habitats.

a, Phylogenomic tree of nitrifiers. The red text indicates groups for which we recovered MAGs; the numbers in the brackets indicate the total number of species in this group in GTDB R220 (ref. [34]), the number of species recovered in MFD, the number of species recovered in MFD not present in GTDB R220 and the total number of MAGs recovered in MFD. The values in parentheses represent the GTDB number of spp. representatives, the MFD number of spp. representatives, the MFD number of spp. representatives not in GTDB, and the MFD total number of MAGs, respectively. b, The distribution of nitrification genes across Danish habitats. The number of reads (reads per kilobase million (RPKM)) assigned to each gene-phylogenetic group (Supplementary Figs. 7 and 9). In cases in which a taxon has polyphyletic amoA or nxrA clades (Supplementary Note 9), an asterisk indicates the aggregation of multiple clades into a single line in the heat map. The samples are clustered with hierarchical clusters within each MFDO2 habitat. The bottom colour panel indicates the MFDO2 habitat. c, The distribution of canonical and potential nitrifiers across Danish habitats based on single-copy marker genes (SingleM). The heat map of short-read metagenomes is based on SingleM with the metapackage supplemented with MAGs from the MFD short-read metagenomes. Taxonomic resolution is marked by prefixes based on GTDB taxonomy: o__ (order), f__ (family) and g__ (genus). Reads assigned to higher taxonomic ranks, such as ‘f__Nitrososphaeraceae’, exclude descendants and consist only of those unassigned to a specific lower rank. pNxrA gene fragments assigned to the ‘Nitrospira_clade_1’ clade in the pNxrA tree (b) probably come from genomes of species in the GTDB genus Nitrospira_C (c), as the abundances of the pNxrA group and Nitrospira_C (based on single-copy marker genes) follow the same trends.

Analysis of the disturbed soil habitats showed similarities in their nitrifier communities, suggesting homogenization due to similar interferences, such as increased N availability, reduced aboveground diversity or physical soil disturbance. The highest relative gene abundances of canonical ammonia oxidizers (AOA and AOB) were observed in fields and greenspaces (Fig. 5b,c). These habitats were dominated by Nitrosospira AOB and Nitrososphaeraceae AOA. AOA distributions have been linked to soil acidity, as well as fertilization management regimes[43]. Furthermore, liming and inorganic fertilizer application in agricultural soils may create conditions in which Nitrosospira can also thrive[43], as seen by the preference of Nitrosospira in fields. Similar to other studies44,45, AOA were more abundant than AOB in agricultural soils, in particular genera within the Nitrososphaeraceae, such as Nitrosocosmicus and several uncharacterized genera lacking isolates (TA-21, TH5893, TH5896, TH1177). Importantly, we were able to link these uncharacterized AOA genera to the major terrestrial amoA clades of uncharacterized groups through phylogenetic analysis of the amoA genes in their genomes (TA-21/NS-δ, TH5893/NS-γ 2.1, TH5896/NS-β 1 and TH1177/NS-ε)44,46 (Supplementary Data 3). By mapping nearly full-length 16S rRNA gene reference sequences to the MAGs, we linked two of these genera, TA-21 and TH5896, to core genera within the disturbed soil habitats (TA-21/MFD_g_198, TH5896/MFD_g_4907) (Supplementary Note 6).

Although AOA are generally abundant in agricultural soils, we identified a single undescribed AOA species, TA-21 sp02254895, that was highly abundant (sylph taxonomic abundance, median = 4.6%, maximum = 25.2%) across nearly all field samples and represented by 320 MFD MAGs (Fig. 5a, Extended Data Fig. 6 and Supplementary Note 8). Furthermore, the same species displayed lower relative abundance in agricultural field subhabitats (MFDO3) permanent grass, low yield (Mann–Whitney U-test, U = 10,628, one-sided, P = 1.23 × 10−5) and fallow fields, spring seeding (Mann–Whitney U-test, U = 41,104, one-sided, P = 0.045) (Fig. 5 and Supplementary Note 8) and was sparsely present in other non-agricultural soils except for urban parks (Mann–Whitney U-test, U = 90,748, one-sided, P = 3.01 × 10−20) and semi-natural grasslands (MFDO2), such as agricultural meadows (MFDO3) (Mann–Whitney U-test, U = 9,219, one-sided, P = 2.3 × 10−5) (Fig. 5, Extended Data Fig. 6 and Supplementary Note 8). The abundance of TA-21 sp02254895 across Denmark varied with land-use intensity and might be an effect of the level of anthropogenic disturbance. Owing to its link to disturbed Danish habitats, we propose the name ‘Candidatus Nitrososappho danica’.

Functional genome annotation of ‘Candidatus Nitrososappho danica’ revealed the potential to use ammonia (amoABC) and urea (ureABC) accompanied by ammonia (amt1/amt2) and urea transporters, CO_2 fixation through the 3-hydroxypropionate/4-hydroxybutyrate cycle[47] (acetyl-CoA/propionyl-CoA carboxylase (accC/pccC), methylmalonyl-CoA mutase (mcmA1, mcmA2), 4-hydroxybutyryl-CoA dehydratase (abfD)) and several genes involved in degradation of peptides (MEROPS IDs: cysteine C44, C26; serine S09C; threonine T01A; and metallopeptidases M38, M41, M48B) and polysaccharides (CAZyme IDs: GT2, GT55, GT81, CE1, CE14, CBM32, GH5). Specifically, GH5 and CBM32 were found in multiple copies (Supplementary Data 4), and have previously been reported to be highly expressed in the TA-21/NS-δ clade[48]. The mixotrophic potential of ‘Candidatus Nitrososappho danica’ might explain discrepancies between TA-21/NS-δ amoA abundance and nitrification and carbon-assimilation rates[49], and future studies are needed to clarify whether this widespread and very abundant organism is growing through autotrophic ammonia oxidation. As N_2O production is much lower from AOA than from AOB43,44,45, understanding the distribution and energy metabolism of archaeal species such as ‘Candidatus Nitrososappho danica’ will prove vital in managing the environmental impact of agricultural soils.

Moreover, recent studies suggest that CMX Nitrospira may be more abundant and important to soil nitrification processes than previously thought[50]. This is of particular interest, as CMX Nitrospira, like AOA, produce less N_2O than canonical AOB do[51]. We identified Nitrospira as part of the core genera of disturbed soil habitats (Supplementary Note 6), but canonical and CMX Nitrospira are difficult to differentiate based on nxrA and 16S rRNA genes[52]. By using amoA gene phylogeny, we were able to assign CMX Nitrospira to its clade A and B subtypes[52], which were found within the genera Nitrospira_D and Palsa-1315 (GTDB[34] R220), respectively. Palsa-1315, first named from MAGs found in a permafrost peatland[53], is probably a new comammox genus[54]. This is supported by a linear correlation (R^2 = 0.48–0.81) between Nitrospira clade B amoA and Palsa-1315 nxrA in various MFDO1 habitats (Extended Data Fig. 7), and by identified amoA and nxrA in the recovered MAGs (Fig. 5a).

Notably, our improved search models showed that CMX clade B, for which no cultured representative is available, was more abundant than CMX clade A in most habitats, especially natural soils (Fig. 5b,c) and sediments (Supplementary Note 9). This challenges previous perceptions that CMX clade B is not abundant in forest soils[55], wetland sediments[56], and acidic or fertilized agricultural soils50,57. Nitrospira clade A amoA was inconsistently identified in sediments and agricultural soils, and was nearly absent from the other habitats investigated (Fig. 5b and Supplementary Note 9). Our analysis highlights CMX clade B as the most abundant ammonia oxidizer in Danish natural habitats, especially the MFDO2 habitats calcareous fens, alluvial woodland and semi-natural humid meadows, while canonical AOB and AOA were more abundant in disturbed habitats. Considering this, we propose the name ‘Candidatus Nitronatura plena’ for the species represented by a circular MAG, to describe the natural, widespread distribution of this most likely complete ammonia oxidizer. Nitrospira_C was the most abundant canonical NOB genus based on single-copy marker genes (Fig. 5c) and reads placed in the nxrA Nitrospira_clade_1 group (Fig. 5b), and displayed similar habitat patterns to the AOA, albeit at a lower abundance. At the national scale, it appears that nitrifier communities clearly reflect different habitat types, with their structure influenced by human impact. Here, we show that we can link specific species across land use types at scale.

Nitrobacter, a widely studied model organism of NOB, is considered abundant in fertilized soil based on nxrA identification[58]. However, our detailed search for NXR-encoding MAGs indicates that Nitrobacter may have been strongly overclassified, as we found NxrA sequences (>600 amino acids) phylogenetically falling between Nitrobacter and Nitrococcus (Extended Data Fig. 8 and Supplementary Notes 9 and 10). While other studies have reported cytoplasmic nxrA sequences clustering near, but outside of, cultivated Nitrobacter representatives in agricultural soils[58], we were able to link these Nitrobacter-like NxrA sequences to Xanthobacteraceae family members, primarily Bradyrhizobium spp., Pseudolabrys spp. and the uncharacterized genus BOG-931 (Fig. 5 and Supplementary Note 10), which are not known to be NOB. In particular, a monophyletic clade of Nitrobacter-like NxrA from BOG-931 grouped closely to Nitrobacter and Nitrococcus NxrA, and the associated MAGs clustered together in a phylogenomic tree (Extended Data Fig. 8 and Supplementary Note 10).

We investigated gene synteny in long-read high-quality MAGs belonging to BOG-931 recovered from MFD samples[59]. Metabolic reconstruction revealed an operon resembling the nxr/nar operon of Nitrobacter winogradskyi and Nitrobacter hamburgensis[60], consisting of cytochrome c class I, nxrA, nxrX, nxrB/narH, narJ and narI, and was flanked by transposases, accompanied by formate/nitrate transporters and cytochrome c oxidase gene clusters (Extended Data Fig. 9 and Supplementary Note 10). Consequently, Nxr-encoding members of BOG-931 could be potential new NOB occurring in many habitats, but confirmation requires culturing (Supplementary Note 10).

The putative Nitrobacter-like nxrA groups were found across fields, forests, grassland formations and greenspaces (Fig. 5b). In fields, Nitrobacter and Nitrobacter-like nxrA genes were present independent of crop type, but were less abundant than canonical Nitrospira NOB, such as Nitrospira_C (Fig. 5b,c). BOG-931 was most abundant in forest soils, and Nitrobacter and Nitrobacter-like NOB have previously been associated with nitrogen amendment in forest soils[61]. Indeed, BOG-931 was detected mainly in soil habitats lacking detected CMX clade B, and was particularly abundant in forests, grassland formations and sphagnum acid bogs of the bogs, mires and fens habitat (Fig. 5b). This suggests niche differentiation between CMX and canonical or potential NOB, and underlines a general need for further investigation of uncultured but abundant nitrifiers, including Nitrobacter nxrA-like containing groups, CMX clade B and AOA TA-21. As the presence and abundance of nitrifiers may be applied to evaluate how human activities affect the nitrogen cycle[44], our results stress the importance of developing and applying reliable methods for quantitatively recording their diversity and distribution. Such methods must cover all important groups, including the newly detected nitrifiers, and provide insights into their response to environmental factors. The most critical of these factors is climate change, whereby the increased temperatures and longer growing seasons may lead to increased or prolonged nitrification activity, and more frequent droughts may lead to increased AOB activity but reduced AOA and CMX activity in soils[62].

Conclusions

Here we provide an atlas of Denmark’s microbial communities, establishing a national baseline of microbial diversity. While many habitats have distinct microbial profiles, some show unexpected similarities undetectable through flora-inferred classification (for example, fields and greenspaces). These may result from land management disturbances, which enhance species diversity while also driving homogenization as communities affected by human disturbance converge. This homogenization extends to function, with nitrifier communities reflecting habitat type and human impact. Integrating gamma diversity metrics into biodiversity assessments may help to prevent national microbiome homogenization. Future assessments could adopt a data-driven approach, as our models show that short-read data can match microbiomes to flora-inferred habitats. The next step is linking microbial species and guilds, such as nitrifiers, to other national research efforts, including historical land use, fertilization regimes and greenhouse gas emissions. Through the identification and characterization of new species, microbially informed agricultural management is within reach, offering a potential strategy to limit N_2O emissions by tailoring inputs to encourage or discourage specific microorganisms. We hope that other national atlases will follow, enabling comparisons of diversity and distribution on other continents. As we stand at the precipice of profound climatic shifts, the MFD dataset will be a vital resource for tracking microbial adaptations and resilience in both disturbed and natural ecosystems and a standard from which to monitor future restoration efforts.

Methods

Sampling

The MFD sample collection includes samples collected as part of the MFD sampling campaign, as well as samples contributed by members of the MFD consortium. The samples taken as part of the MFD sampling campaign were registered and associated with the appropriate metadata using codeREADr (https://www.codereadr.com) using a linear barcode attached to sterile 100 ml sample containers. After collection, the samples were stored between 4 °C and 10 °C for up to 48 h before being deposited at −20 °C for later processing.

As we wanted to cover as much of the Danish environmental landscape as possible, we requested expert collaborators send existing samples from interesting environments or environments that are not easily sampled. These include samples from existing publications, samples collected as part of governmental monitoring, but also samples from collaborators with no current publication. If not otherwise stated, these samples were acquired as frozen sample material. We divided each set of samples into projects, in which samples of the same type (soil, sediment, water) were subjected to the same treatment. Based on this, we constructed summary tables over the different protocols used for sampling and DNA extraction methodology (Supplementary Data 5). Most samples (across the biggest sample groups) were treated similarly, but we acknowledge that the different treatments might affect the results; consequently we applied the appropriate filtering where needed. The number of subsamples and other related sampling metadata are provided at GitHub and in Supplementary Data 6.

Soil samples

Topsoil samples from the MFD sampling campaign were collected as up to five subsamples (0–20 cm), taken within a ∼80 m^2 (5 m radius) sampling area using a weed extractor, which was cleaned with 70% ethanol between sampling sites. As DNA from microorganisms could potentially be overwhelmed by the DNA from whole specimens in the sample material, we visually inspected each subsample with the naked eye and avoided including complete specimens (grass, leaves, sticks or larger animals) in the samples. After specimen removal on site, the subsamples were combined in a sterile plastic bag, the bag closed and the collective sample homogenized by hand before up to 100 ml was transferred to the barcoded sample container (P04_2, P04_4, P04_6, P04_7, P08_1, P08_2, P08_3, P08_5, P08_6, P08_7, P08_8 and P17_1). The samples from projects P19_1, P20_1, P21_1 (ref. [64]) and P25_1 were collected as single subsamples. The subset of topsoil samples from the Land Use and Coverage Area Frame Survey (P04_8)[25] were collected by collaborators from Aarhus University as described in previously[65], in a manner very similar to the MFD sampling campaign.

A subset of the topsoil samples (P01_1)[31] from both natural and agricultural habitats were provided by collaborators from Aarhus University and Copenhagen University. These were collected as described previously[31]. In brief, 81 subsamples, spanning a 9 × 9 grid covering a 40 × 40 m plot, were mixed into a representative sample from which we acquired a subsample. New sample projects were added to extend the existing project with wet terrestrial habitats (P01_2), agricultural and semi-agricultural habitats (P02_1), sites with different agricultural practices (P02_2 (ref. [66])) and urban habitats (P03_1 (ref. [67])). These were all collected as 81 subsamples except in the case of P03_1 which was mixed from 9 subsamples.

Samples from subterranean soils were collected as single samples from different depths using a soil drill (P06_1, P06_2, P06_3). Subsurface soil (P06_1) was collected with PVC liners by percussion hammering using a Geoprobe (NIRAS) drill rig. Soil samples were then collected around the oxic-anoxic interface with 5 ml cut-off syringes through openings cut into the core liners. We acquired the samples from P06_3 as DNA, which had previously been extracted using the DNeasy PowerLyzer PowerSoil Kit (QIAGEN) according to the manufacturer’s protocol. In the case of agricultural soils from croplands, six out of every seventh sample was provided by SEGES. As part of the collection, the individual samples were frozen, crushed to particles below 1 cm in size and dried at 37 °C (P04_3, P04_5), the effect of which was investigated (Supplementary Note 3).

Sediment samples

Surface sediment samples (0–10 cm) from the MFD sampling campaign were collected as up to five sediment subsamples from across the sampling area using a gravity corer, which was cleaned with 70% ethanol between sampling sites. The subsamples were combined in a sterile plastic bag, the bag was closed and the collective sample was homogenized by hand after careful removal of larger debris and any collected water. Up to 100 ml of the homogenized sample was transferred to the barcoded sample container. For the sediment from standing water sources (P05_1, P05_2, P08_5, P09_1, P09_2, P11_1 and P11_3) the top 10 cm was collected, while only the top 5 cm was collected from streams (P10_1, P10_2 and P10_3). Pond sediment (P09_2) was collected from the deepest point of each pond. Stream samples were collected as three subsamples across a 20 m transect of the stream, two at 25% distance from each brim and one in the middle of the stream.

Lake sediments provided by University of Southern Denmark were from either lakes selected for investigation of biotic phosphorus dynamics (P09_3) or a lake restauration initiative (P09_4). For P09_3, the cores were taken from the deepest part of the lake. For P09_4, the cores were taken at five different sampling stations. In both cases, a gravity corer was used for the sampling[68]. Sediment samples from coastal areas (P11_2) were collected at a single point using a HAPS bottom corer, as described previously[69]. Each sample was mixed from 10 subsamples of the sediment core (0–2 cm and 5–7 cm). We acquired the samples as DNA, which had previously been extracted with the DNeasy PowerMax Soil Kit (QIAGEN) according to the manufacturer’s protocol.

We acquired DNA extracts from Aarhus University from multiple sampling campaigns of marine surface and subterranean sediments (P12_1 (ref. [70]), P12_2 (refs. 71,72)). P12_1 holds samples from the Bornholm Basin stations BB01 (13–63 cm) and BB03 (19–73 cm) sampled with a Rumohr corer. Samples from P12_1 were previously extracted with the DNeasy PowerLyzer PowerSoil Kit (QIAGEN) according to the manufacturer’s protocol while phenol-chloroform-isoamyl-alcohol extraction was used for P12_2. We expanded this sample category with sediments from the Baltic Sea provided by WSP Denmark (P12_5). Top sediments (0–30 cm) were collected using a HAPS bottom corer while subterranean sediments (0–300 cm) were collected using a Vibrocore sampler.

Water samples

Besides the samples from fjords (P11_3) the diversity in the natural environments found in the soil and sediment categories is not reflected in this category. The water category is instead made up of samples with a link to the urban environment: drinking water from the waterworks stage (P16_1 (ref. [73]), P16_3 (ref. [74])), tap water (P16_1), potentially polluted groundwater (P16_4) and samples from wastewater treatment plants (P13_1 (ref. [75]), P13_2 (ref. [75])).

Water samples from fjords (P11_3) were collected with a 1 l Ruttner water sampler. The sampler was rinsed three times with water from the locality before the water samples were collected. At each sampling location, five 1 l water samples were randomly collected. The water samples were transferred to 5 l cleansed plastic bottles and stored in a cooler (< 8 h) until they could be filtered in the laboratory. At stations with a halocline, water samples were collected from both above and below the halocline and were treated as two separate events. The collected water was filtered through a mixed cellulose ester membrane (47 mm, 0.22 µm) by dead-end filtration using a sterile filter funnel and a vacuum pump. The amount of water filtered for each sampling site varied from 0.3 l to 1 l. Filters were stored at −20 °C before DNA was extracted using the DNeasy PowerWater Kit (QIAGEN), according to the manufacturer’s protocol. Samples of drinking water (2 l) from the drinking water treatment plants (P16_1) were filtered through a mixed cellulose ester membrane (47 mm, 0.22 µm). The amount of water filtered varied from 0.25 l to 1.8 l. Tap water (5 l) was filtered through a cellulose acetate membrane (47 mm, 0.22 µm). The amount of water filtered varied from 4 l to 5 l. In both cases, filtering was performed as dead-end filtration using a sterile filter funnel and a vacuum pump and the filters were stored at −20 °C before DNA was extracted from the filters using the DNeasy PowerWater Kit (QIAGEN), according to the manufacturer’s protocol. The samples in P16_4 of probable polluted ground water (1 l) were filtered through a cellulose nitrate membrane (47 mm, 0.22 µm) by dead-end filtration using a sterile filter funnel and a vacuum pump. The amount of water filtered varied from 0.5 l to 1 l. Filters were stored at −20 °C before DNA was extracted from the filters using the DNeasy PowerLyzer Kit (QIAGEN) according to the manufacturer’s protocol.

The Technical University of Denmark provided DNA extracted from concentrates. Samples were taken from raw water (abstracted groundwater), filtered water (after secondary sand filters), treated water (after ultraviolet treatment) and water from the distribution network (P16_3). Between 100 l and 250 l of water from the different sampling points was pumped through separate REXEED 25S filters at a constant rate. After sample elution (200 ml) from the REXEED filter, a secondary concentration was conducted using VivaSpin 15R (SATORIUS) filters and centrifugation at 3,500g. DNA was extracted from 100 µl of final concentrate using the NucliSens miniMAG platform and NucliSens Magnetic Extraction Reagents (bioMerieux) according to the manufacturer’s protocol. Samples from wastewater treatment plants were included as DNA samples extracted with the FastDNA Spin Kit for Soil (MP Biomedicals). Wastewater was sampled as both influent (P13_1), using flow proportional sampling, and activated sludge from the aeration tank (P13_2) as described previously[75].

Other samples

We included a last group of samples to encompass the samples that did not directly fit into either of the soil, sediment and water categories. This category covers samples from various surfaces in harbours (P18_1 and P18_2), sand filter material from drinking water treatment plants (P16_2 and P16_3), sludge from anaerobic digesters (P13_3), and scrapings from the walls of a limestone mine and a salt vat (P25_1). The harbour samples (P18_1 and P18_2) were collected as individual scraped-off biofilms and biocrusts from a range of different surfaces (such as fenders and piers). Each sampling location is associated with three individual samples. Sand filter material in P16_2 was collected as described previously[76]. The pooled medium samples were made by homogenizing and combining 20 g subsamples from 20 cm depth intervals. For P16_3 the sand filter material (around 15–40 ml) was collected from 1–2 locations at the top of 12 groundwater-fed rapid sand filters of 11 Danish waterworks using a 1% hypochlorite-wiped stainless-steel grab sampler. Samples from anaerobic digesters (P13_3) were collected from 20 digesters across Denmark, with DNA extracted using the standard DNeasy PowerSoil Pro Kit (QIAGEN) according to published protocols[20].

Subsampling and DNA extraction

Unless otherwise stated in the precious sections, DNA extraction was performed as previously described[77]. The sample containers were thawed at 4 °C and dried soil samples were rehydrated using phosphate-buffered saline before subsampling. The sample material was divided into a total of three Matrix 1.2 ml 2D barcoded tubes (Thermo Fisher Scientific). Of the three 2D Matrix tubes only one was prefilled with the lysing matrix E (MP Biomedical), to which 100 µl of sample material was added. For the two other tubes, 800 µl of sample material was added and deposited at −20 °C in the MFD biobank. The tubes destined for downstream DNA extraction were added to a 96-well SBS rack containing 4 empty positions, 4 reaction blanks and 1 extraction positive control (https://github.com/SebastianDall/HT-Downscaled-Illumina-Metagenomes-Protocol). Linkage of the linear barcodes of the original sample container, the 2D Matrix tubes and location in the final SBS racks was ensured with the use of a Mirage Rack Reader (Ziath) and the software DataPaq (Ziath) forwarding the entries to an SQL server (MongoDB). Pseudolinks were generated for samples acquired as DNA extracts.

DNA extraction was performed using slightly modified protocol of the DNeasy 96 PowerSoil Pro QIAcube HT Kit (QIAGEN). In total, 500 µl CD1 was added to each 2D barcoded Matrix tube; the samples then underwent three 1,600 rpm bead-beating cycles performed at 2-min intervals using the FastPrep-96 (MP Biomedicals). Between the cycles, the samples were kept on ice for 2 min. After lysis, the samples were centrifuged at 3,486g for 10 min using an Eppendorf 5810 benchtop centrifuge (Eppendorf). Then, 300 µl supernatant was transferred by hand to a new S-block containing 300 µl CD2 and 100 µl nuclease-free water (NFW) to meet the requirement of 700 µl for the remaining part of the protocol. The samples were mixed by pipetting and centrifuged at 3,486g for 10 min; the sample transfer step was then performed using the QIAcube HT. All of the subsequent steps were performed according to the manufacturer’s protocol. DNA was quantified using the Qubit 1× HS assay (Invitrogen). Extraction metadata, including a denotation of methodology, can be found in Supplementary Data 6.

MFD ontology

For the MFD data, habitat classification was performed on site by experts in accordance with the relevant field guides when available for the habitats (that is, the natural habitats from macrobial ontologies). Habitat classification was therefore performed by checking the presence of plant indicator species, topographic and abiotic conditions. The MFDO was developed as a link between the classical plant-derived habitat ontologies and the Earth Microbiome Project Ontology (EMPO). The broadest MFDO classification level (Sample type), corresponds to the most specific EMPO level (EMPO level 4)[3], while the detailed levels for natural samples correspond broadly to the Natura 2000 habitat ontology[8]. Finally, missing categories, such as urban, were adapted from the EUNIS ontology[9] to provide a detailed description of non-natural habitats. The MFDO was designed, for the moment, to fit the Danish environment and it was refined with a panel of national experts. The full MFDO and its association to other habitat ontologies (that is, EMPO, Natura 2000 and EUNIS) can be found at GitHub (https://github.com/cmc-aau/mfd_metadata).

Metadata curation

The metadata collected with codeREADr were screened for completeness in the following fields (hereafter, minimal metadata): fieldsample_barcode (the unique sample identifier), project_id (unique identifier of the subproject), longitude and latitude (ISO 6709), sitename (common name of the sampling site), coords_reliable (indicating if the coordinates are reliable, not reliable or masked), sampling_date (sampling date ISO 8601) and five levels of the MFD habitat ontology (mfd_sampletype, mfd_areatype, mfd_hab1, mfd_hab2, mfd_hab3). If a sample presented an incorrect entry, (for example, wrong format, not meaningful for that column or unreadable), the error was corrected using R[78] v.4.2.3, and if a correction was not possible, or the entry absent, the responsible person for the subproject was contacted. The process was iterated until improvements were not possible anymore. In brief, common corrections included case changing, date formatting (using lubridate[79], v.1.9.2) and coordinate projection (project function from terra[80], v.1.7.55). The reference grid mapping and masking of the coordinates were performed using the terra[80] v.1.7.55 package. The European Environment Agency 1-km (ref. [81]) and 10-km (ref. [82]) reference grids of Denmark were projected from EPSG:3035 to EPSG:4326 (function project), whilst the coordinates from MFD samples were encoded into a spatial vector (function vect) and mapped onto the grids (function intersect) to identify their cells of origin. The cells associated with each sample (when the coordinates were present), were reported in the fields cell.1 km and cell.10 km of the metadata. The centroids of the cells were computed (function centroids) and, in the case of samples from subprojects P04_3 and P04_5, the centroids were provided as latitude and longitude, while the coords_reliable field for those samples was set to ‘Masked’. Coordinates from subproject P06_3 were provided already masked as generic locations in the commune of sampling. Concordance between manual annotation of the habitat and government-registered LU (land use) designation was inferred comparing the MFDO for each sample with the Basemap04 (ref. [10]) aggregated LU map. A broad correspondence of terms between MFDO and LU terms was established and, to account for GPS and labelling inaccuracies, any match in a range of 20 m was considered in concordance. Samples that were in disagreement were screened manually on Google Maps and, if the disagreement was confirmed, the coords_reliable field was set to ‘No’. For Fig. 1, the base map was from EuroGeographics. This dataset includes Intellectual Property from European National Mapping and Cadastral Authorities and is licensed on behalf of these by EuroGeographics. The original dataset is available for free online (https://www.mapsforeurope.org). Terms of the licence are available at https://www.mapsforeurope.org/licence. All attribution statements can be found online (www.mapsforeurope.org/attributions). For Extended Data Fig. 1, the base of the map is from Eurostat (Geodata, GISCO, Eurostat; https://ec.europa.eu/eurostat/web/gisco/geodata).

Short-read metagenomic library preparation, sequencing and processing

Metagenomic libraries were prepared with a 1:10 reagent volume reduction of the standard Illumina DNA prep protocol (Illumina) as described previously[77]. Using the I.DOT One (DISPENDIX), 3 µl of up to 20 ng template DNA was prepared before addition of 2 µl BLT/TB1 and subsequent incubation in a thermocycler at 55 °C for 10 min. Tagmentation was stopped by addition 1 µl TSB using the I.DOT One, and incubation in the thermocycler at 37 °C for 15 min. The tagmented DNA was washed twice with 10 µl TWB. The I.DOT One was used to add the PCR master mix, prepared by mixing 2 µl EPM and 2 µl NFW per reaction. The epMotion 96 (Eppendorf) was used to add 1 µl IDT Illumina UD index (Illumina) to each reaction well. The input of genomic DNA was used to determine the applied cycles of the BLT-PCR program: 7 (4.9–20 ng), 8 (2.5–4.9 ng), 10 (0.9–2.5 ng) or 14 (< 0.9 ng). Size-selection was performed on the libraries by addition of 17 µl NFW, before 18 µl of the reaction volume was transferred to a new PCR-plate together with a mixture of 16:18 µl SPB:NFW. After incubation, 50 µl of the supernatant was transferred to a new PCR-plate with 6 µl undiluted SPB. After incubation, the beads were washed twice with 45 µl 80% ethanol and eluted in 20 µl NFW. SPB are 1:1 interchangeable with CleanNGS SPRI beads (CleanNA).

The individual libraries were quantified using a 1:10 diluted upper standard. The pooled libraries were concentrated using 2× volume of SPRI ProNex Chemistry (Promega) beads. Final sequencing libraries were produced by an equimolar combination of the pooled libraries. Quality control was performed using the Qubit 1× HS assay (Invitrogen) and DS1000 or DS1000 HS ScreenTape (Agilent Technologies). Library metadata are provided in Supplementary Data 6.

Metagenomic libraries were sequenced on the Illumina NovaSeq 6000 platform to a median depth of 5 Gb. If a library yielded insufficient data, the library was either repooled or reprepared for a second round of sequencing. The Illumina data were demultiplexed using bcl2fastq2 v2.20.0 (Illumina). The raw reads were trimmed for barcodes, quality filtered and deduplicated with fastp[83] v.0.23.2 with the following options: --detect_adapter_for_pe --correction --cut_right --cut_right_window_size 4 --cut_right_mean_quality 20 --average_qual 30 --length_required 100 --dedup --dup_calc_accuracy 6. Commands were parallelized using GNU-parallel[84] v.20220722 and outputs compressed using pigz[85] v.2.4. Sequencing metadata are provided in Supplementary Data 6.

Nearly full-length bacterial 16S rRNA gene amplicon library preparation, sequencing and processing

A representative set of 426 samples were selected for 16S rRNA amplicon sequencing. These samples comprise 130 samples from the BIOWIDE project (P01_1)[31], as well as 295 samples manually selected to reflect the sample diversity in the full dataset while attempting to maintain the geographical coverage. From samples from subterranean sediments (P12_1), the input DNA was pooled based on the sediment core the samples were derived from. From 14 of the samples, we failed to generate data of sufficient quality leading to a dataset of 412 samples. Here PCR was used to amplify the region V1–V8 of the 16S gene using UMI-tagged target primers enabling downstream chimera filtering and error-correction similar to the method described previously[11]. All of the samples were tagged by the UMI-tailed target primers lu_16S_8F and lu_16S_1391R in a PCR reaction (Supplementary Data 7). The reaction contained 10–20 ng DNA input, 1× SuperFi buffer, 0.2 mM dNTPs, 500 nM of each primer and 2 U of Platinum SuperFi DNA Polymerase (Invitrogen) in a total volume of 50 µl. The PCR program consisted of initial denaturation at 95 °C for 2 min followed by 2 cycles of denaturation (95 °C for 30 s), annealing (55 °C for 1 min) and extension (72 °C for 5 min). The PCR products were then purified with CleanNGS SPRI beads (CleanNA) at a ratio of 0.7× beads per sample. After 5 min of incubation, the beads were washed twice in 80% ethanol and eluted in 18 µl NFW for 5 min. The tagged molecules were then amplified in a second 25 cycle PCR reaction using barcoded primers targeting the UMI-adapter sequence. The PCR reaction contained 15 µl of the purified eluate, 1× SuperFi buffer, 0.2 mM dNTPs, 500 nM of forward and reverse primer and 2 U of Platinum SuperFi DNA Polymerase (Invitrogen) in a total volume of 50 µl. The PCR-program consisted of initial denaturation at 95 °C for 2 min followed by 25 cycles of denaturation (95 °C for 15 s), annealing (60 °C for 30 s) and extension (72 °C for 3 min), followed by a final extension at 72 °C for 5 min. The PCR products were purified with CleanNGS SPRI beads (CleanNA) as described above and eluted in 20 µl NFW. Poorly performing samples underwent a third PCR reaction with 5–10 cycles using up to 50 ng amplicon DNA as input and otherwise identical to the previous PCR reaction.

The barcoded amplicons were multiplexed in pools of 5–6 samples containing a total of 300 ng. The pools were used as input for library preparation for DNA sequencing using the ‘Amplicons by Ligation (SQK-LSK110)’ protocol (version: ACDE_9110_v110_revG_10Nov2020) and loaded onto a MinION R.9.4.1 flow cell (FLO-MIN106D). The flow cells were sequenced for up to 72 h on a GridION platform (Oxford Nanopore Technologies) using the MinKNOW software v.21.05.8 and basecalled using the super-accurate model (r941_min_sup_g507) with Guppy v.5.0.11. Downstream consensus sequences were generated using the longread_umi pipeline v.0.3.2 described previously[11] with slight modifications to ensure compatibility with the updated medaka model (r941_min_sup_g507) and the custom barcode sequences. The quality of the consensus sequences was evaluated based on a ZymoBIOMICS Microbial Community DNA Standard (Zymo Research, D6306) included together with the samples. With UMI sequence coverage of ≥7× corresponding to Q30+ and ≥14× to Q40+. The exact command used to generate the consensus sequences was: longread_umi nanopore_pipeline -d input.fq -v 10 -o analysis -s 140 -e 140 -m 1000 -M 2000 -f GGAATCACATCCAAGACTGGCTAG -F AGRGTTYGATYMTGGCTCAG -r AATGATACGGCGACCACCGAGATC -R GACGGGCGGTGWGTRCA -c 3 -p 2 -q r941_min_sup_g507 -t 20 -T 2 -U “3;2;6;0.3”.

Bacterial and eukaryotic rRNA gene operon library preparation, sequencing and processing

A representative set of 450 samples was selected for both bacterial and eukaryotic rRNA operon sequencing. These samples are the same as those selected for 16S rRNA UMI amplicon sequencing. However, all extracted DNA had been used up for 8 of the samples, resulting in an overlap of only 404 samples, which is why we included 46 other samples. For bacterial rRNA sequencing, PCR was used to amplify around 4,500 bp targeting the 16S and 23S gene using the primers MFD_16S_8F and MFD_23S_2490R (Supplementary Data 7). For eukaryotic rRNA, operon sequencing PCR was used to amplify around 4,500 bp targeting the 18S and 28S gene using the primers MFD_18S_3NDF and MFD_28S_21R (Supplementary Data 7). The PCR reactions contained 10–20 ng DNA input, 1× SuperFi buffer, 0.2 mM dNTPs, 500 nM of each primer and 2 U of Platinum SuperFi DNA Polymerase (Invitrogen) in a total volume of 50 µl. With the addition of 1× SuperFi GC enhancer when amplifying the eukaryotic rRNA operons. The PCR-program consisted of initial denaturation at 98 °C for 1 min followed by 25 cycles of denaturation (98 °C for 15 s), annealing (55 °C for 15 s) and extension (72 °C for 3 min), followed by a final extension at 72 °C for 5 min. The PCR products were then purified with CleanNGS SPRI beads (CleanNA) at a ratio of 0.7× beads per sample. After 5 min of incubation, the beads were washed twice in 80% ethanol and eluted in 20 µl NFW for 5 min. The amplicon DNA was barcoded in a second 8–10 cycle PCR reaction using barcoded primers targeting the introduced adapter sequence. The PCR reaction contained 20 ng of the purified amplicon DNA, 1× SuperFi buffer, 0.2 mM dNTPs, 500 nM of forward and reverse primer and 2 U of Platinum SuperFi DNA polymerase (Invitrogen) in a total volume of 50 µl. With the addition of 1× SuperFi GC enhancer when amplifying the eukaryotic rRNA operons. The PCR program consisted of initial denaturation at 98 °C for 1 min followed by 8–10 cycles of denaturation (98 °C for 15 s), annealing (60 °C for 15 s) and extension (72 °C for 3 min), followed by a final extension at 72 °C for 5 min. The PCR products were purified with CleanNGS SPRI beads (CleanNA) as described above and eluted in 18 µl NFW.

The barcoded amplicons were multiplexed in pools of 92 samples containing a total of 1–2 µg of DNA. The pools were size-selected using SPRI ProNex Chemistry (Promega) with a ratio of 1.2× beads per sample according to the manufacturer’s protocol and eluted in 125 µl. The size-selected and purified pools were shipped for PacBio CCS sequencing on the Sequel II platform (Pacific Biosciences) using the binding kit 3.2. The CCS sequences were further processed using Lima v.2.6.0 (Pacific Biosciences) to filter and demultiplex the data. This was done using the hifi-preset ASYMMETRIC and the following settings --min-score 70, --min-end-score 40, --min-ref-span 0.75, --different, --min-scoring-regions 2. Furthermore, remaining ligation products were removed by identifying partially remaining adapter sequences after filtering. Subsequently all reads were oriented while removing primer sequences and filtering reads below 3.5 kb or above 6.5 kb using cutadapt[86] v.3.4. 16S rRNA genes corresponding to the V1–V8 region were extracted from the rRNA operons using a custom script (trim_RNA_operons.sh) that carries out several steps: first, the rRNA operons were truncated to 1,450 bp using the usearch[87] v.11 command fastx_truncate -trunclen 1450. The trimmed sequences were then trimmed based on the 1391R[88] (Supplementary Data 7) primer using cutadapt[86] v.3.4. The sequences for which the primer could not be found were aligned to the global SILVA v.138.1 SSURef NR99 (ref. [19]) alignment using SINA[89] v.1.6.0, the aligned sequences were trimmed according to the position of the primer binding sites in the alignment. Truncated sequences were removed using a custom script (remove_incomplete_seqs_from_sina_aln.py) that considered sequences that start or end with three or more gaps as truncated. Finally, gaps were removed using the custom script (Remove_gaps_in_fasta.py), whereafter the primer- and alignment-trimmed sequences were combined.

Before diversity analysis, 18S rRNA genes, corresponding to the position between the 3NDf[90] and 1510R[91] primer-binding sites (Supplementary Data 7), were extracted using a custom script (trim_euk_RNA_operons_3ndf-1510R.sh). This script was identical to the script used for processing bacterial rRNA operons except that sequences were trimmed based on the 1391R[88] primer (Supplementary Data 7) with cutadapt[86] v.3.4 and the alignment trimmed based on the corresponding position in the SILVA[19] global alignment using SINA[89] v.1.6.0. The resulting reads were dereplicated using usearch[87] v.11 command usearch -fastx_uniques -sizeout and then resolved into ASVs using usearch -unoise3 -minsize 2. The phylogenetic diversity of the 18S rRNA genes was determined by clustering the ASVs into OTUs at 99% identity with usearch -cluster_smallmem -maxrejects 512 -sortedby other, followed by mapping against the PR2 (ref. [16]) database to determine the percentage identity with the closest hit in the database using usearch -usearch_global -maxrejects 0 -maxaccepts 0 -top_hit_only -id 0 -strand plus. An OTU-table was created by mapping the trimmed raw reads against the 99% OTUs using usearch -otutab -otus. Taxonomy was assigned to the OTUs using the UTAX version of the PR2 database[16] v.5.0.0, the SINTAX classifier through usearch63,87 v.11. For 18S diversity analyses, the taxonomy was inferred with the DADA2 (ref. [92]) v.1.26.0 function assignTaxonomy. OTUs not classified as Eukaryota were discarded before the downstream analyses.

Estimation of the Danish terrestrial diversity

The nearly full-length 16S rRNA UMI gene sequences were mapped against the nearly full-length OTUs clustered at 98.7% sequence similarity to yield a dataset (OTU table) with 412 habitat-representative samples and 107,826 (106,760 after filtering) species-representative OTUs using usearch[87] v.11 with downstream analysis done in R[78] v.4.4.1 using tidyverse[93] v.2.0.0. Similarly, the full-length 18S rRNA gene sequences were mapped against the nearly full-length OTUs clustered at 99% sequence similarity, yielding a dataset of 450 habitat-representative samples and 12,515 (12,469 after filtering) species-representative OTUs.

We made habitat-specific rarefaction curves across samples with more than 4,000 observations from habitats with more than 9 sample representatives (5.9 million observations), as well as a pan-habitat rarefaction curve (6.0 million observations) from the nearly full-length bacterial rRNA gene UMI dataset. For this analysis, we combined the samples from temperate heath and scrub (n = 12) and sclerophyllous scrub (n = 7). Likewise, we made habitat-specific rarefaction curves across samples with more than 6,000 observations from habitats with more than 9 sample representatives (12.0 million observations), as well as a pan-habitat rarefaction curve (12.9 million observations) from the eukaryotic nearly full-length 18S rRNA gene dataset. For this analysis we combined the samples from temperate heath and scrub (n = 13) and sclerophyllous scrub (n = 7). Rarefaction was performed using vegan[94] v.2.6-6-1 (function rarecurve) using a step size of 10,000.

Sample based coverage and Hill diversity indices were calculated after transformation to presence and absence data by using rarefaction and extrapolation with Hill numbers of order q as implemented in the iNEXT[95] v.3.0.1 package (function iNEXT). Hill richness (total number of species), Hill–Shannon (number of common species) and Hill–Simpson (number of dominant species) were estimated using order q = 0, q = 1 and q = 2, respectively. For the total dataset, the end point of extrapolation was set to twice the size of the dataset (nBac = 824, nEuk = 900); and, for habitat-specific estimates, the end point was fixed at 100 samples. For the habitat-specific data, we investigated normality (function shapiro_test) of the community coverage and log-transformed sampling effort and, based on the results, measured the linear correlation with the Pearson correlation coefficient (function cor_test) using the package rstatix[96] v.0.7.2.

Establishment of the MFG 16S rRNA gene reference database

The MFG 16S rRNA reference database was assembled from high-quality bacterial and archaeal 16S rRNA genes obtained from several sources: nearly full-length 16S rRNA gene and rRNA operon amplicons created in this study, SILVA v138.1 SSURef NR99 (ref. [19]), EMP500 (ref. [3]), AGP70 (ref. [11]), MiDAS 4 (ref. [97]) and MiDAS 5 (ref. [20]), and ref. [21]. All bacterial sequences were trimmed between the 8F[98] and 1391R[99] primer-binding sites (Supplementary Data 7), and archaeal sequences between the 20F[100] and the SSU1000ArR[101] primer-binding sites (Supplementary Data 7).

High-quality bacterial and archaeal sequences were obtained from the SILVA v138.1 SSURef NR99 (ref. [19]) ARB-database by exporting them separately in the fastawide format after terminal trimming between positions 1,044 and 41,788 (Bacteria) and positions 1,041 and 32,818 (Archaea) in the global SILVA[19] alignment, corresponding to the primer binding sites (Supplementary Data 7). A custom script (Extract_full-length_16S_rRNA_genes_from_SILVA_alignments.sh) was used to remove truncated sequences based on the presence of terminal gaps in the exported FASTA-alignments. Finally, sequences that contained N’s were removed and U’s were replaced with T’s using two custom scripts (Remove_seqs_with_Ns.py and replace_U_with_T.py).

Owing to the large number of trimmed 16S rRNA gene reads, ASVs were resolved for each dataset individually. Sequences were dereplicated using the usearch[87] v.11 command usearch -fastx_uniques -sizeout and then resolved into ASVs using usearch -unoise3 -minsize 2. ASVs from all individual datasets were combined with the 16S sequences from SILVA v138.1 SSURef NR99 (ref. [19]) and dereplicated using usearch -fastx_uniques. The ASVs were sorted based on their abundance across the MFD dataset by mapping the trimmed sequences against the ASVs with usearch -search_exact -dbmask none -strand plus -matched. The matched sequences were dereplicated using usearch -fastx_unique -sizeout, and sequences which did not originate from the MFD samples were appended. The complete database was processed using AutoTax[18] v.1.7.6 to create the MFG 16S reference database. As the MFG database was found to contain sequences representing chloroplast and mitochondrial 16S rRNA genes, as well as pseudogenes, we performed additional filtering to create the final MFG database. To inform this filtering, we undertook extensive manual evaluation of the phylogenetic tree and sequence alignments in ARB[102] v.7.0 to establish a reproducible method for removing non-16S rRNA gene sequences. These steps included removing (1) all sequences that shared less than 70% identity with their closest match in the SILVA v138.1 SSURef NR99 database[19], as these most likely represent pseudogenes and would lead to inflated phylum level diversity; (2) ASVs with de novo placeholder names and best hits in SILVA v138.1 SSURef NR99[19] against Mitochondria or Chloroplast; (3) ASVs with de novo phyla placeholder names and top hits against Rickettsiales and less than 75% identify, as these also probably represent mitochondrial sequences that are not represented in the SILVA database; (4) ASVs representing de novo phyla covered by only a single ASV; (5) ASVs with a better hit in the MIDORI2 GB257 mitochondrial database[103] than in SILVA[19] v138.1 SSURef NR99, which, at the same time, share >75% identity (> 1,000 bp alignment length) to the MIDORI2 GB257 (ref. [103]) hit. Finally, ASVs that were assigned to de novo phyla but shared >70% identity with a Patescibacteria hit were assigned to Patescibacteria. The species-level clustered (98.7% identity) version of the MFG 16S reference database was created by subsetting the MFG 16S reference database to those representing the 98.7% clustering centroids created during the AutoTax[18] processing based on their ASV numbers.

Database evaluation

To evaluate the coverage of the MFG database, we classified 16S gene fragment reads (13.1 million) extracted from representative metagenomes based on the 10 km EU reference grid of Denmark (see the ‘Spatial thinning’ section) and 16S rRNA gene V4 OTUs clustered at 99% identity (2.26 million) from the GPC project[23] using our database, as well as SILVA 138.1 SSURef NR99 (ref. [19]), GreenGenes2 (ref. [22]) and the complete 16S rRNA database from GTDB R22034 using the SINTAX classifier[63], after which, the percentage of reads classified at different taxonomic ranks was determined. The analysis was conducted using R[78] v.4.3.2 using tidyverse[93] v.2.0.0 and vegan[94] v.2.6-6.1.

Short-read 16S rRNA gene classification

Hidden Markov models (HMM) were made from Rfam[104] v.14.7 seed alignments for Archaea (RF01959), Bacteria (RF00177) and Eukarya (RF01960) using hmmbuild (HMMER[105] v.3.3.2). Metagenomic reads from rRNA gene fragments were extracted from the quality-filtered metagenomic reads using the constructed models with nhmmer[106] (HMMER v.3.3.2) with the settings --incE 1e-05 -E 1e-05 --noali. In the case of multiple hits for the same metagenomic read, the best domain hit was selected based on the bit-score. The 16S reads were filtered for hits within the region between position 8F[98] and 1391R[99] primer binding sites for Bacteria (Supplementary Data 7) and the 20F[100] and SSU1000ArR[101] primer binding sites for Archaea (Supplementary Data 7). The 16S reads were taxonomically annotated using the SINTAX classifier[63] and the MFG 16S reference database with the confidence cutoff set to 0.8. The output was aggregated to the individual taxonomic levels using R[78] v.4.4.1 and the tidyverse[93] v.2.0.0 package. Reads not classified at the given taxonomic level were assigned to ‘unclassified’.

Spatial thinning

To investigate the effect of spatial autocorrelation on community composition, we performed a distance decay analysis across the levels of the MFD ontology. The spatial autocorrelation was modelled using a logarithmic decay model of Hellinger-transformed Bray–Curtis similarity (1 − Bray–Curtis dissimilarity) as a function of spatial distance. Spatial distances were calculated from longitude and latitude with the codep[107] v.1.2-3 package (function geodesics) using the Haversine formula. A pseudocount of 0.1 m was added between samples with a spatial distance of zero. Community similarity was calculated using the metagenomic 16S rRNA gene fragments aggregated at the genus level from samples with more than 1,000 observations. This led to a dataset with 10,001 samples and 38.4 million observations, which was subjected to random subsampling without replacement to a depth of 1,002 observations using ampvis2 (ref. [108]) v.2.8.9 (functions amp_load and amp_subset_samples). After transformation, relative abundances were Hellinger-transformed using vegan[94] v.2.6-6.1 (function decostand) before calculation of Bray–Curtis dissimilarity using the package parallelDist[109] v.0.2.6 (function parDist) and summarized using hexbin[110] v.1.28.3. The modelling was restricted to MFDO1 habitats showing spatial separation of samples, excluded Bornholm, and was limited to spatial distances ≤300 km. These filtering criteria lead to the inclusion of 9,121 samples. We made individual models for comparisons within and between the same habitat categories across all five levels of the habitat ontology. We found that at 10 km and on the MFDO1 level and above, the spatial effect becomes negligible. To address the effect of spatial autocorrelation on community composition we made a spatially thinned dataset using the 10-km reference grid of Denmark[82]. For each MFDO1 habitat, we chose sample representatives by selecting the sample with the lowest mean Bray–Curtis dissimilarity to the other samples of that MFDO1 habitat in the same cell. This led to a spatially thinned subset of 2,348 samples.

Effect of drying on diversity metrics and community composition

We performed 16S rRNA gene amplicon sequencing of the V4 region on replicate samples originating from two different lots at an agricultural experimental station. The samples were processed as triplicates which were either subsampled the day of collection and immediately frozen and stored in the freezer for +6 months and then subsampled, or dried at either room temperature (25 °C), 40 °C, 60 °C or 80 °C overnight and then stored for +6 months. The dried samples were rewetted with PBS before subsampling and DNA extracted using the DNeasy 96 PowerSoil Pro QIAcube HT Kit (QIAGEN) as described above. We investigated normality (function shapiro_test) and homoscedasticity (function levene_test) of the amount of extracted DNA using the package rstatix[96] v.0.7.2. Based on the results obtained, we chose a nonparametric approach to test the significance of the differences. We used a Kruskal–Wallis rank sum test (function kruskal_test) combined with two-sided post hoc pairwise Mann–Whitney U-test comparisons (function wilcoxon_test) from rstatix[96] v.0.7.2. The Bonferroni procedure was applied to adjust for multiple testing.

DNA was diluted to 5 ng μl−1 using NFW and standard amplicon libraries were prepared as described previously[77]. In brief, the amplicon libraries were prepared as one 50 μl reaction, which was subsequently split into two 25 μl reactions. Then, 25 cycles of PCR were performed on the duplicate samples, which were subsequently pooled and cleaned using 0.8× CleanNGS SPRI beads (CleanNA) and washed twice with 80% ethanol and eluted in NFW. Another 8 cycles of library PCR were performed on up to 10 ng of amplicon template and cleaned with 0.8× CleanNGS SPRI beads (CleanNA). The final libraries were quantified and pooled equimolarly to produce the final sequencing libraries. Each 25 μl amplicon PCR reaction consisted of 4 μl sample/NFW (target: 20 ng DNA), 1 µl UV-treated NFW, 25 μl PCRBIO 2× Ultra Mix and 20 μl abV4-C tailed amplicon primer mix (1 μM, 400 nM final concentration). The subsequent 25 μl library PCR was prepared with the cleaned PCR template in 2 μl sample/NFW (target: 10 ng DNA), 0.5 µl NFW, 12.5 μl PCRBIO 2× Ultra Mix (PCR Biosystems) and 10 μl adapter indexes (4 μM). After clean-up, libraries were pooled equimolarly. A detailed protocol can be found at GitHub (https://github.com/SebastianDall/HT-downscaled-amplicon-library-protocol).

The final library was sequenced on the Illumina MiSeq platform. ASV abundance tables were generated by running AmpProc v.5.1 (https://github.com/eyashiro/AmpProc) using the following choices: standard workflow, generate both otu and zotu tables, process only single-end reads, no primer region removal, amplicon region V4 and no taxonomic assignment. The 16S reads were taxonomically annotated using the SINTAX classifier[63] and the MFG 16S reference database with the confidence cutoff set to 0.8.

We filtered the data to samples with more than 10,000 observations, which led to a dataset of 35 samples out of the original 36 and a total of 1.65 million observations. We performed random subsampling without replacement to a depth of 36,975 observations using ampvis2 (ref. [108]) v.2.8.9 (functions amp_load and amp_subset_samples).

We used ampvis2 (ref. [108]) v.2.8.9 to investigate community composition (function amp_heatmap) and to perform exploratory ordination analysis (function amp_ordinate) using Hellinger-transformed Bray–Curtis dissimilarities of ASVs with more than 0.01% relative abundance in any sample. We used ampvis2 (ref. [108]) v.2.8.9 to calculate observed ASV richness (function amp_alphadiv) and Bray–Curtis dissimilarity (function vegdist) after Hellinger-transformation (function decostand) of relative abundances using vegan[94] v2.6-6.1. We investigated normality (function shapiro_test) and homoscedasticity (function levene_test) of both observed ASV richness and Bray–Curtis similarity (1 − Bray–Curtis dissimilarity) using the package rstatix[96] v.0.7.2. On the basis of the results obtained, we chose a nonparametric approach to test the significance of the differences. We used a Kruskal–Wallis rank-sum test (function kruskal_test) combined with two-sided post hoc pairwise Mann–Whitney U-test comparisons (function wilcoxon_test) from rstatix[96] v.0.7.2. The Bonferroni procedure was applied to adjust for multiple testing. We performed a PERMANOVA to access the effect of treatment on the community composition (as measured as Hellinger-transformed Bray–Curtis dissimilarity) and partitioned the variance by contrasts using vegan[94] v.2.6-6.1 (function adonis2). In both cases 9,999 permutations were used.

Evaluate the variable treatment effect in agricultural soils from MFD

We selected samples in the metagenomic 16S rRNA gene fragment dataset from either the pool of dried samples (n = 2.617) or frozen samples (n = 385) but excluded the frozen samples from lowland soils. To ensure spatial separation, we selected the representative samples from the spatially thinned subset while considering the different type of crops. This procedure led to the selection of 30 dried and 30 frozen samples. The sampling points were visualized on a map of Denmark using the ggplot[111] extension ggspatial[112] v.1.1.9 and the base map from rnaturalearth[113] v.1.0.1. We performed a principal coordinate analysis (PCoA) of Hellinger-transformed Bray–Curtis dissimilarities. Principal coordinate decomposition performed with the ape[114] v.5.8 package (function pcoa). We evaluated dispersion of the habitats using vegan[94] v.2.6-6-1 (function betadisper). The significance of the overall dissimilarity between habitats was evaluated using ANOSIM (function anosim) using vegan[94] v.2.6-6-1 with 9,999 permutations. We performed a PERMANOVA to access the effect of treatment on the community composition using vegan[94] v.2.6-6.1 (function adonis2) with 9,999 permutations.

Diversity metrics

Based on the sampling methodology and DNA extraction summaries, we limited the analysis of diversity metrics to surface sediment and topsoil from environments with a minimum of nine sample representatives. We filtered the data to samples with more than 4,000 and 6,000 reads in the nearly full-length 16S rRNA gene UMI dataset and the nearly full-length 18S rRNA gene dataset respectively. For the diversity calculations, we combined the samples from ‘temperate heath and scrub’ and ‘sclerophyllous scrub’. We performed random subsampling without replacement to a level of 4,008 and 6,235 for the 16S and 18S rRNA gene data, respectively using vegan[94] v.2.6-6-1 (function rrarefy). After this procedure, the nearly full-length 16S rRNA UMI dataset comprised 309 habitat-representative samples, 76,052 species-representative OTUs and 1.2 million observations, while the numbers for the nearly full-length 18S were 363 samples, 10,356 species representative OTUs and 2.2 million observations. Finally, the nearly full-length 18S rRNA gene data were transformed to presence and absence.

For the prokaryotic communities, the beta diversity was evaluated from the Bray–Curtis dissimilarity matrix (see the ‘Spatial thinning’ section) calculated from the metagenome-derived 16S rRNA gene fragment dataset. After subsetting to the spatially thinned set of samples and filtering to the environments used in analysis of alpha and gamma diversity, the matrix encompassed dissimilarities between 1,954 samples. For the eukaryotic communities, we calculated Jaccard dissimilarity on the nearly full-length 18S rRNA using the package parallelDist[109] v.0.2.6 (function parDist) due to sparsity of 18S rRNA gene reads in the metagenomic data.

Alpha diversity was calculated as observed OTU richness using ampvis2 (ref. [108]) v2.8.9 (function amp_alphadiv). We investigated normality (function shapiro_test) and homoscedasticity (function levene_test) of the groups using the package rstatix[96] v.0.7.2. Owing to multiple cases of non-normality, a nonparametric approach was applied. Significance of differences in the observed richness was statistically tested using a Kruskal–Wallis rank-sum test (function kruskal_test) and two-sided post hoc pairwise Mann–Whitney U-test comparisons (function wilcoxon_test) from rstatix[96] v.0.7.2. The Benjamini–Hochberg procedure was applied to adjust for multiple testing. A compact letter display was made using the function multcompLetters from package multcompView[115] v.0.1-10 using a confidence threshold of 0.05. Gamma diversity was estimated after transforming the prokaryotic data to presence and absence, by using rarefaction and extrapolation with Hill numbers of order q as implemented in the iNEXT[95] v.3.0.1 package (function iNEXT). The end point of extrapolation was fixed at 100 samples and gamma diversity reported as Hill–Shannon using order q = 1. Differences between groups were inferred from the 95% confidence intervals. Beta diversity was evaluated at both mean within-habitat and mean between-habitat level. Differences in beta diversity were visualized using the stats[78] v.4.4.1 package (function hclust, method ward.D2) on the between-habitat Bray–Curtis dissimilarities, with the confidence of the habitat-splits calculated using 100 iterations with the bootstrap[116] v.0.1 package (function bootstrap). The analysis was conducted using R[78] v.4.4.0 using tidyverse[93] v.2.0.0.

Exploratory PCoA

We explored differences in community composition between different habitats by performing PCoA on the Bray–Curtis dissimilarity matrix calculated from the metagenome-derived 16S rRNA gene fragment dataset (see the ‘Spatial thinning’ section) and from the Jaccard dissimilarity matrix of nearly full-length 18S rRNA gene dataset of presence and absences. For the prokaryotic metagenomic-derived data, we removed samples from subterranean environments, samples from drinking water treatment plants and habitats with less than 20 sample representatives, the matrix encompassed Bray–Curtis dissimilarities between 9,643 samples. For the nearly full-length eukaryotic data, we used the same data as for calculations of alpha and gamma diversity resulting in a matrix of Jaccard dissimilarities between 363 samples. Principal coordinate decomposition was performed with the ape[114] v.5.8 package (function pcoa). We evaluated dispersion of the habitats using vegan[94] v.2.6-6-1 (function betadisper). The significance of the overall dissimilarity between habitats was evaluated using ANOSIM (function anosim) using vegan[94] v.2.6-6-1 with 999 permutations. To evaluate how much of the variance could be explained by the MFDO1 habitat levels we performed a PERMANOVA (function adonis2) using vegan[94] v.2.6-6-1 with 999 permutations. We expanded the analysis using a contrasts analysis also using adonis2 and 999 permutations.

Habitat classification

The habitat classification analysis was performed using R[78] v.4.2.3 according to the workflow described in Supplementary Note 5. The microbial relative abundances at the genus level of the 16S gene fragments were summarized at higher taxonomic levels (family to phylum) by summing up the abundances of populations with the same taxonomy. Taxa observed with a relative abundance >0 in at least 25 samples were retained for subsequent analysis. The resulting five tables were screened for multicollinearity using the function vifcor (th = 0.7) from the package sdm[117] v.1.1_18 wrapped in a block-wise script for efficiency. Considering the block-wise implementation, it is not guaranteed to find the optimal solution, but the shuffling at each iteration increases the chances of approaching it. The microbial data were filtered for a minimum (n = 25) of species observations, spatially thinned (by habitat class) with a minimum distance of 5 km using the function thin_by_dist from tidysdm[118] v.0.9.5. The ontology was used to create five different target variables, one for each ontology level, but only classes with at least 50 observations and whose class names were not ending with ‘NA’ were retained for modelling. The modelling was carried out with the package tidymodels[119] v.1.1.1, which allows the creation of model workflows. The functions cited in this section belong to tidymodels[119] or its dependencies unless otherwise stated. The data were split (stratified by class) in training and test set (70/30 split) using the function initial_split and, in the training data (70% of the filtered set), the minority classes were upsampled using the SMOTE algorithm from the package themis[120] v.1.0.3 with a ratio equal to 0.5. The predictor variables (microbial relative abundances) were centred on the mean, scaled to unit variance and used to build a random forest model (from the package ranger[121] v.0.16.0). The models’ hyperparameters mtry, trees and min_n were tuned using a fivefold cross validation using the function vfold_cv with v = 5 and repeats = 5; leading to 25 fits from fit_resample and an equal number of evaluation points per hyperparameters’ combination. The best model was selected and evaluated on the test set (30% of filtered data). Moreover, the variable importance (impurity) was retrieved for the best model. Several steps in the workflow involve random choices (Supplementary Note 5), therefore, to smoothen the effects of randomness, the workflow was repeated 25 times starting from the spatial thinning. Global metrics such as F1 (micro and macro), PR-AUC and Kappa were collected after the validation using the function collect_metrics. We collected a total of 25 best models, one for each combination between predictors (phylum, class, order, family, genus) and targets (sample type, area type, MFDO1, MFDO2, MFDO3). The analysis of the false negatives was performed by collecting, for each class in each fold and iteration the number of associated misclassified samples, using the function conf_mat from the package yardstick[122] v.1.3.0. A null distribution of false negatives was computed by multiplying the previous number by the fraction of samples in each class (except for the class in exam). A two-tailed paired t-test was performed using the function t_test from the package rstatix[96] v.0.7.2.

Core analysis

Abundant and prevalent genera were identified from the spatially thinned dataset across each level of the MFD ontology using a prevalence and relative abundance filter of 50% and 0.1% respectively. Investigation of shared genera was performed using UpSetR[123] v.1.4.0 and ComplexUpset[124] v.1.3.3. We screened the core genera for candidates related to habitat disturbance and visualized their abundance and prevalence across the different MFDO1-level habitats. We mapped all 16S rRNA genes from metagenomic bins (see below) against the MFG 16S reference database (98.7% identity) using usearch[87] v.11 usearch_global, with the flags -top_hits_only and -strand both. We then identified genera associated with nitrogen cycling and investigated their abundance and prevalence across the MFDO1 habitats.

Metagenomic assembly and binning

Trimmed shallow metagenomic reads were assembled using MegaHit[125] v.1.2.9 with the following options: --k-list 27,43,71,99,127 --min-contig-len 1000. Metagenomes smaller than 1 Mb were omitted from further processing. In total, 10,042 assemblies were used for genome recovery.

To maximize the recovery of low coverage MAGs, the SingleM[40] v.1.0.0beta7 ‘pipe’ subcommand with the default GTDB R214 metapackage[126] was first run on the metagenomes to generate archive OTU tables of single-copy marker gene sequences (combined with SingleM summarize across all samples). Bin Chicken[127] v.0.9.6 was then run on those tables using the command ibis coassemble –max-coassembly-samples 1 –max-recovery-samples 10 –singlem-metapackage S3.2.1.GTDB_r214.metapackage_20231006.smpkg.zb, to match the single-copy protein sequences across samples and choose the 10 most similar samples for each sample for multisample binning. The reads for the selected samples were mapped to the assemblies using Minimap2 (ref. [128]) v.2.24 with the -ax sr option and SAMtools[129] v.1.16.1 with samtools view -Sb -F 2308 - | samtools sort options. The jgi_summarize_bam_contig_depths command of MetaBAT2 (ref. [130]) v.2.12.1 was used on the mapping files to acquire contig coverage values, which were used as input for binning through MetaBAT2 (ref. [130]) with the following options: -m 1500 -s 100000.

The recovered MAGs were quality-assessed using CheckM2 (ref. [131]) v1.0.2 to acquire MAG completeness, contamination values and were taxonomically classified using GTDB-Tk[132] v.2.4.0. Bakta[133] v.1.8.1 with Bakta database v.5.0 (type full) was used to annotate the MAGs and acquire bacterial rRNA and tRNA counts, while for archaeal MAGs, tRNAscan-SE[134] v.2.0.9 and barrnap[135] v.0.9 with the corresponding archaeal databases were used to acquire tRNA and rRNA counts. CheckM2 (ref. [131]) completeness and contamination values, together with the observed rRNA and tRNA counts, were used to classify the MAGs according to MIMAG[136] guidelines, and only medium- or high-quality MAGs were kept for further analysis. The MAGs were dereplicated using dRep[137] v.2.6.2 with the -sa 0.95 -nc 0.4 settings. MAG coverage values were calculated using CoverM[138] v.0.6.1, with the -m mean setting.

Representation of the metagenomic microbial community by these short-read-assembled MAGs in each sample was assessed using SingleM[40] v.0.18.0 (default GTDB R220 reference metapackage[139] v.4.3.0) through the appraise subcommand that compares the OTUs of 59 single-marker copy genes found in the metagenomic reads and MAGs. In brief, the SingleM pipe subcommand was first applied to individual trimmed short-read metagenomes (with a minimum of 100 bp) and individual short-read assembled MAGs to identify OTU sequences of these 59 marker genes in both types of datasets. For each type of dataset (that is, short-read data and MAGs), OTU tables were then combined using SingleM summarize and passed through the SingleM appraise subcommand (under the default cut-off for species-level estimates) to compare the OTU tables of both data types for determining the bacterial and archaeal community recovered by the short-read assembled MAGs.

For ‘Candidatus Nitrososappho danica’ and ‘Candidatus Nitronatura plena’, we conducted a targeted reassembly to retrieve the highest quality possible genome representatives for the type material. Long-read assemblies of MFD06229 (containing ‘Candidatus N. natura’) and MFD09848 (containing ‘Candidatus Nitrososappho danica’) were taxonomically classified with mmseqs2 (ref. [140]) v.14.7e284 using the uniref100 database[141] (12 October 2024 download date). For reassembling MFD06229.bin.1.58 (NCBI: GCA_974707355.1), reads mapping to contigs classified as the Nitrospirota phylum were extracted using the Samtools[129] v.1.20 “view -q 20 -m 1000” command and assembled with myloasm[142] v.0.1.0. The acquired circular contig was extracted as a separate genome bin. For MFD09848.bin.1.115 (NCBI: GCA_974504955.1), reads mapping to contigs of the Nitrososphaerota phylum were extracted with the Samtools view -q 20 -m 1000 command and assembled using Flye[143] (v.2.9.3) with the following settings: --nano-hq, --meta, --extra-params min_read_cov_cutoff = 12. A MAG was then recovered through manual binning and curation by comparing the reassembled contigs to the original MFD09848.bin.1.115 genome.

Novelty and prokaryotic fraction estimation

To assess sample novelty in each shallow metagenome, microbial profiles were estimated by SingleM[40] v.0.18.0 (default GTDB R220 reference metapackage v.4.3.0) using the SingleM pipe subcommand. Essentially, OTUs of 59 single-copy marker genes were first identified from the shallow metagenomes and compared against those found in the reference genome database to assign taxonomic classifications for the identified OTUs. The overall taxonomic profile for each metagenome was then condensed from the OTU tables of all the different markers. Sample novelty was expressed in terms of the proportion of microbial community characterized by the current reference genome database that encompassed most recent advances in genome mining. This was achieved by taking the ratio between the sum of the total coverage at the species level and the sum of total coverage at all taxonomic levels based on the condensed taxonomic profiles (hereafter, known species fraction). To compare novelty between metagenomes from MFD and NCBI public metagenomes, published SingleM[40] profiling results for NCBI datasets were used for comparison with the MFD datasets. NCBI datasets were grouped into NCBI_Water, NCBI_Soil, NCBI_Sediment and NCBI_Human based on the original metadata labelling in the original SingleM publication using the habitat mapping in Supplementary Table 3. NCBI_Human refers to the human gut metagenomes in NCBI, and they were used as a reference point for a well-studied system.

To investigate the improved species representation of the microbial community by the short-read assembled MAGs, the MAGs were added to the default GTDB R220 SingleM metapackage[139] v.4.3.0. This was done using the SingleM supplement subcommand applying dereplication at 95% similarity and CheckM2 (ref. [131]) quality filtering of 50% completeness and 10% contamination for all MAGs to be supplemented. In total, 4,507 dereplicated and novel species representatives from 19,253 MFD MAGs were added to the new metapackage. The microbial community was then reanalysed based on this new metapackage supplemented with the short-read constructed MAGs using the SingleM renew subcommand.

Genome quantification

We quantified the microorganisms across Denmark using the MFD MAG catalogue as reference and the full set of short-read metagenomes with sylph[41] v.0.6.1. First, we indexed the reference and the samples using the sketch command and the default parameters. The command profile was used to profile the sample sketches against the genome sketches. We extended the microbial taxonomy from GTDB[34] R220 to include the dereplication cluster identifier calculated by dRep[137] v.2.6.2 (see the ‘Metagenome assembly and binning’ section). We incorporated the microbial taxonomy using the utility script sylph_to_taxprof.py and then extracted the quantification matrices with the utility script merge_sylph_taxprof.py. The taxonomic abundance was aggregated from individual genome to species clusters in R[78] v.4.2.3 using the tidyverse[93] v.2.0.0 package. For analysis of genome abundances of AOA species cluster TA-21 sp02254895 cluster 98_1, one sample was randomly selected from each 10 km reference cell within each MFDO3 level. Only MFDO3 levels represented in at least 10 different 10 km reference cells were included in the analysis, to exclude habitat types only being present in a confined area of Denmark. We investigated normality (function shapiro_test) and homoscedasticity (function levene_test) of the groups using the package rstatix[96] v.0.7.2. Owing to non-normality, a nonparametric approach was applied for comparison of groups. Pairwise comparison was performed between each group (MFDO3) against all (base mean) using a one-sided Mann–Whitney U-test (function wilcoxon_test).

Gene-centric investigation of short-read metagenomes

A dereplicated set of the species representatives from the GTDB[34] release 214 and the genomes from Earth’s microbiomes[4] GEM database was created using fastANI[144] v.1.32 removing all GEMOTU genomes with >96% average nucleotide identity over >50% aligned fragments to a GTDB[34] species representative. The resulting 97,227 genomes (85,205 GTDB and 12,022 GEMOTU) were annotated using anvi’o[145] v.8 with gene calling using Prodigal[146] v.2.63.

The total protein complement of the 97,227 genomes was used as a query in a DIAMOND[147] v.2.1.8 search against preliminary protein datasets of cytoplasmic NarG/NxrA, the two distinct versions of periplasmic NxrA/NarG (exemplified by Nitrospira and Nitrotoga, respectively), and AmoA/PmoA with a score cut-off of 100. True-positive hits were selected using an alignment score ratio approach as previously described148,149, resulting in 1,068 AmoA/PmoA sequences, and 10,254 cytoplasmic NxrA/NarG sequences, 665 periplasmic NxrA/NarG sequences of the Nitrospira clade, and 581 periplasmic NxrA/NarG sequences of the Nitrotoga clade. To search the short-read metagenome data for AmoA and NxrA/NarG functional genes, we created several new GraftM[39] v.0.14 search packages: Archaeal AmoA, Bacterial AmoA, cytoplasmic NxrA/NarG and periplasmic NxrA/NarG. The AmoA/PmoA sequences were split into 823 bacterial and 245 archaeal sequences to create two specific GraftM packages. Moreover, the two periplasmic NxrA/NarG sequence databases were combined to make one GraftM package.

Multiple-sequence alignment of protein sequences was performed with MAFFT[150] v.7.490. TrimAl[151] v.1.4.1 was used to trim minimum 20% amino acid representation. IQ-TREE[152] v.2.2.0.3 was used to generate phylogenetic trees of protein sequences, using the ultrafast bootstrap approximation option and 1,000 iterations. For NxrA/NarG and bacterial AmoA trees, the ModelFinder[153] option was applied (best-fit models pNXR: LG + R6, cNXR: LG + F + R10, Bacterial amoA: LG + F + R8). The model applied for the archaeal AmoA tree was WAG + R. ARB[102] v.7.0 was used to reroot and group trees, followed by visualization in iTOL[154] v.6. Trees used for GraftM[39] v.0.14 package generation are shown in Supplementary Fig. 10. Inclusion of protein sequences of NxrA and AmoA obtained through this study was filtered based on length of the protein product, with a cut-off of 200 amino acids for AmoA and 600 amino acids for NxrA. Gene phylogeny was ascertained using GraftM[39] v.0.14 on the forward shallow metagenomic reads with search mode hmmsearch+diamond along with a conditional E-value threshold of 10−10. The samples were filtered to include at least 500,000 reads (Supplementary Note 9). The output was aggregated to the individual clades using R[78] v.4.4.1 and the tidyverse[93] v.2.0.0 package. A full overview of all clades without aggregation is shown in Supplementary Fig. 9. Read abundance was normalized by HMM-alignment length (bacterial AmoA: 744 nucleotides, archaeal AmoA: 648 nucleotides, pNXR: 3,411 nucleotides, cNXR: 4,092 nucleotides) reported in RPKM. To reduce the complexity and describe the habitats with interesting nitrifier distributions, only the major soil types from MFDO1 habitats ‘soil, agriculture, fields’, ‘soil, natural, bogs, mires and fens’, ‘soil, natural, forests’, ‘soil, natural, grassland formations’ and ‘soil, urban, greenspaces’ were included in the main analysis. Agricultural samples from reclaimed lowlands were included in the ‘soil, agriculture, fields’ category. Analysis of the remaining habitats is provided in Supplementary Note 9. Read abundances were Hellinger-transformed using vegan[94] v.2.6-6.1 (function decostand) before calculation of Bray–Curtis dissimilarity with the package parallelDist[109] v.0.2.6 (function parDist) and performing the principal coordinate decomposition with the ape[114] v.5.8 package (function pcoa). Samples were clustered within MFDO2 habitats using hierarchical clustering with the stats[78] v.4.4.1 package (function hclust, method=ward.D2). As the MFDO2 for samples within ‘soil, natural, forests’ was not descriptive, being either ‘temperate forests’ or ‘forest (non-habitattype)’, the MFDO3 was used in the clustering of forest samples. A least squared distance linear model was used to calculate the linear correlation between RPKM of Nitrospira clade B amoA (dependent variable) and Palsa-1315 nxrA (independent variable) using the stats[78] v.4.4.1 package (function lm). Spearman’s rank correlation was calculated using stats v.4.4.1 package (function cor.test).

Linkage of AOA MAGs to the major terrestrial amoA clades

The nucleotide sequences of all full-length amoA genes encoded in the 890 AOA genomes in the GlobDB[155] (release 220) were exported using anvi’o[145] (v8 development version). These 679 amoA sequences were combined with the 1,206 amoA sequences in the dataset from ref. [46] and aligned with Muscle5. The alignment was trimmed by removing the first 128 and last 41 positions using TrimAl[151] v.1.5.0 to only include the 591 aligned positions used to create the phylogeny of ref. [46]. A phylogeny was calculated based on the trimmed alignment with IQ-TREE[152] v.2.4.0 using the GTR + F + I + R10 model selected by ModelFinder[153]. The GTDB R220 taxonomy of the GlobDB genomes encoding amoA sequences was matched to the amoA clades by manually inspecting the phylogeny. For the terrestrial Nitrososphaeraceae, the genera assigned by GTDB-Tk[132] v.2.4.0 directly corresponded to the specific clades in the amoA phylogeny defined previously[46]. A translation table for the amoA and GTDB taxonomies of the Nitrososphaeraceae is provided in Supplementary Data 3. For the Nitrosopumilaceae (including the Nitrosotalea genus), a direct translation between amoA clades and GTDB genera could not be made for all clades defined in either amoA or concatenated marker gene phylogenies due to incongruencies in the topologies of the amoA and concatenated marker gene phylogenies.

Phylogenomic investigation of nitrifier genera, and Nitrobacter-like NxrA containing MAGs compared to NxrA gene phylogeny

We identified MAGs encoding Nitrobacter-like NxrA sequences of at least 600 amino acids in length within the family Xanthobacteraceae genera BOG-931, Bradyrhizobium, JAFAXD01, Pseudolabrys and VAZQ01. The MAGs encoding Nitrobacter-like NxrA sequences were selected for phylogenetic analysis, alongside species that belonged to the genus level GTDB[34] R214 groups BOG-931, Bradyrhizobium, JAFAXD01, Pseudolabrys and VAZQ01, and also met the >90% genome completeness and <5% genome contamination thresholds using CheckM2 (ref. [131]) v.1.0.2. The single recovered Nitrobacter MAG from this study was also included, as well as representatives from GTDB[34] R214 for context, for example, the species representatives present for BOG-931, JAFAXD01, VAZQ01 and manually selected isolates from Bradyrhizobium and Pseudolabrys. The specific Nxr groups were assigned based on the updated GraftM package and classification tree. The phylogenomic tree was constructed using GTDB-Tk[132] v.2.3.2 and the de_novo_wf on all bacterial MAGs. The multiple-sequence alignments of 120 single-copy proteins were subset to the genomes of interest using fxtract[156] v.2.3. This alignment was used as input for IQ-TREE[152] v.2.1.2 using the WAG + G model and -B 1000 using UFBoot. The tree was visualized in ARB[102] v.7.0 for rerooting by the Methylopilaceae isolate outgroups and analysis, and further processed in iTOL[154] v.6.

The cytoplasmic NxrA tree was built using Nitrobacter-like NxrA sequences of at least 600 amino acids in length from Xanthobacteraceae-family MAGs, along with known NxrA from Nitrococcus and Nitrobacter, and outgroup NxrA sequences from Nitrospira, Scalindua and Brocadia. Alignment, trimming and tree-generation was done using MAFFT[150] v.7.490, TrimAl[151] v.1.4.1, IQ-TREE[152] v.2.2.0.3 with ultrafast bootstrap approximation and 1,000 iterations, using substitution model LG + F + R10.

Phylogenomic trees for known nitrifier genera as well as the putative genera examined in this study were created by subsetting the GTDB-Tk de_novo_wf trees for the bacteria and archaea. Trees were investigated and refined using ARB[102] v.7.0 and iTOL v.6 as above, with final refinements in Inkscape v.1.4.2.

Metabolic investigation of nitrifier MAGs

Metabolic reconstruction of the short-read MAGs was conducted using DRAM[157] v.1.4.6 and the KEGG database[158] release 109. DRAM.py annotate was run using the default settings, followed by DRAM.py distil. Key nitrification and Calvin–Benson–Bassham-cycle genes were searched based on KO identifiers in the DRAM annotations.tsv output file and incorporated into the genome trees. Gene synteny was displayed using R[78] v.4.4.1 and the gggenes[111] v.0.5.1 package. To annotate carbohydrate active enzymes, dbCAN HMMdb[159] v.13 (released 14 August 2024) was used on the dbCAN3 webserver, and hits were filtered (E < 10−15, coverage >0.80) as described previously[48]. Peptidases were identified with DRAM based on the MEROPS peptidase database[160] (downloaded 1 July 2024) and were filtered to not include any hits to unassigned peptidases or non-peptidase homologues.

Data exploration and visualization

Data analysis was conducted in RStudio 2024.04.2 and 2023.12 using R[78] v.4.2.3 to v.4.4.0 using tidyverse[93] v.2.0.0. Data were read with either data.table[161] v.1.15.4 or readxl[162] v.1.4.3. Summary statistics are either reported as median or as mean ± s.d. where applicable. The colour scale for the MFOD1 levels of the ontology was made using wesanderson[163] v.0.3.7. To ensure that continuous gradients were colour blind friendly, colours from viridisLite[164] v.0.4.2 were used. Plots were made using ggplot from tidyverse[93] v.2.0.0, patchwork[165] v.1.2.0, ggpmisc[166] v.0.5.6, ggpubr[167] v.0.6.0 and ggtext[168] v.0.1.2 and combined using Adobe Illustrator 2024 and Inkscape v.1.4.2.

Sample permissions

All necessary permits for sample collection were obtained before fieldwork. All samples were collected within the Danish Exclusive Economic Zone (EEZ); therefore, the Nagoya Protocol on Access and Benefit-Sharing does not apply. The Danish EEZ does not include any indigenous territories; accordingly, the CARE (collective benefit, authority to control, responsibility, and ethics) principles were not applicable to this work.

Etymology

Description of ‘Candidatus Nitronatura plena’ gen. nov., sp. nov.:

‘Candidatus Nitronatura plena’ (ni.tro.na.tu’ra. From L. neut. n. nitrum, nitrate, and N.L. fem. n. natura, nature; N.L. fem. n. Nitronatura, meaning ‘nitrogen and nature’, symbolizing a lineage associated with nitrogen cycling in natural ecosystems; ple’na. L. fem. adj. plena, full or complete, referring to the capacity for complete ammonia oxidation by clade B comammox). This taxon is represented by the circular closed Nitrospiraceae MAG GCA_974707355.1, recovered from a sphagnum acid bog. The complete protologue is provided in Supplementary Data 8.

Description of ‘Candidatus Nitrososappho danica’ gen. nov., sp. nov.:

‘Candidatus Nitrososappho danica’ (ni.tro.so.sap’pho. From L. masc. adj, nitrosus, full of natron, here intended to mean ‘nitrous’; and N.L. fem. n. Sappho, latinized form of the Greek poet’s name Σαπφώ. N.L. fem. n. Nitrososappho, is used here as a metaphor for resilience and persistence, in this case, of an archaeal ammonia oxidizer found in environmentally disturbed or constrained habitats; da’ni.ca. N.L. fem. adj. danica, Danish, referring to the origin of the sample and wide distribution in Denmark). This taxon is represented by the archaeal MAG (comprising 8 contigs) GCA_974504955.1, recovered from the sediment of an urban rainwater basin; however, the species is also widely present in agriculture. The complete protologue is provided in Supplementary Data 8.

The proposed names were registered at SeqCode under the register list seqco.de/r:xueh5m88.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Data in NCBI are accessible through BioProject accession PRJNA1071982. This includes the sequencing data: bacterial UMI 16S rRNA gene reads (PromethION), bacterial rRNA operon data (PACBIO), eukaryotic rRNA operon (PACBIO) and metagenomes (Illumina), which are available at the NCBI SRA. Moreover, the high-quality MAGs, based on completeness and contamination (>90% completeness <5% contamination), are available at the NCBI GenBank under BioProject PRJNA1071982. The long-read MAG data are in the ENA under BioProject PRJEB586. Data supporting the findings of this study are available at Zenodo[169] (https://doi.org/10.5281/zenodo.17162544). These data include the medium-quality MAGs, metagenome assemblies and MFG 16S rRNA gene reference database (clustered and unclustered). We also uploaded the amplicon data (UMI 16S rRNA gene, 16S from operons and 18S from operons) to the Global Biodiversity Information Facility as separate datasets which can be accessed online (https://doi.org/10.15468/33qqsm, https://doi.org/10.15468/yr5rmw and https://doi.org/10.15468/ea7jvq, respectively). Data (metadata and data files) at GitHub are accessible at https://github.com/cmc-aau/mfd_wiki/wiki. Public data used in this study include GTDB r220 and r214 (https://data.gtdb.ecogenomic.org/releases/), SingleM GTDB R220 reference metapackage v.4.3.0 (https://zenodo.org/records/11323477)[139], SILVA v.138.1 (https://www.arb-silva.de/documentation/release-1381/), PR2 database v.5.0.0 (https://github.com/pr2database/pr2database/releases), MiDAS 4 and 5 databases (https://www.midasfieldguide.org/guide/downloads), AGP70 database (ENA: PRJEB32674; https://www.ebi.ac.uk/ena/browser/view/PRJEB32674)[21]; NCBI (SRA: PRJNA787301; https://www.ncbi.nlm.nih.gov/bioproject/PRJNA787301/) MIDORI2 GB257 database (https://www.reference-midori.info/download.php), Greengenes2 (https://ftp.microbio.me/greengenes_release/2022.10/), GEM database (https://genome.jgi.doe.gov/portal/GEMs/GEMs.home.html), GlobDB release 220 (https://globdb.org/downloads), KEGG database release 109 (https://www.kegg.jp/kegg/docs/relnote.html), dbCAN HMMdb and dbCAN3 server (https://bcb.unl.edu/dbCAN2/), MEROPS Peptidase Database (https://www.ebi.ac.uk/merops/) and UniRef100 database (https://www.uniprot.org/uniref).

Code availability

The code for analyses and figure generation is available in GitHub (https://github.com/cmc-aau/mfd_wiki/wiki).

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Acknowledgements

Funding was provided by the Poul Due Jensen Foundation, PDJF (grant MicroFlora Danica to M.A. and P.H.N.), Villum Foundation (grant 15510 and 50093 to M.A., grant 13351 to P.H.N.), the European Union (ERC grant 101078234 to M.A.), the Velux Foundation (grant 21517 to P.F.T.) and the Carlsberg Foundation (grant CF18-0949 to P.F.T.). C.M.S. was supported by a Novo Nordisk Foundation Postdoctoral Fellowship grant (NNF20OC0065005); D.R.S. and M.W. by the Cluster of Excellence grant ‘Microbiomes drive Planetary Health’ of the Austrian Science Fund FWF (10.55776/COE7); S.T.N.A. by the EMERGE National Science Foundation (NSF) Biology Integration Institute (2022070); B.J.W. by an Australian Research Council Future Fellowship (FT210100521). This study is funded by the European Union Horizon Europe research and innovation program under grant agreement no. 101086179 (AI4SoilHealth). Views and opinions expressed are those of the authors only and do not necessarily reflect those of the European Union or the Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. We thank the members of the Microflora Danica Consortium for their contributions in collecting samples and related metadata across Denmark.

Author information

Author notes

1. These authors contributed equally: C. M. Singleton, T. B. N. Jensen

Authors and Affiliations

1. Center for Microbial Communities, Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark

C. M. Singleton, T. B. N. Jensen, F. Delogu, K. S. Knudsen, E. A. Sørensen, V. R. Jørgensen, S. M. Karst, Y. Yang, M. Sereika, F. Petriglieri, S. Knutsson, S. M. Dall, R. H. Kirkegaard, J. M. Kristensen, C. K. Overgaard, Peter Roslev, Niels Iversen, Kåre L. Nielsen, Nadieh de Jonge, Dan Bruhn, Jeppe L. Nielsen, Torsten N. Kristensen, Chenjing Jiang, Marta A. Nierychlo, Giulia Dottorini, M. Wagner, M. K. D. Dueholm, P. H. Nielsen & M. Albertsen

2. Centre for Microbiome Research, School of Biomedical Sciences, Queensland University of Technology (QUT), Translational Research Institute, Brisbane, Queensland, Australia

B. J. Woodcroft & S. T. N. Aroney

3. Centre for Microbiology and Environmental Systems Science, University of Vienna, Vienna, Austria

D. R. Speth, Andrew Giguere & M. Wagner

4. Department of Agroecology, Aarhus University, Tjele, Denmark

Henning C. Thomsen, Bent T. Christensen, Lis W. de Jonge, Anne-Cathrine S. Danielsen, Cecilie Hermansen & Mogens H. Greve

5. Center for Sustainable Landscapes under Global Change, Aarhus University, Aarhus, Denmark

Anne-Cathrine S. Danielsen, Signe Normand, Urs A. Treier & Bjarke Madsen

6. Department of Ecoscience, Aarhus University, Aarhus, Denmark

Rasmus Ejrnæs & Thomas A. Davidson

7. Section for Ecoinformatics & Biodiversity, Department of Biology, Aarhus University, Aarhus, Denmark

Signe Normand, Urs A. Treier & Bjarke Madsen

8. Center for Electromicrobiology, Department of Biology, Aarhus University, Aarhus, Denmark

Andreas Schramm, Ian P. G. Marshall & Ann-Sofie Dam

9. Section for Microbiology, Department of Biology, Aarhus University, Aarhus, Denmark

Andreas Schramm, Ian P. G. Marshall, Ann-Sofie Dam, Kasper U. Kjeldsen & Kai Finster

10. Department of Biology, Aarhus University, Aarhus, Denmark

Philip F. Thomsen, Eva E. Sigsgaard, Martin J. Klepke & Marie Vestergård

11. Habitatvision, Lystrup, Denmark

Erik Aude & Lene Thomsen

12. SEGES Innovation, Aarhus, Denmark

Camilla Lemming & Rita Hørfarter

13. Department of Chemical and Biochemical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark

Marlene M. Jensen

14. Global Biodiversity Information Facility, Copenhagen, Denmark

Tobias G. Frøslev

15. Globe Institute, University of Copenhagen, Copenhagen, Denmark

Tobias G. Frøslev

16. Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark

Lone Gram, Peter B. Svendsen & Morten Dencker Schostag

17. Center for Microbial Secondary Metabolites, Technical University of Denmark, Kongens Lyngby, Denmark

Lone Gram, Peter B. Svendsen & Morten Dencker Schostag

18. WSP Danmark, Tåstrup, Denmark

Sanne Kjellerup

19. Research Centre for Built Environment, Climate and Water Technology, VIA University College, Horsens, Denmark

Torben L. Skovhus & Ditte A. Søborg

20. Department of Biology, University of Southern Denmark, Odense, Denmark

Kasper Reitzel

21. Nibe, Denmark

Jørgen F. Pedersen

22. Department of Molecular Diagnostics, Aalborg University Hospital, Aalborg, Denmark

Inge S. Pedersen & Mads Sønderkær

23. Department of Clinical Medicine, Aalborg University, Aalborg, Denmark

Inge S. Pedersen & Mads Sønderkær

24. Department of the Built Environment, Aalborg University, Aalborg, Denmark

Jes Vollertsen & Fan Liu

Consortia

The Microflora Danica Consortium

- Henning C. Thomsen

- , Bent T. Christensen

- , Lis W. de Jonge

- , Anne-Cathrine S. Danielsen

- , Cecilie Hermansen

- , Mogens H. Greve

- , Rasmus Ejrnæs

- , Thomas A. Davidson

- , Signe Normand

- , Urs A. Treier

- , Bjarke Madsen

- , Andreas Schramm

- , Ian P. G. Marshall

- , Ann-Sofie Dam

- , Kasper U. Kjeldsen

- , Kai Finster

- , Philip F. Thomsen

- , Eva E. Sigsgaard

- , Martin J. Klepke

- , Marie Vestergård

- , Erik Aude

- , Lene Thomsen

- , Camilla Lemming

- , Rita Hørfarter

- , Marlene M. Jensen

- , Tobias G. Frøslev

- , Lone Gram

- , Peter B. Svendsen

- , Morten Dencker Schostag

- , Sanne Kjellerup

- , Torben L. Skovhus

- , Ditte A. Søborg

- , Kasper Reitzel

- , Jørgen F. Pedersen

- , Andrew Giguere

- , Inge S. Pedersen

- , Mads Sønderkær

- , Jes Vollertsen

- , Fan Liu

- , Peter Roslev

- , Niels Iversen

- , Kåre L. Nielsen

- , Nadieh de Jonge

- , Dan Bruhn

- , Jeppe L. Nielsen

- , Torsten N. Kristensen

- , Chenjing Jiang

- , Marta A. Nierychlo

- & Giulia Dottorini

Contributions

S.M.K., P.H.N. and M.A. conceived the study. C.M.S., T.B.N.J., F.D., E.A.S., V.R.J., S.M.K., Y.Y., K.S.K., M.K.D.D., P.H.N. and M.A. designed the study. V.R.J. and P.H.N. designed sampling protocols, oversaw logistics and mediated contact with collaborators. T.B.N.J. and V.R.J. designed the subsampling procedures, designed metadata templates and mediated the internal logistics. F.D. oversaw the metadata curation and the MFD ontology with help from T.B.N.J. and V.R.J.; T.B.N.J., V.R.J., S.K. and S.M.D. performed the DNA extraction of the samples except in the cases of samples acquired as DNA. T.B.N.J., S.K. and S.M.D. performed the metagenomic library preparation. E.A.S. performed the bacterial 16S rRNA gene and operon library preparations and V.R.J. and C.K.O. performed the eukaryotic operon library preparations. E.A.S. processed all amplicon data. M.K.D.D. generated and evaluated the MFG 16S database and performed the phylogenetic analysis of the 18S amplicon data. E.A.S. and M.K.D.D. performed the analysis of the 16S amplicon data. J.M.K. performed the analysis of the 18S amplicon data. T.B.N.J. preprocessed the metagenomic data and M.S. performed the metagenomic assembly and binning guided by inputs generated by B.J.W., S.T.N.A., Y.Y. and C.M.S.; T.B.N.J. generated the prokaryotic profiles based on 16S gene fragments. T.B.N.J. performed the diversity, distance decay and core community analysis. F.D. performed the analysis on prokaryotic taxa as habitat descriptors. R.H.K. organized data deposition. M.S. performed the quality assessment of the generated MAGs. Y.Y. profiled the microbial communities using single copy marker genes with input from B.J.W. and S.T.N.A.; K.S.K., D.R.S., M.W. and C.M.S. performed the AmoA/PmoA and NxrA gene-centric analysis. C.M.S. and K.S.K. performed the phylogenomic and metabolic analysis of the nitrifier MAGs. F.P. conducted nomenclature analyses. C.M.S., T.B.N.J., F.D., Y.Y., K.S.K., M.K.D.D., P.H.N. and M.A. wrote the manuscript with input from all of the other authors. The Microflora Danica Consortium collected samples and related metadata across Denmark, provided input and specialist knowledge. All of the authors reviewed and agreed with the paper.

Corresponding authors

Correspondence to P. H. Nielsen or M. Albertsen.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Brett Baker and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

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Extended data figures and tables

Extended Data Fig. 1 Distribution of the samples per MFDO1 habitats in the 10 km EU reference grid of Denmark.

The map of Denmark follows the same criteria and scales as Fig. 1a, including the cutout for Bornholm island, with the base map from Eurostat (Geodata - GISCO - Eurostat). Each facet includes only samples of the relevant MFDO1 category as indicated in each title. Most MFDO1 habitats (18, representing 9,975 samples) exhibited broad geographical sample distributions across the country. An exception was MFDO1 “Rocky habitats and caves”, with samples only found on the island of Bornholm, which is geologically different from the rest of Denmark. The 10 km EU reference grid of Denmark was overlaid to the map and each cell where at least one metagenomic sample was present has been shaded. Cells including at least one sample with amplicon data have been marked with a dot in the centre.

Extended Data Fig. 2 Eukaryotic diversity of the Danish habitats.

a. Diversity overview of the selected habitats with each facet addressing a different measure of diversity. The selected 10 MFDO1 habitats are represented in the rows of the multi-facet plot. The n = X number of biologically independent samples per habitat is indicated by the # FL18S column. Dendrogram of between-group (branches) and within-group (nodes) Jaccard dissimilarity. Bootstrap values were calculated using 100 iterations. A heatmap of the relative abundances of the 20 most prevalent eukaryotic Divisions-Subdivisions are shown, taxonomy is inferred with the PR2 database v5.0.0. The three hinges of the box plots correspond to 25th, 50th and 75th percentiles of the distributions whilst the whiskers extend to a maximum of 1.5 times the distance between the 25th and 75th percentile hinges. All individual samples are shown as points (with a jitter to improve visualization). Gamma diversity (Hill-Shannon diversity) is reported with corresponding error bars spanning the 5th-95th percentiles of the distribution. b. Ordination of the 18S rRNA dataset. 18S rRNA sequences from eukaryotic rRNA operon sequencing of 363 representative samples are arranged using PCoA and coloured according to MFDO1 habitat description together with the results from the ANOSIM and PERMANOVA. The visualization depicts the first two components, accounting for 3.3% and 2% of explained variance, respectively. c. Sub-panels of the individual 10 selected MFDO1 habitats are highlighted in different panels and colours together with the results of the contrasts analysis. The samples of each habitat often occupy a distinct portion (of variable size) of the plot.

Extended Data Fig. 3 Distance decay analysis.

The analysis was conducted on genus-aggregated 16S rRNA data subjected to random subsampling without replacement (1,002) reads. The samples were filtered to the samples with reliable coordinates (except from GPS masked “Agricultural” samples) and limited to distances below 300 km, excluding samples from Bornholm. This left 9,121 samples (9,132,242 reads) for the calculation of Hellinger-transformed Bray-Curtis and geographical distances (a total of 184,844,898 comparisons). The influence of spatial distance on the microbial community in the metagenome samples was negligible, except at short distances (< 10 km). In the top panel, points represent mean Bray-Curtis similarity of 5 km bins and error bars span 1 standard deviation.

Extended Data Fig. 4 Core genera across MFDO1 habitats.

a. Upset plot of unique and uniquely shared core genera identified across the 19 MFDO1 habitats. b. The prevalence and mean abundance of selected core genera within the families Nitrosomonadaceae, Nitrososphaeraceae and Nitrospiraceae across the MFDO1 soil habitats in the geographically balanced dataset. *Historically putative nitrifier groups, but capability is unknown. c. Stacked bar plot of the count and the cumulative relative abundance of the identified core and non-core genera as well as the proportion of reads unclassified at the genus level across the investigated MFDO1 habitats.

Extended Data Fig. 5 Novelty of Danish microbes.

a. The known species fraction of the microbial community in each metagenome estimated using SingleM against GTDB R220 (unshaded, see Methods) and with the MFD MAGs included (grey-shaded). The first four boxes represent 81,709 public metagenomes downloaded from NCBI SRA, with human gut metagenomes as a reference point of a well-studied system. MD indicates the median percentage of known species fraction per habitat. The two boxplots per habitat type represent the known species fraction when classifying using the SingleM database with MFD MAGs (+ MFD MAGs) and without MFD MAGs (-MFD MAGs). b. Basic MAG recovery statistics and taxonomic novelty against GTDB R220. The rows of the table line up with the habitats described in a. c. Boxplot of the proportions of the prokaryotic community represented by MAGs reconstructed from short-read metagenomes across different habitats. MD: median, sd: standard deviation. The sample number of the analyses, N/n = X biologically independent samples is indicated for each box. The three hinges of the box plots correspond to 25th, 50th and 75th percentiles of the distributions whilst the whiskers extend to a maximum of 1.5 times the distance between the 25th and 75th percentile hinges. All individual samples are shown as points (with a jitter to improve visualization).

Extended Data Fig. 6 Genome-based quantification of Nitrososphaeraceae AOAs.

a. Heatmap displaying the relative “taxonomic abundance” quantified at the genome level by sylph[41] of AOA (families Nitrososphaeraceae and Nitrosopumilaceae) MAGs recovered in the MFD project and aggregated into species clusters. The AOA abundances shown are relative to the taxonomic abundance of all MFD MAGs. The samples are faceted by MFDO1 habitat type and clustered within each MFDO2 habitat, displayed with a colour bar on the x-axis. On the y-axis, species clusters are shown with GTDB-Tk[132] classification and a species cluster ID. In cases where members of the same species cluster have several GTDB-Tk classifications, the name describing the most MAGs in the species cluster was chosen as the species cluster name. b. Relative taxonomic abundance of the most abundant AOA species cluster TA-21 sp02254895 cluster 98_1 in agricultural samples. To balance the dataset and ensure that habitats were compared on a national, and not local scale, one sample was randomly selected from each 10 km reference cell within each MFDO3 level. Only MFDO3 levels represented in at least 10 different 10 km reference cells were included in the analysis, to exclude habitat types only present in a confined area of Denmark. Samples are coloured by the MFDO2, point jitter is added for visualization, and the mean of each group is indicated in red. Pairwise comparison was made between each group (MFDO3) against all (base mean) using a one-sided (greater) Mann-Whitney U test, p-value was adjusted for multiple comparisons using Benjamini-Hochberg approach and significance level is indicated above each group (* = p < 0.05, ** = p < 0.01, *** = p < 0.001, **** = p < 0.0001). The dotted line represents the mean of all samples (m = 4.65). c. Relative taxonomic abundance of TA-21 sp02254895 cluster 98_1 in “Greenspaces”, “Grassland formations”, and “Forests” samples. One sample was randomly selected from each 10 km reference cell within each MFDO3 level. Only MFDO3 levels represented in at least 10 different 10 km reference cells were included in the analysis. Samples are coloured by the MFDO1, point jitter is added for visualization, and the mean of each group is indicated in red. Pairwise comparison was made between each group (MFDO3) against all (base mean) using a one-sided (less) Mann-Whitney U test, p-value was adjusted for multiple comparisons using Benjamini-Hochberg approach and significance level is indicated above each group (* = p < 0.05, ** = p < 0.01, *** = p < 0.001, **** = p < 0.0001). The dotted line represents the mean of all samples (m = 0.46). The sample number of the analyses, n = X biologically independent samples, is indicated in the x-axis labels of the violin plots.

Extended Data Fig. 7 Correlation of gene read abundance of comammox clade B amoA and Palsa-1315 nxrA.

Relative gene read abundance in selected MFDO1 habitats ‘Agriculture’, ‘Bogs, mires and fens’, ‘Forests’, ‘Urban Greenspaces’, ‘Grassland formations’, and ‘Natural Sediment’. Read abundance is reported in reads per kilobase million [RPKM]. A regression line generated with a least squared distance linear model is displayed in red. The regression coefficient (Reg. coeff) and R^2 were computed using the lm function (stats[78] v4.4.1 package) and Spearman’s rank correlation (ρ) was computed using the cor.test function (stats v4.4.1 package). P-values were adjusted with the Benjamini-Hochberg approach. The sample number of the analyses, n = X biologically independent samples, is indicated in the subplots.

Extended Data Fig. 8 Aligned genome and protein tree of new Nitrobacter-like narG/nxrA encoding MAGs.

Genome tree (left, alignment length 5,035aa) of potential nitrite oxidizers containing Nitrobacter-like NxrA, belonging to the Xanthobacteraceae, primarily Bradyrhizobium spp., genus BOG-931 spp., and Pseudolabrys spp. Protein tree (right) of NxrA sequences (alignment length 1233aa) from Nitrococcus, Nitrobacter, and Nitrobacter-like NxrA recovered from short read MAGs (SR-MAGs). Wedges represent clusters of genomes within the genera that do not contain the NxrA-like sequences of interest. Protein subunits involved in nitrite oxidation or reduction are indicated by: NxrA/NarG, NxrX, NxrB/NarH, NarJ, NxrC/NarI; nitrite/nitrate transport: NarK/NasA; formate_nitrite_transporter, electron transfer: Cyt.c class I; nitrite reduction: NirK, NirS; nitric oxide reduction: NorB, NorC; nitrous oxide reduction: NosZ; carbon dioxide fixation: CbbL, CbbS, PrK; nitrogen fixation: NifD, NifK, NifH. Tree scale bars represent the number of amino acid substitutions per site. ITOL v6 was used for tree display.

Extended Data Fig. 9 Genome tree of BOG-931 short read and long read MAGs of high quality.

Genome tree (left) of potential nitrite oxidizers containing Nitrobacter-like narG/nxrA, belonging to the genus BOG-931. LR-MAGs (MFDxxxxx.bin.xx) included are of HQ based on the MIMAG[136] standard, while SR-MAGs (LIB-MJxxx-xx) with completeness >90% and contamination <5% have been included. Genomes in red encode Nitrobacter-like nxrA sequences. For LR-MAGs, gene synteny is only displayed for contigs that also encode Nitrobacter-like nxrA. In the circular genome MFD09603.bin.c.1, RuBisCO is present on the same contig, with a distance between nxr/nar and RuBisCO gene operon of 1.4 Mbp. Other nitrification genes on the same contig as Nitrobacter-like NarG/cNXR are displayed. In the box (dotted line) are nxr/nar genes from SR-MAGs, along with RuBisCO genes. Here, genes are located on several contigs. The arrangement of genes from different contigs is done manually for visual comparability. Wedges represent clusters of genomes within the genera that do not contain the NxrA-like sequences of interest, displaying the number of genomes collapsed into each clade.

Extended Data Table 1 Comparison of diversity

Supplementary information

Supplementary Information

Supplementary Notes 1–10, Supplementary Figs. 1–10 and Supplementary Tables 1–4, containing information supporting the findings of the study and describing assessment of methods.

Reporting Summary

Supplementary Data 1

All core genera identified across all levels of the MFD ontology. List of core genera identified across the five levels of the Microflora Danica ontology (sample type, area type, MFDO1, MFDO2 and MFDO3). The relative abundance of each genus is reported as median, mean and the s.d. of habitat class-level specific abundance. Besides the taxonomy from phylum to genus, the table contains n_obserservations, the number of samples within an ontology level and habitat a genera is observed in; n_abundant, the number of samples where the genera has ≥0.1% relative abundance; median, mean and s.d. of the relative abundance, the number of samples in the specific habitat; group_size, the number of samples in the habitat category; core, the number of samples the genus needs to have ≥0.1% relative abundance to be considered part of the core; prevalence, the fraction of samples where the genus has ≥0.1% relative abundance; and fidelity, how many habitats of the specific ontology level the genus is observed in.

Supplementary Data 2

The number of core genera across the MFD ontology levels. The number of core genera identified across the five levels of the MFD ontology (sample type, area type, MFDO1, MFDO2, MFDO3). The columns to the right of an ontology level refer to the number of core genera for that habitat ontology class level.

Supplementary Data 3

AOA classifications. Classification matches of AOA genome to amoA clade taxonomy. The table contains genome identifier and GTDB genome taxonomy from the phylum to the species level, presence of the amoA gene in the genome and the specific clades in the amoA phylogeny defined previously[46].

Supplementary Data 4

Functional annotation of nitrifiers. DRAM functional annotation output (annotations.tsv file) for AOA, AOB and NOB described in this study.

Supplementary Data 5

Summary of the sampling and extraction methods. Overview of sample type, sampling methods and number of samples across the different sampling projects in Microflora Danica. Where indicated by an asterisk, the sampling protocol used was very similar to the one used for the Microflora Danica sampling campaign[65].

Supplementary Data 6

All sample metadata including methodology. Full metadata file with information relating to sampling, extraction, library preparation and sequencing. A full explanation of all fields can be found at GitHub (https://github.com/cmc-aau/mfd_metadata). Where indicated by an asterisk, the sampling protocol used was very similar to the one used for the Microflora Danica sampling campaign[65].

Supplementary Data 7

Primer sequences used for UMI 16S and operon sequencing and processing. Primer sequences and primer binding positions referenced in the main and in the methods.

Supplementary Data 8

Protologue tables for ‘Candidatus Nitronatura plena’ and ‘Candidatus Nitrososappho danica’.

Peer Review File

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Singleton, C.M., Jensen, T.B.N., Delogu, F. et al. The Microflora Danica atlas of Danish environmental microbiomes. Nature 649, 971–981 (2026). https://doi.org/10.1038/s41586-025-09794-2

- Received: 02 July 2024

- Accepted: 22 October 2025

- Published: 03 December 2025

- Version of record: 03 December 2025

- Issue date: 22 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09794-2

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Long-read metagenomics reveals phage dynamics in the human gut microbiome

Jakob Wirbel,

Angela S. Hickey,

Daniel Chang,

Nora J. Enright,

Mai Dvorak,

Rachael B. Chanin,

Danica T. Schmidtke &

…

Ami S. Bhatt

Nature volume 649, pages 982–990 (2026)

Abstract

Gut bacteriophages profoundly impact microbial ecology and health1,2,3; yet, they are understudied. Using deep long-read bulk metagenomic sequencing, we tracked prophage integration dynamics in stool samples from six healthy individuals, spanning a 2-year timescale. Although most prophages remained stably integrated into their hosts, approximately 5% of phages were dynamically gained or lost from persistent bacterial hosts. Within a sample, we found that bacterial hosts with and without a given prophage coexisted simultaneously. Furthermore, phage induction, when detected, occurred predominantly at low levels (1–3× coverage compared to the host region), in line with theoretical expectations[4]. We identified multiple instances of integration of the same phage into bacteria of different taxonomic families, challenging the dogma that phages are specific to a host of a given species or strain[5]. Finally, we describe a new class of ‘IScream phages’, which co-opt bacterial IS30 transposases to mediate their mobilization, representing a previously unrecognized form of phage domestication of selfish bacterial elements. Taken together, these findings illuminate fundamental aspects of phage–bacterial dynamics in the human gut microbiome and expand our understanding of the evolutionary mechanisms that drive horizontal gene transfer and microbial genome plasticity.

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Main

Bacteriophages are the most abundant biological entity on Earth, playing crucial roles in shaping microbial communities. Most phages are either lytic or are integrated into their bacterial host’s DNA (prophages), forming a bacterial lysogen. In-depth studies of several model integrated phages (such as λ and Mu) have built the framework for our understanding of lysogens and established foundational principles in molecular biology[1]. Recent advances in metagenomic sequencing and improved analytical tools have shown that lysogens are much more diverse, prevalent and abundant in human microbiomes than previously recognized6,7,8,9,10.

Most human gut phages are prophages[11], although alternative lifestyles, such as phage plasmids or carrier states, also exist12,13,14. Typically, prophages insert their genome into the host chromosome using dedicated integrases of three different classes: tyrosine recombinases, small and large serine recombinases and DDE recombinases. By integrating into the host chromosome, phages ensure replication and vertical transfer within their hosts when conditions are not optimal for lytic replication. Hosts may derive a fitness benefit from prophage-encoded genes, such as antibiotic resistance or virulence factors2,3, but run the risk that prophages may re-enter the lytic cycle, killing their hosts. Prophages can accumulate mutations over time, leading to decay of mobilization machinery[15], whereas other prophage genes may be adopted and maintained by their bacterial host[16]. Similarly, prophages may incorrectly package host genes upon induction, thereby mediating horizontal gene transfer between related bacterial strains or species through processes such as generalized and specialized transduction[17].

Prophages can profoundly impact their bacterial host and the microbial community at large. Yet, it remains challenging to study phages and hosts within the same sample. Distinguishing between phage and bacterial regions of the chromosome has been difficult, prompting researchers to use virus-like particle (VLP) sequencing to enrich for viral sequences[18]. Using this method, studies have suggested that phages in the gut are relatively stable, persisting within their human superhost over 1 year19,20. VLP-based studies have informed our knowledge of phage abundance and stability in the human gut microbiome, but this technique has two notable limitations: focusing on VLPs (1) overlooks prophages that are not actively producing phage particles at the time of sampling; and (2) there is little direct evidence of which prokaryotic host(s) each phage infects. Bulk metagenomics, in which viral and prokaryotic genomes are sequenced simultaneously, slightly addresses these challenges, given new phage prediction tools21,22,23,24. However, most bulk studies are performed with short-read sequencing, which cannot resolve repetitive elements, posing many challenges when studying phages derived from complex communities. For example, phage genomes can have large regions of similarity (phage genomic mosaicism[25]) leading to fragmented assemblies and incomplete genomes. Similarly, if a given prophage is integrated into multiple hosts, short-read assembly would be unable to resolve its hosts, complicating further investigation.

Long-read metagenomic sequencing can address these challenges by yielding more contiguous assemblies, potentially resolving individual phage genomes and the hosts of integrated prophages. Previous studies have demonstrated advantages of long-read sequencing for the study of phages. One study used long-read VLP sequencing to resolve more complete viral genomes and identify structural variations (SVs) within phages6,26. Another study reported improved prophage and CRISPR spacer assembly with long-read sequencing, capturing individualized phage populations that were stable over a 10-day period[27]. Although this has enabled host prediction from complex communities, suggesting extremely broad host range for some phages6,7,28, CRISPR spacers are typically very short (20–50 nucleotides), which may lead to an overestimation of host range[29]. Accurate assembly of prophages into their native host through long-read assembly would provide direct evidence of infection, enabling the study of specific phage–host relationships.

Here we sought to examine the relationship between prophages and their hosts in the human gut using long-read sequencing. To do so, we generated a deep, long-read, longitudinal metagenomic dataset from stool samples collected from six individuals over a 2-year period. This enabled us to study the dynamics of integrated prophages in multiple individuals across longer timescales than have been previously reported. Although we found a small fraction of phages to be gained or lost over 2 years, most phages seem to be stably integrated into their bacterial hosts. Prophages appear to induce at predominantly low levels, with some phages and their hosts existing in mixed populations (induced phage, integrated phage and host without phage) at the same time. A small number of phages assemble into multiple and taxonomically diverse host contexts, providing strong evidence of a broad host range. Unexpectedly, we also identified a group of related prophages (IScream phages), which do not encode their own canonical integrase but instead have probably domesticated bacterial insertion sequence (IS) elements for their integration and excision machinery. In this study, we demonstrated how longitudinal long-read metagenomics can help elucidate diverse aspects of prophage biology in human microbiomes.

Longitudinal long-read metagenomics

To learn more about the biology of integrated phages and their activity over time, we collected stool samples from six healthy adults at two time points, spaced 2 years apart (T1 and T2). We then generated long-read metagenomic DNA sequencing data from these samples on the Oxford Nanopore Technologies (ONT) platform to a depth of approximately 30 billion bases (Gb) (Fig. 1a, Extended Data Fig. 1 and Supplementary Table 1). All samples were also sequenced using short-read shotgun sequencing using the Illumina platform to a depth of 6 Gb. To enable comparison between short-read and long-read sequencing without the bias of different sequencing depths, we subsampled our long-read data to the mean of the short-read data (Methods). Following quality control and host-read removal, short reads were assembled using MEGAHIT[30] and long reads using metaFlye[31]. Both assemblies were subsequently binned into metagenome-assembled genomes (MAGs) (Methods). As expected, long-read assemblies exhibited higher contiguity with a higher mean contig N50 (255.5 kb for long reads versus 7.8 kb for short reads) and fewer contigs within corresponding high-quality bins (P = 2.2 × 10−16; paired Wilcoxon test; Extended Data Fig. 1). By contrast to gene-calling challenges from previous ONT chemistries[32], the average length of predicted genes was similar across both types of assemblies (Extended Data Fig. 1). This demonstrates that ONT reads generated with the latest chemistry (R10.4; median read quality of approximately Q20) can yield accurate assemblies without the need for short-read polishing[33]. Taken together, our long-read assemblies exhibited much higher contiguity than the short-read-based assemblies, without sacrificing quality.

Fig. 1: Improved detection of integrated prophages with long-read sequencing.

a, Schematic of the analysis workflow. DNA from stool samples collected approximately 2 years apart from six healthy individuals was sequenced using both short-read (Illumina) and long-read (ONT) platforms. MEGAHIT and metaFlye were used to assemble the short and long reads, respectively. Phages were then predicted from both assemblies using VIBRANT, geNomad, VirSorter2 and Cenote-Taker3. LR, long read; SR, short read. b, Number of phage regions, number of integrated phage regions and percentage of phage regions found to be integrated, as predicted by geNomad for the assemblies from short-read (6 Gb; n = 12), subsampled long-read (6 Gb; n = 12) and deep long-read (30 Gb; n = 12) sequencing. Bars represent the mean across all samples, and points indicate values for individual samples. c, Lengths of phage regions are shown for short-read (6 Gb; n = 20,467), subsampled long-read (6 Gb; n = 4,442) and deep long-read (30 Gb; n = 9,652) assemblies. Box plots show the interquartile ranges (IQRs) as boxes, with the median represented by a black horizontal line, whiskers extending up to the most extreme points within 1.5-fold IQR and outliers shown as dots. d, Example of fragmentation with short-read sequencing. Filled boxes represent sequence regions predicted to be phage by the different tools. In the lower panel, grey boxes indicate alignments of short-read contigs against the subsampled long-read assembled contig. e, Mean fraction of CheckV quality annotations across samples for phages from the short-read and long-read assemblies. f, Blue bars show the number of phage regions found integrated in each bacterial phylum, combined across all samples, assessed on the deep (30 Gb) long-read assemblies. The number of high-quality bins assigned to each bacterial phylum, summed across all samples, is represented by orange points (see right y axis). Panel a was created using BioRender (https://biorender.com).

Source Data

Prophages in long-read assemblies

To compare phages in both short-read and long-read assemblies, we used the computational phage prediction tool geNomad[23], which combines alignment-free and gene-based models for viral prediction. Across all samples, geNomad found more phage regions in the short-read assemblies than in the downsampled long-read assemblies. However, the percentage of integrated phages was lower (approximately 5% in short-read assemblies versus approximately 60% in long-read assemblies; Fig. 1b). Finding a majority fraction of phages in the long-read assemblies to be integrated is consistent with theoretical expectations[11]. These results were recapitulated using phage annotations from three other commonly used prediction tools: VIBRANT[21], VirSorter2 (ref. [22]) and Cenote-Taker3 (ref. [24]) (Extended Data Fig. 2).

The average length of predicted phage regions was much smaller in short-read than in long-read assemblies (Fig. 1c); therefore, we considered that a single phage region predicted in the long-read assemblies might be represented by multiple smaller phage regions in the short-read assembly. We mapped the short-read contigs to the long-read assemblies and compared phage annotations across sequencing methods. Figure 1d shows the phage predictions and alignments of short-read contigs against a single long-read contig containing a 50-kb region that is predicted by geNomad, VIBRANT, VirSorter2 and Cenote-Taker3 to be an integrated prophage. This region recruits alignments from multiple phage contigs in the short-read assembly but also from contigs not predicted to be phage, indicating likely mis-annotations (Extended Data Fig. 3). Overall, the majority (82%) of long-read integrated phages were found to be fragmented in the corresponding short-read assemblies (Extended Data Fig. 3 and Methods). Consistent with this, a higher fraction of long-read phages was classified as medium or higher quality by CheckV[34], which assesses genome completeness and the presence of phage hallmark genes (Fig. 1e). These results demonstrate the superiority of long-read metagenomics for the accurate and complete assembly of integrated phages.

In the following sections, we focus on deep (30 Gb) versus downsampled (6 Gb) long-read sequencing assemblies as we detected more phage regions with the same quality at this depth (Fig. 1b,c,e).

Long-read sequencing allowed us to assemble the majority of phages as integrated elements into their bacterial hosts; therefore, we could directly determine the taxonomy of the bacterial hosts of each of the integrated phages from our assemblies. To do so, we used taxonomic assignments of the genes in the surrounding host region (Methods). We found a high concordance of host assignment between this approach and existing host prediction methods[35] (approximately 90% agreement up to the family level; Extended Data Fig. 4). To ensure accurate host identification, we focused on phages with agreement between gene-based and high-quality binning host taxonomy. The majority of integrated phages were found in the phyla Bacillota A and Bacteroidota, which reflect the number of high-quality bins present across samples (Fig. 1f). In summary, long-read metagenomic sequencing and assembly improved the detection of integrated prophages and their hosts.

Most prophage induction rates are low

With samples collected from the same individuals over a 2-year interval, we aimed to explore whether phages were acquired or lost independently of their bacterial hosts during this period. In this analysis, we also investigated whether phages could be found in different genetic loci within the same or in different hosts. To compare identical phages over time, we clustered all integrated phages with sufficient coverage (median coverage higher than ten) from the same individual, taking their surrounding host region into account (Extended Data Fig. 5 and Methods). We classified phage clusters on the basis of their assembly: overall, 35% of phage clusters were difficult to classify because of fragmented assemblies or had phages assembled at the ends of contigs, and 13% of the clustered phage genomes were assembled within their host genome in a single time point only (Fig. 2a). The remaining 52% of clustered phage genomes were found in hosts that were assembled in both time points. Of those, 90% of the clustered phage genomes were found in the same position within their bacterial hosts (host context) over the 2-year period. More rarely (approximately 5%), clustered phage genomes were found to be dynamic across the 2 years, meaning phages were lost or gained while the host was present at both time points. Examples of lost or gained phages between time points in an otherwise stable bacterial host are provided as sequencing coverage plots (Fig. 2b,c). Some of these dynamic phages could be the result of a new infection or loss events within the same strain. However, we also found dynamic phages within bacterial species, in which different strains with low shared average nucleotide identity (ANI) (less than 99%; Extended Data Fig. 5) exist at the two time points. In these cases, bacterial strain replacement is probably causing the detection of a ‘dynamic’ phage.

Fig. 2: Phage dynamics and population heterogeneity in the gut.

a, Alluvial (flow) plot showing the percentage of phage clusters and their classification (Extended Data Fig. 5) across our classification pipeline. b, Coverage plot supporting the loss of an integrated phage in T2 for individual D09. c, Coverage plot supporting the gain of an integrated phage in T2 into a host that is already present in T1 for individual D05. d, Left, coverage plot showing population heterogeneity in terms of integration for a prophage in individual D04. The fraction of hosts without phage was calculated based on the reads supporting the integrated phage or the host without phage (see schematic below). Right, this fraction is shown as a box plot for all phages with SV evidence in the same time point (n = 360) and for all phages with SV evidence in the other time point (n = 209) (identified by temporal variability). Box plots show the IQRs as boxes, with the median as a black horizontal line, whiskers extending up to the most extreme points within 1.5-fold IQR and outliers represented as dots. e, Left, coverage plot for a phage with SV evidence for circular phage genomes (see schematic below). The mean coverage of the phage and host region is indicated by dashed black lines (the grey area shows the mean ± 1 s.d.). Right, the coverage ratio between the phage and the surrounding host region is shown as a density plot, with 25th and 75th percentiles indicated by dashed black lines.

Source Data

On the basis of the observed drops in read coverage for dynamic phages, we reasoned that read-level evidence could reveal the exact boundaries of integrated phages. To quantify this systematically, we used Sniffles2 (ref. [36]) to identify SVs from reads mapped across time points (Methods). With the exact boundaries predicted by Sniffles2, we could quantify the prediction error for all included phage prediction tools (Extended Data Fig. 6). We observed deletion SVs in dynamic phages and in those assembled as stably integrated. These results indicate the presence of both hosts with an integrated phage (lysogens) and hosts without an integrated phage (naive hosts) at the same time. In total, we found that naive hosts coexisted with lysogens in approximately 7% of cases, made apparent by incomplete drops in coverage (Fig. 2d). We then quantified the proportion of reads supporting the presence of naive hosts and observed a wide distribution of values for this fraction, indicating that prophage prevalence within a population can be highly heterogeneous (Fig. 2d). In a subset of SV-overlapping phages (approximately 40%), we could detect the deletion SV (evidence for the naive host) only through reads from the other time point, meaning that the proportion of naive hosts within the population changed over time. Using the exact boundaries detected this way, we often observed a small fraction of reads supporting the existence of naive hosts in the original time point, despite falling below Sniffles2 detection thresholds (Fig. 2d). This indicates that heterogeneous prophage prevalence, in which lysogens and naive bacterial hosts can coexist, may be more common than we can detect.

In addition to the detection of naive hosts, SV calling also resulted in the detection of circular phage genomes. Circularization of phage genomes can occur during prophage induction, allowing us to measure the induction rate of some integrated phages in situ. In most cases, the presence of circular phage genomes was not concomitant with an appreciable increase in genome coverage compared to the surrounding host region (Fig. 2e). Instead, we observed the majority of induced phages to be present at 1–3× coverage compared to the surrounding host region. This is consistent with low-level phage induction as opposed to large lytic replication bursts (greater than 10×), the latter of which seems rare in the gut[4]. Some phages with evidence of circular genomes exhibited coverage ratios lower than 1× compared to the host region, which could be explained by the simultaneous presence of lysogens, naive hosts and induced circular phage genomes (Extended Data Fig. 6).

Overall, our data indicate that integrated phages are relatively stable in their hosts over a 2-year time frame, with few cases of new phage integration or loss. Additionally, both phage integration and phage induction are subject to substantial population-level heterogeneity in the gut, with low average rates of phage induction.

Evidence for broad prophage host range

In addition to detecting dynamic phages, we were able to find a small percentage of phage clusters (approximately 5%) that were assembled in multiple host contexts within and across time points (Fig. 2a and Extended Data Fig. 7). Although some well-studied phages (P1 (ref. [37]), PR5 (ref. [38]) and PRD1 (ref. [39])) have been described to infect bacteria of different genera, computational predictions for gut phages have suggested that even broader host ranges may exist7,8,9. These predictions typically rely on CRISPR spacer analysis, which may overestimate the true host range owing to the short spacer length and modularity of phage genomes.

Only a few phages within the same individual were present in multiple host contexts (median n = 11); therefore, we expanded our analysis to assess the host range of closely related phages. We used standard clustering cutoffs[34] for viral species of 95% identity and 85% coverage to cluster all phages across all individuals. Using our high-confidence host identification approach, we determined the broadest host taxonomic level that was shared within a phage cluster. As expected, the majority of phages were restricted to the species level (78%), with only under 20% being restricted to the genus level (Fig. 3c). We found evidence for a small number of phages that demonstrate broader host range; 11 phage species were restricted to the family level, and eight were restricted to the order level, most of which belong to the order Bacteroidales (Supplementary Table 2). A single example of a phage species restricted to the class level was insufficiently supported by read coverage (Extended Data Fig. 8). One phage species was integrated into hosts annotated as Vescimonas coprocola (family Oscillospiraceae), Negativibacillus sp. (family Ruminococcaceae) and Agathobaculum butyriciproducens (family Butyricicoccacea) (Fig. 3b). The integrated prophage was the only region with more than 95% nucleotide identity and a length greater than 10 kb that was shared across the three high-quality genome bins. The taxonomic annotation of each host was also supported by individual gene predictions consistent across the host contig harbouring the phage (Fig. 3c). Although we observed strong evidence of some phages integrated into different bacterial families, we wondered whether some of these cases might be the result of mis-assemblies. One pattern that is typically observed in a mis-assembly is that the majority of read alignments end abruptly at the point of mis-assembly, as opposed to alignments spanning the junction[40]. To rule out mis-assembly of phage integration, we evaluated the alignment ends normalized to coverage across the junction of the host and phage genome (Fig. 3c). For this specific example, we observed a moderate concentration of alignment ends piling up at the boundaries of the phage region. These peaks resulted from reads supporting the presence of circular phage genomes, indicating that this phage species may be capable of independent replication across disparate bacterial families (Supplementary Table 2).

Fig. 3: Long-read assemblies provide evidence for broad host range for integrated phages.

a, Percentage of all viral clusters with members that are restricted to the species, genus, family, order or class taxonomic level. The number of clusters is noted above each bar. b, Chart representing high-quality MAGs for three different species, annotated with phage predictions from geNomad. A single genomic region (greater than 10 kb) is shared between all three genomes with more than 95% nucleotide identity, annotated by a pink link. c, Coverage and synteny plot for the annotated region from b. The top panel shows read coverage, the middle panel shows the number of alignment ends divided by coverage and the bottom panel shows the genes (as arrows) in the three genomes. Genes are coloured according to their taxonomic predictions. d, Chart representing two high-quality MAGs, with a single region shared between them. e, Coverage and synteny plot for the annotated region from d. The top panel shows read coverage, the middle panel shows the number of alignment ends divided by coverage and the bottom panel shows the genes (as arrows) in the three genomes. Genes are coloured according to their taxonomic prediction. The shaded regions in c and e indicate amino acid similarity greater than 80%. Scale bars, 1 Mb (b,d), 25 kb (c,e).

Source Data

We also detected another phage species that was well supported, by high coverage and the lack of alignment end accumulation, to have bacterial hosts in organisms from different families but in the same order: Parabacteroides distasonis (family Tannerellaceae) and Bacteroides stercoris (family Bacteroidaceae) (Fig. 3d,e). We manually inspected the other order-restricted phage species (Extended Data Fig. 8). In four cases, the phage regions were annotated at the beginning of a contig, preventing the validation of phage integration by having bacterial host genomic fragments on both ends of the phage genome. However, an orthogonal assembler (myloasm[41]) confirmed the integration of the phages into the correct context in two of the four cases (Extended Data Fig. 8 and Supplementary Table 2). The final two phage species were part of a larger region shared between Tannerellaceae and Bacteroidaceae and potentially involved in recombination (Extended Data Fig. 8).

Taken together, these examples provide strong assembly-level evidence for a broad host range of some phages, mostly infecting hosts of the order Bacteroidales.

IScream phages

Prophages typically carry enzymes to enable their genomic integration into host DNA (through integrase). When examining the set of prophages with exact genome boundaries from SV evidence (n = 569), we found tyrosine and serine integrases to be most common (Fig. 4a). DDE-type integrases, similar to the integrase used by phage Mu[42], were found predominantly in phages with another integrase, suggesting that these DDE enzymes represent bacterial ISs (mobile, selfish genetic elements[43] similar to the integrase of Mu) instead of genuine phage mobilization machinery.

Fig. 4: Description of the new IScream phage group.

a, Doughnut plot showing the number of phages with different integrase enzymes. Only phages with SV evidence were included. Of phages without annotated integrases, 22 contained genes annotated as IS30 transposases at both ends (schematic shown below). We named these phages IScream phages. b, Genome organization of IScream phages (length > 10 kb) assembled here, visualized with LoVis4u. Gene functionality (annotated as coloured bars below genes) was inferred using pharokka; truncated IS30 elements were manually confirmed. Genes are connected across genomes if the predicted proteins have higher than 25% amino acid identity. Two clusters of closely related IScream phages are annotated with their ANI and the taxonomic classification of their assembled host context on the right side. c, Tree showing the relationship between bona fide bacterial IS30 transposases from the ISfinder database (in grey) and IScream phage outward-directed IS30 (purple) identified in the MGV catalogue. All IS30 transposases were clustered at 70% amino acid similarity before multiple sequence alignment and tree construction. Clusters containing an IS30 from any of the IScream phages assembled here are annotated by cyan dots. d, Circos plot for the B. hansenii ATCC 27552 genome. The outer ring indicates the location of the IS elements, the middle ring shows the predicted integrated phages and the innermost ring shows the location of the circular phage genomes detected by the presence of SV. e, Image of a 2% agarose gel showing various PCR products from the B. hansenii culture with and without DNase treatment. The primer location and expected product size are schematically annotated on the right side. The experiment was repeated three independent times, yielding similar results. Scale bars, 10 kb (b), 1 (c). Illustration in a adapted from SVG Repo (https://www.svgrepo.com/) under a CC0 1.0 Universal Public Domain licence.

Source Data

Among 101 phages lacking an identified integrase, we found 22 that contained IS30 family elements on both ends of the prophage genome. This organization is reminiscent of composite transposons, a type of mobile genetic element in which two IS elements flank a gene cassette, encoding, for example, proteins conferring antibiotic resistance[43]. We considered that these phages are mobilized similar to composite transposons through the IS30 within their genomes. Other phages, such as Mu, are known to be mobilized through a transposase[42]. Here we describe a new group of phages that probably co-opted bacterial IS30 elements for their mobilization. Because these phage genomes are ‘sandwiched’ by IS30 elements, we named them IScream phages.

To explore the IScream phages in more detail, we first focused on genome organization and observed high synteny across the full-length IS30-bound phage genomes (Fig. 4b). Genes for core phage lifestyle functions, such as structural proteins or host lysis, seem to be present in all IScream phages (Extended Data Fig. 9). Two highly similar clusters of IScream phages (ANI > 95%) are present across several individuals, integrated into different bacterial host contexts. In fact, one of the order-restricted broad host range phage clusters within the class Clostridia was identified as an IScream phage (Fig. 4b and Supplementary Table 2). Because we observed only a small number of IScream phages in our data, we screened the Metagenomic Gut Virus (MGV) catalogue[8] and found 1,780 potential IS30-bound phages, revealing that these phages are abundant and prevalent gut residents (Extended Data Fig. 9).

Focusing more on the IS30 transposase proteins potentially used for phage integration, we clustered all IS30 proteins in the IScream phages assembled here. We observed that the IS30 proteins directed outwards of the integrated phage genome to be relatively conserved (mean amino acid identity = 52%), whereas the IS30 proteins on the other side were highly variable in length (183–4,175 nucleotides) and typically truncated or fused to other protein domains, for example, domains involved in conjugation (TraX) or defense against restriction (DarA). These deprecated IS30 proteins also typically lack a classical IS30 catalytic domain (Extended Data Fig. 10), suggesting that the outward-directed IS30 functionally catalyses phage mobilization. This is again similar to composite transposons because one of the IS elements in typical composite transposons can lose its catalytic activity and decay over time[44].

To explore the potential evolutionary history of IS30 domestication by phages, we clustered the full length, outwardly directed IS30 proteins across the phages identified in our dataset, all IS-bound phages in MGV and all bacterial IS30 elements annotated in the ISfinder database[45]. We found all phage-derived IS30 proteins to form a sub-clade most closely related to the IS30 elements in Clostridium thermocellum, Treponema denticola and Halanaerobium hydrogeniformans (Fig. 4c). We additionally found IS30 proteins on phages in meso-American paleofeces[46] that clustered together with other phage IS30 proteins (Extended Data Fig. 10). This suggests that the IScream phages in our data and from a paleofeces sample resulted from a single domestication event that potentially occurred in a host related to one of the three species mentioned above.

To gather further evidence that the IS-bound phages represent bona fide phages that can form virions rather than cryptic prophages or other selfish non-phage elements, we analysed publicly available VLP-enriched sequencing data from neonatal gut samples[47] using Phanta, a virus-inclusive read-level profiling method[48]. We found higher abundance of some MGV-derived IScream phages in VLP rather than in metagenomic shotgun (metaG) sequencing (Extended Data Fig. 9), suggesting viral particle production. We additionally screened Clostridia genomes (Methods) and found two phages flanked by IS30 elements in Blautia hansenii American Type Culture Collection (ATCC) 27552 (Fig. 4d). We obtained and cultured this strain of B. hansenii and performed long-read sequencing of the overnight culture. Consistent with our previous results showing a low level of induction for integrated prophages, we observed circular phage genomes in our overnight culture, indicating spontaneous induction of the phage (Fig. 4d and Extended Data Fig. 10). Phages were isolated by means of polyethylene glycol (PEG) precipitation (Methods). Using polymerase chain reaction (PCR), we found the expected circular phage genome in the PEG precipitate (Fig. 4e). This circular version of the genome, but not the integrated version or the bacterial genome, was protected from DNase treatment, consistent with the presence of phage particles in the culture, in which the capsid protects the phage genome from DNase exposure (Fig. 4e). To visualize the potential phage particles, we used transmission electron microscopy to image lysate from the B. hansenii culture (Methods). We identified phage-like particles (Extended Data Fig. 10), which we suspect represent the IScream phage in question because our long-read sequencing data indicated that this phage was the only predicted phage region within B. hansenii showing evidence of spontaneous induction (Extended Data Fig. 10).

Together, these findings indicate that IScream phages have co-opted bacterial IS30 transposases for phage mobilization while retaining phage activity, highlighting a previously overlooked group of gut-resident phages.

Discussion

Decades of phage research have yielded fundamental insights into molecular biology, genetics and evolution. Many studies focused on a select set of culturable phages have informed the ‘central tenets’ of phage biology1,49. Although valuable, it is unclear how generalizable these core principles are. Here we generated a deep long-read longitudinal metagenomic dataset to derive a more complete understanding of prophage–host interactions within the human gut microbiome. Our findings revealed that (1) prophages are usually stable within their hosts over a 2-year time frame; (2) lysogens and naive hosts can coexist within a population at varying proportions; (3) phage induction, when observed, occurs at predominantly low levels; (4) phages can integrate into bacterial hosts of different families; and (5) some prophages might have domesticated ISs as integrases for integration and excision.

Previous studies using short-read VLP sequencing19,20 or bulk metagenomic sequencing over shorter time frames (10 days[27]) have reported generally high temporal stability and individuality of the virome. Using bulk long-read metagenomic sequencing and a longer sampling period of 2 years, we observed most prophages to be stably integrated, consistent with previous stability estimates. Only rarely do we infer possible new phage infection events. For example, we observed an identical phage present in two different strains of Alistipes putredinis at T1 and T2 from the same individual (Extended Data Fig. 7). Furthermore, for stably integrated prophages, we commonly observed population heterogeneity in terms of integration, in which lysogens and naive hosts coexist.

In a small subset of phages, we detected phage induction through read-level evidence. Within this population, we found that low-level induction is more common than large burst events, consistent with short-read-based surveys and ecological models4,50. In some cases, we found examples in which lysogens, naive hosts and induced circular phage genomes coexist (Extended Data Fig. 6). This could reflect strain-level variation that limits the ability of phages to infect the entire host population, or geographic separation of subpopulations. Mechanisms such as phase variation of surface structures have also been reported to modulate phage susceptibility, providing a regenerating susceptible subpopulation13,51. Because the spatial distribution of induction in the population is unknown, it is difficult to determine if induction is a sporadic or somehow coordinated event[52]. Taken together, these findings are consistent with a previously proposed ecological model, in which phages spread slowly through a susceptible bacterial population[13], potentially constrained by spatial separation[53]. In addition to exploring the presence of a susceptible subpopulation of hosts, low-level phage induction might serve as a successful strategy for prophages to prevent mutation accumulation and deterioration into cryptic prophages[15].

Most cultured phages are thought to have a narrow host range, as determined by plaque-based assays[5]. Although some phages can infect hosts within the same genus37,38,39, some are restricted to the species or strain level[54]. Here we provide direct evidence supporting the existence of phages with broad lysogenic host range, as we found them integrated into bacterial hosts from different families. A recent study by Bignaud et al.[55], which used metagenomic Hi-C data, supports these findings and demonstrated that broad host range is more common than previously recognized. Long reads may allow us to investigate this at scale in future studies. Lysogenic host range does not necessarily imply productive host range (the ability to produce infective particles from several hosts)[5] because we found evidence for circular phage genomes in a single case only (Fig. 3b). Many potential determinants may contribute to broad host range, including prophage-encoded diversity-generating retroelements[56], inversions[57] and polyG tracts[58], although they have not yet been associated with host range of this breadth. The ability of these phages to infect several gut residents may have implications for phage therapy that is being explored as a promising alternative to antibiotics[59]. Although many of these approaches leverage the narrow host range of cultured phages, evaluating host range through isolation and culturing may limit our ability to detect its breadth[60]. The long-read metagenomic analysis we presented here allowed us to better capture the landscape of the host range of gut resident phages.

Finally, we describe the new group of IScream phages, which probably use IS30 transposases of bacterial origin for their mobilization. A previous study had suggested that the distinction between site-specific recombination and transposases might not be well defined[61]. Kiss et al.[61] created a synthetic system in which they replaced the integrase of phage λ with an Escherichia coli IS30 and observed that the IS30 transposase was sufficient to create a functional phage. Although IS30 transposases use DDE chemistry similar to that of the transposable phage Mu[42], here we provide evidence that IS30 may act as recombination machinery for natural phages. We propose that this IS30 was domesticated from a host IS30 element from the class Clostridia. Although it is known that bacteria can domesticate the genes of phages[16], these findings provide an example for how phages might have domesticated originally selfish genetic elements for their own purpose.

Although we were able to expand our understanding of prophage dynamics in the human gut microbiome through long-read sequencing, this study has several limitations. First, our samples were limited to six individuals from a shared geographic area; therefore, our findings may not be generalizable. Second, detection of prophages and their hosts is necessarily linked to sequencing depth. Despite our deep sequencing, we have not yet exhausted the diversity of the microbiome and were unable to detect low-abundance organisms. Third, although de novo assembly allows for reference-free investigation of microbial communities, this approach is not free from errors[40] and potentially collapses population heterogeneity within a sample. Careful analyses of read-level evidence are therefore needed to support assembly-level claims and quantify the presence of mixed populations. Wherever possible, we have used direct read alignment-based approaches to orthogonally validate results derived from assemblies. Finally, phage annotation, host taxonomic classification and SV calling are subject to error. For example, although dynamic phages are enriched for SV calls (37% versus 11% for stably integrated phages), not all of them were found by Sniffles2 owing to internal filtering steps to ensure specificity.

Conclusion

Integrated prophages play fundamental roles in the human gut. They are prevalent and abundant entities that outnumber lytic phages and represent untapped potential for molecular tool development or therapeutic interventions beyond antibiotics. In this study, we revealed key aspects of prophage biology through long-read sequencing, highlighting phage integration dynamics, population heterogeneity, host range and a new group of IS-bound phages. We anticipate that future studies will further elucidate the impact of phage-mediated horizontal gene transfer in the gut[17], characterize the specificity of phage integrases for biotechnological applications and continue to explore how the evolutionary flux between phages and bacteria may lead to genomic innovation.

Methods

Study population and ethics statement

For time point 1, stool samples were collected from six adult volunteers living in the Bay Area, CA, USA. This study involving humans was approved by our institutional internal review board (Stanford IRB 42043; principal investigator: A.S.B.), and informed consent was obtained from all participants. For time point 2, the same individuals were recontacted for a second stool donation, 2 years after the initial one.

The samples for time point 1 were included in the publication of Maghini et al.[62], and short-read metagenomic sequencing reads are available at the National Center for Biotechnology Information’s Sequence Read Archive under the identifier PRJNA940499.

Sample collection and processing

Stool samples were collected without a preservative and stored at −80 °C. All DNA extractions were performed using the QIAamp PowerFecal Pro DNA Kit (QIAGEN; 51804) according to the manufacturer’s instructions, with the exception of using the EZ-Vac Vacuum Manifold (Zymo Research) instead of centrifugation. DNA concentration was measured using a Qubit 3.0 Fluorometer (Thermo Fisher Scientific) with the dsDNA High Sensitivity kit.

Metagenomic short-read sequencing

The samples from T1 had already been sequenced; therefore, we generated new libraries only for samples from T2. Metagenomic sequencing libraries were pooled, and 2 × 150 bp reads were generated using the NovaSeq 6000 platform (Illumina; 20012850) to a final depth of 6 Gb per sample.

Metagenomic long-read sequencing

All samples from both time points underwent long-read metagenomic sequencing using the ONT platform. DNA fragment distribution was assessed using a TapeStation (Agilent; G2992AA). Samples with apparent fragmentation were cleaned up using a bead-based protocol before library preparation[63].

Libraries were prepared using the Native Barcoding Kit V24 (ONT; SQK-NBD114.24) using 1,000 ng of DNA as input. In the pooling step, four samples were combined, resulting in three total libraries. The libraries were loaded onto PromethION R10.4.1 flow cells (ONT; FLO-PRO114M) and sequenced until exhaustion of the flow cells.

Short-read data processing

For short-read sequencing, all raw reads were processed with our in-house NextFlow pipeline (v.22.10.5; ref. [64]; https://github.com/bhattlab/bhattlab_workflows_nf). In short, reads were deduplicated using HTStream SuperDeduper (v.1.3.3), and low-quality bases were trimmed using TrimGalore (v.0.6.7). Reads were then mapped against the human genome (hg38) using bwa (v.0.7.17 31), and all matching reads were discarded. For comparability, T1 samples were downsampled to final library sizes randomly drawn from the distribution of T2 library sizes after preprocessing.

For each sample, metagenomic assembly was performed using MetaHIT (v.1.2.9), and genes were predicted using Bakta (v.1.8.2). Assemblies were binned into draft genomes using MetaBAT (v.2.5), CONCOCT (v.1.1.0) and MaxBin (v.2.2.7), followed by bin consolidation using DAS Tool (v.1.1.6). Bin quality was assessed using CheckM (v.1.2.2), and taxonomic classification was performed using GTDB-Tk (v.2.3.0) using the Genome Taxonomy Database (GTDB) r214.

Long-read data processing

For long-read sequencing, POD5 files were basecalled and de-multiplexed using Dorado (v.0.5.3) using the ‘super-high accuracy’ model (v.3.4.0) to create the final set of FASTQ files. Read quality and length distribution were assessed using NanoPlot (v.1.41.6) before and after the removal of human reads (read mapping against the human genome (v.38) using minimap2 (v.2.26-r1175)). Metagenomic assembly was performed using metaFlye (v.2.9.2-b1786) using the nano-hq flag to use only reads of quality Q20 or higher for initial assembly. Binning and taxonomic classification of bins were performed as described for short reads. The workflows for long-read data processing are also available at https://github.com/bhattlab/bhattlab_workflows_nf.

To better compare the short and long reads, the long reads were subsampled to the same mean depth as the short reads using a custom script, which randomly selected read IDs from the processed reads up to a specified amount of total sequencing. Seqtk subseq (v.1.4-r130) was used to subsample the original FASTQ files for the selected read IDs. Read quality and length distribution were assessed with NanoPlot (v.1.41.6). Reads were assembled and binned using the same workflow described above.

Phage prediction

To predict phages, we applied geNomad (v.1.7.6; ref. [23]), VIBRANT (v.1.2.1; ref. [21]), VirSorter2 (v.2.2.4; ref. [22]) and Cenote-Taker3 (v.3.4.0; ref. [24]) to the short-read and long-read assemblies for each metagenomic sample. For each predicted phage, we used CheckV (v.1.0.1; ref. [34]) to assess the quality and to adjust the boundary predictions on the basis of CheckV host trimming. VIBRANT, geNomad, Cenote-Taker3 and CheckV are included in the NextFlow project available at https://github.com/bhattlab/bhattlab_workflows_nf. VirSorter2 was run separately because it relies on Snakemake (v.5.26.0) for execution. All predicted phages, including full phage contigs and predicted prophages, were collated from the four tools.

Comparison across short-read and long-read sequencing

To compare the phage predictions across short-read and long-read sequencing, we mapped short-read contigs against the (subsampled) long-read assembly using blast+ (v.2.2.31), filtering alignments for identity (99%) and query coverage (90%). To determine if an integrated long-read phage was fully covered by short-read contigs, we required a single short-read contig to align to at least 95% of the phage region. To determine the short-read coverage of integrated phages, we mapped the short reads to the long-read assembly using Bowtie 2 (v.2.5.4) and calculated the per-base coverage using SAMtools (v.1.21).

To compare short-read and long-read phage predictions in more detail, we adapted the segment overlap metric (originally developed to measure overlaps between predicted biosynthetic gene clusters[65]) to measure which fraction of long-read phages was covered by the predicted short-read phages and vice versa (Extended Data Fig. 4). This metric calculates recall by considering each long-read phage prediction as a positive instance. A long-read phage was considered a true positive if covered (up to a variable cutoff of x%) by alignments of one or more short-read phage contigs; otherwise, it was classified as a false negative. Recall, defined as true positives over the sum of all positives, quantifies the fraction of long-read phages that are found by short-read sequencing. Precision was defined on the basis of the short-read phages; those overlapping (to at least x%) a long-read phage were considered true positives, whereas those not overlapping were considered false positives. Precision was then calculated as true positives over the sum of true and false positives, quantifying the fraction of short-read phages found by long-read sequencing.

Clustering of phages across time points

Prophages were clustered across time points within each individual by mapping all phages of T1 and T2 against each other using blast+ (v.2.2.31), and genome identity and coverage were computed using the CheckV companion scripts[34]. To cluster the host regions, we extracted 20-kb regions on either side of integrated prophages, concatenated these phage-surrounding regions and performed the same clustering analysis with the CheckV companion scripts. Additionally, we mapped the original phage region and their host regions against the assembly of the other time point to prevent erroneous classification of dynamic phages not annotated in the other time point. Finally, we quantified the median read coverage in both time points for all phage regions and filtered out all phages with a median coverage of less than ten in both time points.

Using these clustering results, we iteratively consolidated the clustering in the following way (Extended Data Fig. 5): phages with high identity (98%) and genome coverage (90%) were clustered together. If the hosts clustered together as well, they were classified as stably integrated; otherwise, they were classified as phages found in several host contexts. If two phages failed to cluster together but their host regions did, we relaxed the cutoff for genome coverage to classify them as stably integrated because we observed large SVs between phages integrated into the same host context. For all singleton clusters, we checked whether the host region or the phage itself had a high-identity and high-coverage mapping to the assembly of the other time point. Because we observed many false-positive dynamic phages at contig edges, we discounted singleton phages found at the edges of contigs. If a phage and its host region mapped well to the same contig in the other time point, we classified this phage as stably integrated; otherwise, we classified them as either dynamic phages or singletons lost/gained with their host if we found the host region in the assembly of the other time point.

Strain replacement analysis

To evaluate the relationship between paired MAGs within individuals, we identified all high-quality and high-coverage bins that shared the same taxonomic classification on the basis of GTDB-Tk (v.2.3.0) across time points. High-coverage bins were quantified by calculating the median contig coverage on the basis of metaFlye (v.2.9.2-b1786) output within each bin. The shared ANI of paired bins was calculated using FastANI (v.1.34; ref. [66]) and default one-to-one parameters. Mean proportion of strain replacement across individuals was then calculated by setting a minimum shared ANI for the same strain and determining the number of paired MAGs that fall above this value per individual. This was done using an ANI cutoff of all unique ANI values to calculate the strain replacement for different ANI cutoffs.

Identification of SVs overlapping predicted phage regions

To find SVs in our data, we mapped the reads of each time point against the assembly of the other time point within an individual using NGMLR (v.0.2.7; ref. [67]). Binary Alignment Map files were sorted with SAMtools (v.1.9), and structural variants were identified using Sniffles2 (v.2.2; ref. [36]).

Assignment of host taxonomy for integrated prophages

We used iPHoP (v.1.3.3; ref. [35]) to predict hosts for all phages using default parameters and database version iPHoP_db_Aug23_rw. For integrated long-read phages, we annotated their hosts by classifying each annotated gene on a given contig using the mmseqs taxonomy module from MMseqs2 (v.14.7e284; ref. [68]). This module provides a taxonomic annotation on the basis of the GTDB (v.214.1; ref. [69]) database for each gene. For each contig, we then combined the annotations for all genes not located in predicted phage regions at each taxonomic level. Annotations were accepted if more than 50% of genes agreed, disregarding genes without taxonomic annotation.

The iPHoP, MMseqs2-based and binning taxonomic assignments were evaluated for consensus at each taxonomic level (Extended Data Fig. 4). To determine a subset of integrated phages for which we had high-confidence host assignment, we filtered our results for agreement between high-quality bins (more than 90% completeness and less than 5% contamination; ref. [70]) and MMseqs2-based assignment down to the family level.

Clustering of phages on species level for host-range analysis

To evaluate host range within phage species, we clustered all geNomad-annotated phages in all samples at a minimum of 95% ANI and 80% alignment fraction using CheckV (v.1.0.1; ref. [34]) supporting scripts. We specified a minimum of 80% query and target coverage (--min_tcov 80 --min_qcov 80) because we expect phages to be assembled more contiguously by long reads. The resulting cluster membership information was merged with the host annotations from our MMseqs2 taxonomy approach and bin taxonomy, as described above.

To visualize phages found in several bacterial families, we compared bins using AliTV[71], filtering alignments by length and nucleotide identity. Additionally, we calculated their read coverage (removing reads less than 500 bp in length and with more than 1% mismatches to prevent spurious mappings) and the number of read alignment ends per genomic position. A high number of read alignment ends could point towards potential mis-assemblies, as explored in a previous study[40].

To gain orthogonal evidence for the integration of the same phage species into distinct bacterial families, we also assembled our data using myloasm (v.0.1.0; ref. [41]), an alternative assembler to Flye. We used standard parameters, except for --min-reads-contig 3. For all putative phage species in distinct bacterial families, we identified the corresponding myloasm contig and tried to verify the prophage integration (Supplementary Table 2). In the case of two clusters, the phages were found at the edges of contigs, but their integration into the bacterial species could be verified by the broader host context derived from the myloasm assembly (Extended Data Fig. 8).

Gene content for integrated prophages

To assess gene content across all integrated prophages with structural variant evidence, we used pharokka (v.1.7.3) against the pharokka (v.1.4.0) database[72] with the --meta, --skip-mash and --split flags.

Synteny and gene content visualization

Visualization of all gene content and synteny was done using LoVis4u (v.0.1.4.1; ref. [73]). Two of the 22 IScream phages were smaller than 10 kb and therefore were removed from this analysis. For IScream phage synteny visualization, gff files of the identified full-length IScream phages, generated by pharokka annotation (described above), were used as input for LoVis4u visualization with default configuration. For host context visualization, gff files from Bakta were parsed to visualize specified windows and to convert them to a format compatible with LoVis4u. The reformatted gff3 files were used as input for LoVis4u visualization using an updated configuration file specifying mmseqs_min_seq_id = 0.8.

Identification of potential integrase enzymes

To identify potential integrase enzymes in our assemblies, we used a set of Pfam hidden Markov models described in an earlier exploration of mobile genetic elements[74]: PFPF07508 for large serine recombinases; PF00239 for small serine recombinases; PF00589 for tyrosine recombinases; and PF00665, PF13333 and PF13683 for DDE recombinases. We used the hmmsearch command (with the —cut_ga flag) from HMMER (v.3.4; ref. [75]) against all predicted proteins. We then annotated each phage by counting which type of potential integrase was present within the phage boundaries using only phages with evidence from SVs.

Identification of IScream phages in MGV and Clostridia genomes

To explore the prevalence and abundance of IScream phages, we searched for potential IScream phages in public datasets. We ran ISEScan (v.1.7.2.3; ref. [76]) on all viral genome assemblies from MGV and identified genomes that contained one IS30 element starting less than 1 kb from the beginning of the assembly and one IS30 element ending less than 1 kb from the end of the assembly.

We next searched for existing bacterial isolates containing integrated IScream phages. We downloaded all 2,160 class Clostridia genome assemblies annotated as complete or chromosomal level using the NCBI Datasets tool. We ran ISEScan and geNomad on all assemblies and identified genomes that contained a geNomad-annotated prophage region with one IS30 element starting less than 1 kb from the beginning of the prophage region and one IS30 element ending less than 1 kb from the end of the prophage region, which included B. hansenii ATCC 27552 (see Supplementary Table 3 for a complete list of potential IScream phages).

Additionally, we analysed the species-level taxonomic profiles for two public datasets, generated with Phanta (v.1.0; ref. [48]), which included a viral database built on representative genomes from MGV (see the original Phanta publication for details about data processing). Each phage species from this database was classified as potential IScream phage if a genome contained in this species bin was identified to be an IScream phage.

The dataset from Liang et al.[47] included bulk metagenomic sequencing and VLP-enriched sequencing of infants. In this dataset, we quantified the relative taxonomic abundance of IScream-containing phage species in paired bulk and VLP sequencing to identify potential particle formation of IScream phages. Finally, the non-cancer control samples from the dataset of Yachida et al.[77] was used to calculate prevalence and mean abundance of phage species in healthy individuals.

Clustering of IS30 elements

To cluster IS30 elements across IScream phages, we used the ete3 toolkit (v.3.1.3; ref. [78]). First, we built a tree for all IS30 proteins on all IScream phages using the ‘standard_fasttree‘ workflow from ete3, consisting of a multiple sequence alignment with Clustal Omega (v.1.2.4) and tree construction using FastTree (v.2.1.8) (Extended Data Fig. 10). Because this analysis showed a clear separation between outward-directed and inward-directed IS30 proteins, we subsequently focused only on the outward-directed IS30. To gain insights into the evolutionary history of IScream phages, we extracted all outward-directed IS30 proteins from our IScream phages, all potential MGV IScream phages and all bona fide bacterial IS30 elements from the ISfinder database. For the MGV phages, we filtered the outward-directed IS30 proteins to be longer than 600 and shorter than 2,200 nucleotides. All proteins were clustered at 70% amino acid similarity over 80% of the alignment, using MMseqs (v.14.7e284; ref. [79]). A tree was then constructed on the cluster representatives using the ‘standard_fasttree’ workflow in ete3, modified to trim positions in the multiple sequence alignment with more than 90% gaps using trimAl (v.1.4.rev6).

Screening for potential phage IS30 elements in ancient stool metagenomes

To identify potential IScream phages in ancient stool samples, we downloaded the raw data from Wibowo et al.[46], containing sequencing of desiccated paleofecal samples (1,000–2,000 years old) from the southwestern USA and Mexico. Raw reads were processed and assembled as described above, phages were identified with geNomad, and IS elements were detected with ISEScan. For all contigs that contained IS30 elements within predicted phages, we assessed their DNA damage level with DamageProfiler (v.1.1; ref. [80]). To prevent inclusion of modern IScream phage IS30 proteins, we filtered the phage-overlapping IS30 proteins for being on contigs recruiting more than 1,000 reads and showing an estimated 5′C>T damage level or an estimated 3′G>A damage level over 1%, resulting in six potential IScream IS30 open reading frames. Note that these filtering steps are rather specific because many more IS30 proteins are identified on shorter contigs, which are not predicted to be of phage origin. The six ancient IS30 proteins were added to the clustered IS30 proteins from MGV, IScream phages and ISfinder, and the tree was recomputed as described above.

Culturing of the B. hansenii IScream phage

B. hansenii (ATCC 27552) was grown anaerobically (90% nitrogen, 5% carbon dioxide and 5% hydrogen) in an anaerobic chamber (Sheldon Manufacturing) in Brain Heart Infusion (Sigma) supplemented (BHIS) with hemin (5 μg ml−1), l-cysteine (1 mg ml−1) and sodium bicarbonate (0.2%).

B. hansenii was grown overnight in pre-reduced BHIS. The culture was pelleted by means of centrifugation (Eppendorf; 5920R) at 4,000g for 15 min, and DNA was extracted using a DNeasy Blood and Tissue Kit (QIAGEN; 69504) following the manufacturer’s instructions for Gram-positive bacteria. Bacterial genome sequencing was performed by Plasmidsaurus using ONT with custom analysis and annotation.

PEG precipitation of B. hansenii phage particles

B. hansenii was grown overnight in 4 l of BHIS. The supernatant from the culture was harvested by means of centrifugation (Eppendorf; 5920R) at 4,198g for 15 min at 4 °C. Sodium chloride was added to the supernatant to a final concentration of 5 M, and PEG 8000 was added to a final concentration of 10%. This solution was stirred for 30 min at 4 °C to dissolve and then left overnight at 4 °C with no agitation. PEG-precipitated phage particles were harvested by centrifugation at 10,000g for 10 min at 4 °C. The supernatant was removed, and the resulting pellets were air-dried for 3–5 min in an inverted position. The pellets were combined and resuspended in a total of 10 ml of SM buffer (100 mM NaCl, 8 mM MgSO_4·7H_2O and 50 mM Tris–HCl (pH 7.5)). An equal volume of chloroform was added, mixing by inverting and centrifuged at 12,000g for 10 min at 4 °C. The aqueous phase was collected, and chloroform treatment was repeated.

PCR validation of phage particles from B. hansenii IScream phage

PCR primers were designed using NCBI Primer-BLAST under default settings. The PCR product size was targeted to be between 150 bp and 250 bp. PCR primers were designed to target (1) B. hansenii gmk gene; (2) an internal region of the IScream phage; and (3) the upstream junction site of the integrated phage and bacterial chromosome, as well as to span the junction of circularization of the IScream phage region (Fig. 4e; the primers are listed in Supplementary Table 4). PCRs were performed on PEG-precipitated samples directly with and without DNase treatment. For the DNase-treated samples, 50 μl of PEG Prep was treated with 5 μl of 10X TURBO DNase buffer, 1.13 μl of TURBO DNase (Invitrogen; AM2239) and 1 μl of RNase (1 mg ml−1; Invitrogen; AM2270), incubated at 37 °C for 1 h and then heat inactivated at 70 °C for 10 min.

PCRs were performed using Q5 high-fidelity DNA polymerase (annealing temperature of 67 °C, annealing time of 30 s and extension time of 5 s at 72 °C). PCRs were run on a 2% agarose gel. A raw gel image is shown in Supplementary Fig. 1.

Transmission electron microscopy of B. hansenii lysate

A 500-ml culture of B. hansenii was grown overnight, as described above. The supernatant from the culture was harvested after 24 h by means of centrifugation (Eppendorf; 5920R) at 4,198g for 15 min at 4 °C. The supernatant was then filtered using a 0.22-μm filter (Fisher Scientific; 09-719C). The supernatant (5 μl) was placed on glow-discharged 200-mesh carbon/Formvar-coated Cu grids (FF300-Cu) and allowed to settle for 3 min. Grids were washed by touching the sample side with two drops of water. Three drops of 1% uranyl acetate in double-distilled water were then added, and the third drop was incubated on the grid for 1 min at room temperature. The remainder of the last drop was removed with filter paper, and the grid was allowed to dry. The samples were then observed on the JEOL JEM-1400 transmission electron microscope at 120 kV, and photographs were taken using a Gatan Orius digital camera.

Statistical analysis and visualization

All statistical tests were performed in R (v.4.2.2). Data visualization was performed using ggplot2 (v.3.5.1), which is part of the tidyverse (v.2.0.0) suite of tools[81].

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The raw sequencing data for all samples sequenced in this study are available from the European Nucleotide Archive under the study identifier PRJEB88320. The short-read sequencing data from T1 had been included in a paper by Maghini et al.[62] and are available under the identifier PRJNA940499. Data for the MGV catalogue from the publication by Nayfach et al.[8] are available at https://portal.nersc.gov/MGV/. The raw sequencing data of ancient metagenomic samples from the publication by Wibowo et al.[46] are available under PRJNA561510. The raw data for the Phanta-profiled datasets from Liang et al.[47] are available under PRJNA524703 and PRJDB4176 from the study by Yachida et al.[74]. Source data are provided with this paper.

Code availability

The source code developed for the reported analysis and data visualization is publicly available at Zenodo (https://doi.org/10.5281/zenodo.15192469)[82] and at GitHub (https://github.com/bhattlab/long_read_benchmark).

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Acknowledgements

We thank all members of the Bhatt laboratory for enriching discussions and invaluable feedback, especially D. Maghini and Y. Pinto. We also thank members of the Ashley, Altemose and Good laboratories at Stanford University for help with long-read sequencing and for inspiring discussions. We thank X. Zeng from the Fischbach laboratory for providing the B. hansenii ATCC 27552 sample and J. J. Perrino for his help and guidance during transmission electron microscopy imaging. The project described was supported in part by ARRA (award no. 1S10RR026780-01) from the National Center for Research Resources. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Center for Research Resources or the National Institutes of Health. A.S.B. is supported by the Paul Allen Distinguished Investigator Award, and the Bhatt laboratory is supported by a Stand Up To Cancer Grant and NIH R01AI148623, R01AI143757 and U54AG089334 (Human Virome Program centre grant). This research was supported in part by grant NSF PHY-2309135 to the Kavli Institute for Theoretical Physics (KITP). J.W. is a Damon Runyon Quantitative Biology Fellow supported by the Damon Runyon Cancer Research Foundation (DRQ-22-24). A.S.H. and D.T.S. acknowledge support from the National Science Foundation Graduate Research Fellowship Program (DGE-1656518). D.T.S. and M.D. are supported by the Cell and Molecular Biology Training Grant (T32 GM007276). R.B.C. is supported by the A.P. Giannini Foundation. D.C. is supported by NIH T32HG000044. N.J.E. is supported by the Stanford University DARE Fellowship.

Author information

Author notes

1. These authors contributed equally: Jakob Wirbel, Angela S. Hickey

Authors and Affiliations

1. Division of Hematology, Department of Medicine, Stanford University, Stanford, CA, USA

Jakob Wirbel, Rachael B. Chanin & Ami S. Bhatt

2. Department of Genetics, Stanford University, Stanford, CA, USA

Angela S. Hickey, Daniel Chang & Ami S. Bhatt

3. Department of Bioengineering, Stanford University, Stanford, CA, USA

Nora J. Enright

4. Department of Biology, Stanford University, Stanford, CA, USA

Mai Dvorak

5. Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA

Danica T. Schmidtke

Contributions

A.S.B., J.W. and A.S.H. conceived and designed the study. M.D. collected and processed all stool samples. M.D., A.S.H., R.B.C. and J.W. conducted all long-read library preparation and sequencing. Short-read and long-read comparison, phage dynamics and host-range analyses and data visualizations were performed by J.W. and A.S.H. J.W., D.C. and N.J.E. conducted IScream phage analyses and visualization. A.S.H., D.T.S., R.B.C. and D.C. performed the Blautia experiments and interpretation. Funding acquisition was done by A.S.B. The original draft was written by J.W., A.S.H. and A.S.B. All authors contributed to the review and editing of this paper.

Corresponding author

Correspondence to Ami S. Bhatt.

Ethics declarations

Competing interests

A.S.B. is a founder of Stylus Medicine, serves on the scientific advisory board and is a board observer. She also serves on the Scientific Advisory Board of Caribou Biosciences and Cantata Biosciences. The other authors declare no competing interests.

Peer review

Peer review information

Nature thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Comparisons between short-read and long-read sequencing.

a) Mean sequencing depth of Illumina short-read sequencing, subsampled ONT long-read sequencing, and deep ONT long-read sequencing across all samples (n = 12) are shown as bars with points indicating individual samples. b) Mean assembly n50 across different sequencing types and depth as bars while points indicate individual samples (n = 12). c) Comparison of the number of contigs present in assembled bins that were shared between the three sequencing types: Illumina short reads (SR) at a depth ~6 Gb, subsampled ONT reads (LR) to ~6 Gb, and deep ONT reads (DLR) at ~30 Gb, represented as a box plot. Box plots show the interquartile ranges (IQRs) as boxes, with the median as a black horizontal line, whiskers extending up to the most extreme points within 1.5-fold IQR, and all data points are indicated as dots. Corresponding bins were determined by matching bin taxonomic assignment across sequencing approaches. Orange lines track corresponding bins across the sequencing types. A paired Wilcoxon signed rank test was used to compare the number of contigs per bin from short reads at ~6 Gb depth and long reads at ~6 Gb depth (n = 183, p-value < 2.2e-16). d) Histogram of mean read quality score from deep long read sequencing samples. e) Comparison of the length of predicted genes from assemblies of each sequencing type. A one-sided Wilcoxon rank sum test was used to compare the median length of predicted genes between Illumina short reads and ONT long reads subsampled to the same depth. Results showed no significant difference (SR n = 3457575, LR n = 3408004, p = 1). Similarly, one-sided Wilcoxon rank sum test was used to compare the short read assembly with the deep ONT assembly, similarly showing no significant difference (SR n = 3457575, DLR n = 6671897, p = 1). The Wilcoxon test indicated that the median gene lengths from short-read assembly were not significantly greater than median gene lengths from ONT assemblies. Box plots show the IQRs as boxes, with the median as a black horizontal line and the whiskers extending up to the most extreme points within 1.5-fold IQR. Line plots show overall sample distribution on a density scale of zero to one.

Source Data

Extended Data Fig. 2 Comparisons between different phage annotation tools.

For each phage annotation tool (VIBRANT, geNomad, virsorter2, and Cenote-taker3), the average total number of phages annotated, average number of integrated phages annotated, and the average fraction of identified phages that were integrated are presented for each sequencing type and depth (Illumina short reads ~6 Gb (n = 12), Illumina short reads ~12 Gb (T1 only, n = 6)*, ONT long reads ~6 Gb (n = 12), and ONT long reads ~30 Gb (n = 12)). Phage length was compared between entire phage contigs (orange) and integrated phages (purple) for Illumina short reads at ~6 Gb and subsampled ONT long reads at ~6 Gb, indicating that short-read sequencing resulted in more fragmented (and therefore shorter) phage genomes. Box plots show the interquartile ranges (IQRs) as boxes, with the median as a black horizontal line, whiskers extending up to the most extreme points within 1.5-fold IQR, and outliers indicated as dots.

Source Data

Extended Data Fig. 3 Short-read phage fragmentation and segment overlap analysis.

a) Barplot showing the number of integrated phages per sample for downsampled (~6 Gb) and deep (~30 Gb) long-read sequencing. The fill color indicates if the same phage was found to be fragmented in the short-read assembly or found to be completely covered by a single short-read contig. b) Short-read depth of all integrated prophages identified with geNomad in the long-read assemblies. Short reads were mapped to the long-read assemblies and the average depth for all regions identified to be phages was calculated. Dots indicate if the phage was found to be fully covered (n = 467 for long reads (6 Gb), n = 499 for long reads (30GB)) or fragmented (n = 2240 for long reads (6 Gb), n = 5088 for long reads (30 Gb)) when mapping short-read contigs to the long-read assemblies. The right-side panel for each plot shows the average short-read depth as box plots. Box plots show the interquartile ranges (IQRs) as boxes, with the median as a black horizontal line and the whiskers extending up to the most extreme points within 1.5-fold IQR. c) Segment overlap metric (see Methods and ref Carroll et al.[65]) calculated between short-read phages and long-read phages. Across all predictors, only about 20% of long-read phages were found to be sufficiently covered by short-read phages (segment overlap recall). This fraction increased with a more lenient cutoff for how much of the long-read phage had to be covered to be considered a true positive, indicating that only a small part of the long-read phage is covered by short-read phage contigs. Similarly, the segment overlap precision (how many short-read phages overlapped long-read phages) was higher for all phage predictors, but decreases with the overall number of phages predicted to be present. Especially for virsorter2, only about 35% of the ~4000 predicted short-read phages per sample overlapped corresponding long-read phage predictions. This indicates that virsorter2 predicts many short-read contigs to be phage, but does not predict the same sequence as phage when assembled in context. See schematic at the bottom of the figure panel for a visual representation of mapping of short-read phages to long-read phages. d) Same as panel c, but only for integrated phages (disregarding phage contigs). In this analysis, segment overlap precision is ~80%, indicating that most of phages identified to be integrated prophages in short-read assemblies are similarly identified in the long-read assemblies, whereas the coverage of integrated phages in the long-read assemblies remains relatively low (~20%). See schematic at the bottom of the figure panel for a visual representation of mapping of integrated short-read phages to long-read phages. e) Segment overlap metric when comparing across phage predictors, in the long-read assemblies only. See schematic at the bottom of the figure panel for a visual representation for how segment overlap and recall were calculated when comparing predictors.

Source Data

Extended Data Fig. 4 Host taxonomy assignment schematic and benchmarking.

a) Schematic representation of the different approaches to assigning hosts to integrated phages. From the long-read assemblies, we assign bacterial hosts for a given prophages by comparing the bin taxonomic assignment from GTDB-tk (bin membership of the contig containing the integrated prophage, see Methods) and gene-level annotations from mmseqs-taxonomy (see Methods). For this approach, all genes in host regions of the same contig as the integrated prophage are annotated against the GTDB database and a consensus taxonomic assignment is generated by majority rule. The current gold standard for taxonomic prediction of phage hosts is the prediction tool iPHoP, which integrates the predictions from several different approaches. Host prediction via iPHoP has traditionally been necessary, since phages are often assembled as fragments or without host context with short-read sequencing. For each phage genome, iPHoP generates an integrated list of host predictions. b) Mean ratio of the host agreement between the mmseq2-taxonomy based approach and binning taxonomic assignment for integrated prophages across all samples at each taxonomic level. Data are presented as mean values +/− SEM. c) Mean ratio of the host agreement between the mmseq2-taxonomy based approach and iPHoP taxonomic assignment for integrated prophages across all samples at each taxonomic level. Since iPHoP outputs a list of possible hosts, here we looked for agreement at each taxonomic level from any potential host. Data are presented as mean values +/− SEM.

Source Data

Extended Data Fig. 5 Clustering of prophages within individuals.

a) Schematic showing the clustering and classification of integrated phages within individuals. In short, all integrated phages and their adjacent host regions were clustered separately using the CheckV companion scripts for genome identity and coverage calculation based on blast mappings. Clustering was done with high identity (99%) and genome coverage (90%) cutoffs. Then, clusters were refined by comparing between phage and host clusters. b) Histogram showing the number of clusters with the specified number of phage regions. Cluster annotation is indicated according to the figure legend. c) Scatter plot showing the median read coverage in timepoint 1 against coverage in timepoint 2 for each phage cluster, split by their classification. For each cluster, the representative phage genome is shown. Dot shapes indicate the timepoint of assembly. d) Dotplot comparing two contigs within individual D08, showing the gain of a 137 kb integrated phage into an otherwise stable host. The phage region (representing a dynamic phage) is indicated by a shaded pink area. e) Dotplot comparing two contigs within individual D05, showing a cluster classified as ‘same host, different phage’ due to variations in phage annotation. The phage regions are indicated by shaded pink areas. While both contigs align perfectly, the region annotated as phage in both timepoints do not overlap over more than 90%, resulting in disparate clustering outcomes. The reason for this inconsistency is a single nucleotide difference changing the predicted genes in T2 (annotated above). f) Dotplot comparing two contigs within individual D04, showing a cluster classified as ‘same host, different phage’ due to a large structural variation (deletion in T2). The phage regions are indicated by shaded pink areas. The gap in the alignment represents a 6.5 kb deletion in T2 compared to T1, resulting in disparate clustering outcomes because of respective genome coverage below 90%. g) Line graph showing the mean proportion of strain replacement across individuals depending on the ANI cutoff used to call strain replacement. The analysis is based on high-quality and high coverage (>30x median coverage) MAGs that had the same species annotation across timepoints (n = 90). Colored bars indicate the number of dynamic phages that were lost (blue) or gained (orange) from paired MAGs with a shared ANI.

Source Data

Extended Data Fig. 6 Detection of exact phage boundaries through long-read mapping and structural variation calling.

a) Schematic representation of the identification of exact phage boundaries by structural variant (SV) calling. In short, SVs were identified with Sniffles2 on the basis of read mapping within and across timepoints for the same individual. In these cases, linked read alignments (either supporting a deletion or a duplication, represented by blue lines in the schematic) can be used to identify SVs. Duplication SVs can be interpreted as the presence of circular phage genomes. We considered all phage-SV overlaps that covered at least 50% of both phage and SV to be high-quality phages identified with base-pair accuracy. b) Read alignment plot illustrating a phage identified by a deletion SV in individual D02. Read alignments are separated into linked and single alignments. Each line represents the alignment of a single read with dots showing the start and end of each alignment. Linked alignments are colored by strand and connected by a thin grey line to indicate that they originate from the same read. On the top, the coordinates from phage predictions are shown: boundaries of the original phage prediction are shown with 50% shading, while boundary correction with CheckV is shown in full opacity. c) Same plot as b, but for a duplication SV (circular phage genome), identified in individual D05. d) Same plot as b, showing for a phage region present in individual D05 that there is read evidence for the simultaneous presence of circular phage genomes (linked reads supporting a duplication SV), hosts without the integrated phage (linked reads supporting a deletion SV), and hosts with the integrated phage (single alignments) in T2. e) Number of phages predicted by different tools with and without CheckV boundary correction that overlapped SVs called by Sniffles2, colored by their overlap being in range (more than 50% of the phage and the SV region) or not within range. Virsorter2 tends to predict very long phages, resulting in many comparisons where less than 50% of the phage was covered by the SV region. CheckV boundary correction increases the number of Virsorter2 predictions that fall in range. f) Evaluation of the total boundary error calculated as the sum of the absolute difference on either side of the prediction with and without CheckV boundary correction. This is done for all phage-SV overlaps within range (see e). Box plots show the interquartile ranges (IQRs) as boxes, with the median as a black horizontal line, whiskers extending up to the most extreme points within 1.5-fold IQR. A one-sided Wilcoxon rank sum test was used to determine if the phage boundary error with CheckV correction was shorter than the original predicted boundary of each tool. No significant difference was found for the geNomad predictions (Original n = 2161, CheckV-corrected n = 2119, p = 0.2). CheckV-boundary correction significantly decreased the boundary error for VIBRANT (Original n = 1746, CheckV-corrected n = 1688, p = 0.009) and Cenote-taker3 (Original n = 1649, CheckV-corrected n = 1550, p = 0.001), but had the most significant effect on the virsorter2 predictions (Original n = 2027, CheckV-corrected n = 1580, p < 2.2e-16).

Source Data

Extended Data Fig. 7 Genome comparison for two phages assembled into multiple host contexts.

a) Bin comparison between two Alistipes putredinis metagenome-assembled genomes in individual D09, generated by AliTV (see Methods). Boxes represent contigs, green areas indicate phage regions predicted by geNomad, and links represent regions of high identity, with the phage region of interest highlighted in pink. b) Gene synteny plot for the phage region highlighted in a. Gene arrows are colored by their taxonomic prediction from mmseqs-taxonomy (see Methods). c) Bin comparison between Clostridium fessum and Ventrimonas species bins assembled from two timepoints in individual D02, generated by AliTV (see Methods). The only link between the Clostridium and Ventrimonas bins represents the phage region of interest, assembled into multiple host contexts. d) Gene synteny plot for the phage region highlighted in c. Gene arrows are colored by their taxonomic prediction from mmseqs-taxonomy (see Methods). Both contigs representing the Ventrimonas host context are taxonomically consistent (>80% of genes predicted to be Ventrimonas). For panel b and d, the shaded regions between gene arrows indicate amino acid similarity greater than 80%.

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Extended Data Fig. 8 Evidence for phages assembled in taxonomically distinct host contexts.

a) Chart representing high-quality metagenome-assembled genomes for two different species, annotated with phage predictions from geNomad. A single genomic region (>10 kb) is shared between both genomes with >95% nucleotide identity, annotated by a pink link. As the phage region was assembled at the edge of a contig, we sought orthogonal validation by identifying and comparing the corresponding contig generated from an alternative assembly method (myloasm[41]). The myloasm contig is annotated with its individual CheckM completeness and contamination result. The right panel shows the coverage and synteny plot for the annotated phage region. The top panel shows read coverage, the middle panel shows the number of alignment ends divided by coverage, and the bottom panel shows the gene arrows across the three genomes. Genes are colored according to their taxonomic predictions. b) Same plot organization as in a, for a different example phage region. c) Same plot organization as in a. In this example, two phage regions were identified as part of a larger shared region between two genomes found in different bacterial families. This region is more complex, as the comparison of two closely related Bacteroides uniformis genomes reveals complex rearrangement between these closely related genomes, involving the region in question. d) Same plot organization as in a, for a phage found in three bacterial hosts of different classes. All phage regions are at the ends of contigs and could not be verified by myloasm. The shaded regions in the synteny plots indicate amino acid similarity greater than 80%.

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Extended Data Fig. 9 IScream phages have phage-like gene content and are present in MGV.

a) Number of genes classified as different functional groups, identified from pharokka, shown for both IScream phages and all other integrated phages. Both groups of phages were identified through structural variation analysis. Each panel is annotated by a Benjamini-Hochberg-corrected P-value, resulting from testing differences in gene numbers with a two-sided Wilcoxon test (Other phages n = 569, IScream phage n = 22). Box plots show the interquartile ranges (IQRs) as boxes, with the median as a black horizontal line, whiskers extending up to the most extreme points within 1.5-fold IQR, and outliers are indicated as dots. b) Log10-transformed relative taxonomic abundance for all phage species containing IScream phages identified in MGV genomes for data from Liang et al.[47] (see Methods). Each dot represents a phage species within one individual, sequenced either in bulk metagenomic (MetaG) or virus-like particle (VLP)-enriched samples, and are connected by lines to indicate the same individual. Different phage species are indicated by different colors. c) Mean relative taxonomic abundance is plotted against prevalence for all phages identified by phanta in the metagenomes of healthy individuals from Yachida et al.[77] (see Methods). Phage species containing IScream phages identified in MGV genomes are highlighted in cyan.

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Extended Data Fig. 10 IS30 clustering and Blautia hansenii IScream phage.

a) Multiple sequence alignment of all IS30 open reading frames in IScream phages with structural variation evidence, assembled in this study, visualized through the ete3 toolkit[78] (boxes indicate alignments and empty areas indicate gaps in the alignment). The outward- and inward-directed IS30 proteins form two separate clusters, indicated by the tree reconstructed from the multiple sequence alignment. b) Box plot showing the IS30 gene length for outward- and inward-directed IS30 genes from IScream phages assembled here (n = 22). Box plots show the interquartile ranges (IQRs) as boxes, with the median as a black horizontal line, whiskers extending up to the most extreme points within 1.5-fold IQR, and all data points are indicated as dots. c) Boxplot showing the IS30 gene length for outward- and inward-directed IS30 genes from all potential IScream phages identified in MGV (n = 1705). All boxplots show the interquartile ranges (IQRs) as boxes, with the median as a black horizontal line, whiskers extending up to the most extreme points within 1.5-fold IQR, and outliers indicated as dots. d) Evidence for the presence of circular phage genomes from read alignments against the B. hansenii reference genome (NZ_CP022413.2). Read alignments are separated into linked alignments (top part) and single alignments (bottom part). Each line represents the alignment of a single read with dots showing the start and end of each alignment. Linked alignments are colored by strand and connected by a thin grey line to indicate that they originate from the same read. The remainder of this figure panel shows coordinates of phage predictions, read coverage, and the gene content of the phage genome (as determined by structural variation calling). e) Tree constructed from a multiple sequence alignment of IS30 proteins clustered at 70% amino acid identity (outward-directed IS30 proteins from MGV IScream phages, IScream phages assembled here, and bona fide bacterial IS30 elements) together with IS30 proteins found overlapping phages predicted in assemblies of paleofeces. f) Raw, uncropped TEM images of virus-like particles from the lysate of an overnight culture of B. hansenii. Scale bar for 200 nm included on each image.

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Supplementary information

Supplementary Fig. 1

Raw gel image for Fig. 4e.

Reporting Summary

Supplementary Tables

This file contains Supplementary Tables 1–4. Supplementary Table 1. Sequencing information; Supplementary Table 2. Broad host-range examples; Supplementary Table 3. Other IScream phages in Clostridia genomes; Supplementary Table 4. Blautia primer sequences.

Peer Review File

Source data

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Wirbel, J., Hickey, A.S., Chang, D. et al. Long-read metagenomics reveals phage dynamics in the human gut microbiome. Nature 649, 982–990 (2026). https://doi.org/10.1038/s41586-025-09786-2

- Received: 09 May 2025

- Accepted: 21 October 2025

- Published: 26 November 2025

- Version of record: 26 November 2025

- Issue date: 22 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09786-2

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iHALT unlocks liver functionality as a surrogate secondary lymphoid organ

John Gridley,

David Pak,

Anuradha Kumari,

Jacob Shupak,

Brantley Holland,

Yifeng Shi,

Sheetal Trivedi,

Yongtao Wang,

Sudhir Pai Kasturi,

Amit Kapoor,

Raymond T. Chung &

…

Arash Grakoui

Nature volume 649, pages 991–1002 (2026)

Abstract

Upon viral infection, the current paradigm of humoral immunity posits that germinal centre reactions occurring within secondary lymphoid organs (SLOs) yield effector plasma cells that subsequently traffic to infected organs or the bone marrow1,2,3. However, it is not well understood how viral tissue tropism may govern the spatiotemporal dynamics of such responses. Here we demonstrate that infection with a prototypical systemic virus indeed induces liver-trafficking plasma cells generated in SLOs, whereas strictly hepatotropic hepaciviral infection elicits locally primed, virus-specific plasma cells in the liver independently of SLO contribution. Such locally derived progenies emerged from inducible hepatic-associated lymphoid tissue (iHALT) structures containing generative foci of T follicular helper cells, myeloid cells and germinal centre-like B cells, often arising from single founder clones unique to individual periportal structures and locally supporting somatic hypermutation. Critically, the cellular composition, cell–cell contact partners and microarchitecture of such iHALT structures in mice were closely mirrored upon hepaciviral infection in humans. Functionally dependent upon CD40L signalling and cognate B cell receptor specificity, emerging CXCR4+VLA-4+LFA-1+CD44+CD138+ plasma cells were immediately retained along CXCL12+fibronectin+ICAM2+osteopontin+type I collagen+ periportal fibroblast tracts, acting as cognate anchoring pairs that were critical to their maintenance therein. In summary, we characterize humoral immunity exclusively generated and maintained within its extralymphoid site of viral infection in the liver amidst SLO dormancy, in which functional iHALT successfully compensates for strictly hepatotropic virus-induced SLO-evasion strategies to prevent persistent infection.

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Main

Intricately compartmentalized lymphocyte networks exist within SLOs in largely segregated follicular arrangements, benefitting from microarchitectural frameworks that are ideally conducive to their local survival and maintenance. In the context of mounting a T cell-dependent antibody response, the likelihood of experiencing cognate antigen recognition with complementary costimulatory guidance is markedly enhanced in these regions owing to extensive antigen exposure and local trapping mechanisms[4], recirculation of naive lymphocytes with extensive adaptive repertoire diversity, and rational proximity to relevant instructive cell types[5]. Such alignment collectively renders them strategically poised for rapid initiation of de novo humoral responses upon initial antigen encounter in settings of primary infection or immunization. As hubs for B cell affinity maturation, isotype switching and generation of memory B and long-lived plasma cells (LLPCs), the formation of productive germinal centres (GCs) is widely considered to be the unparalleled gold standard for establishment of protective antibody-mediated immunity against most viral infections6,7. Given the delicate network of signalling prerequisites that is required for their structural generation and functional orchestration[5], the theme of SLO idealism extends to GCs via provision of growth factors, supportive fibroblastic reticular cell-laden stromal infrastructure[8], conformationally native antigen presentation on follicular dendritic cells[9], and adjacently pre-existing B and T cell zones that are highly malleable to fine-tuned restructuring upon milieu-derived shifts in local chemokine gradients[10]. It therefore seems unlikely and potentially disadvantageous for a host to disregard these rationally pre-formed structures on perpetual retainer in favour of ‘starting from scratch’ at an extralymphoid site.

Tertiary lymphoid structure (TLS) formation has been observed in settings of chronic inflammation, autoimmunity, cancer and infection. As leukocyte aggregates generated at nonlymphoid sites, TLSs exhibit a wide range of heterogeneity surrounding their precipitating cues, anatomical location, cellular composition and functional outputs. Persistent inflammation is thought to be necessary, but not sufficient, for TLS initiation[11]. As only a subset of patients with cancer develop intratumoural TLSs12,13, the additional prerequisites for this phenomenon are not well understood. Given that this dichotomy is also present in autoimmunity[14], it is thought that interindividual variability in pro-inflammatory interleukin polymorphisms, inherent stromal propensity for high endothelial venule generation[15], and organ type[16] partially dictate such outcomes. Another key driver of TLS formation and maturation is the presence of antigen, which adopts many forms in these contexts, including self-antigens that have broken central tolerance, tumour-specific markers and foreign pathogens. These factors can culminate in igniting a multistep cascade in which lymphoid tissue inducers activate mesenchymal, fibroblast-like lymphoid tissue organizers to deposit extracellular matrix proteins to form the structural scaffolding of the TLS[17]. Lymphoid tissue inducers also induce proximal vascular endothelium to undergo metaplasia towards high endothelial venule formation, thereby enabling extravasation of circulating lymphocytes to this prepared tissue niche that is conducive to their local congregation. Notably, the anatomical fate of locally derived effector lineages is profoundly altered depending on its origins within SLOs or extralymphoid sites, with each setting cultivating a distinct mixture of sphingosine-1-phosphate (S1P) gradients, chemokines, integrins, extracellular matrix components and adhesion molecules, which collectively dictate local residency, indefinite recirculation or peripheral tissue homing. As tissue–blood immunoglobulin concentration discrepancies have been shown to have marked effects on dichotomous infection outcomes[18], such guided anatomical assignments of plasma cell progenies are likely to be critical determinants of autoimmune severity, tumour repression and resolution of infectious disease.

Locally generated adaptive immunity has been observed in infections of the respiratory tract[19], gut[20], central nervous system[21] and liver[22], among other organs. Most of these cases originate from pre-formed, tissue-adjacent lymphocyte hubs, such as dural-associated lymphoid tissue[23], nasal-associated lymphoid tissue[24] or Peyer’s Patches within gut-associated lymphoid tissue[25]. Truly inducible lymphoid structures that are absent in a naive state and form at extralymphoid sites have nonetheless been characterized in the lung, such as inducible bronchus-associated lymphoid tissue during respiratory infection[26] and in the liver during Ehrlichia muris[22] or chronic hepatitis C virus (HCV) infection[27]. The majority still of these settings describe a complementary role assumed by locally generated humoral immunity for tandem cooperation with[26] or direct progenitor seeding from[22] SLO-driven responses. Here we utilize a HCV-related rodent hepacivirus (RHV)28,29, which shares identical genomic structures, polyprotein cleavage patterns and strict hepatotropism with HCV30,31, to compare the nature of its humoral immune responses to those of systemic viral infection. We thereby characterize humoral immunity generated independently of SLOs that is exclusively confined to its site of acute viral infection amidst SLO dormancy, where iHALT is spontaneously generated in the liver of immunocompetent hosts and is functional in preventing persistent hepaciviral infection.

Humoral immunity confined to the liver

Seeking to determine the anatomical distribution of the functional effectors of antibody-mediated immunity in hepatotropic viral infection, we quantified IgG+ antibody-secreting cells (ASCs) generated during RHV infection. We observed minor fluctuations in peripheral blood mononuclear cells (PBMCs) and spleen throughout infection, but a small and consistently increasing number of ASCs accumulated in the bone marrow, resulting in a 16.1-fold increase by 4 weeks post-infection (Fig. 1a). A relatively delayed expansion of IgG+ ASCs was observed in the liver, with a 1.3-fold increase at day 11 and a 280.6-fold increase by week 4 post-infection (Fig. 1a). Whereas bone marrow ASCs did not show any correlation with splenic ASCs (Fig. 1b), a strong correlation was present when compared with those in the liver (Fig. 1c). This expansion of intrahepatic ASCs coincided with an 8.0-fold increase in serum IgG (Fig. 1d). In further characterizing the anatomical distribution of ASCs during RHV infection, we observed a profoundly skewed tissue preference within the liver at week four post-infection in terms of both total and viral E2-specific IgG ASCs (Fig. 1e,f). To determine whether this liver-focused humoral phenomenon specific to this mouse model, we broadened our analysis to include matched PBMC and liver samples from humans infected with HCV, a close genetic relative of RHV30,31. Given the significant skewing in favour of the liver in both RHV infection in mice (Fig. 1h,j) and HCV infections in humans (Fig. 1i,k and Extended Data Fig. 1a–d), this intrahepatic concentration of total and E2-specific IgG ASCs suggests that this represents a conserved response to hepaciviral infection across multiple host species.

Fig. 1: Viral-specific humoral responses are primarily compartmentalized within the liver in strictly hepatotropic viral infection.

a, Quantification of IgG-secreting ASCs during RHV infection in PBMCs, spleen, bone marrow and liver by ELISpot. Connecting line represents mean values. b,c, Correlations between bone marrow ASCs and splenic (b) or intrahepatic (c). Two-tailed nonparametric Spearman correlations with Pearson’s r. d, Serum IgG and intrahepatic ASCs after RHV infection. e,f, Total (e) and E2-specific (f) ASCs at 4 weeks post-RHV infection from n = 2 independent experiments. LN, lymph node. g, Representative ELISpot images of total and E2-specific ASCs in PBMCs and intrahepatic lymphocytes from mice at four weeks post-RHV infection (top) and chronically HCV-infected humans (bottom). h–k, Quantification of total (h,i) and E2-specific (j,k) ASCs from RHV-infected mice (h,j) and HCV-infected humans (i,k), with individual-matched paired comparisons shown between PBMCs and intrahepatic lymphocytes. Data from n = 15 mice from n = 2 independent experiments (j) and n = 9 individuals (k). Two-tailed, paired t-test; P = 0.0082 (i); P = 0.0019 (k). a–h,j, Mouse data are representative or pooled values from at least two independent experiments with at least three mice per group. g,i,k, Human data include nine individuals chronically infected with HCV. d–f, Data are mean ± s.e.m. *P < 0.05, **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001. NS, not significant (P < 0.05).

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SLO dormancy in hepaciviral infection

Although it is well-known that virus-specific plasma cells eventually end up in infected organs, the factors that govern how this process occurs across the spectrum of viral infections with varying organ tropisms are not fully understood. First, to determine whether SLO contributions to the intrahepatic immune response were required components for facilitating hepaciviral clearance, we observed that splenectomized (Fig. 2a,c) and FTY720-treated (Fig. 2b,c) mice were able to acutely resolve RHV infection. These findings demonstrate that both SLO lymphocyte trafficking to the liver and outright splenic presence are dispensable facets of effective anti-RHV immunity. To characterize the origins of RHV- and lymphocytic choriomeningitis (LCMV)-induced intrahepatic ASCs, we initiated FTY720 treatment prior to infection and maintained it throughout to preclude lymphocyte egress from SLOs. Compared with FTY720 treatment, untreated mice exhibited nonsignificant 1.1- and 1.2-fold increases in total IgG (Fig. 2d) and E2-specific (Fig. 2e) ASCs, respectively, in the liver during RHV infection (Fig. 2f). Conversely, untreated mice infected with LCMV generated significant 7.9- and 10.0-fold increases compared with FTY720 treatment in total IgG (Fig. 2g) and viral nucleoprotein-specific (Fig. 2h) ASCs, respectively, in the liver (Fig. 2i). These results support a canonical paradigm of SLO ASC priming with ensuing trafficking to infected organs during systemic infection, whereas strictly hepatotropic infection is conducive to supporting locally generated humoral immunity in the absence of SLO contribution. These observations were not due to the quickly resolving nature of LCMV Armstrong infection, as similar findings were observed with LCMV clone 13 infection (Extended Data Fig. 2a–f). Potential drivers of the lack of such productive iHALT being formed in LCMV infection may include the observed virus localization and lymphocyte restriction near central venules with markedly decreased utilization of oxidative phosphorylation at early time points, in contrast to the periportal preference of RHV and iHALT leukocyte precursors, which exhibit enhanced oxidative phosphorylation usage (Extended Data Figs. 2g–o and 3a,b), representing a classical metabolic signature of GCs[28]. Trafficking of ASCs to the bone marrow following infection with RHV or LCMV was significantly inhibited by FTY720 treatment (Extended Data Fig. 4d–g), suggestive of ASC sequestration in SLOs during LCMV infection and potentially in the liver during RHV infection. This intrahepatic ASC priming in RHV infection prompted us to explore the presence of various cellular components that are typically found in SLO-resident GCs, which revealed significantly enriched populations of GC-like B cells[29] (Fig. 2j and Supplementary Fig. 1a), T follicular helper (TFH) cells (Fig. 2k and Supplementary Fig. 2a) and plasma cells (Fig. 2l and Supplementary Fig. 3a) in the liver during RHV infection. Together, these observations depict two routes by which virus-specific ASCs arrive at infected tissues, that are directly manipulable by spatiotemporal cues of viruses with varying organ tropisms.

Fig. 2: Intrahepatic humoral responses are locally generated amidst SLO dormancy in strictly hepatotropic but not systemic viral infection.

a–c, Serum RHV viraemia in wild-type control (WT) and splenectomized (a), FTY720-treated (b) or splenectomized FTY720-treated (c) mice. GE, genome equivalents. a–i, FTY720 treatment was initiated one day before infection and continued throughout the experiment. Intrahepatic total (d,g), RHV E2-specific (e) and LCMV Armstrong nucleoprotein (NP)-specific (h) ASCs at four weeks post-infection with RHV (d,e) and LCMV Armstrong (g,h). g, P = 0.0026. f,i, Representative intrahepatic ASC ELISpot images from n = 2 independent experiments during RHV (f) and LCMV (i) infection. NP, viral nucleoprotein. j–l, Fluorescence-activated cell sorting (FACS) visualization of intrahepatic CD38lowCD95+ GC B cells (j), CXCR5+PD-1+ TFH cells (k) and CD38lowCD138+ plasma cells (l) alongside representative plots as a proportion of B cells (j,l) or CD4+ T cells (k) from n = 2 independent experiments. RHV versus LCMV: P = 0.0003 (j), P = 0.0085 (k). m–o, Intrahepatic total (m) and E2-specific (n) ASCs at 4 weeks after intravenous (IV) or subcutaneous (SC) RHV infection and representative ELISpot images (o) from n = 2 independent experiments. RHV B1-8i versus RHV (intravenous): P = 0.0003 (m), P = 0.0002 (n). p–r, CD38lowCD95+ GC B cell frequencies in liver-draining (p) and mesenteric (q) lymph nodes and spleen (r) at week 2 post-infection alongside representative FACS plots (s) from n = 2 independent experiments. LDLN, liver-draining lymph node; MLN, mesenteric lymph node. t,u, Frequency of GC-like B cells after LCMV infection (t) and after RHV infection (u). Data are mean ± s.e.m. from n = 2 independent experiments. a–u, Data are representative or pooled values from at least two independent experiments with at least three mice per group. Data in d,e,g,h,j–n,p–s are mean + s.e.m. Two-tailed, unpaired t-test (d,e,g,h); one-way ANOVA with Tukey’s multiple comparisons test (j–n,p–r); two-way ANOVA with Dunnett’s multiple comparisons test (t,u).

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After observing locally primed ASC generation in the liver during RHV infection, we sought to characterize the potential role of various signalling prerequisites in initiating this response, including the CD40–CD40L axis, B cell receptor (BCR) specificity and route of initial viral inoculation. We found that CD40L blockade abrogated intrahepatic GC B cell and ASC generation almost entirely (Fig. 2m–o and Extended Data Fig. 4a), thereby implicating CD4+ T cell costimulatory instruction as a key upstream mediator of this locally generated humoral response in the liver. Further, such interactions were shown to be critical for facilitating viral clearance, as we observed persistent RHV infection following CD40L blockade (Extended Data Fig. 4b). We then sought to address whether this response was merely a result of bystander, cytokine-induced activation within the infected microenvironment, thereby enabling bypassing of cognate BCR signalling for B cell activation and ASC differentiation. As B1-8i mice[32], which express a restricted heavy chain specific for 4-hydroxy-3-nitrophenylacetyl, were unable to generate significant levels of ASCs or GC B cells in the liver (Fig. 2m–o and Extended Data Fig. 4a), we therefore demonstrated that antigen-specific BCR signalling was also a critical prerequisite to these liver-generated antibody responses. To delineate whether RHV-induced intrahepatic ASC responses were simply a result of intravenous viral injection owing to inevitably biased delivery and accumulation in the liver, we characterized intrahepatic humoral immunity in mice infected subcutaneously, which we found to largely resemble intravenous infection (Fig. 2m–o and Extended Data Fig. 4a). Such observations suggest that virus-specific, liver-generated ASC formation was uniquely instructed by strictly hepatotropic replication in hepatocytes during RHV infection rather than arising as a by-product of its initial intravenous delivery to the liver.

After determining that the peak of antigen-specific ASC accrual in the liver was rapidly attained by week 2 in LCMV infection, as opposed to the delayed onset and peak at week 4 in RHV infection (Extended Data Fig. 4c), we sought to similarly examine the appearance of GC B cells across various SLOs and the liver over time as a surrogate for potential seeding sites of ASC progenitors prior to hepatic homing or local generation. First, we examined a time point at which GC formation has been robustly observed during prototypical infection and immunization models; at 2 weeks, LCMV infection accordingly elicited significant expansions in GC B cell frequencies compared with naive mice, with 9.0-, 6.2- and 18.1-fold enhancements in the liver-draining lymph node, mesenteric lymph nodes and spleen, respectively (Fig. 2p–s). By contrast, RHV infection induced only modest, nonsignificant increases in each of these SLOs, with 2.3-, 1.6- and 1.3-fold increases in the liver-draining lymph node, mesenteric lymph nodes and spleen, respectively (Fig. 2p–s). Consequently, LCMV infection induced significantly increased GC B cell generation by week 1 in the liver-draining lymph node and in all other organs by week 2 (Fig. 2t), followed by the rapid dissipation of such populations in accordance with the acutely resolving nature of LCMV Armstrong infection. In direct contrast, no significant increase in GC B cell generation was observed throughout the entire duration of RHV infection in the surveyed SLOs, whereas a delayed expansion was observed solely in the liver and immediately extinguished upon viral resolution (Fig. 2u). These findings cooperatively support a route that enables intrahepatic ASCs to arise from SLO-derived GC progenitors during systemic infection, in contrast to locally expanded lineages within the liver during strictly hepatotropic viral infection.

iHALT lacks compartmentalization of SLOs

Upon determining that liver-generated humoral responses require integration of coordinated signalling events and involve classical GC-associated populations amidst SLO dormancy, it was critical to spatially characterize these processes in relation to SLO-driven responses. Accordingly, we performed spatial transcriptomic analyses of the spleen (Fig. 3a) and mesenteric lymph nodes (Fig. 3b) following systemic viral infection, and the liver (Fig. 3c) after strictly hepatotropic viral infection overlaid with haematoxylin and eosin (H&E) staining. Whereas the spleen and lymph nodes exhibited distinctly segmented expression patterns of Ms4a1 and CD3g (Fig. 3d,f), directly coinciding with B cell follicles and T cell zones (Fig. 3i), the liver displayed diffuse overlap and intermingling of such populations without clear borders. Whereas Siglec1+ macrophages formed a fine layer inside the marginal zone of the spleen and the subcapsular sinus of lymph nodes as means of antigen sampling for facilitating adaptive immune responses[33], the liver maintained its aberrant cellular distribution patterns with highly disordered myeloid cell scattering (Fig. 3i). Transcripts related to DNA double-stranded breaks[34], DNA repair[35] and cell cycle36,37 were specifically enriched within GCs in SLOs, including H2afx, Cdk1, Mki67, Ccna2, Nek2, Uhrf1 and Mef2b (Fig. 3e,i and Extended Data Figs. 5 and 6). As these gene signatures are classically reminiscent of centroblasts undergoing somatic hypermutation (SHM), class-switch recombination and clonal expansion within GCs, these same features were also present within the liver in a subset of GC-like B cells (Fig. 3e,i and Extended Data Fig. 7). Corroborating our previous identification of intrahepatic CD38lowCD95hi B cells bearing a GC-like phenotype[29] (Fig. 2j), this population presented within the liver as central foci of blasting B cells sparsely intertwined with T cells and myeloid lineages, terminally adjacent to differentiated plasma cells as potentially budding progenies (Fig. 3h,i). XBP1+ plasma cells were also observed in SLOs as putative outputs of proximally occurring GC reactions, subsequently exiting via bridging channels to the red pulp of the spleen or to the medullary cords in lymph nodes (Fig. 3h,i). Despite lacking multiple structural components of GCs housed within SLOs, iHALT wielded unconventional distribution of unpartitioned, productive foci that were nonetheless competent in robustly manufacturing plasma cell progenies that were immediately tethered to their generative origins.

Fig. 3: iHALT lacks segmented compartmentalization of SLOs but contains functional, generative foci of blasting B cells, giving rise to adjacent plasma cells.

a–c, H&E staining of formalin-fixed, paraffin-embedded (FFPE) spleen (a) and mesenteric lymph node (b) sections at 4 weeks post-LCMV infection and a liver section at 4 weeks post-RHV infection (c). Regions of interest (ROIs) are enlarged on the right. One tissue sample from each condition was utilized for Visium HD spatial transcriptomics based on similar morphological H&E staining with limited interindividual variability for n = 4 (a), n = 4 (b) and n = 5 (c) mice. d–h, Visium HD spatial transcriptomic of slides shown in a–c. Individual transcript localization is shown as log_2-scaled heat maps of 8-μm bins for Ms4a1 (d), H2afx (e), Cd3g (f), Ccl21a (g) and Xbp1 (h). i, Graph-based subclustering of clusters of interest from sections in a–c were manually annotated and cross-validated with ACT and PanglaoDB cell annotation databases. IFZ, interfollicular zone; SCS, subcapsular sinus. Colours indicate cell type and anatomical zones.

Anchors locally retain oligoclonal ASCs

Following the local generation of plasma cells within iHALT, it remained unclear what signals were responsible for retaining these cells adjacent to such sites as largely immotile effectors. These populations were relatively absent in the hypoxic zone 3 of the liver surrounding central veins, rather being almost exclusively confined to zone 1 in periportal regions (Extended Data Fig. 8e). Marked expression of putative anchoring pairs at junctions of plasma cells tethered to neighbouring fibroblast tracts included CD44, CXCR4, CD138, αLβ2 integrin (LFA-1) and α4β1 integrin (VLA-4) on plasma cells, complementing osteopontin, CXCL12, type I collagen, ICAM2 and fibronectin, respectively, which were primarily expressed on perivascular fibroblasts (Fig. 4a–d). As several of these cognate binding pairs have previously been demonstrated to mediate LLPC attachment to stromal niches in the bone marrow1,38,39,40,41,42,43,44, this suggested they could be acting similarly here to arrest plasma cell outputs at their respective origins. Thus, we utilized in vivo disruption of such associative factors, specifically VLA-4, LFA-1 and either CXCR4 or osteopontin, to functionally validate the role of qualitative trends observed from spatial transcriptomics (Fig. 4a–c) in confirming that plasma cell retention in the liver was indeed dependent on such molecular factors (Fig. 4e,f and Supplementary Table 1).

Fig. 4: iHALT-derived plasma cells are locally retained and anatomically oligoclonal.

a–c, Visium HD spatial transcriptomics from liver tissue at four weeks post-RHV infection. a, Transcript feature sums of log_2-scaled heat maps of 8-μm bins for osteopontin (Spp1), Cxcl12, type I collagen (Col1a1 and Col1a2 (Col1a1/2)), Icam2, fibronectin (Fn1), Cd44, Cxcr4, CD138 (Sdc1), LFA-1 (Itga4/Itgb1) and VLA-4 (Itgal/Itgb7). b, Merged transcript localization of feature sum lists from a. c, H&E image with portal vein (blue) and central vein (yellow) ROIs (left) with associated transcript localization (right). d, Cartoon diagram representing plausible molecular factors responsible for intrahepatic plasma cell retention. LSEC, liver sinusoidal endothelial cell. Created in BioRender. Grakoui, A. (2025) https://BioRender.com/ppmu1j5. e,f, Intrahepatic total (e) and E2-specific (f) ASCs at 4 weeks post-infection with or without acute blockade of anchoring molecules at days 26 and 27 post-infection. AMD, AMD3100; anti-V/L, anti-VLA-4 plus anti-LFA-1; anti-V/L/S, anti-VLA-4, anti-LFA-1 plus anti-SPP1. n = 2 independent experiments. Control versus anti-V/L + AMD: P = 0.0003 (e), P = 0.0182 (f). g–i, Xenium Prime 5K spatial transcriptomics on liver tissue at three weeks post-infection with upstream morphological staining (g), virus, vasculature and plasma cell transcript localization (h), and virus, GC-associated and plasma cell transcript localization in periportal regions (i). j, RHV RNA in serum plotted against intrahepatic E2-specific ASC frequencies at four weeks post-infection. μMT, B6.129S2-Ighmtm1Cgn/J mice lacking mature B cells; dpi, days post infection. k,l, Bulk IgH BCR sequencing at 4 weeks post-infection from n = 3 RHV-infected mice, n = 3 LCMV-infected mice and n = 1 naive mouse. SHM accrual is plotted as nucleotide divergence from germline sequences among distinct clonotypes (k) and IgH V–J gene pairing chord diagrams (l). m–o, Intrahepatic common Igkc transcript localization alongside unique Igkv gene family transcripts with upstream morphological staining (m) and segmented cell borders showing transcript localization of Igkc with Igkv4-51 (n) and Igkc with Igkv15-103 (o). e,f,j, Data are representative or pooled values from at least two independent experiments of at least three mice per group. e,f,k, Data are mean + s.e.m. One-way ANOVA with Tukey’s multiple comparisons test (e,f,k); two-tailed nonparametric Spearman correlations with Pearson’s r (j).

Source Data

Although virus-specific plasma cells had clearly been shown to be locally generated and retained in this case within the liver, it remained unclear whether these makeshift structures offered functional benefit to the host despite the delayed kinetics and microarchitectural impediments presented by SLO dormancy. Nevertheless, it was readily apparent that generation of functional iHALT was critical for facilitating the resolution of infection amidst a lack of such structures and abundant viral transcripts in B1-8i mice (Fig. 4g–i) and the persistent infection observed following various perturbations precluding intrahepatic ASC generation (Fig. 4j). Such dependence was also corroborated by the unhindered clearance of splenectomized FTY720-treated mice (Fig. 2a–c) and lack of GC formation (Fig. 2p–s,u) or ASCs in SLOs (Fig. 1e,f) in wild-type controls, suggesting that such viral persistence in these cases could be attributable to the overtly clear absence of iHALT (Fig. 4j) in light of the essential role of virus-specific IgG in driving viral resolution[45]. As both the spleen in systemic infection and iHALT in strictly hepatotropic viral infection induced similar levels of SHM over naive controls (Fig. 4k and Extended Data Fig. 9f–i), the spleen retained an evenly expanded, highly polyclonal repertoire, whereas iHALT exhibited marked expansion of a few highly dominant clones (Fig. 4l and Extended Data Fig. 9e). In congruence with such global oligoclonality and the notion of local generation, transcripts of specific BCR variable gene families were predominantly restricted to anatomically unique, isolated iHALT clusters, putatively representative of single founder clones giving rise to ensuing GC-like B cell and plasma cell lineages (Fig. 4m–o and Extended Data Fig. 10h). Collectively, individual iHALT structures were anatomically oligoclonal and supported similar levels of SHM compared with SLOs, ultimately giving rise to plasma cells that were locally retained by functionally validated anchoring molecules and exerted indispensable local antiviral functionality.

Mouse iHALT is closely mirrored in humans

Given our extensive characterization of iHALT induced by mouse hepaciviral infection, it was critical to assess the biological relevance of such findings in humans. Upon performing spatial transcriptomics with subcellular resolution and cell segmentation on liver tissue from healthy, autoimmune hepatitis (AIH), hepatitis B virus (HBV)-infected and HCV-infected humans, we observed leukocytic aggregates that were highly similar within specific conditions yet externally heterogeneous across this spectrum of pathological intrahepatic antigen reservoirs and altogether absent in healthy liver tissue (Fig. 5a,b, Extended Data Fig. 11 and Supplementary Table 2). Although AIH induced large, centralized lymphocytic hubs that were highly interconnected and primarily composed of CD4+ T cells (Fig. 5a–g), its distal margins featured rare waves of CD8+ T cells surrounding and directly engaging intact hepatocytes (Extended Data Fig. 12a,b), potentially representing the effector department of such lymphoid regions in exerting active pathological damage. By contrast, HBV infection featured periportal lymphoid aggregates that were highly internally homogeneous and absent of such obvious segmented compartmentalization phases throughout. Although AIH and HBV induced lymphocytic aggregates that were absent in healthy liver tissue, such regions were highly T cell-dominant (Fig. 5e–g) and presented little evidence of locally generated humoral immunity.

Fig. 5: Hepaciviral infection in mouse and human induce intrahepatic lymphoid structures with highly similar cellular composition, organizational microarchitecture and cell–cell contacts.

a,b, Xenium Prime 5K spatial transcriptomics of liver tissue from healthy, AIH, HBV-infected and HCV-infected humans with upstream staining (a) and selected transcript localization (b). c–g, Quantitative analyses from spatial transcriptomics demonstrating number of leukocytic aggregates per mm^2 of tissue (c), aggregate area of leukocytic aggregates (d), and lymphocytic cell-type proportions of B cells (e), CD4+ T cells (f) and CD8+ T cells (g) observed in leukocytic aggregates from individuals with AIH (n = 1), chronic HBV (n = 2) and chronic HCV (n = 2) infection. Data are mean + s.e.m. d–g, One-way ANOVA with Tukey’s multiple comparisons test. AIH versus HBV: P = 0.0101 (g). h, Quantification of cell types and their direct contact partners within leukocytic aggregates from annotated spatial transcriptomics with subcellular resolution and cell segmentation during mouse RHV (orange) and human HCV (blue) infection. HSC, hepatic stellate cell. i–n, RHV-infected mouse liver (i,k,m) and HCV-infected human liver (j,l,n) tissue. Generative GC-like structures were characterized upstream staining (i,j) and GC-associated transcript localization (k,l) and colour-coded cell-type annotation with selected overlaid transcripts (m,n). o–t, RHV-infected mouse liver (o,q,s) and HCV-infected human liver (p,r,t) tissue. Areas of intrahepatic plasma cell residency were characterized by upstream staining (o,p) and plasma cell and hepatic stellate cell and fibroblast-associated transcripts (q,r) with colour-coded cell-type annotation (s,t). Based on similar morphological H&E staining with limited interindividual variability of n = 2 HCV-infected humans and n = 4 RHV-infected mice, Xenium 5K was performed on tissue from n = 2 human and n = 1 mouse livers, from which n = 1 representative tissue of each are shown in i–t.

Source Data

Upon spatially characterizing iHALT during RHV infection with greater resolution and reliable single-cell annotation with related transcript visualization, a plausibly generative hub was observed housing blasting GC-like B cells expressing Mki67, H2afx and Aicda in frequent contact with CD4+ T cells (Fig. 5i,k,m). Notably, HCV-induced lymphoid structures in humans exhibited remarkable similarity to structures observed in mouse RHV infection, featuring a central focus of similarly blasting GC-like B cells in extensive contact with CD4+ T cells (Fig. 5j,l,n). Just outside of these generative foci in both mice and humans, proximally emerging plasma cells were closely retained in the surrounding periportal regions, often directly bordering hepatic stellate cells (Fig. 5o–t). Among such active structures containing GC-like B cells and their ensuing plasma cell counterparts, the highly prominent cell–cell contact patterns favouring GC-like B cells with CD4+ T cells and plasma cells with hepatic stellate cells was again closely recapitulated in mice and humans (Fig. 5h). Collectively, although various settings of liver disease featuring intrahepatic antigen reservoirs elicited lymphoid aggregates of variable composition and organizational structures in humans, hepaciviral infection in mice and humans readily induced lymphoid structures bearing extensive similarity in relation to their cellular composition, cell–cell contact partners and segmented organizational schemes.

Discussion

Virus-specific plasma cells eventually localize within infected organs during the acute phase of a broad spectrum of infections1,3,46. As SLOs possess the microarchitectural infrastructure and rationally juxtaposed cells that are necessary for promptly facilitating cooperative GC reactions, infection-induced chemokine gradients and adhesion molecules typically guide such GC-derived effector lineages to these extralymphoid sites of infection. Accordingly, we show here that ASCs are generated in SLOs and subsequently traffic to the infected liver during systemic infection. However, we also characterize humoral immunity that is generated and maintained exclusively at the site of viral infection in the wake of unproductive SLO dormancy and arises spontaneously in immunocompetent hosts as iHALT, which confers protection from persistent hepatotropic viral infection.

As there was minimal humoral activity in SLOs during RHV infection, it was unclear whether this activity was attributable to the strictly hepatotropic nature of this virus. Furthermore, it remained unknown whether this effect was specific to this mouse model or, rather, represented a conserved phenomenon occurring across multiple host species. We therefore utilized human samples from individuals infected with HCV, a close genetic relative of RHV with identical genomic structure and polyprotein cleavage patterns[30], to confirm that an anatomical skewing of E2-specific ASCs in the liver represented a conserved immunological response to hepaciviral infection in both mice and humans. As we have previously shown that CD20+ B cells are critical for RHV clearance prior to two weeks post-infection but dispensable thereafter[45], this suggests that the local differentiation of GC-like B cells to depletion-resistant intrahepatic ASCs at this time is likely to orchestrate IgG-dependent viral resolution. Although these results confirmed the intrahepatic confinement of functional, virus-specific effectors in mice and humans, it was critical to determine to what extent iHALT may be present in this setting as a potentially generative hub of such outputs. Of note, lymphoid structures were readily observed in HCV-infected human liver tissue, demonstrating high similarity to those seen in mouse RHV infection in terms of cellular composition, generalized microarchitectural arrangement and cell–cell contact partners. Given the delayed development of intrahepatic TLS formation and potent neutralizing antibodies in the chronic phase of HCV infection47,48, it is possible that the timely generation of functional iHALT may dictate perpetual infection outcome prior to the onset of T cell exhaustion[49] and extensive quasispecies diversification[50]. Utilization of RHV to systematically investigate the development of iHALT in an unrestricted fashion and corroborate the biological significance of such findings with static observations in human samples is a highly physiologically relevant mouse model of natural hepatotropic infection and shares multiple features with its hepacivirus relative HCV, including the propensity to cause fibrosis and hepatocellular carcinoma, replicative dependence on host miR-122, identical genomic structures and polyprotein cleavage patterns, strict hepatotropism, viral buoyancy, entry dependence on hepatocyte scavenger receptors and tight junction proteins, correlates of protective adaptive immunity, and the intricacies of iHALT described here and elsewhere30,31,45,51,52,53,54.

Systemic infection ensures extensive antigen exposure in tissues and SLOs via circulating blood, draining lymph and cell–cell contacts[55]. Host immune systems in this setting can therefore generate adaptive immune responses from any of these convenient, rapid and efficient sites. Accordingly, we found that LCMV infection elicited high frequencies of GC B cells in SLOs by one to two weeks post-infection and that intrahepatic ASC accrual was dependent on lymphocyte egress from SLOs. As strictly hepatotropic viral infection conversely did not produce significant ASC or GC B cell responses in SLOs, a delayed generation of GC B cells and TFH cells in the liver at four weeks post-infection supported the development of intrahepatic, virus-specific ASCs that were independent of SLO contribution. Consistent with this apparent lack of functional role, viral clearance was unhindered following combined splenectomy and treatment-induced inhibition of lymphocyte egress from SLOs. As hepatotropic viral infection has been shown to induce a highly inflammatory environment in the liver, it was critical to distinguish whether the humoral responses characterized here were merely by-products of such cytokine-induced bystander activation[56] or, rather, represented coordinated immune interactions crafted by cognate specificity. We proceeded to show that productive iHALT was indeed facilitated by costimulatory guidance, required a diverse, unrestricted BCR repertoire, supported comparable levels of SHM as SLOs, and demonstrated markedly unique oligoclonality among individual periportal structures. Consistent with such signalling intricacies required for functional iHALT orchestration, systemic viral infection of prolonged duration was also unable to elicit locally primed humoral immunity, further reinforcing the notion that kinetically persisting, general inflammation was not alone sufficient to induce iHALT. Extending further to cases of strictly hepatotropic antigen reservoirs, intrahepatic lymphocyte aggregates could be readily visualized during HBV infection and AIH in humans. Although the cellular composition and microarchitecture of these structures deviated markedly from those seen in HCV infection, extensively secreted hepatitis B surface antigen and ensuing effects on systemic antigen availability may contribute to the B cell underrepresentation. Further, although AIH elicited large, centralized CD4+ T cell networks with CD8+ T cells at its peripheral edges surrounding intact hepatocytes, the precise signalling pathways accounting for such heterogeneity across the spectrum of pathological intrahepatic antigen reservoirs and associated disease states remain undefined and represent an intriguing avenue for future study. Given that the local facilitation of hallmark SLO-associated activities such as somatic hypermutation, antigen-specific clonal expansion and plasma cell differentiation were experimentally validated to occur within iHALT alongside downstream functional efficacy of such progenies in exerting antiviral control, such criteria justify its classification as a bona fide inducible lymphoid tissue within the overarching context of TLSs. Such an assignment distinguishes itself from cases restricted to broader, less descript TLS dubbing that are based solely on the observation of leukocytic aggregates of unknown composition, activity, or functionality, thus extending the potential acquisition of iHALT labelling to other pathological settings of experimentally validated SLO mimicry accompanied by functional adaptive effector outputs within the liver.

Regardless of the initial location of adaptive immune effector priming, if and to where the effector cells depart is largely determined by gradients of S1P[57] and various chemokines in conjunction with lymphocyte integrin interaction with local adhesion factors[58]. Essentially, such migratory outcomes are dictated by the competitive proceedings of multiple individual factors vying for control to their own end, such as the increased LFA-1 and VLA-4 integrin expression collectively overpowering potential S1PR1 responsiveness in ultimately maintaining marginal zone B cells as a non-recirculating population in the spleen59,60. As GC-derived plasma cells in the spleen exiting through the bridging channels can traffic to infected organs via CXCR3[46] or permanently arrest at CXCL12-rich regions internally in the red pulp[38] or externally in the bone marrow[38], the specific set of guiding principles present in the liver for locally produced iHALT progenies warranted further investigation. Although iHALT formally lacks multiple cellular components and the segregated compartmentalization of bona fide SLO GCs, it possesses a central, integrative network of GC dark zone-like, blasting B cells, T cells and myeloid cells immediately surrounded by budding plasma cell outputs. In contrast to transitory bridging channels of the spleen or distant medullary sinuses of lymph nodes, the majority of plasma cells in the liver were directly confined to their generative origins within the parenchyma along neighbouring periportal tracts. Consistent with this local maintenance and the aforementioned multivariable function dictating the anatomical fate of plasma cells, these intrahepatic regions shared many of the same features responsible for LLPC tethering to bone marrow stromal niches. Namely, iHALT-derived plasma cells expressed high levels of CD44, CXCR4, CD138, LFA-1 and VLA-4, and directly juxtaposed perivascular fibroblast tracts were high producers of their respective ligand pairs, including osteopontin[42], CXCL12[38], type I collagen[39], ICAM2[43] and fibronectin[44]. Furthermore, plasma cell accrual was rarely observed near central veins in zone 3 of the liver, suggesting that the more highly oxygenated zone 1 periportal regions are inherently more conducive to fostering iHALT development. In light of the concentrated anatomical skewing of functional, virus-specific ASCs in the liver demonstrated to be both locally produced and retained, such molecular anchoring pairs were functionally validated in vivo as significant contributors to plasma cell retention in the liver in direct adjacency to their generative foci in lieu of succumbing to S1PR1-dependent egress to circulation.

Despite forfeiting the rapid kinetics and stromal intricacy of SLO-generated antibody responses, local TLS formation at sites of infection or within tumours claims its own set of unique, potentially compensatory advantages. Locally relevant antigen biasing can facilitate a higher likelihood of cognate lymphocyte recognition focused exclusively on targets of interest in a confined, antigenically concentrated region and, further, ensuing effector lineages are immediately spatially poised for local, potent antibody production without migratory hurdles or suboptimal concentration discrepancies between blood and tissue[18]. As viruses adopt multiple strategies to prolong infection duration and thereby increase the likelihood of quasispecies diversification and T cell exhaustion in order to achieve chronicity, it is plausible that some viruses may exploit perceived host vulnerabilities to delay adaptive immune responses via SLO subversion in settings of strict replicative organ tropism. Here we characterize locally retained humoral immunity that is generated exclusively at its extralymphoid site of infection and is able to compensate for virus-mediated, covert suppression of SLO activity to derail persistent infection.

Methods

Human study participants

Patients undergoing orthotopic liver transplantation at Emory Transplant Center of Emory University Hospital were enrolled in the study in accordance with the Emory University Institutional Review Board (IRB) approval (IRB #00100485). HCV-infected samples utilized in the current study are from individuals chronically infected with HCV without HIV coinfection. Patient characteristics with clinical information and relevant biological variables are summarized in Supplementary Table 1. Written informed consent was obtained from each individual and IRB #00100485 conforms to the guidelines of the 1975 Declaration of Helsinki (revised 2013). Additional human liver samples were obtained in accordance with the protocol approved by the Mass General Brigham IRB, protocol nos. 1999P004983 and 2004P000793.

Animals and ethics statement

C57BL/6J and B6.129P2-Aicdatm1(cre)Mnz/J (AIDcre/cre) mice were obtained from Jackson Laboratories. B6.129P2[C]-Ightm2Cgn/J (B1-8i) mice were obtained from J. Jacob. Predetermined sample size calculations and blinding were not performed, as number of individual data points and experimental design were determined based on experiment type to generate interpretable, reproducible conclusions. Regarding randomization, age-matched mice of identical strains ordered from commercial vendors were randomly divided into cages by animal care staff upon arrival at vivarium facilities. Mice were six to ten weeks of age at the time of study initiation. All biohazard and animal experiments were carried out in accordance with approved protocols from the Emory Institutional Animal Care and Use Committee (IACUC #201700372).

Viruses and infections

RHV inoculum was generated in vivo following serial passage and adaptation to the mouse host. Mice were infected with RHV via retroorbital injection unless otherwise specified with 10^5 viral genome equivalents. Lymphocytic choriomeningitis virus (LCMV) Armstrong was infected intraperitoneally with 2 × 10^5 plaque-forming units (PFU) per mouse and LCMV clone 13 was infected via retroorbital injection with 2 × 10^6 PFU per mouse.

FTY720 treatment and CD40L blockade

To prevent lymphocyte egress from SLOs, FTY720 (Sigma) was administered at a final concentration of 1 mg kg−1 and injected intraperitoneally three times weekly. To disrupt CD40–CD40L signalling, 250 μg anti-CD154 (CD40L, clone MR1, InVivoMab) was injected intraperitoneally 3 times weekly.

Disruption of associative anchor molecules

For assessing the functional role of cognate associa designated slide area. These slides were processed tive anchoring pairs on intrahepatic plasma cell retention, interventional treatments were acutely administered intraperitoneally at 200 μg each on days 26 and day 27 post-infection, after which ASC enumeration was conducted at day 28 post-infection. Such interventions included specified combinations of anti-CD49d (VLA-4, clone PS/2, InVivoMab), anti-CD11a (LFA-1, clone M17/4, InVivoMab), anti-SPP1 (osteopontin, clone 103D6, InVivoMab), and AMD3100 octahydrochloride (Tocris Bioscience). Persistent disruption of such factors was achieved via administration of such treatment at 100 μg 3 times weekly beginning at day 11 post-infection, again being assessed for intrahepatic ASC generation at day 28 post-infection.

Splenectomy

To eliminate splenic antiviral contributions, mice were subjected to general anaesthesia with nebulized isoflurane, after which the spleen was exteriorized and separated from proximal mesentery and vasculature by cauterization. Mice were given postoperative analgesia and permitted to recover for one week prior to infection.

Virus quantification

RHV titres were determined through PCR with reverse transcription (RT–qPCR) following RNA extraction from serum. RNA was extracted from 10 μl of serum using the Pure Viral Nucleic Acid Kit (Roche) and eluted in 30 μl. RT–qPCR was performed following amplification of cDNA reverse transcribed from viral RNA with primers specific for the NS3 region of RHV, TaqMan Fast Virus 1-Step Master Mix for qPCR (Applied Biosystems), and TaqMan QSY Probe (Applied Biosystems). A standard curve was generated using a linearized plasmid encoding the RHV NS3 protein.

Hepatic leukocyte isolation

Isolation of liver-infiltrating leukocytes was performed following perfusion of the liver with PBS through the hepatic portal vein and being passed through 70-μm cell strainers (Fisher). Cells were then isolated via 37% Percoll (Cytiva) gradient density centrifugation at 500g for 15 min with slow brake, followed by lysis of residual RBCs in ACK buffer.

Construction and purification of RHV E2 glycoprotein monomer

The purified RHV E2 ectodomain (eE2) monomer was generated as previously described[45]. In brief, an insert containing the RHV E2 ectodomain (amino acids 413–648) upstream of a PreScission protease-cleavable protein A (PA) tag was cloned into the pJG lentiviral vector. Lentiviruses were generated by cotransfecting pJG-RHV E2 with accessory plasmids psPAX2 and pMDG2 into HEK293T cells (purchased from ATCC [CRL-3216] and authenticated and tested negative for mycoplasma by vendor) with Lipofectamine 3000 (Invitrogen). FACSort-purified stable transduction yielded RHV-E2–PA in the supernatant, which was purified by IgG fast flow affinity column (GE Healthcare). The protein A tag was cleaved in-column by PreScission protease (GE Healthcare). Soluble protease was captured by the GSTrap FF column (GE Healthcare) connected in tandem with the IgG FF column before elution of RHV E2.

ELISpot

Total and antigen-specific ASC responses were assessed via spontaneous IgG ELISpot assays without prior cell stimulation. For mouse experiments, plates (Millipore) were directly coated with anti-IgG H+L (Jackson Immuno), RHV eE2 or LCMV NP (provided by R. Ahmed), all in PBS overnight at 4 °C. For human ELISpot, plates were coated with anti-IgG (Mabtech), HCV eE2 (J6, genotype 2a[61]), HBV HBcAg (Prospec), or hepatitis E virus (HEV) protein p239 (provided by Z. Feng). Following blocking, lymphocytes harvested from bone marrow, liver, lymph nodes, spleen and PBMCs were added to ELISpot plates in IMDM (Cytiva HyClone) supplemented with 10% fetal calf serum and 1% penicillin/streptomycin without prior stimulation and left in an incubator with 5% CO_2 at 37 °C overnight. Cryopreserved PBMCs and intrahepatic leukocytes from HCV-infected individuals were thawed and directly plated in the same manner for human ELISpot. Following 16 h of incubation, plates were washed and detected for mouse with anti-Fcγ horseradish peroxidase (HRP) (Jackson) or for human with anti-IgG biotin (Mabtech) and streptavidin-HRP (Mabtech). Plates were developed with ELISpot TMB substrate (Mabtech) for 7 min, quenched and washed with deionized water, and left to dry in the dark overnight until imaging with Immunospot CTL counter and Image Acquisition 4.5 software (Cellular Technology).

ELISA

Serum IgG was determined following the coating of ELISA plates with anti-mouse H+L IgG antibody (Jackson Immuno) at 4 °C overnight. Plates were washed twice and blocked at 37 °C for one hour. Sera was diluted in binding buffer and incubated for 1 h at room temperature. After washing, anti-mouse Fcγ HRP (Jackson Immuno) was then added for 1 h at room temperature. After washing, 50 μl of TMB substrate solution (Thermo) was added prior to quenching with 50 μl of H_2SO_4. Absorbances were read at 450 nm and total IgG concentration was determined from standard curves (Sigma).

Flow cytometry

Leukocytes isolated from various organs were subjected to Fc blocking (Tonbo Biosciences) and subsequently surface stained for 30 min at 4 °C with BV421 anti-CD19 (Biolegend), BV605 anti-CD138 (Biolegend), PE anti-CD38 (BD Biosciences), PE-Cy7 anti-CD95 (BD Biosciences), BUV395 anti-mouse CD4 (BD Biosciences), BV785 anti-CD279 (Biolegend), and biotinylated anti-CXCR5 (Thermo). Cells were then stained with Ghost Dye Violet 510 (Tonbo Biosciences) and streptavidin-APC (Thermo), fixed in Cytofix (BD Biosciences), and washed in FACS buffer prior to acquisition. Data were acquired with BD FACSDiva Software v.9.3.1 and analysed with Flowjo v.10.10.0.

Immunohistochemistry

Liver tissue was fixed in 4% formalin for at least 24 h, embedded in paraffin, and 10-mm sections were cut and placed on charged slides. Sections were dewaxed using xylene and ethanol gradient, followed by heat induced antigen retrieval. Subsequently, the sections were permeabilized in a Triton X–gelatin solution for 20 min, washed 3 times with PBS and blocked with 5% BSA at room temperature for 1 h. Next, the sections were incubated for 1 h with anti-mouse IgG at room temperature. Afterward, the slides were washed in PBS and mounted using a DAPI-containing mounting medium. Images were captured using a Nikon A1RHD confocal microscope at Emory University.

BCR sequencing

For BCR sequencing, whole organs were first lysed and digested in RLT using TissueRuptor (Qiagen), followed by RNA extraction with RNeasy Mini Extraction Kit (Qiagen). Samples were amplified using iR-RepSeq+ Mouse BCR IgH primers from a total RNA input of 1,200 ng. Next-generation sequencing libraries were generated encompassing the BCR heavy chain with unique molecular identifiers being incorporated during the reverse transcription step and first-strand cDNA selection with removal of remnant primers via SPRIselect bead purification (Beckman Coulter). After 2 additional rounds of amplification and purification, libraries were multiplexed and sequenced on 10% of a Nextseq 1000 P1 600 cycle. Sequencing data were analysed using the iRmap program62,63. In brief, Ig heavy chain (IgH) sequence reads were de-multiplexed and compared to an IMGT reference library[64] for mapping to germline V, D, J and C sequences. For generation of clonal lineage trees, representative IgH sequences of expanded BCR clones were selected for clonal lineage analysis. Phylogenetic trees were created using IgPhyml from the immcantation framework (v.4.0.0)65,66 and plotted using the alakazam R library[67] with unique CDR3 nodes being defined by a hamming distance of one.

Visium HD spatial transcriptomics

Spatial transcriptomic libraries were generated using the Visium HD Spatial Gene Expression Reagent Kit (10x Genomics, PN-1000668) according to the manufacturer’s instructions at UAB Flow Cytometry and Single Cell Core. First, the FFPE tissues were assessed for RNA quality and the samples with DV200 >50% were selected for the assay. In brief, the tissue slides were prepared with 5-μm sections at the UAB Comparative Pathology Lab and were subjected to H&E staining. The 6.5 mm × 6.5 mm ROIs were imaged at 20× magnification using a Lionheart FX (Biotek) at the UAB High Resolution Imaging Facility. The tissue sections were then decrosslinked and hybridized with Visium Mouse Transcriptome Probes v.2. The hybridized probes were captured by the Visium HD Slide oligos using the CytAssist instrument (10x Genomics), amplified, and the final sequencing libraries were generated. The libraries were subjected to 2× 150 bp pair-end sequencing with a sequencing depth of 275 million reads per sample on an Illumina NovaSeq plus instrument (Azenta Life Sciences). Demultiplexing and sequence analysis, along with alignment to H&E-stained images, were carried out using the SpaceRanger pipeline. Individual transcript localization, graph-based clustering, and clustered gene expression heat maps were generated in Loupe Browser v.8.0.0. Graph-based clusters were subjected to manual annotation and cross-validated with ACT[68] and PanglaoDB[69] cell annotation databases.

Xenium Prime spatial transcriptomics

FFPE tissue sections from mouse and human liver samples were arranged into tissue microarrays from H&E-stained samples and 5-μm sections were mounted onto Xenium slides at Acepix Biosciences. Such arrays of 8 tissue cores (4 mm diameter each) were arranged to fit within the 12 × 24 mm designated slide area. These slides were processed using the Xenium In Situ platform (10x Genomics) at UAB Flow Cytometry and Single Cell Core following the manufacturer’s protocol. In brief, the samples were probe hybridized with the Xenium Prime 5K Mouse/Human Pan Tissue and Pathways panel, along with custom panels of 100 additional genes each, based on rational selection and design. Following probe hybridization, ligation and rolling circle amplification, the Xenium cell segmentation cocktail for morphology-based cell partition was also included in the workflow. The slides were then loaded onto the Xenium analyser instrument for automated imaging and signal decoding. Transcript assignments, segmented cell boundaries, and unsupervised clustering were performed in the instrument by Xenium Onboard Analysis software and visualized using Xenium Explorer v.3.2. Objective selection criteria for intrahepatic leukocytic aggregates in human liver tissue required all of the following: transcript density for LTB ≥4 × 10−4, MS4A1 or CD4 ≥4 × 10−3, CXCL13 ≥3 × 10−4 and CCL19 ≥10−3 and total transcript count of LTB ≥10 and MS4A1 or CD4 ≥50. Downstream analyses were conducted in Xenium Explorer 3 with manual cell-type annotation of individually segmented cells utilizing ACT[68] and PanglaoDB[69] cell annotation databases.

Statistical analyses

Statistical analyses were performed with Prism v.10 (GraphPad). Specific tests used are provided in each figure legend. Statistical significance was denoted as indicated: *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001 and ****P ≤ 0.0001.

Obtaining biological materials

Biological materials used in this study can be made available to the scientific community by directly contacting the corresponding author.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The data supporting the findings of this study are available in the article. Mouse BCR sequencing data are located at https://doi.org/10.5281/zenodo.17345590 (ref. [70]), mouse spatial transcriptomics data are located at https://doi.org/10.5281/zenodo.17346168 (ref. [71]), and human spatial transcriptomics data are located at https://doi.org/10.5281/zenodo.17354774 (ref. [72]). Source data are provided with this paper.

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Acknowledgements

We thank J. Jacob, Z. Feng and R. Ahmed for providing valuable reagents; E. Elrod, A. Pradhan, S. Watry and N. Pazira for technical assistance; J. Magliocca, D. J. Lo, S. Kim, M. Kazimi and F. Siddiqui for providing liver samples, along with the nursing staff and consenting team members; S. Liu, C.-W. Sun, M. Basu, E. Dessasau, K.-J. Chang, P. Laaguiby, R. Mote, J. Brown, W. Pan, B. Brown, D. Weber, K. Gill, A. Saini and Emory University Integrated Cellular Imagining Core Facility (RRID:SCR_023534); and the veterinary and animal care staff of Emory National Primate Research Center for their assistance. This project was supported by NIH grants R01AI136533 and U19AI159819 to A.G., ORIP/OD P51OD011132 (formerly NCRR P51RR000165) to the Emory National Primate Research Center (A.G.), R01AI151175 and R01AI183877 (A.G. and A. Kapoor), and R01AI185926 (A. Kapoor). J.G. was supported by Emory Vaccinology Training grant T32 5T32AI074492-14 and NIAID F32 fellowship award F32AI189066. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author information

Authors and Affiliations

1. Division of Microbiology and Immunology, Emory National Primate Research Center, Emory Vaccine Center, Atlanta, GA, USA

John Gridley, David Pak, Anuradha Kumari, Jacob Shupak, Brantley Holland, Yifeng Shi, Sudhir Pai Kasturi & Arash Grakoui

2. Center for Vaccines and Immunity, The Research Institute at Nationwide Children’s Hospital, Columbus, OH, USA

Sheetal Trivedi & Amit Kapoor

3. Liver Center, Gastrointestinal Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

Yongtao Wang & Raymond T. Chung

4. Division of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA

Arash Grakoui

Contributions

J.G. conceived and designed the study, carried out experiments, analysed and interpreted data, and wrote the manuscript. D.P. conceived and designed the study, carried out experiments, and analysed and interpreted data. A. Kumari, J.S., B.H., Y.S., S.T. and Y.W. assisted with experiments, and analysed and interpreted data. S.P.K. helped design the study and provided intellectual input. A. Kapoor and R.T.C. analysed and interpreted data and provided intellectual input. A.G. conceived and designed the study, analysed and interpreted data, acquired funding, and wrote the manuscript, with all authors contributing to writing and providing feedback.

Corresponding author

Correspondence to Arash Grakoui.

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The authors declare no competing interests.

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Nature thanks Antonio Bertoletti, Percy Knolle, Nina Le Bert, Robert Thimme and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Uninfected human liver samples do not have cross-reactive E2-specific ASCs and intrahepatic ASCs in HCV-infected humans demonstrate high specificity for E2.

Intrahepatic leukocytes were incubated overnight without prior stimulation on plates coated with respectively labeled antigens demonstrating (A) lack of E2-specific ASCs in uninfected human liver samples and (C) specificity for E2 over other irrelevant proteins. Representative ELISpot images are shown from liver samples of (B) uninfected and HCV-infected humans as well as (D) from an HCV-infected donor (ET196). (A) Mean + SEM. Statistical tests performed were (A) two-tailed, unpaired t-test and (C) one-way ANOVA with Tukey’s multiple comparisons test. (A) p = 0.0045, (C) HBcAg vs. p239: p = 0.9896, HBcAg vs. E2: p = 0.0059, E2 vs. p239: p = 0.0038. Statistical significance was denoted as **=(p ≤ 0.01).

Source Data

Extended Data Fig. 2 Systemic infection of prolonged duration still does not induce locally generated humoral immunity and harbors virus and immature leukocyte aggregates confined to zone 3 with marked reduction of oxidative phosphorylation compared to early iHALT precursors.

(A, C) Total and (B, D) virus-specific ASCs were quantified in the liver at week 4 post-infection for (A, B) RHV and (C, D) LCMV clone 13 infection with (E, F) representative ELISpot images showing (A, B) representative and (C, D) pooled data from (A-D) n = 2 independent experiments. Mice were either untreated (WT) or splenectomized and treated with FTY720 prior to infection with treatment being maintained throughout (splX + FTY720 [D -1]). Virus localization was determined by viral transcript density for individual portal and central zones at week 1 post-infection during (G) RHV and (H) LCMV clone 13 infection from n = 10 portal and n = 10 central zone selections from a Xenium 5 K spatial transcriptomics run of n = 1 mouse liver per group. Transcript density of genes associated with oxidative phosphorylation usage were quantified in (I) portal zone hepatocyte regions and (J) immature lymphocytic clusters at week 1 post-infection. Following (K) upstream staining, (L-O) individual transcript localization was determined in the liver at week 1 post-infection. (A-D, G-J) Mean + SEM. (A-D, G-J) Two-tailed, unpaired t-tests were performed. (A) p = 0.9168, (B) p = 0.1987, (C) p = 0.0008, (D) p < 0.0001, (G) p = 0.0261, (H) p = 0.0282. Statistical significance was denoted as *=(p ≤ 0.05), **=(p ≤ 0.01), ***=(p ≤ 0.001), and ****=(p ≤ 0.0001).

Source Data

Extended Data Fig. 3 Selection of intrahepatic portal and central zones.

(A, B) Portal and central zones used for quantification of various transcripts (Extended Data Fig. 4) were selected based on expression of various zone-specific transcripts, such as Hal (portal) and Cyp2e1 (central) at week 1 post-infection with either (A) RHV or (B) LCMV clone 13 infection.

Extended Data Fig. 4 Humoral dynamics during RHV and LCMV infection.

(A) CD38lowCD95+ GC-like B cell frequency in the liver is shown at 4 weeks post-RHV infection for uninfected controls, mice treated with αCD40L beginning at day 11 post-infection onwards three times weekly, BCR-restricted B1-8i mice, and WT mice infected either intravenously or subcutaneously. (B) RHV serum viremia is shown for WT mice that were untreated or treated with αCD40L three times weekly beginning at day 11 post-infection. (C) Mean + SEM intrahepatic antigen-specific ASCs were shown kinetically throughout RHV (blue) and LCMV (red) infection. Total (D, F) and antigen-specific (E, G) ASCs were shown in the bone marrow at 4 weeks post-infection with RHV (D, E) or LCMV (F, G) infection with or without FTY720 treatment beginning at day -1 prior to infection and being maintained throughout thereafter three times weekly. (A, C-G) Mean + SEM. (A, C-G) Statistical tests performed were (A) one-way ANOVA with Tukey’s multiple comparisons test and (D-G) two tailed, unpaired t-test. (A) p = 0.0498, (D) p = 0.0001, (E) p = 0.0482, (F) p = 0.0375, (G) p = 0.0045. Statistical significance was denoted as *=(p ≤ 0.05), **=(p ≤ 0.01), and ***=(p ≤ 0.001).

Source Data

Extended Data Fig. 5 Spatial transcriptomic clustering of Visium HD annotated clusters in the spleen following LCMV infection.

(A-C) Individual transcripts added to Feature List were scaled as heatmaps of graph-based clusters generated in Loupe Browser v8.0.0. Annotated clusters corresponding to Fig. 3i are shown at 4 weeks post-infection with LCMV in the spleen.

Extended Data Fig. 6 Spatial transcriptomic clustering of Visium HD annotated clusters in the mesenteric lymph nodes following LCMV infection.

(A-C) Individual transcripts added to Feature List were scaled as heatmaps of graph-based clusters generated in Loupe Browser v8.0.0. Annotated clusters corresponding to Fig. 3i are shown at 4 weeks post-infection with LCMV in the mesenteric lymph nodes.

Extended Data Fig. 7 Spatial transcriptomic clustering of Visium HD annotated clusters in the liver following RHV infection.

(A-C) Individual transcripts added to the Feature List were scaled as heatmaps of graph-based clusters generated in Loupe Browser v8.0.0. Annotated clusters corresponding to Fig. 3i are shown at 4 weeks post-infection with RHV in the liver.

Extended Data Fig. 8 Plasma cell localization during strictly hepatotropic viral infection is primarily confined to periportal zone 1 of the liver and absent near central veins.

(A-F) Visium HD spatial transcriptomic outputs at 4 weeks post-RHV infection in the liver displayed as log2-scaled heatmaps of 8 μm bins for single-parameter panels and feature sums for multiple-parameter lists. (A) Transcripts characteristic of central zone (zone 3) are shown in orange (Glul, Cyp2e1) while those characteristic of portal zone (zone 1) are shown in green (Hal, Arg1). (B) Co-expression of central (orange) and portal (green) zone-related transcripts shown in (A) as combined feature sums. (C) Merged expression of central (red, Glul and Cyp2e1) and portal (green, Hal and Arg1) combined transcripts. (D) Expression of various plasma cell-related transcripts as shown in blue (Xbp1, Derlr3, Jchain, Irf4). (E) Merged expression of transcripts indicative of the central zone (red, Glul and Cyp2e1) and plasma cells (Xbp1, Derl3, Jchain, Irf4). (F) Merged expression of transcripts indicative of the portal zone (green, Hal and Arg1) and plasma cells (Xbp1, Derl3, Jchain, Irf4). (F) Co-expression of plasma cell-related transcripts (Xbp1, Derl3, Jchain, Irf4) as feature sum list. (A, F) From morphologically similar H&E staining with limited interindividual variability conducted on n = 5 mice, data is shown from n = 1 mouse liver tissue with which Visium HD spatial transcriptomics was conducted.

Extended Data Fig. 9 Strictly hepatotropic viral infection induces iHALT with oligoclonal expansion and comparable levels of SHM compared to the spleen during systemic infection.

(A-D) Proportion of IGHJ gene family usage in n = 1 naïve spleen, n = 3 LCMV-infected spleens (week 4 post-infection), n = 3 RHV-infected livers (week 4 post-infection), and n = 1 naïve liver with (A-D) two-tailed, unpaired t-tests. (E) Chord diagrams representing IgH V-J gene family pairing. Representative clonal lineage trees displaying nucleotide divergence from germline clones obtained from (F) naïve spleen, (G) naïve liver, (H) RHV-infected liver, and (I) LCMV-infected spleen. (A-D) Mean + SEM. (A-D) Two-tailed, unpaired t-tests were performed for LCMV spleen vs. RHV liver. For (A-D) LCMV spleen vs. RHV liver: (A) p = 0.0039, (B) p = 0.0099, (C) p = 0.2334, (D) p = 0.0780. Statistical significance was denoted as **=(p≤0.01).

Source Data

Extended Data Fig. 10 iHALT is unhindered by splenectomy, exhibits polarized IgG+ cell localization, and harbors isolated BCR clonal distribution.

(A) Number of total IgG+ ASCs in the liver at week 4 post-RHV infection following splenectomy one week prior to infection or FTY720 administration beginning at day 11 post-infection onward with representative ELISpot image. (B) Correlation between % plasma cells (CD38lowCD138+ of total B cells) and % GC B cells (CD38lowCD95+ of total B cells) in the liver at 4 weeks post-infection with RHV. (C) Representative H&E staining of FFPE sections from spleen at week 4 post-LCMV infection and liver at week 4 post-RHV infection. (D) Immunofluorescence staining of FFPE sections at 4 weeks post-RHV infection. Localization of representative Ig kappa gene families displayed as log2-scaled heatmaps of 8 μm bins at 4 weeks post-infection in (E) spleen (LCMV), (F) mesenteric lymph nodes (LCMV) and (G) liver (RHV). (H) From liver tissue at 4 weeks post-infection with RHV following FTY720 treatment, H&E (top) is shown for corresponding ROIs where Igkv19–93 localization is shown as log2-scaled heatmaps of 8 μm bins (bottom). (I) Correlation between intrahepatic CD38lowCD95+ GC B cells and RHV serum viremia at 4 weeks post-infection. (A-B) Data shown are representative or pooled values from 2-3 independent experiments of 3–9 mice per group. Visium HD spatial transcriptomics was conducted with liver tissue from n = 1 representative mouse following similar H&E morphological staining with limited interindividual variability from (C) n = 4 (spleen) and n = 4 (liver), (E) n = 4, (F) n = 4, (G) n = 5, and (H) n = 4 mice. (D) Representative image shown from immunofluorescent staining that was performed with liver tissue from n = 4 mice. (A) Mean + SEM. Statistical tests performed were (A) one-way ANOVA with Tukey’s multiple comparisons test and (B, I) two-tailed nonparametric Spearman correlation with Pearson’s r coefficient. (A) Uninfected vs. WT: p < 0.0001, WT vs. FTY720 D + 11: p = 0.9939, FTY720 D + 11 vs. Splenectomy: p = 0.9853. Statistical significance was denoted as ****=(p ≤ 0.0001).

Source Data

Extended Data Fig. 11 Selection of intrahepatic lymphoid aggregates in human liver tissue.

(A-F) For various labeled liver conditions, intrahepatic lymphoid aggregate selections utilized for downstream analysis (Fig. 5) were made based all of the following criteria being met: transcript density of LTB ≥ 4e−4, MS4A1 or CD4 ≥ 4e−3, CXCL13 ≥ 3e−4 or CCL19 ≥ 1e−3 and total transcript count of LTB ≥ 10, MS4A1 or CD4 ≥ 50.

Extended Data Fig. 12 Spatial transcriptomic characterization of intrahepatic lymphoid aggregates in human AIH and HBV infection.

Xenium Prime 5 K spatial transcriptomics was performed with liver tissue obtained during human (A, B) AIH and (C, D) HBV infection. Depicted are regions of interest showing (A, C) upstream morphological staining and (B, D) expression of various color-coded transcripts and annotated cell types. Data shown from (A, B) n = 1 individual with AIH and (C, D) n = 1 individual chronically infected with HBV from which liver tissue was selected for spatial transcriptomics conducted on (A, B) n = 1 individual and (C, D) n = 2 individuals with limited interindividual variability.

Supplementary information

Supplementary Information

This file contains Supplementary Figs. 1–3 and Tables 1 and 2. Supplementary Figs. 1–3 provide flow cytometry gating strategies for GC B, TFH and plasma cells in mice. Supplementary Table 1 lists the antibodies utilized in this study and relevant associated information, including dilution, conjugate (if applicable), vendor, catalogue number, lot number and validation. Supplementary Table 2 provides information about human liver donor samples utilized in this study with the following abbreviations used in the table: Dx, diagnosis; unk, unknown; HCC, hepatocellular carcinoma; L.O.D., limit of detection.

Reporting Summary

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Gridley, J., Pak, D., Kumari, A. et al. iHALT unlocks liver functionality as a surrogate secondary lymphoid organ. Nature 649, 991–1002 (2026). https://doi.org/10.1038/s41586-025-09803-4

- Received: 23 January 2025

- Accepted: 23 October 2025

- Published: 26 November 2025

- Version of record: 26 November 2025

- Issue date: 22 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09803-4

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Architecture of the neutrophil compartment

Daniela Cerezo-Wallis,

Andrea Rubio-Ponce,

Mathis Richter,

Emanuele Pitino,

Immanuel Kwok,

Giovanni Marteletto,

Ana Cristina Guanolema-Coba,

Changming Shih,

Run-Kai Huang,

Ana Moraga,

Natalia Borbaran Bravo,

Samuel Doré,

Sergio Callejas,

David G. Aragonés,

Daniel Jiménez-Carretero,

Daniel Martin,

Samuel Ovadia,

Tommaso Vicanolo,

Georgiana Crainiciuc,

Jon Sicilia,

Tong Deng,

Anjelica Martin,

Jing Zhang,

Maria Isabel Cuartero,

Diego Moncada Giraldo,

Alicia Garcia-Culebras,

Alejandra Aroca-Crevillen,

Sandra Martín-Salamanca,

Carlos Torroja,

Max Ruiz,

Irene Ruano,

Melissa S. F. Ng,

Jian Hou,

You Wang,

Ming Zhang,

Jun Pu,

Ana Herruzo,

David Chang van Oordt,

Seokyoon Chang,

Alexander E. Downie,

Fei Chen,

Andrea L. Graham,

William C. Gause,

Pierre O. Fiset,

Jonathan D. Spicer,

Holger Heyn,

Maria A. Zuriaga,

Juan A. Bernal,

Irina A. Udalova,

Maria A. Moro,

Katrien de Bock,

Ana Dopazo,

Jose J. Fuster,

Fátima Sánchez-Cabo,

Juan C. Nieto,

Gabriel F. Calvo,

Julia Skokowa,

Oliver Soehnlein,

Daniela F. Quail,

Logan A. Walsh,

Lai Guan Ng,

Andrés Hidalgo &

…

Iván Ballesteros

Nature volume 649, pages 1003–1012 (2026)

Abstract

Neutrophils exhibit remarkable phenotypic and functional diversity across tissues and diseases1,2, yet the lack of understanding of how this immune compartment is globally organized challenges translation to the clinic. Here we performed single-cell transcriptional profiling of neutrophils spanning 47 anatomical, physiological and pathological scenarios to generate an integrated map of the global neutrophil compartment in mice, which we refer to as NeuMap. NeuMap integrates and expands existing models3,4 to generate fundamental new insights; it reveals that neutrophils organize in a finite number of functional hubs that distribute sequentially during maturation to then branch out into interferon-responsive and immunosuppressive states, as well as a functionally silent state that dominates in the healthy circulation. Computational modelling and timestamp analyses identify prototypical trajectories that connect these hubs, and reveal that the dynamics and preferred paths vary during health, inflammation and cancer. We show that TGFβ, IFNβ and GM-CSF push neutrophils along the different trajectories, and projection of chromatin accessibility sites onto NeuMap reveals that the transcription factor JUNB controls angiogenic and immunosuppressive states and promotes tissue revascularization. The architecture of NeuMap appears to be conserved across sex, environmental and genetic backgrounds, as well as in humans. Finally, we show that NeuMap enables inference of the pathophysiological state of the host by profiling blood neutrophils. Our study delineates the global architecture of the neutrophil compartment and establishes a framework for exploration and exploitation of neutrophil biology.

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Main

Millions of neutrophils are produced every day by the bone marrow through a well-defined series of differentiation steps before their release into the circulation as terminally differentiated, non-proliferative cells that eventually infiltrate most tissues5,6. Work over the past decade has unveiled substantial heterogeneity of neutrophils and delineated a vast array of transcriptional and phenotypic states, of which only a few have been assigned functional roles[2]. Paradoxically, the fundamental organization of the neutrophil compartment remains uncharacterized, a limitation that hinders their functional classification, knowledge of their physiological relevance, and clinical value.

Previous efforts to define the transcriptional organization of this compartment has reported linear trajectories when profiling neutrophils from the bone marrow, spleen, blood and inflamed tissues, and suggested active reprogramming of granulopoiesis by disease7,8. It is unlikely, however, that these profiles encompass the entire transcriptional diversity of neutrophils, given the vast variety of microenvironments, infectious agents and malignant cells that inhabit or invade mammalian tissues, their transcriptional plasticity, and the wealth of functional states already identified across healthy and disease conditions[2]. Thus, fundamental questions remain about the following: (1) the actual number of possible transcriptional and functional states that neutrophils can acquire; (2) how these phenotypic states relate to each other; (3) the specific stage(s) that are reprogrammed by disease; and (4) the signalling and transcriptional programmes that instruct the diversity of neutrophils in living tissues. We posited that understanding the global architecture of the neutrophil compartment might provide insights into these unknowns and facilitate the conversion of this phenomenal army of cells into therapeutic allies.

Transcriptional cartography of neutrophils

To generate a comprehensive transcriptional map of neutrophil diversity in C57BL/6J mice, we conducted single-cell RNA sequencing (scRNA-seq) of lineage-negative (B220, CD3, CD19, NK1.1, Ter119) cells isolated from the bone marrow and CD11b+LY6G+ cells obtained from 13 tissues of C57BL/6J mice housed in specific pathogen-free conditions at different developmental stages, sex, age and pathological perturbations (Fig. 1a, Extended Data Fig. 1a, Supplementary Fig. 1 and Supplementary Table 1). We used the BD Rhapsody platform[9] and included blood from healthy adult male mice as an internal reference to assess integration quality and control for batch effects across datasets (Methods). We then applied dimensionality reduction techniques to visualize the transcriptional diversity of 129,829 neutrophils collected across 47 biological conditions (Fig. 1a and Extended Data Fig. 1b).

Fig. 1: NeuMap, an integrative map of the neutrophil transcriptome.

a, Scheme of the tissues and biological conditions used to generate NeuMap using scRNA-seq. Further details in Supplementary Table 1. b, UMAP visualization by tissue of origin. c, Score value of gene sets for specific biological processes. Complete gene lists in Supplementary Table 2. APC, antigen-presenting cell; OxPhos, oxidative phosphorylation. d, UMAP visualization of neutrophils extracted from health or diseased conditions. e, Scheme of the functional compartmentalization of NeuMap. Each hub is defined by areas containing the top 85% K-mass score. f, Stacked bars showing the proportion of cells from different organs and conditions in each transcriptional hub. Please note that not all 47 conditions are included in the panel. Tissues from healthy young male and female mice are labelled in red, and treatments or disease for each tissue are indicated at the bottom of each bar.

Source Data

The resulting transcriptome embedding, which we refer to as a NeuMap (Fig. 1a,b), enabled visualization of the transcriptional space of the entire neutrophil compartment and revealed general properties by examining its topology. For example, granulopoiesis was identifiable as a linear structure spanning genes associated with proliferation, maturation and granule formation3,10 (Fig. 1c and Extended Data Fig. 1c,d) that was enriched in neutrophils from the bone marrow and spleen (Fig. 1b and Extended Data Fig. 1e). The transcriptional map expanded and lost its linear topology as we incorporated neutrophils from healthy peripheral tissues (blood, lung, liver and intestine) and a broad set of pathological conditions (Extended Data Fig. 1b). Finally, this map converged in a defined end structure featuring programmes associated with hypoxia and cancer (Fig. 1c), without generating transcriptional areas that are separated from the main body of the map. NeuMap also revealed a separate path connecting the initial and end states that was independent of the main granulopoietic path and was abundant in the lungs and inflamed tissues (Fig. 1b and Extended Data Fig. 1e). The integration of all neutrophils in a single, interconnected structure is consistent with the constant production and maturation of neutrophils[11] and suggests that most transcriptional programmes are accessible by neutrophils emanating from the granulopoietic programme.

We found that the anatomical and pathophysiological context (including age and sex) dictated the distribution of cells in different regions of NeuMap (Fig. 1b,d and Extended Data Fig. 1e–g). These distributions were also consistent with those obtained by mapping neutrophils from existing datasets of healthy and tumour-bearing mice onto NeuMap3,4,12 (Extended Data Fig. 1h). Finally, this ‘cartographic’ inspection of NeuMap revealed that neutrophils from healthy individuals were sufficiently diverse to build the general scaffold of NeuMap, whereas those from inflammatory or tumoural conditions extend from these homeostatic states rather than creating new programmes (Extended Data Fig. 1b).

By scoring NeuMap cells against various gene signatures, we identified physiologically relevant regions within the NeuMap embedding (Fig. 1c, Extended Data Fig. 2a and Supplementary Table 2), including the proliferative and granule-synthesis regions described above, distinct metabolic states (oxidative phosphorylation, hypoxia and glycolysis), chemotactic and antimicrobial functions, antiviral responses and signatures associated with cancer, which overlapped with areas of predicted immunosuppression and angiogenesis (Fig. 1c and Extended Data Fig. 2a).

To define functional regions more precisely, we performed unbiased clustering (Extended Data Fig. 2b–e) and optimal grouping of clusters and functional signatures. We identified seven distinct transcriptional regions, or hubs, distributed in NeuMap (Fig. 1e and Extended Data Fig. 2f), which we interpreted as predictive of the main functional states of neutrophils in health, inflammation, infection and cancer. These hubs included the following: (1) pre-neutrophil (PreNeu)-like neutrophils10,13, defined by expression of mKi67 and Ltf, and oxidative respiration14,15 (Supplementary Table 3); (2) an ‘immature hub’ undergoing active maturation and granule synthesis that was positive for Mmp8 and Cebpe, a regulator of granule synthesis10,16; (3) Cd52+ neutrophils that lacked a distinct transcriptional signature, had low mRNA content (Extended Data Fig. 2a), and were enriched in blood (immuno-silent hub); (4) a conspicuous interferon-response signature (IFN-response hub) marked by expression of Ifit1 and Cd274, which featured signatures of antiviral response (Fig. 1c) and was similar to the G5b state reported in mice and humans during infection[3] (Extended Data Fig. 1h); (5) and (6) hubs that shared signatures associated with immunosuppression and angiogenesis—one featured expression of Cd14 and Ptgs2 and was typical of lung and liver neutrophils[1] (immunosuppression I hub (IS-I)), and the other expressed high levels of Vegfa and Cd274 and was prominent in tumoural neutrophils (immunosuppression II hub (IS-II)); and (7) a final hub (antigen (Ag) presentation hub) featuring increased expression of genes associated with MHCII (H2 and Cd74), reminiscent of neutrophils reported to mediate antigen presentation1,17,18, connected the PreNeu and IS-I hubs (Fig. 1e), and is not explored further here. Neutrophils from different tissues or pathophysiological conditions were associated with the different functional hubs (Fig. 1f). For example, Cd14+Ptgs2+ IS-I neutrophils were abundant in the gut, liver and lungs, interferon-responsive neutrophils dominated in infected, inflamed and ischaemic conditions irrespective of the tissue, and Cd274+ and Vegfa+ IS-II neutrophils were prominent in tumours but were largely absent from healthy tissues (Fig. 1e,f and Extended Data Fig. 2f–h).

By analysing multiple other conditions, we found that the basic structure of NeuMap in defined hubs was conserved across relevant biological variables, including sex, housing conditions and genetic strains (Balb/c), and in the presence of Tet2-associated clonal haematopoiesis[19], both at baseline and during atherosclerosis (Extended Data Fig. 3a–e). Profiling of neutrophils from 10 human tissues, including samples from healthy individuals, colorectal cancer and blood from a patient with systemic lupus (Supplementary Table 4 and Supplementary Fig. 2), also revealed substantial conservation of the transcriptional structure of the neutrophil compartment in humans (Extended Data Fig. 4a,b). We could identify six hubs (H1–H6; Extended Data Fig. 4c,d), each enriched in neutrophils from the different tissues and conditions (Extended Data Fig. 4e). Notably, cross-species comparison revealed strong conservation between the six human and seven mouse hubs. The main difference was that the human hub 6 combined features of mouse IS-II and APC hubs and was enriched in human lung and tumour tissues (Extended Data Fig. 4f, g).

In summary, NeuMap offers insights into the transcriptional structure of the neutrophil compartment across multiple tissues and physiological conditions at single-cell resolution, enables the integration of profiles and signatures from existing datasets, and uncovers a conserved and limited set of transcriptional states across different anatomical sites, disease conditions, genetic backgrounds, microbiome statuses and species.

Molecular, functional and spatial profiling

We next explored the potential of this integrative map to uncover new biological properties of neutrophils. NeuMap indicated that most neutrophils from the lungs, liver and intestine localized within the Cd14+ Ptgs2+ IS-I hub, predicting immunosuppressive and angiogenic functions in these organs (Fig. 1f, Extended Data Fig. 1e and Fig. 2a). To validate this prediction, we isolated neutrophils from the lung and compared their angiogenic and immunosuppressive properties with neutrophils from the blood, bone marrow and spleen, which localized in different hubs (Fig. 1e). Using an in vivo Matrigel plug model, we found that co-injection of lung neutrophils potently induced neovascularization compared with neutrophils from the other tissues (Fig. 2b). Similarly, co-incubation of lung neutrophils with activated CD8+ OT-I T cells had the most potent suppressive activity in a cytotoxicity assay against ovalbumin (OVA)-expressing B16 melanoma target cells (Fig. 2c), together confirming the predicted functional properties of neutrophils on the basis of their distribution in NeuMap.

Fig. 2: NeuMap illuminates molecular and functional properties of lung neutrophils.

a, Heat map showing contribution of bone marrow, spleen, blood and lung neutrophils to the top 5% of cells for the indicated functional gene signatures. Cell number was downsampled to 1,000 per tissue. Ag present., Ag presentation; IS, immunosuppression; max, maximum; min, minimum; TAN, tumour-associated neutrophils; VascGF, vascular growth factors. b, In vivo Matrigel plug assay assessing angiogenic potential of neutrophils from indicated tissues. Left, representative Doppler imaging; right, quantification. Data are mean ± s.e.m. from n = 8 (lung) and n = 4 (spleens and bone marrow) biologically independent mice. Two-tailed t-test. c, OT-I T cell killing of B16OVA cells in the presence of tissue neutrophils. Dashed line, OT-I only; nil, untreated OT-I. Data are mean ± s.e.m. from n = 6 control and 9 independent experiments. One-way ANOVA with Dunnett’s multiple comparisons. d, Dogma-seq analysis mapping neutrophils onto NeuMap. Left, top transcription factor motifs that are enriched in the IS-I hub. Right, enrichment score for JUNB binding sites. e, Left, contour plots of control Junbfl/fl and JunbΔN lung neutrophils mapped onto NeuMap. Right, heat map of differentially expressed genes (DEGs) (Supplementary Table 6). f, K-mass projection of neutrophils from ischaemic hindlimbs and kinetics of revascularization in control and JunbΔN mice. Data are mean ± s.e.m. from n = 7–9 biologically independent mice per group. Two-way ANOVA with Tukey correction. LDPI, laser doppler perfusion imaging. g,h, Spatial transcriptomics of immunosuppression scores (g) and interferon-response scores (h) in neutrophils from naive, LLC and flu-infected lungs. i, UMAP clustering of lung neutrophils from tumour-bearing, flu-infected and healthy mice by multiparametric staining. j–l, UMAP and representative immunofluorescence of lung sections from naive mice (j), tumour-bearing mice (k) and flu-infected mice (l), showing cluster-defining markers. Data are from n = 3 biologically independent mice per condition (one section per mouse). Scale bars: 50 µm (main image (top)); 20 µm (expanded view (middle and bottom)). m, Distribution of neutrophil clusters from i in intratumoural (IT), tumour-adjacent (AD), intravascular (intra) and extravascular (extra) tissue.

Source Data

To gain insights into transcriptional regulators of the IS-I hub, we performed single-cell assay for transposase-accessible chromatin (ATAC) sequencing combined with RNA sequencing (Dogma-seq)[20] of neutrophils from the same four tissues at steady-state or during conditions of inflammation and cancer. A genome-wide search for transcription factor binding sites revealed that the IS-I hub was enriched in motifs bound by the AP-1 complex, SMAD and NF-κB isoforms (Fig. 2d and Extended Data Fig. 5a, b), suggesting control of the angiogenic and immunosuppressive properties of lung neutrophils by these factors. By contrast, the immature and immuno-silent hubs were enriched in motifs for members of the CEBP and KLF families, consistent with their roles in neutrophil maturation13,21. IRF- and STAT-related binding sites were largely restricted to the IFN-response hub, AP-1 and Bhlhe sites dominated in IS-II neutrophils, and SMAD and AP-1 binding motifs in the antigen-presenting hub. Finally, CEBP, NF-κB and SMAD binding sites were accessible in most hubs of NeuMap (Extended Data Fig. 5a,b), suggesting broad control of neutrophil transcription by these factors.

To investigate the predicted role of AP-1 in regulating the properties of neutrophils in the IS-I hub, we generated mice with neutrophil-specific deletion of Junb (MRP8cre; Junbfl/fl or JunbΔN mice), a component of the AP-1 complex. Bulk transcriptome analysis of lung neutrophils from control versus JunbΔN mice revealed enrichment in genes associated with immunosuppression, angiogenesis and cancer, and this signature projected over the IS-I and IS-II hubs of NeuMap (Extended Data Fig. 5c), and scRNA-seq analyses revealed altered distribution of lung and liver neutrophils onto NeuMap, as predicted (Fig. 2e and Extended Data Fig. 5d). These alterations, however, were partial, suggesting the contribution of other transcriptional regulators in the IS-I hub. These alterations were cell-intrinsic because transfer of bone marrow neutrophils to the lungs of wild-type mice induced expression of genes involved in immunosuppression and angiogenesis only in control, but not in JUNB-deficient neutrophils (Extended Data Fig. 5e). Of note, although JunbΔN neutrophils differentiated normally (Extended Data Fig. 5f), their capacity to suppress T cell activity in vitro and to promote Matrigel vascularization in vivo was lost (Extended Data Fig. 5g,h). Consistently, lungs from JunbΔN mice showed reduced endothelial cell proliferation and numbers (Extended Data Fig. 5i,j) during young age or after irradiation[1], and subcutaneous Lewis lung carcinoma (LLC) tumours in these mice recruited neutrophils with reduced expression of cancer-associated markers, including CD14, Sca1 and PD-L1 (Extended Data Fig. 5k,l). This correlated with blunted endothelial cell proliferation and increased T cell infiltration in the tumours, and reduction in tumour growth in JunbΔN mice (Extended Data Fig. 5m–o), indicating that neutrophils also require active JUNB signalling to acquire a pro-tumoural state (IS-II hub).

We used a model of hindlimb ischaemia to examine JUNB-dependent reprogramming of neutrophils during neovascularization in a different tissue. scRNA-seq of neutrophils from ischaemic limbs showed that they distributed between the IS-I and IS-II hubs (Fig. 2f) and JunbΔN mice showed impaired restoration of blood flow in the ischaemic limb compared with littermate controls (Fig. 2f), together demonstrating that AP-1 regulates the regenerative properties of neutrophils across tissues.

To further explore the idea that NeuMap classifies neutrophils by functional state, rather than only by anatomical location, we profiled neutrophils from the same tissue subjected to different challenges. Indeed, whereas neutrophils from naive lungs localized mainly in the IS-I hub, those from influenza virus (flu)-infected lungs shifted towards the IFN-response and Ag presentation hubs, and those exposed to LLC tumours moved towards the IS-II hub (Extended Data Fig. 6a), indicating that both tissue and physiological context shape the phenotype and function of neutrophils. We then used spatial transcriptomics of lung sections from naive, flu-infected and tumour-bearing mice to directly associate the distribution of neutrophils in different transcriptional hubs with their microanatomical localization. We annotated cell types and functional signatures in different regions of the lung samples (Extended Data Fig. 6b–d) and identified the spots that contained neutrophils. Using similarity scores for the IFN-response and IS-I hubs (Fig. 2g,h), we found a high immunosuppression signature in neutrophils from border tumour areas compared with those from the tumour core, adjacent tissue or naive lungs. By contrast, the interferon-response score was high in neutrophils around infected bronchioles but was almost undetectable in naive lungs (Fig. 2g,h and Extended Data Fig. 6c). Conversely, projection of the signature of the spatially identified neutrophils onto NeuMap revealed that neutrophils from tumours distributed in areas that included the IS-I and IS-II hubs, whereas those from the flu-infected lungs overlapped with the IFN-response hub (Fig. 2g,h). The association between the IS-II hub and neutrophils in border areas of tumours may explain the aggressive nature of the invasive tumour front[22], and we confirmed these spatial associations in a pancreatic ductal adenocarcinoma (PDAC) spatial dataset[7] (Extended Data Fig. 6e). Notably, IFN-response, IS-I and IS-II neutrophil signatures could also be detected in the border zone of infarcted areas of the myocardium (dataset from ref. [23]; Extended Data Fig. 6e), suggesting that different stresses can elicit similar responses in neutrophils. These analyses also revealed that neutrophils from different hubs have distinct spatial associations with various types of T cells, macrophages and fibroblasts (Extended Data Fig. 6f), providing insights into where these neutrophils are educated in the tissue and their potential effect on other immune and non-immune cells.

To examine the association of the hubs in NeuMap with protein expression and spatial distribution, we performed cyclic labelling of lung sections under the same conditions of infection and cancer. We selected a panel of eight antibodies that were predicted to identify neutrophils from the different hubs present in the lungs (Extended Data Fig. 6g, h) and performed unbiased clustering and uniform manifold approximation and projection (UMAP) embedding to identify eight patterns of neutrophils by protein content (clusters 1–8 in Fig. 2i–l). Each phenotypic cluster identified neutrophils associated with different perturbations and regions of the tissue (Fig. 2m and Extended Data Fig. 6h,i). For example, cluster 1 lacked specific markers and was typical of neutrophils from naive lungs and non-affected tissue adjacent to tumours (Fig. 2j,k), MHCII+CD14+CD11bhi neutrophils (cluster 5) were intratumoural, and PD-L1+IFIT1+ neutrophils (clusters 6 and 7) were abundant in flu-infected sites (Fig. 2k,l and Extended Data Fig. 6i,j).

Overall, these findings highlight the potential of NeuMap to integrate transcriptional, phenotypic, anatomical and functional profiles of neutrophils across tissue microenvironments and physiological states.

Transcriptional trajectories in NeuMap

To explore the transcriptional dynamics in NeuMap, we examined the distribution of neutrophils from several tissues across the different transcriptional hubs in conditions of health, cancer (PDAC) and acute inflammation (from lipopolysaccharide (LPS) injection) (Fig. 3a–c). The distribution of the neutrophils in NeuMap varied for each condition; neutrophils from healthy mice distributed through the immature, immuno-silent, IS-I and IFN-response hubs (Fig. 3a). By contrast, tumour-bearing mice had few neutrophils in the immuno-silent and IFN-response hubs, and instead shifted towards the IS-I and IS-II hubs (Fig. 3b). Finally, most neutrophils from LPS-treated mice localized in the IFN-response hub (Fig. 3c). Of note, the distribution of neutrophils in NeuMap remained relatively constant for each tissue across all conditions (Extended Data Fig. 7a), suggesting that both tissue and physiological state determine the transcriptional diversity of neutrophils.

Fig. 3: Transcriptional trajectories in NeuMap.

a–c, K-mass score (representing cell density) of neutrophils from the bone marrow, blood, spleen, lungs and livers of naive mice (a), tumour-bearing mice (PDAC tumours) (b) and LPS-treated mice (c), projected onto NeuMap. RNA velocity analyses were performed for each of the conditions and the main developmental trajectories are highlighted with red arrowheads. d, K-mass score of the mapped time-stamped neutrophils from steady-state, inflammation (LPS) and tumour-bearing (LLC) mice onto NeuMAP. Neutrophils were tracked at 24 h (bone marrow, black dots), 36 h and 72 h (blood, spleen and lung, orange-red scale) after tamoxifen-induced labelling of Ly6g-tdTomato cells. e, Network model highlighting the trajectories identified in a–d, showing genes and transcription factors enriched for each trajectory. Transcription factors were selected when identified by both EnrichR and chromatin accessibility analyses. Note that we did not find any transcription factor enriched in the immuno-silent path. For a complete list see Supplementary Table 7. f, Inference of preferred trajectories for neutrophils from healthy, tumour-bearing and inflamed mice from the data in a–d.

We utilized RNA velocity analysis[24] to infer the transcriptional trajectories of neutrophils in NeuMap in conditions of health, cancer and inflammation. Whereas the transition vectors from the PreNeu to the immature hub were present in all groups, the downstream trajectories varied across conditions. Healthy mice favoured the transition from the immature to the immuno-silent hub, tumour-bearing mice transitioned towards the IS-I and IS-II hubs, and inflamed mice transitioned towards the IFN-response hub (Fig. 3a–c). We validated these predicted trajectories by genetic-tracing experiments using iLy6GtdTomato mice[1] to label neutrophils with tdTomato in the marrow and tracking potential alterations in granulopoiesis and neutrophil fates in tissues[1] (Extended Data Fig. 7b–d). Maturation paths were similar for the three conditions in the bone marrow, but they followed separate trajectories in the other tissues (Fig. 3d and Extended Data Fig. 7c). Notably, neutrophils from LPS-treated mice activated an additional route of maturation directed towards the IFN-response hub (maturation path II; Fig. 3e and Extended Data Fig. 7c). This trajectory was similar to the canonical maturation path I (shown in Fig. 3e) but lacked activation of genes typically associated with homeostatic maturation (Cd101, Cxcr4, Sell or Csf3r), whereas the expression of inflammatory genes (Icam1 and Cd274) was increased (Extended Data Fig. 7c), suggesting an accelerated and activated type of granulopoiesis. Consistently, we found reduced frequency and intensity of CD101 expression and increased presence of PD-L1+ICAM1+ neutrophils in the bone marrow after LPS treatment (Extended Data Fig. 7e), and accelerated transit of neutrophils across tissues in mice treated with LPS (Extended Data Fig. 7f).

When comparing the trajectories in peripheral tissues, we noticed that tdTomato+ neutrophils from all conditions transited between the different transcriptional hubs defined in NeuMap (Fig. 1e) and began branching out into two separate trajectories by 36 h (Fig. 3d). However, the preferred pathways differed between conditions; neutrophils in healthy and tumour-bearing mice favoured the IFN-response hub by 72 h, whereas inflammation favoured their transition to the immuno-silent hub. We confirmed this pattern in a model of zymosan-induced peritonitis (Extended Data Fig. 7g). Notably, only neutrophils from tumour-bearing mice transited from the IS-I hub to the IS-II hub (Fig. 3d and Extended Data Fig. 7d).

We modelled these findings in a network of transcriptional transitions and hubs in NeuMap, and used pseudotime analysis to identify genes that were specifically activated in those transitions (Fig. 3e and Extended Data Fig. 7h). We found, for example, that the canonical maturation programme (path I) involved the simultaneous activation of inflammatory, anti-inflammatory and interferon-regulated genes (for example, Dusp1, Nlrp3 and Ifitm1; Fig. 3e). This profile was consistent with this path splitting into at least two trajectories, one leading to the immuno-silent hub and the other leading to the IFN-response hub, suggesting that at this early stage, neutrophils activate broad genetic programmes without committing to only one. Representation of the preferred trajectories of neutrophils onto NeuMap suggested that, although the global structure of this network of trajectories is conserved, each condition has preferred transcriptional paths that are followed by neutrophils (Fig. 3f). Thus, mapping of temporal series onto NeuMap infers transcriptional dynamics of neutrophils as they mature in the bone marrow and transition into peripheral tissues to acquire new properties.

Deterministic signalling drives NeuMap trajectories

We sought to identify cues that controlled the transition of immature neutrophils to the main hubs. We first took advantage of a dataset containing the single-cell transcriptomes of lymph node leukocytes exposed to 86 different cytokines[25] and performed in silico screening by mapping the neutrophils from this dataset onto NeuMap (Fig. 4a). Most cytokines induced profiles associated with a single hub; for example, interferons and IL-36 directed neutrophils to the IFN-response hub, GM-CSF and APRIL directed them to the IS-II hub, and IL-1β, IL-1α and TNF directed them to a region between the IS-I and IS-II hubs (Fig. 4a,b).

Fig. 4: Signals that drive neutrophil maturation along the different paths.

a, In silico screening of cytokine induction of transcriptional profiles[25]. Heat map shows lymph node-derived neutrophils treated in vivo with cytokines or PBS (arrowhead). Cells were classified into NeuMap hubs using Seurat LabelTransfer; proportions are colour-coded by hub. b, Distribution of transcriptomes from cytokine-treated neutrophils projected onto NeuMAP. c, Heat map of 21 markers in bone marrow-derived neutrophils (left) and UMAP projection (right) defining five states (immature, mature, inflammation/infection, IS-I and IS-II). Data from n = 4 independent experiments. d, Contour plots of neutrophils from c after 24 h treatment with cytokines or conditioned media from LLC (CM LLC) and PDAC (CM PDAC). e, Radar chart summarizing neutrophil distributions from d. f, Scheme, haematoxylin and eosin (H&E) staining and NeuMAP projection (K-mass) of bone marrow neutrophils cultured with TGFβ, IFNβ or GM-CSF. Micrographs show representative nuclear morphologies (immature ringed versus multilobulated). Scale bars, 10 µm. g, Heat map visualization of the proportion of bone marrow neutrophils from TgfbrΔN, IfnarΔNand Csf2rΔN mutants and Cre− controls in the mature, inflammation/infection (infl./infect.) and cancer phenotypic clusters after treatment with the indicated cytokines for 24 h, determined by flow cytometry. Data are mean ± s.e.m. from n = 3 biologically independent mice per group. One-way ANOVA followed by Dunnett’s multiple comparison test. h, Contour plots showing distribution of neutrophils from indicated genotypes in NeuMAP; shifts (arrowheads) are quantified in Extended Data Fig. 8f. i, Functional assays of neutrophils treated with vehicle or cytokines, measuring migration (n = 3–4), bacterial killing (n = 6–7), phagocytosis (n = 4), NET formation (n = 10–13), immunosuppression (n = 4–6) and angiogenesis (n = 7–8). Data are mean ± s.e.m. from n = 2 independent experiments. One-way ANOVA followed by Dunnett’s multiple comparison test. FC, fold change.

Source Data

We validated these observations by exposing bone marrow neutrophils to eight different cytokines or conditioned media from two cancer cell lines (LLC and FC1242). We profiled the cells using a custom panel of 21 targeting markers associated with different areas of NeuMap (Fig. 4c) to categorize neutrophils into 5 possible profiles (Fig. 4c and Extended Data Fig. 8a–c). We found that TGFβ favoured maturation (CD101hiLY6Ghi); IFNβ induced an inflammatory-like phenotype (PD-L1hiCD14lowCX3CR1hi); and GM-CSF induced a cancer-associated profile (CD101lowPD-L1hiCD14hidcTRAIL-R1hiMHC-II+). Finally, tumour-conditioned media induced phenotypes that resembled GM-CSF treatment but were milder and more biased towards the immunosuppressive phenotype (Fig. 4c–e and Extended Data Fig. 8a–d). Notably, we confirmed that these phenotypes mirrored the predicted transcriptional states in NeuMap by scRNA-seq of the cultured bone marrow neutrophils and projection onto NeuMap (Fig. 4f), suggesting that these cytokines drive the different transcriptional trajectories identified in NeuMap (Fig. 3e).

To formally demonstrate this, we generated mice with neutrophil-specific deficiency in receptors for TGFβ (TgfbrΔN mice), type I interferon (IfnarΔN) and GM-CSF (Csf2rΔN). In vitro treatment of bone marrow neutrophils from these mutant mice prevented the phenotypic changes elicited by their respective cytokines, resulting in reduced maturation-, inflammation- and cancer-associated phenotypes (Fig. 4g and Extended Data Fig. 8e). We then performed scRNA-seq of neutrophils from the relevant tissues of TgfbrΔN (bone marrow), IfnarΔN(blood) and Csfr2ΔN(LLC tumour) mice and their respective controls and mapped their distribution in NeuMap. Neutrophils from TgfbrΔN mice showed delayed maturation, loss of the IFN-response hub in IfnarΔN mice and a shift away from the IS-II hub in Csf2rΔN mice (Fig. 4h and Extended Data Fig. 8f), confirming that these cytokines reprogramme neutrophils to defined transcriptional states in vivo.

We then used multimodal profiling of chromatin accessibility and gene expression at single-cell resolution[20] to identify transcription factors that are potentially involved in the induction of these programmes, which we validated using the HOXB8 system with CRISPR-mediated deletion of selected transcription factors[21] (Extended Data Fig. 8g,h). Deletion of Cebpb, Rfx2 and Runx1 impaired neutrophil maturation and subsequent cytokine-driven polarization. By contrast, Irf5 was required for the infection/inflammation profile, and Relb was required for the acquisition of the cancer/immunosuppressive phenotype in response to GM-CSF or PDAC-conditioned medium. Finally, Junb deletion had broad effects on differentiation, including the IS-I/IS-II phenotype (Extended Data Fig. 8g,h), and this could be rescued by its enforced expression in Junb−/− HOXB8 cells (Extended Data Fig. 8i–k).

We finally used this in vitro strategy to enrich for neutrophils in transcriptional states associated with different regions of NeuMap and assess their core functional properties, including chemotaxis, phagocytosis and neutrophil extracellular trap (NET) formation, as well as bactericidal, immunosuppressive, or angiogenic activities (Fig. 4i and Supplementary Fig. 3a–f). TGFβ induced moderate migratory and immunosuppressive activities. IFNβ, by contrast, impaired migration but activated phagocytosis and NET formation, suggesting activation of an antimicrobial programme. Finally, GM-CSF impaired migration and enhanced phagocytosis, but additionally activated immunosuppressive and angiogenic properties (Fig. 4i and Supplementary Fig. 3e,f), consistent with scenarios of tissue repair and cancer1,26,27.

Thus, these data suggest that the transcriptional transitions and functional states (hubs) defined in NeuMap are elicited by defined signals and transcription factors.

Conservation and predictive properties of NeuMap

We speculated that the rich transcriptional resolution of NeuMap could be harnessed to visualize neutrophil states across species, pathophysiological conditions and response to therapies. We first examined signatures associated with a favourable response to immunotherapy in a mouse model of lung cancer[27]. Visualization of these signatures onto NeuMap revealed altered trajectories and a shift from the IS-II hub towards the IFN-response hub in the responding group (Fig. 5a).

Fig. 5: Predictive potential of NeuMAP.

a, K-mass score of neutrophils from mouse lung cancer with or without with anti-CD40 immunotherapy[27], mapped onto NeuMAP. b, Signature scores from human neutrophils isolated from blood of healthy individuals or patients with severe COVID-19 (ref. [31], influenza A[28] or systemic lupus[29], or from lungs of patients with cancer[12]. c, Representative images from spatial transcriptomics of lung sections from patients with lung adenocarcinoma, with healthy tissue, adjacent tissue and tumour lesion from the same individual. Top, H&E-stained sections. Middle, spatial distribution of neutrophil gene signature scores. Bottom, enlarged views of indicated regions showing neutrophils from different clusters or hubs. Data are from n = 8 patients, with 2 formalin-fixed paraffin-embedded (FFPE) tumour sections and 1 adjacent non-tumorous section per patient. Representative images are shown. Scale bar: 200 µm (main image); 50 µm (expanded view). d, Left, UMAP embedding of neutrophil transcriptomes from the spatial transcriptomic dataset across all regions, identifying transcriptional clusters shown in c. Middle, bar plot showing relative abundance of each neutrophil state in healthy, adjacent and tumour areas. Right, heat map showing mean score per hub for each human cluster gene set (scaled by signature). Kruskal–Wallis test,P < 0.001 for all hubs. e, Percentage of nearest neighbouring cells to neutrophils from different clusters or hubs. Numbers indicate cells scored per group. AT2, alveolar type 2 cell; TAM, tumour-associated macrophage. f, Overview of 18 physiological and pathological conditions from which single-cell blood neutrophil transcriptomes were obtained. 80 w.o., 80 weeks old. g, Top, UMAP analysis of blood neutrophils from f, coloured by sample origin. Overlap across samples and NeuMap hubs (defined in Fig. 1) measured by Bhattacharyya index. Bottom, projection of blood neutrophil transcriptomes onto reference NeuMAP embedding; overlap is quantified in a correlation matrix. h, Bhattacharyya indices showing overlap of each sample with NeuMap regions (Extended Data Fig. 10C), represented as a barcode for each condition in a hierarchical tree. Drawings in a–c,f were created in BioRender. Cerezo Wallis, D. (2025) https://BioRender.com/pfm336w.

Source Data

We then explored whether the transcriptional hubs defined in NeuMap persisted across species and pathological states, as suggested by our profiling of the human neutrophil compartment (Extended Data Fig. 4). Projection of neutrophil signatures extracted from published human datasets of infection, autoimmunity and cancer12,28,29,30 (Fig. 5b and Extended Data Fig. 9a) onto the mouse NeuMap revealed that severe COVID-19 was associated with the PreNeu hub (Fig. 5b), in line with previous reports[31]. By contrast, active flu infection or systemic lupus localized in the IFN-response hub, and neutrophils from lung tumours localized in the IS-II hub (Fig. 5b and Extended Data Fig. 9a), in agreement with studies in humans28,32,33. Integration of neutrophil signatures from multiple human and mouse cancer types revealed a preferential association with the IS-II, Ag-presenting and IFN-response hubs (Extended Data Fig. 9a–c). Validating this finding, we found that exposure of human neutrophils differentiated from CD34+ progenitors to IFNβ- and GM-CSF-elicited responses that strongly mirrored those in mice (Extended Data Fig. 9d).

We then examined human neutrophils in situ by high-resolution spatial transcriptomic analysis of 12 human lung specimens from healthy and lung adenocarcinoma samples (Fig. 5c). We identified five neutrophil transcriptomic profiles (clusters 1–5) that matched with the various hubs in the mouse NeuMap (Fig. 5d). For example, the healthy lung tissue was enriched in cluster 1 and cluster 2 neutrophils, which associated with the IS-I and Ag-presenting hubs, respectively, reflecting the findings in mice (Fig. 1f). By contrast, tumoural regions were enriched in cluster 4 and cluster 5, which shared features with the IS-II and Ag-presenting hubs (Fig. 5c,d). Neighbourhood analyses revealed differential associations of neutrophils from each cluster with distinct cell lineages (Extended Data Fig. 9e), together supporting the transcriptional, phenotypic and functional conservation of the mouse and human neutrophil compartments (Extended Data Fig. 4).

Finally, we investigated whether the precise distribution of blood neutrophils in NeuMap could help predict the pathophysiological state of the host. We performed scRNA-seq analysis of neutrophils from the blood of mice exposed to 18 different conditions, including cancer, microbial infections (viral and bacterial), sterile inflammation and physiological states (pregnancy, embryos and old age) (Fig. 5f). We found that projecting these neutrophils onto the full NeuMap markedly reduced their transcriptional overlap, as quantified using the Bhattacharyya index (Fig. 5g and Extended Data Fig. 10a,b). Thus, we used NeuMap as a scaffold to project the distributions of blood neutrophils and generated ten ‘diagnostic regions’ to enhance the spatial resolution and separability of the samples (Extended Data Fig. 10c). Using the overlap of blood neutrophils over these ten regions (Extended Data Fig. 10d), measured by their Bhattacharyya indices, we generated distribution ‘barcodes’ for each sample (Fig. 5h). Notably, these barcodes could discriminate between young and old male mice, pregnant female mice, atherosclerosis-prone Apoe−/− mice and those with early stages of cancer. Similarly, we could discriminate between different types of tumours or infections, as well as mice with active liver cholestasis versus those in remission (Fig. 5h and Extended Data Fig. 10c). Thus, NeuMap captures the transcriptional diversity of neutrophils with sufficient resolution to enable inference of host physiology by assessing the distribution of blood neutrophils, a feature with considerable diagnostic potential.

Discussion

NeuMap provides a transcriptional and functional map of the neutrophil compartment across organs, developmental stages and pathophysiological conditions. It reveals that, despite the wealth of transcriptional states reported in the literature1,2,3,9,18,31,34, the neutrophil compartment is organized as a finite collection of transcriptional states—or hubs—that can be associated with recognizable biological properties and appears to be conserved between mice and humans (Supplementary Fig. 4). Further expansion and exploration of NeuMap should be a collective effort for the field.

We highlight three salient features of our study. First, the transcriptome of the neutrophil compartment is organized as a single structure without obvious branching or separated clusters, both in mice and humans, possibly reflecting the short lifespan of neutrophils and the continuous replenishment of the entire compartment1,7. Second, NeuMap reveals several functional hubs, mirroring the heterogeneity of neutrophil states reported in multiple studies1,3,7,9,18,33,34. The relatively small number of hubs, however, contrasts with the remarkable diversity of scenarios in which neutrophils have important roles. It aligns, however, with the observation that many of the populations reported in different studies converge into similar signatures and functions, as shown, for example, in the context of cancer1,7,12,26,35,36. Thus, NeuMap can be used as a reference platform to uncover core properties of neutrophils across environments and diseases. Third, we emphasize the transcriptional dynamism of the neutrophil compartment, as captured by NeuMap and validated in the timestamp analyses that illuminate trajectories connecting the different hubs. This suggests that interfering with these trajectories may be more effective than targeting terminally differentiated neutrophils, a strategy that still dominates neutrophil-based therapies[5].

Our study is limited by the relatively small number of pathophysiological conditions analysed. Perturbations associated with allergy, autoimmunity, mucosal inflammation or diseases associated with old age32,37,38,39,40, as well as developmental processes remain uncharted in our NeuMap. It is also likely that additional cues not explored here (including cytokines, chemokines, signalling lipids, metabolites or mechanical cues) and other transcriptional regulators contribute to the specification of neutrophils. Finally, our study highlights the collective nature of the compartment and hints that some properties of the collective differ from the sum of its individual components, a notion that may have a major impact on understanding its evolutionary logic and defining how neutrophils contribute to health and disease.

Methods

Mice

All experiments were performed on 6-to-24-week-old C57BL/6 male and female mice. Young mice were defined as 8 to 12 weeks old, and old mice were defined as 22 to 24 months old at the time of analysis. Mice were maintained under specific pathogen-free conditions with chow and water provided ad libitum. mouse lines used were on the C57B1/6 J background and housed under specific pathogen-free conditions at the Centro Nacional de Investigaciones Cardiovasculares Carlos III, Singapore Immunology Network or Yale University. All mouse husbandry and experimentation was conducted using protocols approved by local animal ethics committees and authorities. Mice (Mus musculus) were maintained in racks with individual ventilation cages according to current Spanish, Singapore and US legislation (RD 53/2013 and EU Directive 63/2010, respectively). Mice have access to dust- and pathogen-free bedding, as well as sufficient nesting and environmental enrichment materials, to facilitate nesting. All mice were kept in environmental conditions of 45–65% of relative humidity, temperature of 21–24 °C, and a light:dark cycle of 12 h:12 h. Mice with neutrophil-specific deficiency in Tgfbr2 (TgfbrΔN) were generated by crossing MRP8CRE mice with Tgfbr2fl/fl mice[41]. Similarly, we generated neutrophil-specific mutants by crossing Junb-floxed[42], Csf2r-floxed[43] and Ifnar1-floxed mice[44] with the MRP8CRE driver. Apoe–/– mice (B6.129P2-Apoetm1Unc; Taconic M&B). Ly6gcreERT2 mice were crossed with Rosa26Tdtomato mice as in ref. [1], resulting in the iLy6gtdTom mice used in our fate mapping experiments. Gavage administration of tamoxifen (2 mg per mouse) was performed to induce CRE recombinase activity in 6-to-12-week-old male iLy6gtdTom mice. JAXBoy (PtprcK302E) from Jackson laboratories and Tet2−/− mice[19] were used for adoptive bone marrow cell transfer. Eight-week-old male Germ-free mice (C57Bl/6) were kindly provided by the laboratory of N. Palm. In brief, Germ-free C57BL/6 mice were bred and maintained under sterile conditions in flexible film isolators (Class Biologically Clean) in the Palm laboratory Gnotobiotic Facility at Yale School of Medicine. Mice were housed in a temperature- and humidity-controlled room under a dark:light cycle of 12 h:12 h. All animal protocols were approved by the Yale University Institutional Animal Care and Use Committee (protocol 11513).

For the rewilding experiments, litters of mice were generated from multiple breeding pairs and randomly assigned to either remain in the institutional vivarium (laboratory mice) or be released into the outdoor enclosures (rewilded mice) to control for the microbiota at the onset of the experiment. Outdoor enclosures were previously described[45] and the protocols for releasing laboratory mice into the outdoor enclosure facility and then returning them to vivaria were approved by Princeton University (protocol 1982) and Rutgers University (protocol PROTO999900794). All protocols were approved by the corresponding local authorities of Madrid, Singapore, Rutgers, Princeton and Yale University.

Mouse models of disease

Stroke

Thrombotic occlusion of the middle cerebral artery was induced by the ferric chloride (FeCl_3) stroke model. In brief, mice were anaesthetized and maintained at 2% sevoflurane in a mixture of 0.2 l min−1 O_2:0.8 l min−1 air and temperature was kept at 36.5–37 °C using a heating blanket. The scalp was opened, and the middle cerebral artery was visualized with a stereomicroscope (PZMIV, World Precision Instruments). A piece of Whatman filter paper strip soaked in freshly prepared FeCl_3 (20%) was placed over the intact dura mater on the artery for 10 min and then removed to allow the formation of a thrombus. Following surgery, individual mice were returned to their cages with free access to water and food. Brains were collected 24 h after surgery to perform transcriptomics analysis.

Liver cholestasis

For the liver injury model, mice were fed a 0.1% of 3,5-diethoxycarbonyl-1,4-dihydrocollidine (DDC)-supplemented diet for 3 weeks before sample collection, housed with a 12 h:12 h light:dark cycle, and permitted ad libitum consumption of water as described[46]. An additional group of mice was fed a 0.1% DDC-supplemented diet for three weeks and afterward allowed to recover for three days under standard mouse diet to study the reversibility of the cholestatic phenotype.

Influenza A infection

A stock of the virus strain A/PR8/34 (H1N1) was diluted, and 100 plaque-forming units were administered intranasally to isoflurane-anaesthetized 8-to-12-week-old male mice in 50 μl of PBS. Mouse weight was monitored daily after infection and mice that presented weight loss of more than 20% of their initial body weight were euthanized and considered deceased. For transcriptomic studies, blood and lungs were collected on day 4 after infection.

Pancreatitis

Acute pancreatitis was induced by intraperitoneal injections of 50 µg kg−1 of cerulein (Sigma-Aldrich), every hour, for a total of 7 administrations. Mice were euthanized 24 h after the first injection.

Orthotopic pancreatic tumour model

Mice were anaesthetized with ketamine/xylazine, and had their abdomen shaved and swabbed with antiseptic. A 5 mm vertical incision was made in the skin and abdominal layer at a point 1 cm down from the xiphoid process of the sternum, and 1 cm to the right of the midline. The pancreas was exposed, 10^5 FC1242 cells were resuspended in phosphate-buffered saline (PBS) and mixed with Matrigel (BD) in a 1:1 ratio and were injected as a volume of 50 µl into the body of the pancreas to form a visible bolus using a 30G insulin needle. The pancreas was then returned to the abdominal cavity. The abdominal layer was closed with absorbable 5/0 sutures, while the skin was closed with non-absorbable 5/0 sutures. Superglue was applied over the sutures to ensure that they did not come undone after surgery. Mice were resuscitated with saline and were subcutaneously administered Buprenorphine (10 mg kg−1) and Enrofloxacin (Baytril, 1.5 mg kg−1) for the 2 days following surgery. Mice were euthanized at week 5 following surgery and tissues were collected for transcriptomic analysis.

Orthotopic breast tumour model

Mice were implanted orthotopically with 5 × 10^5 E0771 breast cancer cells in 50 μl Matrigel into the thoracic mammary gland of C57BL6/J mice. Additionally, the same procedure was followed using the BALB/c-derived 4T1 breast tumour cell cancer in BALB/c mice. Mice were euthanized at week 4 after implantation and tumours were collected for transcriptomic analysis.

Orthotopic lung cancer model

We administered 2 × 10^5 LLC cells in 100 µl PBS intravenously into the lateral tail vein of 8-week-old C57BL6/J mice. Mice were euthanized at week 1, 2 or 3 after the implantation and bloods and lungs were used for transcriptomics analysis.

Subcutaneous lung cancer model

Mouse LLC1 implants were generated in 8-week-old C57BL/6 mice by subcutaneous implantation of 0.5 × 10^6 cells (1 injection site per mouse). Tumour growth was followed every 2 days by measuring the 2 orthogonal external diameters using a calliper. Tumour volume was calculated as V = π/6 × L × W × H, where L, W and H represent length, width, and height, respectively. Tumours were excised and processed for flow cytometry analysis when they reached 0.5 cm^3.

Caecal ligation and puncture-induced sepsis

Caecal ligation and puncture were performed as described[47]. In brief, the peritoneal cavity of ketamine/xylazine-anaesthetized mice was exposed with a small incision and the caecum was exteriorized. 80% of the caecum distal to the ileo-caecal valve was ligated using non-absorbable 7-0 suture. A 23G needle was then used to puncture both walls of the distal end of the caecum, and a small drop of faeces was extruded through the perforation. The ligated and punctured caecum was relocated inside the peritoneal cavity and both peritoneum and skin were closed. Blood was extracted three days after the puncture.

Peritonitis

Male 8-to-12-week-old mice were injected intraperitoneally with zymosan (1 mg, intraperitoneal injection, Sigma). After 2 h and 72 h we performed a peritoneal lavage for transcriptomic studies.

Myocardial infarction

Male 8-to-12-week-old mice were intubated, and temperature controlled throughout the experiment at 36.5 °C to prevent hypothermic cardioprotection. Thoracotomy was then performed, and the left anterior descending artery was ligated with a nylon 8/0 monofilament suture for 45 min. At the end of the ischaemia, the chest was closed, and mice were kept with 100% O_2 and given analgesia with buprenorphine (subcutaneous injection, 0.1 mg kg−1) as described previously[47]. Mice were euthanized 24 h or 72 h h after surgery and the heart was isolated for transcriptomics studies.

Bleomycin-induced fibrosis

We administered 1 mg kg−1 of bleomycin sulfate to 8-to-12-week-old mice as previously described[48]. In brief, bleomycin was dissolved in saline and was instilled into the tracheal lumen through a cannula under isoflurane (2.5% in oxygen) anaesthesia. Bleomycin was injected at day 0 and at day 4. Mice were euthanized three weeks after bleomycin injection and lungs were collected for transcriptomics analysis.

Staphylococcus aureus Infection

Mice were intravenously infected with 2.5 × 10^7 CFU of S. aureus (RNU4220 strain) and monitored for weight loss. For single-cell transcriptomic studies, blood was collected five days after infection.

Candida infection

Mice were intravenously infected with 1.5 × 10^5Candida albicans conidia (SC5314 strain), blood for single-cell transcriptomic studies was extracted at day 6 after infection[47].

LPS-induced inflammation

For transcriptomic studies, 400 ng of LPS (Sigma) were injected intravenously. Blood and tissues were collected 24 h after injection. An intraperitoneal lethal dose of LPS (40 mg kg−1) was used as a model of endotoxic shock. Mice were monitored daily for weight loss. A weight loss of more than 20% of initial body weight was considered a lethal event and mice were euthanized at humane endpoints.

High-fat diet

Apoe−/− mice were fed for 6 weeks with a control or high-fat diet (HFD, 10.7% total fat, 0.75% cholesterol; Sniff) before blood extraction.

Dextran sulfate sodium colitis

To induce colitis, mice received for 9 days water with 1.5% dextran sulfate sodium salt (MP Biomedicals) as previously described[49]. Blood was collected on day 9 after dextran sulfate sodium treatment.

Hindlimb ischaemia

Hindlimb ischaemia experiments were performed as described[50]. In brief, mice were anaesthetized with isoflurane, the hindlimb was shaved, and, following a small incision in the skin, both the proximal end of the femoral artery and the distal portion of the saphenous artery were ligated. The artery and all side-branches were dissected free; after this, the femoral artery and attached side-branches were excised. Immediately after surgery, perfusion was measured by laser Doppler imaging of plantar regions of interest (ROIs) (Moor Instruments) and calculated as ratio of left (ligated) versus right (unligated) values. Ischaemic muscle samples for transcriptomics analysis were collected one day after surgery.

Model of clonal haematopoiesis and PCSK9-induced hypercholesterolemia

To model TET2 loss-of-function-driven clonal haematopoiesis, we performed an adoptive bone marrow transfer without pre-conditioning. Ten-week-old unirradiated JAXBoy (PtprcK302E) recipient mice were intravenously injected with a total of 1.5 × 10^7 unfractionated CD45.2+ Tet2−/− bone marrow cells, administered as 3 consecutive daily doses of 5 × 10^6 cells[51]. Donor cells were collected from age-matched littermate Tet2−/− mice (8 to 10 weeks old) by flushing femurs and tibias following euthanasia. To induce hypercholesterolaemia, a recombinant AAV vector encoding a gain-of-function form of mouse PCSK9 (pAAV/D377Y-mPCSK9) was delivered via a single tail vein injection[52]. One week later, mice were placed on either a high-cholesterol western diet (Envigo, TD.88137; 42% calories from fat, 0.2% cholesterol) or a matched control diet for 13 weeks. At endpoint, adoptive bone marrow transfer mice were euthanized and Tet2–/– (CD45.2+) or wild-type (CD45.1+) neutrophils were isolated from peripheral blood and bone marrow by cell sorting. Cells were processed for scRNA-seq as described below.

Sample preparation and flow cytometry-assisted cell sorting

Mice were euthanized and Blood was taken through cardiac puncture with a 1 ml syringe attached to a 26G needle filled with 50 ml of 0.5 M EDTA. After blood collection, mice were perfused via the right ventricle with 10 ml of PBS to remove circulating blood cells. Tissues, including lung, tumours, muscle, heart, placenta and pancreas, were collected, cut into small pieces, and digested with Liberase TM (Sigma) and DNase I (Sigma) for 30 min at 37 °C. Following digestion, tissues were passed through 70-µm nylon mesh sieves using syringe plungers to obtain single-cell suspensions.

Bone marrow cells were obtained by flushing femurs with PBS containing 2 mM EDTA and 2% FBS using a 23G needle. Spleens were mechanically dissociated through 70-µm mesh filters. For colon isolation, intestines were cleaned, cut longitudinally and washed in PBS. After a 30-min incubation in 100 mM EDTA at 37 °C with shaking to remove epithelial cells, colon tissue was cut and digested in Liberase TM and DNase I for 30 min at 37 °C. Ear (skin) samples were processed by separating the dorsal and ventral sides, cutting them into small pieces, and digesting them in Liberase TM and DNase I for 90 min at 37 °C. The resulting suspensions were filtered as above. Peritoneal lavage was performed by injecting 10 ml of cold PBS into the peritoneal cavity, followed by gentle massage of the abdomen and careful aspiration of the fluid using a syringe and needle. The collected fluid was centrifuged, and the pellet was resuspended in fluorescence-activated cell sorting (FACS) buffer for staining.

For meninges isolation, mice were euthanized and decapitated. The skull was opened along the sagittal midline, and the brain was removed to expose the dura. The meninges were peeled off using fine forceps and placed directly into digestion solution on ice. For the brain, infarcted regions were dissected and digested for 30 min at 37 °C in an enzyme cocktail containing: 50 U ml−1 collagenase; 8.5 U ml−1 dispase; 100 µg ml−1 Nα-tosyl-L-lysine chloromethyl ketone hydrochloride; 5 U ml−1 DNase I in 9.64 ml HBSS without calcium, magnesium, or phenol red (Fisher Scientific, 14175-095). After digestion, brains were ground using a 2 ml glass-glass grinder, filtered through a 70-µm filter, and centrifuged. Cell pellets were resuspended in 7 ml of 35% Percoll, overlaid with 5 ml HBSS to form a gradient, and centrifuged at 800g for 30 min at 4 °C (no brake). The myelin layer and supernatant were discarded, and the final cell pellet was washed and resuspended in FACS buffer.

All single-cell suspensions were lysed in RBC lysis buffer (eBioscience) for 4 min and stained with the following antibodies: CD11b-PE (Clone M1/70, BioLegend, 1:200); LY6G-AF647 (Clone 1A8, eBioscience, 1:200); DAPI (1:10,000). Neutrophils were sorted as live (DAPI-negative), CD11b+LY6G+ cells using a FACS Aria sorter (BD Biosciences) at the Centro Nacional de Investigaciones Cardiovasculares (CNIC) Cytometry Unit. Bone marrow neutrophils were captured as lineage-negative (B220, CD18, NK.1.1, Ter119, CD3).

Cancer cell culture

The C57BL6/J syngeneic mouse LLC, E0771 breast luminal B and the BALB/c-derived 4T1 breast tumour cell cancer cell lines were from the American Type Culture Collection. The pancreatic adenocarcinoma FC1242 cell line (gift from D. Engle) was derived from Pdx1cre; KrasG12D/+; null/+ (KPC) mice. B16-OVAGFP cells were provided by the laboratory of D. Sancho. All cells were cultured in DMEM (Thermofisher) supplemented with 10% FBS (Thermofisher) and 100 μg ml−1 penicillin/streptomycin (Thomas Sci).

In vitro mouse neutrophil culture and analysis

Primary mouse neutrophils were obtained from the femurs and tibias of healthy C57BL/6J mice, or indicated genetically modified mouse model, by centrifugation. Erythrocytes were lysed using Red Blood Cell Lysis Solution (Qiagen; 79217). Cell strainer-filtered single-cell solutions were sorted in BD Aria Cell Sorter as DAPI− CD45+CD11b+LY6G+CD101+ mature, and DAPI− CD45+CD11b+LY6G+CD101− immature neutrophils. Cells were seeded in 96-well plates, 50,000 cells per well, and cultured with complemented DMEM medium (vehicle), or with the indicated treatments. G-CSF (574606, BioLegend), TGFβ (7666-MB-005/CF, R&D), IFNβ (581302, BioLegend), CXCL12 (578704, BioLegend), IL-1β (401-ML, R&D Systems), and GM-CSF (315-03-20UG, Thermofisher) were used at a concentration of 10 ng ml−1. Conditioned medium of LLC or KP-PDAC cells was obtained after 24 h culture of 80% confluence cells. Neutrophils were collected after 24 h or 48 h of treatment, and flow cytometry was performed using the following antibodies, all diluted 1:200 unless indicated otherwise: CCR5-BUV615-P (BD Biosciences, 752321, clone C34-3448), CD101-PE-Cy7 (eBioscience, 25-1011-82, clone MOUSHI 101), CD106-BUV563 (BD Biosciences, 741246, clone 429), CD115-BUV737 (BD Biosciences, 750948, clone AFS98), CD11b-BV510 (BioLegend, 101263, clone M1/70), CD11b-PE (BioLegend, 101208, clone M1/70), CD14-APC-Cy7 (BioLegend, 123318, clone Sa14-2), CD150-PE-Cy5 (BioLegend, 115911, clone TC15-12F12.2), CD16/32-PerCP-Cy5.5 (BioLegend, 101324, clone 93), CD274-BV421 (BioLegend, 124315, clone 10F-9G2), CD44-BV570 (BioLegend, 103037, clone IM7), CD45-APC (BioLegend, 103112, clone 30F11), CD74-BUV661 (BD Biosciences, 741572, clone In-1), KIT-BV605 (BioLegend, 135121, clone ACK2), CX3CR1-FITC (BioLegend, 149020, clone SA011F11), DC-Trail-R1-biotinylated (R&D Systems, BAF2378, polyclonal), I-A/I-E-BUV496 (BD Biosciences, 750281, clone M5/114.15.2), ICAM1-PE-Dazzle 594 (BioLegend, 1161130, clone YN1/1.7.4), LY6C-BV711 (BioLegend, 128037, clone HK1.4), LY6G-PE (BioLegend, 127608, clone 1A8), Sca1-BUV395 (BD Biosciences, 563990, clone D7), TLR4-BV786 (BD Biosciences, 741015, clone MTS510). Streptavidin-BV650 (BioLegend, 405231) was included at 1:500.

Secondary staining was performed with Streptavidin-BV650 (Biolegend). Cells were analysed in a SymphonyA4 Flow Cytometer. The data were analysed using FlowJo v.10 software. FlowAI[53] was used for quality control of flow data, followed by dimensionality reduction using the UMAP_R plugin[54]. Initial clusterization was performed with FlowSOM[55] and ClusterExplorerPlugin, and UMAP parameters were embedded in each sample for statistical analysis of neutrophil phenotypes.

RNA isolation, reverse transcription PCR

Total RNA was prepared with the RNA Extraction RNeasy Plus Mini-kit (QIAGEN) and RNA was reverse-transcribed with the High-Capacity cDNA Reverse Transcription kit (Applied Biosystems) according to the manufacturer’s protocol. Real-time quantitative PCR (SYBR-green, Applied Biosystems) assays were performed with an Applied Biosystems 7900HT Fast Real-Time PCR System sequencer detector. Expression was normalized to the expression of the 36b4 housekeeping gene. Primer sequences are as follows: 36b4: forward 5′-ACTGGTCTAGGACCCGAGAAG-3′, reverse 5′- TCCCACCTTGTCTCCAGTCT-3′; Ptgs2: forward 5′-TGAGCAACTATTCCAAACCAGC-3′, reverse 5′-GCACGTAGTCTTCGATCACTATC-3′; Nr4a1: forward 5′-TTGAGTTCGGCAAGCCTACC-3′, reverse 5′-GTGTACCCGTCCATGAAGGTG-3′; Il1b: forward 5′-AGTGAGGAGAATGACCTGTTC-3′, reverse 5′-CGAGATGCTGCTGTGAGATT-3′; Tnfaip3: forward 5′-GAACAGCGATCAGGCCAGG-3′, reverse 5′-GGACAGTTGGGTGTCTCACATT-3′; Cebpe: forward 5′-GCAGCCACTTGAGTTCTCAGG-3′, reverse 5′GATGTAGGCGGAGAGGTCGAT-3′; Ltf: forward 5′-TGAGGCCCTTGGACTCTGT-3′, reverse 5′-ACCCACTTTTCTCATCTCGTTC-3′.

Functional assays

T cell cytotoxicity assay

B16F10–OVA-GFP (10^4 cells) were seeded in 96-well culture dishes for 24 h, in RMPI medium with glutamine (Thermofisher) containing 10% heat-inactivated FBS (Thermofisher), 100 μg ml−1 penicillin/streptomycin (Thomas Sci); 200 nM glutamine, 1% non-essential amino acids (MEM amino acids; Gibco), 1% sodium pyruvate (Gibco) and 0.01% β-mercaptoethanol (Gibco). Neutrophils from sorting or in vitro cultures were co-culture at a 2:1 ratio with SIINFEKL-activated OT-1 T effector cells for 3 h. Neutrophil-OT-I cells were then seeded on top of B16F10-OVAGFP target cells 1:2 ratio. After 24 h, live cells were stained with 0.4 g l−1 crystal violet (Sigma-Aldrich, HT90132). The area covered by target cells was quantified from micrographs of the plates using the ImageJ software.

In vivo Matrigel plug assay

Fifty thousand neutrophils sorted from tissues of interest were resuspended in 500 µl of growth-factor-depleted Matrigel (Corning) and injected subcutaneously in the lower back of anaesthetized mice to form plugs. At days 3 and 7 after implantation, the same number of sorted neutrophils was resuspended in 50 µl of PBS and injected directly into the plug respectively. On day 9 after implantation, Doppler laser perfusion imaging was performed at the lower back region that contained the Matrigel plugs. One ROI was defined for each observable Matrigel plug, and the amount of flux variation in each ROI was quantified. Only ROIs that were not obscured by hair regrowth were used.

Chemotaxis assay

Chemotaxis assays were performed as described[47]. In brief, bone marrow neutrophils were plated in 6.5 mm polycarbonate transwells with 5-mm pores (Corning) in RPMI medium 48 h after cytokine treatment. 20 ng ml−1 CXCL1 (R&D) was added to the bottom well. Transwells were incubated for 2 h at 37 °C and transmigrated cells were collected from the bottom well and stained for cytometric analysis. The number of transmigrated cells was assessed by the presence of a known number of Truecount beads (BD Biosciences).

3D Doppler imaging of tumour vascularization

Subcutaneous LLC tumour vascularization was imaged using Vevo Imaging Systems once they reached 500 mm^3. In brief, mice were anaesthetized in an isoflurane vapourizer chamber, and the backs were thoroughly shaved. The mice were placed in the imaging platform and images were captured using the power colour Doppler-3D mode. A total of 100 images were captured to generate a 3D reconstruction of the vasculature. Vevo LAB software was used to calculate the Volume and per cent vascularization of tumours. Per cent vascularization is determined by calculating the percentage of pixels in the volume that have a power Doppler signal associated with them, the presence of this signal indicates the presence of blood flow.

NET formation assay

Forty-eight hours after cytokine treatment, 5 × 10^4 bone marrow neutrophils were plated with RPMI medium on poly-l-lysine-covered 8-well μ-Slides (Ibidi), and left 30 min to adhere. Subsequently, cells were incubated for 2 h with 100 nM PMA or vehicle. Cells were then fixed using 4% PFA for 10 min, permeabilized with PBS with 0.1% Triton X-100, 1% goat serum plus 5% BSA and stained with antibodies against cit-H3, DNA (Sytox-green, Molecular Probes) and MPO. Whole-slide z-stack tilescan images were acquired with a Leica SP5 confocal microscope, and analysed using Imaris software (v.9.5, Bitplane)[47].

Bacterial killing assay

Forty-eight hours after cytokine treatment, bone marrow neutrophils were resuspended in fresh medium along with live S. aureus (ATCC) that were grown in tryptic soy broth. For the in vitro assays, neutrophils and bacteria (10^4 CFU in 200 μl) were incubated at 37 °C for 60 min. The cells are then plated onto tryptic soy plates in a serial dilution. Bacterial colonies on the plates were counted the following day.

Phagocytosis assay

Forty-eight hours after cytokine treatment, bone marrow neutrophils were resuspended in fresh medium along with fluorescent latex beads (SIGMA) followed by flow cytometric analyses.

Analysis of human neutrophils

Isolation and expansion of human bone marrow CD34+ HSPCs

Bone marrow samples were obtained from healthy donors under informed consent approved by the ethics committee of the University Hospital Tübingen. CD34+ haematopoietic stem and progenitor cells (HSPCs) were isolated through Ficoll gradient centrifugation followed by magnetic bead-based separation using the EasySep Human CD34+ Cell Selection Kit II (Stem Cell Technologies, 17856). CD34+ cells (n = 4; purity 95.4 ± 1.9%) were cultured at a density of 5 × 10^5 cells per ml in StemSpan SFEM II haematopoietic stem cell medium (Stem Cell Technologies, 09655), supplemented with 1% penicillin/streptomycin, 20 ng ml−1 IL-3, 20 ng ml−1 IL-6, 20 ng ml−1 TPO, 50 ng ml−1 SCF and 50 ng ml−1 FLT-3L (all cytokines purchased from R&D Systems). Cells were cultured under standard conditions (37 °C, 5% CO_2) and frozen for future use.

For granulocytic differentiation in vitro, cells were seeded at a density of 2 × 10^5 cells per ml. During the first 8 days of differentiation (days 0–7), cells were maintained in a myeloid cell expansion medium—RPMI 1640 supplemented with 10% FCS, 1% penicillin/streptomycin, 5 ng ml−1 SCF, 5 ng ml−1 IL-3 and 1 ng ml−1 G-CSF. The medium was changed every two days. On day 8 of culture, the medium was replaced with a granulocytic cell differentiation medium (RPMI 1640 supplemented with 10% FCS, 1% penicillin/streptomycin and 1 ng ml−1 G-CSF). The medium was changed every 2 days until day 14. On day 13 of differentiation, cells were collected and counted. 800,000 cells were lysed for RNA isolation, 50,000 cells for FACS, and 40,000 cells for cytospins. The remaining cells were resuspended in fresh granulocytic differentiation medium at a seed density of 2 × 10^5 cells per ml and divided into 4 groups. Group one was maintained in granulocytic differentiation medium, group two was treated with 10 ng ml−1 TGFβ, group three was treated with 10 ng ml−1 IFNβ (refreshed after 24 h), and group 4 was treated with 10 ng ml−1 GM-CSF. RNA-seq analyses were performed 48 h after stimulation.

HOXB8 cell cultures and differentiation

HOXB8-immortalized myeloid progenitors were routinely tested for mycoplasma contamination and cultured in RPMI 1640 medium supplemented with 10% fetal calf serum (FCS), 10 μM β-mercaptoethanol (Thermo Fisher Scientific), 4% supernatant from SCF-producing CHO cells, 1% penicillin/streptomycin and 1 μM β-oestradiol (Sigma-Aldrich) to maintain the progenitor state. Neutrophil differentiation was initiated by β-oestradiol withdrawal and continued culture in medium supplemented with 1% SCF-containing supernatant. Differentiation into neutrophils was achieved by culturing cells in RPMI 1640 medium containing 10% FCS, 30 μM β-mercaptoethanol, 4% SCF supernatant, and 20 ng ml−1 granulocyte colony-stimulating factor (G-CSF) under standard tissue culture conditions (37 °C, 5% CO_2).

CRISPR–Cas9-mediated knockout

Knockouts of selected transcription factors in HOXB8 progenitors have been previously described[21]. In brief, HOXB8 progenitors were transduced with lentiCas9-v2 lentiviral vectors encoding guide RNAs (gRNAs) targeting the following genes and exons: Cebpb (exon 1; gRNA: AGGCTCACGTAACCGTAGT); Klf6 (exon 1; gRNA: TCGCTGTCGGGAAAACAGGG); Runx1 (exon 3; gRNA: TAGCGAGATTCAACGACCTC); Rfx2 (exon 5; gRNA: CTGCTGGGGGCGTAAAGCTG); Relb (exon 4; gRNA: CTGCACGGACGGCGTCTGCA); Irf5 (exon 2; gRNA: ACCCTGGCGCCATGCCACGAGG); and Junb (exon 1; gRNA: GGAACCGCAGACCGTACCGG).

JUNB overexpression

Lentiviral vectors for JUNB overexpression were generated by transient transfection of HEK293T cells using the calcium phosphate precipitation method. Cells were co-transfected with: (1) a transfer plasmid containing the Junb cDNA under the control of the human PGK promoter; (2) packaging plasmid psPax2; and (3) envelope plasmid pMD2.G encoding VSV-G. The medium was replaced 24 h after transfection. At 72 h, virus-containing supernatant was collected, clarified by centrifugation (2,000 rpm, 5 min, 4 °C), filtered (0.45 μm), and concentrated via ultracentrifugation (26,000 rpm, 2 h, 4 °C). Viral pellets were resuspended in cold PBS, aliquoted, and stored at −80 °C.

Lentiviral transduction of HOXB8

HOXB8 progenitors were transduced by spinoculation. In brief, 5 × 10^5 cells were plated per well in 6-well plates with 1 ml of medium. Lentiviral particles were added at a multiplicity of infection (MOI) of 11.24 for the vector pRRL-hPGK-JUNB-IRES-eGFP and MOI = 1.8 for the pRRL-hPGK-IRES-eGFP empty vector, and cells were centrifuged at 1,000g for 90 min at 30 °C. Following transduction, cells were collected, washed, and resuspended in fresh culture medium at a final concentration of 5 × 10^4 cells per ml.

Plasmid construction

To construct the JUNB expression vector, the human PGK (hPGK) promoter was PCR-amplified with ClaI and XbaI restriction sites and cloned into the ClaI/XbaI sites of the pRRL-CMV-IRES-eGFP vector, replacing the CMV promoter. The Junb coding sequence was amplified from mouse cDNA using primers containing BglII and XhoI sites and inserted into the BamHI and XhoI sites of the modified vector. Cloning was performed using the following primers: hPGK forward (ClaI): 5′-TTTTTTATCGATGGGTAGGGGAGGCGCTTT-3′; hPGK reverse (XbaI): 5′-TTTTTTTTAGACGAAAGGCCCGGAGATGA-3′; Junb forward (BglII): 5′-TTTTTTAGATCTGCCACCATGTGCACGAAAATGGAACA-3′; Junbreverse (XhoI): 5′-TTTTTTCTCGAGTCAGAAGGCGTGTCCCTT-3.

Culture of HOXB8 cells

For the flow cytometry and bulk RNA-sequencing experiments, HOXB8 progenitors at day 3.5 of differentiation were seeded in 96-well plates at a density of 50,000 cells per well. Cells were cultured in complete RPMI medium (vehicle) or treated with GM-CSF (10 ng ml−1) for 48 h, following the same conditions described for primary bone marrow neutrophil cultures. Vehicle or GM-CSF treated cells were collected at 48 h after treatment for the analysis.

Bulk RNA sequencing of mice and human-derived neutrophils

RNA from isolated mouse neutrophils was extracted using RNAeasy micro kit (Quiagen). RNA quality was checked using capillary electrophoresis (Agilent). Samples were submitted for whole RNA next generation sequencing in the Genomics Unit of CNIC. Total RNA (200 ng) was used to generate barcoded RNA-sequencing libraries using the NEBNext Ultra RNA Library preparation kit (New England Biolabs). Libraries were sequenced with HiSeq2500 (Illumina) to generate 50-nucleotide single reads, with a minimum of 8 million reads per sample. For RNA-seq of human-derived neutrophils, we isolated RNA from a total of 800,000 differentiated neutrophils collected on day 13 and 15. We used the NucleoSpin RNA Mini Kit (Macherey-Nagel, 740955.50), following the manufacturer’s instructions. RNA concentration was assessed with Qubit 2.0 (Thermo Fisher), and a total of 400 ng RNA was sequenced. RNA integrity was assessed using Agilent Bioanalyzer 2100, with RNA integrity number (RIN) between 9.8 and 10.0. RNA samples were processed by Novogene for library preparation and sequencing, and all samples passed the quality control criteria. Strand-specific libraries were generated on the basis of Novogene’s standard protocol. Samples were sequenced on an Illumina platform to produce 150 bp pairwise reads (PE150) per sample.

FastQ files for each sample were obtained using CASAVA (v.1.8) software (Illumina). Reads were further processed with RTA (v.1.18.66.3). FastQ files for each sample were obtained using bcl2fastq (v.2.20.0.422) software (Illumina). Sequencing reads were further processed as follows: Illumina adapters were trimmed and low-quality reads removed with Cutadapt (v.4.9)[56] (mismatch rate = 1 mismatch every 10 bp, overlap = 5 bp, minimum read length = 30 bp). Quality control of the processed reads was done with fastQC (v.0.12.1). RSEM (v.1.3.1) was used to quantify expression levels against the mouse genome reference GRCm38 or the human genome reference GRCh38, depending on the analysis[57] (default options). The processing of the counts and differential expression analysis was performed using limma (v.3.32.2)[58] and EdgeR (v.3.20.1)[59]) which were also used to perform pairwise differential expression analyses. To identify genes whose expression significantly varies across conditions, we applied a Likelihood Ratio Test (LRT) using DESeq2 (v.1.30.1)[60], allowing the detection of global effects of a factor without the need to specify individual contrasts. The resulting significant genes were then clustered using the k-means algorithm from the stats package (v.4.0.3).

Single-cell transcriptomics on mouse neutrophils

scRNA-seq of sorted tissue neutrophils

For single-cell analysis, all samples were collected between ZT1 and ZT5. Tissues were dissected and dissociated into a single-cell suspension by enzyme digestion. The resulting suspensions were filtered through cell strainers, and sorted in BD Aria Cell Sorter as DAPI−CD11b+LY6G+ cells, and loaded into a BD Rhapsody cartridge. For the generation of single-cell whole-transcriptomes, we used a BD Rhapsody system according to the manufacturer’s instructions. In brief, cell suspensions from each condition were incubated with Sample Tags (BD) for 20 min at room temperature. Cells were then washed three times and pooled in a single tube. Cell viability and concentration were assessed using a Countess III cell counter (Thermo Fisher). Sixty thousand cells were loaded into a Rhapsody Single Cell Analysis System cartridge. Cell capture and cDNA synthesis were performed according to manufacturer’s instructions; cells were isolated into nanowells by gravity, then cells were lysed and mRNAs together with sample tags oligonucleotides were released and captured by the beads present in the nanowells. Each bead contained a unique oligonucleotide named ‘cell label’ to identify each individual bead. All beads present in the cartridge were collected and cDNA synthesis took place in a single reaction. At this point, each cDNA and Sample Tag oligonucleotide were attached to its corresponding cell label oligonucleotide. Two separated indexed libraries were prepared for whole-transcriptome analysis and sample tag demultiplexing following the manufacturer’s instructions. The average size of the libraries was calculated using the 2100 Bioanalyzer (Agilent), and the concentration was determined using the Qubit fluorometer (Thermofisher). Finally, libraries were combined and sequenced together in a paired-end run (60 × 42) using a NextSeq 2000 system (Illumina) and a P2 flow cell. Output files were processed with NextSeq 1000/2000 Control Software Suite v.1.4.1. FastQ files for each sample were obtained using BCL Convert v.3.6.3 software (Illumina).

Construction of NeuMap and projection of external data

Rhapsody analysis pipeline v.1.9.1 was run locally. This pipeline includes steps for, alignment to mouse genome reference (GRCm38 with the gencodevM19-20181206) quantification and filtering of low-quality cells and tagging of doublets, which were also filtered out of the downstream analyses[61]. After BD Rhapsody’s pipeline automatic quality filtering, a second round was performed manually, where cells with a mitochondrial content over 20% or with over 300 total gene counts were discarded. Cell Annotation was performed using R package SingleR and the Immgen database for each dataset individually. All subsequent downstream analyses were implemented using R (v.4.0.3) and the package Seurat (v.4.0.5)[61]. The Seurat suite was used to integrate the neutrophils from all datasets using Seurat’s integration implementation. This method uses common sources of variation across the different datasets and aligns the cells so those in similar biological states cluster together. The integrated dataset was used to perform the unbiased cluster analysis and the construction of NeuMap. Additionally, we used the integrated NeuMap to generate a reference which we later used to analyse additional and external datasets by projecting cells onto our reference and annotate the new data using our custom labels using Seurat’s MapQuery and TransferData. This method is technology agnostic, so we could reliably project cells from external datasets sequenced in different platforms onto NeuMap62,63.

Definition of hubs

Functional hubs were selected by performing unbiased clustering at different resolutions using Seurat’s function FindClusters(). Resolutions used ranged between 0.05 and 0.3. Clusters from different resolutions were selected because they best represented the expression of functional signatures projected onto NeuMap. Areas shown in the figures correspond to the q15 quantile of the KMASS algorithm, which calculates the density of cells in specific areas. For clarity, hubs in figures are shown as the area with the accumulation of 85% of cells for each selected cluster/hub. Analyses were performed on the complete set of cells for each cluster or hub. The FindAllMarkers() function from Seurat was used to calculate DEGs across the hubs. Only genes detected in a minimum of 25% of the cells and with an average of at least 0.25-fold difference (log scale) between the groups in either of the groups were tested.

Kernel density estimation

The MASS R package (v.7.3.61) was used for two-dimensional kernel density estimation (K-mass score), with n = 100 grid points in each direction.

Signature projection

The signatures used for illustration of functional states are contained in Supplementary Table 2. All signatures were calculated by Seurat’s AddModuleScore() function. We used two different sources for the functional signatures: (1) previous publications, for which we provide the whole list of genes reported and used in Supplementary Table 2; and (2) public databases such as gene ontology (GO) and gene set enrichment analysis (GSEA). In those cases the whole gene list from the functional category was tested. For signatures from human data, human genes were mapped to their corresponding mouse homologue to calculate the enrichment score using the Mouse Genome Informatics (MGI) database. We used Seurat R package (v.5.2.1) AddModuleScore() function to calculate the scores. To generate visualization heat maps across NeuMap hubs, we first calculated enrichment scores for each cell. Scores were then averaged by hub and scaled per signature for comparison. To assess whether gene signature scores significantly differed across NeuMap hubs, we applied the Kruskal–Wallis test to each signature, testing the null hypothesis that score distributions were identical across hubs. The resulting test statistics were compared to a chi-squared distribution, with degrees of freedom equal to the number of hubs minus one. To correct for multiple comparisons, we adjusted P values using the Benjamini–Hochberg false discovery rate method.

Velocyto analysis

The analysis of expression dynamics in scRNA-seq data was performed using velocyto (v.0.17.17)[24], a package that allows estimating RNA velocities distinguishing between spliced and unspliced mRNAs in standard scRNA-seq protocols. The command line tool in Python implementation was adapted to be able to work with BAM files generated by BD Rhapsody, using samtools[64] to format the files, mainly by removing all possible alignments with antibodies and renaming the UMI barcode tag to ‘UB’ instead of ‘MA’. Velocyto was then executed with default parameters and the GRCm38 reference genome with the gencodevM19-20181206 transcriptome annotation. After concatenation of the spliced and unspliced data from all experiments, the results were merged with the outputs from single-cell analyses performed with Seurat in R, and scVelo[65] was used for further processing. Pre-processing included gene selection by detection (the minimum number of both unspliced and spliced counts was set to 30), and by variability (keep 2,000 highly variable genes (HVGs)), normalization, and log1p transformation. First and second order moments were computed among 50 nearest neighbours in the principal component analysis (PCA) space using 30 components. Cell-based RNA velocities were estimated by modelling the transcriptional dynamics of splicing kinetics using the stochastic model available in scVelo. Finally, these velocities were projected onto the previously computed UMAP and visualized at the cellular level or as velocity vector fields through streamlines.

In some experiments we performed Pseudotime analysis. Samples were pre-processed using the standard Monocle3 pipeline. To address batch effects, samples were integrated using the Batchelor algorithm (v.1.20.0). Dimensionality reduction and clustering were performed within Monocle3 (v.1.3.7), and pseudotime values were computed for the integrated dataset. To evaluate the significance of differences in pseudotime values between Cre− control and Tgfbr2-mutant immature cells, we applied a non-parametric Wilcoxon rank-sum test with continuity correction (W = 373,675; P value = 0.005548).

Comparison of cell distribution in the different hubs

To test differences in how neutrophils are distributed in the different hubs, we classified cell hubs for both mutant and control samples on the basis of the k-nearest neighbours algorithm (k = 5) of the cells projected onto Neumap. The observed hub proportions were calculated for each sample, and differences were determined by subtracting the proportions in control from the proportions in mutant cells. To assess the statistical significance of these differences, we used a bootstrap approach. For this, we generated a null distribution of hub proportion differences by merging each control–mutant pair into a mixed population. From this combined dataset, 10,000 resampled pairs were drawn with replacement, matching the sample sizes of the original control and mutant datasets. The differences in hub proportions between the resampled mutant and control groups were then calculated. The null distributions for each hub were verified to have a mean of 0.0, as expected under the null hypothesis. Finally, P values for the observed differences were computed by determining the fraction of resampled differences that were as extreme as or more extreme than the observed differences. To estimate 95% confidence intervals, the quantiles corresponding to the 0.025 and 0.975 percentiles of the null distributions were calculated.

Single-cell multiome using Dogma-seq

To simultaneously profile chromatin accessibility and gene expression at single-cell resolution, we used the Chromium Next GEM Single Cell Multiome ATAC + Gene Expression platform (10x Genomics). We collected neutrophils from bone marrow, spleen and lung (dataset 1) and LLC and spleen with LPS (dataset 2) from 8-to-12-week-old C57BL/6 mouse healthy blood was sequenced in both datasets as a quality control reference. Single-cell suspensions were prepared as described above. After staining, cells were washed, resuspended in sorting buffer, and incubated with DAPI (NBP2311561, Novus Biologicals) for 15 min prior to sorting.

Live CD11b+LY6G+ neutrophils were sorted in equal proportions from each organ. Cells were then pooled and lysed in 100 µl of cold DIG lysis buffer (20 mM Tris-HCl, 150 mM NaCl, 3 mM MgCl_2, 0.005% digitonin, 2 U μl−1 RNase inhibitor) for 5 min on ice. Lysis was quenched with 1 ml of cold DIG wash buffer, followed by centrifugation at 500g for 5 min. Nuclei were resuspended in 100 µl of 10x Genomics Nuclei Buffer supplemented with 1 mM DTT (Sigma) and 2 U µl−1 RNase inhibitor (Roche) to a final concentration of 3,400 nuclei per µl. After additional washes and centrifugation, samples were processed for library preparation at the Yale Center for Genome Analysis.

Library construction was performed following the 10x Genomics protocol (Chromium Next GEM Multiome ATAC + GEX v.1.1, CG000338 rev. E). In brief, nuclei underwent transposition using the ATAC transposition mix and were loaded onto the Chromium Controller for GEM generation, barcoding, and reverse transcription. Separate libraries were constructed for ATAC and gene expression using standard amplification and indexing steps. Libraries were quantified using Bioanalyzer (Agilent) and Qubit (ThermoFisher), pooled, and sequenced on an Illumina NovaSeq 6000 platform (paired-end, 150 bp) with a target depth of 75 million reads per sample.

Data processing

Initial quality control and cell filtering

DNA accessibility and gene expression from each cell were analysed simultaneously using Seurat (v.4.0.5 and v.4.3)[61] and Signac (1.14.0)[66] R packages. Per cell quality control metrics were evaluated using the DNA accessibility, and transcriptional data were obtained. Cells that did not pass the following criteria were removed from downstream analysis: number of counts in the ATAC data 100 < (nCount_ATAC) < 100,000; number of counts in the gene expression data 100 < (nCount_RNA) < 5,000; number of genes in the gene expression data 100 < (nFeature_RNA) < 2,500; ratio of mono-nucleosomal to nucleosome-free fragments (nucleosome_signal) < 2; ratio of fragments centred at the transcription start site (TSS) to fragments in TSS-flanking regions > 1; percentage of mitochondrial gene expression < 5.

ATAC data annotation

R packages Signac (v.1.14.0) and Seurat (v.5.1.0) were used to analyse single-cell chromatin data and gene expression, respectively. Full genome sequences for M. musculus (mouse) were used as provided by UCSC (mm10, based on GRCm38.p6), and annotated using Ensembl M. musculusannotations v.79.

Cell-type identification and neutrophil subset classification

We used R package SingleR (v.2.8.0)[67] to annotate cell types against the ImmGen database[68]. Cells annotated as ‘neutrophils’ were subset and re-analysed by running a new round of FindVariableFeatures() in which outlier features were identified and ScaleData() to re-scale the expression of the neutrophil subset.

Mapping onto NeuMap

We used Seurat v.5.1. FindTransferAnchors() function to identify pairwise correspondences (anchors) between the reference and query datasets using the transcriptomics data. This function uses canonical correlation analysis and mutual nearest neighbours to identify cells with similar gene expression profiles across the two datasets. The top 2,000 variable features shared between the reference and query datasets were used for anchor identification.

The query dataset was mapped onto the NeuMap reference using the MapQuery() function. This step projected the query cells from the Dogma-seq into NeuMap embedding space, allowing direct comparison and visualization of the dogma cells relative to NeuMap. Hub annotations from NeuMap were transferred to the query dataset using the TransferData() function. This function predicts cell labels for each query cell on the basis of the similarity scores computed from the anchors. The predicted labels were assigned to the query dataset, enabling downstream analysis of chromatin state in cells from each hub. Additionally, we assessed the confidence scores provided by TransferData() for each predicted label, retaining only high-confidence predictions (predicted.id.score ≥ 0.7) for downstream analysis.

Merging of the datasets and peak calling

We created a common peak set, and quantified this peak set in each experiment using Signac (v.1.14.0) and GenomicRanges (v.1.58.0)[69] prior to merging the objects. Once both datasets contained an assay with the same set of features, we used Seurat (v.5.2.1) R package to merge the datasets.

We used the Signac R package (v.1.14.0) to call peaks with the CallPeaks() function. The CallPeaks() function used MACS2 (v.2.2.9.1)[70] to run. Peaks were called for cells assigned to each hub separately. Only cells with a predicted.id.score ≥ 0.7 were retained for peak calling. Peaks on nonstandard chromosomes and in genomic blacklist regions were removed. After quality control and predicted score filtering 1,962 for dataset 1 and 8,155 neutrophils remained for dataset 2.

ATAC data processing

R package Signac (v.1.14.0) standard processing pipeline was applied to the combined data: term frequency-inverse document frequency normalization was applied via RunTFIDF(), top features were identified using FindTopFeatures() with a minimum cut-off of 5 and singular value decomposition was performed on the normalized data running RunSVD().

Differential peak analysis

Differential accessibility peaks were identified using FindAllMarkers(), considering only positive markers and a minimum percentage of cells expressing the feature (min.pct = 0.1). The closest genes to the differentially accessible peaks were annotated using the ClosestFeature(). The results were merged and filtered to retain significant peaks and marked for uniqueness.

Motif enrichment analysis

A position frequency matrix set was retrieved from the JASPAR2020 database via the homonim R package (v.0.99.10), filtering for vertebrate transcription factors in the CORE collection. Motif information was added to the dataset with the BSgenome.Mmusculus.UCSC.mm10 genome as reference. Enriched motifs in the differentially accessible peaks per hub were then identified.

Transcription factor activity

Chromatin accessibility variability analysis was performed using R package chromVAR (v.1.28.0)[71], with the BSgenome.Mmusculus.UCSC.mm10 genome as the reference. This step computes motif activity scores for each cell, representing the inferred transcription factor activity based on chromatin accessibility.

Single-cell transcriptomics on human neutrophils

Samples collection and processing

Human samples were collected in Renji Hospital, Shanghai, China, under the Renji Hospital Ethics Committee protocol KY2024-090-B, in accordance with the Declaration of Helsinki, following informed consent from all participants. Samples were collected from healthy donors, patients, or perfused organ donors. Specifically, healthy donor samples (bone marrow, peripheral blood and umbilical cord blood) were randomly collected without self-selection or recruitment bias. Other healthy tissues were obtained from anonymous acute-death donors without chronic inflammation to minimize the confounding effects of death shock on the organs. Systemic lupus erythematosus (SLE) patient samples (umbilical cord and peripheral blood) were randomly selected from pregnant patients with an active disease state (SLEPDAI > 5) and without other chronic inflammatory comorbidities.

Blood and bone marrow were collected in BD vacutainer K2E (EDTA) tubes (BD Healthcare, 367525) to prevent coagulation. Erythrocytes were lysed in 5–10 ml 1× red blood cells (RBC) lysis buffer (diluted from 10× BD Pharm Lyse, 555899) for 5 min for twice to deplete erythrocytes and then washed and re-suspension. Spleen, lung, omentum, mesentary fat, perirenal fat, liver, colon and rectum tissues were minced into small pieces and digested for 30 min at 37 °C in a mixture of collagenase IV (385 U ml−1, Sigma) and DNase I (2.5 mg ml−1, Sigma) and the samples were homogenized into single-cell suspension using syringe plungers and passed through 70-μm cell strainers (15-1070, BIOLOGIX). Then the samples were lysed in 2 ml 1× RBC lysis buffer (diluted from 10× BD Pharm Lyse, 555899) for 3 min to deplete erythrocytes and then washed and resuspended. Endometrium was cut into small pieces and enzymatically digested with the Tumor Dissociation Kit (130-095-929, Miltenyi Biotec). After digestion, the cell suspension was filtered through 70-μm cell strainers and subjected to a 3-min erythrocyte lysis with 2 ml 1× RBC lysis buffer, followed by washing and re-suspension.All single-cell suspensions were incubated with Fc-blocker (Human TruStain FcXTM, 422302, Biolegend) for 30 min on ice, then stained for 30 min at 4 °C in the dark with Fixable Viability Stain 700 (564997, BD Biosciences) (1:1,000), and the following antibodies: Anti-CD19-PE-Cy7 (clone HIB19, BioLegend, 302216; 1:200); anti-CD3-PE-Cy7 (clone HIT3a, BioLegend, 300316; 1:200); anti-CD45-APC-Cy7 (clone HI30, BioLegend, 304014; 1:200); anti-CD56-PE-Cy7 (clone 5.1H11, BioLegend, 362510; 1:200). All antibodies were used at 1:200 dilution unless otherwise indicated. After washing with FACS buffer, cells were sorted on a FACS Aria III cell sorter (BD Biosciences). After washing with FACS buffer, cells were sorted on a FACS Aria III cell sorter (BD Biosciences).

Library preparation

Single-cell suspensions were processed on the BD Rhapsody Express System (BD Biosciences). In brief, cells and beads were loaded onto the BD Rhapsody cartridge. Lysis, reverse transcription and exonuclease I digestion were performed using BD Rhapsody Enhanced Cartridge Reagent Kit (BD Bioscience, 664887) and the BD Rhapsody cDNA Kit (BD Bioscience, 633773). The whole-transcriptome libraries were prepared by following the BD Rhapsody single-cell whole-transcriptome amplification workflow with the BD Rhapsody WTA Amplification Kit (BD Bioscience, 633801), including random priming and extension (RPE), RPE amplification PCR and whole-transcriptome amplification index PCR. Libraries were quantified using a High Sensitivity DNA chip (Agilent) on a Bioanalyzer 2100 and the Qubit High Sensitivity DNA assay (Thermo Fisher Scientific) and then sequenced on a NovaSeq X Plus (Illumina). Raw sequencing data (.fqstq files) were processed with the BD Rhapsody analysis pipeline.

Data processing and cell annotation

Seurat v.5.2.1 package standard pipeline was used for the analysis of the single-cell data. The percentage of mitochondrial content was calculated and cell cycle gene expression scores were obtaned using the cell-cycle gene list by Tirosh et al.[72] and DAM stage genes[73] expression scores. Cells with a percentage of mitochondrial content over 20% were removed from downstream analysis. Likewise cells with a number of detected features below 300 were removed. Cells were manually annotated by selecting the clusters in each dataset with highest expression score for known neutrophil markers in different states as described12,74.

Data integration

scRNA-seq datasets were integrated using the reciprocal principal component analysis (RPCA) method implemented in Seurat v.5.2.1 package. Data normalization and identification of variable features (n = 2,000) were performed independently for each dataset using variance stabilizing transformation. Integration anchors were identified using RPCA reduction with k.anchor=15, and datasets were integrated with k.weight=50. Principal component analysis was performed on the integrated data using the top 15 components for UMAP dimensionality reduction (seed = 42).

Gene module scoring and hub identification

We used Seurat v.5.2.1 AddScoreModules() to assess the activity of specific gene sets within cell clusters. Gene lists of interest shown here were obtained from public data and repositories (Supplementary Table 2), from ref. [18]. For each gene list, we used AddModuleScore() to calculate the aggregated module score against a set of control genes with similar expression, thus ensuring the score was not biased by overall expression levels. To identify human to mouse hub similarities gene lists of interest were obtained from NeuMap hub gene lists. For each gene list, we used AddModuleScore() to calculate the aggregated module score against a set of control genes with similar expression. For all gene module scoring, we reduced the control gene parameter to 80 to ensure sufficient background correction while maintaining computational feasibility within the reduced gene number in the integrated dataset.

Clustering was performed using the Leiden algorithm at several resolution. Clusters with high expression of functional scores were selected as hubs, keeping only cells uniquely assigned to each specific cluster or hub. This approach led to the identification of six human NeuMap regions shown in Supplementary Fig. 4d. For visualization purposes, we used two-dimensional kernel density estimation on UMAP coordinates. For figure visualization, we calculated density values per hub using the kde2d function from the MASS R package (n = 100 grid points) (v.7.3.61), selected cells above the 80th percentile of density (top 20% most accumulated cells) to define core regions representative of each defined hub. The FindAllMarkers() function with default parameters from Seurat was used to calculate DEGs shown is Supplementary Table 4 across the human hubs. Spatial boundaries around these high-density regions were computed using the concaveman algorithm (v.1.1.0) to generate concave hull polygons that capture the geometric extent of each cell state cluster. For the stacked bars plots, all samples are downsampled to 1,000 cells for consistency with previous mouse NeuMap bars and comparability among samples with varying sizes.

Spatial transcriptomics using Visium OCT

Visualization of gene expression in naive lung (n = 1), tumour-bearing lungs (n = 2) and flu-infected lung (n = 1) was performed using the 10x Visium Spatial Gene Expression Kit (10x Genomics; PN1000184) following the manufacturer’s protocol. The OCT blocks were cut using a cryostat (Leica; PN-CM1520) and first cuts were used for RNA extraction (Qiagen; PN-74034), and RNA quality was assessed using the Agilent RNA 6000 Pico chips (Agilent; PN- 5067-1513), ensuring a minimum RIN number of 7. Second, 10-μm sections were cut on the Visium Spatial Tissue Optimization Slide (PN-1000193) to assess optimal tissue permeabilization time. FInally, a 10-μm section was mounted on a Visium Spatial Gene Expression Slide and then stained for H&E staining and imaged using the NanoZoomer S210 (Hamamatsu; NP-C13239) to assess tissue morphology and quality. Following protocol instructions, the sections were then permeabilized for 18 min, then tissue was lysed, and reverse transcription was performed followed by second strand synthesis and cDNA denaturation. Spatially barcoded, full-length cDNAs were amplified by PCR for 16 and 17 cycles, depending on the initial concentration previously determined by qPCR. Indexed sequencing libraries were generated via end repair, A-tailing, adaptor ligation, and sample index PCR. Size distribution and concentration of full-length GEX libraries were verified on an Agilent Bioanalyzer High Sensitivity chip (Agilent). Finally, sequencing of GEX libraries was carried out on a NovaSeq 6000 sequencer (Illumina) aiming at approximately 40,000 pair-end reads per spot.

Data pre-processing

For the analysis of the spatial transcriptomic data, SpaceRanger software (10x Genomics, v.1.3.0) was used to map the sequenced reads, correct amplification bias and obtain the count matrix. The mouse genome (mm10) was used as the reference. The filtered feature expression matrices generated were then used as input for downstream analysis with Seurat[75] (v.4.4.0) in R (v.4.3.1).

Quality control and data normalization

To ensure quality of the data, spots not overlapping tissue were removed previously to the SpaceRanger mapping with the Loupe Browser software (10X Genomics). Quality metrics were calculated on a per-slide basis to preserve biological variability. Differences in total UMI across spots were adjusted by log-normalization using the NormalizeData() function from Seurat. This function divides the raw gene counts for each cell by the total counts of that cell and multiplies it by the scale factor (10,000), which is then log-normalized as log(1+x). Genes not expressed in any spot overlapping tissue were also removed.

Feature selection and dimensionality reduction

To annotate the distinct lung regions of each Visium slide, we used the FindVariableFeatures() function to extract the top 3,000 HVGs and capture major axes of biological variability. Data were then scaled with ScaleData() to z-score. Principal component analysis was performed, and the top 50 principal components were retained for subsequent analysis steps.

Clustering and annotation

To perform clustering, the FindNeighbors() function was applied together with the Leiden[76] community detection algorithm. Sample-specific resolutions were chosen, ranging from 0.2 to 0.6 in the function FindClusters(). Lastly, DEGs for each cluster were identified with FindAllMarkers() function and Wilcoxon rank-sum test. When needed, clusters showing high heterogeneity were sub clustered and markers re-calculated. Each region of the lungs was then annotated considering the DEGs together with the haematoxylin and eosin staining.

Downstream analysis

To estimate cell-type composition in each spatial transcriptomic spot, we performed deconvolution using a single-cell reference dataset from the LungMap project[62], using the seeded non-negative matrix factorization (NMF) regression approach implemented in SPOTlight (v.1.0.3)[77]. Spots with a predicted composition of neutrophils of ≥10% were annotated as neutrophil-enriched.

Signature scoring was performed using decoupleR[78] (v.2.8.0). In brief, the univariate linear model (ulm) approach was applied to compute similarity (enrichment) scores by testing the association between gene expression and the neutrophil hub signatures derived from our single-cell RNA-seq data, thereby quantifying signature activity within each Visium spatial transcriptomics spot.

To map neutrophil signatures from the spatial transcriptomic data onto NeuMap, we first carried out differential expression analysis across healthy, flu-infected, and cancer samples using the FindAllMarkers() function in Seurat (v.4.3), with a logistic regression framework. Genes were included if expressed in at least 25% of cells in one group and showed a minimum log-fold change of 0.25. Significance was defined by adjusted P values (Benjamini–Hochberg correction) with a threshold of P ≤ 0.05.

The top 50 significantly DEGs (ranked by log_2 fold change) were selected for module scoring using Seurat’s AddModuleScore() function. For each gene set, a module score was computed by averaging expression levels and comparing against a background of control genes with matched expression, thereby controlling for overall expression bias.

Analyses of PDAC and myocardial infarction (MI) models

Spatial transcriptomic data were analysed for the PDAC and MI mouse datasets7,23 and used the Seurat package (v.5.1.0) in R, with three biological replicates included for each condition. Raw count matrices were first filtered to remove unexpressed genes. Low-quality spots were excluded on the basis of thresholds for the number of detected genes, total UMI counts, and mitochondrial gene content. Spots with abnormally low or high total counts, low gene detection, or mitochondrial percentages exceeding dataset-specific thresholds were considered low quality and discarded. We normalized using SCTransform79,80. Highly variable features were identified using the FindVariableFeatures() function, selecting the top 10% of genes by variability within each dataset. Data were then scaled using ScaleData() to centre gene expression values and apply z-score transformation. Dimensionality reduction was performed using PCA via RunPCA() on the selected HVGs. Neighbourhood graphs were constructed with FindNeighbors() on the basis of the first 20 principal components, followed by clustering with the Leiden algorithm and a resolution parameter set to 0.5 using FindClusters(). Low-quality clusters lacking underlying tissue structure were identified and removed. UMAP embeddings were computed on the same 20 principal components using RunUMAP() for visualization.

For single-cell referencing, we used a publicly available dataset (GSE141017and GSE176092)23,80. Quality control was applied by removing unexpressed genes and low-quality cells on the basis of gene counts, UMI counts, and mitochondrial gene content. The dataset was normalized using SCTransform, followed by identification of HVGs (top 10%), scaling, and PCA using 30 (MI) to 40 (PDAC) components. Batch correction across samples was performed using FindIntegrationAnchors() and IntegrateData(). Clustering was performed with the Leiden algorithm (resolution = 0.5) after computing neighbours using the top 30–40 principal components. UMAP was used for visualization. We annotated cell-type identities using SingleR (v.2.6.0) with reference profiles from the MouseRNAseq ImmGen databases via the celldex package (v.1.14.0) and curated marker genes from the dataset’s own clustering results. Marker genes for each cell type were extracted using FindAllMarkers(). Cell-type-specific markers were used in subsequent annotation and deconvolution steps. Cell-type deconvolution of spatial transcriptomic data were conducted using the SPOTlight package (v.1.0.3), using a seeded NMF regression approach. Spots with neutrophil compositions of 10% or higher were labelled accordingly.

For neutrophil hub signature scoring and subtype analysis, we identified the top 15 marker genes for each hub, and module scores were calculated using AddModuleScore(). Seeded K-means clustering (K = 7) was performed on neutrophil-labelled spots using the hub marker signatures. Clusters were annotated on the basis of the most specific and abundant signature. Clusters with no dominant signature were left unclassified. This procedure was repeated for macrophage and T cell subtype signatures81,82. For MI-associated fibroblasts[83], a different strategy was used by classifying the cell subtypes in the reference single-cell dataset and predicting their respective abundances directly via deconvolution. Spatial annotations were derived by integrating information from the Leiden clusters, cell-type deconvolution, histological inspection, and expression of cancer-specific signatures. GSEA and over-representation analyses were used to characterize and differentiate distinct tumour cores. Finally, to investigate spatial relationships between spots, we constructed a graph using the igraph::graph() function (v.2.1.4) on the basis of a distance matrix computed from spot coordinates (stats::dist()). The graph was tuned to include only immediate neighbours on the basis of the 2D spatial grid structure.

Spatial analysis of human lung specimens using Visium HD

Human tissue microarray samples were used under protocol 2019-5253, which was reviewed and approved by the McGill University Health Centre (MUHC) Research Ethics Board, specifically by the MUHC co-Chair of the Comité d’éthique de la recherche du CTGQ panel. Human lung tissue specimens were obtained through protocols approved by the McGill University Health Centre Institutional Review Board (IRB 2014-1119). From these samples, tissue microarrays were constructed by a pathologist on the basis of intratumoural neutrophil abundance, using 1-mm cores sampled from FFPE pulmonary invasive adenocarcinomas with high-grade predominant solid architecture and adjacent non-tumorous lung tissue. Samples were derived from eight patients. Sections (5 μm) were mounted onto Visium CytAssist HD slides (10x Genomics) and processed following the manufacturer’s protocol. In brief, FFPE tissue sections underwent deparaffinization, decrosslinking, probe hybridization, ligation and extension, followed by spatial barcoding and sample indexing. Final library quality and fragment size were assessed using an Agilent Bioanalyzer High Sensitivity chip. Libraries were sequenced at the McGill University Genome Centre on an Illumina NovaSeq X Plus platform (1.5B reads, PE100 per lane). Spatial gene expression data were processed using Space Ranger (10x Genomics), and high-resolution spatial transcriptomic profiles were generated for downstream analysis.

FASTQ files from the Visium HD experiment were mapped to the GRCh38-2020-A reference genome using the Visium_Human_Transcriptome_Probe_Set_v2.0_GRCh38-2020-A probe set with Space Ranger (v.3.1.1, 10x Genomics). Sample areas were manually selected using Loupe Browser, and the registration file H1-RTF6MBB-A1-fiducials-image-registration.json was provided to spaceranger count via the --loupe-alignment argument. High-resolution H&E and CytAssist images were passed with the --image and --cytaimage arguments, respectively.

Cell-level transcriptomic profiles were reconstructed using the Bin2cell package[84] (v.0.3.2). First, an AnnData object was created with b2c.read_visium, then filtered to retain bins with at least one count and genes expressed in at least three spots (min_counts = 1, min_cells = 3). The H&E image was scaled using b2c.scaled_he_image with mpp = 0.38. Due to variability in bin sizing across the array, 2 μm bins exhibit slight differences in width/height, leading to a striped appearance. To correct this artefact, the b2c.destripe function was applied. Nuclei segmentation was then performed on the H&E using b2c.stardist with parameters: stardist_model = “2D_versatile_he”, prob_thresh = 0.1, and nms_thresh = 0.1. Segmented nuclei were expanded using b2c.expand_labels with algorithm = “volume_ratio”. To recover additional cells not segmented via H&E, the Stardist fluorescence model was applied to a σ-smoothed gene expression image generated using b2c.grid_image (mpp = 0.38, sigma = 5). b2c.stardist was re-run with stardist_model = “2D_versatile_fluo” and the same thresholds. Cells not segmented with the H&E-based model were assigned secondary labels from the fluorescence model. Finally, bins were grouped into cells with the b2c.bin_to_cell function. To reconstruct the Segmentation Polygon Mask we converted the.npz mask output from Bin2cell into a data frame using pd.DataFrame.from_dict (pandas v.2.2.3). This file was then processed in R (v.4.4.1) using the concaveman package (v.1.1.0) to generate cell polygons for visualization.

SingleCellExperiment[85] objects were created in R by importing the Bin2cell.h5ad files with the h5ad2sce function from the schard package (v.0.0.1). Low-quality cells were removed if they met any of the following criteria: fewer than 10 counts, fewer than 10 unique genes, area <8 µm^2, or >15% mitochondrial reads. Additionally, cells labelled only through gene expression segmentation (secondary labels) were excluded upon manual inspection for falling outside tissue boundaries. In a second quality control step, the isOutlier function from the scuttle package (v.1.16.0) was used to flag outliers in transcript count and transcript density (log_2(counts/area)) using parameters nmads = 2.5, type = “both”, and log = FALSE. Identified outliers were excluded from downstream analysis. For pre-processing, quality-filtered SingleCellExperiment objects were normalized using logNormCounts from scuttle. HVGs were identified with modelGeneVar (scran[6] v.1.34.0), blocking by patient ID (block = sce$patient). HVGs were selected using getTopHVGs (fdr.threshold = 0.05). PCA was performed using runPCA (scater v.1.34.0)[86] on the HVGs (subset_row = hvgs), and the first 15 principal components were retained on the basis of elbow plot inspection. UMAP dimensionality reduction was computed using runUMAP (scater).

To cluster cells, we built a shared nearest-neighbour graph using buildSNNGraph from scran with type = “jaccard” and use.dimred = “PCA”. Louvain community detection was performed using cluster_louvain (igraph[7] v.2.1.1) at multiple resolutions. DEGs between clusters were identified using findMarkers (scran) with direction = “up”. Top-ranked markers for each cluster were selected from the ‘Top’ column of the results and inspected manually to guide spatial annotation. Hallmark gene sets (for example, angiogenesis, hypoxia) were obtained from MSigDB using the msigdbr package (v.7.5.1) with species = “Homo sapiens” and category = “H”. Immune cell-type signatures[87] were retrieved using category = “C8” and filtered to retain adult lung signatures while excluding fetal profiles. Signature scoring was performed using AUCell (v.1.28.0). Gene expression rankings per cell were computed with AUCell_buildRankings, followed by AUC calculation with AUCell_calcAUC. For visualization, cluster-level mean AUC scores were obtained using aggregateAcrossCells (scuttle[86]) with statistics = “mean” and use.assay.type = “AUC”.

To assign neutrophil clusters to NeuMap hubs, human gene signatures corresponding to spatial clusters C1–C5 were first converted to mouse orthologs using homology data from the Mouse Genome Informatics (MGI) database (https://informatics.jax.org). We then computed gene module scores for each hub using the AddModuleScore() function in Seurat. Module scores were calculated by averaging the expression of hub-associated genes and comparing them to control gene sets with similar expression levels, thereby normalizing for baseline expression and minimizing bias. For visualization, enrichment scores were averaged by hub across clusters and scaled per signature to enable comparison in the resulting heat map.

Neighbourhood analysis

Spatial transcriptomic data were analysed using Seurat (v.4.3) in R (v.4.2). A spatial proximity graph was constructed by computing the k-nearest neighbours (k = 6) from each spot’s x,y coordinates using the RANN package (v.2.6.2). Edges exceeding 250 units in Euclidean distance were excluded to account for realistic cell–cell interaction radii. An undirected graph was generated using the igraph package, with edge weights corresponding to physical distances. We implemented two complementary functions to quantify spatial cell-type context: (1) neighbourhood frequency analysis. For a given set of target spots (for example, neutrophils), their first-order neighbours were identified within the spatial graph. For each neighbour, the cell-type label was extracted, the number of neighbouring cells of each type was counted per target spot and aggregated across all targets to calculate the mean and 95% confidence interval for each cell type. This allowed identification of the most frequently co-localized cell types around a given population. (2) neighbourhood composition by Hub: In a separate analysis, both the target spot and its neighbours were pooled to represent a local “neighbourhood.” Cell types within each neighbourhood were classified using a curated cell-type annotation. For each neutrophil hub, cell-type counts were summed and normalized to percentages, excluding spots annotated as ‘unassigned’ to avoid skewing proportion estimates. This enabled comparative analysis of cellular composition across microenvironments.

MACSima imaging cyclic staining

Sample preparation and image acquisition

Multiplex immunohistochemistry of lungs from naive, flu-infected, or tumour-induced mice was performed using a MACSima imaging system (Miltenyi Biotec). In brief, cyclic immunofluorescence imaging consisting of repetitive cycles of immunofluorescent staining, sample washing, multi-field imaging, and signal erasure by photobleaching was performed. Cryosectioned fixated lungs from the 3 groups were placed on microscopy slides and MACSwell sample carriers were mounted and blocked using a blocking buffer containing 10% BSA and 2% goat serum for 1 h at room temperature before lungs were preincubated with an antibody to IFIT1 (ab236256, Abcam, 1:100) overnight at 4 °C. Thereafter, nuclei were counterstained with DAPI before samples were placed in the MACSima imaging system. Neutrophil subsets were identified using the following antibodies: anti-CD11b-APC (clone M1/70.15.11.5, Miltenyi Biotec, 130-113-239; 1:50); anti-CD45-FITC (clone REA737, Miltenyi Biotec, 130-110-658; 1:50); anti-CXCR2-PE (clone SA044G4, BioLegend, 149303; 1:50); anti-Ly6C-PE (clone REA796, Miltenyi Biotec, 130-111-916; 1:50); anti–MHC-II-APC (clone REA813, Miltenyi Biotec, 130-112-388; 1:50); anti-PD-L1-APC (clone 10 F.9G2, BioLegend, 124312; 1:50); anti-CD14-PE (clone Sa14-2, BioLegend, 150106; 1:50) Anti-IFIT1 (Polyclonal, Abcam, Ab70023; 1:50). Additionally, a conjugated anti-rabbit antibody (polyclonal, Sigma-Aldrich, F9887; 1:100) in the first cycle to identify IFIT1. The tissue location was characterized using anti-Podoplanin-PE (clone 8.1.1, BioLegend, 127408; 1:50).

Data analysis and visualization

Images were stitched and pre-processed using MACS iQ View Analysis Software (Miltenyi Biotec, v.1.3.2) and representative overlay pictures were displayed. For downstream analysis, cells were segmented on the basis of the DAPI signal using the StarDist plugin in ImageJ (US National Institutes of Health) and the donut algorithm in MACS iQ View. The segmented data were then exported for further analysis to FlowJo (BD Biosciences), where neutrophils were identified as LY6Ghi cells using a threshold value. In flu-infected lungs, neutrophils were characterized as intravascular or extravasated on the basis of the podoplanin signal in the segmented neutrophils. Neutrophils from all ROIs were concatenated and phenotypically analysed by dimensional reduction using the UMAP plugin and unsupervised clustering using the FlowSOM plugin. FlowJo and R were used for visualization.

Mathematical modelling for blood neutrophil diagnosis

To model the predictive value of the distribution of blood neutrophils in NeuMap, we used the density overlap represented by the Bhattacharyya index. We favoured density versus spatial overlap to reduce the impact of outliers in our calculations. We also favoured using ten selected regions of NeuMap over the seven hubs delineated in Fig. 1e to gain spatial resolution of cell distribution over the specific areas of NeuMap where blood neutrophils from the tested conditions tended to concentrate. In separate analyses, we found that the spatial overlap was not based on densities, and the use of seven hubs provided significantly less resolution in the overlap barcodes (not shown).

Density state estimation

The UMAP coordinates \(\{{x}_{j},{y}_{j}\}\) of the neutrophils collected from the tissue hubs and blood samples were pipelined into a computational approach to estimate the probability density functions (PDFs) over the Neumap transcriptomic space associated with these datasets. The kernel density estimators \(\hat{f}\,(x,y)\) were of the form[88],

$$\hat{f}\,(x,y)=\frac{1}{n{h}_{x}{h}_{y}}\mathop{\sum }\limits_{j=1}^{n}K\left(\frac{{x}_{j}-x}{{h}_{x}}\right)K\left(\frac{{y}_{j}-y}{{h}_{y}}\right),$$

where K is the Epanechnikov kernel

$$\begin{array}{cc}K(u)=\left\{\frac{3}{4}(1-{u}^{2}),\right. & \mathrm{for}\,|u|\le 1,\,0,\,\mathrm{otherwise},\end{array}$$

with n, hx and hy denoting the number of points and the bandwidths along two orthogonal directions, respectively. This kernel choice, which is symmetric and normalized along each orthogonal direction, ensures a smooth, bounded and efficient computation of the density estimates. The determination of each kernel’s bandwidths, which influences the smoothness and accuracy of the resulting PDF estimate, was made via the Sheather–Jones method[65]. This technique uses a data-driven approach that minimizes the mean integrated square error of the estimated density function. By iteratively adjusting the bandwidths hx and hy and evaluating their performance, Sheather–Jones effectively balances bias and variance, resulting in accurate density estimates, particularly for tissue hubs and blood samples that present non-Gaussian distributions or multimodality.

Overlap integration

Upon estimating the PDFs of our datasets, we assessed the degree of overlap between pairs of these functions. For this purpose, we used the Bhattacharyya index, which yields a measure of the amount of overlap between two PDFs \(f(x,y)\) and \(g(x,y)\). The Bhattacharyya index was defined by the integral expression

$$\mathrm{BC}(\,f,g)=\iint \sqrt{f(x,y)g(x,y)}{\rm{d}}x{\rm{d}}y,$$

where the double integral runs over the entire domain defined by NeuMap. The Bhattacharyya index gives a scalar value in the interval [0, 1], enabling a direct interpretation of the overlap: values near 1 suggest a high degree of similarity, or almost perfect overlap, between the two distributions, indicating that the neutrophil states are nearly indistinguishable within the dimensionally reduced transcriptomic space. Conversely, values near 0 denote little to no overlap, pointing to distinct neutrophil states with minimal similarity in their respective distributions within the dimensionally reduced transcriptomic space.

To calculate the Bhattacharyya index, we used an adaptive Monte Carlo method[89] that combines adaptive importance and stratified samplings over multiple iterations, thus optimizing the sample distribution around the peaks of the PDFs and thereby reducing the standard deviation in the estimates. This approach yielded accurate values of the multidimensional integrals and hence offered robust measures of the overlap between the PDFs. While the Bhattacharyya index does not constitute a true probability measure, its bounded nature makes it a valuable score for comparing the similarity of data distributions in a normalized manner. From the computed Bhattacharyya indexes, the resulting barcodes were generated. To perform our calculations, we used the R programming language to analyse all datasets. Specifically, we used the MASS, graphics, stats and vegas R packages.

Estimation of neutrophil lifetimes

To quantify the neutrophil half-lives and transit times, we used an age-structured mathematical model as previously proposed[1]. This model effectively captures the temporal variation in the proportion of labelled neutrophils following the administration of the BrdU pulse. Let \(u=u(t,a)\) denote the density of neutrophils, which at time t, have an age a. We assume that their age ranges in the interval \(a\in [0,{a}_{\max }]\), where \({a}_{\max }\) is the maximum age (or the maximum lifespan) a neutrophil can achieve in the different examined tissues. In practice, this age can be taken sufficiently large without appreciably altering the numerical results for the entire neutrophil population. To describe the temporal dynamics of the age distribution in neutrophils, we considered the following first-order linear transport partial differential equation

$$\frac{\partial u}{\partial t}+\frac{\partial u}{\partial a}=-\frac{u(t,a)}{\tau (a)}+\phi (t,a).$$

(1)

The left-hand side of equation (1) represents the temporal change in the number of neutrophils along with their corresponding age. The first term on the right-hand side accounts for neutrophil death. The death time, \(\tau (a)\), generally depends on the age of the neutrophil. The introduction of a flux function, \(\phi (t,a)\), encapsulates the net recruitment of neutrophils entering or leaving the target tissue. Assuming that at time \(t=0\) no BrdU+-labelled neutrophils of any age have yet arrived at tissue i, the initial condition is \(u(0,a)=0\). Therefore, the exact solution to (1), obtained using the method of characteristics for first-order partial differential equations, is

$${u}_{i}(t,a)={{\rm{e}}}^{-{\int }_{0}^{t}\frac{{\rm{d}}\xi }{{\tau }_{i}(a-\xi )}}{\int }_{0}^{t}{\phi }_{i}(\xi ,a-t+\xi ){{\rm{e}}}^{{\int }_{0}^{\xi }\frac{{\rm{d}}\eta }{{\tau }_{i}(a-t+\eta )}}{\rm{d}}\xi .$$

(2)

Equipped with equation (2), we computed the total number of neutrophils at time t and tissue i, irrespective of their age, via the following integral

$${n}_{i}(t)={\int }_{0}^{{a}_{\max }}{u}_{i}(t,a){\rm{d}}a.\,$$

(3)

To connect equation (2) with the different scenarios addressed in the experiments, the net flux \({\phi }_{i}(t,a)\) corresponded to one synchronous wave of neutrophils after administration of the BrdU labelling. The chosen functional forms for \({\tau }_{i}(a)\) and \({\phi }_{i}(t,a)\) in our modelling were

$${\tau }_{i}(a)={\tau }_{i},\,{\phi }_{i}(t,a)={\alpha }_{i}{{\rm{e}}}^{-\frac{{(t-{t}_{i})}^{2}}{2{{\sigma }_{i}}^{2}}},$$

(4)

where τi, αi, ti, and σi are constant parameters. Inserting equations (2) and (4) into equation (3), we arrive at the following exact formula for the total number of neutrophils at a given tissue i at time t

$${n}_{i}(t)={m}_{i}{{\rm{e}}}^{-\frac{t}{{\tau }_{i}}}\left[\mathrm{Erf}\left(\frac{{\sigma }_{i}^{2}+{t}_{i}{\tau }_{i}}{\sqrt{2}{\sigma }_{i}{\tau }_{i}}\right)-\mathrm{Erf}\left(\frac{{\sigma }_{i}^{2}+({t}_{i}-t){\tau }_{i}}{\sqrt{2}{\sigma }_{i}{\tau }_{i}}\right)\right],$$

(5)

where

$${m}_{i}=\sqrt{\frac{{\rm{\pi }}}{2}}{\alpha }_{i}{{\sigma }_{i}{a}_{\max }{\rm{e}}}^{\frac{{\sigma }_{i}^{2}+{2t}_{i}{\tau }_{i}}{2{\tau }_{i}^{2}}},$$

(6)

is a normalization parameter for tissue i, and \(\mathrm{Erf}(x)=\frac{2}{\sqrt{{\rm{\pi }}}}{\int }_{0}^{x}{{\rm{e}}}^{-{\xi }^{2}}{\rm{d}}\xi \) is the error function. The parameters τi, mi, ti, and σi for blood, bone marrow and spleen were computed via a nonlinear regression analysis using the corresponding time series measured from day 1 to day 7 for each tissue compartment. Once these four parameters were found for each tissue i, the mean half-life time \({t}_{1/2}^{(i)}\) and transit time \({t}_{\mathrm{tran}}^{(i)}\) were estimated from the normalized profile (equation (5)). To do that, the transit time was identified as the time at which this unimodal profile achieves its maximum (100% of the BrdU-labelled neutrophils in tissue i). Subsequently, the 50% level was set as a reference for the mean half-life time \({t}_{1/2}^{(i)}\). The approach for calculating these two lifetimes is illustrated in Extended Data Fig. 7f. The bands shown in this figure correspond to a confidence level of 0.75.

To carry out the density state estimation and overlap integration, we used the R programming language to analyse all datasets. Specifically, we used the MASS v.7.3.61, graphics v.4.4.3, stats v.4.0.3 and v.4.4.3., and vegas v.2.1.4 R packages. The nonlinear regression and statistical analysis were performed with Matlab (R2024a) using the functions fitnlm and coefCI.

Quantification and statistical analysis

Data from experiments are represented as mean values ± s.e.m. All parameters analysed followed normal distribution as tested by D’Agostino–Pearson test unless indicated in the figure legend. Unpaired two-tailed t-test was used when two groups were compared, and comparison of more than two datasets was done using one-way analysis of variance (ANOVA) with Tukey’s post-test or two-way ANOVA. Log-rank analysis was used for Kaplan–Meier survival curves. Statistical analysis was performed using GraphPad software. Statistics on the RNA sequencing are indicated in the analysis section. A P value below 0.05 (*) was considered statistically significant; P ≤ 0.01 (**) and P ≤ 0.001 (***), as well as nonsignificant differences (NS), are indicated accordingly.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

All the transcriptomics data are available in Gene Expression Omnibus (GEO) as a Super-series GSE266680. Data used for the hNeuMap are available at the Chinese Academy of Sciences (https://ngdc.cncb.ac.cn/gsa-human/), with accession number HRA013413. The raw sequence data used to build the human NeuMap (Extended Data Fig. 4) have been deposited in the Genome Sequence Archive[90] in the National Genomics Data Center[91], China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (GSA-Human: HRA013413). Source data are provided with this paper.

Code availability

Visualization and analysis of NeuMAP is available at the single-cell Data Analysis and Visualization (scDAVIS) web-based tool: https://bioinfo.cnic.es/scdavisr/ (User: neumap; password: 0gl4d1h).

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Acknowledgements

We thank all members of our laboratories for insightful feedback; E. Menet, Z. Zhao, E. Prieto, N.A Muñoz, R. Nieto and M. Vitón for help with sorting and cytometric analyses; L. Cabezuela and E. Santos for animal husbandry; V. Labrador, E. Arza and the Microscopy Unit of the CNIC for help with microscopy; L. Carramolino and J. Bentzon for providing plasmids encoding PCSK9; J. Li and H. Beach for help with data visualization; E. Wagner and N. Palm for the generous gift of mice; and The Thoracic Oncology Clinical Database and Biobank at the McGill University Health Centre for access to tissue and clinical data. L.G.N is supported by Chinese Ministry of Science and Technology grant 2023YFC2306300 and National Natural Science Fund of China grant 92374205, W2431020. D.C.-W. was supported by the Cancer Research Institute/Irvington Postdoctoral Fellowship (CRI CRI3511) and A.G.-C. was supported by a predoctoral grant from MCIN (PREP2022-000391). I.B. was supported by grants from MICIN (RYC2020-029563-I and PID2022-140534NB-I00) and the BBVA Foundation (Leonardo fellowship; LEO22-2-2596). A. Hidalgo was supported by the Worldwide Cancer Research foundation (Grant 23-0115) and 1RO1AI165661 from the US National Institutes of Health. D.G.A. is supported by contract 2023-CDT-11616 from a project with grant number TED2021-132296B-C55 funded by the Spanish MCIN/AEI/10.13039/501100011033. G.F.C. is supported by grants PID2022-142341OB-I00 funded by the Spanish MCIN/AEI/10.13039/501100011033 and European Union “NextGenerationEU/PRTR”, and SBPLY/23/180225/000041 funded by Junta de Comunidades de Castilla-La Mancha, Spain, and European Regional Development Fund. O.S. receives support from the Deutsche Forschungsgemeinschaft (CRC TRR332 A2 & Z1, CRC1009 A13, CRC1123 A6, 502158695), the Leducq Foundation, and IZKF and the IMF of the Medical Faculty Münster. L.A.W. acknowledges funding from the Bachynski Family Foundation and D.F.Q. receives support from Canada Foundation for Innovation (CFI 42884). The CNIC is supported by the MCIN, the Instituto de Salud Carlos III, and the Pro-CNIC Foundation and is a Severo Ochoa Center of Excellence (grant number CEX2020-001041-S funded by MCIN/AEI/10.13039/501100011033). M.S.F.N. is supported by the National Medical Research Council Young Individual Research Grant (MOH-OFYIRG24jan-0012). I.K. receives funding from the National Medical Research Council Young Individual Research Grant (MOH-OFYIRG24jan-0032) and the Skin Research Institute of Singapore Joint ReseaRch Grant (SRIS_JRG_2003). C.S. is supported by National Natural Science Foundation of China (grant 32300756), J.H. is supported by Noncommunicable Chronic Diseases-National Science and Technology Major Project (2023ZD0501300), M.Z. is supported by National Natural Science Foundation of China (grant 82370751) and J.P. is supported by National Natural Science Foundation of China (grant 82421001).

Author information

Author notes

1. These authors contributed equally: Daniela Cerezo-Wallis, Andrea Rubio-Ponce

2. These authors jointly supervised this work: Lai Guan Ng, Andrés Hidalgo, Iván Ballesteros

Authors and Affiliations

1. Vascular Biology and Therapeutics Program and Department of Immunobiology, Yale University, New Haven, CT, USA

Daniela Cerezo-Wallis, Andrea Rubio-Ponce, Giovanni Marteletto, Samuel Ovadia, Tong Deng, Anjelica Martin & Andrés Hidalgo

2. Centro Nacional de Investigaciones Cardiovasculares Carlos III, Madrid, Spain

Daniela Cerezo-Wallis, Andrea Rubio-Ponce, Ana Cristina Guanolema-Coba, Ana Moraga, Sergio Callejas, Daniel Jiménez-Carretero, Daniel Martin, Tommaso Vicanolo, Georgiana Crainiciuc, Jon Sicilia, Maria Isabel Cuartero, Alicia Garcia-Culebras, Alejandra Aroca-Crevillen, Sandra Martín-Salamanca, Carlos Torroja, Maria A. Zuriaga, Juan A. Bernal, Maria A. Moro, Ana Dopazo, Jose J. Fuster, Fátima Sánchez-Cabo, Andrés Hidalgo & Iván Ballesteros

3. Institute for Experimental Pathology (ExPat), Centre for Molecular Biology of Inflammation (ZMBE), University of Münster, Münster, Germany

Mathis Richter & Oliver Soehnlein

4. Centre Nacional d’Anàlisi Genòmica, Barcelona, Spain

Emanuele Pitino, Max Ruiz, Irene Ruano, Holger Heyn & Juan C. Nieto

5. Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), 8 A Biomedical Grove, Immunos, Singapore, Singapore

Immanuel Kwok & Melissa S. F. Ng

6. Shanghai Immune Therapy Institute, Shanghai Jiao Tong University School of Medicine Affiliated Renji Hospital, Shanghai, China

Changming Shih, Run-Kai Huang, Alicia Garcia-Culebras & Lai Guan Ng

7. Departamento de Biología Celular e Histología, Facultad de Medicina, Universidad Complutense de Madrid, Madrid, Spain

Ana Moraga & Julia Skokowa

8. Department of Oncology, Haematology, Clinical Immunology and Rheumatology, University Hospital, Tübingen, Germany

Natalia Borbaran Bravo, Daniela F. Quail & Logan A. Walsh

9. Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Quebec, Canada

Samuel Doré & Gabriel F. Calvo

10. Department of Mathematics and MOLAB-Mathematical Oncology Laboratory, Universidad de Castilla-La Mancha, Ciudad Real, Spain

David G. Aragonés & Katrien de Bock

11. Department of Health Sciences and Technology, Laboratory of Exercise and Health, ETH Zürich, Zurich, Switzerland

Jing Zhang

12. Unidad de Investigación Neurovascular, Departamento de Farmacología y Toxicología, Facultad de Medicina, Universidad Complutense and Instituto de Investigación Hospital 12 de Octubre, Madrid, Spain

Maria Isabel Cuartero

13. Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA

Diego Moncada Giraldo

14. Department of Hematology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

Jian Hou

15. Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

You Wang

16. Shanghai Key Laboratory of Gynecologic Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

You Wang

17. Department of Urology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

Ming Zhang

18. State Key Laboratory for Systems Medicine for Cancer, Division of Cardiology, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

Jun Pu

19. Media and Immersive Experience Center, The Design School, Arizona State University, Arizona State University, Temple, AZ, USA

Ana Herruzo

20. Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA

David Chang van Oordt, Seokyoon Chang, Alexander E. Downie & Andrea L. Graham

21. Department of Medicine, Center for Immunity and Inflammation, New Jersey Medical School, Rutgers, The State University of New Jersey, Newark, NJ, USA

Fei Chen & William C. Gause

22. Department of Pathology, McGill University, Montreal, Quebec, Canada

Pierre O. Fiset

23. Department of Surgery, McGill University Health Center, Montreal, Quebec, Canada

Jonathan D. Spicer

24. Universitat de Barcelona (UB), Barcelona, Spain

Holger Heyn

25. Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK

Irina A. Udalova

26. CIBER en Enfermedades Cardiovasculares (CIBER-CV), Madrid, Spain

Jose J. Fuster

27. School of Medicine, Westlake University, Hangzhou, China

Lai Guan Ng

28. Department of Neuroscience and Biomedical Sciences, Universidad Carlos III de Madrid, Madrid, Spain

Iván Ballesteros

Contributions

Conceptualization: I.B., A. Hidalgo and L.G.N. Data curation: D.C.-W., A.R.-P., M. Richter, E.P., J.C.N., I.K., G.M., D.G.A., D.J.-C., C.T., D.M.G., C.S., R.K.-H., J. Sicilia and I.B. Formal analysis: D.C.-W., A.R.-P., M.R., E.P., J.C.N., I.K., D.G.A., D.J.-C., D.M.G., G.F.-C., J. Sicilia and I.B. Funding acquisition: A. Moraga, M.A.M., K.d.B., A.D., H.H., G.F.C., F.S.-C., O.S., D.F.Q., L.A.W., L.G.N., A. Hidalgo and I.B. Investigation: D.C.-W., A.R.-P., M. Richter, I.K., A.C.G.-C., N.B.B., S.D., S. Callejas, A.A.-C., M.A.Z., D.M., S.M.-S., T.D., S.O., T.V., G.C., A.M., J.Z., M.I.C., I.R., M.Z., M.S.F.N., D.C.v.O., A.E.D., F.C., S. Chang, A. Martin and I.B. Methodology: D.C.-W., A.R.-P., E.P., G.M., G.F.C., A. Hidalgo and I.B. Project administration and supervision: A. Hidalgo. Resources: A. Moraga, M.A.M., K.d.B., A.L.G., W.C.G., J.D.S., H.H., G.F.C., F.S.-C., A.D., I.A.U., J.J.F., J.A.B., O.S., D.F.Q., L.A.W., J.H., Y.W., M.Z., J. Skokowa, A. Martin, J.P., L.G.N., A. Hidalgo and I.B. Software: A.R.-P., E.P., G.M., D.G.A. and D.J.-C. Validation: D.C.-W., A.R.-P., A.C.G.-C., P.O.F. and I.B. Visualization: D.C.-W., A.R.-P., A. Hidalgo and I.B. Writing: A. Hidalgo and I.B.

Corresponding authors

Correspondence to Lai Guan Ng, Andrés Hidalgo or Iván Ballesteros.

Ethics declarations

Competing interests

J.C.N. is a scientific advisor of Omniscope. The other authors declare no competing interests.

Peer review

Peer review information

Nature thanks Ash Alizadeh, Paul Kubes and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 NeuMAP, a map of the neutrophil transcriptome.

(A) Plots showing the sorting strategy to isolate neutrophils from the indicated tissues. Note the different gating for the bone marrow. (B) Evolution of the NeuMAP by the cumulative addition of neutrophils from the indicated tissues. (C) Score expression on the NeuMap of published gene sets associated with neutrophil differentiation and maturation. (D) Score values of genes expressed in primary, secondary, and specific granules onto the NeuMAP. (E) K-mass score of neutrophils by the indicated tissue of origin in the NeuMAP. (F) Contour plots of the combined neutrophils from the indicated tissues in various conditions of disease. (G) Contour plots of the combined neutrophils from the indicated tissues of males, females, and old (80 week-old) individuals. (H) Mapping of neutrophils from the indicated studies, showing their distribution in the NeuMap.

Extended Data Fig. 2 Identification of clusters and transcriptional hubs in the NeuMap.

(A) Score expression of gene sets from specific biological processes. See Supplementary Table 2 for a complete gene list. (B) Clustering of the NeuMap with different levels of resolution. The clusters that best captured the functional areas were selected across the different resolutions to annotate the transcriptional hubs of the NeuMAP (as in Fig. 1). (C) Clustering tree of 151,960 neutrophils from R0.05 to R0.3, with clusters labeled according to their size. The arrow intensity reflects the proportion of cells and the color of the number of cells assigned to the other clusters. (D) Heatmap of the differentially expressed genes for each transcriptional hub. For a complete gene list see Supplementary Table 3. (E) Clustering of the NeuMap at resolution 0.2. (F) Transcriptional hubs from (B) and mRNA expression of the indicated genes. (G) Heatmap showing the enrichment scores of the functional signatures in the transcriptional hubs (H) Heatmap showing the proportion of cells in all the conditions included in the NeuMap that contribute to the top 5% expressing cells for each indicated functional gene signature. The number of cells per tissue was downsampled to 1000 prior to the calculation of the contribution proportion.

Source Data

Extended Data Fig. 3 Architecture of the NeuMap across sex, environmental conditions, mouse strains, and genetic alterations.

(A) Experimental strategy and projection onto the NeuMap to assess neutrophil distribution across transcriptional hubs. (B) K-mass density plots and stacked bar graphs showing hub distribution of neutrophils from healthy tissues and influenza-infected lungs, separated by sex (male and female). (C) K-mass scores and hub distributions of neutrophils isolated from indicated tissues in naïve male mice housed under germ-free (GF), specific pathogen-free (SPF), or rewilded (RW) environmental conditions. (D) K-mass scores and hub distributions of neutrophils from healthy tissues, flu-infected lungs, and tumors in C57BL/6 J and BALB/c mice, showing conserved hub structure across mouse strains. (E) K-mass scores and hub distributions of wild-type and Tet2−/− neutrophils isolated from non-irradiated bone marrow chimeras, under control (upper panels) or hypercholesterolemic (lower panels) conditions, illustrating hub preservation across genetic backgrounds. WD, Western diet.

Source Data

Extended Data Fig. 4 Architecture of the human neutrophil compartment (human NeuMap).

(A) UMAP visualization of human neutrophils colored by tissue of origin. Data were downsampled to 3,000 cells per cluster for visualization. A complete list of samples is provided in Supplementary Table 4. (B) Two-dimensional kernel density estimation showing cell density distributions across tissues. (C) Module scores for selected biological processes (left) and for human neutrophil signatures recently described in ref. [18] across 12 cancer types (right). Gene lists are provided in Supplementary Table 2. (D) Functional compartmentalization of the human NeuMap. Each hub is defined as the area containing the top 85% K-mass score. For a complete gene list of differentially expressed genes for each transcriptional hub see Supplementary Table 4. (E) Stacked bar plots showing the proportion of neutrophils across different organs and conditions within each hub, defined in (D). (F) Score values of murine hubs across the human NeuMap, showing preferential distributions matching the hubs defined in the human NeuMap. (G) Gene Module scores of murine hubs projected onto human neutrophil hubs, scaled by row. CRC, colorectal cancer; BM, bone marrow; SLE, systemic lupus erythematosus. Drawings in b,g were created in BioRender. Cerezo Wallis, D. (2025) https://BioRender.com/pfm336w.

Source Data

Extended Data Fig. 5 Characterization of JunbΔN mice.

(A) Heatmap showing the fold enrichment in binding sites for the indicated transcriptional factors (TF) associated with each hub. *, statistically significant motif enrichment. (B) chromatin accessibility scores for the indicated TF projected onto the NeuMAP, as determined by scATAC-sequencing combined with scRNA-seq (Dogma-seq). For a complete list of enriched motifs see Supplementary Table 5. (C) Heatmap of differentially expressed genes obtained by bulk RNA-seq of neutrophils from the BM, blood, and lung of wildtype mice, and lung of JunBΔN mice, as indicated in the table at right. Bottom right, JunB mRNA levels and signature of JunB target genes are projected in the NeuMAPs. For a complete list of DEGs see Supplementary Table 6. (D) K-mass values of neutrophils obtained from BM, blood, lungs, spleen, and livers of control Junbfl/fl and JunBΔN mice. (E) Expression of the indicated genes by RT-PCR analysis of Junbfl/fl and JunBΔN BM neutrophils, before and after intratracheal transfer into the lungs of WT mice. (F) Quantification of immature and mature myeloid cell progenitors and mature populations in the BM of control Junbfl/fl and JunBΔN mice. Eo, eosinophils, Mono, monocytes, Imm. Neu, immature neutrophils, Mat.neu, mature neutrophils; CMP, common myeloid progenitor; cMoP, common monocyte progenitor; GMP, granulocyte-monocyte progenitor. Data is from 7 WT and 5 JunBΔN mice. No significant differences found as determined by two-tailed Student’s t-test. Data are presented as mean ± SEM. Box plots represent median (center line), interquartile range (box limits, 25th–75th percentiles), whiskers (min–max), and all individual data points are shown. (G) Quantification of OT-1 T cell killing of B16OVA target cells in the presence of neutrophils from the indicated tissues of control Junbfl/fl and JunBΔN mice. Data is data are mean ± SEM 6 mice per group. Significant differences shown in figure, as determined by two-tailed Student’s t-test (H) Quantification of the vascularization of Matrigel plugs co-injected with blood or lung neutrophils from control and JunBΔN mice, as assessed by Doppler analysis. Data are mean ± SEM from 9 WT and 6 JunBΔN mice. p = 0.0136 in control mice and p = 0.9541 in JunBΔN mice as determined by two-tailed Student’s t-test (I) Percentage of proliferative endothelial cells (Ki67+ Sca1 + CD31 +) in lungs from control and JunBΔN mice after irradiation (left) or during organismal growth (4-week-old mice; right). Data are mean ± SEM from 11-8 mice irradiated, and 15-11 mice growth. p = 0.11 for irradiated or p = 0.06 for growing mice as determined by two-tailed Student’s t-test. (J) Representative images and quantification of the density of CD31 + ERG+ endothelial cells (MRP14) in control and JunBΔN mice. Data are mean ± SEM from 3 regions from 3 independent mice per group. Differences determined by two-tailed Student’s t-test. (K) Experimental setup and multiparametric cytometric analysis (21 markers) of lung neutrophils from LLC tumors implanted in CreNEG control and JunBΔN mice. The UMAP projections show the distribution of neutrophils from each mouse and the expression of CD14, Sca1 and PDL1. Right, percentage of control and JunB-deficient neutrophils that acquire the “Cancer” phenotype. Data are mean ± SEM from 6 JunBΔN, and –7 WT mice. p = 0.0414 as determined by two-tailed Student’s t-test. (L) Ratio of CreNEG control and JunBΔN neutrophils that infiltrate the lungs relative to the numbers in blood, in naïve and LLC tumor-bearing mice, using transplantation chimeric mice. Data are mean ± SEM from 5 mice per group. (M) Number of proliferative endothelial cells (BECs) in the lungs of healthy and tumor-bearing mice 3 weeks after tumor implantation. BECs were analyzed by flow cytometry. Data mean ± SEM from 5 mice per the tumor-bearing group; p = 0.0453 as determined by by two-tailed Student’s t-test. (N) Absolute number of CD4 or CD8 T cells infiltrating LLC tumors in control and JunBΔN mice; data are mean ± SEM from 7 WT and10 JunBΔN mice. (O) Volume of subcutaneous LLC implanted tumors in control versus JunBΔN mice over time; data are mean ± SEM from 6 WT and 5 JunBΔN. Differences determined by Two-way ANOVA with Tukey correction.

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Extended Data Fig. 6 Spatial characterization of neutrophils.

(A) K-mass score of neutrophils from lungs of naïve, tumor-bearing, and flu-infected mice. (B) Score of the indicated signatures onto the spatial transcriptomic dataset in the naïve, tumor-bearing, and flu-infected lung sections. (C) Enrichment scores of neutrophils for the IS-II and IFN-response signatures. Statistics Wilcoxon pairwise Test; *** p < 0.001 (D) H/E staining of lungs from tumor-bearing (LLC), and flu-infected lung sections. Deconvolution using LungMap Project3 allows annotation of cells the tissues used for spatial transcriptomics. Right, bars show the percentage distribution of neutrophils assigned to the indicated hubs in the different annotated regions of the tissue. (E) Analyses as in (D) performed in a PDAC tumor (from ref. [7]) and an infarcted myocardial tissue (one day after ischemia; from ref. [23]). (F) Percent of closest neighbouring cells (macrophages, T cells or fibroblasts) featuring the indicated profiles to neutrophils from the different hubs. The numbers on top indicate the number of neighbouring cells around neutrophils from each hub, when available. (G) Expression of the indicated genes encoding for surface markers in the NeuMAP, and (H) expression of the respective proteins represented in the UMAP generated with neutrophils stained using the MACSima platform (see Methods). (I) Heatmap showing the expression of the indicated proteins in the neutrophil clusters identified using MACSima. (J) Mean fluorescence expression intensity of the indicated proteins in neutrophils in different regions of the tissues analyzed by multiparametric immunofluorescence. Ex, extravascular; In, intravascular; AT, adjacent tissue; IT, intratumoral.

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Extended Data Fig. 7 Dynamics of neutrophil maturation.

(A) Fraction of neutrophils in the different transcriptional hubs for the indicated tissues and conditions of naive, tumor-bearing or LPS treated mice (B) Experimental setup for the timestamp experiment in iLy6GtdTomato mice, showing the frequency of Tomato+ neutrophils in the indicated tissues at different times after tamoxifen administration. Neutrophils were collected from BM (24 h), and tissues (36 and 72 h). (C) Upper panels, cell density (K-mass) in the NeuMap of pulse-labelled neutrophils from the BM (left) or tissues (right) of naïve, tumor-bearing, and flu-infected mice. Middle panels, pseudotime scores of BM neutrophils from the three conditions. Lower panels, expression of relevant maturation and activation genes projected in the NeuMap. (D) Heatmap showing the distribution of Tomato+ neutrophils along the different transcriptional hubs over time, determined from the timestamp experiment. (E) Flow cytometric plots and quantification of mature blood neutrophils (CD101 + Ly6G + ), and expression of PDL1 and ICAM1 in BM neutrophils in response to LPS treatment, and kinetics of the percentage of PDL1 + ICAM1+ cells over time after LPS treatment. Mean ± SEM from 3 replicates. Two-tailed unpaired T-test (bar graph) p = 0.022 (CD101), p = 0.001 (PDL1) and p = 0.000037. Two-way Anova with Šídák’s multiple comparisons test (time-course) p < 0.00001 (F) In vivo kinetics of neutrophils from the indicated organs showing their transition from the BM to blood and to spleen of naïve or LPS-treated mice, determined by BrdU staining. Mean ± SD from 4 independent mice. (G) Transcriptional distribution in the NeuMAP of peritoneal-infiltrating neutrophils at 1 and 3 days of zymosan injection. (H) Representative genes that change along the trajectories defined in Fig. 3e. For the complete set see Supplementary Table 7.

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Extended Data Fig. 8 Analysis of ex vivo neutrophil cultures.

(A) Heatmap showing expression of 21 markers in mature neutrophils treated with the indicated cytokines and conditioned media for 24 h. G, G-CSF, CM LLC, conditioned medium of LLC cells; CM PDAC, conditioned medium of FC1242 pancreatic cancer cells; GM, GM-CSF. (B) Projection of marker expression in the UMAP obtained by multiparametric flow cytometry. (C) Percentage of neutrophils classified as “Mature”, “Inflammation/infection”,”immunosuppression“, and “Cancer” after ex vivo culture for 24 and 48 h with the indicated cytokines and conditioned media. Data is from 4 independent experiments. ***, p < 0.001 compared with the vehicle group at the respective time point, as determined by Two-way ANOVA with Dunnett’s multiple comparison test. Mature: Control vs. TGFβ, Cxcl12, IL1β, IFNβ, CM LLC, CM PDAC and GM-CSF p < 0.000001. Inflammation/Infec= Control vs. IFNβ p < 0.000001. Immunosuppression: Control vs. CM LLC, CM PDAC and GM-CSF p < 0.000001. Cancer: Control vs. GM-CSF p < 0.000001. (D) Percentage of viable cells after ex vivo culture of neutrophils for 48 h with the indicated treatments. Data are mean ± SEM from 3–7 individual experiments. One-way ANOVA with Dunnett’s multiple comparison test. Vehicle vs. TNFα and G-CSF, p < 0.000001. Vehicle vs. CM PDAC p = 0.0041 (E) BM neutrophils from control CreNEG, TgfbrΔN, IfnarΔN and Csf2rΔN mice were cultured ex vivo for 24 and 48 h with TGFβ, IFNβ and GM-CSF. The plots show the percentage of neutrophils with a “Mature” phenotype after treatment with TGFβ, an “Inflammation/infection” phenotype after treatment with IFNβ, and a “Cancer” phenotype after treatment with GM-CSF. Data is from 3 independent experiments and statistics are as in (C). as determined by two-Way ANOVA with Tukey’s multiple comparison test. + TGFβ: TgfbRΔN vs. CreNeg, IfnarΔN, and Csf2rΔN p < 0.000001. + IFNβ: IfnarΔN vs. CreNeg, Csf2rΔN, and TgfbRΔN p < 0.000001. + GM-CSF: Csf2rΔN vs. IfnarΔN, CreNeg, and TgfbRΔN p < 0.000001 (F) Changes in the distribution of neutrophils from TgfbRΔN, Ifnar1ΔN and Csf2rΔN mice across the transcriptional hubs of the NeuMap, determined by scRNA-sequencing relative to CreNEG control mice. The arrowheads indicate the more relevant hubs examined for each mutant, as determined from Fig. 4h. P values comparing the distribution of control vs. mutant cells were calculated using a bootstrap approach (see Methods). The figure shows the delta proportions of neutrophil hubs between the mutant and control mouse. Bars show confidence intervals of the random distributions. TgfbrΔN: IFN-resp p = 0.0017, IS-I p = 0.0037. IfnarΔN: PreNeus p = 0.0137, Immature p = 0.0043, Immune-Silent p = <0.00001, IFN-resp. P = 0.0002, Ag-Present. P < 0.00001. Csf2rΔN: Immature p = 0.0239, Immune-silent p = 0.0252, IS-I p < 0.00001, IS-II p < 0.00001 (G) Transcription factor (TF) enrichment across NeuMap transcriptional hubs, identified by DOGMA-seq. Dot size indicates odds ratio, and color intensity reflects the adjusted p-value. (H) Functional profiling of HoxB8-derived neutrophils lacking the indicated TFs (Cebpb, Irf5, Klf6, Rfx2, Runx1, Relb, JunB) or wild-type controls, after 48 h treatment with the indicated cytokines or PDAC-conditioned medium. Heatmaps show the proportion of cells in each phenotypic cluster (Immature, Mature, Inflammation/Infection, immunosuppression, Cancer), based on flow cytometry marker expression. Data are from three independent experiments. *p < 0.05, **p < 0.01, ***p < 0.001, One-way ANOVA with Dunnett’s multiple comparison test. (I) Schematic representation of JunB overexpression in HoxB8 progenitors using lentiviral transduction. Representative cytospin images of control and JunB-overexpressing neutrophils at day 5 post-estrogen withdrawal confirm comparable polymorphonuclear morphology. (J) Heatmap showing differentially expressed IS-I/IS-II-associated genes identified by bulk RNA sequencing of HoxB8-derived neutrophils (wild-type; JunbWT, JunB knockout; Junb−/−, or JunB-overexpressing; OE) cultured for 48 h with vehicle or 10 ng/ml GM-CSF. (K) UMAP contour plots derived from multiparametric flow cytometry (21 markers) of HoxB8-derived neutrophils from the same experimental groups as in (J), after 48-hour culture with vehicle or GM-CSF. Heatmaps below indicate the proportion of cells within the IS-I/IS-II-associated gate for each condition. Data represent n = 3 independent experiments. ***p < 0.001, one-way ANOVA with Dunnett’s multiple comparison test.

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Extended Data Fig. 9 Predictive potential of the NeuMAP.

(A) Enrichment score of signatures obtained from human neutrophils from four types of cancer (lung, liver and pancreas, and glioblastoma, GBM). Shown are the associations of different clusters of neutrophils from these human cancers with hubs of inflammation/Infection (left), Angiogenesis/Immunosuppression (middle), and Antigen presentation (right). Shown are the cluster names from the indicated studies. See also Fig. 5a for similar projections in infectious, autoimmune disease and lung cancer. Right, heatmap of average enrichment scores for each hub signature, scaled by signature. (B) Signature scores from intratumoral neutrophil populations (T1–T3) from ref. [7] (left), and from those described by ref. [92] (C1-C6) (Right). (C) Signature scores from intratumoral neutrophil identified by ref. [18] across 12 types of human cancers. (D) Human neutrophils derived from bone marrow CD34 + HSPC were cultured with the indicated cytokines and their RNA sequenced. Enrichment scores were computed for each cytokine-induced signature and mapped onto NeuMAP. Right, heatmap showing mean signature scores across hubs. Scale bar 200 µm (E) Spatial transcriptomic maps of lung tissue from patients with lung adenocarcinoma, comparing healthy, adjacent, and tumor regions. Cell types were annotated by deconvolution and overlaid as spatial coordinates. Right, neighbourhood composition using a curated cell annotation. The exact n value for each cell type is indicated next to the corresponding bar in the graph. Data are presented as mean ± SEM. Drawings in a–e were created in BioRender. Cerezo Wallis, D. (2025) https://BioRender.com/pfm336w.

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Extended Data Fig. 10 Diagnostic potential of blood neutrophils projected in the NeuMap.

(A) Blood neutrophils from 18 conditions were projected onto the blood-only UMAP or the NeuMap based on their transcriptional profile and are shown as densities or K-mass. (B) Distribution of cells with their assigned transcriptional hub (by color) in the blood-only UMAP and in the NeuMAP (left), showing the overlapping areas of the different hubs. (C) PCA analysis of blood sample distribution based on the Bhattacharyya index obtained using the 7 transcriptional hubs or the 10 expanded diagnosis regions. (D) Representative examples of the distribution of the blood neutrophils from four conditions onto the expanded diagnostic regions that we used to calculate the Bhattacharyya indices.

Supplementary information

Supplementary Information

Supplementary Figs. 1–4.

Reporting Summary

Supplementary Table 1

Identification of samples (65 individual samples) and biological conditions (47 different conditions) used to build the NeuMap. All samples are single-cell transcriptomes from purified neutrophils generated in this study.

Supplementary Table 2

Gene signatures used to identify functional hubs in NeuMap.

Supplementary Table 3

DEGs in neutrophils from the seven functional hubs identified in Fig. 1 and Extended Data Fig. 2.

Supplementary Table 4

Identification of samples used to build the human NeuMap and gene list of DEGs for each transcriptional hub shown in Extended Data Fig. 4. All samples are single-cell transcriptomes from purified neutrophils generated in this study.

Supplementary Table 5

Analysis of Enriched Motifs found in tissue neutrophils and clusters of the Dogma-seq analysis.

Supplementary Table 6

LRT analysis of neutrophils from bone marrow, blood and lungs of control mice, and JunbΔN lungs from the bulk RNA-sequencing dataset, shown in and Fig. 2e and Extended Data Fig. 3c.

Supplementary Table 7

Pseudotime trajectory analysis, from the proposed developmental trajectories in Fig. 3e and Extended Data Fig. 6h.

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Cerezo-Wallis, D., Rubio-Ponce, A., Richter, M. et al. Architecture of the neutrophil compartment. Nature 649, 1003–1012 (2026). https://doi.org/10.1038/s41586-025-09807-0

- Received: 16 August 2024

- Accepted: 24 October 2025

- Published: 03 December 2025

- Version of record: 03 December 2025

- Issue date: 22 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09807-0

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Fasting boosts breast cancer therapy efficacy via glucocorticoid activation

Nuno Padrão,

Tesa M. Severson,

Sebastian Gregoricchio,

Ana Guijarro,

Catrin Lutz,

Daniel Taranto,

Stefan Hutten,

Francesca Ligorio,

Angelica Persia,

Merel Roest,

Joyce Sanders,

Ji-Ying Song,

Sara Pires-Oliveira,

Maria Donaldson Collier,

Hugo Horlings,

Livia Pisciotta,

Filippo de Braud,

Claudio Vernieri,

Leila Akkari,

Jos Jonkers,

Alessio Nencioni,

Irene Caffa &

…

Wilbert Zwart

Nature volume 649, pages 1013–1021 (2026)

Abstract

The majority of breast cancers are driven by oestrogen receptor-α (ERα) activation, and endocrine therapy represents the mainstay treatment for these patients[1]. However, resistance is common and tumours often progress after years of endocrine suppression[2]. Periodic fasting enhances the efficacy of standard endocrine therapy and delays acquired drug resistance, although the underlying mechanisms remain unclear[3]. Here we show that fasting induces extensive epigenetic reprogramming in ERα-positive breast cancer xenografts when combined with endocrine therapy, with large-scale activation of glucocorticoid receptor (GR) and progesterone receptor signalling and concomitant reduction in the activity of activator protein-1 (AP-1) family members. GR-driven gene programmes are selectively activated in in vivo models of ERα-positive breast cancer during fasting, and GR knockout hinders the anti-tumour effects of fasting combined with tamoxifen. Exogenous administration of GR ligands recapitulates fasting-enhanced anti-oestrogen action, thus promoting tumour regression. Patients undergoing a cyclic fasting-mimicking diet exhibited increased blood progesterone and cortisol concentrations. Additionally, tumours collected after the fasting-mimicking diet showed an inverse correlation of GR activation with proliferation markers, providing clinical confirmation of our observations in animal models. Our results indicate that GR activation has a pivotal role in the ability of fasting to enhance endocrine therapy activity in breast cancer and suggest that corticosteroid administration should be evaluated as an adjuvant to endocrine therapy in this setting.

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Main

Hormone receptor-positive (HR+) breast cancer accounts for 75% of all breast cancer diagnoses, and endocrine therapies represent the mainstay of treatment for patients with HR+ breast cancer, in both adjuvant and metastatic settings[1]. Yet, the efficacy of standard endocrine therapies is limited by primary or acquired resistance[2]. Periodic fasting enhances the efficacy of endocrine therapies against HR+ breast cancer and delays acquired therapy resistance in animal models[3]. Clinical studies indicate that cycles of water-only fasting or fasting-mimicking diets (FMDs; low-calorie, low-protein and low-sugar, vegan diets that recreate the metabolic effects of fasting[4]) are feasible and safe in patients with different tumour types, such as breast, melanoma, colorectal, lung and gynaecological cancers5,6,7.

We previously reported that fasting enhances the efficacy of endocrine therapies in HR+ breast cancer[3]. However, the mechanisms that underlie this effect remain unknown. Moreover, adjuvant endocrine regimens involve five to ten years of continuous daily treatment[8], making prolonged combined dietary intervention during endocrine therapy highly challenging to adhere to. Here we investigated the biological mechanisms at the basis of fasting enhancement of tamoxifen (TMX) efficacy, one of the most commonly utilized endocrine therapies, with the aim of identifying therapeutic strategies that phenocopy beneficial effects of fasting or FMD in patients with HR+ breast cancer, and which could be potentially adopted in place of fasting or FMD.

Fasting reprogrammes the cancer epigenome

In mice xenografted with the human HR+ breast cancer cell line MCF7, weekly cycles of 48 h fasting showed synergistic in vivo anti-tumour effects when combined with TMX (Fig. 1a,b and Extended Data Fig. 1a,b), confirming our previous observations[3]. To comprehensively define the biological effect of fasting on tumour cell biology, we performed extensive multiomic analyses on the collected tumour material, including transcriptomics, proteomics, immunohistochemistry and chromatin immunoprecipitation with sequencing (ChIP–seq) for the active enhancer/promoter mark H3K27ac[9] and many transcription factors (Fig. 1a). A deep analysis of H3K27ac active enhancer/promoter profiles revealed profound epigenomic reprogramming in tumours collected from mice exposed to TMX plus fasting (Fig. 1c–e, Extended Data Fig. 1c–g and Extended Data Table 1) compared with those of tumours that were treated with TMX or fasting alone (Extended Data Fig. 1c,d,f,g). To comprehensively identify transcription factors that potentially act through regulatory elements with altered epigenetic states after fasting, we intersected the genomic coordinates of fasting-affected H3K27ac sites in TMX-treated tumours with data from a publicly available ChIP–seq database[10] (n = 13,976) (Fig. 1f and Supplementary Information). H3K27ac sites with decreased signal upon fasting showed enriched occupancy for AP-1 transcription factor family members (including FOSL2, JUN, FOSL1, FOS and JUND)11,12 (Fig. 1f, top), which are known to enhance breast cancer growth and proliferation[13]. In agreement with this finding, AP-1 inhibition effectively blocked proliferation of the HR+ breast cancer cell lines MCF7 and T47D (Extended Data Fig. 1h), as previously reported[14]. The H3K27ac sites that were gained after fasting in TMX-treated tumours revealed an enrichment for ERα occupancy in silico (Fig. 1f, bottom), which was confirmed experimentally by ERα ChIP–seq in the same tumours (Extended Data Fig. 1i and Extended Data Table 1). Strong in silico enrichment at fasting-induced H3K27ac regions was also observed for other steroid hormone receptors (SHRs): GR, progesterone receptor (PR) and androgen receptor (AR). (Fig. 1f, bottom). All three SHRs serve as tumour suppressors in ERα+ breast cancer15,16,17,18, yet how their function is affected by dietary interventions remains unknown.

Fig. 1: Fasting induces H3K27ac changes in TMX-treated xenografts.

a, Schematic representation of the treatment cycles of mice xenografted with MCF7 cells. After tumours reached a palpable size, mice were treated with the different treatment arms. After four weeks of treatment, tumours were collected and the represented multiomics profiling was performed. TF, transcription factor. Adapted from Servier Medical Art (https://smart.servier.com), CC BY 4.0. b, Xenograft tumour growth in six-to-eight-week-old female athymic nude mice randomized in control arm (ad libitum diet, n = 6) or fasting alone (48 h weekly, n = 6), TMX alone (n = 6) or TMX combined with fasting (n = 6) treatment arms. n represents number of tumours per treatment group. Data are mean ± s.e.m. P values by mixed-effect model with Tukey’s multiple test correction (Supplementary Information) and two-tailed Student’s t-test (P values from the last day are represented). c, Heat map depicting the differentially enriched H3K27ac regions between TMX alone (n = 3) or TMX plus fasting (n = 6). n represents number of tumours per treatment group. Colour scale represents the average normalized read counts. d, Representative snapshots of differentially enriched regions for H3K27ac between TMX (blue) and TMX plus fasting (green). The genomic coordinates are annotated. e, Heat map of differentially enriched H3K27ac signal in xenografts treated with TMX or TMX plus fasting. f, In silico GIGGLE analysis for factor enrichment at TMX-enriched H3K27ac sites (top left, n = 3) or TMX plus fasting-enriched H3K27ac sites (bottom left, n = 6). n represents number of tumours per treatment group. Right, average binding motifs of the depicted transcription factors, using HOMER software. In box plots, boxes indicate the first and third quartiles, the centre line indicates the median, and the whiskers indicate the first and third quartiles expanded by 1.5× the interquartile range (Supplementary Information).

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Fasting activates intratumoural GR and PR

Our in silico analyses suggested altered activity for many SHRs after fasting. To further explore the role of these SHRs in fasting-induced anti-tumour effects, we performed immunohistochemistry analysis to quantify the expression and subcellular localization of ERα, PR and GR in tumours collected from mice exposed to ad libitum diet, fasting, TMX or TMX plus fasting (Fig. 2a). We found that ERα and PR reside mostly in the nucleus, irrespective of their ligand state, as expected17,19, and both their amount and subcellular localization were unaffected by fasting (Fig. 2a). By contrast, fasting—alone or in combination with TMX—strongly increased the nuclear localization of GR, an effect that is typically seen upon GR activation[20] (Fig. 2a).

Fig. 2: Fasting enhances GR and PR chromatin binding and decreases enhancer activity at JUN sites.

a, Representative immunohistochemistry staining for ERα, GR and PR in MCF7 xenografted tumours in the different treatment arms (n = 4). n represents number of mice per treatment group. Scale bars, 100 μm. b, Heat map depicting ChIP–seq signal for GR and PR in xenografts for all treatment arms. Regions altered in response to fasting in TMX-treated mice are shown. c, Average ChIP–seq signal for GR and PR for all conditions at sites that are enriched in the TMX plus fasting condition, with data centred on the peak, within a ±2-kb window. Data are mean ± s.e.m. d, ChIP–seq heat map signal for JUN in xenografts for all treatment conditions at H3K27ac regions that are differentially enriched in TMX and TMX plus fasting. e, Average JUN ChIP–seq signal for all conditions at H3K27ac regions that are enriched in TMX (left) and TMX plus fasting (right) conditions. Data are centred at the JUN peak, with a ± 2-kb window around the peak. Data are mean ± s.e.m. f, Schematic representation of blood collection schedule. Blood was collected from MCF7 xenografted mice before initiating the first cycle of fasting for all treatment conditions. Blood collection was repeated after the fourth cycle of fasting, when the mice were euthanized. Adapted from Servier Medical Art (https://smart.servier.com), CC BY 4.0. g, Corticosterone and progesterone levels in blood of mice before and after four cycles of TMX plus fasting (n = 9). n represents number of mice per treatment group. Data were analysed by two-tailed Wilcoxon signed rank test. h, Schematic representation of serum collection from patients with breast cancer who were being treated with endocrine therapy, before and after five days of FMD. Adapted from Servier Medical Art (https://smart.servier.com), CC BY 4.0. i, Cortisol and progesterone concentrations in serum of patients with ERα+ breast cancer, as described in h. Data were analysed by two-tailed Student’s paired t-test. j, Cortisol concentrations in serum of patients with ERα+ breast cancer before and after five days of FMD. Data were analysed by two-tailed Student’s paired t-test.

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SHRs exert their function by binding specific genomic regions to drive expression of their target genes. ChIP–seq analyses of GR and PR showed strong chromatin interactions upon fasting (Fig. 2b,c, Extended Data Fig. 2a,b and Extended Data Table 1). In line with our in silico predictions and motif enrichment analyses (Fig. 1f, bottom), H3K27ac regions that were enhanced with fasting were enriched for GR and PR occupancy, both after fasting alone and after fasting with TMX (Extended Data Fig. 2c). Whereas overall JUN chromatin binding was increased in tumours from fasted mice (Extended Data Fig. 2d and Extended Data Table 1), JUN binding was not observed in the newly engaged H3K27ac sites upon dietary intervention (Fig. 2d,e). When analysing sites that lost H3K27ac, we found that JUN occupancy remained unaltered. These data imply that fasting resulted in a loss of enhancer action at AP-1 sites, without affecting AP-1 chromatin binding at these sites. For newly gained enhancers, AP-1 was found not to occupy these regions, whereas both GR and PR did.

GR and PR are SHRs that depend on their cognate ligands for activation, namely cortisol (or corticosterone in mice) and progesterone, respectively. Therefore, we measured the levels of both hormones in mouse blood before and after the four-week treatment (which included four fasting cycles) (Fig. 2f,g) and in the serum of patients with breast cancer who were undergoing endocrine therapy in combination with a five-day FMD regimen as part of a clinical trial (ClinicalTrials.gov ID: NCT05748704) (n = 15) (Fig. 2h,i and Extended Data Table 2). In mice, fasting alone or fasting combined with TMX increased the concentrations of circulating corticosterone and progesterone (Fig. 2g and Extended Data Fig. 3a). Analogously, FMD also increased cortisol and progesterone levels in patients with breast cancer who were undergoing endocrine therapy (Fig. 2i), as well as in patients with breast cancer undergoing FMD without concomitant endocrine therapy in the DigesT study (n = 35; ClinicalTrials.gov ID: NCT03454282) (Fig. 2j). Fasting and FMDs were previously found to reduce blood IGF1, insulin and leptin concentrations (hereafter collectively referred to as fasting-reduced factors (FRFs)), in mice and humans with breast cancer3,6,21,22. The re-introduction of these FRFs reduced the beneficial effects of fasting on tumour growth[3] (Extended Data Fig. 3b) and prevented the increase of circulating corticosterone and progesterone levels in mice treated with TMX plus fasting (Extended Data Fig. 3a).

Collectively, these results indicate that fasting (in mice) and a FMD (in patients) increase the blood levels of cortisol and progesterone, promoting GR and PR activation in HR+ breast cancer cells. Furthermore, fasting switches AP-1 occupied sites from an active epigenetic state to a repressed chromatin state, without affecting AP-1 binding.

Fasting activates GR-responsive genes

To understand the transcriptional consequences of an altered epigenome upon fasting, and whether and how such changes contribute to enhance endocrine therapy anti-tumour activity, we performed RNA-sequencing (RNA-seq) analyses. Consistent with the observed tumour regressions (Fig. 1b), RNA-seq analysis of MCF7 xenografts that were treated with TMX plus fasting revealed an inhibition of pathways related to cell proliferation (MYC[23] and E2F targets[24]) (Fig. 3a,b and Extended Data Fig. 3c,d). In agreement with our previous study[3], the nutrient sensor[25] mTOR showed decreased activity after fasting (Fig. 3b and Extended Data Fig. 3d). These data were confirmed by proteomic analysis (Extended Data Fig. 3e,f). Since fasting increased corticosteroid levels in mice and patients with HR+ breast cancer (Fig. 2g,i,j) and increased chromatin binding of GR (Fig. 2b,c and Extended Data Fig. 2c), we utilized a pan-cancer applicable GR-activity gene signature[16] that confirmed elevated GR transcriptional activity in tumours collected from mice treated with TMX plus fasting (Fig. 3c). Unexpectedly, TMX treatment alone significantly reduced the expression of the GR-activity signature (Fig. 3c). GR activation was recently reported to exert anti-tumour effects in breast cancer by increasing the expression of the transcriptional suppressor ZBTB16 (also known as PLZF)[16]. Of note, ZBTB16 was the second most upregulated GR-signature gene in mice that received both TMX and weekly fasting cycles (Fig. 3d). Consistent with these findings, increased chromatin occupancy of both GR and PR was observed at the ZBTB16 gene locus after fasting, which was accompanied by a marked increase in the active enhancer–promoter marker H3K27ac at the same site (Fig. 3e).

Fig. 3: Fasting enhances GR transcriptional activity and increases expression of the transcriptional suppressor ZBTB16.

a, Volcano plot depicting genes that are differentially expressed between TMX and TMX plus fasting treated MCF7 xenografts. Differential gene expression was determined by two-sided Wald test. b, Gene set enrichment analysis for Hallmark pathways. Shown are the pathways that are differentially enriched upon treatment with TMX or TMX plus fasting. Normalized enrichment score (NES) was calculated by weighted Kolmogorov–Smirnov test and P value was determined by permutation-based testing with multiple Benjamini–Hochberg hypothesis correction. DN, downregulation; UP, upregulation. c, Enrichment plot of a pan-cancer GR-activity signature for all depicted conditions. NES was calculated by weighted Kolmogorov–Smirnov test and P value determined by permutation-based testing with multiple Benjamini–Hochberg hypothesis correction. d, Differential expression analyses for GR-activity signature genes, based on log_2-transformed fold change ranking, comparing TMX and TMX plus fasting conditions. e, Snapshots of H3K27ac, ERα, GR, PR and JUN ChIP–seq signal at the ZBTB16 gene locus in TMX-treated and TMX plus fasting-treated xenografts. f, Enrichment plot for GR-activity signature in matched tumour samples of patients with breast cancer, before and after five days of FMD. NES was calculated by weighted Kolmogorov–Smirnov test and P value was determined by permutation-based testing with multiple Benjamini–Hochberg hypothesis correction. g, Correlation plot between of G2M and E2F Hallmarks, and the GR-activity signature in matched tumour samples from patients with breast cancer, before and after five days of FMD. Two-sided Spearman’s linear correlation between gene set variation analysis (GSVA) enrichment scores of the indicated gene sets was calculated, and R and P values are shown.

To confirm our xenograft-based observations in patients with breast cancer, we applied the same GR-activity signature to transcriptomic data derived from matched tumours specimens (pre- and post-FMD) from patients with HR+ breast cancer undergoing a five-day FMD in the clinical trial NCT03454282. Consistent with the data obtained in mice, GR transcriptional activity was increased after FMD in these patient samples (Fig. 3f and Extended Data Fig. 4a,b). Moreover, we found that GR activity was negatively correlated with two Hallmark gene sets of tumour proliferation (Hallmark of E2F targets and G2M Checkpoint) (Fig. 3g). In line with the increased PR chromatin binding (Fig. 2b,c, Extended Data Fig. 2a,b and Extended Data Table 1) and the increased progesterone levels (Fig. 2g,i and Extended Data Fig. 3a), PR transcriptional activity was also increased in patients after FMD (Extended Data Fig. 4b,c and Extended Data Table 3).

Overall, these results indicate that fasting selectively activates transcriptional programmes that are under the control of GR and PR, both of which are known for their tumour suppressor function in HR+ breast cancer16,17.

GR activation mimics fasting effects

The beneficial effects of PR activation in HR+ breast cancer have previously been reported[17] and are being explored in the phase 2 clinical trial PIONEER (ClinicalTrials.gov ID: NCT03306472). However, the role of GR activation in this type of cancer remains poorly understood. Here we focus on GR and its role in the enhancement of endocrine therapy activity through fasting. To determine whether the anti-proliferative effects of fasting are critically mediated by GR action, we knocked out GR in MCF7 cells (Extended Data Fig. 5a,b) and generated mouse xenografts from these GR-knockout (GR-KO) cells. As expected, GR-KO MCF7 cells did not respond to the GR agonist dexamethasone (Dexa) (Extended Data Fig. 5c–f). GR-KO and control MCF7 mouse xenografts were exposed to weekly 48 h fasting cycles, with or without TMX treatment (treatment schedule as in Fig. 1a). Whereas in control tumours, TMX and fasting synergistically blocked tumour growth (Fig. 4a,b and Extended Data Fig. 6a,b), confirming our previous observation[3] (Fig. 1b), this synergy was lost when GR was knocked out (Fig. 4a,b and Extended Data Fig. 6c,d).

Fig. 4: GR activity drives beneficial effects of fasting in endocrine response.

a, Non-targeted control (NT) or GR-KO MCF7 xenograft tumour growth in six-to-eight-week-old female athymic nude mice treated with fasting (NT, n = 8; GR-KO, n = 6), TMX (NT, n = 9; GR-KO, n = 7) and TMX plus fasting (NT, n = 11; GR-KO, n = 9). Tumour growth was normalized to the respective control tumours b, Per cent MCF7 NT or GR-KO tumour volume compared with the respective control (control: NT, n = 8; GR-KO, n = 6; fasting: NT, n = 8; GR-KO, n = 5; TMX: NT, n = 6; GR-KO, n = 6; TMX plus fasting: NT, n = 10; GR-KO, n = 7). Data are mean ± s.d. Comparison by two-sided Student’s t-test on the last day. c, Schematic representation of the four treatment cycles. Mice were randomized into control arm, Dexa, TMX or TMX plus Dexa treatment arms. BC, breast cancer. Adapted from Servier Medical Art (https://smart.servier.com), CC BY 4.0. d, MCF7 xenograft outgrowth in six-to-eight-week-old female athymic nude mice in the different treatment arms (control, n = 8; TMX, n = 9; Dexa, n = 6; fasting, n = 8; TMX plus fasting, n = 11; TMX plus Dexa, n = 7). e, HR+ breast cancer PDX outgrowth in six-to-eight-week-old female NSG mice in the different treatment arms (control, n = 4; TMX, n = 4; Dexa, n = 4; TMX plus Dexa, n = 5). f, MCF7 xenograft outgrowth in six-to-eight-week-old female athymic nude mice in the different treatment arms (control, n = 7; TMX, n = 7; Dexa, n = 5; TMX plus Dexa, n = 6). After four weeks, all treatments were stopped and tumours were allowed to re-grow. Data are mean ± s.e.m. g. Survival curves of immunocompetent mice engrafted with TSAE1 cells and treated with either TMX alone (n = 10) or combined with Dexa (n = 10). Log-rank test is depicted. h. Proposed model for fasting-enhanced TMX response in breast cancer cells. a,b,d–g, n, number of tumours per treatment group. a,d,e, Data are mean ± s.e.m. P values by mixed-effect model with Tukey’s multiple test correction (Supplementary Information) and two-tailed Student’s t-test (P values of last day are represented).

Source data

Since GR appeared to have a central role in fasting-mediated enhancement of endocrine therapy for HR+ breast cancer, we hypothesized that the beneficial effects of fasting could be mimicked by glucocorticoid administration. To test this hypothesis, we compared the anti-tumour activity of Dexa with fasting, alone or in combination with TMX, in MCF7 xenografts (Fig. 4c). Notably, combined Dexa and TMX phenocopied the anti-tumour activity of fasting plus TMX (Fig. 4d). Mice treated with Dexa did not undergo weight changes, whereas weight loss was observed in fasted mice (Extended Data Fig. 6e). The ability of Dexa to enhance endocrine therapy activity was further validated in a second HR+ breast cancer xenograft model (T47D; Extended Data Fig. 6f,g) and in a HR+ patient-derived xenograft (PDX) model (IDC186; Fig. 4e and Extended Data Fig. 6h,i). Consistent with the ability of combined fasting plus endocrine therapy to exert carry-over anti-tumour activity, we found that one month of treatment of MCF7 xenograft-bearing mice with Dexa plus TMX delayed tumour growth, after treatment withdrawal, by a factor of two, compared with fasting or TMX alone (Fig. 4f and Extended Data Fig. 7a).

We previously reported that fasting and FMDs lower insulin, IGF1 and leptin plasma concentrations in mice and humans with breast cancer3,6,21,22. Thus, we assessed whether the ability of Dexa to phenocopy the anti-tumour activity of fasting would reflect similar effects on these FRFs. Leptin and c-peptide were not affected, whereas serum IGF1 concentration decreased in response to Dexa (Extended Data Fig. 7b). Finally, in mice that were treated with TMX, Dexa attenuated TMX-induced uterus hyperplasia, a relatively common side effect of TMX, which we previously reported to be effectively prevented by fasting[3] (Extended Data Fig. 7c).

Dexamethasone is a potent immunomodulator, with significant adverse effects when chronically administered[26]. Therefore, we next determined whether the combined effect of Dexa and TMX might be less effective or even detrimental in an immunocompetent HR+ breast cancer model, by affecting anti-tumour immunity. In allografts of the ERα+ TSAE1 mouse breast cancer cell line27,28 in BALB/c mice, combined treatment of Dexa with TMX significantly reduced tumour growth (Extended Data Fig. 7e,f) and increased the survival of these mice compared with TMX treatment alone (Fig. 4g).

To better understand whether and how treatment with Dexa would modulate the peripheral immune landscape, particularly with respect to the effects specific for TMX, we performed immune profiling of BALB/c mice bearing TSAE1 tumours from the different treatment groups (control, TMX, Dexa and TMX plus Dexa). We found no overt changes in leukocyte populations and in their proliferative capacities in response to the different treatments. However, combined Dexa plus TMX treatment led to a significant reduction in PD-L1 expression in neutrophils, non-classical monocytes and dendritic cells as compared with control treatment (Extended Data Fig. 7f).

Cumulatively, our findings indicate that the beneficial effects of fasting in enhancing endocrine therapy efficacy in breast cancer are mediated through GR activation, and that corticosteroid administration can be used to replace fasting to increase endocrine therapy activity.

Discussion

Dietary restriction represents a timely and promising research field in oncology. In particular, fasting or FMD regimens hold promise to achieve strong anti-tumour effects, with the ability to simultaneously activate multiple anti-tumour mechanisms[3]. Yet, concerns about the risk of malnutrition and impact on quality of life in relation to these diets remain. Thus, the search for fasting mimetics that could recreate its benefit in terms of anticancer effects is warranted. Our study defines GR agonists, such as Dexa, as a therapeutic intervention that phenocopies the beneficial effects of fasting, enhancing endocrine therapy efficacy. GR agonists have been used in the clinic for decades, including as anticancer agents (for example, for haematological malignancies such as lymphomas or multiple myeloma), anti-inflammatory and anti-allergic drugs, and antiemetics. However, their prescription as drugs that can modify the activity of endocrine therapy for HR+ breast cancer in currently not foreseen.

Fasting has direct metabolic consequences, such as lower serum glucose, decreased insulin levels and, consequently, decreased AKT–mTOR signalling, which have been attributed anti-tumour effects[3], including in breast cancer. Our data show that the ability of fasting to enhance the anti-tumour effects of endocrine therapy for breast cancer is largely mediated through GR signalling. SHRs show substantial genomic crosstalk in breast cancer cells. In particular, GR, AR and PR all share genomic regions with ERα throughout the genome[29] and serve as tumour suppressors in HR+ breast cancer16,17,18. Although the crosstalk between SHRs has been studied extensively at the biological level, suggesting novel therapeutic opportunities in breast cancer treatment[17], the physiological causes of this interplay remain unknown. We show here that fasting increases levels of cortisol (or corticosterone) and progesterone, selectively activating GR and PR genetic programmes, to increase the effect of endocrine therapy in breast cancer (Fig. 4h).

PR agonist treatment is being evaluated in combination with letrozole in a phase 2 randomized clinical trial in patients with ERα+ breast cancer (ClinicalTrials.gov ID: NCT03306472). However, the MIPRA clinical trial showed that PR inhibition by mifepristone also reduced tumour cell proliferation in patients with HR+ breast cancer (ClinicalTrials.gov ID: NCT02651844)[30]. These contradictory results can be partially explained by the use of mifepristone, which is a potent PR inhibitor but, when used at high doses or depending on the time of the day when the drug is taken (night versus morning), also acts as a GR agonist31,32,33. GR activation promotes a luminal HR+ breast cancer phenotype associated with improved prognosis and reduced cell proliferation[16]. Here we show that GR activation enhances the effects of the endocrine therapy, and exogenously administered GR agonists phenocopy the favourable effects of fasting.

Chronic treatment with corticosteroids may exert undesirable effects on the immune system, bones, muscles and endocrine system[26]. Our in vivo studies using an immunocompetent mice model revealed that Dexa significantly delayed tumour growth and extended survival of mice bearing an HR+ tumour compared with TMX alone (Fig. 4g). Moreover, immune profiling of these mice indicated a balanced immune state, with no signs of pronounced activation or suppression. We observed a significant reduction in PD-L1 expression in some immune cell populations when Dexa was combined with TMX (Extended Data Fig. 7f). Since low PD-L1 expression is considered to be a marker of immune cell activation and increased anticancer activity of the immune system[34], it is possible that the anti-tumour effects that we observed with TMX plus Dexa in this immunocompetent mouse breast cancer model also reflected favourable systemic effects of Dexa on anti-tumour immunity, although this mechanism needs to be confirmed through further studies.

GR stimulation is most effective at decreasing breast cancer cell proliferation in the luminal A patient population[16], which is consistent with our data. Several clinical trials from the late 1980s and early 1990s evaluated the therapeutic contribution of glucocorticoids to endocrine therapy in patients with breast cancer, finding that the glucocorticoids only modestly improved response rates35,36,37. However, these clinical trials enrolled patients with unknown receptor status. Since GR agonism drives tumour migration and proliferation in triple-negative breast cancer[38], the limited benefit of glucocorticoid administration in the earlier studies may be explained by the enrollment of a significant fraction of patients with triple-negative breast cancer35,36,37.

Our study positions glucocorticoid administration as a novel therapeutic strategy that mimics the effects of fasting in HR+ breast cancer cancer treatment, substituting the need for dietary restriction with a clinically approved and safe therapeutic agent.

Methods

Animal experiments

All mouse experiments were performed in accordance with institutional guidelines for animal care and use established in the Principles of Laboratory Animal Care (directive 86/609/EEC). Animal work was only initiated upon approval by the Italian Istituto Superiore di Sanità (ISS) with authorization no. 40/2022, protocol 22418.167 or by the Animal Ethics Committee of the Netherlands Cancer Institute. Six-to-eight-week-old female athymic nude mice (purchased from Envigo) were used in the experiments at the Animal Facility of the IRCCS Ospedale Policlinico San Martino (Genoa). These mice were housed in Sealsafe Plus GM500 individually ventilated cages (IVCs) held on DGM Racks at 22 ± 2 °C and approximately 50–60% relative humidity under a 12 h:12 h light:dark lighting cycle and with food (standard diet, 4RF18, Mucedola) and water ad libitum. Mice were acclimatized for one week before experiments were initiated. To allow MCF7 xenograft growth, a 17β-oestradiol-releasing pellet (Innovative Research of America) was inserted in the intra-scapular subcutaneous region under anaesthesia conditions, the day before cell injection. Xenografts were established by subcutaneous injection of 5 × 10^6 MCF7 cells to both flanks of the mouse (experiments in Figs. 1a,b, 2 and 4a,b), or orthotopic injection of 3 × 10^6 MCF7 cells into the fourth abdominal fat pad (experiments in Fig. 4d and Extended Data Fig. 3a,b). Treatment was initiated when the tumours appeared as established palpable masses (~2 weeks after cell injection). In each experiment, mice were randomly assigned to receive one of the following treatments or their combinations, as indicated: control (ad libitum diet); TMX (45 mg kg−1 per day in peanut oil, oral gavage3,39), fasting (water only, for 48 h every week3,40), Dexa (4 mg kg−1 every other day in physiological solution, intraperitoneal[41]), IGF1 (200 μg kg−1 body weight, intraperitoneal twice a day on the days of fasting); insulin (20 mU kg−1 body weight, intraperitoneal once a day on the days of fasting); leptin (1 mg kg−1 body weight, intraperitoneal once a day on the day of fasting). During the 48 h of fasting, mice were individually housed in a clean, new cage to reduce coprophagy and the intake of the residual chow. Body weight was measured immediately before, during and after fasting. Fasting cycles were repeated every seven days to allow for complete recovery of body weight before a new cycle. The size of the tumours was measured twice a week and tumour volume was calculated using the formula: tumour volume (in mm^3) = (w^2 × W) × π/6, where w and W are lengths of the minor side and major side (in mm), respectively. The maximum tumour volume that was permitted by our Institutional Animal Care and Use Committee (IACUC) was 1,500 mm^3, and in none of the experiments were these limits exceeded. Tumour masses were isolated at the end of the last fasting cycle, weighed, divided into two parts, snap frozen in liquid nitrogen and stored at −80 °C. Ten slices of 50 μm per tumour sample were subsequently utilized for ChIP–seq, proteomics and RNA-seq analyses. Sample size estimation was performed using PS (power and sample size calculation) software (Vanderbilt University). By this approach, we estimated that the number of mice that were assigned to each treatment group would reach a power of 0.85. The type I error probability associated with our tests of the null hypothesis was 0.05. Mice were assigned to the different experimental groups in a random fashion. Operators were unblinded, as blinding during animal experiments was not possible because mice were subject to a specific diet supply and daily treatment.

To establish mammary intraductal cell line-derived xenograft (MIND-CDX) models, T47D cells were intraductally injected as previously described39,42. Specifically, 1 × 10^6 T47D cells were dissociated to single cells with 0.05% trypsin and injected intraductally into the abdominal/inguinal mammary glands (both sides) of 8-week-old female NSG mice (Jackson Laboratory) with a 34G needle. To ensure stable outgrowth, T47D MIND-CDX mice were supplemented with 17β-oestradiol (Sigma, E2758) via the drinking water at a concentration of 4 µg ml−1 starting 7 days prior to tumour inoculation via intraductal injection. E2 supplementation was maintained throughout the experiment. To establish TSAE1 allograft models, BALB/c mice (Jackson Laboratory) were intraductally injected with of 1 × 10^4 single cells in PBS as described above (one gland). The IDC186 MIND-PDX model was established from a pre-menopausal Caucasian breast cancer patient, confirmed positive for ERα (95%) and PR (95%) but negative for HER2 (Extended Data Fig. 6h). To generate the PDX model, 5 × 10^4 single cells in PBS were intraductally injected into one of the abdominal mammary glands of 8-week-old female NSG mice (Jackson Laboratory) and supplemented with 17β-oestradiol (Sigma, E2758) as described above.

The xenograft model cohorts were monitored three times per week and tumours were palpated and measured via calliper in two dimensions. Mice were enrolled into treatment groups when the largest tumour per animal measured 50 mm^3 for T47D and IDC186 xenografts and 25 mm^3 for TSAE1 allografts, respectively. Mice were randomly allocated into treatment groups and received the following treatments: (1) vehicle treatment (corn oil, daily, oral gavage); (2) TMX (45 mg kg−1 in corn oil, daily, oral gavage); (3) Dexa (4 mg kg−1, 3 times per week, intraperitoneal injection); or (4) TMX plus Dexa. Mice were treated for 28 consecutive days for the TSAE1 and IDC186, 56 days for T47D (with a 1-week treatment break between days 28 and 35), or until the cumulative mammary tumour burden reached a volume of 1,500 mm^3 and thus the maximally permitted disease end point. At euthanasia, mammary glands and full female reproductive tracts were collected in formalin, stained against haematoxylin and eosin (H&E) according to routine procedures, and uteri were analysed for histopathological abnormalities. Tumour measurements and post-mortem analysis were performed in blinded fashion. H&E slides were reviewed by a trained pathologist (J.-Y.S.) in a blinded manner. Slides were digitally processed using a PANNORAMIC 1000 whole slide scanner (3DHISTECH) and captured with the Slidescore software (www.slidescore.com).

Clinical studies of FMD in patients undergoing endocrine therapy for HR+ breast cancer

The NCT05748704 trial was conducted at the IRCCS Ospedale Policlinico San Martino (Genoa), between December 2022 and February 2024 and was approved by the Comitato Etico Regione Liguria. This trial consists of a single-arm phase I/II clinical study of a FMD with solid tumours who are candidates to receive active medical or radiotherapy treatment (or with medical treatment or radiotherapy already ongoing). The nutritional intervention consists of a low-calorie diet lasting 5 days and aimed at providing between 800 and 1,000 kcal day−1 (tentatively 10% carbohydrates, 15% proteins and 75% lipids). Throughout the clinical study, patients have received dietary counselling for the intervals between FMD cycles, aiming at providing an appropriate intake of proteins, essential fatty acids, vitamins and minerals[43] and have also been invited to perform light/ or moderate daily muscle training to enhance muscle anabolism[44]. Study primary outcomes were the effects of the FMD regimen on the circulating levels of factors with pro- or anti-oncogenic activity (including insulin, IGF1, IGFBP1, IGFBP3, leptin, adiponectin, IL-6, TNF and IL-1β), as well as the effect of FMD cycles on leukocyte subpopulations with a role in tumour growth control, such as regulatory T cells, myeloid-derived suppressor cells (MDSCs) as well as natural killer (NK) cells, and its stem cell pool (for example, haematopoietic stem cells, endothelial stem cells, mesenchymal stem cells). Additional information on this trial is available at https://clinicaltrials.gov/ct2/show/NCT05748704. Patient serum for subsequent ELISA assays of circulating growth factors and adipokines has been routinely collected before and after the first, sixth and twelfth FMD cycle. Informed consent was obtained from all patients participating in the clinical trial.

The DigesT study (ClinicalTrials.gov ID: NCT03454282) trial was conducted between July 2018 and December 2020 and in accordance with the Declaration of Helsinki and the principles of Good Clinical Practice. The study protocol was approved by the Institutional Review Board (IRB) and the Ethics Committee of Fondazione IRCCS Istituto Nazionale dei Tumori Milan (INT 157/17). All patients provided written informed consent before any study procedures, as well as for the use of clinical and biological data for research purpose. The FMD nutritional intervention consisted in a 5-day, plant-based, calorie-restricted (up to 600 kcal on day 1; up to 300 kcal on days 2, 3, 4 and 5), low-carbohydrate, low-protein, nutritional regimen, as previously published[6]. Enrolled patients initiated the FMD 12–15 days before surgery, and underwent blood sampling after at least 8 h complete fasting on the morning (08:30 to 10:00) of FMD initiation (pre-FMD), and on the morning of FMD completion (post-FMD). Tumour samples for RNA-seq analyses were obtained diagnostic core biopsies performed at baseline (Pre-) and from matched surgical specimens (Post-). The primary outcomes of the study were to measure the absolute and relative changes in population of peripheral blood mononuclear cells before and after the FMD. Additional information on this trial is available at https://clinicaltrials.gov/ct2/show/NCT03454282.

ChIP–seq

Snap-frozen xenografted tumours were double fixed using 2 mM of disuccinimidyl glutarate diluted in solution A (50 mM Hepes, 100 mM NaCl, 1 mM EDTA, 0.5 mM EGTA) for 25 min followed by 1% formaldehyde for 20 min, at room temperature. Cells were then lysed and sonicated accordingly to the protocol previously described[45], with the difference of have performed 15 cycles of 30 s on, 30 s off in the sonication step (BioRuptor Pico, Diagenode). Obtained nuclear lysates were incubated overnight with 50 μl of protein A coated Dynabeads magnetic beads (10008D, Invitrogen) conjugated with 5 μg of ERα (06-935, Millipore), H3K27ac (39133, Active Motif), GR (12041S, Cell Signaling), PR (8757S, Cell Signaling) or JUN (9165S, Cell Signaling) antibodies. The resulting immunoprecipitated DNA was submitted for library preparation using the KAPA library kit (KK8234, Roche) and subsequently paired-end sequenced on the Illumina NovaSeq 6000 system with read length of 51 bp. ChIP–seq analyses were performed using an in house pipeline publicly available at https://github.com/sebastian-gregoricchio/ChIP_Zwart (v.0.1.2) with default parameters. In brief, all samples were aligned to reference genome Hg38/GRCh38 using Burrows-Wheeler Aligner[46] (BWA v.0.5.10). Reads were filtered based on mapping quality (MAPQ ≥ 20), and duplicate reads were marked with Picard MarkDuplicates (v.2.19.0). MACS2 (v.2.1.2) was used to perform peak calling over input ChIP–seq samples; only peaks with a q-value < 0.01 were retained. DeepTools[47] (v.2.5.3) was used to calculate the fraction of reads in peaks (FRiP) and normalized ChIP–seq signal. For visualization purposes, Reads Per Genomic Content (RPGC) normalization (1× coverage) signal was averaged among the replicates per each condition using deeptools bigwigCompare. Genome browser snapshots were generated using the R v.4.0.3 environment and Rseb[48] (v.0.3.1) (https://github.com/sebastian-gregoricchio/Rseb). Tornado plots were generated using deepTools (v.2.5.3). Differential peak analyses were performed using diffBind[49] (v.3.0.15). Peaks were defined as differential when the |log_2(fold change)| >1.5 and adjusted P value <0.05. Genomic location annotation of the peaks was performed using ChIPSeeker[50] (v.1.26.2) defining the promoter region as −2 kb:transcription start site:+1 kb. Transcription factor binding enrichment from public available datasets—GIGGLE analyses[51]—were performed using the tool available at the of CistromeDB website (http://cistrome.org/db/).

Cell lines

The MCF7, T47D and HEK293T cell lines were purchased from the American Type Culture Collection (ATCC). TSAE1 cells were a gift from C. Isacke laboratory[28]. All cell lines were kept in DMEM, high glucose, pyruvate (Gibco) and supplemented with 10% fetal bovine serum (FBS, Capricorn Scientific) and 1% penicillin-streptomycin (5,000 U ml−1, Life Technologies). For ligand treatment, 4-hydroxytamoxifen (HY16950; MedChemExpress) and SR11302 (HY-15870; MedChemExpress) were reconstituted in DMSO, and used in the described concentrations and time points. All cell lines were cultured at 5% CO_2 at 37 °C, were subjected to regular Mycoplasma testing, and underwent authentication by short tandem repeat profiling (Eurofins Genomics).

CRISPR–Cas9-mediated knockout cell line generation

GR-targeting single-guide RNA (NR3C1; ATGACTACGCTCAACATGTT) and non-targeting (NT) control guide RNA (GTATTACTGATATTGGTGGG) were separately cloned into the lentiCRISPR v.2 vector[52]. Using H3K293T cells, the CRISPR vectors were co-transfected with third-generation viral vectors using polyethyleneimine (PEI, Polysciences). After lentivirus production, the medium was collected and added to the MCF7 cells. Two days after infection, cells were selected for 2 weeks with 2 μg ml−1 puromycin (Sigma Aldrich), and knockout efficiency was confirmed by western blot and immunofluorescence.

Immunoblotting

Total protein lysates were obtained using Laemmli buffer complemented with 1× complete protease inhibitor cocktail (Roche) and 1× phenylmethylsulfonyl fluoride (PMSF). Forty micrograms of protein per sample was resolved in a NuPAGE 4–12% Bis-Tris gel (NP0335BOX, Invitrogen) in 1× NuPAGE MOPS SDS Running Buffer (NP00012, Invitrogen) and sequentially transferred to a 0.45-μm nitrocellulose membrane (Santa Cruz Biotechnology). Protein detection was performed using antibodies raised to detect GR (1:1,000, 12041S, Cell Signaling) and β-actin (1:10,000, MAB1501R, Merck Millipore). Odyssey CLx Imaging system (Li-Cor Biosciences) and ImageStudio Lite v.5.2.5 (LI-COR Biosciences) software were used to scan and visualize the proteins.

Immunofluorescence

Cells were fixed with 2% paraformaldehyde (103999, Merck) for 10 min, washed twice with PBS and subsequently permeabilized with 0.5% Triton/PBS (Triton X-100, Sigma Aldrich). Following two PBS washing steps, cells were blocked for 1 h in 1% bovine serum albumin (BSA, A8022, Sigma/Merck)/PBS solution before incubation with antibody against GR (1:100, 12041S, Cell Signaling). After two additional PBS washes, cells were incubated with Alexa Fluor 488 goat anti-rabbit IgG (1:1,000, A-11008, ThermoFisher Scientific) and DAPI (ProLong Gold Antifade Mountant, P36930, ThermoFisher Scientific) and signal detected using laser confocal microscopy (SP5, Leica).

Immunohistochemistry

Immunohistochemistry of the formalin-fixed, paraffin-embedded (FFPE) tumour samples was performed on a BenchMark Ultra (Ki-67) or a Discovery Ultra (ERα, PR, GR) automated stainer (Ventana Medical Systems). In brief, paraffin sections were cut at 3 µm, heated at 75 °C for 28 min and deparaffinised in the instrument with EZ prep solution (Ventana Medical Systems). Heat-induced antigen retrieval was carried out using Cell Conditioning 1 (CC1, Ventana Medical Systems) for 32 min (Ki-67, ERα, PR) or 64 min (GR) at 95 °C. Ki-67 was detected using the clone 30-9 (Ready-to-Use, 32 min at 37 °C, Roche Diagnostics/Ventana), GR using the clone D6H2L (1/600 dilution, 1 h at 37 °C, 12041, Cell Signalling), ERα using the clone SP1 (Ready-to-Use, 32 min at room temperature, Roche Diagnostics/Ventana) and PR using the clone 1E2 (Ready-to-Use, 32 min at room temperature, Roche Diagnostics/Ventana). In order to reduce background signal for the PR staining, after primary antibody incubation slides were incubated with normal antibody diluent (Roche Diagnostics) for 24 min. Bound Ki-67 antibody was detected using the OptiView DAB Detection Kit (Ventana Medical Systems). GR and ERα bound antibody was visualized using Anti-Rabbit HQ (Ventana Medical systems) for 12 min at 37 °C followed by Anti-HQ HRP (Ventana Medical systems) for 12 min at 37 °C and the ChromoMap DAB detection kit (Ventana Medical Systems). PR bound antibody was detected using OmniMap anti-Rabbit HRP (Ventana Medical systems) for 12 min at room temperature. followed by ChromoMap DAB detection kit (Ventana Medical Systems). Slides were counterstained with Hematoxylin and Bluing Reagent (Ventana Medical Systems). A PANNORAMIC 1000 scanner from 3DHISTECH was used to scan the slides at a 40× magnification and uploaded to the Slidescore software (www.slidescore.com). Digitized slides were further processed using QuPath (v.0.6.0)[53]. The analysis protocol begins with tissue detection using a pixel classifier, which differentiates foreground from background (white) pixels. Next, manual annotation is performed with the brush tool to delineate and exclude stroma areas from the region of interest. To identify Ki-67-positive and negative cells, the native cell detection tool is used with the optical density sum option for image analysis. Default parameters are applied, with adjustments made only to the pixel size (0.25 µm), based on the slide resolution, and the target cell size (8 µm) for accurate cell identification. Finally, the scoring is calculated as the proportion of positive cells relative to the total number of detected cells, providing a quantitative assessment of Ki-67 expression. All quantifications were compared and approved by a trained pathologist (J.S.).

RNA-seq analyses

MCF7 xenograft tumour RNA was isolated by homogenizing the tissue sample in 1 ml of RLT buffer (79216, Qiagen) and 1% β-mercaptoethanol using the Qiagen TissueLyserII (85300, Qiagen) for 6 min with frequency setting 30 (1 s−1) in combination with 5-mm stainless steel beads (69989, Qiagen). The total RNA was isolated using the RNeasy Mini Kit (74106, Qiagen), including an on column Dnase digestion (79254, Qiagen), according to the manufacturer’s instructions (Rneasy Mini Handbook, Qiagen). Quality and quantity of the total RNA was assessed by the 2100 Bioanalyzer using a Nano chip (Agilent). Total RNA samples having RNA integrity number (RIN) >8 were subjected to library generation. Strand-specific libraries were generated using the TruSeq Stranded mRNA sample preparation kit (Illumina, RS-122-2101/2) according to the manufacturer’s instructions (Illumina, document 1000000040498 v.00). In brief, polyadenylated RNA from intact total RNA was purified using oligo-dT beads. Following purification the RNA was fragmented, random primed and reverse transcribed using SuperScript II Reverse Transcriptase (Invitrogen, 18064-014) with the addition of actinomycin D. Second strand synthesis was performed using polymerase I and RnaseH with replacement of dTTP for dUTP. The generated cDNA fragments were 3′ end adenylated and ligated to IDT xGen UDI(10 bp)-UMI(9 bp) paired-end sequencing adapters (Integrated DNA Technologies) and subsequently amplified by 12 cycles of PCR. The libraries were analysed on a 2100 Bioanalyzer using a 7500 chip (Agilent), diluted and pooled equimolar into a multiplex sequencing pool. The libraries were sequenced with 51 paired-end reads on a NovaSeq 6000 using a Reagent Kit v.1.5 (100cycles) (Illumina). After sequencing, data was aligned to the human reference genome Hg38/GRCh38 using HISAT2[54] (v.2.1.0) and the number of reads per gene were calculated using HTSeq count[55] (v.0.5.3). Gene expression differences, between the conditions, were determined using DESeq2[56] (v.1.22.2) with |log_2(fold change)| > 1 and adjusted P value <0.05 cut-offs. Differentially expressed genes were ranked by log_2(fold change expression) and used for gene set enrichment analysis (GSEA) on the Hallmark gene set from msigdbr (v.7.5.1), a previously published GR-activity signature[16] or a newly developed PR-activity signature[17] (Extended Data Table 3), using clusterProfiler[57] (v.3.18.1), (pvalueCutoff = 0.05, pAdjustMethod = “BH”). GSEA enrichment plots have been generated using the plot.gsea function from the Rseb package[48] (v.0.3.2).

Tumour RNA was extracted from FFPE tumour specimens from patients with breast cancer using the MasterPure Complete DNA and RNA Purification Kit (Lucigen, LGC Biosearch Technologies) following the manufacturer’s instructions. RNA quality was evaluated using Agilent RNA 6000 Nano Kit (Agilent Technologies) on the Agilent 2100 Bioanalyzer (Agilent Technologies). RNA-seq libraries were prepared using TruSeq Stranded Total RNA Library Prep Gold (20020598, Illumina) according to the manufacturer’s protocol and sequenced using 50 bp paired-end sequencing mode on Illumina Novaseq 6000 platform (Illumina). Differential gene expression analysis comparing Post- versus Pre- samples was performed using negative binomial distribution and Benjamini–Hochberg false discovery rate (FDR) with the Bioconductor package DESeq2, applying Wald tests on normalized counts to obtain log_2(fold change) and P values for each gene. To evaluate the activity of pathways of interest (GR-activity signature and PR-activity signature16,17 (Extended Data Table 3) and Hallmark gene sets) we performed GSEA using the Bioconductor package clusterProfiler. GSEA was performed on genes ranked by the absolute value of log_10(P value) scaled by the sign of log_2(fold change), tested against gene lists of interest. The enrichment score and NES were computed for each gene set, and nominal P values were estimated by permutation testing. Multiple testing correction was applied using the Benjamini–Hochberg procedure to obtain FDR q values. To estimate the activity of pathways of interest at a single sample level we performed GSVA. Differences in enrichment scores between Post- and Pre- samples were determined by the paired Wilcoxon test. To correlate the activity of GR-activity signature and Hallmark gene sets of interest, their GSVA enrichment scores were correlated through Spearman’s linear correlation. All analyses were performed with R studio software (v.2023.03).

Proteomics

For protein digestion, frozen tissues were lysed in boiling guanidine HCl (GuHCl) lysis buffer as previously described[58]. Protein concentration was determined with a Pierce Coomassie (Bradford) Protein Assay Kit (Thermo Scientific), according to the manufacturer’s instructions. After dilution to 2 M GuHCl, aliquots corresponding to at least 1.05 mg of protein were digested twice (4 h and overnight) with trypsin (Sigma Aldrich) at 37 °C, enzyme:substrate ratio 1:75. Digestion was quenched by the addition of formic acid (final concentration 5%), after which the peptides were desalted on a Sep-Pak C18 cartridge (Waters). From the eluates, aliquots were collected for proteome analysis, the remainder being reserved for phosphoproteome analysis (not included in this work). Samples were vacuum dried and stored at −80 °C until LC–MS/MS analysis.

Prior to mass spectrometry analysis, the peptides were reconstituted in 2% formic acid. Peptide mixtures were analysed by nano LC–MS/MS on an Orbitrap Exploris 480 Mass Spectrometer equipped with an EASY-NLC 1200 system (Thermo Scientific). Samples were directly loaded onto the analytical column (ReproSil-Pur 120 C18-AQ, 2.4 μm, 75 μm × 500 mm, packed in house). Solvent A was 0.1% formic acid/water and solvent B was 0.1% formic acid/80% acetonitrile. Samples were eluted from the analytical column at a constant flow of 250 nl min−1. For single-run proteome a 90-min gradient was employed containing a 78-min linear increase from 6 to 30% solvent B, followed by a 10-min wash.

Raw data were analysed by DIA-NN (v.1.8)[59] without a spectral library and with ‘Deep learning’ option enabled. The Swissprot Human database (20,395 entries, release 2021_04) was added for the library-free search. The Quantification strategy was set to Robust LC (high accuracy) and MBR option was enabled. The other settings were kept at the default values. The protein groups report from DIA-NN was used for downstream analysis in Perseus (v.1.6.15.0)[60]. Values were log_2-transformed, after which proteins were filtered for at least 100% valid values in at least one sample group. Missing values were replaced by imputation based on a normal distribution using a width of 0.3 and a minimal downshift of 2.4. Differentially expressed proteins were determined using a Student’s t-test (threshold: FDR: 5% and S0: 0.1).

Differentially protein levels were ranked by log_2(fold change) × P value and used for GSEA analysis on Hallmarks from the community-contributed functional database from the web-based Gene Set Analysis Toolkit (WebGestalt[61]). GSEA enrichment plots were generated as previously described.

ELISA

Mouse whole blood was collected in Eppendorf tubes. It was allowed to coagulate for 2 h at room temperature, centrifuged for 20 min at 4,000 rpm, then aliquoted into PCR tubes and stored at −80 °C until subsequent use. Whole blood from patients was collected in Vacuette Serum Clot Tubes (BD), centrifuged 20 min at 2,100 rpm then aliquoted into small tubes and stored at −80 °C until use. ELISA assays to detect mouse serum levels of cortisone and progesterone were purchased from R&D System and ALPCO respectively while ELISA assays to detect human serum levels of cortisol and progesterone were purchased from R&D System and Enzo, respectively.

Flow cytometry

Breast cancer nodules were macrodissected from TSAE1-bearing mice and processed to generate single-cell suspensions using the Tumour Dissociation Kit (Miltenyi Biotec, 130-096-730) in conjunction with the gentleMACS Octo Dissociator, following the manufacturers’ protocols. The resulting cell suspensions were passed through a 100-µm cell strainer and subsequently washed with fluorescence-activated cell sorting buffer (PBS containing 5% FBS). To ensure the removal of red blood cells, samples were treated with an erythrocyte lysis solution. Following this, samples were pre-incubated with an anti-CD16/CD32 antibody (1:400, 553142, BD Bioscience) and then stained with antibodies specific for extracellular markers, adhering to standard staining protocols. After staining for surface markers, cells were labelled with a live/dead viability dye and subsequently fixed and permeabilized using a fixation/permeabilization solution (eBioscience, Invitrogen) for intracellular staining. All antibodies utilized for flow cytometry were titrated to account for lot-dependent variations, as described in Extended Data Fig. 7f. Immune cell populations were classified as follows: lymphoid cells (CD45+CD11b−), myeloid cells (CD45+CD11b+), neutrophils (CD45+CD11b+Ly6G+), non-classical monocytes (CD45+CD11b+Ly6G−Ly6C−) and dendritic cells (CD45+F4/80−CD11chiMHCIIhi) (for gating strategy used, see Supplementary Fig. 1). Sample acquisition was performed using a five-laser Aurora spectral flow cytometer (Cytek Biosciences), and data analysis was conducted using FlowJo v.10 software.

Statistical analysis

Statistical analyses were performed using GraphPad Prism software v.10.4.1 (GraphPad Software) or in R v.4.0.2 (R Core Team 2020, https://www.R-project.org). Paired t-test or one-way analysis of variance (ANOVA) was used to calculate changes in the majority of the analyses (unless otherwise stated) and only P values <0.05 were considered significant. Two-tailed Wilcoxon signed rank test was used to compare cortisol plasmatic concentration measured before FMD (Pre-FMD) and after FMD (Post-FMD). For the animal experiments, a linear mixed-effects model was utilized to assess whether the mass volume exhibits a statistically significant trend relative to the treatment over time. The model was constructed using the two-way ANOVA or mixed-effect model (in case of missing data) from GraphPad Prism software v.10.4.1. The fixed-effects model matrix was generated by the interaction of time (measured in days after injection) and treatment. The random-effects term was specified as the interaction between the treatment group and the blocking factor of the mouse ID. Subsequent pairwise post hoc multiple comparisons were conducted using the same software. Statistical significance was determined by P values less than 0.05. Linear mixed-effect model results are presented in the Supplementary Information.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

All mouse data generated or analysed during this study are included in this published article (and its supplementary information files). The ChIP–seq and RNA-seq data relative to mice xenografts have been deposited to the GEO database (GSE260486). The ChIP–seq pipeline is publically accessible[62]. RNA-seq data for patients with breast cancer are deposited on the European Genome-Phenome Archive (EGA) under accession number EGAS00001004944. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE[63] partner repository with the dataset identifier PXD049477. Source data are provided with this paper.

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Acknowledgements

We thank S. Prekovic and the members of the Zwart/Bergman and Nencioni laboratories for valuable feedback, suggestions and input throughout the project. We acknowledge the NKI Animal facility and pathology for all the support with mouse experiments, NKI genomics core facility for the next generation sequencing and bioinformatics support, the NKI Proteomics/Mass Spectometry Facility for the proteomic and bioinformatics analyses, and the NKI-AVL Core Facility Molecular Pathology and Biobanking (CFMPB) for supplying NKI-AVL Biobank material and/or lab support. W.Z. is further supported by Dutch Cancer Society, Alpe d’HuZes and a VIDI grant (9171640) from the Netherlands Organization for Scientific Research (NWO). This work was supported in part by the Associazione Italiana per la Ricerca sul Cancro (AIRC; 22098 to A.N. and MFAG 26482 to I.C.).

Author information

Authors and Affiliations

1. Division of Oncogenomics, The Netherlands Cancer Institute, Amsterdam, The Netherlands

Nuno Padrão, Tesa M. Severson, Sebastian Gregoricchio, Merel Roest, Maria Donaldson Collier & Wilbert Zwart

2. Oncode Institute, Utrecht, The Netherlands

Nuno Padrão, Tesa M. Severson, Sebastian Gregoricchio, Catrin Lutz, Daniel Taranto, Stefan Hutten, Merel Roest, Maria Donaldson Collier, Leila Akkari, Jos Jonkers & Wilbert Zwart

3. Department of Internal Medicine and Medical Specialties, University of Genoa, Genoa, Italy

Ana Guijarro, Angelica Persia, Livia Pisciotta, Alessio Nencioni & Irene Caffa

4. Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands

Catrin Lutz, Stefan Hutten & Jos Jonkers

5. Division of Tumor Biology and Immunology, The Netherlands Cancer Institute, Amsterdam, The Netherlands

Daniel Taranto & Leila Akkari

6. Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy

Francesca Ligorio & Filippo de Braud

7. IFOM ETS, the AIRC Institute of Molecular Oncology, Milan, Italy

Francesca Ligorio & Claudio Vernieri

8. IRCCS Ospedale Policlinico San Martino, Genoa, Italy

Angelica Persia, Livia Pisciotta, Alessio Nencioni & Irene Caffa

9. Department of Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands

Joyce Sanders, Sara Pires-Oliveira & Hugo Horlings

10. Experimental Animal Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands

Ji-Ying Song

11. University of Milan, Milan, Italy

Filippo de Braud & Claudio Vernieri

12. Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands

Wilbert Zwart

Contributions

N.P. and W.Z. conceived the study. N.P. and I.C. performed most of the experiments. N.P., T.M.S., S.G. and F.L. performed the computational and statistical analyses. I.C., A.G., C.L. and S.H. performed the in vivo experiments. D.T. performed the FACS experiments and analysis. M.R. produced and provided the GR-KO cells. J.S. and J.-Y.S. performed the pathological analyses. S.P.-O. performed the immunohistochemistry quantifications. F.L., C.V., F.d.B., A.N., I.C. and A.P. were involved in the clinical trials and translational analyses in patient samples. M.D.C., H.H., L.P., L.A., J.J., A.N., I.C. and W.Z. contributed to the study design and supervision. N.P., I.C., A.N. and W.Z. wrote the manuscript with input of all authors. All authors evaluated the results and edited the manuscript.

Corresponding authors

Correspondence to Irene Caffa or Wilbert Zwart.

Ethics declarations

Competing interests

A.N. and I.C. hold intellectual property rights on clinical uses of FMDs. C.V. and F.d.B. hold intellectual property rights on clinical uses of FMDs. F.L. reports honoraria as a speaker from Novartis, Pfizer and Accademia Nazionale di Medicina. C.V. and F.d.B. report roles in advisory boards, consultancy activity or having received honoraria as speakers from Pfizer, Novartis, Eli Lilly, Astra Zeneca, Daiichi Sankyo, Menarini Stemline, Gilead, MSD and Istituto Gentili. The other authors declare no competing interests.

Peer review

Peer review information

Nature thanks Marcus Goncalves, Stephen Hursting and Eneda Toska for their contribution to the peer review of this work. Peer review reports are available.

Additional information

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Extended data figures and tables

Extended Data Fig. 1 Fasting reduces tumour growth and exerts epigenetic changes in ERα+ xenografts.

a. Representative immunohistochemistry stainings for Ki-67 in MCF7 xenografts from mice treated with ad-libitum diet (control, n = 6), tamoxifen (TMX, n = 5), fasting (n = 6) and combination (TMX+Fasting, n = 6). n, represents different tumour samples analysed. b. Percentage of tumour cells stained for Ki-67 in the different depicted conditions. Data are presented as mean ± SD. P-values represents one-way ANOVA with Dunnet’s multiple test correction. c. PCA plot on H3K27ac ChIP-seq data, for all 4 conditions. d. Genomic distribution of the H3K27ac ChIP-seq peaks for all 4 conditions. e. Volcano plot of the differentially-enriched H3K27ac sites between TMX and TMX+Fasting. f. H3K27ac ChIP-seq data for all 4 conditions, depicting representative regions that are differentially-enriched between TMX and TMX+Fasting. The genomic coordinates are annotated. g. Heat map showing H3K27ac ChIP-seq signal for all 4 conditions at differentially-enriched H3K27ac regions in comparing TMX or TMX+Fasting treated xenografts (top). Regions were sorted on H3K27ac signal intensity. Data are centred at H3K27ac peak, within a ± 2.5 kb window. Average density plots for ChIP-seq signal for H3K27ac in xenografts treated with the respective conditions (bottom). Data are presented as mean values ± SEM. h. Cell viability of MCF7 and T47D cells treated with tamoxifen and increased concentrations of AP-1 inhibitor SR11302, alone or in combination. Data are from three biological replicates and are presented as mean ± SD. P-values represents Kruskal-Wallis followed by Dunn post-hoc correction. i. Heat map depicting ERα ChIP-seq signal for all 4 conditions at differentially-enriched H3K27ac regions, comparing TMX versus TMX+Fasting treated xenografts (top). Regions were sorted on ERα signal. Data are centred on the ERα peak, within a ± 2.5 kb window. Average density plots are shown for ChIP-seq signal for ERα in MCF7 xenografts treated with the respective conditions (bottom). Data are presented as mean values ± SEM.

Source data

Extended Data Fig. 2 GR and PR chromatin binding is induced by fasting treatment.

a. PCA plots for GR, PR and c-JUN ChIP-seq regions between the 4 conditions in MCF7 xenografts. b. Genomic distribution of GR, PR and c-JUN ChIP-seq regions between the 4 conditions. c. Heat map of GR and PR ChIP-seq signal for all 4 conditions, at differentially-enriched H3K27ac sites between TMX and TMX+Fasting (top). Regions were sorted on H3K27ac signal. Data are centred at H3K27ac peak, within a ± 2 kb window. Average density plots is shown for ChIP-seq signal of GR and PR in xenografts for all 4 treatment conditions (bottom). Data are presented as mean values ± SEM. d. Heat map depicting ChIP-seq signal for c-JUN in MCF7 xenografts for all 4 treatment conditions, visualizing differentially-enriched c-JUN sites, comparing TMX and TMX+Fasting regions (top). Regions were sorted according to decreasing c-JUN signal. Data are centred at c-JUN peak, within a ± 2 kb window. Average density plots is shown for c-JUN ChIP-seq signal for all 4 conditions in TMX+Fasting enriched regions (bottom). Data are presented as mean values ± SEM.

Extended Data Fig. 3 Tumour growth and systemic effects of fasting-reduced factors treatment and differential transcriptional activity between xenografts treated with tamoxifen alone or combined with fasting.

a. Fold-change to baseline levels of corticosterone and progesterone levels on mice blood after 4 cycles of all treatment conditions. Data are shown as mean ± SD and analysed by one-way ANOVA followed by Tukey’s multiple comparison test. b. MCF7 xenograft tumour growth in six/eight-week-old female athymic nude mice in the different treatment arms. Control n = 14, TMX n = 12, Fasting n = 15, TMX+Fasting n = 12, TMX+Fasting+FRFs n = 7. n, number of tumours per treatment group. Data are shown as mean ± SEM and P-values are determined by mixed effect model with Tukey’s multiple test correction (see Supplementary File 1) and two-tailed Student’s t-test (P-values of last day are represented). c. PCA plot based on gene expression data between MCF7 xenografts for all treatment conditions. d. Enrichment plots of the differentially enriched Hallmarks performed on transcriptomic data comparing TMX (top) and TMX+Fasting (bottom) xenografts. NES is calculated by weighted Kolmogorov-Smirnov test and P-value determined by permutation-based testing with multiple Benjamini-Hochberg (BH) hypothesis correction. e. GSEA for Hallmark gene sets performed on bulk proteomic data from xenografts treated with TMX alone or TMX+Fasting. f. Representative enrichment plots of the two top-differentially enriched Hallmarks performed on bulk proteomic data, when comparing TMX-enriched vs TMX+Fasting-enriched pathways. NES is calculated by weighted Kolmogorov-Smirnov test and P-value determined by permutation-based testing with multiple Benjamini-Hochberg (BH) hypothesis correction.

Source data

Extended Data Fig. 4 Transcriptomic analyses of BC patients treated with 5 days FMD diet.

a. Gene set enrichment analysis for Hallmarks pathways. Differentially-enriched pathways between pre- and post-5 days FMD in BC patients are shown. NES is calculated by weighted Kolmogorov-Smirnov test and P-value determined by permutation-based testing with multiple Benjamini-Hochberg (BH) hypothesis correction. b. GSVA enrichment scores of the GR- and PR-activity signature in transcriptomic matched samples from BC patients pre-and post-5 days of FMD. Each boxplot indicates the 25th and 75th percentiles of the distribution of GSVA ESs, while the horizontal line inside the box indicates the median value of the distribution. Dots indicate measurements in individual patients. P-values refer to the two-sided paired Wilcoxon test. c. Enrichment plot for PR-activity signature in matched tumour samples of BC patients Pre- and Post- 5 days FMD. NES and P-values are depicted. NES is calculated by weighted Kolmogorov-Smirnov test and P-value determined by permutation-based testing with multiple Benjamini-Hochberg (BH) hypothesis correction.

Extended Data Fig. 5 GR-KO MCF7 xenografts generation and response to dexamethasone treatment, in vivo.

a. Western blot for GR in NT (non-targeting) and GR-KO MCF7 cells; Actin was used as loading control. Three biological replicates were performed (n = 3). For gel source data, see Supplementary Fig. 1. b. Immunofluorescence for DAPI and GR in NT and GR-KO MCF7 cells. Two biological replicates were performed (n = 2). c. Xenograft MCF7 GR-KO tumour volume in six/eight-week-old female athymic nude mice treated with vehicle or Dexa. Data are shown as mean ± SEM. Control n = 6, Dexa n = 5. n, number of tumours per treatment group. d. Representative IHC images for GR in MCF7 GR-KO and NT xenografts treated with vehicle or Dexa. GR-KO control n = 5, GR-KO dexa n = 4, NT n = 3. n, number of tumours analysed. e. Volcano plot of the differentially expressed transcripts between vehicle and Dexa treated MCF7 GR-KO xenografts. Differential gene expression was determined by two-sided Wald test. f. Enrichment plot of a pan-cancer GR-activity signature for the depicted conditions. NES is calculated by weighted Kolmogorov-Smirnov test and P-value determined by permutation-based testing with multiple Benjamini-Hochberg (BH) hypothesis correction.

Source data

Extended Data Fig. 6 Tumour outgrowth and body weight analyses for respective xenografts under all treatment conditions.

a. MCF7 NT xenografts tumour growth in six-eight-week old female athymic nude mice treated with the respective depicted conditions (Control n = 8, TMX n = 9, Fasting n = 8, TMX+Fasting n = 11). n, number of tumours analysed. Data are shown as mean ± SEM and P-values are determined by mixed effect model with Tukey’s multiple test correction (see Supplementary File 1) and two-tailed Student’s t-test (P-values of last day are represented). b. Body weight changes (%) in mice during the 4 treatment cycle from the experiment performed in a. Data are shown as mean ± SD. Control n = 4, TMX n = 5, Fasting n = 5, TMX+Fasting n = 6. n, number of mice analysed. c. MCF7 GR-KO xenografts tumour growth in six-eight-week old female athymic nude mice treated with the respective depicted conditions (Control n = 7, TMX n = 7, Fasting n = 6, TMX+Fasting n = 9). n, number of tumours analysed. Data are shown as mean ± SEM and P-values are determined by mixed effect model with Tukey’s multiple test correction (see Supplementary File 1) and two-tailed Student’s t-test (P-values of last day are represented). d. Body weight changes (%) in mice during the 4 cycle treatments from the experiment performed in c. Data are shown as mean ± SD. Control n = 4, TMX n = 4, Fasting n = 4, TMX+Fasting n = 6. n, number of mice analysed. e. Body weight changes (%) in mice during 4 weeks of depicted treatments from Fig. 4f. Data are shown as mean ± SD. Control n = 4, TMX n = 5, Fasting n = 5, TMX+Fasting n = 6, Dexa n = 4, TMX+Dexa n = 4. n, number of mice analysed. f. T47D xenograft tumour outgrowth in six-eight-week old female NSG mice in the different treatment arms (Control n = 16; TMX n = 16; Dexa n = 19 and TMX+Dexa n = 13). n, number of tumours analysed. Data are shown as mean ± SEM and P-values are determined by mixed effect model with Tukey’s multiple test correction (see Supplementary File 1) and two-tailed Student’s t-test (P-values of last day are represented). g. Body weight changes (%) in mice during 7 weeks of depicted treatments from f. Data are shown as mean ± SD. (Control n = 8; TMX n = 8; Dexa n = 9 and TMX+Dexa n = 7). n, number of mice analysed. h. Representative immunohistochemistry stainings for H&E, ERα (n = 3), GR (n = 1) and PR (n = 3) in the IDC186 PDX model. n, number of independent tumour samples. i. Body weight changes (%) in mice during 4 weeks of depicted treatments from Fig. 4e. Data are shown as mean ± SD.

Source data

Extended Data Fig. 7 Effects of Dexa treatment in circulating FRFs, TMX-induced uteri hyperplasia and PD-L1 expression levels in several immune-populations.

a. MCF7 xenografts tumours volume in six-eight-week old female athymic nude mice treated with the depicted conditions (Control n = 7; TMX n = 7, Fasting n = 8; TMX+Fasting n = 8). After one month, all treatments were stopped and tumours were allowed to grow. Data are shown as mean ± SEM. b. Fold-change differences to basal conditions of circulating c-peptide, leptin and IGF-1 (FRFs) levels in in six-eight-week old female athymic nude mice xenografted with MCF7 cells, treated with the respective depicted conditions. Control n = 4, TMX n = 4, Fasting n = 4, TMX+Fasting n = 6, Dexa n = 4, TMX+Dexa n = 4. n, number of mice per treatment group. Data are shown as mean ± SD and analysed by two-tailed Student t-test. c. Microphotographs of H&E staining of mouse uteri after TMX (left panel) and TMX + Dexa (right panel) treatments. Black arrows indicate luminal epithelia and blue arrows indicate glandular epithelia from the endometrium. TMX n = 9, TMX+Dexa n = 8. n, represents different mice uteri analysed. d. TSAE1-engrafted tumour growth in immune-competent mice treated with either TMX alone (n = 17) or combined with Dexa (n = 19) for up to 26 days. n, number of tumours analysed. Data are shown as mean ± SEM and P-value is determined by mixed effect model. e. Body weight changes (%) in mice during the cycle treatments from the experiment performed in e. Data are shown as mean ± SD. TMX n = 10, TMX+Dexa n = 10. n, number of mice analysed. f. Bar plots showing the mean fluorescence intensity for selected markers for systemic myeloid cells and lymphoid cells (CD45 + CD11b+ and CD45 + CD11b- respectively), neutrophils (CD45 + CD11b + Ly6G + ), non-classical monocytes (CD45 + CD11b + Ly6G- Ly6C-) and dendritic cells (CD45 + F4/80- CD11c high MHCII high) from TSAE1-bearing mice treated with three rounds of control treatment, TMX, Dexa, or TMX+Dexa (7 days). Control n = 5, TMX n = 5, Dexa n = 5, TMX+Dexa n = 4. n, number of mice per treatment group. Data are shown as mean ± SD. Statistical significance was determined by one-way ANOVA with Tukey’s multiple comparison test.

Source data

Extended Data Table 1 ChIP-seq analysis quality controls

Extended Data Table 2 HR + BC patients treatment scheme from clinical trial NCT05748704

Extended Data Table 3 PR-activity gene signature

Supplementary information

Supplementary Information

This file contains Supplementary Figs. 1 and 2 and Supplementary Tables 1–10.

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Source data

Source Data Figs. 1, 2 and 4 and Source Data Extended Data Figs. 1, 3 and 5–7

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Padrão, N., Severson, T.M., Gregoricchio, S. et al. Fasting boosts breast cancer therapy efficacy via glucocorticoid activation. Nature 649, 1013–1021 (2026). https://doi.org/10.1038/s41586-025-09869-0

- Received: 29 February 2024

- Accepted: 05 November 2025

- Published: 10 December 2025

- Version of record: 10 December 2025

- Issue date: 22 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09869-0

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Cytosolic acetyl-coenzyme A is a signalling metabolite to control mitophagy

Yifan Zhang,

Xiao Shen,

Yuan Shen,

Chao Wang,

Chengping Yu,

Jiangxue Han,

Siyi Cao,

Lin Qian,

Miaolian Ma,

Shijing Huang,

Wenyu Wen,

Miao Yin &

…

Qun-Ying Lei

Nature volume 649, pages 1022–1031 (2026)

Abstract

Acetyl-coenzyme A (AcCoA) sits at the nexus of nutrient metabolism and shuttles between the canonical and non-canonical tricarboxylic acid cycle1,2, which is dynamically regulated by nutritional status, such as fasting[3]. Here we find that mitophagy is triggered after a reduction in cytosolic AcCoA levels through short-term fasting and through inhibition of ATP-citrate lyase (encoded by ACLY), mitochondrial citrate/malate antiporter (encoded by SLC25A1) or acyl-CoA synthetase short chain family member 2 (encoded by ACSS2), and the mitophagy can be counteracted by acetate supplementation. Notably, NOD-like receptor (NLR) family member X1 (NLRX1) mediates this effect. Disrupting NLRX1 abolishes cytosolic AcCoA reduction-induced mitophagy both in vitro and in vivo. Mechanically, the mitochondria outer-membrane-localized NLRX1 directly binds to cytosolic AcCoA within a conserved pocket on its leucine-rich repeat (LRR) domain. Moreover, AcCoA binds to the LRR domain and enhances its interaction with the nucleotide-binding and oligomerization (NACHT) domain, which helps to maintain NLRX1 in an autoinhibited state and prevents the association between NLRX1 and light chain 3 (LC3). Furthermore, we find that the AcCoA–NLRX1 axis underlies the KRAS-inhibitor-induced mitophagy response and promotes drug resistance, providing a metabolic mechanism of KRAS inhibitor resistance. Thus, cytosolic AcCoA is a signalling metabolite that connects metabolism to mitophagy through its receptor NLRX1.

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Main

Cells selectively degrade mitochondria through two pathways: the PINK1–Parkin pathway4,5, and the mitophagy receptor-mediated pathway4,5. NLRX1 is the only mitochondria-localized NLR and functions as a mitophagy receptor that directly binds to LC3 to mediate mitophagy in response to mitochondrial damage6,7. Fasting offers benefits to human health through improving mitochondrial quality[8]. The mitophagic structure has been observed in muscles after 24 h of fasting[9] and remains intact after Pink1 deficiency in mice[10]. Although several mitophagy receptors could be transcriptionally upregulated by fasting[11], it remains unclear whether a specific mitophagy receptor mediates fasting-induced mitophagy in a selective manner. Other open questions include the identification of novel mechanisms of mitophagy receptor-mediated selective mitophagy and delineation of mitophagy-regulated tumour cell death in cancer biology.

In the non-canonical tricarboxylic acid (TCA) cycle, mitochondrial citrate is exported to the cytosol via SLC25A1 and is broken down into oxaloacetate and AcCoA via ACLY to produce malate and fatty acids, respectively[2]. AcCoA-reduction-induced autophagy depends mainly on its canonical function as the substrate of protein acetylation3,12,13. Whether AcCoA mediates nutritional signals of mitophagy in a non-canonical manner, such as through acetylation and AMPK independently, is of great interest.

Cytosolic AcCoA links to mitophagy

To investigate fasting-induced mitophagy response, we first analysed changes of metabolites in mice serum after overnight starvation (the fasting effect was reflected by weight loss shown in Extended Data Fig. 1a). Among them, the glucose concentration dropped significantly from about 11–13 mM to 5–7 mM (Extended Data Fig. 1b). The glutamine concentration also decreased by about 30% (Extended Data Fig. 1c). As glucose and glutamine are the primary carbon sources for cell growth, we tailored a mild starvation medium (SM): 5 mM glucose and 2 mM glutamine. Mitophagy response was induced in various cells cultured with SM as indicated by decreased levels of mitochondrial proteins TIM23, MT-CO2 and HSP60, and increased LC3 recruitment on mitochondria (Extended Data Fig. 1d), which was blocked by bafilomycin A1 (BafA1), suggesting that this reduction in mitochondrial mass was due to autophagy (Extended Data Fig. 1d). mt-Keima is a highly sensitive reporter for mitophagy measurement[14]. SM-induced mitophagy was determined by an increased acidic mitophagy reporter mt-Keima signal and a reduction in the mitochondrial DNA (mtDNA)/nuclear DNA (nDNA) ratio (Extended Data Fig. 1e,f), which could be reversed by resupplementation with normal medium (NM) (Extended Data Fig. 1g–i). To determine whether this mitochondrial reduction was due to decreased mitochondrial biogenesis, we assessed the expression levels of both genes encoding regulators of mitochondrial biogenesis (PPARGC1A (encoding PGC1α), NRF1 and NRF2) and nuclear-encoded mitochondrial housekeeping genes (TFAM, TIM23 and HSP60). Although there was a slight decrease in the NRF1 expression level in A549 cells after SM culture, possibly due to an altered metabolic state (Extended Data Fig. 1j), other genes remained unaffected after SM culture or resupplemention with NM (Extended Data Fig. 1j,k). Notably, U-2 OS cells did not respond to SM-induced mitophagy compared with other cells (Extended Data Fig. 1d,e).

AMPK is a master regulator of autophagy in response to nutrient deficiency[15]. Compared with the markedly increased level of phosphorylated AMPK (p-AMPK) and p-ULK1-S555 triggered by AMPK activator oligomycin, SM culture did not induce apparent AMPK activation (Extended Data Fig. 1l), consistent with the previous report that low-glucose (5 mM) medium did not induce AMPK activation[16]. Autophagy is also inhibited by mTORC1 activation[17]. Compared with the mTORC1 inhibitor Torin-1, SM culture did not affect mTORC1 activation status (Extended Data Fig. 1m). Thus, SM-induced mitophagy occurs in an AMPK- and mTOR-independent manner.

We next hypothesized that the various mitophagic responses after SM culture might be due to different intrinsically metabolic traits of cells. Mass spectrometry (MS) analysis showed that the levels of AcCoA, malate and certain fatty acids were decreased in HeLa, A549 and MCF7 cells, but not in U-2 OS cells, after SM culture (Extended Data Fig. 1n). The levels of several amino acids were elevated after SM treatment of HeLa cells compared with other mitophagy-responsive cells, such as A549 and MCF7 cells. It might be a cell-type-specific response to increased nutrient uptake or autophagic turnover of intracellular proteins, rather than a general response correlating with SM-induced mitophagy in our tested cell lines. Notably, these metabolites are linked to a non-canonical TCA cycle. Moreover, compared with U-2 OS cells, SM-responsive cells had significantly high protein levels of ACLY, fatty acid synthase (FASN), isocitrate dehydrogenase 1 (IDH1), SLC25A1 and AcCoA carboxylase 1 (ACC1), all linked to cytosolic AcCoA metabolism (Extended Data Fig. 1o). We further confirmed that cytosolic but not mitochondrial AcCoA levels decreased after SM culture of responsive cells (Extended Data Fig. 1p).

We therefore investigated whether cytosolic AcCoA was implicated in SM-induced mitophagy. Inhibiting ACLY or SLC25A1 via their inhibitors—potassium hydroxycitrate tribasic monohydrate (HC) and SB204990 (SB) (ACLY inhibitors) and 1,2,3-benzenetricarboxylic acid hydrate (BTC) (SLC25A1 inhibitor) (Fig. 1a)—reduced the cytosolic AcCoA levels (Extended Data Fig. 2a) and enhanced mitophagy, as shown by elevated acidic mt-Keima signals and decreased mitochondrial mass (Fig. 1b,c and Extended Data Fig. 2b–d). Congruously, knockdown of ACLY or SLC25A1 induced mitophagy in tested cells cultured with NM (Extended Data Fig. 2e–g). Acetate contributes to cytosolic AcCoA pools[18]. Accordingly, ACSS2 knockdown also induced mitophagy in NM (Extended Data Fig. 2g–i). By contrast, mitochondrial content remained intact after knocking down ACC1 or FASN (Extended Data Fig. 2j,k). Notably, adding acetate abolished SM-induced or ACLY-knockdown-induced mitophagy (Extended Data Fig. 2l–q). We found that ACLY or SLC25A1 inhibition mildly upregulated PPARGC1A and NRF2 expression levels in HeLa and A549 cells (Extended Data Fig. 3a), which may contribute to mitophagy-stimulated mitochondrial turnover. However, the expression levels of nuclear-encoded mitochondrial genes, including TIM23 and HSP60, showed no decrease after a reduction in cytosolic AcCoA (Extended Data Fig. 3a–d), suggesting that cytosolic AcCoA reduction does not substantially alter mitochondrial biogenesis. Moreover, knockdown of the autophagy upstream gene FIP200 or the core machinery gene ATG7 (ref. [19]) blocked SM-induced or SLC25A1- or ACLY-inhibition-induced mitophagy (Extended Data Fig. 3e,f). Collectively, these results show that a reduction in cytosolic AcCoA induces mitophagy in vitro.

Fig. 1: NLRX1 is required for cytosolic AcCoA-reduction-induced mitophagy.

a, Schematic of AcCoA metabolism in the cytosol. b, Flow cytometry analysis of HeLa-tet-on-mt-Keima cells treated with HC (20 mM), SB (100 μM) or BTC (5 mM) for 16 h. n = 3 biological replicates. Data are mean ± s.e.m. Statistical analysis was performed using ordinary one-way analysis of variance (ANOVA) and Dunnett’s multiple-comparison test. c, Immunoblotting images of HeLa cells treated as in Fig. 1b with or without BafA1 (200 nM). n = 3 biological replicates. d, Schematic of genome-wide CRISPR screening of HC-induced mitophagy. e, Mitochondrial (mito)-related genes depleted in mitophagic cells treated with HC. Previously reported mitophagy receptors (MRs) are highlighted in red. NLRX1 was the top-ranked mitophagy receptor. P values were calculated using MAGeCK software. f, Flow cytometry quantification of control (sgNC) or NLRX1-knockout (sgNLRX1) HeLa-tet-on-mt-Keima cells with the indicated treatment for 16 h. n = 3 biological replicates. Data are mean ± s.e.m. Statistical analysis was performed using two-way ANOVA with Bonferroni’s multiple-comparison test. g, Immunoblot images of HeLa cells treated as indicated. n = 3 biological replicates. h, Immunoblot images of liver tissues of WT or Nlrx1−/− mice given intraperitoneal injection of PBS or HC (100 mg per kg) for 4 h. n = 3 mice per group. i,j, Confocal microscopy analysis of mt-Keima signals in liver tissues of WT or Nlrx1−/− mice after HC treatment. i, Representative images of liver tissue sections. Scale bar, 5 μm. j, Quantification of the mt-Keima signal in Fig. 1i. Values are normalized to the red/green signal in the WT + PBS group. n = 24 images from 4 mice per group. Data are mean ± s.e.m. Statistical analysis was performed using two-way ANOVA with Bonferroni’s multiple-comparison test.

Source Data

Fasting led to a significant reduction in cytosolic AcCoA levels in the brain and gastrocnemius but not in liver tissue[20] (Extended Data Fig. 4a). Meanwhile, mitochondrial AcCoA levels in the brain and gastrocnemius tissues were not affected (Extended Data Fig. 4a). Accordingly, the mitochondrial mass decreased in starved gastrocnemius and brain but not in liver tissues, as determined by the levels of mitochondrial proteins TIM23 and CYTB, mitochondrial LC3 recruitment and the mtDNA/nDNA ratio (Extended Data Fig. 4b,c), corroborating the linkage between mitophagy response and cytosolic AcCoA levels in vivo. Consistent with that, fasting-induced mitophagy was demonstrated by elevation of acidic mt-Keima signals in gastrocnemius and brain but not liver tissues (Extended Data Fig. 4d), while fasting-upregulated Nrf2 levels in gastrocnemius tissue probably contributed to mitochondrial turnover (Extended Data Fig. 4e). Refeeding restored levels of cytosolic AcCoA, mitophagy (Extended Data Fig. 4f–j) and Nrf2 expression without affecting other mitochondrial-biogenesis-related gene expression (Extended Data Fig. 4k). Furthermore, intraperitoneal administration of sodium acetate increased cytosolic AcCoA levels and blocked fasting-induced mitophagy in gastrocnemius tissue without affecting mitochondrial biogenesis (Extended Data Fig. 4l–p). Intraperitoneal injection with HC[3] to inhibit ACLY in vivo decreased the cytosolic but not mitochondrial AcCoA levels (Extended Data Fig. 4q) and induced mitophagy without affecting mitochondrial biogenesis (Extended Data Fig. 4r–u). Thus, mitophagy could be regulated by cytosolic AcCoA levels both in vitro and in vivo.

NLRX1 mediates cytosolic AcCoA mitophagy

We next investigated how cytosolic AcCoA reduction triggers mitophagy. SM culture or inhibition of SLC25A1 or ACLY had little effect on mitochondrial functions, including mitochondrial membrane potential (Extended Data Fig. 5a), mitochondrial reactive oxygen species (ROS) (Extended Data Fig. 5b), ATP production (Extended Data Fig. 5c), mitochondrial protein import (Extended Data Fig. 5d), permeability transition (Extended Data Fig. 5e) and oxidative phosphorylation rate (Extended Data Fig. 5f). Cells remained intact in different experimental conditions (Extended Data Fig. 5g). Moreover, neither PINK1 stabilization (Extended Data Fig. 5h) nor the mitochondrial recruitment or the E3 activity of Parkin was observed after SM culture or inhibition of SLC25A1 or ACLY compared with activating effects of carbonyl cyanide m-chlorophenyl hydrazone (CCCP)[19] (Extended Data Fig. 5i,j). Alternatively, we hypothesized about whether the mitophagy receptor is involved in this mitophagy response. To this end, a genome-wide CRISPR screening (19,050 genes, 6 single guide RNAs (sgRNAs) per gene) assay was performed to systemically and unbiasedly investigate mitophagy receptors involved in HC-induced mitophagy response in HeLa-tet-on-mt-Keima reporter cells (Fig. 1d). ULK1, ATG4A, WIPI2 and LAMP1 were identified among the top candidates lost in the top mitophagic cells after HC treatment (Extended Data Fig. 5k), demonstrating the robustness of the screening assay. The complete list of the screening was shown in Supplementary Table 1. Although transient knockdown of ACLY, SLC25A1 or ACSS2 induced mitophagy in multiple cell lines (Extended Data Fig. 2e–i), these genes were not identified as significantly enriched genes during the genome-wide CRISPR screening assay. There might be a compensatory effect during generating the knockout cells[21]. To explore which mitophagy receptor mediates this pathway, we listed all mitochondrial candidates using the MitoCarta 3.0 database[22]. Gene Ontology (GO) pathway enrichment analysis of the top 100 genes revealed the carboxylic acid metabolic process as the top candidate pathway (Extended Data Fig. 5l), highlighting the role of metabolism in mitophagy. NLRX1 ranked first among the screen-hit mitophagy receptors23,24 and was ranked tenth of all mitochondrial proteins (ranked by negative log-transformed fold change and P < 0.05) (Fig. 1e and Supplementary Table 2). Although the function of NLRX1 in mitophagy has been reported6,7, the upstream signal triggering NLRX1 activation remains unclear. Indeed, NLRX1 deficiency significantly impaired SM-induced, SLC25A1- or ACLY-inhibition-induced, and ACLY-, SLC25A1- or ACSS2-knockdown-induced mitophagy response, on the basis of the analysis results of mitochondrial proteins, LC3 recruitment on mitochondria, mtDNA/nDNA levels and acidic mt-Keima signal levels (Fig. 1f,g and Extended Data Fig. 6a–o).

Cytosolic AcCoA reduction initiates general autophagy through EP300-mediated protein acetylation[3]. Notably, NLRX1 depletion had little effect on p62 degradation (Extended Data Fig. 7a,b), overall protein acetylation (Extended Data Fig. 7c) and LC3 lipidation (Extended Data Fig. 7d) after cytosolic AcCoA reduction, and LC3 recruitment to mitochondria was largely blunted in NLRX1-deficient cells (Fig. 1g and Extended Data Fig. 6a,d). NLRX1 deficiency did not affect cytosolic AcCoA levels in the tested cells (Extended Data Fig. 7e). Furthermore, NLRX1 deficiency did not alter general-starvation-induced (Earle’s balanced salt solution, EBSS) autophagosome formation, on the basis of LC3 staining, transmission electron microscopy (TEM) analysis (Extended Data Fig. 7f,g) or p62 degradation (Extended Data Fig. 7h). However, EBSS-induced mitophagy was significantly abolished by BafA1 or NLRX1 deficiency (Extended Data Fig. 7h,i). Thus, cytosolic AcCoA-reduction-induced mitophagy is highly selective and requires the mitophagy receptor NLRX1.

HC- or fasting-induced mitochondrial degradation and LC3 recruitment on mitochondria were profoundly negated in both the liver and gastrocnemius tissues of Nlrx1−/− mice (Fig. 1h and Extended Data Fig. 7j–l). To further explore NLRX1-mediated mitophagy in vivo, we established an AAV-delivered mt-Keima reporter system in Nlrx1−/− mice. The basal acidic mt-Keima signals were decreased in both the liver and gastrocnemius tissues of Nlrx1−/− mice (Fig. 1i,j and Extended Data Fig. 7m). Consistent with that, HC treatment or fasting-induced acidic mt-Keima signals were abolished in liver and gastrocnemius tissues of Nlrx1−/− mice (Fig. 1i,j and Extended Data Fig. 7m). Moreover, Nlrx1 knockdown using AAV delivery system abolished HC- or fasting-induced mitochondria reduction in vivo (Extended Data Fig. 7n–q). NLRX1 deficiency or knockdown did not affect mouse weight during ad libitum feeding or after fasting (Extended Data Fig. 7r,s). Collectively, these results show that NLRX1 controls cytosolic-AcCoA-reduction-induced mitophagy both in vitro and in vivo.

NLRX1 contains an N-terminal mitochondrial targeting sequence (MTS), which could be cleaved after importing into the mitochondria[25]. The uncleaved NLRX1 is retained in the cytosol to promote LC3 lipidation and recruitment to mitochondria after mitochondria protein import stress (MPIS), including treatment of CCCP or MitoBloCK-6 (MB-6), the inhibitor of the mitochondria protein import pathway MIA40–ERV1 (ref. [7]). Indeed, uncleaved NLRX1 in the cytosol was found in CCCP-treated cells, but was not detected when reducing AcCoA in cells with endogenous or exogenous NLRX1 (Extended Data Fig. 8a,b), consistent with the result that SM culture or inhibition of ACLY or SLC25A1 induced little protein import stress (Extended Data Fig. 5d). Furthermore, restoration of full-length but not the cytosol-retained NLRX1(∆N-ter) (NLRX1 lacking MTS signal)[26] rescued the mitophagy induced by SM culture or inhibition of ACLY or SLC25A1 (Extended Data Fig. 8c).

Next, we established split green fluorescent protein (GFP) reporters localizing in the cytosol (cytoGFP) or mitochondrial matrix (matrixGFP)[27] and found the presence of NLRX1 in both the mitochondrial matrix and outer membrane (Extended Data Fig. 8d–f). About 45% of cells had outer-mitochondrial-membrane-localized NLRX1, while 70% of cells had matrix-localized NLRX1, and the distribution was unaffected after SM culture (Extended Data Fig. 8f). Importantly, the intact mitochondrial membrane before and after AcCoA reduction (Extended Data Fig. 5e) suggested that only outer-membrane-localized NLRX1 could gain access to the cytosolic LC3-decorated autophagosome to mediate mitophagy. To demonstrate that the cytosol-facing NLRX1 is sufficient to induce cargo degradation, we substituted the endoplasmic reticulum (ER)-localization sequence of FAM134b with the MTS of NLRX1 to generate a chimeric ER-localized NLRX1, which was exposed to the cytosolic face6,28 (Extended Data Fig. 8g,h). ACLY or SLC25A1 inhibition induced ER degradation, evidenced by decreased ER protein CLIMP63 in NLRX1(ER) but not NLRX1(Cyto) cells (Extended Data Fig. 8i), which were blocked by BafA1 (Extended Data Fig. 8j). Together, the mitochondrial-outer-membrane-localized NLRX1 mediates cytosolic AcCoA reduction-induced mitophagy.

MPIS-led mitochondrial dysfunction and mitophagy depend on NLRX1 (ref. [7]). Indeed, both the MPIS inducer MB-6 and CCCP caused cytosolic AcCoA reduction in HeLa cells (Extended Data Fig. 8k,l), and acetate supplementation blocked MB-6 or CCCP-induced mitophagy (Extended Data Fig. 8m,n). To examine how MPIS decreased AcCoA, we surveyed the literature and found that among the metabolic proteins that contribute to cytosolic AcCoA production, the pyruvate dehydrogenase (PDH) complex that converts pyruvate into AcCoA[3] was significantly decreased in the mitochondria after MB-6 treatment, in a proteomics analysis[29] (Extended Data Fig. 8o). We speculated that MB-6 might reduce cytosolic AcCoA through downregulating PDH complex importation in mitochondria (Extended Data Fig. 8p). Indeed, knockdown of the PDH complex (PDH-E1, encoded by PDHA1 and PDHB) significantly decreased cytosolic AcCoA levels (Extended Data Fig. 8q), while acetate supplementation restored the mitochondrial content reduced by silencing of PDH-E1 (Extended Data Fig. 8r). Notably, PDH-E1 knockdown in combination with MB-6 slightly decreased MT-CO2 levels compared with MB-6 alone (Extended Data Fig. 8s), indicating that the mitophagic effect of MB-6 might partially depend on PDH-E1. Besides the PDH complex, CCCP induced more-potent protein import suppression by affecting citrate synthase, SLC25A1 and mitochondrial pyruvate carrier (Extended Data Fig. 8t), leading to cytosolic AcCoA reduction (Extended Data Fig. 8l). Thus, cytosolic AcCoA reduction acts as the unified signalling metabolite to control NLRX1-mediated mitophagy response.

NLRX1 senses cytosolic AcCoA

AcCoA levels are commonly linked to protein acetylation[30]. However, SM culture with or without acetate did not alter NLRX1 acetylation levels in HEK293T cells stably expressing wild-type NLRX1–Flag (Extended Data Fig. 9a). To define whether NLRX1 directly senses cytosolic AcCoA, we incubated synthesized biotin–AcCoA (biotin was conjugated to the amino group of AcCoA) with cell lysates, followed by streptavidin pull-down. Notably, the interaction of both exogenous and endogenous NLRX1 and biotin–AcCoA was strongly enhanced after SM culture compared with under normal conditions, while supplementation with acetate reduced the association between biotin–AcCoA and NLRX1 (Fig. 2a,b). By contrast, biotin–AcCoA hardly pulled-down NLRP3, another NLR protein (Extended Data Fig. 9b). Notably, supplementing excess AcCoA in mitochondrial lysates competed for the interaction between NLRX1 and biotin–AcCoA, with competition saturated above 0.5 mM (Fig. 2c). To determine whether NLRX1 directly binds to AcCoA, we obtained the purified recombinant NLRX1 (87–975 amino acids) with a N-terminal MBP (maltose-binding protein) tag using insect cells (Extended Data Fig. 9c). In an in vitro pull-down assay, recombinant MBP–NLRX1 bound to biotin–AcCoA, and increasing concentrations of AcCoA competed with biotin–AcCoA for NLRX1 binding. The saturated competition was above 25 µM (Extended Data Fig. 9c), indicating the direct association.

Fig. 2: NLRX1 directly senses AcCoA.

a,b, Acetate (Ace) blocks SM-induced binding between NLRX1 and biotin–AcCoA. HEK293T NLRX1–HA cells (a) and A549 cells (b) were treated as indicated for 6 h with or without acetate (10 mM), and biotin–AcCoA was used to pull down NLRX1. n = 3 biological replicates. c, AcCoA competitively decreases the interaction between biotin–AcCoA and NLRX1. Top, representative immunoblotting image. Bottom, quantification of competitive binding between NLRX1 and biotin–AcCoA. n = 4 biological replicates. Data are mean ± s.e.m. Statistical analysis was performed using ordinary one-way ANOVA with Tukey’s multiple-comparison test. d, Biotin–AcCoA binds to the LRR domain of NLRX1. n = 3 biological replicates. e,f, Molecular docking analysis of the binding of AcCoA and NLRX1. e, AcCoA binds to the classical pocket of NLRX1–LRR. f, Four critical residues (Glu729, Lys754, Gln758, Arg958) at NLRX1–LRR are critical for binding to AcCoA. g, NLRX1(4A) blocks the interaction between NLRX1 and AcCoA. n = 3 biological replicates. h, MBP–RR(WT) binds to AcCoA with a Kd of about 6.6 µM. Purified recombinant MBP–LRR(WT) or MBP was incubated with 2 µM [3H]AcCoA and the indicated concentrations of AcCoA for scintillation counting, presented as disintegrations per minute (DPM). n = 5 (MBP–LRR(WT)) and n = 3 (MBP). Data are mean ± s.d. i, MBP–LRR(4A) impairs the AcCoA affinity of the LRR domain. The indicated recombinant protein was incubated with 2 µM [3H]AcCoA for scintillation counting. n = 3 biological replicates. Data are mean ± s.e.m. Statistical analysis was performed using ordinary one-way ANOVA with Dunnett’s multiple-comparison test.

Source Data

To explore the binding domain of AcCoA on NLRX1, we performed a domain mapping assay and found that the LRR domain was crucial for NLRX1–AcCoA binding (Fig. 2d). In silico molecular docking analysis using the experimental structure of LRR[31] illustrated that the LRR domain of NLRX1 contains four conserved sites (Glu729, Lys754, Gln758 and Arg958) that form a pocket and are potentially crucial for AcCoA binding (Fig. 2e,f and Extended Data Fig. 9d). Among these, the Lys754 and Arg958 sites bind to the negative phosphate groups, and the Gln758 site binds to the acetyl-group of AcCoA, with an AcCoA-interaction mode similar to that of bacterial hybrid malic enzymes that have the phosphotransacetylase domain[32]. Indeed, mutation of all four sites to alanine (E729A, K754A, Q758A and R958A; hereafter 4A) significantly blocked the biotin–AcCoA pull-down (Fig. 2g).

We next purified MBP–LRR(WT) or MBP–LRR(4A) protein from a bacterial expression system using gel filtration (Extended Data Fig. 9e). Competition binding with increasing concentrations of unlabelled AcCoA to radiolabelled AcCoA showed that recombinant MBP–LRR directly bound to AcCoA, and the dissociation constant (Kd) value was approximately 6.6 µM (Fig. 2h), which is within the reported range of cytosolic AcCoA concentrations[33]. Moreover, the AcCoA affinity of MBP–LRR(4A) was significantly impaired (Fig. 2i). Collectively, these results show that NLRX1 senses AcCoA by directly binding at the conserved pocket on the LRR domain.

Besides AcCoA, we also tested the association between NLRX1 and other related CoA metabolites, such as CoASH, malonyl-CoA and succinyl-CoA. According to the results of pull-down experiments, no interaction between recombinant NLRX1 and malonyl-CoA or succinyl-CoA was detected, while CoASH was associated with recombinant NLRX1 to a lesser extent than AcCoA was (Extended Data Fig. 9f). Furthermore, the result of competition assay of CoASH to biotin–CoASH-bound NLRX1 demonstrated that their association is specific (Extended Data Fig. 9g). The in silico docking analysis revealed a more-favourable binding energy for AcCoA to NLRX1 (ΔG = −7.804 kcal mol−1) than CoASH (ΔG = −5.56 kcal mol−1) (Extended Data Fig. 9h). CoASH was predicted to bind to NLRX1 through three major sites (Glu729, Lys754 and Arg958) (Extended Data Fig. 9h), compared with the four AcCoA-bound sites (Glu729, Lys754, Arg958 and Gln758) (Fig. 2f). We further confirmed that AcCoA has stronger NLRX1-binding affinity than CoASH using the competition assays. Increasing concentrations of AcCoA effectively reduced the interaction between biotin–CoASH and NLRX1, but not vice versa (Extended Data Fig. 9g), corroborating the in silico prediction result that NLRX1 has a higher binding affinity for AcCoA than for CoASH (Extended Data Fig. 9h). We next tested the intracellular association between NLRX1 and CoASH or AcCoA using a cellular thermal shift assay. AcCoA addition substantially increased the stability of NLRX1 protein level compared with CoASH (Extended Data Fig. 9i), indicating that AcCoA has stronger binding to NLRX1 than CoASH within cells. Notably, the CoASH level remained steady or slightly increased after nutrient starvation[33]. Thus, NLRX1 binds to AcCoA rather than to other related CoA metabolites, under physiological conditions.

Mitochondrial and cytosolic AcCoA are different pools because AcCoA cannot diffuse freely across cellular membranes[1]. ACLY or SLC25A1 inhibition decreased AcCoA levels in the cytosol but not in the mitochondria (Extended Data Fig. 2a), indicating that the cytosolic AcCoA pool is sufficient to induce NLRX1 activation. Compared with the neutral pH (around 7.2) in the cytosol, the mitochondrial matrix has a highly alkaline environment (pH of around 8.0)[34]. The isoelectric points of NLRX1 protein are about 6.8–7.4 according to different scales[35], while two positive charge residues (Lys754, Arg958) among the four major AcCoA-binding amino acids are predicted to associate with the negative phosphate group of AcCoA (Fig. 2f). Thus, to define whether the pH affects the association between NLRX1 and AcCoA, the mitochondria were isolated from HEK293T NLRX1–HA cells and lysed using buffers with different pH values (6.8, 7.4 or 8.0). The amount of NLRX1 obtained through biotin–AcCoA pulldown was substantially decreased in pH 8.0 buffer compared with in the neutral buffers (Extended Data Fig. 9j), suggesting that alkaline condition could disrupt the association between NLRX1 and AcCoA. In summary, NLRX1 mainly senses cytosolic AcCoA.

AcCoA controls NLRX1 oligomerization

NLRX1 has a conserved LC3-binding site (LIR) on the NACHT domain, which recruits LC3-decorated autophagosome for mitochondrial degradation after activation[6]. Notably, SM and CCCP promoted the colocalization of LC3 with NLRX1 at both the endogenous and exogenous levels, which was repressed by addition of acetate (Fig. 3a and Extended Data Fig. 10a–c). Meanwhile, ACLY or SLC25A1 inhibition also increased colocalization of NLRX1 with LC3 (Extended Data Fig. 10d,e). Moreover, we reintroduced WT or LIR-deficient (∆LIR) NLRX1 into NLRX1-knockout cells to determine the induction of mitophagy. Notably, SM culture or ACLY or SLC25A1 inhibition did not trigger mitophagy in NLRX1-deficient (empty vector) cells (Fig. 3b,c and Extended Data Fig. 10f–h). Reconstitution of cells with NLRX1 WT successfully restored mitophagy after the stimulation, but reconstitution with NLRX1(∆LIR) did not (Fig. 3b,c and Extended Data Fig. 10f–h). Collectively, these results show that cytosolic AcCoA reduction-induced mitophagy requires the association of NLRX1 with LC3.

Fig. 3: AcCoA triggers NLRX1 autoinhibition.

a, Acetate blocks SM-triggered colocalization of endogenous NLRX1 and LC3. HeLa cells with HA-tagged knock-in (NLRX1–HA KI) were treated with SM for 12 h with or without acetate (10 mM), followed by confocal microscopy analysis. Left, representative microscopy images. Scale bars, 10 μm. Right, quantification of Pearson’s colocalization coefficient. n = 21 (mock), n = 88 (SM) and n = 34 (SM + acetate) cells. Data are mean ± s.e.m. Statistical analysis was performed using Kruskal–Wallis test with Dunn’s multiple-comparison test. b, NLRX1(ΔLIR) does not induce mitophagy. Immunoblot (IB) images of total HeLa cell or mitochondrial lysates as indicated. n = 3 biological replicates. c, NLRX1(ΔLIR) induces mitophagy, on the basis of the mt-Keima reporter analysis. n = 3 biological replicates. Data are mean ± s.e.m. Statistical analysis was performed using two-way ANOVA with Bonferroni’s multiple-comparison test. d, Schematic of NLRX1 oligomerization and association with LC3 in response to AcCoA signalling. e, Addition of AcCoA but not CoASH promotes the association of the NACHT domain with the WT LRR domain. n = 3 biological replicates. f, NLRX1 oligomerization is controlled by cytosolic AcCoA. Total lysates from HEK293T cells expressing HA-tagged WT NLRX1 were treated as indicated for SDS–agarose electrophoresis. n = 3 biological replicates. g,h, SM promotes NLRX1 WT but not NLRX1(4A) oligomerization (g) or binding to GST–LC3 (h). Cells were treated as indicated for SDS–agarose electrophoresis (g) or GST pull-down (h). n = 3 biological replicates. i, AAV-mediated NLRX1(4A) constitutively induces mitophagy in the liver tissue of C57BL/6 mice. Liver tissue or mitochondrial lysates were used for immunoblotting. The ratio represents CYTB/tubulin. j, Quantification of relative CYTB/tubulin levels in Fig. 3i. n = 4 mice per group. Data are mean ± s.e.m. Statistical analysis was performed using ordinary one-way ANOVA with Dunnett’s multiple-comparison test. EV, empty vector.

Source Data

Previous studies showed the mode of NLRX1 autoinhibition: under a quiescent state, the LRR domain associates with the NACHT domain to inhibit the association of NLRX1 with LC3 at the LIR site. After activation, LRR disassociates with NACHT through an unknown mechanism to release the autoinhibition, promoting oligomerization of NLRX1 and its binding to LC3 (ref. [6]). On the basis of these premises, we hypothesized that AcCoA could stabilize the autoinhibition state of NLRX1 by enhancing the interaction between LRR and NACHT (Fig. 3d). Indeed, biotin–AcCoA did not bind to the NACHT domain but, when co-transfected with the LRR domain, biotin–AcCoA readily pulled-down NACHT, indicating the formation of the NACHT–AcCoA–LRR complex (Fig. 2d). Moreover, adding AcCoA but not CoASH in cell lysates co-expressing NACHT and LRR domains strengthened LRR binding to the NACHT domain (Fig. 3e). By contrast, LRR(4A) decreased binding to NACHT under NM (Extended Data Fig. 10i) and did not respond to AcCoA after SM culture (Fig. 3e). Furthermore, ACLY or SLC25A1 inhibition reduced the intensity of the monomer band while enhanced the oligomeric band of NLRX1 WT under indicated cross-link conditions (Fig. 3f). Adding acetate blocked SM- or CCCP-promoted oligomerization and restored the monomer band of NLRX1 WT (Fig. 3f and Extended Data Fig. 10j). Consistent with that, the oligomerization level of NLRX1(4A) was higher than that of NLRX1 WT and could not be upregulated by SM culture (Fig. 3g). Moreover, SM culture strengthened the association of NLRX1 WT with LC3 (Fig. 3h). Compared with NLRX1 WT, NLRX1(4A) constitutively increased binding to LC3 (Extended Data Fig. 10k) under NM and did not respond to SM culture (Fig. 3h).

On the basis of the results above, we first found that reintroduction of NLRX14A into NLRX1-deficient HeLa cells promoted acidic mt-Keima signal levels under basal conditions, but reintroduction of NLRX1ΔLIR did not (Extended Data Fig. 10l). BafA1 abolished the difference among NLRX1(4A) and other groups (Extended Data Fig. 10l), demonstrating the suppressive effect of AcCoA binding on NLRX1-mediated mitophagy. Furthermore, NLRX14A reintroduction into NLRX1-deficient cells significantly decreased the mtDNA/nDNA level compared with empty vector, NLRX1 WT or NLRX1(ΔLIR), which could be blocked by BafA1 (Extended Data Fig. 10m). Moreover, the introduction of Nlrx14A into liver or gastrocnemius tissues using AAVs constitutively induced mitophagy in the ad libitum-fed state, while Nlrx1WT overexpression did not significantly alter mitophagy (Fig. 3i,j and Extended Data Fig. 10n,o). In summary, NLRX1-mediated mitophagy is controlled by cytosolic AcCoA levels through direct binding both in vitro and in vivo.

Mitophagy drives KRASi resistance

KRAS mutations drive up to 30% of human cancers; specifically, more than 90% of patients with human pancreatic ductal adenocarcinoma (PDAC) have KRAS mutations. Thus, KRAS inhibitors (KRASi) have broad therapeutic potential for cancer therapy[36]. However, acquired drug resistance also emerges and limits the clinical benefit[37]. Whether metabolic rewiring participates in KRASi-induced resistance remains unclear. Mitophagy has been implicated in drug resistance, such as chemoresistance[38], we therefore wondered whether mitophagy participates in KRASi-induced resistance. Notably, the KRAS(G12D) inhibitor MRTX1133 (ref. [39]) and the pan-RAS inhibitor RMC-6236 (ref. [36]) decreased ACLY expression and cytosolic AcCoA levels in mouse KPC (KrasG12D/+Trp53R172H/+) cells and human PDAC AsPC-1 cells with KRASG12D (Fig. 4a,b and Extended Data Fig. 11a,b). Consistent with that, both inhibitors could induce mitophagy analysed by mitochondrial protein, acidic mt-Keima signal and mtDNA/nDNA level (Fig. 4c,d and Extended Data Fig. 11c–f), which could be blocked by acetate (Extended Data Fig. 11g–l). Importantly, KRASi-induced mitophagy was almost completely abolished in NLRX1-deficient cells (Fig. 4e,f and Extended Data Fig. 11m–p). Collectively, these results show that KRASi induces NLRX1-dependent mitophagy through downregulating ACLY–AcCoA axis metabolism.

Fig. 4: KRAS inhibitors enhance drug resistance by NLRX1-dependent mitophagy.

a–d, KRASi decreases Acly mRNA (a) and cytosolic AcCoA levels (b) and induces mitophagy (c,d). KPC cells were treated for 24 h and analysed using quantitative PCR with reverse transcription (RT–qPCR) (a); liquid chromatography coupled with MS (LC–MS) (b), normalized to cell numbers; immunoblotting (c); or flow cytometry for cells expressing mt-Keima reporter (d). n = 3 biological replicates. Data are mean ± s.e.m. Statistical analysis was performed using ordinary one-way ANOVA with Dunnett’s multiple-comparison test (a and d) and unpaired two-tailed Student’s t-tests (b). e–h, Nlrx1 knockout abolishes the KRASi-induced mitophagic response (e,f), and elevates ROS levels (g) and NADP+/NADPH ratio (h). KPC cells were used for immunoblot analyses (e), and cells expressing mt-Keima reporter were used for flow cytometry (f), CM-H2DCFDA staining (g) and the NADP+/NADPH colorimetric assay (h). n = 3 biological replicates. Data are mean ± s.e.m. Statistical analysis was performed using two-way ANOVA with Bonferroni’s multiple-comparison test. i, Nlrx1 depletion decreases KPC cell viability after MRTX1133 treatment. Dose–response curves for MRTX1133 treatment, based on 5-day CellTiter-Glo assays. Half-maximal inhibitory concentration (IC_50) values are displayed at the top. n = 6 biological replicates. j, Nlrx1 knockout exacerbates the suppressive effect of MRTX1133 on tumour cell growth in vivo. NSG mice given subcutaneous injection of KPC cells were treated with vehicle or MRTX1133 (30 mg per kg, twice a day) when the tumour volume reached around 300 mm^3. n = 5 mice per group. Data are mean ± s.e.m. Statistical analysis was performed using two-way ANOVA with Bonferroni’s multiple-comparison test. k,l, Impaired mitophagy and increased ROS production in Nlrx1-deficient tumours with MRTX1133 treatment. The mice were treated as in Fig. 4j, and after 6 d of treatment, tumours were collected for immunoblotting (k; n = 3 mice per group) or CM-H2DCFDA staining with counting by Image J (l; n = 30 images from 5 mice per group). Data are mean ± s.e.m. Statistical analysis was performed using two-way ANOVA with Bonferroni’s multiple-comparison test.

Source Data

Given that cellular redox management is a hallmark of cancers with KRAS mutations40,41 and mitochondria are the centre of ROS production, we speculated that mitophagy is a stress response to KRASi therapy in tumour cells to sustain cell survival. Indeed, both ROS levels and the cellular NADP+/NADPH ratio were significantly elevated in NLRX1-deficient cells after KRASi treatment (Fig. 4g,h and Extended Data Fig. 11q), indicating that mitophagy inhibition increased oxidative response. Notably, KRASi exhibited a better tumour-killing effect on NLRX1-deficient PDAC and lung cancer cells with KRAS mutations (Fig. 4i and Extended Data Fig. 11r). Moreover, adding the antioxidant N-acetyl-L-cysteine (NAC) blocked low-dose-MRTX1133-induced cell death in Nlrx1-deficient KPC cells, highlighting the critical role of oxidative stress in mitophagy inhibition-induced cell death (Extended Data Fig. 11s). Mitophagy inhibitor Mdivi-1 combined with KRASi exhibited a synergized suppressive effect on cancer cells compared with KRASi alone (Extended Data Fig. 11r,t), suggesting the potential of targeting ACLY–AcCoA–NLRX1-axis-mediated mitophagy in anti-tumour therapy. Moreover, MRTX1133 administration decreased tumour volume, and Nlrx1 deficiency could synergistically enhance the suppressive effect of MRTX113 on tumour growth (Fig. 4j and Extended Data Fig. 11u,v). Notably, MRTX1133 suppressed Acly expression to a similar level in both control and Nlrx1-deficient tumours, while mitochondrial protein reduction was observed only in control tumours, not in Nlrx1-deficient tumours (Fig. 4k). Consistent with the in vitro results, the basal ROS levels were the same in both control and Nlrx1-deficient tumours. However, MRTX1133 significantly decreased ROS level in the tumour sections of the control group but elevated it in Nlrx1-deficient tumours (Fig. 4l and Extended Data Fig. 11w). Collectively, these results show that KRAS inhibition induces an Nlrx1-dependent mitophagy response to elicit drug resistance.

Discussion

Our data demonstrate that NLRX1 could function as the key mitophagy receptor to selectively mediate reduced cytosolic AcCoA-induced mitophagy both in vivo and in vitro without affecting general autophagy receptor p62 degradation. NLRX1 has previously been shown to mediate mitophagy after mitochondrial damage such as bacterial infection and mitochondria protein import stress6,7. Here we show that cytosolic AcCoA is the bona fide ligand of NLRX1 in an acetyaltion- and AMPK-independent manner that underlies nutrient stress and mitochondria damage to control its activation.

The NLR family could not only detect pathogen-associated molecular patterns but also sense ‘altered-self’ signals to activate subsequent cascades, such as inflammasomes or autophagy for surveillance42,43. A common working mode for this family is sensing ligands directly through the LRR domain, getting oligomerized and then initiating downstream signalling pathways[42]. Notably, we found that MPIS-induced cytosolic AcCoA reduction and the mitophagy could be countered by the addition of acetate, proving that cytosolic AcCoA functions as the altered-self signal to control NLRX1’s activation under various conditions. Thus, our study provides a unified model of NLRX1-mediated mitophagy.

Although mitophagy has been implicated in drug resistance[38], here we identify that rewired metabolism-driven mitophagy could be used by KRAS inhibitors to decrease oxidative stress and sustain tumour cell survival. Thus, mitophagy inhibition could be a sensitization strategy for KRAS inhibitors to enhance their anti-tumour efficacy. It will be interesting to explore whether this AcCoA–NLRX1 axis is also present in other physiological or pathological situations.

Methods

Mouse studies

Wild-type C57BL/6 male mice (6–8 weeks of age) were purchased from BIKAI. Nlrx1−/− mice were purchased from Cyagen. NSG mice were purchased from Shanghai Model Organisms Center. All mice were housed in the specific-pathogen-free animal facility of Fudan University with the following environmental parameters: temperature maintained at 21–25 °C, relative humidity at 45–65% and a 12 h–12 h light–dark cycle.

All mice were randomly separated into each experiment group. Mice were fasted from 10:00 for 24 h with free access to water without food. PBS or HC (59847, Sigma-Aldrich; 100 mg per kg) was intraperitoneally injected into mice at 10:00 for 4 h. For acetate administration, mice were fasted for 24 h and PBS or sodium acetate (S5636, Sigma-Aldrich; 1 g per kg) was intraperitoneally injected 10 h and 1 h before mice were euthanized. For serum collection, mice were fasted overnight for 16 h with free access to water. For food reintroduction, mice were fasted for 24 h and then re-fed for another 24 h. Mice were euthanized and the indicated tissues were collected for subsequent analysis.

For the KPC model used in the MRTX1133 therapy experiment, 1 × 10^6 KPC cells were subcutaneously injected into 6- to 8-week-old NSG mice. The vernier calliper measurements begun when the tumours reached around 200 mm^3. Tumour volume measurements were recorded three times per week using the formula 0.5 × length × width^2. A blinded study design was used in the mouse tumour experiment to prevent bias during data collection and assessment, thus mice were randomized into control and treatment groups, and treated by intraperitoneal injection with vehicle (10% DMSO + 90% (20% SBE-β-CD in saline) or MRTX1133 in vehicle (30 mg per kg, twice a day) when the tumour volume reached around 300 mm^3. Tumours were collected after 6 days of treatment. All of the animal experiment procedures, including the maximal tumour volume, were approved by ethics committee of Department of Laboratory Animals, Fudan University.

AAV production and infection in vivo

Plasmids for the AAV2/9 system, including pAAV RC2/9 plasmids, pAAV helper plasmids and transgene plasmids with the CMV or U6 promoter were used for global expression or the knockdown of genes in vivo respectively as previously described[44]. The plasmids were mixed with PEI solution and transfected into HEK293T cells. Then, 60–72 h after transfection, the cells and medium were collected by centrifugation (3,500 rpm, 4 °C, 5 min). 5× polyethylene glycol (40% PEG 8000, 2.5 M NaCl) was added to the supernatant and incubated at 4 °C overnight followed by centrifugation (3,000 rpm, 4 °C, 5 min) to collect the virus pellet. Meanwhile, the cell pellet was resuspended with lysis buffer (150 mM NaCl, 20 mM Tris-Cl, pH 8.0) and lysed by three freeze–thaw cycles between liquid N_2 and a 37 °C water bath followed by centrifugation (5,500 rpm, 4 °C, 10 min) to obtain the supernatant. Then the supernatant was mixed with the virus pellet. The mixture was purified by Optiprep (D1556-250mL, Sigma-Aldrich) gradients (17%, 25%, 40% and 60%) centrifugation (40,000 rpm, 16 °C, 2 h). The viral fraction was collected from the 40% gradient, then washed three times with PBS using 100 kDa columns (3,500 rpm, 4 °C, 30 min).

AAVs were administered to C57BL/6J mice through gastrocnemius injection (5 × 10^10 copies, 25 μl per mouse, three sites) or tail injection (1 × 10^11 copies, 150 μl per mouse). All experiments were performed 3–4 weeks after AAV injection. The efficiency of Nlrx1 knockdown or overexpression mediated by AAV delivery was validated by immunoblotting.

Plasmids, reagents and antibodies

Plasmids

WT NLRX1 (HA tag), the NACHT domain (amino acids 160–483, HA tag), LRR domain (amino acids 669–975, Flag tag), ΔLRR (amino acids 1–668, HA tag), 4A (four sites, Glu729, Lys754, Gln758 and Arg958, were mutated to Ala, HA tag) of NLRX1, NLRX1-GFP11 (the C terminus of NLRX1 without stop codon was fused to the linker GGSGGGS and the GFP11 tag RDHMVLHEYVNAAGIT), NLRX1-GFP11-IRES-RFP (the C terminus of NLRX1-GFP11 fused to IRES and RFP), HSP60-GFP11 (the C terminus of HSP60 without stop codon was fused to the linker and the GFP11 tag), GFP11-TOM20 (the GFP11 and the linker fused to the N terminus of TOM20) and HA-NLRP3 were constructed into pcDNA3.1 vector; WT NLRX1-HA, ΔLIR-NLRX1 (amino acid deletion 461–466, HA tag), NLRX1(ΔN-ter) (amino acids 156–975, HA tag), NLRX1(Cyto) (amino acids 87–975, Flag tag), NLRX1(ER) (the N terminus of NLRX1(Cyto) fused to the amino acids 81–250 of FAM134B, Flag tag) were generated as previously described[6]; cytoGFP(1–10), matrixGFP(1–10) (the N terminus of GFP1–10 was fused to the MTS of COX8, residues 1–36) and GFP-Parkin were generated into the pLVX-hygro vector, and GFP-LC3B was generated into pQCXIH. Constructs encoding mt-Keima and MTS-eGFP were generated into the pLVX or pLVX-Tet-On vector.

Guide RNAs targeting human or mouse NLRX1 were designed online (http://www.e-crisp.org/E-CRISP/) and inserted into the pLentiCRISPR v2 vector.

For NLRX1–HA knock-in cell generation, the guide RNA (5′-TCTGGAAGCTGAGACACTGG-3′) was cloned into the pX458 plasmid. The homology arm of NLRX1-800-stop codon-+800 cloned into pcDNA 3.1 with a mutation at the PAM site from CGG to CCG and HA tag (TACCCCTACGACGTCCCCGACTACGCC) sequence was inserted before the stop codon.

Metabolites

AcCoA sodium salt (AcCoA) (A2056), CoASH (C4780), malonyl-CoA (M4263) and sodium acetate (acetate) (S5636) were obtained from Sigma-Aldrich. Succinly-CoA (HY-137808) was obtained from MCE. Biotin was conjugated to the amino groups (-NH2) of AcCoA by EZ-Link sulfo-NHS-LC-biotin (A39257, Thermo Fisher Scientific) according to the manufacturer’s instructions.

Antibodies

Anti-TIM23 (mouse, 611223, BD Biosciences, 1:5,000), anti-MT-CO2 (rabbit, ab79393, Abcam, 1:3,000), anti-CYTB (rabbit, 55090-1-AP, Proteintech, 1:5,000), anti-HSP60 (goat, sc-13115, Santa Cruz Biotechnologies, 1:1,000), anti-LC3 (rabbit, 3868S, CST, 1:1,000), anti-HA (mouse, 901513, BioLegend, 1:1,000), anti-Flag (mouse, F3165, Sigma-Aldrich, 1:3,000), anti-NLRX1 (rabbit, 17215-1-AP, Proteintech, 1:1,000), anti-PINK1 (rabbit, BC100-494SS, Novus Biologicals, 1:1,000), anti-ACLY (rabbit, 15421-1-AP, Proteintech, 1:1,000), anti-ACSS2 (rabbit, 16087-1-AP, Proteintech, 1:1,000), anti-FASN (rabbit, 10624-2-AP, Proteintech, 1:3,000), anti-ACC1 (rabbit, 21923-1-AP, Proteintech, 1:2,000), anti-IDH1 (rabbit, 12332-1-AP, Proteintech, 1:3,000), anti-p62 (rabbit, 18420-1-AP, Proteintech, 1:3,000), anti-acetylated-lysine (rabbit, 9441, CST, 1:1,000), anti-phospho-AMPKα (rabbit, 40H9, CST, 1:1,000), anti-AMPKα (rabbit, 10929-2-AP, Proteintech, 1:3,000), anti-ULK1 (rabbit, 8054, CST, 1:1,000), anti-phosphorylated ULK1 (Ser757) (rabbit, 6888, CST, 1:1,000), anti-phosphorylated ULK1 (Ser555) (D1H4) (rabbit, 5869, CST, 1:1,000), anti-S6 kinase (S6K) (rabbit, 9202, CST, 1:1,000), anti-phospho-S6K (Thr389) (mouse, 9206, CST, 1:1,000), anti-cytochrome c (rabbit, 556432, BD Biosciences, 1:1,000), anti-Parkin (rabbit, Proteintech, 66674-1-Ig, 1:500), anti-CLIMP63 (mouse, ENZ-ABS-669-0100, ENZO, 1:500), anti-FIP200 (rabbit, 17250-1-AP, Proteintech, 1:3,000), anti-ATG7 (rabbit, 10088-2-AP, Proteintech, 1:3,000), anti-tubulin (rabbit, 11224-1-AP, Proteintech, 1:5,000), anti-actin (mouse, 66009-1-Ig, Proteintech, 1:5,000) were used in immunoblotting. Anti-LC3 (rabbit, PM036, MBL, 1:100), anti-HA (mouse, 901513, BioLegend, 1:1,000), anti-TOM20 (mouse, 612278, BD Biosciences, 1:1,000) and anti-HSP60 (goat, sc-13115, Santa Cruz Biotechnologies, 1:1,000) were used in immunofluorescence. The fluorescent secondary antibodies goat anti-mouse Alexa Fluor 594 (A11032, Invitrogen, 1:1,000), donkey anti-rabbit Alexa Fluor 594 (A21207, Invitrogen, 1:1,000), donkey anti-mouse Alexa Fluor 488 (A21202, Invitrogen, 1:1,000) and donkey anti-goat Alexa Fluor 647 (A21447, Invitrogen, 1:1,000) were used in immunofluorescence.

Inhibitors

HC (59847), BTC (51520) and CCCP (C2759) were from Sigma-Aldrich. SB (HY-16450), actinomycin D (HY17559), MRTX1133 (HY-134813), RMC-6236 (HY-148439), Torin-1 (HY-13003) and Mdivi-1 (HY-15886) were from MCE. Bafilomycin A1 (S1413) was from Selleck. Oligomycin (9996) was from CST.

Cell culture and cell line generation

HEK293T, HeLa, A549, MCF7 and U-2 OS cells were purchased from ATCC and AsPC-1 cells were purchased from National Collection of Authenticated Cell Cultures (NCACC), Shanghai. Sf9 cells were purchased from Invitrogen. KPC (KrasG12D/+Trp53R172H/+) cells were obtained from Z.-G. Zhang. HEK293T, HeLa, A549, MCF7, U-2 OS, KPC cells and AsPC-1 cells were cultured in DMEM (Invitrogen) or RPMI-1640 (Invitrogen) supplemented with 10% FBS (BI) and 1% penicillin–streptomycin (HyClone). Sf9 cells were cultured in SF900 II SFM (Gibco). The SM is the DMEM formula with 5 mM glucose, 2 mM glutamine, 1 mM pyruvate and supplemented with 10% dialysed serum (BI) and 1% penicillin–streptomycin (HyClone). All cell lines were grown at 37 °C and 5% CO_2 and were tested to be mycoplasma free using the mycoplasma detection kit (40612ES25, YEASEN). A notable exception was the Sf9 cell line, which was maintained under distinct conditions: incubation at 28 °C with shaking on a horizontal shaker at a rotational speed of 100 rpm.

NLRX1-knockout cells were generated using the CRISPR–Cas9 system. pLentiCRISPR v2 vectors carrying sgRNA were mixed in Opti-MEM and transfected into cells with polycation polyethylenimine (PEI) (Sigma-Aldrich) and selected by puromycin for 3 days to get NLRX1-deficient cells. Single cells were seeded into 96-well plates and validated by sequencing and immunoblotting to get NLRX1-knockout cells. To generate NLRX1–HA-tag knock-in cells (HeLa-NLRX1(HA-KI)), the plasmid pX458 together with donor DNA (amplification of plasmid pcDNA3.1 containing the homology arm of NLRX1) with a ratio of 1:1 in Opti-MEM were transfected with Lipo3000 (Invitrogen) into HeLa cells. After 48 h transfection, GFP-positive cells were sorted and seeded into the 96-well plate (single clone per well) by flow cytometry. The knock-in cells were validated by sequencing and immunoblotting.

To generate cells with the inducible expression of mt-Keima, HeLa cells were infected with viruses expressing pLVX-Tet3G-rtTA and selected with G418 (800 μg ml−1) for 1 week to get HeLa-rtTA cells. HeLa-rtTA cells were then infected with viruses expressing mt-Keima, followed by blasticidin (10 μg ml−1) selection for an additional 1 week to generate HeLa-Tet-On-mt-Keima cells. The expression of mt-Keima was induced by doxycycline (1 μg ml−1) for 6 h.

To generate stable cell lines, cells were infected with indicated viruses together with 10 μg ml−1 polybrene. After 48 h, cells were selected with 2 μg ml−1 puromycin, 50 μg ml−1 hygromycin, 10 μg ml−1 blasticidin or 800 μg ml−1 G418 for 1–2 weeks. The overexpression or knockdown efficiency was verified by immunoblotting.

For transient gene overexpression, cells were transfected with indicated plasmids using Lipo3000 (for HeLa cells) or PEI (for HEK293T cells). Gene expression was validated by immunoblotting or immunofluorescence 24–48 h after transfection.

Virus packing

Lentiviral or retroviral vectors carrying the indicated genes, together with packaging plasmids psPAX2 and pMD2.G or VSVG and GAG were transfected into HEK293T cells. After 48–72 h, the supernatants were collected, filtered with a 0.45-μm filter and concentrated with PEG8000.

Gene knockdown by siRNA

siRNAs were transfected by Lipofectamine RNAiMAX (Invitrogen) according to the manufacturer’s instructions and, after 48 h, the transfected cells were treated as indicated and collected for subsequent analysis.

Mitochondria isolation

For mitochondria isolation, cells were washed with cold PBS twice and collected with 1 ml cold mitochondrial lysis buffer as previously described[45]. The buffer containing 210 mM mannitol, 70 mM sucrose, 5 mM Tris–HCl (pH 7.5) and 1 mM EDTA (pH 8.0), was adjusted to pH 7.5 with protease inhibitors. Then the cell suspension was transferred to the Dounce Tissue Grinder (P1110, T2690, Sigma-Aldrich) and lysed by 26 strokes. The homogenate was centrifuged at 1,300g for 10 min at 4 °C and the supernatant was collected in new tubes followed by centrifugation (10,000g, 4 °C, 20 min) to generate the supernatant as the cytosolic fraction and the cell pellet as the mitochondrial fraction.

Immunoblotting, immunoprecipitation and GST-pull-down assay

For immunoblotting, cells were lysed in 1× SDS buffer, boiled at 95 °C for 10 min and analysed by SDS–PAGE. For LC3 analysis, cells were lysed by buffer F (20 mM Tris-HCl, pH 7.4, 150 mM NaCl, 10% glycerol, 0.5% NP-40 and protease inhibitors) and centrifuged for 15 min at 4 °C. The supernatants were collected and boiled with 3× SDS and analysed by SDS–PAGE. For biotin–AcCoA pull-down assays, cells were collected and mitochondria were purified as described above. Mitochondria pellets were lysed by buffer C (50 mM HEPES, pH 7.5, 150 mM NaCl, 1% NP-40, 2 mM EDTA and protease inhibitors) and centrifuged for 15 min at 4 °C; the supernatants were then collected for the biotin–AcCoA-binding assay. Streptavidin beads (3419, CST) were incubated with biotin or biotin-labelled AcCoA in PBS for 1 h at room temperature, the beads were washed once with PBS and then incubated with cell lysates overnight at 4 °C with rotation. On the second day, the beads were washed four times with buffer C, boiled with 1× SDS and analysed by SDS–PAGE. For GST–LC3 pull-down assays, GST beads (AGM90049, AOGMA) were incubated with recombinant GST–LC3 for 4 h at 4 °C, then washed with buffer C and incubated with cell lysates at 4 °C for 4 h with rotation. The beads were washed four times with buffer C, boiled with 1× SDS and analysed by SDS–PAGE. For LRR-domain binding with the NACHT domain, cells were lysed with lysis buffer (0.5% Triton X-100, 20 mM HEPES pH 7.6, 150 mM NaCl, 12.5 mM β-glycerophosphate, 1.5 mM MgCl_2, 2 mM EGTA with protease inhibitors). AcCoA was co-added with Flag beads (A2220, Sigma-Aldrich) into the lysates overnight at 4 °C with rotation. The beads were washed and protein samples were processed as described above.

For AMPK activation and mTOR inhibition analysis, HeLa, A549 and MCF7 cells were treated with SM (DMEM containing 5 mM glucose, 2 mM glutamine and 1 mM pyruvate sodium with 10% dialysed serum and 1% penicillin–streptomycin) for 16 h. Oligomycin (5 μM, 5 min) or Torin-1 (100 nM, 16 h) was used as a positive control. Cells were washed with precooled PBS twice and lysed with precooled lysis buffer containing 20 mM Tris-HCl, pH 7.4, 150 mM NaCl, 1 mM EDTA, 1 mM EGTA, 1% Triton X-100, 2.5 mM pyrophosphate, 50 mM NaF, 5 mM β-glycerol phosphate, 50 nM calyculin A, 1 mM Na_3VO_4 and protease inhibitors. The lysates were centrifuged at 17,000g for 10 min at 4 °C, and the supernatant was boiled with 3× SDS and analysed by SDS–PAGE[46].

For all immunoblotting analyses, proteins were separated by 10–12% SDS–PAGE, transferred to PVDF membranes (Amersham), blocked by 5% non-fat milk in 0.1% Tween-20/PBS buffer for 1 h at room temperature, and immunoblotted by antibodies according to molecular mass. All uncropped raw immunoblotting data are provided in Supplementary Fig. 1.

Purification of recombinant NLRX1 proteins

Human NLRX1 without the N-terminal MTS (amino acids 87–975) was cloned into the pFastBac His vector with an additional N-terminal MBP tag. The vector was then transfected into DH10Bac to get recombinant bacmids, which were further transfected into SF9 insect cells to get amplified baculovirus. SF9 cells were infected with amplified baculovirus for 3 days and cells were collected and lysed in HEPES buffer (20 mM HEPES pH 7.5, 150 mM NaCl) with protease inhibitors and 0.5 mM Tris (2-carboxyethyl) phosphine (TCEP) with sonication. After centrifuging with 10,000 rpm for 1 h, supernatants containing recombinant NLRX1 were purified with Ni-NTA (QIAGEN) and gel-filtration chromatography on the Superdex 200 column (GE Healthcare). The purified recombinant MBP–NLRX1–His was confirmed by immunoblotting using NLRX1 antibody and used for biotin–AcCoA pull-down assay.

NLRX1 LRR domain (629–975) or LRR(4A) were cloned into the pMAL-c5X vector with an N-terminal expressed MBP tag. Constructs were transfected into Escherichia coli BL21 (DE3) cells, which were incubated in LB medium (50 μg ml−1 ampicillin) for 6 h at 37 °C with shaking. Protein expression was induced with 0.2 mM isopropyl-β-D-thiogalactopyranoside overnight at 18 °C. Cells were collected and resuspended in HEPES buffer with protease inhibitors and 0.5 mM TCEP. The proteins were further purified by gel-filtration chromatography on the Superdex 200 column (GE Healthcare Life Sciences) equilibrated with the HEPES buffer with 0.5 mM TCEP. Dextrin beads (SA077025, Smart-Lifesciences) were used to purify recombinant proteins, washed with HEPES buffer and eluted with 5 mM maltose.

[3H]AcCoA binding assay

Recombinant MBP–NLRX1-LRR proteins (50 μg) and equal amounts of MBP or MBP–LRR(4A) proteins were incubated with dextrin beads for 2.5 h at 4 °C with rotation. Beads were washed twice with lysis buffer 2.0 (HEPES buffer with 2 mM MgCl_2, 0.5 mM TCEP and 0.05% Tween-20). The beads were incubated with 2 μM [3H]AcCoA (NET290250UC, Perkin Elmer) and the indicated concentrations of cold AcCoA for 1 h at room temperature. The tubes were flicked every 10 min. The beads were then washed four times with lysis buffer 2.0 and quantified using the TriCarb scintillation counter (PerkinElmer). The binding affinity Kd was calculated as previously described[47].

Protein oligomerization analysis

Protein oligomerization analysis was conducted as previously described6,48. Cells were washed twice with PBS and centrifuged at 3,000 rpm for 5 min at 4 °C. After resuspending in PBS, cell pellets were pipetted 24 times with a 22-gauge needle and centrifuged at 13,000 rpm for 1 h at 4 °C followed by gentle sonication in PBS. The samples were divided into two parts—one part was cross-linked with 1 mM glutaraldehyde for 10 min at 16 °C and the other was not cross-linked as inputs. The samples were boiled at 95 °C for 10 min and analysed by immunoblotting after SDS–agarose or SDS–PAGE electrophoresis.

Molecular modelling for AcCoA binding to NLRX1

The NLRX1 structure was from the Protein Data Bank[31] (PDB: 3UN9). The simulation of AcCoA or CoASH docking to LRR of NLRX1 was performed by Schrödinger Computational Suite, Maestro v.11.5.011, MMshare v.4.1.011, release 2018-1, platform Windows-x64. All structure figures were prepared in Pymol (http://www.pymol.org).

Flow cytometry

Cells were treated as indicated and washed with PBS, collected in DMEM or RPMI1640 and centrifuged at 800g for 5 min. Cells were stained with 100 nM TMRM (T668, Invitrogen), 5 μM MitoSOX (M36008, Invitrogen) or 10 μM CM-H2DCFDA (HY-D0940, MCE) in DMEM or RPMI1640 for 30 min at 37 °C and 5% CO_2. After the incubation, cells were washed twice with PBS and resuspended in DMEM or RPMI1640 followed by flow cytometry.

The split GFP system used for monitoring mitochondrial outer membrane or matrix protein localization has been previously described27,49,50. In brief, HeLa cells stably expressing cytoGFP(1–10) or matrixGFP(1–10) were transfected with construct encoding NLRX1(G11)-IRES-RFP. After 36–48 h, cells were treated as indicated and flow cytometry was performed by Beckman coulter CytoFLEX S instrument. NLRX1 localization on the mitochondrial outer membrane or matrix was calculated on the basis of the GFP+RFP+/RFP+ ratio. For flow cytometry, 1 × 10^4 to 2 × 10^4 cells were collected using the Beckman Courtier instrument, and the data were analysed by FlowJo v.10.8.1 software or CytExpert 2.5.

ATP measurement and cell death assay

For intracellular ATP production measurement, the ATP Assay Kit (S0026, Beyotime) was used according to the manufacturer’s instructions. In brief, cells were washed twice with PBS and lysed with lysis solution for 20 min on ice followed by centrifugation (12,000g, 5 min, 4 °C). ATP assay working solution and the supernatants were co-added into a black 96-well plate. The luminescence was measured using a microplate reader (BioTek).

For the cell death assay, LDH was detected using the CytoTox 96 Non-Radioactive Cytotoxicity Assay kit (G1782, Promega) according to the manufacturer’s instructions. In brief, cells were treated as indicated and the supernatants were transferred to a fresh 96-well plate. An equal volume of LDH detection working solution was added to each well plate and incubated at 37 °C for 30 min. The LDH positive control in the kit was used as the positive control. Finally, the absorbance signal was measured at 490 nm using a microplate reader (BioTek).

Measurement of NADP(H)

For the measurement of NADP(H), the NADP+/NADPH Assay Kit (Beyotime, S0180) was used according to the manufacturer’s instructions. In brief, cells were washed twice with PBS, resuspended with extraction buffer and then centrifuged (12,000g, 5 min, 4 °C). The supernatant was divided into two equal parts. One part was used for total NADPH measurement. The other part was incubated for 30 min at 60 °C to decompose NADP+. The G6PDH assay working solution was then added to the supernatants in the 96-well plate and the mixture was incubated for 10 min. Finally, the absorbance signal was measured at 450 nm with a microplate reader (BioTek).

2D cell proliferation

For 2D cell proliferation assay, cells were seeded into the black 96-well plate and treated with serial dilutions of MRTX1133 or RMC-6236, Mdivi-1 (20 μM) or DMSO. Then, 50 μl of CellTiter-Glo reagent was added to 50 μl of medium-containing cells and the contents were mixed for 2 min. The plate was next incubated at room temperature for 10 min. The luminescence was measured using a microplate reader (BioTek). Luminescence signal was normalized to DMSO treated cells (percentage DMSO = (lumtreated/mean(lumDMSO)) × 100). A log[inhibitor] versus response-variable slope (four parameters) model was used to calculate the IC_50.

Metabolite extraction and GC–MS

For intracellular metabolite measurements in Extended Data Fig. 1n, cells were cultured in 10 cm dishes and treated with SM for 16 h. When cell confluency was about 80–100%, the medium was removed, cells were washed with cold PBS twice, collected in extraction buffer (acetonitrile:isopropanol:water, 3:3:2, v/v/v). The resuspended cells were placed in liquid N_2 for 5 min and thawed on ice for 5 min and the freeze–thaw cycle was repeated four times and the samples were then centrifuged (12,000 rpm, 4 °C, 10 min) to collect the supernatants. For serum valine, leucine and isoleucine measurements in Extended Data Fig. 1c, blood was collected from the eyes and clotted at room temperature for 1 h and then centrifuged (3,000 rpm, 10 min). Next, 20 μl serum was diluted with 80 μl precooled methanol and vortexed, then centrifuged at 12,000g for 15 min to remove proteins. The cell or serum supernatants were then evaporated by freeze-vacuum and analysed by gas chromatography coupled with MS (GC–MS). The pellets were resuspended in 75 μl acetonitrile at 60 °C for 15 min, then oximated by MTBSTFA (N-tert-butyldimethylsilyl-N-methyltrifluoroacetamide) in 50 μl pyridine and further incubated in 60 °C for 1 h. The samples were centrifuged and the supernatants were transferred to glass vials. Then, 1 µl of each sample was injected and analysed on the Agilent 7890B-5977B GC–MS system with DB-5MS (0.25 mm internal diameter, 0.25 μm film, with 30 m empty column, Agilent J&W). Metabolite m/z ratios were compared with those of previous studies[51]. Each metabolite was quantified by the retention time and peak area by MassHunter Workstation (Agilent). The final intracellular metabolites and amino acid level was normalized to actin level of immunoblotting or volume of serum.

Metabolite extraction and LC–MS

Cytosolic and mitochondrial AcCoA levels were determined as previously described[45]. In brief, approximately 1 × 10^7 to 2 × 10^7 cells were lysed with 1 ml cold mitochondrial lysis buffer and the cytosolic and mitochondria fractions were then isolated as described above. The cytosolic fraction was quenched by 50% (w/v) trichloroacetic acid in water (the final concentration of trichloroacetic acid is 10%) and the mitochondria fraction was resuspended in 1 ml 10% (w/v) trichloroacetic acid. The mitochondria fraction was placed into liquid N_2 for 5 min and thawed on ice for 5 min and the freeze–thaw cycle was repeated four times followed by centrifugation (17,000g, 4 °C, 10 min). The mitochondria supernatants and cytosolic fractions were then purified using Oasis HLB 1cc (30 mg) SPE columns (Waters). Columns were washed with methanol, equilibrated with water, loaded with the cytosolic and mitochondrial fractions, washed with water and eluted with elution buffer (25 mM ammonium acetate in methanol). The elutions were evaporated by freeze-vacuum, resuspended in 20 μl 20% acetonitrile in water and analysed by LC–MS. For in vivo AcCoA measurement, tissues were obtained from C57BL/6J mice, and 50–100 mg tissue was collected in 1 ml precooled 10% trichloroacetic acid. The samples were then homogenized and lysed on a rotating shaker (30 min, 4 °C) followed by centrifugation (12,000g, 10 min, 4 °C). The supernatant was purified, dried and resuspended as described above, and analysed using LC–MS. Then, 5 µl of each sample was injected and analysed using SHIMADZU LC-30AB LC system coupled to the QTRAP7500 Mass Spectrometer (SCIEX). Hydrophilic interaction chromatography (HILIC) with the BEH column (1.7 µm, 2.1 mm × 100 mm; Waters) was used. Mobile phase A was as follows: ammonia with 10 mM ammonium formate and 0.2% ammonia. Mobile phase B was acetonitrile. The flow rate was 0.2 ml min−1 and the column temperature was set at 40 °C. Linear gradient: 0 min, 80% B; 3 min, 50% B; 10 min, 50% B; 10.1 min, 80% B; 15 min, 80% B. Multiple reaction monitoring (MRM) technology using MS/MS was used for specific detection of AcCoA. The LC–MS system was operated in negative ionization mode. The source parameters included curtain gas (CUR) at 40 psi; collision active dissociation (CAD) gas at 6; ion source gas 1 (GS1) at 40 psi, GS2 at 70 psi; ion spray voltage (IS) at 4,500 V; ion source temperature (TEM) at 450 °C. The specific transition was recorded as follows: AcCoA 808.0945 > 407.9000. The final AcCoA level was normalized to the actin immunoblot level, or the tissue weight or cell number, as described in the figure legends.

For serum amino acid measurement (proline, glutamate acid, serine, asparagine, glutamine, arginine, glycine, alanine, aspartic acid, tyrosine, histidine, lysine, methionine, phenylalanine, threonine and tryptophan, while cysteine was too low to be detected), blood was collected from eyes and clotted at room temperature for 1 h followed by centrifugation (3,000 rpm,10 min). Then, 20 μl serum was diluted with 80 μl precooled methanol and vortexed, then centrifuged at 12,000g for 15 min to remove proteins. The supernatants were evaporated by freeze-vacuum, resuspended in 100 μl 80% methanol in water and analysed by LC–MS. Then, 1 µl of each sample was injected and analysed using UPLC-H Class LC system (Waters) coupled to the 6500 QTRAP Mass Spectrograph (SCIES). The ultimate AQ-C18 column (5 µm, 2.1 mm × 250 mm; Welch) was used at room temperature. Mobile phase A was as follows: water (0.1% formic acid, v/v); and mobile phase B was acetonitrile (0.1% formic acid, v/v). Linear gradient: 0–1 min, 0% B; 1–14 min, 0–90% B; 14–16 min, 90% B; 16–16.1 min, 90–0% B; 16.1–20 min, 0% B. The flow rate was 0.2 ml min−1. MRM technology using MS/MS was used for specific detection of various amino acids. The LC–MS system was operated in positive ionization mode. The source parameters included CUR at 40 psi; CAD at medium; GS1 at 40 psi, GS2 at 40 psi; IS at 5,500 V; and TEM at 500 °C. All LC–MS analysis was performed by the Metabolic Platform at the Fudan University. Data were analysed using Skyline (22.2.0.527) software to calculate the peak area values.

Seahorse analysis

The mitochondrial oxygen consumption rate (OCR) in HeLa cells was measured with the Seahorse XFe96 equipment (Agilent) using the Cell Mitochondrial Stress Test kit (103015-100, Agilent) according to the manufacturer’s instructions. In brief, 0.8 × 10^4 cells were seeded onto an XFe96 cell culture microplate (Agilent) per well and treated with CCCP (10 μM), SB (100 μM), BTC (5 mM), HC (20 mM) and SM for 16 h. Before analysis, cells were washed twice and equilibrated with XF DMEM in a 37 °C incubator without CO_2 for 1 h. Oligomycin (final concentration: 1.5 μM), FCCP (final concentration: 2 μM), and rotenone/antimycin A (final concentration: 0.5 μM) were used in OCR analysis. Data were analysed by Seahorse Wave Desktop Software (Agilent).

CRISPR screening

To generate lentivirus for screening, we used the genome-wide GeCKO v2.0 Human library[52] in the lentiCRISPR v2 vector (Addgene, 1000000048), which contains six sgRNAs per gene (123,411 sgRNAs targeting 19,050 genes). A total of 1 × 10^8 HEK293T cells was seeded into six T225 flasks. Each flask was transfected with 20 μg of plasmid library, 10 μg of psPAX2 and 5 μg of pMD2.G using PEI. After 48 h of transfection, the supernatant was collected, centrifuged at 3,000 rpm for 5 min, filtered through a 0.45-μm filter and stored at −80 °C. Virus titres were determined using puromycin selection. The lentiviral library was used to infect HeLa cells stably transfected with doxycycline-inducible mt-Keima reporter at a multiplicity of infection of approximately 0.3 with 8 μg ml−1 polybrene. Then, 48 h after infection, 1 μg ml−1 puromycin was added to the cells and selected for 5 days, followed by an additional 2 days of expansion in puromycin-free medium. During selection, cells were maintained at >500 cells per sgRNA.

To induce mitophagy, cells were first treated with 1 μg ml−1 doxycycline overnight to induce mt-Keima expression, followed by 20 mM HC treatment for 16 h. Treated cells were trypsinized, filtered through a 40-μm cell strainer and resuspended in PBS containing 2% FBS. Cell sorting was performed using a BD FACSAria II instrument with two channels: 405 nm excitation for mt-Keima at pH 7 and 562 nm excitation for mt-Keima at pH 4, with a 610 nm emission bandwidth[53]. The top 25–30% and bottom 25–30% cells were sorted to represent mitophagy-enhanced and -inhibited cell subsets, respectively. A total of 10^7 cells was sorted for each group in two biological replicates for subsequent sequencing.

Genomic DNA was extracted from both the mitophagy-enhanced and -inhibited groups. Sequencing libraries were prepared by two rounds of PCR to amplify target DNA fragments, followed by the ligation of index and adapter sequences. The prepared libraries were then subjected to paired-end sequencing (2 × 150 bp) on the Illumina NovaSeq 6000 platform. After sequencing, 20 bp gRNA sequences were extracted and aligned to the GeCKO v2.0 library reference sequence. The alignment results for all library sequences were counted to obtain the number of matched reads. Sequencing depth and coverage were calculated to assess data reliability and accuracy. Two rounds of screening data were analysed using MAGeCK software[54] with the default settings, and the results were ranked by negative log_2-transformed fold change and P value. Mitochondrial genes were defined using the mitoCarta3.0 database[22]. Volcano plots for genome-wide and mitochondria-targeted analyses of the CRISPR screening were generated using the R package ggplot2. GO biological processes enriched in the top 100 mitochondrial genes from the screen were analysed using the Metascape database[55].

qPCR analysis

To quantify the mtDNA/nDNA ratio, genomic DNA was isolated from cells or tissues using the TIANamp Genomic DNA Kit (DP304-02, TIANGEN) according to the the manufacturer’s instructions, and qPCR was conducted to amplify the mitochondria genome (MT-CYTB, MT-CO1, MT-ATP6 in human; mt-Cytb, mt-Co1, mt-Atp6 in mouse) or nuclear genome (RPL13A in human; Rpl13a in mouse) separately as previously described[6]. Total RNAs were extracted using the RNA Easy Fast Tissue/Cell Kit (TIANGEN, DP451) and reverse transcribed using the PrimeScript RT Reagent kit (TaKaRa, RR047A) according to the manufacturer’s instructions. The qPCR was performed on the ABI QuantStudio 7 Flex system using the TB Green Premix ExTaq kit (TaKaRa, RR820A). The relative fold changes were calculated using \({2}^{-\Delta \Delta {C}_{{\rm{t}}}}\) method. qPCR primer sequences are provided in Supplement Table 3.

Immunofluorescence and confocal microscopy

For colocalization analysis of exogenous NLRX1 with exogenous LC3, HeLa cells stably expressing NLRX1 with a C-terminal HA tag (HeLa-NLRX1-WT) were cultured on glass coverslips and transfected with the vector expressing GFP–LC3. For colocalization analysis of endogenous NLRX1 with endogenous LC3, HeLa cells with HA tag knock-in (HeLa-NLRX1(HA-KI)) were used. For colocalization analysis of GFP–Parkin with mitochondria, HeLa cells stably expressing GFP–Parkin were generated (HeLa-GFP–Parkin). Cells were treated as indicated in the figure legends and washed with PBS twice, fixed with 4% PFA for 10 min, permeabilized with 0.1% Triton X-100 in PBS for 10 min, blocked with 5% BSA in PBS for 1 h at room temperature and incubated with primary antibodies (diluted in 5% BSA) at 4 °C overnight. The next day, cells were washed three times with PBS and incubated with fluorescent secondary antibodies for 2 h at room temperature and then washed three times with PBS. After incubation with DAPI for 5 min and mounted with antifade reagent, samples were observed with the ×60 oil objective of confocal microscopy (Leika SP5 and Olympys FV3000).

For analysis of mitochondrial protein import, HeLa-rtTA cells were transfected with vectors expressing Tet-On-MTS-EGFP. After 6 h of transfection, 0.25 μg ml−1 doxycycline was added to induce MTS-EGFP expression. Then cells were stained with 50 nM Mitotracker Deep Red FM (M22426, Thermo Fisher Scientific) for 20 min and washed with PBS twice, and fresh medium was added for living cell imaging using Olympys confocal microscope (FV3000) with a ×60 oil objective in two channels: 488 nm excitation and 520 nm emission for eGFP and 640 nm excitation and 685 nm emission for Mitotracker Deep Red FM. Three replicates with a total of 100 cells per condition were analysed29,56.

The split GFP system used for monitoring mitochondrial outer membrane or matrix protein localization was described above. In brief, HeLa cells stably expressing cytoGFP(1–10) or matrixGFP(1–10) were transfected with constructs encoding NLRX1-GFP11, HSP60-GFP11 or GFP11-TOM20 vectors. After 24 h, living cells were observed using Olympus confocal microscope (FV3000) with a ×60 oil objective in the channel with 488 nm excitation and 520 nm emission for GFP. Images were processed by deconvolution using OLYMPUS CellSens Dimension Desktop (v.4.1.1) to improve resolution, remove background fluorescence and recover the real distribution.

Mitophagy reporter assay

For measuring in vivo mitophagy, 6–8-week-old mice were given intramuscular injection of AAV-mt-Keima (3 × 10^11 copies, 25 μl per mouse, three sites) or intravenously (1 × 10^11 copies, 150 μl per mouse). After 4–6 weeks, mice were fasted for 24 h or given intraperitoneal injection of HC for 4 h, and gastrocnemius or liver tissues were collected. The samples were cut into sections with a thickness of 6 μm and observed using Olympus confocal microscope (FV3000) with a ×60 oil objective in two channels: 445 nm excitation for mt-Keima pH 7 and 561 nm excitation for mt-Keima pH4 with a 570–695 nm emission bandwidth. For mitophagy index quantification, total mitochondrial area and mitolysosome area were calculated by green fluorescence area and red-only puncta area gated by a fixed threshold individually using Image J software. Mitophagy level was quantified by the ratio of mitolysosome area/mitochondrial area.

For measuring in vitro mitophagy, cells were treated as indicated, and the flow cytometry was performed by Beckman coulter CytoFLEX S instrument in two channels (BV605 and PE-Texas Red channels) through two lasers (405 nm and 562 nm) and emission at 610 nm. The gating strategy was provided in Supplementary Fig. 2. Data were analysed using CytExpert 2.5. The ratio of the mitophagic percentage was calculated and the quantification was pooled from three independent biological replicates.

Cytochrome c release analysis

HeLa cells were treated as indicated, washed with cold PBS and lysed with digitonin lysis buffer (150 mM NaCl, 50 mM HEPES pH 7.4, 25 μg ml−1 digitonin with protease inhibitors) for 10 min on ice. The lysates were then centrifuged at 2,000g for 10 min at 4 °C. The supernatants were centrifuged at 20,000g at 4 °C twice, and the final supernatant was the cytosolic fraction for the detection of cytosolic cytochrome c57,58.

Tissue mitochondrial isolation

Cytosolic and mitochondrial fraction isolation was determined as previously described[20]. In brief, for mitochondria isolation from gastrocnemius tissue, mice were killed and muscles were removed, washed with cold PBS supplemented with 10 mM EDTA and minced. The muscles were incubated with PBS supplemented with 10 mM EDTA and 0.05% trypsin for 30 min followed by centrifugation (200g, 10 min, 4 °C). The pellet was resuspended with IBm1 (67 mM sucrose, 50 mM Tris/HCl, 50 mM KCl, 10 mM EDTA and 0.2% BSA, pH adjusted to 7.4) and transferred to the Dounce Tissue Grinder and lysed by 15 strokes followed by centrifugation (700g, 10 min, 4 °C). The supernatant was then centrifuged at 8,000g for 10 min at 4 °C. The supernatant is the cytosolic fraction. Then pellet was resuspended with IBm2 (0.25 M sucrose, 3 mM EGTA/Tris and 10 mM Tris/HCl, pH adjusted to 7.4) followed by centrifugation (8,000g, 10 min, 4 °C) to obtain the mitochondrial pellet.

For mitochondria isolation from liver, mice were killed and livers were collected, washed with cold IBc (10 mM Tris–MOPS, 1 mM EGTA/Tris, 0.2 M sucrose, pH adjusted to 7.4) and minced. Then the liver suspension was transferred to the Dounce Tissue Grinder and lysed by ten strokes, followed by centrifugation (600g, 10 min, 4 °C). The supernatant was then centrifuged at 7,000g for 10 min at 4 °C. The supernatant was the cytosolic fraction. The pellet was then resuspended with IBc followed by centrifugation (7,000g, 10 min, 4 °C) to obtain the mitochondrial pellet. For LC–MS, both the cytosolic and mitochondrial fractions were performed as described above. For immunoblotting analysis, both the cytosolic and mitochondrial fractions were boiled with 3× SDS and analysed by SDS–PAGE.

Electron microscopy

HeLa cells were treated with indicated conditions, transformed into suspension cells using a cell shovel and centrifuged at 2,000 rpm for 10 min. The pellets were fixed in 2.5% glutaraldehyde for 1 h at room temperature and then overnight at 4 °C. The next day, after washing three times with 0.1 M PBS, the pellets were fixed with 1% osmic acid at room temperature for 1 h, washed three times with double-distilled H_2O, dehydrated in a graded ethanol series, slowly infiltrated with 100% acetone and 50% acetone (acrylic resin: acetone,1:1, v/v) for 2 h and embedded in acrylic resin at 60 °C for 48 h. The embedded samples were cut into sections with a thickness of 70 nm, and the sections were stained with 2% uranyl acetate at room temperature for 10–20 min and lead citrate stain the sections for 5 min. Lastly, samples were observed by electron microscope and images were captured by FEI Tecnai G2 spirit electron microscope.

Thermal shift assay

HeLa cells were lysed using lysis buffer and centrifuged at 12,000 rpm for 10 min at 4 °C. PBS, AcCoA (500 µM) or CoASH (500 µM) were added to cell lysates, heated to graded temperatures (44.6–65 °C, 3 min) and centrifuged at 12,000 rpm for 10 min at 4 °C. Soluble proteins were extracted and analysed by immunoblotting.

Statistical analysis

All data were analysed using GraphPad Prism (v.8.3.0) or Excel, with n ≥ 3 biological replicates unless otherwise specified. Data are presented as mean ± s.e.m. or mean ± s.d. as indicated. For the CRISPR screen data in Fig. 1e and Extended Data Fig. 5k, n = 2 biological replicates. All statistical tests were two-tailed. Statistical parameters, including scale bars and statistical significance, are shown in the figures and the figure legends. Two-group comparisons were analysed using unpaired t-tests. Multiple comparisons among more than two groups were performed using one-way ANOVA. A two-way ANOVA was used when two categorical variables were analysed. Post hoc analysis was conducted to identify specific group differences following ANOVA. P < 0.05 was considered to be statistically significant.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

All data supporting the findings of this study are available in the Article and its Supplementary Information. The CRISPR screen data have been deposited in the Genome Sequence Archive in National Genomics Data Center, China National Center for Bioinformation (Beijing Institute of Genomics) of the Chinese Academy of Sciences (GSA-Human: HRA013003). The metabolomic data are available at the Metabolomics Workbench under study ID ST004160. Source data are provided with this paper.

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Acknowledgements

We thank C. Zhang for providing AAV-related plasmids; J. Han and Z. Yin for technical support of 3H-AcCOA; F. X. Chen for pLVX-Tet3G-rtTA and pLVX-Tet-On vectors; the members of the Lei Laboratory for discussions throughout this study and technical help; and the staff at the Biomedical Core Facility of Fudan University for technical support. J. W. Locasale advised on experimental design and manuscript preparation. The Metabolomics Workbench is supported by US National Institutes of Health grants U2C-DK119886 and OT2-OD030544. This work was supported by National Key R&D Program of China (no. 2020YFA0803402 to Q.-Y.L.), the National Natural Science Foundation of China (nos. 81790253, 91959202, 82121004 and 82330092 to Q.-Y.L.; no. 81902823 to Y.Z.; no. 82472873 to M.Y.), the Innovation Program of Shanghai Municipal Education Commission (no. 2023ZKZD11), the Shanghai Municipal Science and Technology Major Project, and the New Cornerstone Science Foundation to Q.-Y.L., China Postdoctoral Science Foundation Grant (nos. 2019M651375 and 2020T130112 to Y.Z.) and the Ministry of Science and Technology of China (no. 2019YFA0508401 to W.W.).

Author information

Author notes

1. These authors contributed equally: Yifan Zhang, Xiao Shen, Yuan Shen, Chao Wang

Authors and Affiliations

1. Fudan University Shanghai Cancer Center & School of Basic Medical Sciences & Institutes of Biomedical Sciences; Cancer Institutes; Key Laboratory of Breast Cancer in Shanghai; Shanghai Key Laboratory of Radiation Oncology; The Shanghai Key Laboratory of Medical Epigenetics; State Key Laboratory of Brain Function and Disorders, School of Basic Medical Sciences, Fudan University, Shanghai, China

Yifan Zhang, Xiao Shen, Yuan Shen, Chao Wang, Chengping Yu, Jiangxue Han, Siyi Cao, Lin Qian, Miao Yin & Qun-Ying Lei

2. Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China

Yifan Zhang, Xiao Shen, Yuan Shen, Chao Wang, Chengping Yu, Jiangxue Han, Siyi Cao, Lin Qian, Miao Yin & Qun-Ying Lei

3. National Key Laboratory of Plant Molecular Genetics, Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, China

Miaolian Ma

4. Department of Neurosurgery, Huashan Hospital, the Shanghai Key Laboratory of Medical Epigenetics, Institutes of Biomedical Sciences, State Key Laboratory of Brain Function and Disorders and MOE Frontiers Center for Brain Science, National Center for Neurological Disorders, School of Basic Medical Sciences, Fudan University, Shanghai, China

Shijing Huang & Wenyu Wen

5. New Cornerstone Science Laboratory, Frontier Innovation Center, School of Basic Medical Sciences, Fudan University, Shanghai, China

Qun-Ying Lei

Contributions

Y.Z. and X.S. designed the experiments. Y.Z., X.S., Y.S. and C.W. co-performed experiments, co-analysed data and co-wrote the manuscript. J.H. and S.C. performed RT–qPCR analysis. C.Y. performed part of experiments. L.Q. and M.M. performed protein purification. S.H. and W.W. helped with molecular docking and binding sites prediction. M.Y. provided intellectual discussion and co-wrote the manuscript. Q.-Y.L. conceptualized, supervised the study, designed experiments, analysed data and co-wrote the manuscript.

Corresponding author

Correspondence to Qun-Ying Lei.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Kivanc Birsoy, Luca Scorrano and Haitao Wen for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Mild starvation induces mitophagy in an AMPK- and mTOR-independent manner.

a-c, The characterization of fasting mice. 6–8-week-old male mice were ad libitum-fed or fasted overnight for 16 h with free access to water. The mice body weight (a), n = 6 mice per group; the glucose level (b), n = 5 mice per group; the serum level of amino acids (c), the indicated amino acids measured by mass-spectrometry while the cysteine level was too low to be detected (valine, leucine and isoleucine were analysed by GC-MS while other amino acids analysed by LC-MS). n = 4 mice per group. Data shown as the mean ± s.e.m. Unpaired two-tailed Student’s t-test. d-f. SM induces mitophagy in a cell-line-dependent manner. Total cell or Mito lysates as indicated for immunoblotting (d), cells expressing mt-Keima reporter for flow cytometry analysis (e) or qPCR analysis of mtDNA/nDNA (f). n = 3 biological replicates. Data shown as the mean ± s.e.m. Unpaired two-tailed Student’s t-test for all groups except U-2 OS group in (e) (Mann-Whitney U-test) and MCF7 mtATP6 group in (f) (Welch’s t-test). g-i, Re-feed with NM could rescue SM-induced mitophagy. Total cell or Mito lysates from the indicated cells treated with SM for 16 h, followed by the NM for another 16 h for immunoblotting (g), cells expressing mt-Keima reporter for flow cytometry analysis (h) or qPCR analysis of mtDNA/nDNA (i). n = 3 biological replicates. Data shown as the mean ± s.e.m. Ordinary one-way ANOVA and Dunnett’s multiple comparisons test (h,i). j,k, RT-qPCR analysis of Mito biogenesis-related gene and nuclear-encoded Mito gene expression levels. Cells treated as indicated. n = 3 biological replicates. Data shown as the mean ± s.e.m. Unpaired two-tailed Student’s t-test (j) and Ordinary one-way ANOVA and Dunnett’s multiple comparisons test (k) except MCF7 HSP60 in (k) (Kruskal-Wallis test and Dunn’s multiple comparisons test). l,m, SM does not affect AMPK or mTOR pathway. The indicated cells were treated with SM, Torin 1 (100 nM) for 16 h or oligomycin (Oligo, 5 μM) for 5 min for immunoblotting. n = 3 biological replicates. n, Heat map of key intracellular metabolites level. Upper panel: the flow chart that the indicated cells were treated with complete medium (Mock) or SM for 16 h for Mass Spectrometry. Bottom panel: data calculated as relative metabolite ratios of SM/Mock and shown as mean values from 4 biological replicates for all metabolite except AcCoA (3 biological replicates). Data normalized to Actin. o, Metabolic enzyme protein expression level in different cell lines by immunoblotting. n = 3 biological replicates. p, SM reduces Cyto AcCoA level in a cell-line-dependent manner. Cells cultured with SM for 16 h for the relative abundance of Mito or Cyto AcCoA level by LC-MS. Data normalized to Tubulin (Cyto) or HSP60 (Mito) levels. n = 3 biological replicates. Data shown as the mean ± s.e.m. Unpaired two-tailed Student’s t-test.

Source Data

Extended Data Fig. 2 Cytosolic AcCoA level controls mitophagy in vitro.

a-d, SLC25A1 or ACLY inhibition decreases Cyto AcCoA level (a) and induces mitophagy (b-d). Indicated cells treated with HC (20 mM), SB (100 μM), BTC (5 mM) for 16 h for Mito or Cyto AcCoA analysis by LC-MS (a), immunoblotting (b), cells expressing mt-Keima reporter for flow cytometry analysis (c) or qPCR analysis of mtDNA/nDNA (d). n = 3 biological replicates. Data shown as the mean ± s.e.m. Ordinary one-way ANOVA and Dunnett’s multiple comparisons test (a,c,d). e-i, SLC25A1, ACLY or ACSS2 knockdown induces mitophagy. The indicated cells transfected with indicated siRNAs were cultured with or without Baf A1 (200 nM). Total cell or Mito lysates for immunoblotting (e,h), cells expressing mt-Keima reporter for flow cytometry analysis (f,i) or qPCR analysis of mtDNA/nDNA (g). n = 3 biological replicates. Data shown as the mean ± s.e.m. Ordinary one-way ANOVA and Dunnett’s multiple comparisons test (f,g) or unpaired two-tailed Student’s t-test (i). j,k, ACC1 or FASN knockdown does not affect mitophagy. The indicated cells were transfected with indicated siRNAs. Total cell or Mito lysates for immunoblotting (j), cells expressing mt-Keima reporter for flow cytometry analysis (k). n = 3 biological replicates. Data shown as the mean ± s.e.m. Ordinary one-way ANOVA and Dunnett’s multiple comparisons test (k). l-n, Ace blocks SM-induced mitophagy. The indicated cells cultured with SM for 16 h with or without 10 mM Ace. Total cell or Mito lysates for immunoblotting (l), cells expressing mt-Keima reporter for flow cytometry analysis (m) or qPCR analysis of mtDNA/nDNA (n). n = 3 biological replicates. Data shown as the mean ± s.e.m. Ordinary one-way ANOVA and Dunnett’s multiple comparisons test (m,n). o-q, Ace blocks ACLY knockdown-induced mitophagy. The indicated cells transfected with indicated siRNAs cultured with or without 10 mM Ace. Total cell or Mito lysates for immunoblotting (o), cells expressing mt-Keima reporter for flow cytometry analysis (p) or qPCR analysis of mtDNA/nDNA (q). n = 3 biological replicates. Data shown as the mean ± s.e.m. Ordinary one-way ANOVA and Dunnett’s multiple comparisons test (p,q).

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Extended Data Fig. 3 Mitochondria reduction induced by decreased cytosolic AcCoA is dependent on autophagy.

a-d, RT-qPCR analysis of Mito biogenesis-related gene and nuclear-encoded Mito gene expression levels in indicated cells. Cells treated as indicated for 16 h. n = 3 biological replicates. Data shown as the mean ± s.e.m. Ordinary one-way ANOVA and Dunnett’s multiple comparisons test for all groups except HeLa HSP60 in (b), A549 PPARGC1A and all HSP60 in (c), A549 (NRF1, HSP60) and MCF7 HSP60 in (d) (Kruskal-Wallis test and Dunn’s multiple comparisons test). e,f, ATG7 or FIP200 knockdown blocks SM- and SLC25A1 or ACLY inhibition-induced mitophagy. HeLa cells were transfected with siRNA targeting control (siNC), ATG7 (siATG7), or FIP200 (siFIP200), respectively, followed by indicated treatments for 16 h. Mitophagy was determined by indicated mitochondria markers (e) or qPCR analysis of mtDNA/nDNA (f). n = 3 biological replicates in (e and bottom panel of (f)) and n = 6 biological replicates in (upper panel of (f)). Data shown as the mean ± s.e.m. Two-way ANOVA and Bonferroni’s multiple comparisons test (f).

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Extended Data Fig. 4 Cytosolic AcCoA controls mitophagy in vivo.

a-d, 24 h-fasting reduces Cyto AcCoA level (a) and induces mitophagy (b-d) in gastrocnemius (Gastro) and brain but not liver tissues. C57BL/6 male mice fasted for 24 h, and then Cyto and Mito fractions from Gastro, brain, and liver tissues or total lysates were harvested for LC-MS (a) or immunoblotting analyses (b). n = 6 mice for all groups except for the Brain samples (n = 5 mice) in (a) and n = 3 mice per group in (b). Data shown as the mean ± s.e.m. Unpaired two-tailed Student’s t-test for all groups except Cyto of Brain (Mann-Whitney U-test) and Cyto of Gastro (Welch’s t-test). c, qPCR analysis of mtDNA/nDNA (n = 4 mice per group). Data shown as the mean ± s.e.m. Unpaired two-tailed Student’s t-test except Gastro mtAtp6 and Brain mtCytb (Mann-Whitney U-test). d, Relative mt-Keima signal in each group. 10-week-old male mice expressing AAV-delivered mt-Keima reporter treated as indicated. Values normalized to ad libitum level of mitophagy. n = 42 (Gastro, ad libitum, from 5 mice) and n = 58 (Gastro, fasted, from 6 mice), n = 40 (Brain, from 4 mice) and n = 49 (Liver, from 4 mice) images. Data shown as the mean ± s.e.m. Mann-Whitney U-test. e, Mito biogenesis-related gene and nuclear-encoded Mito gene expression levels (n = 6 mice per group). Data shown as the mean ± s.e.m. Unpaired two-tailed Student’s t-test except liver Ppargc1a and Nrf1 (Welch’s t-test). f-k, Refeeding could rescue fasting-induced reduction of Cyto AcCoA level (f) and mitophagy (g-k). 6-week-old male mice fasted for 24 h and ad libitum-fed for another 24 h. f, Cyto Gastro tissues harvested for LC-MS. n = 5 mice per group. Data shown as the mean ± s.e.m. Ordinary one-way ANOVA and Dunnett’s multiple comparisons test. g, Relative mt-Keima signal in each group. 10-week-old male mice expressing AAV-delivered mt-Keima reporter were treated as indicated, and Gastro tissues were harvested for confocal microscopy. Values normalized to the ad libitum level of mitophagy. n = 24 images from 4 mice per group. Data shown as the mean ± s.e.m. Welch one-way ANOVA and Dunnett’s T3 multiple comparisons. h,i, Total or Mito Gastro lysates for immunoblotting. n = 3 mice for all groups except for the fasted groups (n = 4 mice) in (h) and n = 3 mice for all groups in (i). j, qPCR analysis of mtDNA/nDNA (n = 4 mice per group). k, Mito biogenesis-related gene and nuclear-encoded Mito gene expression levels (n = 6 mice per group). Data in (j, k) shown as the mean ± s.e.m. Ordinary one-way ANOVA and Dunnett’s multiple comparisons test for all groups except Nrf2 in (k) (Welch one-way ANOVA and Dunnett’s T3 multiple comparisons). l-p, Ace blocks fasting-induced decreased Cyto AcCoA level (l) and mitophagy (m-p). l,m, C57BL/6 male mice fasted for 24 h, and PBS or Ace (1 g/kg) was intraperitoneally injected 10 h and 1 h before sacrifice. Then Cyto and Mito fractions from Gastro tissues or total lysates were harvested for LC-MS (l) or immunoblotting (m). n = 4 mice per group in (l) and n = 3 mice per group in (m). Data shown as the mean ± s.e.m. Welch one-way ANOVA and Dunnett’s T3 multiple comparisons. n, Relative mt-Keima signal in each group. 10-week-old male mice expressing AAV-delivered mt-Keima reporter were treated as indicated. Values normalized to the ad libitum level of mitophagy. n = 24 images from 4 mice per group. Data shown as the mean ± s.e.m. Welch one-way ANOVA and Dunnett’s T3 multiple comparisons. o, qPCR analysis of mtDNA/nDNA (n = 4 mice per group). p, Mito biogenesis-related gene and nuclear-encoded Mito gene expression level (n = 6 mice per group). Data in (o, p) shown as the mean ± s.e.m. Ordinary one-way ANOVA and Dunnett’s multiple comparisons test except mtCo1 in (o) (Welch one-way ANOVA and Dunnett’s T3 multiple comparisons) and Nrf1 in (p) (Kruskal-Wallis test and Dunn’s multiple comparisons test). q-u, Acly inhibition decreases Cyto AcCoA level (q) and induces mitophagy (r-u) by intraperitoneally HC (100 mg per kg) injection for 4 h. q,r, Cyto and Mito fractions from liver tissues or total lysates harvested for LC-MS (q) or immunoblotting (r). n = 6 mice per group in (q) and n = 3 mice per group in (r). Data shown as the mean ± s.e.m. Welch’s t-test. s, 10-week-old male mice expressing AAV-delivered mt-Keima reporter treated as indicated. Left panel: representative images of liver tissue sections shown. Scale bar, 10 μm. Right panel: relative mt-Keima signal values in each group normalized to PBS level of mitophagy. n = 54 (PBS, from 4 mice) and n = 93 (HC, from 5 mice) images. Data shown as the mean ± s.e.m. Mann-Whitney U-test. t, qPCR analysis of mtDNA/nDNA (n = 4 mice per group). u, Mito biogenesis-related gene and nuclear-encoded Mito gene expression level (n = 6 mice per group). Data in (t,u) shown as the mean ± s.e.m. Unpaired two-tailed Student’s t-test.

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Extended Data Fig. 5 Effects of Cytosolic AcCoA reduction on mitochondrial functions and PINK1-Parkin pathway.

HeLa cells were cultured with SM, HC (20 mM), SB (100 μM), BTC (5 mM), or CCCP (10 μM) for 16 h in (a-h), CCCP (10 μM) for 1 h in (i, j), MitoBloCK-6 (MB-6) (100 μM) for 6 h in (d), or actinomycin D (10 μM) for 16 h in (e). a, SM and SLC25A1 or ACLY inhibition do not decrease Mito membrane potential according to flow cytometry analysis of TMRM staining. Left panel: histogram plot of the TMRM fluorescence. Right panel: relative quantitative analysis of the fluorescence level of TMRM. Data shown as the mean ± s.e.m. Ordinary one-way ANOVA and Dunnett’s multiple comparisons test. n = 3 biological replicates. b, Effects of SM and SLC25A1 or ACLY inhibition on Mito ROS production according to flow cytometry analysis of Mito ROS production by MitoSOX staining. Left panel: histogram plot of the MitoSOX fluorescence. Right panel: relative quantitative analysis of the fluorescence level of MitoSOX. Data shown as the mean ± s.e.m. Ordinary one-way ANOVA and Dunnett’s multiple comparisons test. n = 3 biological replicates. c, Effects of SM and SLC25A1 or ACLY inhibition on intracellular ATP level according to luminescence determination. Data shown as the mean ± s.e.m. Kruskal-Wallis test and Dunn’s multiple comparisons test. n = 4 biological replicates. d, Effects of SM and SLC25A1 or ACLY inhibition on Mito protein import. HeLa-rtTA cells transfected with the vector expressing doxycycline (dox)-inducible MTS-EGFP and treated with dox (0.25 μg per ml) for 6 h before stimulation. Total Mito were stained using Mitotracker (purple). Left panel: representative cell images of MTS-EGFP (green) localization compared with mitochondria (purple) shown. Scale bar, 10 μm. Right panel: quantitative analysis of the proportion of cells with improperly localized MTS-EGFP (localized in the cytosol or nucleus). n = 26 (Mock), n = 32 (SM), n = 27 (HC), n = 28 (SB), n = 27 (BTC) and n = 29 (MB-6) images. Data shown as the mean ± s.e.m. Kruskal-Wallis test and Dunn’s multiple comparisons test. e, SM and SLC25A1 or ACLY inhibition do not induce mitochondria permeability transition determined by Cytochrome c release. Cyto fractions were isolated after the indicated treatment, and Cytochrome c level was determined by immunoblotting. n = 3 biological replicates. f,g, Effects of SM and SLC25A1 or ACLY inhibition on oxygen consumption rate (OCR) at routine rates (f), cell death according to analysis of LDH release (g). n = 3 biological replicates. Data shown as the mean ± s.e.m. Ordinary one-way ANOVA and Dunnett’s multiple comparisons test. h-j, SM and SLC25A1 or ACLY inhibition do not induce PINK1 protein stabilization (h), Parkin’s E3 activity (i) and localization on mitochondria (j). n = 3 biological replicates (h). i,j, HeLa cells stably expressing GFP-Parkin were generated, and after treatments, immunoblotting analyses of ubiquitinated GFP-Parkin were determined (i) and Mito marker TOM20 was stained (j). n = 3 biological replicates (i). j, Left panel: representative cell images of colocalization of GFP-Parkin with TOM20 are shown Scale bar, 10 μm. Right panel: the statistical analysis of Parkin colocalization with mitochondria. n = 10 images for all groups except for the Mock and CCCP groups (n = 11 images). Data shown as the mean ± s.e.m. Kruskal-Wallis test and Dunn’s multiple comparisons test. k, Volcano plot of the whole genome depleted genes in mitophagic cells treated with HC. Autophagy-related genes were highlighted in red. l, Gene ontology analysis of Fig. 1e. The P values were calculated using MAGeCK software (k) or Metascape database (l).

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Extended Data Fig. 6 Cytosolic AcCoA reduction induces NLRX1-dependent mitophagic response.

a-c, NLRX1 is required for SM-induced mitophagic response. The indicated control (sgNC) or NLRX1 knockout (sgNLRX1) cells cultured with SM for 16 h. d-i, NLRX1 is required for SLC25A1 or ACLY inhibition-induced mitophagic response. d-f, The indicated control (sgNC) or NLRX1 knockout (sgNLRX1) cells were treated with HC (20 mM) and BTC (5 mM) for 16 h. g-i, The indicated control (sgNC) or NLRX1 knockout (sgNLRX1) cells were treated with SB (100 μM) for 16 h. j-o, NLRX1 is required for ACLY/SLC25A1/ACSS2 knockdown-induced mitophagic response. The indicated control (sgNC) or NLRX1 knockout (sgNLRX1) cells were transfected with the indicated siRNAs. a, d, g, j, m, Total cell or Mito lysates for immunoblotting. n = 3 biological replicates. b, e, h, k, n, Cells expressing mt-Keima reporter treated as indicated for flow cytometry analysis. c, f, i, l, o, qPCR analysis of mtDNA/nDNA. b, c, e, f, h, i, k, l, n, o, n = 3 biological replicates. Data shown as the mean ± s.e.m. Two-way ANOVA and Bonferroni’s multiple comparisons test.

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Extended Data Fig. 7 NLRX1 depletion specifically inhibits mitophagy but not general autophagy.

a-c, NLRX1 does not affect p62 degradation (a,b) or pan-protein acetylation (c) upon treatments of SM and SLC25A1 or ACLY inhibition. Control (sgNC) or NLRX1 knockout (sgNLRX1) HeLa cells cultured with SM, HC (20 mM), or BTC (5 mM) for 16 h for total lysates immunoblotting. n = 3 biological replicates. d, NLRX1 deficiency showed little effect on LC3 lipidation. Control (sgNC) or NLRX1 knockout (sgNLRX1) HeLa cells cultured with SM or HC (20 mM) for 16 h with or without Baf A1 (100 nM) for immunoblotting. The ratio represents LC3 II/LC I. n = 3 biological replicates. e, NLRX1 knockout does not affect the level of Cyto AcCoA. The level of AcCoA in the indicated control (sgNC) or NLRX1 knockout (sgNLRX1) cells was detected by LC-MS. n = 3 biological replicates. Data shown as the mean ± s.e.m. Unpaired two-tailed Student’s t-test. f-i, Control (sgNC) or NLRX1 knockout (sgNLRX1) HeLa cells cultured with EBSS for 2 h with or without Baf A1 (100 nM) for confocal microscopy (f), electron microscopy (EM) (g), immunoblotting (h), or cells expressing mt-Keima reporter for flow cytometry analysis (i). f, Left panel: representative cell images are shown. Scale bar, 10 μm. Right panel: the statistical analysis of the numbers of LC3 puncta per cell. n = 47 cells. Data shown as the mean ± s.e.m. Two-way ANOVA and Bonferroni’s multiple comparisons test. g, Asterisk indicates autophagosome/autolysosome, scale bar, 1 μm. h,i, n = 3 biological replicates. Data in (f,i) shown as the mean ± s.e.m. Two-way ANOVA and Bonferroni’s multiple comparisons test (i). j, Blunted mitophagy after HC treatment in the liver of Nlrx1−/− mice. Wild type (WT) or Nlrx1−/− mice intraperitoneally injected with PBS or HC (100 mg per kg) for 4 h, and qPCR analysis of total liver tissue mtDNA/nDNA (n = 3 mice per group). Data shown as the mean ± s.e.m. Two-way ANOVA and Bonferroni’s multiple comparisons test. k-m, NLRX11 is required for fasting-induced mitophagy in Gastro tissue. WT or Nlrx1−/− mice fasted for 24 h. k, Total or Mito Gastro lysates for immunoblotting. n = 3 mice per group. l, qPCR analysis of mtDNA/nDNA. WT Ad libitum or WT fasted group (n = 4 mice), Nlrx1−/− Ad libitum or Nlrx1−/− fasted group (n = 3 mice). Two-way ANOVA and Bonferroni’s multiple comparisons test. m, Analysis of fasting-induced mitophagy in Nlrx1−/− mice using AAV-delivered mt-Keima reporter. Left panel: representative Gastro images shown. Scale bar, 10 μm. Right panel: relative mt-Keima signal values in each group normalized to the WT Ad libitum group. n = 24 images from 4 mice per group. Data shown as the mean ± s.e.m. Two-way ANOVA and Bonferroni’s multiple comparisons test. n,o, NLRX1 is required for ACLY inhibition-induced mitophagy in liver tissue. Nlrx1 was knocked down by AAV-carried shRNA in liver tissue. Control (shNC) or Nlrx1 knockdown (shNlrx1) C57BL/6 male mice intraperitoneally injected with HC for 4 h, then liver tissues harvested for immunoblotting (n) or qPCR analysis of mtDNA/nDNA (o). n, Left panel: representative immunoblotting. Right panel: relative quantification of TIM23/Tubulin or CYTB/Tubulin level. n = 5 mice in (n) and n = 6 mice in (o). Data shown as the mean ± s.e.m. Two-way ANOVA and Bonferroni’s multiple comparisons test. p,q, NLRX1 is required for fasting-induced mitophagy in Gastro tissue. Nlrx1 was knocked down by AAV-carried shRNA in Gastro tissue. shNC or shNlrx1 C57BL/6 male mice fasted for 24 h, then Gastro tissues were harvested for immunoblotting (p) or qPCR analysis of mtDNA/nDNA (q). p, Left panel: representative immunoblotting. Right panel: relative quantification of TIM23/Tubulin or CYTB/Tubulin level. n = 6 mice in (p) and n = 3 mice in (q). Data shown as the mean ± s.e.m. Two-way ANOVA and Bonferroni’s multiple comparisons test. r,s, Nlrx1−/− or Nlrx1 knockdown does not affect mice weight. WT and Nlrx1−/− mice or shNC or shNlrx1 C57BL/6 mice were ad libitum-fed or fasted for 24 h with free access to water and mice weight were determined. n = 6 mice per group. Data shown as the mean ± s.e.m. Two-way ANOVA and Bonferroni’s multiple comparisons test.

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Extended Data Fig. 8 Cytosol-facing NLRX1 mediates Cytosolic AcCoA reduction-induced cargo degradation through autophagy.

a,b, CCCP but not SM and SLC25A1 or ACLY inhibition induces endogenous NLRX1 (a) or exogenous NLRX1-HA (b) retention in the cytosol. Indicated cells cultured with SM, HC (20 mM), BTC (5 mM) for 16 h, or CCCP (30 µM) for 20 h. Cyto and Mito fractions for immunoblotting. n = 3 biological replicates. c, NLRX1 lacking MTS signal (∆N-ter) loses mitophagic response upon treatments of SM and SLC25A1 or ACLY inhibition. HeLa NLRX1-knockout cells stably expressed NLRX1 WT or NLRX1(∆N-ter) cultured with SM, HC (20 mM), BTC (5 mM) or SB (100 µM) for 16 h for total lysates immunoblotting. n = 3 biological replicates. d, A schematic of the split green fluorescent protein (GFP) system used to report NLRX1 localization on different compartments of mitochondria. NLRX1 fused with a C-terminal GFP11 expressed in cells stably expressing GFP1–10 in the cytosol (cytoGFP1–10) or Mito matrix (matrixGFP1–10) along with the red fluorescent protein (RFP) as the translation normalization marker. NLRX1 localization in either compartment led to the complementation of GFP fluorescence. e,f, NLRX1 is simultaneously present in the Mito matrix and outer membrane, and the distribution is not affected by SM. e, Representative cell images shown. HSP60GFP11 and GFP11TOM20 serve as the Mito matrix and outer membrane localization controls, respectively. Images processed by a deconvolution algorithm through the Cellsens Dimension Desktop from Olympus company. Scale bar, 10 μm. f, GFP and RFP fluorescence analysed by flow cytometry, and the percentages of GFP+-RFP+/RFP+ calculated to reflect the distributions of NLRX1. n = 3 biological replicates. Data shown as the mean ± s.e.m. Unpaired two-tailed Student’s t-test. g, A schematic of NLRX1(Cyto) or NLRX1(ER) chimeric protein generation. Substitution of the MTS of NLRX1 with the ER-localization sequence of FAM134b (NLRX1(ER)) leads to NLRX1 localization on ER with a topology facing the cytosol while NLRX1 lacking MTS (NLRX1(Cyto)) localizes in the cytosol. h, Validation of NLRX1(ER) localization on ER. HeLa cells stably expressing NLRX1(ER) or NLRX1(Cyto) with Flag tag were fixed and stained for Flag and the ER marker CLIMP63. Quantification of Pearson’s colocalization coefficient between Flag and CLIMP63. n = 38 (NLRX1(Cyto)), n = 19 (NLRX1(ER)) cells. Data shown as the mean ± s.e.m. Unpaired two-tailed Student’s t-test. i,j, NLRX1(ER) but not NLRX1(Cyto) promotes SLC25A1 or ACLY inhibition-induced CLIMP63 reduction, while Baf A1 could block NLRX1(ER)-mediated CLIM63 reduction. Cells treated with HC (20 mM) or BTC (5 mM) for 16 h with or without Baf A1 (100 nM). Total lysates for immunoblotting. n = 3 biological replicates. k, Mito protein import stress (MPIS) decreases Cyto AcCoA level. HeLa cells treated with MPIS inducer MitoBloCK-6 (MB-6, 100 μM) for 16 h for Cyto AcCoA level analysis by LC-MS and normalized to Actin level. n = 3 biological replicates. Data shown as the mean ± s.e.m. Unpaired two-tailed Student’s t-test. l, CCCP decreases Cyto AcCoA level. HeLa NLRX1-knockout cells stably expressed NLRX1 WT treated with 20 μM CCCP for 6 h for Cyto AcCoA level analysis by LC-MS and normalized to Actin level. n = 3 biological replicates. Data shown as the mean ± s.e.m. Unpaired two-tailed Student’s t-test. m,n, Ace blocks MPIS- and CCCP-induced mitophagy. Indicated cells were treated with MB-6 (100 μM) for 16 h or 20 μM CCCP for 6 h with or without 10 mM Ace for total lysates immunoblotting. n = 3 biological replicates. o, MB-6 inhibits PDH complex importation into mitochondria. Data were collected from a published data resource[29] and shown as log_2 of the mean MB-6/DMSO ratio and P value. p, A schematic of a working model illustrating that MB-6 inhibits the import of PDH-E1 into mitochondria, thereby blocking pyruvate-derived AcCoA production. PDH-E1, the complex of PDHA1 and PDHB. q, PDH-E1 knockdown decreases Cyto AcCoA level. HeLa cells were transfected with the indicated siRNAs for Cyto AcCoA analysis by LC-MS and normalized to Actin. n = 4 biological replicates. Data shown as the mean ± s.e.m. Unpaired two-tailed Student’s t-test. r, Ace could rescue PDH-E1 knockdown-induced mitophagy. HeLa cells were transfected with the indicated siRNAs with or without 10 mM Ace for immunoblotting. n = 3 biological replicates. s, MB-6 induces weak mitophagy after PDH-E1 deficiency. HeLa cells were transfected with the indicated siRNAs with or without MB-6 (100 μM) treatment for 16 h for immunoblotting. n = 3 biological replicates. t, CCCP inhibits multiple protein importation into mitochondria. Data collected from a published data resource[29] and shown as log_2 of the mean CCCP/DMSO ratio and P value.

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Extended Data Fig. 9 NLRX1 directly binds to AcCoA at conserved sites within the LRR domain.

a, NLRX1 acetylation is not affected by AcCoA level. HEK293T cells stably expressing NLRX1 with Flag tag (NLRX1-Flag) cultured with SM for 16 h with or without 10 mM Ace for immunoprecipitation. n = 3 biological replicates. b, AcCoA binds with NLRX1 but not NLRP3. Constructs of NLRP3 with HA tag (HA-NLRP3) or NLRX1 with HA tag (NLRX1-HA) were transfected in HEK293T cells, followed by culture with SM for 6 h and biotin-AcCoA pull-down to enrich HA-tagged proteins for immunoblotting. n = 3 biological replicates. c, AcCoA competitively decreases the interaction of biotin-AcCoA with recombinant NLRX1. MBP-NLRX1 (87-975aa) purified from insect cells. AcCoA were added as indicated concentrations and biotin-AcCoA was used to pulldown NLRX1, followed by immunoblotting. Left panel: Coomassie staining of gel-filtrated NLRX1 recombinant with MBP tag (MBP-NLRX1) from insect cells. Upper panel of right panel: representative immunoblotting image. Bottom panel of right panel: quantification of competitive binding between NLRX1 and biotin-AcCoA. n = 3 biological replicates. Data shown as the mean ± s.e.m. Ordinary one-way ANOVA and Tukey’s multiple comparisons test. d, 729/754/758/958 sites of NLRX1 are conserved across species. e, Purification of NLRX1-LRR WT and NLRX1-LRR(4A). Left panel: Gel filtration of recombinant MBP, MBP-NLRX1-LRR WT, or MBP-NLRX1(4A) proteins on a Superose 6 column (UV A280). Right panel: Coomassie staining of gel-filtrated recombinant proteins from E. coli. n = 3 biological replicates. f, Affinity of recombinant NLRX1 with acyl-CoAs. MBP-NLRX1 purified from insect cells. Biotin-conjungated acyl-CoAs were used to pull down NLRX1, followed by immunoblotting analysis. n = 3 biological replicates. g, AcCoA exhibits higher affinity with recombinant NLRX1 than CoASH. MBP-NLRX1 (87-975aa) was purified from insect cells. Biotin-AcCoA or CoASH was used to pull down NLRX1 in the presence of indicated metabolites, followed by immunoblotting. n = 3 biological replicates. h, Molecular docking shows that CoASH binds to NLRX1-LRR. Left panel, illustrates three critical residues (Glu729, Lys754, Arg958) at NLRX1-LRR involved in binding to AcCoA. Right panel, the binding affinity calculated by docking analyses. i, Cellular thermal shift assay (CETSA) confirms endogenous NLRX1 associates with AcCoA. HeLa cells cultured with SM for 6 h, lysed and supernatants were collected. Lysates incubated with AcCoA (500 μM), CoASH (500 μM) or control (PBS), followed by heating at different temperatures (range 44.6 to 65 °C). Soluble fractions for immunoblotting. j, pH affects the binding between NLRX1 and AcCoA. HEK293T cells stably expressing NLRX1-HA cultured with SM for 6 h. Isolated Mito lysate in buffers with different pH values were subjected to biotin-AcCoA pull-down assay, followed by immunoblotting. n = 3 biological replicates.

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Extended Data Fig. 10 AcCoA reduction promotes NLRX1 oligomerization and binding to LC3.

a, Ace blocks the colocalization of NLRX1-HA and GFP-LC3 triggered by SM. NLRX1-knockout HeLa cells stably expressing NLRX1 WT were transfected with the vector expressing GFP-LC3 and treated with SM for 10 h with or without 10 mM Ace, followed by confocal microscopy. Left panel: representative cell images are shown. Scale bar, 10 μm. Upper panel of the right panel: validation of the expression of HA-tagged NLRX1 by immunoblotting. Bottom panel of right panel: quantification of Pearson’s colocalization coefficient. n = 19 (Mock), n = 39 (SM) and n = 32 (SM+Ace) cells. Data shown as the mean ± s.e.m. Kruskal-Wallis test and Dunn’s multiple comparisons test. b, Ace blocks the colocalization of endogenous NLRX1 and LC3 triggered by CCCP. HeLa cells with HA-tagged knock-in (NLRX1-HA KI) were generated. Cells treated with 20 μM CCCP for 6 h with or without 10 mM Ace, followed by confocal microscopy. Left panel: representative cell images shown. Scale bar, 10 μm. Upper panel of the right panel: Validation of NLRX1-HA KI HeLa cells by immunoblotting. Bottom panel of right panel: quantification of Pearson’s colocalization coefficient. n = 39 (Mock), n = 90 (CCCP) and n = 34 (CCCP + Ace) cells. Data shown as the mean ± s.e.m. Kruskal-Wallis test and Dunn’s multiple comparisons test. c, Ace blocks the colocalization of NLRX1-HA and GFP-LC3 triggered by CCCP. HeLa-NLRX1WT cells treated with 20 μM CCCP for 1.5 h with or without 10 mM Ace, followed by confocal microscopy. Left panel: representative cell images are shown. Scale bar, 10 μm. Right panel: quantification of Pearson’s colocalization coefficient between NLRX1-HA and GFP-LC3. n = 23 (Mock), n = 46 (CCCP) and n = 33 (CCCP + Ace) cells. Data shown as the mean ± s.e.m. Kruskal-Wallis test and Dunn’s multiple comparisons test. d, SLC25A1 or ACLY inhibition promotes the colocalization of endogenous NLRX1 with LC3. HeLa-NLRX1(HA KI) cells were treated with HC (20 mM), BTC (5 mM), or SB (100 μM) for 12 h, followed by confocal microscopy. Left panel: representative cell images shown. Scale bar, 10 μm. Right panel: quantification of Pearson’s colocalization coefficient. n = 27 (Mock), n = 30 (HC), n = 50 (BTC) and n = 36 (SB) cells. Data shown as the mean ± s.e.m. Kruskal-Wallis test and Dunn’s multiple comparisons test. e, SLC25A1 or ACLY inhibition promotes the colocalization of NLRX1-HA with GFP-LC3. HeLa-NLRX1WT cells were treated with HC (20 mM), BTC (5 mM), or SB (100 μM) for 10 h, followed by confocal microscopy. Right panel: representative cell images shown. Scale bar, 10 μm. Left panel: quantification of Pearson’s colocalization coefficient between NLRX1-HA and GFP-LC3. n = 33 (Mock), n = 46 (HC), n = 29 (BTC) and n = 48 (SB) cells. Data shown as the mean ± s.e.m. Kruskal-Wallis test and Dunn’s multiple comparisons test. f-h, NLRX1 LIR deletion mutant (NLRX1ΔLIR) fails to induce mitophagy after SLC25A1 or ACLY inhibition. NLRX1 knockout-HeLa cells stably expressing NLRX1WT or NLRX1(ΔLIR) with HA tag were treated with HC (20 mM), BTC (5 mM), or SB (100 μM) for 16 h. Total cell or Mito lysates for immunoblotting (f), cells expressing mt-Keima reporter for flow cytometry analysis (g) or qPCR analysis of mtDNA/nDNA (h). n = 3 biological replicates. Data shown as the mean ± s.e.m. Two-way ANOVA and Bonferroni’s multiple comparisons test. i, LRR(4A) decreases the binding with NACHT in the complete medium. NACHT-HA and Flag-LRR WT or Flag-LRR(4A) were co-expressed in HEK293T cells for Flag-LRR immunoprecipitation to determine the binding. n = 3 biological replicates. j, Ace blocks CCCP-induced NLRX1 oligomerization. HEK293T cells stably expressing NLRX1 with HA tag (NLRX1-HA) cultured with 20 μM CCCP for 3 h with or without 10 mM Ace for SDS–agarose electrophoresis. n = 3 biological replicates. k, NLRX1(4A) mutant increases the binding with GST-LC3 in the complete medium. HEK293T cells transfected with constructs expressing NLRX1 WT or NLRX1(4A) for GST pull-down assay. n = 3 biological replicates. l,m, Baf A1 blocks NLRX1(4A) constitutively induced-mitophagy. NLRX1WT, NLRX14A, or NLRX1∆LIR were transfected into NLRX1-knockout HeLa cells, respectively. Baf A1 (200 nM) was added 3 h before harvesting. NLRX1-knockout cells expressing mt-Keima for flow cytometry analysis (l). qPCR analysis of mtDNA/nDNA (m). n = 3 biological replicates. Data shown as mean ± s.e.m. Ordinary one-way ANOVA and Dunnett’s multiple comparisons test. n,o, AAV-mediated NLRX1(4A) overexpression constitutively induces mitophagy. Total or Mito Gastro tissue lysates harvested for immunoblotting (n). Left panel: representative immunoblotting, the ratio represents CYTB/Tubulin. Right panel: quantification of relative CYTB/Tubulin level. n = 3 mice per group. Data shown as mean ± s.e.m. Ordinary one-way ANOVA and Dunnett’s multiple comparisons test. qPCR analysis of mtDNA/nDNA (o) in the liver tissue (left panel) or Gastro tissue (right panel). n = 4 mice per group except EV group in Gastro tissue (n = 3 mice). Data shown as mean ± s.e.m. Ordinary one-way ANOVA and Dunnett’s multiple comparisons test for all groups except the liver mtCO1 group (Welch one-way ANOVA and Dunnett’s T3 multiple comparisons).

Source Data

Extended Data Fig. 11 KRAS inhibitors downregulate ACLY for drug resistance through mitophagy.

a, KRASi decrease ACLY mRNA level in a dose-dependent manner. AsPC-1 cells were treated with MRTX1133 or RMC-6236 over a two-point dose response for 24 h for RT-qPCR analysis. n = 3 biological replicates. Data shown as the mean ± s.e.m. Ordinary one-way ANOVA and Dunnett’s multiple comparisons test. b, KRASi decrease Cyto AcCoA levels. AsPC-1 cells were treated with MRTX1133 (10 nM), or RMC-6236 (10 nM) for 24 h and the Cyto AcCoA level was measured by LC-MS and normalized by cell number. n = 3 biological replicates. Data shown as the mean ± s.e.m. Welch’s t-test for MRTX1133 analysis and unpaired two-tailed Student’s t-test for RMC-6236 analysis. c-f, KRASi decrease ACLY protein level and induce mitophagy in a dose-dependent manner. KPC (e) or AsPC-1 (c,d,f) cells treated with MRTX1133 or RMC-6236 over a three-point dose (c) or a two-point dose (d-f) for 24 h with or without Baf A1 (200 nM). Total cell or Mito lysates for immunoblotting (c), cells expressing mt-Keima reporter for flow cytometry analysis (d), or qPCR analysis of mtDNA/nDNA (e,f). n = 3 biological replicates. Data shown as the mean ± s.e.m. Ordinary one-way ANOVA and Dunnett’s multiple comparisons test (d-f). g-l, Ace blocks KRASi-induced mitophagy. KPC (g,h,k) or AsPC-1 (i,j,l) cells treated with MRTX1133 (10 nM), or RMC-6236 (10 nM) for 24 h with or without 10 mM Ace. Total cell or Mito lysates for immunoblotting (g,i), cells expressing mt-Keima reporter for flow cytometry analysis (h,j), or qPCR analysis of mtDNA/nDNA (k,l). n = 3 biological replicates. Data are shown as the mean ± s.e.m. Ordinary one-way ANOVA and Dunnett’s multiple comparisons test (h,j,k,l). m-p, NLRX1 is required for KRASi-induced mitophagic response. Control (sgNC) or NLRX1 knockout (sgNLRX1) KPC (o) or AsPC-1 (m,n,p) cells treated with MRTX1133 (10 nM), or RMC-6236 (10 nM) for 24 h. Total cell or Mito lysates for immunoblotting (m), cells expressing mt-Keima reporter for flow cytometry analysis (n), or qPCR analysis of mtDNA/nDNA (o,p). n = 3 biological replicates. Data are shown as the mean ± s.e.m. Two-way ANOVA and Bonferroni’s multiple comparisons test (n-p). q, Elevated ROS level in Nlrx1-depleted AsPC-1 cells after KRASi treatment. Control (sgNC) and NLRX1-depleting (sgNLRX1) AsPC-1 cells were treated with MRTX1133 (10 nM), or RMC-6236 (10 nM) for 24 h for CM-H2DCFDA staining by flow cytometry analysis. n = 3 biological replicates. Data shown as the mean ± s.e.m. Two-way ANOVA and Bonferroni’s multiple comparisons test. r, NLRX1 depletion and mitophagy inhibitor Mdivi-1 decrease cell viability upon KRASi treatment. IC_50 values calculated based on the dose-response of MRTX1133 or RMC-6236 in control (sgNC) or NLRX1-depleting (sgNLRX1) cells by 3-day (AsPC-1 and A549) or 5-day (KPC) cell-titer-glo assays. n = 3 biological replicates for all analyses, except for KPC cells (MRTX1133, n = 6 biological replicates), A549 cells (Mdivi-1, n = 6 biological replicates), and AsPC-1 cells (sgNLRX1, n = 4 biological replicates). s, NAC rescues low-dose MRTX1133-induced cell death in Nlrx1-deficient cells. Control (sgNC) or Nlrx1-depleting (sgNlrx1) KPC cells were treated with or without 1 nM MRTX1133, and NAC (4 mM) was added on the second day. Four days later, cell viability was determined by cell-titer-glo assays. n = 3 biological replicates. Two-way ANOVA and Bonferroni’s multiple comparisons test. t, Mitophagy inhibitor Mdivi-1 decreases cell viability in KPC cells. Dose-response curves for MRTX1133 in combination with mitophagy inhibitor Mdivi-1 (20 μM) in KPC cells based on 5-day cell-titer-glo assays. IC_50 values are displayed on the top panel. n = 6 biological replicates. u,v, Nlrx1 knockout exacerbates the suppressive effect of MRTX1133 on tumour cell growth in vivo. sgNC and sgNlrx1 KPC cells were subcutaneously injected in NSG mice for vehicle or MRTX1133 (30 mg per kg, twice a day) treatment when tumour volume reached around 300 mm^3. Tumour weight (u), images (v) of KPC-derived allograft in NSG mice. n = 5 mice per group. Data shown as the mean ± s.e.m. Two-way ANOVA and Bonferroni’s multiple comparisons test. w, MRTX1133 upregulates ROS production in Nlrx1-deficient tumour. Tumours were harvested after 6 days of indicated treatments. ROS level in tumour tissue sections determined by CM-H2DCFDA staining. n = 5 mice per group. Scale bar, 10 μm.

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Supplementary information

Supplementary Information

Supplementary Fig. 1 (uncropped western blots) and Supplementary Fig. 2 (gating workflow for mitophagy signal).

Reporting Summary

Supplementary Table 1

List of all genes from the genome-wide CRISPR screen.

Supplementary Table 2

List of mitochondrial-related genes from genome-wide CRISPR screen.

Supplementary Table 3

List of primers used in this study.

Peer Review File

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Zhang, Y., Shen, X., Shen, Y. et al. Cytosolic acetyl-coenzyme A is a signalling metabolite to control mitophagy. Nature 649, 1022–1031 (2026). https://doi.org/10.1038/s41586-025-09745-x

- Received: 17 September 2024

- Accepted: 13 October 2025

- Published: 12 November 2025

- Version of record: 12 November 2025

- Issue date: 22 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09745-x

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Inhibitors supercharge kinase turnover through native proteolytic circuits

Natalie S. Scholes,

Martino Bertoni,

Arnau Comajuncosa-Creus,

Katharina Kladnik,

Xuefei Guo,

Fabian Frommelt,

Matthias Hinterndorfer,

Hlib Razumkov,

Polina Prokofeva,

Martin P. Schwalm,

Florian Born,

Sandra Roehm,

Hana Imrichova,

Brianda L. Santini,

Eleonora Barone,

Caroline Schätz,

Miquel Muñoz i Ordoño,

Severin Lechner,

Andrea Rukavina,

Iciar Serrano,

Miriam Abele,

Anna Koren,

Stefan Kubicek,

Stefan Knapp,

Nathanael S. Gray,

Giulio Superti-Furga,

Bernhard Kuster,

Yigong Shi,

Patrick Aloy &

…

Georg E. Winter

Nature volume 649, pages 1032–1041 (2026)

Abstract

Targeted protein degradation is a pharmacological strategy that relies on small molecules such as proteolysis-targeting chimeras (PROTACs) or molecular glues, which induce proximity between a target protein and an E3 ubiquitin ligase to prompt target ubiquitination and proteasomal degradation[1]. Sporadic reports indicated that ligands designed to inhibit a target can also induce its destabilization2,3,4. Among others, this has repeatedly been observed for kinase inhibitors5,6,7. However, we lack an understanding of the frequency, generalizability and mechanistic underpinnings of these phenomena. Here, to address this knowledge gap, we generated dynamic abundance profiles of 98 kinases after cellular perturbations with 1,570 kinase inhibitors, revealing 160 selective instances of inhibitor-induced kinase destabilization. Kinases prone to degradation are frequently annotated as HSP90 clients, therefore affirming chaperone deprivation as an important route of destabilization. However, detailed investigation of inhibitor-induced degradation of LYN, BLK and RIPK2 revealed a differentiated, common mechanistic logic whereby inhibitors function by inducing a kinase state that is more efficiently cleared by endogenous degradation mechanisms. Mechanistically, effects can manifest by ligand-induced changes in cellular activity, localization or higher-order assemblies, which may be triggered by direct target engagement or network effects. Collectively, our data suggest that inhibitor-induced kinase degradation is a common event and positions supercharging of endogenous degradation circuits as an alternative to classical proximity-inducing degraders.

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Main

In addition to proximity-inducing modalities, sporadic accounts of inhibitor-induced target degradation have been reported, for example, for inhibitors of gene-regulatory proteins such as BCL6 (refs. 2,3) or EZH2 (ref. [4]) and, most frequently, for kinase inhibitors5,6,7. Many kinases require chaperones such as HSP90 for folding and maintained stability[8]. After kinase binding, some inhibitors, including clinical agents, have been shown to disrupt kinase–HSP90 interactions, which in turn results in kinase destabilization[9]. This process of chaperone deprivation is well established and is exemplified by the degradation of HER2 by neratinib[10] and the degradation of LMTK3 by C28 (ref. [11]). Given that hyperactive mutant kinases are frequently more reliant on HSP90, chaperone deprivation has also been associated with preferential degradation of mutant over wild-type (WT) kinases, such as the destabilization of EGFR(G719S) by erlotinib[12]. However, detailed studies, for example, of the mechanism of mutant selective PI3Kα degradation by taselisib and inavolisib13,14, revealed that mechanisms can go beyond the widely accepted framework of chaperone deprivation.

Owing to the high conservation of the ATP-binding site in the kinome, many orthosteric inhibitors bind unselectively. Promiscuous inhibitors can act as selective destabilizers, as exemplified by sorafenib, which binds to many kinases[15] but destabilizes only BRAF(V600E) or RET(M918T)12,16. Importantly, this polypharmacology brings about an opportunity of ‘network drugging’, whereby an inhibitor induces kinase degradation without direct engagement. Mechanistically, this can occur through modulation of an upstream kinase that activates a phosphodegron, leading to degradation of a downstream kinase[17]. Network effects can also be involved with directly acting inhibitors. For example, mutant PI3Kα degradation by inavolisib is dependent on hyperactive HER2 signalling[14].

The identification of monovalent kinase degraders has largely been serendipitous. We therefore lack systematic insights into their pervasiveness and mechanistic principles beyond chaperone deprivation. Owing to the importance of kinase inhibitors in modern medicine (80 FDA approved drugs as of January 2024, another 180 in clinical trials[18]), efforts to quantify and mechanistically dissect inhibitor-induced kinase degradation could identify therapeutic opportunities, explain adverse effects or outline principles of degrader design beyond proximity induction.

To systematically identify inhibitor-induced kinase destabilization, here we map the dynamic abundance profiles of 98 kinases after cellular perturbations with 1,570 kinase inhibitors. Our efforts cover 88 canonical (WT) and 10 mutant kinases. In total we identify 232 compounds that downregulate protein levels of at least one kinase and 66 kinases that are affected by at least one compound. We find that the predisposition of mutant kinases quantitatively and qualitatively differs from their WT counterparts. Even though destabilized kinases are enriched for HSP90 clients, many instances cannot be explained by chaperone deprivation. Notably, we encounter that the propensity of a kinase to be destabilized by an inhibitor is not correlated with its degradability by PROTACs, differentiating inhibitor-induced degradation from degradation by proximity induction[19].

Follow-up of three previously undescribed kinase degraders suggests an underlying mechanistic principle in which inhibitors accelerate endogenous degradation circuits that preferentially recognize a particular kinase state, a concept that we refer to as supercharging. By inducing these states, inhibitors destabilize the cellular pool of a kinase. We describe several mechanisms that lead to kinase degradation. For example, inhibitors can induce degradation-prone kinase states by modulating kinase activity (exemplified by LYN), perturb intracellular kinase localization (BLK) and induce higher-order kinase assemblies (RIPK2). Collectively, our findings highlight a unifying framework of inhibitor-induced target degradation.

Charting a monovalent degrader map

To assess how inhibitors affect kinase levels, we opted for a scalable luminescent reporter setup using a lentiviral expression system in which 98 kinase open reading frames (88 canonical, 10 mutants) are expressed as nanoluciferase (Nluc) fusions in K562 cells (Fig. 1a, Extended Data Table 1 and Supplementary Data 1). As this setup informs on target abundance rather than stability, control measures were put into place, enabling us to segregate temporal inhibitor effects from global perturbations of transcription or translation. Moreover, we profiled control cell lines expressing long- and short-lived non-kinase control target (GFP–Nluc and destabilized GFP (dGFP)–Nluc, respectively; Methods). This panel of a total of 100 cell lines was dynamically assayed against an annotated library of 1,570 kinase inhibitors at regular time intervals (2, 6, 10, 14 and 18 h). Compounds were selected for minimal assay interference, excluding Nluc quenchers and compounds that showed cytotoxicity during the assayed time window (Methods and Supplementary Data 1). Scoring of the resulting dataset was performed using a multitiered scheme (Fig. 1a and Methods). This resulted in a total of 232 compounds that score and elicit destabilization across 66 of the tested kinases, including 7 mutants (Fig. 1a,b, Extended Data Table 1 and Supplementary Data 1). Among all of the hits (Supplementary Fig. 9), we identified 160 unique kinase–compound pairs, which are denoted as selective (Fig. 1b). Reporter half-life was only weakly correlated with the frequency of scoring (Spearman’s rank correlation = −0.381, P = 0.0034373), supporting that the implemented controls successfully filtered out most global perturbations interfering with transcription or translation (Extended Data Fig. 1a).

Fig. 1: Kinase degradation (KinDeg) map across 1,570 monovalent kinase-targeting compounds.

a, KinDeg map. Top left, breakdown of analysed kinases, including 88 canonical and 10 mutated kinases (highlighted in pink). Top right, the drug screen setup, including a breakdown of controls. The kinase tree illustration was reproduced courtesy of Cell Signaling Technology. Middle, data processing and hit calling to classify whether a compound perturbs kinase abundance. Example data are depicted for a small molecule with more than one downregulated kinase (hit > 1; pelitinib) and for a selectively perturbed kinase (hit = 1; alantolactone). POC, per cent over control. Bottom, the resulting binary KinDeg map sorted according to the observed degradation frequencies across kinases and compounds, including adjacent histograms of the summed scores in both dimensions. b, Breakdown of hit scores across the kinases and compounds (subscripts indicate mutated kinases). c, Stratified kinases for scoring at least once (downregulated) or non-scoring kinases with respect to their HSP90 client status as defined previously[8]. Statistical analysis was performed using pairwise two-sided Fisher’s exact tests. d, t-Distributed stochastic neighbour embedding (t-SNE) plot of the compound target landscape (Methods) across the used drug screening library with annotated HER2 binders (violet) and HER2 destabilizing screening hits (dark green) forming a co-cluster. e, Comparisons of experimentally derived half-lives across mutant and canonical kinase pairs. f, Representative hit comparison and JD calculations across matched canonical and mutant EGFR, RAF and RET data. Number and colour annotations are as defined in e. The full dataset is provided in Supplementary Data 1. g, Temporal screening trajectories of the top three hits selected for detailed mechanism of action elucidation. Data are the mean ± confidence intervals of m = 2 technical replicates.

Across all kinases, HER2, ABL1 and the mutant kinase RAF1(S257L) emerged as the most frequently degraded kinases. No specific family was enriched among kinases prone to destabilization (Extended Data Fig. 1b). When comparing our data to a recently published survey that assessed PROTAC-induced global kinase degradability[19], no global correlation was observed, suggesting that inhibitor-induced degradation mechanistically differs from degradation based on proximity induction (Extended Data Fig. 1c). By contrast, when assessing the HSP90 status of each kinase[8], both strong and weak clients had a markedly higher prevalence of being destabilized compared to non or not-defined clients (Fig. 1c). This points to an outsized contribution of chaperone deprivation to the observed degradation events, even though we cannot exclude that individual client kinases are degraded in a chaperone-independent manner. Given the established strong correlation of HSP90 clients, these observations also extend to the HSP90 co-chaperone CDC37 within the kinome[8]. Among the annotated HSP90 clients was also the frequently destabilized HER2. Closer examination of the HER2-degrading compounds in our survey revealed that we identified inhibitors such as AV-412, afatinib or neratinib, which have previously been implicated as HER2 degraders that function through chaperone deprivation10,20,21,22. Moreover, our data revealed destabilizing inhibitors, such as WZ4002 or dacomitinib. Notably, many of the identified HER2 destabilizers feature covalent warheads. Consistent with a direct effect, introducing a C805S mutation[23] in HER2 prevented inhibitor-induced degradation (Extended Data Fig. 1d,e). Further supporting an on-target effect, the identified degraders formed a definite cluster when mapped on their target space[24] (Fig. 1d).

With HER2 degraders exemplifying directly acting degraders, we next set out to identify degradation events driven by network modulation. Mapping the experimentally identified ABL1 destabilizers on their target space, we observed a dispersed distribution, yet we identified one cluster of hit compounds which coincided with dual PI3Kα and mTOR inhibitors (Extended Data Fig. 1f). Although we cannot exclude that the short half-life (1.5 h) of ABL1 results in this prevalence of scoring, we note that the compounds failed to score in the control dGFP (half-life, 1.1 h) cell line (Extended Data Fig. 1g and Supplementary Fig. 5), suggesting an effect beyond low baseline stability. As the screen was conducted in the BCR–ABL-driven leukaemic cell line K562, many of the most potent ABL1 inhibitors were eliminated from the screening library owing to their acute cytotoxicity. This probably contributed to an under-representation of directly acting ABL1 degraders. Akin to HER2 and ABL1, we analysed all remaining kinases (Extended Data Fig. 2). However, clustering of the hits on a per kinase basis failed to reveal generalizable trends, probably due to the limited number of identified degraders for most kinases and limited availability of comprehensive binding data for the assayed compounds. Likewise, binding modes of inhibitors did not significantly differ between hit and non-hit compounds (type I enrichment odds ratio = 0.74, P > 0.05; type II enrichment odds ratio = 1.3, P > 0.05).

Focusing on mutant kinases, we initially identified a decrease in protein half-life for activating over non-activating mutations (Fig. 1e), suggestive of an activity–stability trade-off[25]. Overall, we find distinctive degradation patterns with little overlap comparing WT and mutants (Jaccard distances (JD) ≥ 0.8; Fig. 1f). Although in some cases the degradation frequency corresponds to the reduced protein stability, we identify most hits for EGFR(G719S), which is more stable than the strongly transformative mutants. These observations imply that, mechanistically, the enhanced degradability of mutant kinases goes beyond the reduced half-life and could be rooted in altered signalling and interactomes. Finally, orthogonal to destabilization, we identified multiple inhibitor-induced stabilization events covering 64 of our tested kinases, including also 5 mutant kinases as well as ABL1 and CDK2 (Supplementary Data 1 and Extended Data Fig. 3a–c). ABL1, for example, was stabilized by multiple binders targeting the allosteric myristic-acid-binding site (Extended Data Fig. 3d,e).

After determining the frequency and general features of inhibitor-induced degradation, we next set out to dissect the mechanism of action of three selective inhibitor–kinase pairs with different HSP90 client status[8]: LYN (strong client, degraded by SRC inhibitor 3 (ref. [26]), hereafter SI-3), BLK (weak client, degraded by TAK285 (ref. [27])) and RIPK2 (non-client, degraded by RIPK-IN-4 (ref. [28]), hereafter RI-4) (Fig. 1g and Extended Data Fig. 1h).

Rapid destabilization of LYN by SI-3

A notable effect in our dataset was the degradation of the SRC-family kinase LYN by the compound SI-3 with near-complete protein ablation after 2 h (Fig. 1g). Using a previously described fluorescence protein stability reporter[29], we validated that SI-3 affects LYN stability (Fig. 2a). The effect of SI-3 was also confirmed through immunoblotting for endogenous LYN, revealing that degradation occurs in the nanomolar range (Fig. 2b) and already minutes after ligand exposure (Fig. 2c). Out of a total of 260 quantified kinases, LYN was the most significantly destabilized kinase in a quantitative expression proteomics experiment (Extended Data Fig. 4a,b). Consistent with the annotation as a SRC-family inhibitor and previously published kinome selectivity profiling[26], we established that SI-3 inhibits recombinant LYN protein with a half-maximum inhibitory concentration (IC_50) of 56.7 nM (Extended Data Fig. 4c). Notably, even though 25 out of the 1,570 profiled inhibitors are annotated to bind to LYN, SI-3 was the only inhibitor that prompted LYN degradation (Extended Data Fig. 4d). Additional manual validation of LYN degradation on the endogenous protein levels across a selected set of LYN binders equally could not identify any further degraders (Extended Data Fig. 4e).

Fig. 2: LYN is rapidly degraded by SI-3 through a canonical activity–stability switch.

a, Flow cytometry stability reporter data of SI-3-induced LYN degradation (8 h). b, Dose-dependent degradation of endogenous LYN by SI-3 (half-maximum degradation concentration (DC_50) < 9 nM, maximal degradation (Dmax) = 96%, quantified in Supplementary Fig. 6b). n = 3. c, Time-dependent, SI-3-induced endogenous LYN degradation (quantified in Supplementary Fig. 6c). n = 3. d, Chemical rescue. n = 3. Carfilz., carfilzomib. e, The results of the ubiquitin–proteasome system (UPS)-focused FACS-based CRISPR–Cas9 screen. Essential genes (Methods) or CBL and proteasome subunits are highlighted in cases in which P < 0.05 (one-sided MAGeCK) and log_2-transformed fold-change (log_2[FC]) > 1.585. n = 2. f, Differential BioID results after SI-3 treatment. Enriched prey is highlighted for a log_2[FC]> 2 and −log_10[Padj] > 2 (two-sided, analysis of variance (ANOVA), with P value adjustment using the Benjamini–Hochberg method). n = 3. g–j, Normalized BFP was calculated as the ratio to mCherry and normalized (norm.) to the respective genetic perturbations (Methods). g, Flow cytometry assay of the LYN stability reporter after genetic perturbation of a control gene (sgAAVS1), CBL or CBLB, or both E3s (CBL and CBLB dual KO, DKO). Statistical analysis was performed using two-way ANOVA with Sidak correction. NS, not significant (P > 0.9999). n = 3. h, The results of chemoproteomics analysis for SI-3, depicting selected hit kinases and LYN (insufficient competition). All other hits are shown in Supplementary Fig. 6g–i. KD: 34.60 nM (CSK), 2.987 µM (LCK) and 11.43 µM (FRK). LFQ, label-free quantification. i, Flow cytometry analysis of SI-3-mediated destabilization of LYN WT or gatekeeper (GK) stability reporter, or WT LYN in the presence of overexpressed CSK WT or GK. OE, overexpression. Statistical analysis was performed using unpaired t-tests; NS, P = 0.376936. n = 3. j, Flow cytometry measurement of SI-3-mediated destabilization of LYN WT, LYN(Y32A), LYN(Y316A) or LYN(Y32A/Y316A) (double mutant, DM). n = 3. All flow cytometry was performed using KBM7-iCas9 cells. SI-3 was administered at 156 nM unless specified otherwise. Data are mean ± s.d. n represents biological replicates.

Although SI-3-induced LYN degradation was ubiquitin dependent (TAK243), it was not sensitive to inhibition of neddylation (MLN4924), thereby excluding dependency on a cullin-RING E3 ubiquitin ligase (CRL) (Fig. 2d). Moreover, degradation was not rescued by pharmacological inhibition of the proteasome (carfilzomib) or lysosomal acidification (bafilomycin A1, BafA1). Only simultaneous inhibition of both degradation pathways prevented LYN degradation. Albeit annotated as a strong HSP90 client[8], pharmacological inhibition of HSP90 did not induce LYN degradation both on the endogenous and reporter setup, therefore excluding chaperone deprivation as the underlying mechanism (Fig. 2b and Extended Data Fig. 4f).

To decipher the mechanism of SI-3-induced LYN degradation, we opted for a two-pronged discovery approach to (1) identify which genes are required to induce LYN degradation after SI-3 treatment and (2) chart how SI-3 treatment changed the interactome of LYN. To map genes required for SI-3-dependent LYN degradation, we performed a fluorescence-activated cell sorting (FACS)-based CRISPR–Cas9 screen in KBM7 cells with an inducible Cas9 (iCas9) allele[29] and the LYN stability reporter. This revealed the E3 ligase CBL as the strongest enriched gene (Fig. 2e). CBL had previously been associated with the turnover of multiple SRC-family kinases30,31, including LYN32,33. In support of a physiologically relevant interaction, we identified CBL as the strongest hit not only in SI-3 treated cells, but also in vehicle-treated (DMSO) conditions (Extended Data Fig. 4g). To map how SI-3 altered LYN interactions, we used a BioID34,35 setup in which LYN was expressed as fusion to the miniTurbo (mT) biotin ligase, enabling the identification of proteins that are recruited to LYN after cellular SI-3 treatment by mass spectrometry (MS). Consistent with the CRISPR–Cas9 screen, this led to the identification of CBL as the strongest recruited effector (Fig. 2f and Extended Data Fig. 4h). Moreover, we found the closely related ligase CBLB as a strongly enriched LYN interactor in a SI-3-dependent manner, suggestive of a potential functional redundancy. Indeed, population-level knockout (KO) of CBL was insufficient to rescue SI-3-induced LYN degradation (Fig. 2g), but substantially increased the baseline levels (Extended Data Fig. 4i), therefore explaining why it scored as a hit in the CRISPR–Cas9 screen. Likewise, single KO of CBLB showed no significant rescue. Only combined genetic disruption of both effectors fully rescued degradation in the stability reporter and endogenously (Fig. 2g and Extended Data Fig. 4j). Having identified this redundancy, we next addressed whether the two E3s would separately be responsible for licensing LYN’s proteasomal or lysosomal degradation. Combining single ligase KO with pharmacological inhibition of either degradation pathway revealed that CBLB-mediated degradation preferentially co-opts the proteasomal machinery, while degradation mediated by CBL relies on both proteasomal and lysosomal degradation (Extended Data Fig. 5a). Consistent with the ability to engage both cellular degradation routes, SI-3 induced LYN ubiquitination involved Lys48- and Lys63-linked ubiquitin chains (Extended Data Fig. 5b). Similar to other SRC-family members, LYN is N-terminally anchored to the membrane through lipid modifications. To assess whether membrane association is required for degradation, we genetically and chemically disrupted LYN’s membrane association. While degradation is independent of membrane anchorage, we noted that cytosolic LYN is exclusively degraded by SI-3 through the proteasomal route (Extended Data Fig. 5c–e).

Physiological activation of LYN has been associated with the degradation of LYN through CBL and CBLB[32]. Counterintuitively, this connects LYN activation to the degradation mechanism for the inhibitor SI-3, prompting us to more closely investigate the cellular target spectrum of SI-3. Chemoproteomic profiling revealed 11 high-confidence targets, including 8 kinases (Fig. 2h and Supplementary Fig. 6g–i). We identified CSK (KD = 34.60 nM) as the most potently engaged kinase target, confirming previously reported recombinant assay data of SI-3 (IC_50 = 4 nM CSK)[26]. In contrast to our in vitro data (Extended Data Fig. 4c), LYN was engaged only at much higher concentrations in the cellular context (Fig. 2h). Orthogonally, we validated SI-3 target engagement with CSK using a NanoBRET displacement assay (half-maximum effective concentration (EC_50) = 15 nM; Extended Data Fig. 5f). On the basis of the known role of CSK as a negative regulator of SRC kinases, including LYN, we therefore hypothesized that SI-3, despite directly binding to LYN, might induce indirect LYN degradation through its preferential inhibition of CSK. Similar observations have been made with a chemical genetics setup[33]. To validate this hypothesis, we genetically modified either LYN or CSK to impair drug binding of SI-3 and assessed the consequences for SI-3-induced LYN degradation. Mutating the gatekeeper residue of LYN (T319I)[36] did not alter the degradation capacity. However, overexpression of the CSK gatekeeper mutant (T266M)[37] rescued degradation markedly (Fig. 2i). This suggested that inhibition of CSK is the dominant driver of SI-3-mediated LYN destabilization and that SI-3 functions by perturbing the intrinsic regulatory network. Consequently, blocking CSK-induced LYN activation through pharmacological inhibition of LYN rescued the effect of SI-3-induced LYN degradation on endogenous protein levels (Extended Data Fig. 5g), phosphorylation of CSK’s target site LYN Tyr508 was reduced after SI-3 treatment (Extended Data Fig. 5h,i) and mutating Tyr508 to alanine rendered LYN resistant to SI-3 degradation (Extended Data Fig. 5j).

Previous research identified Tyr32 as a phosphodegron that is important for CBL recognition[33]. However, LYN(Y32A) was not resistant to SI-3-induced degradation, pointing to an additional, redundant phosphodegron (Fig. 2j). Indeed, while degradation of WT LYN was dependent on both CBL and CBLB, we found that degradation of LYN(Y32A) solely depended on CBL (Extended Data Fig. 5k), suggesting that this E3 recognizes the elusive phosphodegron. Turning back to our BioID dataset, we identified increased phosphorylation of Tyr316 in LYN after SI-3 treatment (Extended Data Fig. 5l). Supporting a functional role of Tyr316, we found that the double-mutant LYN(Y32A/Y316A) was almost completely inert to SI-3-induced degradation (Fig. 2j). The degradation of the single LYN(Y316A) mutant was comparable to the degradation of WT LYN, suggesting a redundancy of both phosphodegrons in SI-3-induced LYN degradation. Contrary to LYN(Y32A), LYN(Y316A) retained dependency on both E3 ligases CBL and CBLB for SI-3 induced degradation (Extended Data Fig. 5m).

In summary, our data identified SI-3 as uniquely differentiated inhibitor that is sufficiently selective for CSK over LYN to exploit an endogenous activity–stability switch that ensures immediate and near-complete LYN degradation after its activation.

γ-Secretase governs BLK degradation

From several inhibitors that cause downregulation of BLK in our assay, we focused on the selective hit TAK285 (Fig. 1g). First, we validated that TAK285 affects BLK stability (Extended Data Fig. 6a,b). TAK285-induced BLK degradation was ubiquitin and proteasome dependent, but independent of neddylation or lysosomal degradation (Extended Data Fig. 6c). TAK285 equally affected endogenous BLK levels without downregulating any other kinase (Fig. 3a). BLK is a weak HSP90 client[8] and showed sensitivity towards chaperone deprivation by HSP90 inhibition at comparable kinetics (Extended Data Fig. 6b,c). We therefore hypothesized that TAK285 functions through the HSP90 regulatory axis.

Fig. 3: BLK is degraded by TAK285 in a γ-secretase-dependent manner.

a, Expression proteomics of TAK285-treated NALM-6 cells. Kinases are highlighted where P < 0.05 (one-way ANOVA) and |log_2[FC]| > 0.5. n = 3. b, Genome-wide FACS-based CRISPR–Cas9 screen. Essential genes, BLK, γ-secretase and proteasome subunits are highlighted in cases in which P < 0.05 (one-sided MAGeCK) and a log_2[FC] > 2. n = 2. c, Flow cytometry assay for BLK stability reporter cells pretreated with DAPT followed by DMSO, TAK285 or HSP90i (6 h; NS, P = 0.9978 (DMSO), 0.9966 (HSP90i)). n = 3. d, Immunoprecipitation in BLK-Nluc-3×Flag K562 cells after pretreatment with alkynyl myristic acid (AMA, 100 µM), followed by 1 h carfilzomib (1 µM) and/or DAPT plus DMSO or TAK285. In-gel fluorescence (top, TAMRA) and Flag immunoblotting (bottom) analysis of the immunoprecipitated fractions. e, Stability reporter data for the indicated constructs treated with DMSO or TAK285 (left) (6 h; NS, P ≥ 0.9999 (FUS1), 0.9969 (SRC full length (FL))). n = 3. Right, FUS2 stability reporter data after pretreatment with DAPT (2 h) followed by DMSO or TAK285 (6 h; NS, P = 0.7333) n = 3. f, DMS data for the TAK285-treated (6 h) BLK stability reporter panel, displayed as the DMSO-normalized log_2[FC] of sorted fractions. n = 3. g, Flow cytometry analysis of unique domain stability reporter fusions treated (6 h) with DMSO or TAK285. n = 3. pt., pretreatment. h, Flow cytometry analysis as in g for the indicated stability reporters. i, AlphaFold3-derived model of the BLK γ-secretase complex (Methods). Critical H-bonds are shown at the top right and the positioning of L3 is shown at the bottom right. Normalization of flow cytometry data was performed against the respective genotype/pretreatment unless specified otherwise. Data are mean ± s.d. n represents biological replicates. For c and e, statistical analysis was performed using two-way ANOVA with Tukey’s test for multiple comparisons. For a–h, inhibitor concentrations were as follows: TAK285 (2.5 µM), HSP90i (10 µM) and DAPT (12.5 µM).

To map the underpinning genetic determinants, we performed a FACS-based, genome-wide CRISPR–Cas9 screen (Fig. 3b and Extended Data Fig. 6d). Notably, we identified all four members of the γ-secretase complex (APH1A, NCSTN, PSEN1 and PSENEN) as the most strongly enriched hits, suggesting a functional link to BLK degradation by TAK285. We validated involvement of the γ-secretase by pharmacological inhibition through DAPT (Fig. 3c and Extended Data Fig. 6e,f), as well as by genetic ablation (Extended Data Fig. 6g). In contrast to TAK285, BLK degradation induced through an HSP90 inhibitor (HSP90i) was independent of γ-secretase function. Thus, TAK285-induced BLK degradation is functionally differentiated from chaperone deprivation. Notably, γ-secretase subunits also scored as hits in steady-state (vehicle-only) conditions of the CRISPR–Cas9 screen (Extended Data Fig. 6d), which we orthogonally confirmed by genetic ablation of PSENEN (Extended Data Fig. 6h). This implied a role of γ-secretase in native BLK turnover. Supporting this, we identified an interaction between BLK and NCSTN when performing proximity labelling-based proteomics (Extended Data Fig. 6i). Mining of previously reported immunoprecipitation–MS data similarly revealed baseline interactions between NCSTN and BLK[38]. We orthogonally confirmed the interaction of BLK and γ-secretase in vitro (Extended Data Fig. 6j; KD 2.53 µM). Contrary to a traditional molecular-glue-like mechanism, this affinity is not enhanced by TAK285 (Extended Data Fig. 6j; KD 3.49 µM). Collectively, the data support a physical interaction between BLK and the γ-secretase that is functionalized after TAK285 treatment.

TAK285 only partially inhibited BLK in vitro (around 37%, IC_50 > 30 µM; Supplementary Fig. 7g) and competition experiments suggested a mechanism that is independent of orthosteric BLK binding (Extended Data Fig. 6k). Moreover, TAK285 did not bind to γ-secretase, suggestive of a network effect (Extended Data Fig. 6l). To identify the potential intermediate kinase targeted by TAK285, we performed two orthogonal, dose-ranging chemoproteomics experiments. Kinobead profiling[39] revealed three binding partners: the established TAK285 target EGFR, the kinase MAP2K5 (MEK5) and the non-kinase protein ERCC2 (Supplementary Data 6). BLK was not identified as direct interactor (Extended Data Fig. 6m), supporting biochemical data. Orthogonal to the Kinobead profiling, we performed direct target enrichment through a tethered TAK285 analogue (Supplementary Data 7), which revealed MAP2K5 as the only overlapping target. However, KO of MAP2K5 did not alter TAK285-mediated BLK degradation (Extended Data Fig. 6n). Collectively, these data suggested that TAK285-induced BLK degradation is independent of orthosteric BLK binding or direct γ-secretase engagement, and does not further augment the affinity between BLK and γ-secretase but instead depends on a network effect driven by a yet elusive target.

TAK285 alters the localization of BLK

To further decipher this mechanism, we focused on understanding the contribution of the γ-secretase, an intramembrane protease that is best known for its involvement in Notch-1 and APP. Thus far, all reported protein targets are type I transmembrane proteins. However, recent evidence revealed that γ-secretase can cleave lipid-anchors, specifically on membrane-tethered small molecules[40]. We therefore surmised that TAK285 could induce γ-secretase cleavage of the myristoylation anchor of BLK, thereby releasing BLK into the cytosol and unveiling an unstable N terminus that is rapidly turned over. Indeed, we observed membrane dissociation of BLK after TAK285 treatment (Extended Data Fig. 7a,b). Moreover, orthogonal perturbation of the membrane association of BLK through pharmacological inhibition of N-myristoyltransferases 1/2 (IMP-1088) also destabilized BLK. Consistent with our hypothesis, cytosolic BLK could not be further destabilized by TAK285 (Extended Data Fig. 7c). Leveraging a TAMRA click reaction with alkynyl myristic acid, we could next validate that TAK285 treatment induces a γ-secretase-dependent loss of myristoylation on BLK (Fig. 3d). Collectively, these data support a mechanism whereby TAK285 treatment decreases BLK myristoylation to cause a membrane-to-cytosol transition of BLK where it is rapidly turned over.

Myristoylation of BLK and other SRC kinases occurs at the N-terminal unique domain[41]. Consistent with a critical role of BLK’s N-terminal domain in determining the specificity of the TAK285-induced degradation, we found that a domain swap to the N-terminal domain of SRC retained membrane association (Extended Data Fig. 7d) but disabled TAK285-mediated degradation (Fig. 3e). Inversely, fusing BLK’s unique domain to SRC was sufficient to enable TAK285-mediated and γ-secretase-dependent degradation (Fig. 3e). We therefore turned our attention towards deciphering the role of the N-terminal unique domain of BLK. Mutating all residues that could directly be modified through upstream phosphorylation networks initially led us to identify that BLK(S6A) strongly abrogated inhibitor-induced degradation (Extended Data Fig. 7e). The conserved Ser6 residue had previously been associated with regulating myristoylation and membrane association of the related SRC-family kinase LCK[42]. Accordingly, BLK(S6A) lost membrane association, appeared predominantly cytoplasmatic and had a lower baseline stability (Extended Data Fig. 7d,f). Thus, the BLK(S6A) mutant mirrors the effects of pharmacological myristoylation inhibition on BLK WT.

To gain an unbiased per-residue-resolved map of the unique domain, we next opted for a deep mutational scanning (DMS), mutating every residue of the unique domain (residues 2–57) to each other possible amino acid. Cells were transduced with stability reporter variant libraries, drug treated and sorted based on BFP expression (Fig. 3f and Extended Data Fig. 7g). IMP-1088 dependent destabilization was mainly abrogated by mutations on G2, the position that is myristoylated in cellulo. TAK285 showed clear dependencies on residues Gly2, Leu3, Val4 and Ser6. Focusing on the TAK285-specific positions, we could identify variants (L3A, L3G, L3V, V4S) that disrupted TAK285-mediated degradation (Extended Data Fig. 7h). Notably, these variants remained N-terminally myristoylated, anchored to the membrane and therefore sensitive to IMP-1088 treatment (Extended Data Fig. 7h–k). Systematically shortening unique domain fusions to BFP highlighted the first seven amino acids as minimal motif required for TAK285-mediated degradation (Fig. 3g). Moreover, mutating solely residues Ser3 and Asn4 in SRC to the corresponding residues of BLK rendered SRC degradable by TAK285 in a γ-secretase-dependent manner (Fig. 3h and Extended Data Fig. 7l). Vice versa, the opposite mutations in BLK convey resistance to TAK285 degradation. In summary, while G2 mutations globally alter myristoylation status and shift BLK into the cytosol, mutations on Leu3 and Val4 more specifically impair TAK285-mediated delocalization and degradation.

Enabled by this residue-level, functional understanding, we predicted the complex of BLK and γ-secretase using AlphaFold3 (ref. [43]) (Fig. 3i). Notably, the most confident model directly placed the N-terminal domain within the active site of γ-secretase. Specifically, across molecular dynamics simulations, we consistently observe that the peptide bond between the myristic acid and BLK Gly2 is coordinated by two hydrogen bonds between the two catalytic residues Asp385 and Asp275 of the γ-secretase, therefore providing a structural rationale for the experimentally observed loss in BLK myristoylation and the ensuing cytoplasmic localization (Fig. 3d and Extended Data Fig. 7a,b). Furthermore, we noted that the functionally relevant Leu3 is placed in a thermodynamically favourable hydrophobic pocket just neighbouring the catalytic site (Fig. 3i). This provides a rationale for why identified Leu3 mutations prevented TAK285-induced degradation and provides an explanation as to why SRC WT is refractory to TAK285 treatment unless mutated in positions 3 and 4 (Fig. 3e,h) to mimic BLK’s minimal degradation sequence (Fig. 3g).

In conclusion, we found that TAK285 induces a γ-secretase-dependent dissociation of the membrane-associated BLK into the cytoplasm where BLK is intrinsically unstable. TAK285-induced destabilization is encoded by its unique N-terminal domain and critically mediated by its myristoylation status.

Lysosomal degradation of RIPK2 by RI-4

Finally, we turned our attention to RIPK2, a cytoplasmic kinase that is involved in the clearance of bacterial pathogens by linking activation of the pattern recognition receptors NOD1/NOD2 to intracellular signalling[44]. Degradation of RIPK2, but not inhibition of its kinase activity suppresses the NOD2 signalling pathway, establishing the motivation to identify monovalent and bivalent RIPK2 degraders45,46. Indeed, kinome abundance trajectories revealed nine inhibitors potentially destabilizing RIPK2. Among those inhibitors, RI-4 (Fig. 1g) prompted the most selective and potent degradation response (Extended Data Fig. 8a) and was therefore selected for further mechanistic workup. In accordance with RI-4’s role as a RIPK2 inhibitor[28], recombinant binding assays confirmed RIPK2 engagement, suggesting a directly induced degradation event (Extended Data Fig. 8b). We validated that RI-4 functions at the level of protein stability (Fig. 4a,b) and confirmed degradation of endogenous RIPK2 (Fig. 4c). Quantitative expression proteomics established RI-4’s degradation selectivity (Extended Data Fig. 8c).

Fig. 4: RI-4 destabilizes RIPK2 through TMUB1-facilitated multimerization and macroautophagy.

a, Flow-based stability reporter for RI-4-induced (2.5 µM, 18 h) RIPK2 degradation. b, As in a, but after time-ranging DMSO, 2.5 µM RI-4 or 10 µM HSP90i treatment. n = 3. c, Endogenous RIPK2 destabilization in RKO-iCas9 cells (2.5 µM, 24 h). n = 3. d, Genome-wide FACS based CRISPR–Cas9 screen. Essential genes or hits are highlighted in cases in which P < 0.05 (one-sided MAGeCK) and log_2[FC] > 1.585. Proteasome subunits are highlighted irrespective of cut-offs. n = 2. e, Flow-based RIPK2 stability reporter data after BafA1 pretreatment (100 nM, 2 h) followed by DMSO or RI-4 (2.5 µM, 18 h). n = 3. f, Immunoblot of RIPK2 stability reporter (RKO-iCas9) expressing a control sgRNA (Ctrl, sgAAVS1) or sgRNAs targeting FIP200 (KO1/KO2). n = 3. g, Western blot (WB) quantification of f. h, Microscopy images (BFP) of RIPK2(FL) or RIPK2(∆CARD) stability reporters after RI-4 treatment (2.5 µM) in RKO-iCas9 cells. n = 3, m = 2. Scale bar, 25 µm. Brightness was adjusted per genotype (equally adjusted images are shown in Supplementary Fig. 8b and DMSO controls are shown in Extended Data Fig. 8h). i,j, Quantification of RIPK2 stability (i) and the mean number of RIPK2 foci per cell (j) as shown in h. n = 3 and m = 2. Data are normalized to t = 0 h per condition. k, The log_2[FC] of interactors for 1 h, 4 h RI-4 treatment versus DMSO, or 18 h of RI-4 and BafA1 co-treatment versus BafA1, filtered for GO terms associated with ubiquitin or kinase function. l, Flag co-immunoprecipitation in K562 RIPK2-Nluc-3×Flag cells after 2 h DMSO or RI-4 (2.5 µM) treatment (asterisk denotes short isoform). n = 3. m, Representative immunofluorescence of concanavalin A1 (ConA; green) and TMUB1 (magenta) in RIPK2 stability reporter RKO-iCas9 cells after 2 h RI-4 treatment. n = 3. Scale bar, 12.5 µm. For e and g, statistical analysis was performed using two-way ANOVA with Tukey’s test for multiple comparisons. All data are mean ± s.d. n represents biological replicates and m represents technical replicates.

To reveal cellular effectors that are required for RIPK2 degradation, we again ran a CRISPR–Cas9 screen, which revealed enrichment of hits involved in lysosomal degradation (GO Biological Process 2025: endosomal vesicle fusion (GO: 0034058, P = 0.003409); phagolysosome assembly (GO: 0001845, P = 0.003959)) (Fig. 4d). Moreover, we identified the ubiquitin-like (UBL) domain containing TMUB1 as strongly enriched[47]. Consistent with the screening data, pharmacological inhibition of lysosome acidification rescued induced RIPK2 degradation (Fig. 4e). Moreover, RIPK2 degradation was abrogated in cells deficient for the macroautophagy mediator FIP200 (ref. [48]), but not after KO of PSMB5, supporting that RIPK2 degradation by RI-4 depends on macroautophagy (Fig. 4f,g and Extended Data Fig. 8d,e).

Investigating RI-4-induced changes in RIPK2 abundance using microscopy, we observed that RIPK2 assembled into foci before degradation (Fig. 4h–j). These structures were reminiscent of RIPosomes—physiologically relevant, higher-order RIPK2 assemblies that form after endogenous activation[49]. We confirmed RIPK2 assembly formation by co-transducing the RIPK2-stability reporter (RIPK2-BFP-P2A-mCherry) and an orthogonal RIPK2–GFP reporter, highlighting co-localization after RI-4 treatment (Extended Data Fig. 8f,g). A key feature of physiological RIPK2 assembly is the essential role of RIPK2’s CARD domain[50]. Likewise, RI-4-induced RIPK2 assemblies are CARD-domain dependent, therefore implying a functional resemblance (Fig. 4h–j and Extended Data Fig. 8h). Supporting observations made with RIPK2-stability reporters, clearance of RI-4-induced RIPK2 assemblies was rescued through BafA1 treatment, highlighting another similarity to RIPosomes[49] (Extended Data Fig. 8i–k). Moreover, clearance of RI-4-induced RIPosomes is ubiquitin dependent (Extended Data Fig. 8l,m).

To assess interactors of the RI-4-induced RIPosomes and to identify potentially involved E3 ligases, we conducted dynamic BioID profiling (Supplementary Data 5). GO enrichment highlighted ubiquitin and specifically Lys63-dependent ubiquitination in the process of RIPosome clearance (Extended Data Fig. 9a). Stratifying for terms containing ubiquitin or kinase-associated terms, we identified many previously established components associated with RIPosome clearance such as TNFAIP3, CYLD, N4BP1 and CCDC50 (Fig. 4k). Identification of SQSTM1 (p62) substantiated a direct involvement of the autophagy machinery. Moreover, we detected two E3 ligases that scored as interactors: cIAP1 (encoded by BIRC2) and XIAP. Both ligases contain an IAP domain and have been previously implicated in turnover and ubiquitin-dependent regulation of RIPK2. KO of both ligases impaired degradation (Extended Data Fig. 9b–d), highlighting their functional and potentially redundant role in RIPK2 degradation. RIPK2(I212D) has been shown to disrupt IAP domain binding[51]. Supporting the functional relevance of cIAP1 and XIAP, RIPK2(I212D) phenocopied the BIRC2/XIAP double KO, retaining RI-4’s ability to induce multimer formation while RIPosome clearance was impaired (Extended Data Fig. 9e–g).

As we could not detect peptides of TMUB1 in the BioID dataset, we separately set out to dissect its contribution. TMUB1 depletion revealed a marked delay in the assembly formation, a reduction in the total number of observed foci and, consequently, delayed degradation (Extended Data Fig. 9h–j). Immunoprecipitation further highlighted a drug-induced interaction between RIPK2 and TMUB1 (Fig. 4l and Extended Data Fig. 9k), which we confirmed by immunofluorescence staining (Fig. 4m and Extended Data Fig. 9l,m). This implies that TMUB1 is an early-acting facilitator. Taken together, our data support a model in which RI-4 induces higher-order assemblies of RIPK2 through involvement of the UBL-domain-containing protein TMUB1. These assemblies mimic multimers that are formed in response to physiological stimuli by pathogens. Pathogen-induced and inhibitor-induced assemblies are subsequently turned over through macroautophagy.

Discussion

Analysis of dynamic abundance profiles of 98 kinases after cellular exposure to 1,570 annotated kinase inhibitors revealed that inhibitor-induced kinase degradation is a frequent phenomenon. Known HSP90 clients are enriched among the degraded kinases, suggesting chaperone deprivation as a widespread mechanism of inhibitor-induced degradation. However, in-depth mechanistic investigation of three degraded kinases with graded HSP90 dependency revealed a differentiated, yet shared mechanism of action. In all cases, inhibitor-induced kinase degradation further elevated physiological turnover mechanisms by inducing kinase states that are primed for degradation. Mechanistically, different phenomena can manifest in these unstable states, including altered kinase activity (LYN), localization (BLK) or assembly states (RIPK2).

For the investigated inhibitors, we found that induction of these states can be triggered by direct target engagement, or through network drugging. This highlights that potent off-target degradation can result from on-target inhibition, emphasizing the relevance of unbiased profiling approaches. Indeed, systematic proteomics campaigns have revealed a breadth of proteome-wide effects52,53. One of our most unexpected findings is the drug-induced cleavage of the myristoylation anchor of BLK through the γ-secretase, resulting in rapid turnover of BLK in the cytosol. While the direct TAK285 target mediating this effect remains elusive, this outlines the feasibility of precise pharmacological manipulation of protein anchoring to the membrane to control protein function and stability. Disrupting membrane association of disease-relevant proteins is a longstanding challenge. However, to date, it has predominantly been attempted by enzymatic inhibition of promiscuous enzymes, such as farnesyltransferase inhibition, which suffered from limited efficacy due to compensatory lipidation and from toxicities54,55. Additional research will be required to further our understanding of the generalizability and mechanistic principles of γ-secretase-dependent target dissociation.

We did not detect a pronounced overlap between kinases that are prone to be destabilized by inhibitors and kinases that are primed for degradation through proximity-inducing modalities, such as PROTACs. Nevertheless, there is evidence that also proximity-inducing molecular glue degraders can be prospectively furnished to re-establish physiological degradation, as exemplified by molecular-glue degraders of mutant β-catenin[56]. Future research will be required to understand the scope of supercharging endogenous degradation events beyond kinases. In addition to BCL6 for which induced multimerization enables degradation through its native E3 ligase SIAH1, certain selective oestrogen receptor degraders destabilize the oestrogen receptor through the E3 ligase UBR5, which is responsible for physiological endoplasmic reticulum turnover after oestradiol exposure[57]. Moreover, a recent study reports directly acting degraders of IDO1 (ref. [58]) that function by accelerating IDO1 degradation through the endogenous E3 CRL2KLHDC3. Collectively, these studies indicate supercharging of physiological degradation routes as a general mechanism of ligand-induced protein degradation that is complementary to proximity-inducing modalities such as PROTACs or molecular-glue degraders.

Methods

Cell lines and cell culture

KBM7 cells (obtained from T. Brummelkamp) and KBM7 iCas9 cells (a gift from J. Zuber) were grown in IMDM (Thermo Fisher Scientific) supplemented with 10% heat-inactivated FBS (Sigma-Aldrich) and 1% penicillin–streptomycin (Gibco). RKO iCas9-GFP and iCas9-BFP (gifted by J. Zuber), K562 (purchased from ATCC) and NALM-6 (obtained from A. Villunger) cells were cultured in RPMI 1640 (Thermo Fisher Scientific) supplemented with 10% FBS and 1% penicillin–streptomycin. HEK293T lentiviral packaging cells (obtained from Clontech), HEK293T (purchased from ATCC) and Flp-In T-REx 293 (obtained from Invitrogen) cells were cultured in DMEM (Thermo Fisher Scientific) supplemented with 10% FBS and 1% penicillin–streptomycin.

For competitive Kinobead pull-downs, Jurkat, MCF7, K562, COLO-205 and MV-4-11 cells were cultured in RPMI 1640 medium (Biochrom) supplemented with 10% (v/v) FBS (Biochrom). SK-N-BE(2) cells were grown in DMEM/Ham’s F-12 (1:1) supplemented with 10% (v/v) FBS and OVCAR-8 cells were cultured in IMDM medium (Biochrom) supplemented with 10% (v/v) FBS.

Cell lines were cultured at 37 °C and 5% CO_2 in a humidified incubator and were regularly tested for mycoplasma contamination.

Plasmids and cloning

All plasmid preparation, unless specified otherwise, was performed in stable competent Escherichia coli (NEB) or, in the case of destination vectors, in One Shot ccdB Survival 2 T1R Competent Cells (Invitrogen) according to the manufacturer’s instructions.

The pLEX305-ccdB-Nluc-3×Flag luminescent reporter vector was generated as a destination vector starting from pLEX_305-ccdB-dTAG destination vector (Addgene, 91798) by restriction digest with AgeI and MluI and T4 DNA ligation (NEB) of a synthesized gene block (TWIST) containing the Nluc sequence and a C-terminal 3×Flag tag (5′-CGGGCAAAACCGGTGTCTTCACACTCGAAGATTTCGTTGGGGACTGGCGACAGACAGCCGGCTACAACCTGGACCAAGTCCTTGAACAGGGAGGTGTGTCCAGTTTGTTTCAGAATCTCGGGGTGTCCGTAACTCCGATCCAAAGGATTGTCCTGAGCGGTGAAAATGGGCTGAAGATCGACATCCATGTCATCATCCCGTATGAAGGTCTGAGCGGCGACCAAATGGGCCAGATCGAAAAAATTTTTAAGGTGGTGTACCCTGTGGATGATCATCACTTTAAGGTGATCCTGCACTATGGCACACTGGTAATCGACGGGGTTACGCCGAACATGATCGACTATTTCGGACGGCCGTATGAAGGCATCGCCGTGTTCGACGGCAAAAAGATCACTGTAACAGGGACCCTGTGGAACGGCAACAAAATTATCGACGAGCGCCTGATCAACCCCGACGGCTCCCTGCTGTTCCGAGTAACCATCAACGGAGTGACCGGCTGGCGGCTGTGCGAACGCATTCTGGCGGACTACAAGGACCACGACGGTGACTACAAGGACCACGACATCGACTACAAGGACGACGACGACAAGTAGTAAACGCGTTGACGATGG-3′).

To generate a destabilized version of the vector, an identical gene block was synthesized with the addition of the PEST sequence (Promega) 5′-AATTCTCACGGCTTTCCGCCTGAGGTTGAAGAGCAAGCCGCCGGTACATTGCCTATGTCCTGCGCACAAGAAAGCGGTATGGACCGGCACCCAGCCGCTTGTGCTTCAGCTCGATCAACGTC-3′ upstream of the stop codon. pENTR223 Gateway entry vectors for the kinases were obtained from Hahn/Root Labs Human Kinases ORF Kit59,60 (Addgene Kit, 1000000014) with the exception of FYN and MAPK4, which were purchased separately (BCCM, LMBP ORF81088-E05, LMBP ORF81100-B12). pRK5-HA-ubiquitin, pRK5-HA-ubiquitin_K48R and pRK5-HA-ubiquitin_K63R plasmids were provided by G. Versteeg. A pENTR221-GFP vector was generated by BP Gateway cloning (Invitrogen), starting from the PCR-amplified GFP sequence of pCAG-GFP[61] (gifted by C. Cepko, Addgene, 11150) and insertion into the empty pDONR221 (Invitrogen, 12536017). Final luminescent reporter vectors were generated by LR Gateway cloning according to the manufacturer’s recommendations (Invitrogen, incubation was routinely run overnight at 25 °C before heat inactivation and transformation). The correct insert size was assessed by analytical digest and in-frame cloning was verified by sequencing (Microsynth).

Single point mutations, with the exception of BLK S5A and S6A, were generated from the respective pENTR223 plasmids using either the Q5 site-directed mutagenesis kit (primers are shown in Supplementary Table 1, method, SDM, NEB) or by Q5 (NEB) PCR amplification (primers are shown in Supplementary Table 1, method, PCR), followed by 1 h of DpnI digest (NEB) and direct transformation into DH5α E. coli (NEB).

Stability vectors were generated by digesting the previously published plasmid backbone pRRL_SFFV_empty_BFP_P2A_mCherry[29] with SalI and BamHI, before insertion of the PCR-amplified kinase of interest using the NEBuilder HiFi DNA Assembly Master mix (NEB) according to the manufacturer’s instructions. BLK G2I, G2L, L3A, L3G, L3V, V4S, S5A and S6A stability reporter plasmids were generated analogously using the corresponding mutated primer pairs. Vectors for domain-swap experiments, truncated versions of the BLK stability reporter (with the exception of 1–7) and the ABL1(C464W) stability reporter were generated similarly by amplifying the respective DNA sequences from each kinase and performing two- or three-part assemblies. For the aforementioned truncated BLK version 1–7, oligos (Supplementary Table 3) were annealed, phosphorylated and ligated into the corresponding vector. Primers were designed using the NEBuilder Assembly tool (the sequences are provided in Supplementary Table 2).

pRRL_SFFV_CSK* EF1a_iRFP670 and pRRL_SFFV_BLK*_GGGS_3×Flag EF1as_BFP were generated by restriction digest with SalI and XhoI (or BamHI (3×Flag)) of pRRL.SFFV.DACF16.EF1as.iRFP670[29] or pRRL_SFFV_empty_GGGS_3×Flag EF1as_BFP and insertion of the PCR-amplified CSK/BLK fragments using the NEBuilder HiFi DNA Assembly Master Mix (NEB) (the sequences are provided in Supplementary Table 2).

The gateway vectors pcDNA5_FRT_ccdB_3×Flag_miniTurbo and pSTV6_ccdB_ 3×Flag_miniTurbo (provided by A. Gingras), as well as pRRL_EF1a_ccdB_emGFP_IRES_HygroR (gifted by G. Superti-Furga) formed the basis for the generation of the remaining kinase reporter vectors via LR gateway cloning (Invitrogen). For the LYN and RIPK2 BioID dataset pENTR223_LYN, pENTR223_RIPK2 or pENTR221_GFP were cloned into pcDNA5_FRT_ccdB_3×Flag_miniTurbo, and, for the BLK BioID dataset, pENTR223-BLK or pENTR223-APH1A (BCCM, LMBP ORF81047-H06) and pENTR221-GFP were cloned into pSTV6_ccdB_3×Flag_miniTurbo.

sgRNAs were cloned into a single sgRNA vector pLenti-U6-IT-EF1a-Thy1.1-P2A-Neo or dual sgRNA vector pLentiDual-hU6-IT-mU6-IT-EF1a-Thy1.1-P2A-Neo (both gifts from J. Zuber) as previously reported[29]. The sgRNA sequences are described in Supplementary Table 4 and were designed using VBC score[62].

The saturated mutagenesis library for BLK was ordered directly from GenScript and cloned starting from the BLK stability reporter plasmid. No barcoding was applied.

All inserted DNA sequences were verified by Sanger sequencing (Microsynth).

Cell line generation by lentiviral transduction

With exception of the generation of the LYN and RIPK2 BioID cell lines (see the ‘Generation of cell lines through flp recombinase’), all cell lines were generated by transduction of lentivirus. For virus production, HEK293T lentiviral packaging cells were transfected at 70% confluence with the to-be-packaged plasmid in addition to the two packaging plasmids (pCMVR8.74 helper, pMD2.G envelope, both gifted by D. Trono (Addgene, 22036 and 12259)) using polyethylenimine (PEI MAX MW 40000, Polysciences). Viral supernatants were collected 60 h after transfection and cell debris was removed using a 0.45-μm poly-ethersulfone filter.

For transduction, 1 million cells (K562 for luminescent reporters, HEK293T, KBM7, KBM7 iCas9 or RKO iCas9-GFP/BFP for all other reporters) per 2 ml were transduced with 250 µl virus solution and 8 µg ml−1 polybrene. If required, the virus volume was adjusted to achieve the desired transduction efficiency. Then, 24 h after transduction, cells were expanded. For luminescent reporter cell lines, selection was performed with puromycin (1 µg ml−1, Gibco) starting 48 h after cell recovery. Subsequently cell pools were subjected to quality control by means of immunoblot analysis using the C-terminal Flag epitope tag, as well as assessment of luminescence levels using NanoGlo Luciferase Assay System (Promega). For the latter, 10^5 cells were seeded for each reporter cell pool in 30 µl on a 384-well plate and the luminescence was measured on the Victor X3 2030 Multilabel Reader (Perkin Elmer).

The three cell lines generated for the BLK BioID experiment (performed in KBM7) were likewise selected with puromycin (1 µg ml−1, Gibco). Generated cell pools were assessed after cell recovery for construct expression 24 h after doxycycline treatment (1 µg ml−1, PanReac AppliChem). Both correct fusion size and biotinylating efficiency were tested by immunoblotting. The latter was conducted by an additional incubation of cells with 100 µM biotin for varying timeframes and blotting for the biotinylated proteome using an anti-biotin antibodies (see the ‘Immunoblotting’ section).

sgRNA-vector-containing cells were selected using G418/neomycin (1 mg ml−1, Gibco) 72 h after transduction. Completion of selection or sgRNA transduction efficiency was assessed by staining with APC anti-mouse Thy1.1 antibody (1:400, 202526, BioLegend) in human TruStain FcX Fc receptor blocking solution (1:1,000, 422302, BioLegend) for 5 min at 4 °C, followed by two PBS washes and subsequent analysis by flow cytometry. Genetic KOs were generated by induction of the tightly inducible Cas9 cassette by doxycycline (0.4 μg ml−1, PanReac AppliChem) for a timeframe of 48 h up to 1 week (the incubation times per sgRNA are provided in Supplementary Table 4) before analysis using immunoblotting, imaging or flow cytometry.

All fluorescent reporter cell lines were either used directly for flow cytometry or selected by FACS using the CytoFLEX SRT Benchtop Cell Sorter (CytExpert SRT-Software (v.1.1.0.10007). In the first round of sorting, pools of reporter-positive cells (Supplementary Fig. 2) were enriched. For selected cell lines, single cells were sorted, expanded and used for flow cytometry, FACS-based CRISPR–Cas9 screens or imaging experiments. Specifically, the main stability reporters generated in KBM7 iCas9 for LYN WT, BLK WT and RIPK2 WT were used as clonal cell lines. Moreover, the BLK(WT)–GFP reporters in RKO iCas9-BFP cells were also used as clones. The remainder of the stability reporters generated for imaging purposes in RKO iCas9-GFP cells were used as sorted pools. The KBM7 iCas9 LYN(Y32A) stability reporter was used as a sorted cell pool, whereas the suite of KBM7 iCas9 BLK mutant stability reporters were used without sorting and instead analysis was performed on the reporter-positive cell gate. For the latter, a matched unsorted BLK WT stability reporter was used as a control in the corresponding datasets. All genetic KOs were performed in a pooled format after G418 selection as detailed above.

Generation of cell lines through flp recombinase

Flp-In T-REx 293 cells were transfected with 200 ng of LYN-mT, RIPK2-mT and GFP-mT plasmids and 2 µg pOG44 vector (Invitrogen, V600520) using Lipofectamine 2000 (Invitrogen) according to the manufacturer’s instructions in a 6-well format. Then, 24 h after transfection, cells were expanded to a 10-cm dish and after an additional 24 h, cell selection was initiated with 200 µg ml−1 hygromycin B (Roth) and maintained for at least four weeks. Subsequently, expression and biotinylation capacity (as described for the BioID cell lines generated by lentiviral transduction) was performed.

Temporal luminescence drug screen

Compounds from the kinase inhibitor library including 10 PROTAC controls (see the ‘Compounds’ section) and respective transcription and translation compounds were dispensed through an Echo 550 system into white 1,536-well plates (PerkinElmer, 6004684) at the appropriate concentrations (0.5–10 µM; Supplementary Data 1; 10 µM for CHX and 1 µM for NVP-2). Plates were sealed and stored at −20 °C. On the day of the drug screen, the plates were equilibrated to room temperature. Next, 5 µl of 1:100 endurazine (Promega) in buffered RPMI (complete RPMI supplemented with 50 mM HEPES pH 7, Sigma-Aldrich, H0887) were prelaid into each well using a liquid dispenser (Thermo Fisher Scientific, Multidrop Combi). Then, 5 µl of cells at a density of 640,000 cells per ml in buffered RPMI were dispensed on top. Cells were transferred to an incubator (humidified chamber, 37 °C, 5% CO_2) and the luminescence signal was measured every 4 h from 2 h to 18 h after seeding using the EnVision plate reader (Revvity). Raw luminescence signals were subsequently normalized for intraplate effects. Finally, compound effects were quantified by calculating percentage of control (POC) based on averaged, outlier-corrected DMSO (100%) and positive control (CHIR-99021; 0%) wells, for each plate and timepoint individually (Supplementary Fig. 4a). Only compounds that passed an initial preselection step were used for the final drug screen. The prescreen was performed following the identical steps but only for the two control cell lines GFP-Nluc and dGFP-Nluc and at two concentrations (2.5 µM or 10 µM for 10 mM stock compounds and 0.5 µM and 2 µM for 2 mM stock concentrations). Compounds were eliminated if any of the normalized POC data were smaller than 48 or larger than 150 or if the relative change relative to the 2-h timepoint was bigger than 0.58. In cases in which only the higher concentrations fulfilled these criteria, the corresponding lower concentration was used. In total, 1,620 compounds including 10 PROTACs were used for the drug screen (Supplementary Data 1).

Data analysis of luminescence drug screen

After the normalization of the initial drug-screening data, we performed additional data processing to obtain a binary active/inactive classification for each compound–kinase pair.

First, we implemented a filter to exclude compound–kinase pairs exhibiting high variability across replicates (s.d. > 30; Fig. 1) consistently across all five timepoints, resulting in the removal of 138 compound–kinase pairs. Overall, the proportion of pairs having 0 timepoints with a high s.d. was 99.67%. We further filtered out compounds that exhibited high reactivity against all kinases, considering them false positives due to their low initial 2-h timepoint (35 compounds with a median POC across kinases <70). We further excluded the three non-small molecules disitertide (TFA), Pep2m myristoylated (TFA) and pm26TGF-β1 (TFA) from our analysis. To ensure comparability of time series and to eliminate bias towards absolute POC values, we centred the compound–kinase series around 100 POC relative to the 2-h timepoint. This centring process was first applied across kinases and then across compounds.

We used the time series of CHX, NVP2 and DMSO controls to assess whether compound–kinase pairs significantly deviated from each control. For each kinase and timepoint, we independently calculated the normalized compound z score. A compound was considered to significantly reduce the kinase readout if it exhibited a substantial reduction (2 sigma) compared with the null model of the controls. We used the same methodology to calculate z scores of compounds concerning the distributions of all other compounds. We also calculated the z scores normalizing against only the initial 2-h timepoint to capture significant changes relative to the initial conditions.

This process resulted in eight normalization schemes: against CHX, NVP2, DMSO and compounds, considering both timepoint-independent and initial-timepoint-dependent situations. Each normalization offered varying selectivity over the compound–kinase time series, and we then expressed the scores as the count of significantly decreased timepoints (2 sigma).

Finally, we conducted a parameter scan to define a query for selecting hit compounds by combining the scores and specifying the minimal number of significantly deviated timepoints for each normalization scheme and the overall total combined through ‘or’ operators. We determined the normalization score thresholds for the query by minimizing the false-discovery rate (FDR). This was achieved using the 10 PROTAC controls and their respective kinase targets as a reference for true positives as well as a manually curated inclusion list. The query that reflected our constraints is as follows:

$$\begin{array}{c}[(\mathrm{DMSO\_norm}\ge 5){||}(\mathrm{CHX\_norm}\ge 2){||}(\mathrm{CPD\_norm}\ge 5)\\ \,{||}(\mathrm{CHX\_norm}2{\rm{h}}\ge 5)]{||}(\mathrm{TOT}\ge 10)\end{array}$$

meaning that all of the positive kinase–compound pairs have to globally score 10 or more, or having a normalized score above the determined threshold in at least one of the individual screens (Supplementary Fig. 4b). For the rare instance of a missing timepoint (mainly associated with the kinase reporters for CDK4, CDK7 and CDK9), the score was corrected by +1. One compound was excluded from further data analysis due to scoring in >10 instances. The final KinDeg scores are shown in Supplementary Data 1 (including the annotation of excluded compounds). The final hit kinase trajectories are shown in Supplementary Fig. 9.

The screening data were used to fit the half-lives of each kinase. This was performed by fitting the equation 100 × e(−x × tau) in Python (v.3.7.6) and the package scipy (v.1.4.1) to each kinase’s CHX screening trajectory. t-SNE plots were generated with sklearn and matplotlib (v.1.0.1 and v.3.5.3, respectively) from ChEMBL drug-binding data processed as described in the Chemical Checker (CC)[24] and compounds were characterized with CC global bioactivity signatures. Chaperone client status was mapped from a previous study[8] to the respective canonical kinases (Supplementary Data 1) and respective pairwise comparisons were calculated using a Fisher’s exact test, applying the fisher_exact function from Python’s scipy.stats module (v.0.12.2). The JD values between kinase hit profiles were calculated as 1 − Jaccard similarities (JS), where the JS is the size of the intersection divided by the size of the union of two compound (hit) sets. Kinome trees were depicted using http://www.kinhub.org/kinmap/index.html (ref. [63]).

To assess whether the data were enriched for type I, II or allosteric inhibitors, we manually annotated our hit compounds for their respective binding mode (Supplementary Data 1) using the available literature data, structural properties of the inhibitors as well as structural data where available. We further used the data available in the PKIDB database64,65 to annotate the remaining compounds.

Finally, for scoring of the stabilization events, we used a similar approach to the degraders. However, we focused on compounds exhibiting a substantial readout increase (2 sigma) relative to the controls’ null models. Importantly, we excluded the CHX normalization scheme from this analysis. Following this query:

$${\rm{D}}{\rm{M}}{\rm{S}}{\rm{O}}{\rm{\_}}{\rm{n}}{\rm{o}}{\rm{r}}{\rm{m}}\ge 5||{\rm{C}}{\rm{P}}{\rm{D}}{\rm{\_}}{\rm{n}}{\rm{o}}{\rm{r}}{\rm{m}}\ge 5$$

We further excluded compounds that scored in the GFP-Nluc-3×Flag control cell line or that scored in >10 instances. With these boundary conditions, we identified 204 stabilization events across 64 kinases and 128 compounds. The associated data are provided in Supplementary Data 1.

Immunoblotting

Cell pellets (1–2 million cells per treatment) were lysed in urea lysis buffer (8 M urea, 1% CHAPS, 50 mM Tris-HCL pH 8) for 30 min with shaking at 4 °C and 1,200 rpm. Next, the samples were cleared by centrifugation for 15 min (20,000g, 4 °C) and quantified using the Pierce BCA protein assay kit (Thermo Fisher Scientific) according to the manufacturer’s instructions. Finally, the samples were diluted with Bolt LDS sample buffer (4×) (Invitrogen) supplemented with final concentration (f.c.) 10% β-mercaptoethanol (Sigma-Aldrich) and denatured for 10 min at 70 °C. Then, 20 µg per protein sample was separated on the Bolt 4–12% Bis-Tris Plus Gel (10–17 wells) (Invitrogen) using the Colour Prestained Protein Standard, Broad Range (10–250 kDa, NEB) as a marker. After transfer to a nitrocellulose membrane, membranes were stained by Ponceau-S. Next, the membranes were blocked with 5% milk in TBS-T (30 min, room temperature) and then incubated with primary antibodies overnight at 4 °C in TBS-T. The next day, the membranes were washed three times with TBS-T followed by incubation for 1 h at room temperature with the respective secondary antibodies if required. Finally, the membranes were again washed three times before analysis on the Chemidoc system using Pierce ECL Western Blotting Substrate (Thermo Fisher Scientific). The following antibodies and dilutions were used: GAPDH (1:5,000, Santa Cruz Biotechnology, sc-365062), GAPDH (1:5,000; Santa Cruz Biotechnology, sc-47724), vinculin (1:500; Szabo Scandic, SACSC-25336), Flag (1:2,000; Cell Signaling Technology, 2368), LYN (1:1,000; Cell Signaling Technology, 2796), BLK (1:1,000; Cell Signaling Technology, 3262), RIPK2 (1:1,000; Cell Signaling Technology, 4142S), phosphorylated LYN (Tyr507) (1:1,000; Cell Signaling Technology, 2731), FIP200 (1:1,000; Cell Signaling Technology, 12436), CDK9 (1:1,000; Cell Signaling Technology, 2316), TMUB1 (1:1,000, Abcam, EPR14066), cCBL (1:1,000; Cell Signaling Technology, 2747), phosphorylated LYN (Tyr397) (1:1,000; Cell Signaling Technology, 70926), HRP-conjugated anti-biotin (1:1,000; Cell Signaling Technology, 7075), peroxidase-conjugated goat anti-rabbit IgG (1:10,000; Jackson ImmunoResearch 111-035-003), peroxidase-conjugated goat anti-mouse IgG (1:5,000; Jackson ImmunoResearch, JAC115035003). For quantifications the accompanying ChemiDoc ImageLab software (v2.4.0.03) was used, normalized to the respective loading control and plotted as fold changes with respect to each genotype’s DMSO control or 0-h timepoint. The data were plotted as the mean from three independent biological replicates ± s.d. Replicates and uncropped images are shown in Supplementary Fig. 1.

Compounds

Carfilzomib (Cay17554-5) and BafA1 (Cay11038) were purchased from Cayman, HSP90i (4-(4-(23-dihydro-14-benzodioxin-6-yl)-5-methyl-1H-pyrazol-3-yl)-6-ethylresorcinol), 385920) was obtained from Calbiochem. All other small-molecule inhibitors were sourced from MedChemExpress. These include the kinase inhibitor library (Supplementary Data 1; 1,996 compounds, HY-L009), MLN4924 (HY-70062), TAK-243 (HY-100487), TAK-285 (TAK285, HY-15196), Src inhibitor 3 (SI-3, HY-130254), RI-4 (HY-107978), AV-412 (HY-10346), neratinib (HY-32721), afatinib (HY-10261), WZ4002 (HY-12026), nintedanib (HY-50904), DAPT (HY-13027), alkynyl myristic acid (HY-140335), THAL-SNS-032 (dCDK9, HY-123937), NVP-2 (HY-12214A), dabrafenib (HY-14660), PLX 4720 (HY-51424), ibrutinib (HY-10997), ONO-4059 (HY-18951), R406 (HY-11108), dasatinib (HY-10181), asciminib (HY-104010), DPH (HY-12070) and GNF-2 (HY-11007). Cycloheximide (CHX) was purchased from Cell Signaling Technology (2112S).

All compounds were dissolved in DMSO (Sigma-Aldrich, D1435) as 1 mM, 10 mM, 20 mM, 25 mM or 100 mM stock solutions. Working dilutions were prepared as 1,000× or 2,000× stock solutions. The kinase inhibitor library was delivered as 2 mM or 10 mM stock solutions (Supplementary Data 1).

Flow cytometry

Cells were treated with the compounds at the concentrations and timeframes indicated in the respective figure legends, and the fluorescent channels of interest were subsequently analysed on a LSR Fortessa (BD Biosciences) using the BD FACSDiva software (v.9.0). The data were analysed using FlowJo (v.10.6.2) as outlined in Supplementary Fig. 2 and the resulting mean BFP and mCherry values were exported for further processing. BFP/mCherry ratios were calculated after background subtraction (from matched WT cells) and normalized to either each pretreatment or genetic variant (referred to as normalized BFP in the figure legends) or normalized to a specific condition as indicated in the respective subscripts, for example, DMSO in Fig. 3c. Decay functions were fitted using Y = (Y_0 − plateau) × e(−K × X) + plateau and dose responses fitted using Y = bottom + (top − bottom)/(1 + (IC_50/X)n) where n is the Hill slope using the in-built functions of GraphPad Prism (v.10.0.3) and nonlinear regression fitting. Matched mCherry flow histograms to Figs. 2a and 4a and Extended Data Fig. 6e are shown in Supplementary Figs. 6a, 7a and 8a, respectively.

FACS-based CRISPR–Cas9 screen

The screens were performed as previously described[29]. First, cells were transduced at an multiplicity of infection (MOI) of 0.1–0.2 with lentivirus containing the respective sgRNA library, prepared as described in the ‘Cell line generation by lentiviral transduction’ section to achieve a 1,000× representation per sgRNA. For LYN, the previously published UPS-focused sgRNA library[66] (7,801 sgRNAs) and, for BLK and RIPK2, a genome-wide library62,67 was used. Then, 72 h after transduction, the transduction rate was assessed by staining with APC anti-mouse Thy1.1 antibody (1:400, 202526, BioLegend) and human TruStain FcX Fc receptor blocking solution (1:1,000, 422302, BioLegend) for 5 min at 4 °C. Next, selection with G418 (1 mg ml−1, Gibco) was initiated. Cells were maintained in G418-positive medium for at least 14 days, splitting cells every 48–72 h. For the screen, Cas9 expression was induced with doxycycline (0.4 μg ml−1, PanReac AppliChem) and, after 72 h, cells were treated with DMSO or the respective inhibitors (SI-3, 156 nM, 8 h; TAK285, 6 h; RI-4, 2.5 µM, 18 h). Cells were centrifuged for 5 min at 500g and stained with APC anti-mouse Thy1.1 antibody (1:400, 202526, BioLegend), Zombie NIR Fixable Viability Dye (1:1,000, BioLegend) and human TruStain FcX Fc receptor blocking solution (1:1,000, 422302, BioLegend) for 5 min at 4 °C. Subsequently cells were fixed with BD fixation buffer 4% (Thermo Fisher Scientific, Pierce) for 45 min at 4 °C followed by two washes with PBS and resuspension in FACS buffer (PBS, 5% FBS and 1 mM EDTA) for storage at 4 °C. All staining steps were performed in the dark. Cells were sorted within 48 h of fixation.

Sorting was performed on a BD FACS Aria Fusion (70-µm nozzle, BD Biosciences, BD FACSDiva software, v.8.0.2). First, cells were strained through a 35-μm nylon mesh. Next, cells were sorted for the 5% highest and lowest BFP-expressing cells as well as 30% of the mid-fraction (the gating strategy is shown in Supplementary Fig. 2). For each replicate and condition, cells corresponding to at least a 500-fold (genome-wide) or 1,000-fold (UPS-focused) library representation were sorted.

After sorting, the high, low and mid fractions were pooled per replicate and lysed overnight (14 h) at 55 °C with shaking at 1,200 rpm in lysis buffer (10 mM Tris-HCl, 150 mM NaCl, 10 mM EDTA, 0.1% SDS) supplemented with proteinase K (New England Biolabs). The next day, RNase was removed with DNase-free RNase (Thermo Fisher Scientific) for 2 h at 37 °C. The lysates were stored at −20 °C until further processing.

For DNA extraction, two rounds of phenol extraction (UltraPure Buffer-Saturated Phenol, Thermo Fisher Scientific, 15513039) using phase Lock Gel tubes (VWR, 7332477) followed by isopropanol precipitation overnight at −20 °C were performed. Next, the samples were barcoded using a two-step PCR protocol (AmpliTaq Gold polymerase, Invitrogen, 4311818). After each PCR step, amplicons were cleaned up with Mag-Bind TotalPure NGS beads (Omega Biotek) according to the the manufacturer’s protocol for double-sided selection. Final NGS libraries were pooled at equimolar ratios and sequenced on the HiSeq 3000 or NovaSeq 6000 platform (Illumina).

The resulting reads were trimmed using fastx-toolkit (v.0.0.14) and subsequently aligned (Bowtie2, v.2.4.5) and quantified (featureCounts, v.2.0.1). The corresponding workflows are available at GitHub (https://github.com/ZuberLab/crispr-process-nf/tree/566f6d46bbcc2a3f49f51bbc96b9820f408ec4a3 and https://github.com/ZuberLab/crispr-mageck-nf/tree/c75a90f670698bfa78bfd8be-786d6e5d6d4fc455). Gene-level enrichment was calculated by comparing each high or low population to the corresponding mid population using the median-normalized read counts. The resulting log_2[FC] and P values as well as the number of scoring and total quantified sgRNAs per gene are provided in Supplementary Data 3. Essential genes were retrieved from DepMap (23Q4)[68].

FACS-based DMS

The screen was conducted similar to the CRISPR–Cas9 screen, first transducing cells at an MOI of 0.1–0.2 followed by FACS-based enrichment of double-positive cells. Cells were treated before the screen with DMSO, TAK285 (2.5 µM, 6 h) or IMP-1088 (1 µM 24 h). Cells were then fixed (see the ‘FACS-based CRISPR–Cas9 screen’ section) and sorted for 5% high or low or 30% mid BFP level cells. After DNA extraction, samples were barcoded by two-step PCR with customized primer sets. The samples were finally sequenced using the NovaSeq 6000 platform (Illumina) run in PE150.

For the analysis, we adapted our previously established pipeline[69]. In brief, the raw sequencing reads were converted to fastq format with samtools (v.1.17). Demultiplexing of paired-end reads was performed using cutadapt (v.4.4), matching read 1 5′ barcodes were provided in a separate FASTA file, with no trimming applied (--action=none). Demultiplexed paired-end FASTQ files were converted to unaligned BAM format using Picard’s FastqToSam tool (v.3.0.0) and trimmed using Trim Galore (v.0.6.6) in paired-end mode with Nextera adapter trimming enabled. Short reads were aligned to the BLK unique domain sequence and SAM files were generated using the mem algorithm from the bwa software package (v.0.7.17). The SAM file was converted to BAM format using samtools (v.1.15.1) and mutation calling was performed using the AnalyzeSaturationMutagenesis tool from GATK (v.4.1.8.1). Next, the relative frequencies of variants were calculated for each position and variants that were covered by less than 1 in 30,000 reads were excluded from further analysis. Read counts for each variant were then normalized to the total read counts of each sample and log_2[FC] values comparing high/low-to-mid fractions of each condition were calculated. P values were adjusted for multiple testing using the Benjamini–Hochberg procedure to control the FDR. The resulting significant (adjusted P< 0.05) log_2[FC] low-to-mid and high-to-mid comparisons per condition are provided in Supplementary Data 4. For DMSO-normalized results, finally, log_2[FC] values were calculated with respect to the respective DMSO high-to-mid or low-to-mid log_2[FC]. Heat maps were generated using the pheatmap (v.1.0.12) package in R (v.4.1.0).

Immunofluorescence staining

Cells were seeded in PhenoPlate 96-well microplates (Revvity) and subjected to drug treatment after 24 h of pre-attachment. After treatment, cells were fixed with BD Cytofix for 10 min at room temperature. After three PBS washes, cells were permeabilized with 0.2% sodium citrate, 0.1% Triton X-100 for 5 min at room temperature. Next, cells were washed three times with PBS after a 30 min block with BSA (0.024 g ml−1) and incubated overnight at 4 °C with the respective antibodies diluted in blocking solution (1:500, TMUB1, Abcam, EPR14066). The next day, cells were washed three times with PBS followed by an incubation for 2 h at room temperature with secondary antibodies (1:500, Alexa-Fluor 647, Cell Signaling Technology, 4414) and 1:1,000 concanavalin A–Alexa Fluor 488 (Thermo Fisher Scientific, C11252). Finally, cells were washed three times and kept in 100 µl PBS. The samples were imaged within the next 24 h.

High-content confocal imaging and data analysis

Cells were imaged using the PerkinElmer Opera Phenix automated microscope run on the Harmony software (v.4.9 or later) and using the pre-set filter settings for DAPI (BFP), AF-488 (GFP), AF-647 (TMUB1), mCherry and brightfield. Exposure was set to <400 ms per channel. BFP and GFP, as well as AF-647 and mCherry channels were separated during acquisition. Cells were seeded 24 h before imaging into 384-well or 96-well (CellCarrier Ultra, Revvity) plates to achieve a final cell density of 40–60%. Drugs were added immediately before imaging as indicated in the figure legends. All of the experiments were acquired with a ×40 air objective, with exception of the immunofluorescence data, which were acquired with a ×63 water objective.

Cells were segmented using cellpose[70] (0.6.5-foss-2020b) using either the mCherry (RIPK2) or GFP (BLK) channel and an adjusted diameter of 38, 50 or 80, respectively. Next, relevant features and fluorescence were extracted using custom-built cellprofiler pipelines (4.1.3-foss-2020b).

In all instances, ConvertImageToObjects (convert to boolean image (no), preserve original labels (yes)) was used to generate the primary objects. Next, for RIPK2, EnhanceOrSuppressFeatures was applied (Operation = Enhance, Type = Speckles, Size 6, Speed and accuracy = Fast) followed by IdentifyPrimaryObjects (diameter = 2-20, thresholding strategy = global, method = manual, threshold = 0.0016, smoothing scale 1.3488, method clumped objects&draw lines between clumped objects = Intensity, automatic smoothing and distance calculation enabled, holes filled in after both thresholding and declumping). RelateObjects was applied to assign the resulting speckles per cell object. Finally, MeasureObjectIntensity and MeasureObjectSizeShape were applied for measuring the respective parameters across the speckles and cell objects, before exporting the data to a database for further processing through self-written Python scripts. For the RIPK2 data associated with Fig. 4h–j and Extended Data Fig. 8h, due to the different absolute BFP fluorescence values of the constructs for RIPK2 WT and RIPK2(∆CARD), two steps were added before EnhanceOrSuppressFeatures. Namely, ExpandOrShrinkObjects was applied to eliminate cell boundaries (Operation = shrink by a specified number of pixels, pixels = 4) followed by ImageMath, which was used to calculate the BFP to mCherry ratio. The thresholds in IdentifyPrimaryObjects were thus adapted to 0.2 instead of 0.0016. For RIPK2(I212D) and RIPK2 WT stability reporter data, an additional step of prefiltering cells with less than 0.01 mCherry signal was added before segmentation of the speckles. In the TAK243 dataset and the extended KO data for XIAP and BIRC2, the threshold for IdentifyPrimaryObjects was adjusted to 0.0024 and 0.002 respective to the total BFP signal per acquired dataset.

For the immunofluorescence staining and GFP co-localization experiment, a similar approach as above was conducted. After primary object identification, RIPK2 foci were again identified using EnhanceOrSuppressFeatures (Operation = Enhance, Type = Speckles, Size 6, Speed and accuracy = Fast) followed by IdentifyPrimaryObjects (diameter = 2-20, thresholding strategy = global, method = manual, threshold = 0.0015, smoothing scale 1.3488, method clumped objects&draw lines between clumped objects = Intensity, automatic smoothing and distance calculation enabled, holes filled in after both thresholding and declumping). For TMUB1, IdentifyPrimaryObjects was applied with a threshold of 0.035 and, for GFP, a threshold of 0.015 was used. Each speckle was first assigned to a corresponding cell using the RelateObjects function, followed by the RelateObjects function run on the foci per condition.

For the BLK–GFP cell clones, only the module MeasureObjectIntensity was applied after object classification. Corresponding data were exported to a spreadsheet for further processing.

In all instances, Python (v.3.7.6) was used to annotate the resulting data (condition, replicate) and normalize the data. Normalized data were then exported and depicted in GraphPad Prism (v.10.0.3). In all cases, data were averaged per biological replicate of the mean values per cell. The s.d. was correspondingly calculated across the biological replicates.

NanoBRET

The assay was performed as described previously[71]. In brief, full-length CSK and LYN were obtained as plasmids cloned in frame with an N-terminal Nluc-fusion (gift from Promega). Plasmids were transfected into HEK293T cells using FuGENE HD (Promega, E2312), and proteins were allowed to express for 20 h. Serially diluted inhibitor and NanoBRET K4 Tracer (Promega, TracerDB: T000037) at the Tracer KD concentration taken from TracerDB[72] were pipetted into white 384-well plates (Greiner 781207) using an ECHO acoustic dispenser (Labcyte). The transfected cells were added and reseeded at a density of 2 × 10^5 cells per ml after trypsinization and resuspending in Opti-MEM without Phenol Red (Life Technologies). The system was allowed to equilibrate for 3 h (37 °C, 5% CO_2) before the bioluminescence resonance energy transfer (BRET) measurements. To measure BRET, NanoBRET NanoGlo Substrate and extracellular Nluc Inhibitor (Promega, N2540) were added according to the manufacturer’s protocol, and filtered luminescence was measured on the PHERAstar plate reader (BMG Labtech) equipped with a luminescence filter pair (450 nm BP filter (donor) and 610 nm LP filter (acceptor)). Competitive displacement data were then analysed using GraphPad Prism (v.10.0.3) software using a normalized three-parameter curve fit with the following equation: Y = 100/(1 + 10(X − log[IC_50])).

Commercial recombinant binding/inhibitory assays

In vitro kinase inhibitory or kinase binding assays were performed using the SelectScreen platform (Thermo Fisher Scientific). TAK285 (BLK) and SI-3 (LYN) were screened using the Z′-LYTE assay, while RI-4 (RIPK2) was screened using the LanthaScreen Eu Kinase Binding Assay according to their respective assay availability. Threefold dilutions were performed starting from 30 µM and in presence of ATP, using its standard apparent KM per kinase.

MST binding assay

Protein purification was performed as previously described[73]. Purified γ-secretase complex, modified γ-secretase complex (PS1 fused to GFP) and full-length BLK protein (fused to GFP) were diluted in buffer containing 25 mM HEPES pH 7.4, 150 mM NaCl and 0.1% (w/v) Digitonin.

TAK285 serial dilutions were mixed with purified γ-secretase (GFP-tagged). The mixture was loaded onto MO-K022 capillaries at room temperature. Microscale thermophoresis (MST) analyses were conducted on the Monolith NT.115 (NanoTemper) system with 20% LED power and 60% MST power. The MST data were analysed using MO.Affinity Analysis (v.2.3).

Characterization of the γ-secretase and BLK interaction was performed using GFP-tagged BLK (with or without 20 μM TAK285) as the target protein and addition of serially diluted untagged γ-secretase. Samples were measured as described above.

Immunoprecipitation

Cell pellets (10 million cells) were lysed in 900 µl IP lysis buffer (50 mM Tris-HCL (pH 7.4), 150 mM sodium chloride, 0.1% Triton X-100, 1 mM EDTA and 5 mM magnesium chloride, 1× protease inhibitors) followed by lysate clearance, protein quantification and immunoprecipitation as described in the ‘Immunoprecipitation, on-bead TAMRA click and in-gel fluorescence’ section. After immunoprecipitation and sample washing, proteins were then directly eluted using 70 µl as final volume before analysis using immunoblotting. For blocking, 5% BSA in TBS-T was used instead of 5% milk in TBS-T and the phosphorylated LYN Tyr507 or phosphorylated LYN Tyr397 antibody was diluted 1:1,000 in TBS-T containing 3% BSA and 0.1% sodium azide.

Immunoprecipitation, on-bead TAMRA click and in-gel fluorescence

Cell pellets (15 million cells per condition) were lysed in 900 µl NP40 lysis buffer (DPBS with 1.5 mM magnesium chloride, 1% NP40, 1× protease inhibitors, 1× benzonase) for 30 min on ice. The lysates were cleared by centrifugation (20 min, 4 °C, 20,000g) and quantified using the Pierce BCA Protein Assay Kit (Thermo Fisher Scientific) according to the manufacturer’s instructions. The samples were then normalized to 1 mg per input and preactivated anti-Flag magnetic beads (Sigma-Aldrich) were added, followed by the incubation for 3 h at 4 °C on a rotating wheel. The beads were washed three times with lysis buffer. After removal of the supernatant, 56 µl of click-mix (170 µM TAMRA (5-TAMRA-Azide, CLK-FA008, Jena Biosciences), 230 µM copper sulfate, THPTA 1.15 mM, HCl 5 mM, sodium ascorbate 5 mM, in PBS) were added per sample. Finally, 18 µl of elution buffer (4× Laemmli buffer supplemented with f.c. 10% β-mercaptoethanol) were added and the samples were boiled at 95 °C for 10 min before loading 20 µl of supernatant and analysis using SDS–PAGE. Before the transfer for immunoblotting and its analysis (see section ‘Immunoblotting’), the SDS–PAGE gel was imaged on the ChemiDoc system using the Alexa 546 channel and Alexa 647 for the ladder.

Ubiquitination assays

HEK293T LYN-Nluc-3×Flag cells were seeded into 10-cm culture dishes to reach around 70% confluency on the day of transfection. Transfections were performed using Lipofectamine 2000 (Thermo Fisher Scientific) according to the manufacturer’s protocol. In brief, 30 μl of Lipofectamine 2000 was diluted in 720 µl Opti-MEM (Thermo Fisher Scientific) and mixed gently. In parallel, 12 µg of plasmid DNA (pRK5-HA-Ubiquitin, pRK5-HA-ubiquitin(K48R) or pRK5-HA-ubiquitin(K63R)) was diluted in 720 µl Opti-MEM. Both solutions were incubated separately at room temperature for 5 min, combined and incubated for 10 min. The resulting solution was then added dropwise to the cells. Cells were split the next day and subjected to treatments 72 h after transfection. After the treatments, cells were collected with ice cold PBS and, after an additional wash with ice-cold PBS, snap-frozen on dry ice. Cell pellets were lysed in 1 ml of lysis buffer containing 50 mM Tris-HCl (pH 7.4), 150 mM NaCl, 0.1% Triton X-100, 1 mM EDTA, 5 mM MgCl_2, 5% glycerol, freshly added protease inhibitors (Thermo Fisher Scientific) and Benzonase nuclease (Sigma-Aldrich).

For Flag pull-down assays, 500 µg of clarified lysate was incubated with 25 µl of anti-Flag magnetic beads (Sigma-Aldrich) for 3 h at 4 °C on a rotating wheel. The beads were washed three times with lysis buffer and bound proteins were eluted by boiling at 95 °C for 10 min in 70 μl of lysis buffer supplemented with 4× SDS sample buffer.

Data plotting and statistical analysis

All data are represented as the mean of technical or biological replicates ± s.d. or ±confidence intervals. Datapoints were calculated as described in the respective sections.

Imaging data, all data related to the drug screen, proteomics, CRISPR screen, as well as in vitro kinase binding/inhibitory assay were plotted with seaborn (v.0.12.2) and matplotlib (v.3.4.2) in Python (v.3.7.6). Standard packages such as numpy (v.1.21.5), pandas (v.1.0.1) and scipy (v.1.4.1) were correspondingly used for data handling, processing, normalization, statistical calculations and/or data fitting. Immunoblot quantifications, MST results and data associated with flow cytometry (except for flow histograms) were plotted in GraphPad Prism (v.10.0.3). Statistical tests and data fitting for the corresponding datasets were calculated directly with in-build functions as detailed in the respective sections. Flow histograms were exported from FlowJo (v.10.6.2). Representative images of microscopy experiments were prepared using Fiji (ImageJ, v.2.1.1/1.53i).

Preparation of BioID MS samples

Bait-mT expression was induced 24 h before initiating cell treatments using 1 µg ml−1 doxycycline. The next day, the respective inhibitors (RI-4 2.5 µM, TAK285, 2.5 µM or SI-3, 156 nM) or vehicle control (DMSO, across all cell lines including the two GFP-mT versions) and 100 µM biotin (Sigma-Aldrich, B4501) were added to the cells for 1 h. For BLK-mT, an additional condition was generated including 2 h carfilzomib (1 µM) pretreatment before TAK285 addition. For RIPK2-mT, additional conditions were generated including the co-treatment with BafA1 (100 nM). After the treatments, 20 million cells per condition and replicate were collected by centrifugation followed by two washes in ice-cold PBS. The resulting cell pellets were snap-frozen on dry ice and stored at −80 °C until further processing.

The following protocol was adapted from a previous study[74]. All steps were carried out with Protein LoBind tubes (Eppendorf) and HPLC-grade reagents. In brief, for lysis, cell pellets were resuspended in 250 µl lysis buffer (PBS supplemented with 1% SDS (Sigma-Aldrich, 71736), 2 mM magnesium chloride (Invitrogen, AM9530G), protease inhibitors (Thermo Fisher Scientific, 78437) and benzonase (Merck, US170746-3)). The samples were vortexed and incubated at 37 °C (300 rpm, 30 min) followed by centrifugation at 18,000g (4 °C, 30 min). The supernatant was transferred to fresh tubes and the protein concentration was measured using the Pierce 660 nm protein assay reagent (Thermo Fisher Scientific, 22660) according to the manufacturer’s instructions. Per sample, 1 mg of total protein was diluted up to a final volume of 300 µl with lysis buffer. Next, 30 µl of 50 mM TCEP (Sigma-Aldrich, 75259, diluted in H_2O) was added, the samples were vortexed and incubated for 1 h at 56 °C with shaking at 300 rpm. Then, 80 µl of 1 M HEPES (pH 7.5, AppliChem, A6916) was added, followed by 45 µl of 200 mM iodoacetamide (Sigma-Aldrich, I1149). The samples were again vortexed and incubated at 25 °C and 300 rpm. Pierce Streptavidin Agarose (Thermo Fisher Scientific, 20353) resin was prepared by centrifugation for 30 s followed by two PBS washes. Next the protein samples were added and incubated on a rotator for 1 h at room temperature in the dark. Finally, the samples were washed twice using 1× pre-washed Mini BioSpin columns (Bio-Rad, 7326207) with wash buffer 1 (0.2% SDS in 1× PBS), followed by 16 washes with wash buffer 2 (8 M urea in 1× PBS) and four washes with PBS. For elution, the slurry was resuspended in 2× digestion buffer (50 mM ammonium bicarbonate, 200 mM guanidine hydrochloride, 1 mM calcium chloride, in H_2O) and transferred again to a fresh tube. Subsequently, the supernatant was removed and 250 µl digestion buffer and freshly supplied 10 µl of trypsin solution (0.1 μg μl−1, Promega, V5117) were added, before incubation overnight on a rotating wheel (14 h).

The next day, the beads were centrifuged briefly (30 s) and the supernatant was transferred into a fresh tube. Resin was washed once using 200 µl H_2O, which was added to the already separated supernatant. Peptides were cleaned up with self-made stage tips columns. These were prepared from 1 mm circles of an Epore C18 disc inserted into a 200-µl tip. On top of the C18 disc, 24 µl Oligo R3 solution (15 mg ml−1 in acetonitrile (ACN)) was added before 1 min of centrifugation (1,000g). The column was then washed twice with 100 μl ACN (1,000g for 1 min) and equilibrated twice with 200 μl 0.1% TFA (3 min at 1,000g). The samples were acidified with 30% TFA (1% final concentration) before loading of the samples in two fractions onto the column (1,000g for 3 min). One wash with 200 μl 0.1% TFA (3 min at 1,000g) was followed by a double elution step using 50 μl elution buffer (90% ACN, 0.01% TFA, in H_2O) each. The eluted peptides were dried using a vacuum centrifuge (45 °C) and stored at −20 °C. Next, the samples were TMT-labelled with the TMTpro 18-plex Label Reagent Set (Thermo Fisher Scientific, A52045) according to the manufacturer’s instructions. Subsequently, labelled peptides were pooled and fractionated using on-tip high-pH fractionation. Then, 1 ml of 20 mM ammonium formate (pH 10) was added per 320 µl of pooled sample and added again to self-made C18 columns, prepared as stated above except for the final wash steps, which were performed with 200 μl of 20 mM ammonium formate pH 10 instead of ACN. The samples were loaded in fractions of 250 µl followed by a wash with 200 µl of 20 mM ammonium formate pH 10. Centrifugation at each step was carried out for 3 min and 1,000g. Elution was carried out in five fractions (2 min at 1,000g) with buffers containing 20 mM ammonium formate (pH 10) and different percentages of ACN (16%, 20%, 24%, 28%, 80%). First, 50 µl was used per respective buffer, followed by 20 µl per buffer (2 min 1,000g each). Next, all fractions were dried using a vacuum centrifuge at 45 °C and the resulting dried peptides were stored at −20 °C until data acquisition.

Sample preparation for full-proteome profiling

Per condition, 20 million cells were lysed in 300 µl lysis buffer (50 mM HEPES, pH 8 supplemented with 1 mM PMSF, protease inhibitor cocktail (Sigma-Aldrich) and 2% SDS). Cells were homogenized by pipetting and incubated at room temperature for 20 min. Next, the samples were sonicated (Covaris S2 high-performance ultrasonicator) for 150 s. The lysates were clarified by centrifugation at 20,000g for 5 min at room temperature. Extracted protein amounts were determined by BCA (Pierce BCA Protein Assay, 23227). For each sample, 200 µg (K562) or 100 µg (NALM-6) of protein was digested using a filter-aided sample preparation (FASP) protocol essentially according to published procedures[75].

In brief, proteins were reduced by addition of DTT (final concentration 83.3 mM), followed by incubation at 95 °C for 5 min. After cooling the samples to room temperature, the samples were mixed with 200 µl freshly prepared 8 M urea in 100 mM Tris-HCl at pH 8.5 (UA-buffer) and added onto FASP filter units (Merck Millipore). For buffer-exchange, the samples were centrifuged at 14,000g for 15 min at 20 °C and residual SDS was washed by an additional washing step with 200 µl UA-buffer. All of the subsequent centrifugation steps were done at 14,000g for 15 min at 20 °C. Proteins were alkylated by addition of iodoacetamide (50 mM final concentration) and incubated for 30 min at room temperature in the dark. The samples were washed three times with 100 µl UA-buffer followed by three washes with 100 µl TEAB buffer (Sigma-Aldrich). Proteins were digested by addition of sequencing-grade trypsin at a ratio of 1:50 at 37 °C overnight.

To collect peptides, 50 µl of 50 mM TEAB buffer was added and samples were centrifuged. Filters were additionally washed with 50 µl of 0.5 M NaCl and the flowthroughs of both washing steps were pooled. Peptides were cleaned-up by C18 with peptide desalting spin-columns (Thermo Fisher Scientific). The peptides of each condition were labelled with TMTpro 18plex reagents (K562) or TMTpro 6plex (NALM-6) according to the manufacturer’s instructions (Thermo Fisher Scientific). After 1 h of labelling, 1 µl of each channel was pooled together, quenched and cleaned-up by C18 and concentrated under reduced pressure. This test mix was measured by data-dependent acquisition (DDA) in the Orbitrap for both MS1 and MS2. Quantification was performed at the MS2 level. The test mix was used to calculate the median signal intensity of each TMTpro channel. The ratios to the lowest median channel intensity were derived and all channels were normalized to equalize the labelling efficiency. The pooled channels were quenched, and the samples were cleaned up by C18. As an additional quality control of channel normalization, another test pool was injected. After pooling all of the samples, an aliquot of 100 µl corresponding to roughly 450 µg was cleaned up by C18 and resuspended in 10 mM ammonium formate buffer pH 10. Peptides were separated on an C18 reversed-phase column (150 × 2.0 mm Gemini-NX, 3 µm C18 110 Å, Phenomenex) by liquid chromatography (LC) into 96 time-window-based fractions operating at 50 µl min−1 constant flow rate. A total of 36 fractions were collected, using a previously described pooling strategy[76]. The samples were fractionated into glass vials with 5 µl 30% TFA to acidify samples after fractionation. The fractions were dried under reduced pressure and reconstituted in 0.1% TFA for MS analysis. Additional information with regard to the reagents is provided in Supplementary Table 5.

LC–MS/MS data acquisition of BioID and full proteome samples

MS data were acquired on the Orbitrap Fusion Lumos Tribrid mass spectrometer (Thermo Fisher Scientific) coupled to the Dionex Ultimate 3000 RSLCnano system (Thermo Fisher Scientific) interfaced with the Nanospray Flex Ion Source (Thermo Fisher Scientific). Peptides were loaded on a trap column (PepMap 100 C18, 5 μm, 5 × 0.3 mm, Thermo Fisher Scientific) at a constant flow rate of 10 µl min−1 with 0.1% TFA in HPLC-grade H_2O.

Next, the trap column was switched in-line, and peptides were separated on an analytical column (50 cm, 75 mm inner diameter) in-house packed with ReproSil-Pur 120 C18-AQ, 3 µm (Dr. Maisch HPLC) fitted to an ESI emitter fused silica (20 μm inner diameter × 7 cm length × 365 μm outer diameter; orifice inner diameter, 10 μm; CoAnn Technologies) kept at 50 °C. For the analysis, an analytical gradient of 190 min operated at a constant flow rate of 230 nl min−1 was used. The HPLC was operated with buffer A (0.4% formic acid in HPLC-grade H_2O), and buffer B (0.4% formic acid in ACN).

The analytical gradient comprised the following steps: 0–4 min, constant 6% buffer B; 4–5 min, from 6 to 9% buffer B; 5–146 min, increase to 30% buffer B; 146–154 min, increase to 65% buffer B; and a flush at 100% buffer B. The column was re-equilibrated at 6% buffer B from 167–190 min. The samples were acquired in DDA mode using a maximum of ten dependent scans (TopN approach) with synchronous precursor selection (SPS) enabled. Peptides were ionized by applying a constant voltage of 1.8 kV. MS1 precursor survey scans for MS2 and MS3 levels were acquired with scan range of 400–1,600 m/z and a resolution of 120,000 (at 200 m/z) in the Orbitrap. The automatic gain control (AGC) was set to ‘standard’ with a maximum injection time of 50 ms. Precursor ions were filtered by charge state (2–5) excluding undetermined charge states with a dynamic exclusion (60 s with a ±10 ppm window), and monoisotopic precursor selection. The MS1 precursor intensity threshold was set to 5.0 × 10^3. For MS data analysis, a charge-state filter was used to select precursors for data-dependent scanning. In MS2 analysis, spectra were obtained using one charge state per branch (from z = 2 to z = 5) in a dual-pressure linear ion trap (ITMS2). Ions were isolated using a quadrupole isolation window with an isolation window of ±0.7. Fragmentation was achieved by collision-induced dissociation (CID) with a fixed normalized collision energy of 35% and an CID activation time of 10 ms. For MS2 scans, the normalized AGC target was set to 200% with a maximum injection time of 35 ms. For MS3 scans, precursor ions were isolated using SPS waveform with varying isolation windows for charge stats: 1.3 m/z for z = 2, 1.2 m/z for z = 3, 0.8 m/z for z = 4 and 0.7 m/z for z = 5. Fragment ions were further fragmented by high-energy collision-induced dissociation (HCD) at a fixed activation energy at 45% collision energy. The AGC target was set to 300% with a maximum injection time of 100 ms. The Orbitrap scan range was set to 100–500 m/z at a resolution of 50,000. Xcalibur v.4.3.73.11 and Tune v.3.4.3072.18 were used to operate the instrument.

Processing of BioID raw MS-injections

MS-raw files were processed with the Proteome Discoverer software (PD, Thermo Fisher Scientific, v.2.4.1.15). For the LYN dataset, a subset of 9 TMT-channels (126, 127N, 127C, 132C, 133N, 133C, 134N, 134C, 135N) were used, whereas, for the RIPK2 experiment, and the BLK and APH1A experiment the full channel set was processed. The three BioID datasets were processed independently.

The peptide identification search was performed using Sequest HT, searching for fully tryptic peptides with a maximum of two missed cleavages and a minimum peptide length of 6 and a maximum of 144 amino acids. The precursor mass tolerance was set to 10 ppm and fragment ion mass tolerance was restricted to 0.6 Da. Spectra were searched against the canonical human protein database obtained from UniProtKB (download 5 November 2021, 20,304 sequences) appended with an in-house-generated list of common laboratory contaminants (298 sequences) and streptavidin. As variable modification methionine oxidation (+15.994 Da), deamidation (0.984 Da), phosphorylation on serine, threonine and tyrosine (+79.966 Da), N-terminal specific acetylation (+42.011 Da), methionine loss (−131.040 Da) and acetylation with methionine loss (−89.030 Da) with a maximum number of three variable modification of the same type per peptide. Carbamidomethylation (+57.021 Da) of cysteine residues and TMT 18-plex labelling of peptide N termini and lysine residues (+304.207 Da) were used as static modifications. PSM and peptide FDR were controlled by Percolator at 1% respectively. The obtained results were filtered to include only spectrum matches with a Sequest HT cross-correlation factor (Xcorr) larger or equal to 0.9. Phosphosites needed a minimum site-probability of 75, corresponding to the high-confidence threshold. For protein abundance inference, only high-confidence proteotypic peptides were included.

Protein and peptide intensities were derived from TMTpro reporter ion intensities. The reporter abundances were based on signal-to-noise (S/N) values if applicable, otherwise reporter ion intensities were used. Correction of isotopic impurities was enabled. A co-isolation threshold for isolation interference of precursors was set to maximum 80%. Moreover, to remove noisy signals, an average TMTpro reporter ion S/N threshold smaller or equal to 10 was used with an additional SPS mass matches threshold of 65%, removing peptides with strong interferences. The obtained data were normalized using the sum total peptide amount and scaled to the average. For normalization and to derive protein abundances, all quantified peptides were used. Protein ratios and log_2[FC] values were directly calculated from the grouped protein abundances, without missing value imputation. Abundance changes were tested for their significance using ANOVA on individual proteins across biological triplicates. P values were corrected for multiple testing using the Benjamini–Hochberg procedure.

Data analysis and representation of BioID data

The protein-level PD output was used for further analysis. For the LYN BioID experiments, 4,325 UniProtKB accessions were identified; for RIPK2 BioID experiments, 3,962 accessions were identified; and, for BLK, APH1A BioID experiments, 2,962 UniProtKB accessions were found. From the datasets, proteins flagged as contaminates and proteins without quantification values were removed, resulting in 3,564 UniProtKB accessions for the LYN dataset, 3,334 UniProtKB accessions for the RIPK2 dataset and 1,870 UniProtKB accessions for the BLK, APH1A dataset. For subsequent data analysis, the PD-derived normalized intensities, log_2[FC] and the adjusted P value (Benjamini–Hochberg corrected) were used. For the LYN experiment, the analysis focused on significantly changed proteins after SI-3 treatment versus the vehicle/baseline control (DMSO). To identify enriched proximity interactions, a combined threshold of a log_2[FC] ≥ 2 and an adjusted P ≤ 0.01 against GFP controls were used. The same thresholds were used to identify differentially changed interactions in the LYN SI-3-treated samples against LYN DMSO-treated control. For the RIPK2 and BLK/APH1A datasets, additional scoring of proximity interaction partners was performed using SAINTq[77]. For this, the total sum normalized protein intensities per replicate and condition were grouped together and scored against GFP-negative controls (DMSO). SAINTq was performed on protein level (parameters: normalise_control = false, input_level = protein, compress_n_ctrl = 3, and max score across bait replicates). For BLK/APH1A, proteins with a log_2[FC] ≥ 0.5, a SAINTq-score ≥ 0.99 and a BFDR ≤ 0.01 were considered to be high-confidence proximity interaction partners. For the RIPK2 dataset, a more stringent log_2[FC] cut-off of log_2[FC] ≥ 1 was used. As a further filter, the CRAPome[78] frequency was mapped to each prey protein, using for BLK/APH1A a 10% frequency and for RIPK2 a 20% frequency threshold. Bait proteins were excluded from the CRAPome filter. Moreover, for BLK/APH1A, prey proteins that were annotated as kinases and type I transmembrane proteins in UniProtKB were filtered. These annotated interactors were further filtered for significant changes against GFP negative controls, using the adjusted P value (Benjamini–Hochberg corrected) from the ANOVA hypothesis test performed within PD. The obtained proximity interactors were intersected between BLK and APH1A, revealing on one hand bait-specific and on the other hand shared preys. For the RIPK2 dataset, protein interactions for each condition (RI-4, 1 h; RI-4, 4 h; and BafA1, 18 h) were used as the input for gene set enrichment analysis for GO molecular function (2023) terms using Enrichr. All proteins covered in significantly enriched terms (adjusted P ≤ 0.05) were subset from the comparison of treated versus control (RI-4, 1 h and 4 h versus DMSO; and RI-4 + BafA1, 18 h versus BafA1). All interactions found in at least two conditions versus the GFP negative controls were selected for further visualization. The subset of obtained proximity interactors were grouped into broader molecular function terms. For visualization the log_2[FC] and adjusted P value against treatment controls (DMSO or BafA1, respectively) were used. Data analysis and visualizations were generated employing the statistical software R (v.4.3.1). The resulting processed datasets are provided in Supplementary Data 5. Normalization results and additional individual volcano plots or scatter plots of SAINTq results are provided in Supplementary Fig. 6d–f (LYN), Supplementary Fig. 7b–d (BLK/APH1A) and Supplementary Fig. 8c–e (RIPK2).

Processing and data analysis of full proteome profiling data

The full proteome datasets were processed in Proteome Discoverer v.2.4.1.15, deriving protein intensities using the TMTpro 18 or TMT 6-plex reporter ion quantities.

Peptide identification search was performed using Sequest HT searching for fully tryptic peptides of a minimum of 6 to up to 144 amino acids length and allowing for a maximum of 2 missed cleavage sites. Precursor mass tolerance was set to 10 ppm and fragment ion mass tolerance was restricted to 0.6 Da. The search was performed against the canonical human protein database obtained from UniProtKB (download 12 November 2020) appended with an in-house-generated list of common laboratory contaminants and streptavidin.

As variable modification methionine oxidation (+15.994 Da) and N-terminal specific acetylation (+42.011 Da), methionine loss (−131.040 Da) and acetylation with methionine loss (−89.030 Da) were set. The maximum number variable modification of the same type was limited to 3. As a static modification, carbamidomethylation (+57.021 Da) of cysteine residues and tandem mass tag (TMT) 18-plex/6-plex labelling of peptide N termini and lysine residues (+304.207 Da) were set. PSM and peptide FDR were controlled with Percolator at 1% respectively. Obtained results were filtered to include spectrum matches with a Sequest HT cross-correlation factor (Xcorr) ≥ 1 and strict Percolator target FDR filters. For further analysis, only peptides scored with high confidence and proteins identified with at least 1 proteotypic peptide were used. Protein and peptide intensities were derived from TMTpro reporter ion intensities. The reporter abundances were based on S/N values if applicable, otherwise reporter ion intensities were used. Correction of isotopic impurities was enabled. Co-isolation threshold of 70% for isolation interference of precursors was used. Moreover, to remove noisy signals, an average TMTpro reporter ion S/N threshold of ≤10 was used. A SPS mass match threshold of at least 65% was applied. For reporter-ion-based quantification, unique and razored peptides were considered. The obtained data were normalized using the sum total peptide amount. For normalization and to derive protein abundances, all quantified peptides per protein were used. Protein ratios and log_2[FC] values were calculated from the grouped protein abundances, without missing value imputation. To test for differentially abundant proteins, ANOVA for individual proteins across all biological replicates (n = 3) was performed. For further analysis of degradation selectivity in K562, the 7,665 protein groups with a high confidence score and quantitative values were used. For the NALM-6 cells, the 7,437 protein groups were used for further analysis. For each drug-treated condition the log_2[FC] and P values were derived against DMSO/baseline control conditions. Depending on the duration of the treatment, either the 8- or the 18-h negative control was used. The resulting processed datasets are provided in Supplementary Data 2.

Kinobead profiling

Cells were lysed in 0.8% IGEPAL, 50 mM Tris-HCl pH 7.5, 5% glycerol, 1.5 mM magnesium chloride, 150 mM sodium chloride, 1 mM sodium orthovanadate, 25 mM sodium fluoride, 1 mM DTT, protease inhibitors (SigmaFast, Sigma-Aldrich) and phosphatase inhibitors (prepared in-house according to phosphatase inhibitor cocktail 1, 2 and 3 from Sigma-Aldrich). The cell lysate mixes used for compound profiling were generated either from COLO-205, K562, SK-N-BE(2), MV-4-11 and OVCAR-8 cell lysates (standard 5 CL (cell line) mix) or Jurkat and MCF7 cells mixed at equivalent ratios; the protein concentration was determined using the Bradford assay.

Kinobeads pull-down experiments were performed as previously described[79]. In brief, 2.5 mg of the cell lysate mixture was pre-incubated with increasing compound concentrations (DMSO, 3 nM, 10 nM, 30 nM, 100 nM, 300 nM, 1 µM, 3 µM, 30 µM) for 45 min at 4 °C in an end-over-end shaker in either of the two lysate mixes. Next, the lysates were incubated with Kinobeads (17 µl settled beads) for 30 min at 4 °C. The beads were washed and bound proteins were reduced with 50 mM DTT in 8 M urea, 40 mM Tris HCl (pH 7.4) for 30 min at room temperature. After alkylation with 55 mM CAA, proteins were digested with trypsin overnight at 37 °C. Peptides were desalted using C18 StageTips and dried down in a SpeedVac. Peptides were analysed using LC–MS/MS on the Dionex Ultimate3000 nano HPLC system coupled online to an Orbitrap Fusion Lumos (Thermo Fisher Scientific) mass spectrometer. Peptides were delivered to a trap column (100 µm × 2 cm, packed in-house with Reprosil-Gold C18 ODS-3.5 µm resin, Dr. Maisch, Ammerbuch) and washed at a flow rate of 5 µl min−1 in solvent A (0.1% formic acid, 5% DMSO in HPLC-grade water). Peptides were then separated on an analytical column (75 µm × 40 cm, packed in house with Reprosil-Gold C18 3 µm resin, Dr. Maisch) using a 52-min gradient ranging from 4 to 32% solvent B (0.1% formic acid, 5% DMSO in ACN) in solvent A at a flow rate of 300 nl min−1. The mass spectrometer was operated in a data-dependent mode, automatically switching between MS1 and MS2 spectra. MS1 spectra were acquired over a m/z range of 360–1,300 m/z at a resolution of 60,000 in the Orbitrap using a maximum injection time of 50 ms and an AGC target value of 4 × 10^5. Up to 12 peptide precursors were isolated (isolation width of 1.7 Th, maximum injection time of 75 ms, AGC value of 5 × 10^4), fragmented by HCD using 30% normalized collision energy and analysed in the Orbitrap at a resolution of 15,000. The dynamic exclusion duration of fragmented precursor ions was set to 30 s.

Peptide and protein identification and quantification was performed using MaxQuant (v.1.5.3.30) by searching the tandem MS data against all canonical protein sequences as annotated in the UniProtKB reference database using the embedded search engine Andromeda. Carbamidomethylated cysteine was set as a fixed modification and phosphorylation of serine, threonine and tyrosine, oxidation of methionine and N-terminal protein acetylation as variable modifications. Trypsin/P was specified as the proteolytic enzyme and up to two missed cleavages were allowed. The minimum length of amino acids was set to seven and all data were adjusted to 1% PSM and 1% protein FDR. LFQ and match between runs were enabled within MaxQuant.

For the Kinobeads competition binding assays, protein intensities were normalized to the respective DMSO control and IC_50 and EC_50 values were deduced by a four-parameter log-logistic regression using an internal pipeline that uses the drc package[79] in R. An apparent dissociation constant (Kd,app) was calculated by multiplying the estimated EC_50 by a protein-dependent correction faction. The correction factor of a protein is defined as the ratio of the amount of protein captured from two consecutive pull-downs of the same DMSO control lysate. Targets of the compounds are annotated manually. A protein is considered a target if the resulting binding curve shows a sigmoidal curve shape with a dose-dependent decrease of binding to the beads. Moreover, the number of unique peptides and MSMS counts per condition as well as the protein intensity in the DMSO control are taken into account. The resulting fitted parameters in addition to the normalized intensities are provided in Supplementary Data 6.

TAK285 chemoproteomics

To generate the TAK285 affinity matrix, the terminally amine-tethered TAK285 probe (synthesis is described in the Supplementary Methods) was immobilized to Sepharose beads as previously described[80].

For the competition assay, NALM-6 cell lysates were prepared as previously described[80]. The protein amount of cell lysates was determined using the BCA assay and adjusted to an Igepal concentration of 0.4% and protein concentration of 5 mg ml−1 by diluting with Igepal-reduced lysis buffer. The cell lysate was pre-incubated with different doses of TAK285 or the DMSO vehicle control for 45 min at 4 °C on a shaker, followed by incubation with 18 μl TAK285 affinity matrix for 30 min at 4 °C on a shaker. The beads were washed (once with 1 ml of lysis buffer without protease inhibitors and with only 0.4% Igepal, twice with 1 ml of lysis buffer without protease inhibitors and with only 0.2% Igepal, three times with 1 ml of lysis buffer without protease inhibitors and without Igepal), and the captured proteins were denatured with 8 M urea buffer, alkylated with 55 mM iodoacetamide and digested with trypsin according to standard procedures.

The resulting peptides were desalted by StageTip desalting[81]. To construct a StageTip, five C18 discs were packed into a 200 μl pipette tip. The StageTips were activated with 200 μl ACN (all centrifugation steps at 250g), washed with 200 μl buffer B (0.1% formic acid in 50% ACN) and equilibrated with 200 μl buffer A (0.1% formic acid in double-distilled H_2O). The peptide samples were acidified to a final concentration of around 0.3% formic acid (pH > 2) and loaded on to StageTips. The loading step was repeated with the flow-through. Peptides attached to the C18 material were washed twice with 200 μl buffer A and eluted by adding twice 40 μl of buffer B and collecting the flow-through. The eluent was vacuum-dried and stored at −20 °C until LC–MS/MS measurement.

LC–MS/MS measurement of the TAK285 competition assay

For proteomic data acquisition, a nanoflow LC–ESI-MS/MS setup, comprising a Dionex Ultimate 3000 RSLCnano system coupled to a Fusion Lumos mass spectrometer (both Thermo Fisher Scientific), was used in positive ionization mode. MS data acquisition was performed in DDA mode. For proteome analyses, half of the competition pull-down peptides were delivered to a trap column (Acclaim PepMap 100 C18, 3 μm, 5 × 0.3 mm, Thermo Fisher Scientific) at a flow rate of 5 μl min−1 in HPLC-grade water with 0.1% (v/v) TFA. After 10 min of loading, peptides were transferred to an analytical column (ReproSil Pur C18-AQ, 3 μm, Dr. Maisch, 500 mm × 75 μm, self-packed) and separated using a stepped gradient from minute 11 at 4% solvent B (0.4% (v/v) formic acid in 90% ACN) to minute 61 at 24% solvent B and minute 81 at 36% solvent B at a 300 nl min−1 flow rate. The nano-LC solvent A was 0.4% (v/v) formic acid HPLC-grade water.

MS1 spectra were recorded at a resolution of 60,000 using an AGC target value of 4 × 10^5 and a maximum injection time of 50 ms. The cycle time was set to 2 s. Only precursors with charge state 2 to 6 that fall in a mass range between 360 to 1,300 Da were selected and dynamic exclusion of 30 s was enabled. Peptide fragmentation was performed using HCD and a normalized collision energy of 30%. The precursor isolation window width was set to 1.3 m/z. MS2 spectra were acquired at a resolution of 30,000 with an AGC target value of 5 × 10^4 and a maximum injection time of 54 ms.

Data analysis of the TAK285 competition assay

Protein identification and quantification was performed using MaxQuant (v.2.4.9.0) by searching the LC–MS/MS data against all canonical protein sequences as annotated in the Swiss-Prot reference database (downloaded April 2024) using the embedded search engine Andromeda. Carbamidomethylated cysteine was set as fixed modification and oxidation of methionine and amino-terminal protein acetylation as variable modifications. Trypsin/P was specified as the proteolytic enzyme, and up to two missed cleavage sites were allowed. Precursor tolerance was set to 10 ppm, and fragment ion tolerance was set to 20 ppm. The minimum length of amino acids was set to seven, and all data were adjusted to 1% peptide spectrum matches and 1% protein FDR. LFQ[82] and match between runs was enabled.

To search the proteomics data for dose-dependently competed proteins, we submitted the data to the CurveCurator pipeline[83]. This tool automatically calculates protein LFQ intensities at each competition concentration relative to the DMSO control, plots dose–response curves and applies customized statistics for calling proteins dose-dependently regulated. The associated data are provided in Supplementary Data 7.

BLK γ-secretase complex prediction and molecular dynamics simulations

The BLK–γ-secretase complex was predicted using AlphaFold3, with template information enabled for γ-secretase[43]. BLK was modelled starting at Gly2. The complex was prepared using CHARMM-GUI Membrane Builder[84]: the structure was automatically oriented using the PPM 2.0 method and inserted into a POPC bilayer using the replacement method. An N-terminal myristoylation was added at Gly2 during the setup process. The system was solvated with TIP3P water and neutralized with 0.15 M NaCl. GROMACS (v.2023.2) input files were generated according to CHARMM-GUI’s standard protocol, comprising energy minimization (step 6.0), six-step equilibration (steps 6.1–6.6), and production dynamics (step 7), which were extended to 50 ns. Simulations were repeated in triplicate with different initial velocities. MM/GBSA binding free-energy estimates were computed, and interface contacts were analysed using GetContacts (https://getcontacts.github.io/). Depictions were generated with VMD (v.1.9.4).

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Data associated with the drug screening such as hit scores as well as data associated with compounds or kinases are provided in Supplementary Data 1. Drug screening data have been deposited online (https://science.aithyra.at/KinDegData). Additional CRISPR–Cas9 screening data generated in the revision process have been deposited alongside. All processed sequencing and proteomics data are provided in Supplementary Data 2–7. Moreover, the proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository under dataset identifiers PXD062184 (in vivo biotinylation experiments), PXD053130 and PXD059599 (full proteome profiling) and PXD064676 (TAK285 chemoproteomics). Human protein fasta files were retrieved from UniProtKB (Taxonomic identified 9606, status reviewed, downloaded on 1 December 2019 or 29 April 2024; https://www.uniprot.org/) and have been deposited alongside the respective MS data. The Kinobeads data have been deposited at the ProteomeXchange Consortium via the MASSIVE partner repository under data set identifier MSV000095265 alongside with the used human protein fasta files (UniProtKB, Taxonomic identified 9606, status reviewed, downloaded on the 22 March 2016).

Code availability

All analysis was performed with previously published analysis pipelines or was performed using standard data analysis processing and associated packages as described in the Methods.

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Acknowledgements

We thank the current and former members of the Winter laboratory, in particular M. Cigler for discussions and editorial contributions, as well as M. Schulmann for assistance in DNA preps; the staff at the Core Facility Flow Cytometry of the Medical University of Vienna for access to flow cytometry instruments and assistance with flow cytometry cell sorting as well as the CeMM Biomedical Sequencing Facility for NGS sample sequencing; T. Hannich of the MDP ProMet-facility at CeMM for technical assistance with proteomics data acquisition; J. Zuber at the Research Institute of Molecular Pathology for sharing iCas9 cell lines and plasmids; A.-C. Gingras for providing the BioID plasmids; G. Versteeg for providing the ubiquitin plasmids; S. Martens and M. Gyrd-Hansen for discussions. CeMM, the Winter laboratory and the Superti-Furga laboratory are supported by the Austrian Academy of Sciences; AITHYRA by the Austrian Academy of Sciences as well as the Boehringer Ingelheim Stiftung. The Winter laboratory is further supported by funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement 851478), as well as by funding from the Austrian Science Fund (FWF, projects P7909, P36746 and P5918723) and the Vienna Science and Technology Fund (WWTF, project LS21-015); N.S.S. by the FWF postdoctoral Esprit fellowship ESP 426 and Marie Skłodowska-Curie postdoctoral fellowship (grant agreement no. 101029199); S.L. by the EMBO postdoctoral fellowship (ALTF 236-2024); M.M.O. by a PhD fellowship from the Boehringer Ingelheim Fonds (BIF); and P.A. by the Generalitat de Catalunya (RIS3CAT Emergents VEIS: 001-P-001647 and 2021 SGR 00876) and the Spanish Ministerio de Ciencia, Innovación y Universidades (PID2020-119535RB-I00). A.C.-C. is a recipient of an FI fellowship (2020 FI_B 00094). S. Knapp and M.P.S. acknowledge the support by the Structural Genomics Consortium (SGC), a registered charity (no. 1097737) that receives funds from Bayer, Boehringer Ingelheim, Bristol Myers Squibb, Genentech, Genome Canada through Ontario Genomics Institute, EU/EFPIA/OICR/McGill/KTH/Diamond Innovative Medicines Initiative 2 Joint Undertaking (EUbOPEN grant 875510), Janssen, Pfizer and Takeda. S. Knapp is also supported by the German Cancer Research Center DKTK, the German Cancer Aid project TACTIC and the Frankfurt Cancer Institute (FCI). M.P.S. is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), CRC1430 (project ID 424228829).

Author information

Authors and Affiliations

1. CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria

Natalie S. Scholes, Katharina Kladnik, Fabian Frommelt, Matthias Hinterndorfer, Hana Imrichova, Brianda L. Santini, Eleonora Barone, Caroline Schätz, Miquel Muñoz i Ordoño, Severin Lechner, Andrea Rukavina, Iciar Serrano, Miriam Abele, Anna Koren, Stefan Kubicek, Giulio Superti-Furga & Georg E. Winter

2. Institute for Research in Biomedicine (IRB Barcelona), Barcelona, Spain

Martino Bertoni, Arnau Comajuncosa-Creus & Patrick Aloy

3. Beijing Frontier Research Center for Biological Structure, Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing, China

Xuefei Guo

4. AITHYRA Research Institute for Biomedical Artificial Intelligence of the Austrian Academy of Sciences, Vienna, Austria

Matthias Hinterndorfer & Georg E. Winter

5. Department of Chemistry, Stanford School of Humanities and Sciences, Stanford University, Stanford, CA, USA

Hlib Razumkov

6. Department of Chemical and Systems Biology, ChEM-H, Stanford School of Medicine, Stanford University, Stanford, CA, USA

Hlib Razumkov & Nathanael S. Gray

7. Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany

Polina Prokofeva & Bernhard Kuster

8. Institute of Pharmaceutical Chemistry, Goethe University, Frankfurt am Main, Germany

Martin P. Schwalm, Florian Born, Sandra Roehm & Stefan Knapp

9. Stanford Cancer Institute, Stanford School of Medicine, Stanford University, Stanford, CA, USA

Nathanael S. Gray

10. Center for Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria

Giulio Superti-Furga

11. German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany

Bernhard Kuster

12. Bavarian Biomolecular Mass Spectrometry Center (BayBioMS), Technical University of Munich, Freising, Germany

Bernhard Kuster

13. School of Life Sciences, Westlake University, Hangzhou, China

Yigong Shi

14. Institució Catalana de Recerca I Estudis Avançats (ICREA), Barcelona, Spain

Patrick Aloy

Contributions

N.S.S. and G.E.W. conceptualized the study and wrote the manuscript with input from all of the authors. N.S.S. designed and performed most of the described experiments, data analysis and generated the figures. M.B. and A.C.-C. generated the drug screen scoring scheme. P.A. supervised the corresponding data analysis. K.K. performed experiments, quantified immunoblots and handled BioID experiments. X.G. performed MST experiments. F.F. assisted with the BioID experiments, performed the data analysis and generated the respective figure panels. A.R. and I.S. handled expression proteomics sample preparation and processing. M.A. oversaw MS experiments. E.B. and H.R. performed experiments. B.L.S. handled the AlphaFold3 structure predictions and associated molecular dynamics simulations. A.K. assisted with the drug screen, and performed the initial data processing and normalization. M.H. and C.S. supported CRISPR–Cas9 screens. M.P.S. performed NanoBRET experiments. F.B. generated the TAK285-tethered analogue. S.R. performed hit annotation. S.L. performed the TAK285 chemoproteomics and data analysis. P.P. performed and analysed the Kinobead profiling. H.I., C.S. and M.M.i.O. analysed sequencing data. S. Kubicek supervised the drug screen. S. Knapp oversaw the NanoBRET assays. N.S.G. gave critical input for the manuscript. Y.S. oversaw MST experiments. G.S.-F. and B.K. supervised the proteomics experiments. G.E.W. has overall responsibility for the presented study.

Corresponding author

Correspondence to Georg E. Winter.

Ethics declarations

Competing interests

S. Kubicek, G.S.-F. and G.E.W. are scientific founders and shareholders of Proxygen and Solgate and shareholders in Cellgate Therapeutics. G.E.W. is on the scientific advisory board of Nexo Therapeutics. The G.E.W. and G.S.-F. laboratories received research funding from Pfizer. B.K. is a founder and shareholder of OmicScouts and MSAID. He has no operational role in either company. N.S.G. is a founder, scientific advisory board member and equity holder in Syros, C4, Allorion, Lighthorse, Voronoi, Inception, Matchpoint, CobroVentures, GSK, Shenandoah (board member), Larkspur (board member) and Soltego (board member). The Gray laboratory receives or has received research funding from Novartis, Takeda, Astellas, Taiho, Jansen, Kinogen, Arbella, Deerfield, Springworks, Interline and Sanofi. The other authors declare no competing interests.

Peer review

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Nature thanks Tony Hunter and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Neither half-life nor heterobifunctional degradability determine monovalent destabilization frequencies.

a Comparison of half-life per each kinase fitted from CHX screening data plotted against the summed hit scores. Top: Half-life KDE plot with adjusted bw value of 0.5. Middle: 2D KDE plot and scatter plot of number of hits (#Hits = score) and half-life (h). b Summed destabilization score (#Hits) per canonical kinase mapped onto the kinome tree (Illustration reproduced courtesy of Cell Signaling Technology, Inc. (www.cellsignal.com)). c Comparison of monovalent degrader scores to previously reported scoring frequencies by heterobifunctional degrader molecules (heterobifunctional score) adapted from Donovan, et al.[18]. The data was normalized to the highest scoring kinase per study and depicts the sorted top 32 downregulated kinases by monovalent small molecules. In total, 41 kinases were detected to be downregulated in at least one instance for both studies. d Immunoblot analysis of cell lines in (e) (n = 3). e Luminescent reporter assay of K562 HER2WT or HER2C805S Nluc-3xFLAG reporter cell lines treated for 15 h with the indicated compounds (all 10 µM except for AV-412 (2.5 µM)) shown as normalized luminescence per genetic construct (two-way ANOVA, Sidak corrected) (n = 3). f tSNE plot of compound target landscape focusing on ABL1 hits in comparison to annotated ABL1 binders or dual PI3Kα and MTOR binders. g Drug screening data comparing ABL1 to the mean of all other kinases and the dGFP control. Continued in SI Fig S5. (m = 2, error bars correspond to CI for individual trajectories and SD for the mean of all kinases). h Chemical structures of the three selected examples for mechanism of action elucidation. All data shown as mean of the replicates ± SD unless specified otherwise; n = biological, m = technical replicates.

Extended Data Fig. 2 Hit and drug binding profile depicted as tSNE.

Overlay of the target compound space of all screened inhibitors as annotated in ChEMBL (light grey, see Supplementary Data 1) with all identified hits and annotated kinase binders per kinase shown in the indicated colour code.

Extended Data Fig. 3 Global assessment of inhibitor induced kinase stabilization.

a Breakdown of hit scores across kinases and compounds. b Resulting binary kinase stabilization map sorted according to the observed stabilization frequencies across kinases and compounds including adjacent histograms of the summed scores in both dimensions. c Example trajectories of screening data for DPH stabilizing ABL1 selectively, as well as Abemaciclib stabilizing CDK2 selectively (m = 2, error bars correspond to CI for individual trajectories and SD for the mean of all kinases). d KBM7 iCas9 stability reporter validation for dose-dependent stabilization using DPH and GNF-2 for either ABL1WT or ABL1C464W constructs measured by flow cytometry (n = 3). e Same as in (d) shown for Asciminib. All data shown as mean of the replicates ± SD unless specified otherwise; n = biological, m = technical replicates.

Extended Data Fig. 4 SI-3 selectively destabilizes LYN via a dual phosphodegron and dual E3 mechanism of action.

a Expression proteomics of K562 cells after 2 h of SI-3 treatment (156 nM) (One-way ANOVA), (n = 3). b Highlighted Src kinase family members and CSK abundance changes including p-values for the expression proteomics experiment shown in (a). c Z-LYTE’ SI-3 Lyn inhibitory assay (m = 2, error bars = CI). d Drug screen trajectories of compounds annotated as LYN binders (Supplementary Data 1) depicted for the LYN reporter (pink) and compared to the mean response of all other kinase reporters (black). Compounds that are annotated to bind both LYN and CSK (ChEMBL) have further been highlighted in green. Axis annotations: y-limits (POC) = 0-150 and x-limits (Time (h)) = 0-20; LYN error bars = CI and all other trajectories are shown as SD. e Immunoblot of LYN endogenous protein levels for 2 h drug treatments across a panel of compounds (2.5 µM) reported to engage LYN and CSK (n = 3). f Temporal destabilization profile of LYN stability reporter for DMSO, SI-3 (156 nM) or HSP90i (10 µM) (n = 3). g UPS-focused CRISPR/Cas9 screen data for vehicle (DMSO) treated cells (one-sided MAGeCK) (n = 2). h Immunoblot of representative enrichment for the respective BioID conditions (Fig. 2f) (n = 3). i Genetic k.o. (CTRL = sgAVVS1, KO CBL or KO CBL-B) of LYNWT or LYNY32A stability reporter cell lines measured by flow cytometry. Depicted are baseline normalized values to each respective CTRL sample (n = 3). j Immunoblot for KO of CBL, CBL-B or DKO monitoring endogenous LYN levels upon SI-3 treatment (n = 3). n = biological replicates, m = technical replicates, all data shown as mean of replicates ± SD unless specified otherwise.

Extended Data Fig. 5 SI-3 selectively destabilizes LYN via a dual phosphodegron and dual E3 mechanism of action.

a Chemical rescue upon genetic k.o. for sgAAVS1 (CTRL) or single CBL/CBL-B KO. Cells were pre-treated for 2 h with 1 µM Carfilzomib (Carfil.) or 100 nM Bafil. A1 or both (C&B) followed by 8 h of 156 nM SI-3 treatment and flow cytometric analysis (Two-way ANOVA with Tukey’s test for multiple comparisons, ns > 0.9999, n = 3). b FLAG immunoprecipitation (IP) followed by HA immunoblot of lysates of 293 T FlpIn Lyn-Nluc-3xFLAG cells transfected with HA-ubiquitin (Ub, UbK48R or UbK63R) after 2 h of pre-treatment (pt.) with 1 µM Carfilzomib and 100 nM Bafil. A1, followed by 30 min of DMSO or SI-3 treatment (n = 3). c FACS stability reporter assay for LYN WT, G2A or S6A variants treated for 8 h with DMSO or 156 nM SI-3. d FACS stability reporter assay for LYN pre-treated for 24 h with IMP-1088 (1 µM) or DMSO, followed by 8 h of DMSO or 156 nM SI-3. e FACS stability reporter assay of LYN WT or G2A, pre-treated with rescue compounds from (a) followed by 8 h of DMSO or 156 nM SI-3 treatment (Two-way ANOVA with Tukey’s test for multiple comparisons, ns > 0.9999, n = 3). f NanoBRET measurement of tracer displacement from CSK by SI-3 (n = 2, m = 2). g Immunoblot analysis of K562 cells pre-treated with Nintedanib (10 µM, 2 h) followed by 2 h of SI-3 (156 nM) treatment (n = 3). h FLAG immunoprecipitation (IP) in K562 Lyn-Nluc-3xFLAG or parent (WT) control upon 2 h pre-treatment (pt.) with 1 µM TAK243 or DMSO followed by 1 h of DMSO or SI-3 treatment (n = 3). i Quantification of (h) using TAK243 pre-treated (degradation impaired) samples and comparing pY508 or pY397 LYN to total LYN. The quantification was performed on the IP fractions (FLAG IP) for the bands corresponding to LYN-Nluc-3xFLAG and normalized to the DMSO control lane (two-sided, unpaired t-test, n = 3). j FACS stability reporter assay for LYN WT, Y397A or Y508A treated for 8 h with DMSO or 156 nM SI-3 (n = 3). k Genetic k.o. (CTRL = sgAVVS1, KO CBL or KO CBL-B) for LYNY32A stability reporter cell line and SI-3 degradation assessment (8 h, 156 nM) measured by flow cytometry (Two-way ANOVA with Tukey’s test for multiple comparisons, ns > 0.9999, n = 3). l Phospho-peptide quantification of LYN pY316 measured in the BioID dataset and shown as normalized peptide intensity (two-sided, unpaired t-test, n = 3). m FACS stability assay for LYN Y32A upon KO of CBL, CBL-B or both (DKO) treated for 8 h with DMSO or 156 nM SI-3 (Two-way ANOVA with Tukey’s test for multiple comparisons, n = 3). n = biological replicates, m = technical replicates, all data shown as mean of replicates ± SD unless specified otherwise.

Extended Data Fig. 6 TAK285 degrades BLK in a γ-secretase dependent manner.

a Schematic of the stability reporter design used in the cell line background KBM7 iCas9. Dose response titration of TAK285 to determine the degradation of the BLK stability reporter as measured by flow cytometry. b Flow cytometric analysis of the time-dependent destabilization of BLK (stability reporter). TAK285 was treated at 2.5 µM and HSP90i at 10 µM. t1/2: TAK285 = 1.172 h, HSP90i = 1.740 h; Dmax: TAK285 = 82.49%, HSP90i = 53.68%, R^2 value: TAK285 = 0.9994, HSP90i = 0.9902 (n = 3). c Chemical rescue of TAK285 and HSP90i mediated BLK stability reporter degradation (n = 3). TAK243 (1 µM), Carfilzomib (1 µM), MLN4924 (1 µM) and Bafil. A1 (100 nM) were pre-treated for 2 h, followed by 6 h of 2.5 µM TAK285 treatment prior to flow cytometry. d Matched genome-wide CRISPR/Cas9 data to Fig. 3b shown for vehicle control (DMSO) (one-sided MAGeCK) (n = 2). e Representative BLK stability reporter flow cytometry histogram plot for 2 h DMSO or 12.5 µM DAPT pre-treated cells followed by 6 h 2.5 µM TAK285 treatment. f Immunoblot of NALM-6 cells pre-treated for 2 h with DMSO or DAPT (12.5 µM) followed by 18 h 2.5 µM TAK285 or 10 µM HSP90i treatment (n = 3). g Genetic k.o. of PSENEN (KO; γ-secretase subunit) treated for 6 h with DMSO, TAK285 (2.5 µM) or HSP90i (10 µM) followed by flow cytometric analysis (Two-way ANOVA with Tukey’s test for multiple comparisons, ns = >0.9999 (DMSO), 0.9998 (HSP90i); n = 3). h PSENEN k.o. (KO) baseline BLK stability values measured by flow cytometry and depicted normalized to CTRL (sgAAVS1) (n = 3). i Kinase and type I transmembrane protein interaction partners for BLK and APH1A mapped by BioID. Interaction partners were scored in baseline (DMSO) conditions against GFP controls and grouped as shared or bait-specific interactors. ADAM10 and the γ-secretase subunit NCSTN are highlighted in green. Interactors were ordered in descending log_2FC. Dot size corresponds to log_2FC of each protein against the GFP control. Black dot outlines indicate significantly scored interaction partners within each condition. The colour gradient represents the -log_10 of the adjusted p-value (adj. p-val, BH) (n = 3). j MST measurements of complex formation for BLK and γ-secretase as the ligand in absence or presence of TAK285 (n = 3). k Chemical competition using 2 h of 10 µM Acalabrutinib pre-treatment followed by 6 h of 2.5 µM TAK285 or 1 µM TL-12-186 treatment and analysis by flow cytometry across two stability reporters: BLKWT or the cysteine mutant BLKC319S (Two-way ANOVA with Tukey’s test for multiple comparisons, ns = >0.9999 (WT), 0.9004 (C319S); n = 3, depicted normalized to BLKWT as indicated in the axis label). l MST measurement for γ-secretase and TAK285 as ligand (n = 3). m Dose-ranging chemoproteomics (Kinobead profiling) in Jurkat and MCF7 cell lysates. MAP2K5 was identified as only binder (solid pink line, KD = 2.55 µM), while no binding was detected for BLK (dashed light pink line). n Flow cytometric analysis of BLK stability reporter cells with sgRNAs targeting either AAVS1 (CTRL) or MAP2K5 (KO1, KO2) (Two-way ANOVA with Tukey’s test for multiple comparisons, ns from top to bottom: 0.7873, 0.9924, >0.9999, >0.9999; n = 3). All values represent the mean values ± SD; n = biological replicates, m = technical replicates.

Extended Data Fig. 7 TAK285 re-localizes BLK in a γ-secretase dependent manner.

a Representative microscopy images for a BLK-GFP clonal cell line (RKO iCas9-BFP) assessed for different treatment conditions. Cells were pre-treated (pt.) for 1 h with TAK243 (1 µM) and DAPT (12.5 µM) followed by DMSO or TAK285 (2.5 µM) treatment (tr.) for the indicated timeframe. White arrows highlight localization patterns (n = 2 (clonal cell lines), m = 2). See SI Fig S7e for additional example. b Quantified GFP intensity from images shown in (b) and additional timepoints. The mean total GFP abundance per cell was normalized per 0 h DMSO or TAK243 pre-treatment (n = 2 (clonal cell lines), m = 2). c Inhibition of myristoylation and measurement of BLK stability after TAK285 treatment. Myristoylation was inhibited 24 h prior to 6 h of 2.5 µM TAK285 treatment using the NMT1/2 inhibitor IMP-1088 (1 µM). Left: shown as DMSO-normalized data. Right: normalized per pre-treatment (x; DMSO or IMP-1088, respectively) (n = 3). d Representative microscopy images for localization of different BLK stability reporter pools (BFP-P2A-mCherry) in RKO iCas9 GFP cell line from Fig. 3e and (e). e Stability reporter mutant panel of all phospho-susceptible residues in the unique domain of BLK, assessed after 6 h 2.5 µM TAK285 treatment using flow cytometry (n = 3). f Baseline stability values for BLKWT or BLKS6A stability KBM7 iCas9 reporter cells from (e) measured by flow cytometry (n = 3). g DMS data for IMP-1088 treated (1 µM, 24 h) BLK DMS stability reporter library depicted as normalized log_2FC to DMSO (n = 3). h Orthogonal validation of selected mutations treated with either DMSO, TAK285 (6 h, 2.5 µM) or IMP-1088 (1 µM, 24 h) and analysed by flow cytometry (n = 3). i On bead TAMRA azide click for individual BLK mutants after FLAG immunoprecipitation (IP, n = 3). j Quantification of (i). k Representative microscopy images for individual BLK stability reporter mutations (n = 3). l Flow cytometric analysis of KBM7 iCas9 stability reporter SRCS3L, N4V cells pre-treated with DAPT, followed by DMSO or TAK285 (2.5 µM, 6 h) treatment. Data matched to Fig. 3h (Two-way ANOVA with Tukey’s test for multiple comparisons, ns = 0.8754) (n = 3). All values represent the mean values ± SD; n = biological replicates, m = technical replicates. Scalebars = 25 µm.

Extended Data Fig. 8 RI-4 degrades RIPK2 by inducing kinase multimerization, leading to its clearance via macro-autophagy.

a Drug screen data shown as mean values ± SD for all kinases vs RIPK2 only for the 10 h timepoint. Top and bottom dotted line indicated RI-4’s degradation window (m = 2). b RI-4 LanthaScreen drug binding data. (m = 2, error bar = CI). c Expression proteomics of 10 µM RI-4 treated K562 for 18 h (One-way ANOVA), (n = 3). d Quantification of (e) and respective replicates for CDK9 or RIPK2 abundance for either dCDK9 or RI-4 treated samples, respectively. Values are shown in relation to Vinculin and normalized to DMSO (Two-way ANOVA with a Tukey’s multiple comparison test, ns = 0.3953 (KO1); 0.8258 (KO2) (n = 3). e Example immunoblot of PSMB5 genetic k.o. of RKO iCas9 RIPK2-BFP stability reporter harbouring cells expressing either a control sgRNA (CTRL, sgAAVS1) or sgRNAs targeting PSMB5 (KO1/KO2) treated for 6 h with either DMSO, 2.5 µM RI-4 or 1 µM dCDK9 (n = 3). f Representative normalized fluorescent profiles of RIPK2-GFP and RIPK2-BFP expressing RKO iCas9 cells upon RI-4 treatment (2 h, 2.5 µM) (n = 3). g Representative images of RIPK2 stability reporter cells co-expressing RIPK2-GFP upon RI-4 treatment (2 h, 2.5 µM) (n = 3). h Representative microscopy images (BFP channel) of RIPK2FL (full length) and RIPK2∆CARD stability reporter in RKO iCas9 cells for vehicle control (DMSO) treatments matched treatments shown in Fig. 4h–j. i Representative microscopy images (BFP channel) of RIPK2 stability reporter upon RI-4 treatment in RKO iCas9 cells for either DMSO or Bafil. A1 pre-treated (2 h) cells followed by DMSO or RI-4 (2.5 µM) treatment for a total timeframe of 24 h. j, k Quantification of RIPK2 stability (j) or mean number of RIPK2 foci per cell (k) as shown in (i) (n = 3, m = 2). l, m Quantification of normalized BFP (l) or mean RIPK2 foci number (m) per cell of RIPK2 stability reporter cells (RKO iCas9) pre-treated (1 h) with DMSO or 1 µM TAK243 followed by DMSO or 2.5 µM RI-4 treatment (n = 3, m = 2). All values represent the mean values ± SD unless specified otherwise; n = biological replicates, m = technical replicates. Scale bars = 50 µm (h, i), 10 µm (g).

Extended Data Fig. 9 RI-4 induced, ubiquitin-mediated and TMUB1 facilitated RIPK2 degradation.

a Top 10 enriched GO terms (Molecular Function 2023) for scored interactors of RIPK2 miniTurbo vs GFP for 1 h of RI-4 (2.5 µM) treatment (one-sided Fisher Exact Test, significant terms p-val. <0.01). b Representative microscopy images (BFP channel) of RIPK2 stability reporter upon RI-4 (2.5 µM) treatment in RKO iCas9 cells expressing a control sgRNA (CTRL, sgAAVS1) or a sgRNA targeting BIRC2 or XIAP or both (DKO). Scale bar = 25 µm. c, d Quantification of RIPK2 stability (c) or mean number of RIPK2 foci per cell (d) as shown in b and the respective DMSO controls (n = 3). e Representative microscopy images (BFP channel) of RIPK2 stability reporter (WT or I212D) upon RI-4 (2.5 µM) treatment in RKO iCas9 RIPK2 KO cells. Scale bar = 25 µm. f, g Quantification of RIPK2 stability (f) or mean number of RIPK2 foci per cell (g) as shown in e and the respective DMSO controls (n = 3). h Representative microscopy images (BFP channel) of RIPK2 stability reporter upon RI-4 (2.5 µM) treatment in RKO iCas9 cells expressing a control sgRNA (CTRL, sgAAVS1) or a sgRNA targeting TMUB1 (TMUB1 KO1). Scale bar = 50 µm. l, m Quantification of RIPK2 stability (l) or mean number of RIPK2 foci per cell (m) as shown in (h) as well as for TMUB1 KO2 and the respective DMSO controls (n = 3, m = 2). k Quantification of bait normalized TMUB1 levels after co-immunoprecipitation from Fig. 4l and respective replicates. l, m Quantification of Fig. 4m for total number of foci (TMUB1 or RIPK2) as well as percentage foci co-localization. All values represent the mean values of the biological replicates ± SD; n = biological replicates, m = technical replicates. (k-m) Statistical significance was assessed using an unpaired two-tailed Student’s t-test.

Extended Data Table 1 Screened kinases and hit number breakdowns

Supplementary information

Supplementary Information

Supplementary Methods, Supplementary Tables 1–5, Supplementary Figs. 1–8 and Supplementary References.

Reporting Summary

Supplementary Fig. 9

Temporal trajectories of all hits.

Supplementary Data 1

Concatenated data associated with drug screen.

Supplementary Data 2

Expression proteomics.

Supplementary Data 3

CRISPR–Cas9 screening data.

Supplementary Data 4

DMS data.

Supplementary Data 5

BioID data.

Supplementary Data 6

Kinobead profiling.

Supplementary Data 7

TAK285 chemoproteomics.

Peer Review File

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Scholes, N.S., Bertoni, M., Comajuncosa-Creus, A. et al. Inhibitors supercharge kinase turnover through native proteolytic circuits. Nature 649, 1032–1041 (2026). https://doi.org/10.1038/s41586-025-09763-9

- Received: 15 July 2024

- Accepted: 15 October 2025

- Published: 26 November 2025

- Version of record: 26 November 2025

- Issue date: 22 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09763-9

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Abstract

The nuclear export of mRNA is an important step in eukaryotic gene expression[1]. Despite recent molecular insights into how newly transcribed mRNAs are packaged into ribonucleoprotein complexes (mRNPs)2,3, the subsequent events that govern mRNA export are poorly understood. Here we uncover the molecular basis underlying key events of human mRNA export, including the remodelling of mRNP-bound transcription–export complexes (TREX), the formation of export-competent mRNPs, the docking of mRNPs at the nuclear pore complex (NPC), and the release of mRNPs at the NPC to initiate their export. Our biochemical and structural data show that the ATPase UAP56 (also known as DDX39) acts as a central molecular switch that directs nucleoplasmic mRNPs from TREX to NPC-anchored TREX-2 complexes through its ATP-gated mRNA-binding cycle. Collectively, these findings establish a mechanistic framework for a general and evolutionarily conserved mRNA export pathway.

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Main

Eukaryotic gene expression requires the nuclear export of newly synthesized mRNA through the NPC for translation in the cytoplasm. To prevent the translation of aberrant RNAs, mRNA export is selective for mature mRNA ribonucleoprotein complexes (mRNPs).

Mature mRNPs are marked by specific proteins, which they acquire during the capping, splicing, cleavage and polyadenylation of their precursor mRNAs1,4. By recognizing these maturation marks, the transcription–export complex (TREX) assembles on the surface of packaged mRNPs and selects maturing mRNAs for export1,2,5. TREX also aids in mRNA packaging and thereby ensures genome integrity by preventing the formation of harmful RNA–DNA hybrids, called R-loops[6]. However, packaged TREX–mRNP complexes cannot be directly exported7,8. Instead, they undergo a two-step remodelling process. First, TREX is disassembled to generate export-competent mRNPs[1]. Second, these remodelled mRNPs engage the NPC, where the mRNA export factor, NXF1–NXT1, facilitates mRNP transport across the NPC’s selective permeability barrier9,10,11. Although the factors that are required for mRNA export were identified decades ago9,12,13,14, the mechanistic basis of the different steps, leading to the remodelling of mRNPs for nuclear export, as well as how mRNPs navigate through these steps, remains unclear.

Here, using a combination of biochemistry, in silico protein–protein interaction screening, cryo-electron microscopy (cryo-EM), and cellular assays, we identify a general mechanism for mRNA export that assigns molecular functions to key mRNA export proteins and their complexes.

UAP56 bridges the THO complex to mRNPs

Newly made nuclear mRNPs form compact globules2,3,15, which are decorated with TREX complexes on their surface[2] (Fig. 1a). To investigate how TREX–mRNP complexes are subsequently remodelled for nuclear export, we focused on how TREX interacts with mRNPs after their recognition and packaging. In humans, TREX comprises a tetramer of the six-subunit THO complex, each containing THOC1, THOC2, THOC3, THOC5, THOC6, THOC7, four UAP56 (in yeast, Sub2) molecules and various mRNA export adaptors such as ALYREF (in yeast, Yra1)9,13. ALYREF interacts directly with mRNP-bound maturation marks, such as the exon junction complex (EJC) or the cap-binding complex, through its RNA-recognition motif domain2,16,17. Moreover, ALYREF binds to UAP56 through its N- and C-terminal UAP56-binding motifs (N- and C-UBM)9,18, although only the C-UBM had been observed in structures2,19. UAP56 is a DExD-box ATPase, whose two RecA lobes, RecA1 and RecA2, can clamp onto RNA together with ATP[19]. In the cryo-EM structures of native TREX–mRNP complexes[2], the four UAP56 molecules are primed for mRNA clamping[2]; the UAP56 RecA1 and RecA2 lobes are coordinated through interactions with their cognate THOC2 subunit19,20,21,22 (Fig. 1b,c), and the UAP56 RecA1 lobe connects to the mRNP by binding to the ALYREF C-UBM2,18 (Fig. 1b–d).

Fig. 1: UAP56 controls TREX–mRNP assembly and disassembly.

a, Schematic of the TREX–mRNP complex. For mRNP export, THO must dissociate from the TREX–mRNP complex and the mRNP must be remodelled. b,c, The revised cryo-EM structure of a human TREX–mRNA complex contains the ALYREF N- and C-UBMs, shown in two views (b), and a magnification of the UAP56 interfaces with THOC2 and ALYREF, viewed from the top (c). See also Extended Data Fig. 2g. Shades of green, THO; shades of pink, UAP56; purple, ALYREF; grey sphere, mRNP. d, The domain organization of ALYREF and UAP56. Regions included in the atomic model are indicated with a black line. RBD, RNA-binding domain; RRM, RNA recognition motif. e, Schematic of the RNA- and ATP-dependent UAP56 catalytic cycle. f, Experiment schematic for GCI analysis. g, GCI-derived binding kinetics for the recombinant tetrameric THO complex (immobilized) probed with UAP56, or UAP56, ATP and RNA. Sensograms (pink line), fitted model (black) and a binding kinetics summary table are shown. n.d., not detected. h, Ultraviolet (UV) cross-linking of UAP56 to radioactive [α^32P]ATP or [γ^32P]ATP, with or without 15-nucleotide poly-uridine RNA (experiment scheme on top). After removing excess ATP and RNA from immobilized UAP56, the nucleotide is cross-linked to UAP56 with UV light at 254 nm and visualized by SDS–PAGE using an autoradiograph (middle) and Coomassie-staining (bottom). The radioactive signal in the ‘+RNA’ condition is quantified from three independent experiments. i, Size-exclusion chromatography of UAP56 and ATP with or without 15 nucleotide poly-uridine RNA. UV traces at 280 nm (black) and 260 nm (grey) are shown. See Extended Data Fig. 3g for additional controls. mAU, milli-absorbance units.

ALYREF has a UBM at its N terminus[23]. While it is thought that this N-UBM mimics the C-UBM in binding to the RecA1 lobe of UAP5618,20,21,22, the amino acid sequences of the two UBMs differ despite each being highly conserved (Extended Data Fig. 1a). When we re-examined published TREX–mRNP maps[2], we identified a low-resolution density consistent with an AlphaFold2 prediction of the ALYREF N-UBM with the UAP56 RecA2 lobe (Fig. 1b,c and Extended Data Fig. 1b–g). Furthermore, cryo-EM of a minimal, reconstituted TREX–mRNP complex revealed the THO–UAP56 protomer at 4.1 Å resolution, enabling us to unambiguously assign the ALYREF N-UBM on the UAP56 RecA2 lobe (Extended Data Fig. 2b–g and Extended Data Table 1). Mutating either UBM-binding site in UAP56 selectively abolished the binding to N-UBM or C-UBM peptides in vitro (Extended Data Fig. 3a) and caused severe growth defects in human K562 cells (Extended Data Fig. 3b–d).

Thus, ALYREF binds to two distinct sites on UAP56, forming unique composite surfaces that could be used for mRNA export (see below). Furthermore, we observe UAP56 as the only protein bridging between the THO complex and ALYREF-bound mRNPs (Fig. 1b,c).

RNA clamping releases UAP56 from the THO complex

To facilitate the nuclear export of mRNPs, TREX disassembles through an unknown mechanism1,7,8. Given the central position of UAP56 in TREX, bridging between THO and the mRNP, we investigated whether the ATP-dependent mRNA-clamping of UAP56 (ref. [19]) might have a role in TREX disassembly. To test this, we used grating-coupled interferometry (GCI) to measure the binding affinity of the recombinant and surface-immobilized THO complex to either UAP56 alone or to UAP56 preincubated with ATP, or with RNA, or with both ATP and RNA (Fig. 1f,g and Extended Data Fig. 3e,f). Only the latter pre-incubation, with both ATP and RNA, allows UAP56 to adopt its RNA-clamped conformation[24]. While the THO complex bound to isolated UAP56, irrespective of the addition of either ATP or RNA, with KD values of approximately around 0.24–0.39 µM (Fig. 1g and Extended Data Fig. 3e,f), THO exhibited no measurable binding affinity for RNA-clamped UAP56 formed in the presence of ATP and RNA (Fig. 1g and Extended Data Fig. 3e).

This suggested that TREX dissembles once UAP56 clamps onto RNA. DExD-box family ATPases, including UAP56, act as RNA clampases that clamp rather than translocate on RNA. Some DExD-box ATPases clamp RNA with the highest affinity immediately after ATP hydrolysis, in their ADP- and inorganic phosphate (Pi)-bound state25,26,27. Consistently, we observed the near-complete hydrolysis of ATP in RNA-clamped UAP56 complexes (Fig. 1h). The resulting RNA-clamped complexes were stable, including during size-exclusion chromatography (Fig. 1i and Extended Data Fig. 3g), showing that UAP56 can form longer-lived RNA-bound complexes containing ADP-Pi. The lack in affinity of UAP56–ADP-Pi–RNA complexes for THO indicated that RNA clamping may be important to dissociate UAP56 from the THO complex. Supporting this model, mutation of the DExD-box ATPase motif in UAP56, which prevents ATP hydrolysis and RNA binding in vitro[24], leads to mRNA export defects in yeast[28] and impairs human cell viability (Extended Data Fig. 3b–d).

Notably, when we pre-formed THO–UAP56 complexes using recombinant proteins, we observed inefficient complex disassembly after ATP and RNA addition (Extended Data Fig. 3h). Given that TREX disassembly would require the coordinated release of all four THO-bound UAP56 molecules and because we observed no cooperative binding between UAP56 and tetrameric, dimeric or monomeric THO complexes (Fig. 1g and Extended Data Fig. 3e), we hypothesized that other factors, in addition to RNA-clamping by UAP56, may assist the efficient disassembly of multivalent TREX–mRNPs (Fig. 2a).

Fig. 2: SARNP assists TREX–mRNP disassembly.

a, Schematic of TREX–mRNP disassembly, which may require additional disassembly factors for efficient remodelling. b, Comparison of the abundance of mRNP-associated proteins in purified TREX–mRNPs[2] versus the immunoprecipitation (IP) of nuclear GFP–UAP56 obtained from MS. The heat map is coloured according to the log_2-transformed fold change (log_2[FC]) after normalizing (norm.) to UAP56 levels. c, Domain organization (top) and multiple-sequence alignment (bottom) of human SARNP (UniProt: P82979) and its five UCMs. Residues invariant or conserved among the five UCMs (UCM-1–5) are highlighted in orange. d, Model of a clamped UAP56–ADP-Pi–RNA complex bound to SARNP and ALYREF. The model was obtained by superposing structures of RNA-clamped UAP56 (Protein Data Bank (PDB): 8ENK)[33], open UAP56–ALYREF N- and C-UBMs (Fig. 1) and a UAP56–SARNP UCM-1 AlphaFold2 Multimer prediction (Extended Data Fig. 3i–l). Pink, UAP56; black, RNA; orange, SARNP; purple, ALYREF. e, Native TREX–mRNP disassembly assay. Experiment schematic (left) and MS results (right, heat map) of bead-retained mRNP-associated proteins after adding the ALYREF N-UBM, SARNP UCM-1 or an UCM-1–N-UBM fusion are shown. The heat map is coloured according to the log_2[FC] compared with the buffer control, after normalizing to the mean THO complex subunit levels. Additional details are provided in Extended Data Fig. 5e,f. WB, western blot.

SARNP is a multivalent TREX disassembly factor

To identify factors that assist TREX disassembly, we compared the protein composition of endogenously purified TREX-bound mRNPs[2] with the nuclear UAP56 protein interactome in human cells (Fig. 2b and Supplementary Table 1). This revealed the protein SARNP (CIP29; in yeast, Tho1), which was absent from purified TREX–mRNPs but highly enriched in the UAP56 interactome. SARNP is broadly conserved across eukaryotes and has been implicated in mRNA export in yeast[29], plants30,31 and humans32,33. Moreover, SARNP binds to human mRNAs in vivo34,35 and RNA-clamped UAP56 in vitro32,33 in the absence of the THO complex[31], consistent with the anticipated activities of a factor that aids TREX disassembly.

Using AlphaFold2, we predicted a direct interaction between the human UAP56 RecA2 lobe and SARNP, which was indistinguishable from a recent crystal structure of a chimeric human UAP56–yeast SARNP complex[33] (Fig. 2c,d and Extended Data Figs. 3i–l and 4a,b). Mutation of the highly conserved residue R106D in the SARNP motif (residues 81–115) or of D283R in the UAP56 RecA2 lobe disrupted their interaction in vitro (Extended Data Fig. 4a–c). Consistently, the UAP56(D283R) mutant caused a growth defect in human K562 cells (Extended Data Figs. 3b–d and 4d,e) and impaired the cellular UAP56–SARNP interaction (Extended Data Fig. 4f). Owing to its biochemical activities described below, we refer to SARNP’s UAP56-interacting peptide as the UAP56-clamping motif (UCM). The UCM is found five times in human SARNP[33] with the consensus sequence R/KxxxRAxRFG, and all five UCMs bind to UAP56 (Fig. 2c and Extended Data Fig. 4g,h). Mutation of the central R in these five UCMs abolished the interaction of full-length SARNP with UAP56, while truncation of SARNP’s annotated N-terminal SAP domain had no effect (Extended Data Fig. 4i).

The binding site for the SARNP UCMs on the UAP56 RecA2 lobe is located directly adjacent to the newly identified binding site for the ALYREF N-UBM (Figs. 1b,c and 2d), suggesting that UCM and N-UBM might bind synergistically. To investigate this, we first showed that purified SARNP UCM-1 (hereafter UCM), ALYREF N-UBM and, additionally, ALYREF C-UBM peptides could bind to UAP56 simultaneously in vitro (Extended Data Fig. 4j–l). We next determined the affinities between UAP56 and isolated UCM-1 peptide (10 ± 2 µM) or N-UBM peptide (28 ± 8 µM) (Extended Data Figs. 3a and 5a). We then generated different structure-guided ‘single-chain fusions’ with UAP56 to stabilize low-affinity peptide–UAP56 interactions (Extended Data Fig. 5b,c). We observed enhanced binding of an ALYREF N-UBM peptide to a UAP56–UCM fusion protein (Extended Data Fig. 5b,c), indicating that the synergistic binding of N-UBM and UCM peptides could be important in mRNPs, examined further below.

Notably, binding of a SARNP UCM to UAP56 would sterically clash with the interaction between UAP56 and the THOC2 MIF4G domain in TREX–mRNPs (Extended Data Fig. 4a,b). UCM binding to UAP56 could thereby prevent RNA unclamping and rebinding of UAP56 to THOC2, keeping UAP56 mRNA-clamped, and promoting TREX disassembly and the directionality of these steps. To test this model, we reconstituted the THO–UAP56 complex on beads and examined its integrity after addition of purified ALYREF N-UBM, SARNP UCM peptide, a SARNP UCM-1–ALYREF N-UBM fusion peptide or full-length SARNP, in the presence of ATP and RNA. Among these conditions, the recombinant THO–UAP56 complex disassembled most efficiently when adding the UCM–N-UBM fusion peptide (Extended Data Fig. 5d). These combined data indicate that the synergistic and multivalent binding of SARNP and the ALYREF N-UBM to RNA-clamped UAP56 promotes the efficient dissociation of UAP56 from the THO–UAP56 complex, thus disassembling TREX.

To challenge this model in a more native setting, we assessed whether SARNP and mRNA-clamping by UAP56 could disassemble TREX on endogenous mRNPs. We immobilized TREX–mRNPs purified from the nuclear extract of human K562 cells through the GFP-tagged THO subunit THOC1 (refs. 2,20) and added ATP, or ATP together with either the ALYREF N-UBM, the SARNP UCM or a UCM–N-UBM fusion peptide (Fig. 2e). We measured the release of UAP56–mRNPs from immobilized THO complexes using mass spectrometry (MS) and western blotting (Fig. 2e, Extended Data Fig. 5e,f and Supplementary Table 2). While the addition of the N-UBM alone did not result in mRNP release (Fig. 2e and Extended Data Fig. 5e), addition of the SARNP UCM or of full-length SARNP resulted in detectable mRNP release (Fig. 2e and Extended Data Fig. 5e,f). We note that this release may have been aided by endogenous ALYREF, which co-purifies with mRNPs[2]. This release effect was further enhanced by the addition of the UCM–N-UBM fusion (Fig. 2e and Extended Data Fig. 5e). Consistent with a role downstream of THO, when we acutely depleted SARNP in human K562 cells using the dTAG system, we observed no decrease in UAP56’s interaction levels with either ALYREF or THO (Extended Data Fig. 5g,h). Collectively, these data support a role for UAP56 as the central bridge between the THO complex and the mRNP and demonstrate that the ATP-dependent RNA-clamping of UAP56, assisted by ALYREF and SARNP, is sufficient to disassemble TREX–mRNPs.

SARNP stabilizes UAP56–RNA complexes in vitro

To prevent reassociation of the mRNP with THO, UAP56 must stably clamp onto mRNA. We hypothesized that SARNP and ALYREF may stabilize RNA-clamped UAP56. Indeed, we observed that SARNP, as well as individual or joint fusions of ALYREF N-UBM and a SARNP UCM to UAP56 enhanced the UAP56–RNA interaction in vitro with up to around sixfold higher RNA affinity (KD = 0.12 ± 0.03 µM) compared with wild-type (WT) UAP56 (KD = 0.78 ± 0.09 µM) (Extended Data Fig. 5i,j). SARNP and UAP56 RNA clamping may thereby not only promote THO release but also stabilize UAP56–mRNP complexes. RNA-clamped UAP56 could therefore determine the downstream fate of the mRNP, such as mRNP docking at the NPC.

UAP56–RNA binds to the NPC-anchored TREX-2 complex

Single-molecule tracking experiments of mRNAs in yeast and human cells revealed that mRNPs transiently dock at NPCs before their nuclear export36,37,38,39, but the mechanism underlying these events is unclear (Fig. 3a). To identify proteins that might engage with UAP56–mRNPs after TREX disassembly, we generated a list of putative UAP56 interactors based on their greater than twofold enrichment in UAP56 immunoprecipitates from K562 cell nuclear extract (Fig. 2b). We then performed a pairwise AlphaFold2 Multimer interaction screen between each of these candidates and UAP56 and ranked the results by their interface prediction TM (ipTM) scores (Fig. 3b and Supplementary Table 3). Top-scoring candidates included known UAP56 interactors, such as THOC2, SARNP, and the N- or C-UBM containing export adaptors ALYREF, CHTOP, UIF and PHAX[18], as well as new putative interactors with roles in nuclear mRNA metabolism, including RBM26, RBM27 and NCBP340,41 (Extended Data Fig. 6).

Fig. 3: UAP56 binds to the NPC-anchored TREX-2 complex.

a, Schematic of the UAP56–mRNP complex, which docks at the NPC through an unknown mechanism before export. b, AlphaFold2 Multimer in silico screen predicts novel UAP56 interactors. An experiment schematic (left) and the screen results (right) are shown. UAP56–candidate predictions are ranked by the average interface pTM score (ipTM average). Predicted interactors of interest are highlighted with colours. c, The domain organization of UAP56 (pink) and TREX-2 complex subunits (blue). Regions included in the atomic model are indicated with a black line, and rigid-body fits are shown with a dotted line. NupH, nucleoporin homology. d, Reconstitution of a recombinant UAP56–TREX-2M complex (Extended Data Fig. 7c). SDS–PAGE analysis (Coomassie stain) of a representative in vitro protein pull-down is shown. e, Cryo-EM structures of human TREX-2M (left) and UAP56–TREX-2M (right) viewed from the front. The UAP56 RecA2 lobe, marked with an asterisk, is putatively fitted based on a low-resolution density (Extended Data Fig. 8). Blue, SEM1; dark blue, PCID2; light blue, GANP; shades of pink, UAP56.

Among the top-ranking predicted UAP56 interactors were GANP and PCID2, which are two of the five subunits of the NPC-anchored TREX-2 complex14,42,43,44,45,46 (Fig. 3b,c). TREX-2 is required for mRNA export, but its molecular functions are unclear14,47. The human TREX-2 complex consists of GANP, PCID2, SEM1, ENY2, and CETN2 or CETN3 (yeast, Sac3, Thp1, Dss1, Cdc31 and Sus1, respectively)14,42,43,44,45,46. The GANP subunit scaffolds the four other subunits and anchors TREX-2 to the nuclear basket of the NPC14,48,49. The predicted interaction between UAP56 and TREX-2 therefore suggested a model in which TREX and TREX-2 act in a linear pathway: UAP56–mRNPs, after their release from THO, might dock at the NPC through TREX-2 to facilitate mRNA nuclear export.

To investigate whether UAP56 binds to GANP and PCID2, we purified a minimal recombinant TREX-2 complex (previously termed TREX-2M; ref. [14]) comprising the GANP Sac3 domain (residues 582–1004), PCID2 and SEM1 (Fig. 3c). In in vitro pull-down experiments, TREX-2M bound to UAP56 in a stochiometric complex, confirming their direct interaction (Fig. 3d and Extended Data Fig. 7a). This UAP56–TREX-2M complex could still bind to the ALYREF N-UBM, C-UBM or SARNP UCM peptides (Extended Data Fig. 7a,b).

To reveal the molecular interfaces of the UAP56–TREX-2 complex, we determined cryo-EM structures of the TREX-2M complex in isolation (3.5 Å resolution) and bound to UAP56 (3.5 Å resolution) (Fig. 3e, Extended Data Figs. 7c–g and 8 and Extended Data Table 1). The cryo-EM structure of the human TREX-2M complex in isolation was similar to reported structures of the yeast TREX-2M complex50,51,52,53 (Extended Data Fig. 9a,b), exhibiting a V-shaped architecture made of the GANP Sac3 domain and PCID2 (Fig. 3e). SEM1 is largely unstructured and binds to PCID2 (Fig. 3e). In our UAP56–TREX-2M structure, the N-terminal half of PCID2 rotates slightly outwards to accommodate UAP56, and regions in the GANP N terminus become ordered compared with apo TREX-2M (Fig. 3e). Although we performed these experiments with RNA-bound UAP56, the cryo-EM structure shows UAP56 in an RNA-unbound conformation. While the UAP56 RecA1 lobe is well resolved and binds to the ‘V’ formed by GANP and PCID2, the RecA2 lobe is mobile (Fig. 3e and Extended Data Fig. 8b,d,e). These findings suggested that UAP56 might facilitate the docking of its bound mRNPs at the NPC through interactions with TREX-2, and that TREX-2 may subsequently remodel UAP56–mRNP complexes (Fig. 4a).

Fig. 4: UAP56–TREX-2 interfaces and mRNA release.

a, Schematic of an UAP56–mRNP docked at the NPC, which needs to be released from TREX-2 for mRNP export. b, Details of UAP56–TREX-2M interfaces. TREX-2 regions that are not involved in UAP56 binding (inset) are omitted for clarity. Side chains of key interface residues and AMP-PNP are shown as sticks. Colours are as defined in Fig. 3. c, GCI-derived binding kinetics for TREX-2M (immobilized) probed with UAP56 or UAP56ΔNTD (residues 44–428). Sensograms (pink line), the fitted model (black) and a binding kinetics summary table are shown. d, RNA-export tethering assay. λN-tagged proteins bind to a reporter RNA construct containing boxB RNA aptamers (top). When exported, the reporter RNA is translated into GFP, which is quantified by fluorescence-activated cell sorting (FACS) (top) (Extended Data Fig. 9i,j). The box plots show the median (centre line), interquartile range (25th–75th percentiles; box limits), and the whiskers extend to the 5th and 95th percentiles. Minimum n = 40,000 cells in three independent experiments. a,u., arbitrary unit; PCID2 int, PCID2 interface. e, Mutation of the UAP56 NTD–PCID2 interface in PCID2 accumulates nuclear poly(A) RNA. Shown are representative cells (left; z-projection; Extended Data Fig. 11b) and the ratios of the nucleocytoplasmic (N/C) poly(A) RNA FISH signal (right) after the depletion of endogenous PCID2 or GANP for 16 h, or after the depletion and rescue of endogenous PCID2 with WT or mutant PCID2 constructs M1 (PCID2 NTD-binding mutant (K374D/K388D)) and M2 (PCID2 GANP-binding mutant (D356R/A365F)). Scale bar, 10 µm. Four replicates per condition, with >70 cells per replicate. Pairwise significance testing was performed using two-sided Welch t-tests, with false-discovery rate (FDR) correction for multiple testing; ***P < 0.001. Details and exact P values are provided in Extended Data Fig. 11c,d. Avg., average. f, TREX-2M stimulates UAP56’s ATPase activity in vitro, measured as ATPase rates (molecules of ATP hydrolysed per protein per second) with and without 15 nucleotide poly-uridine RNA. Data are mean ± s.d. from four independent samples. g, UAP56 RNA-unclamping assay. Bead-immobilized 15-nucleotide poly-uridine RNA was incubated with UAP56 and ATP to form UAP56–ADP-Pi–RNA complexes, which were then challenged with recombinant THO or TREX-2M complexes. Bead-remaining UAP56–ADP-Pi–RNA complexes were analysed using SDS–PAGE (Coomassie-staining).

The conserved NTD of UAP56 binds to TREX-2

UAP56 has an unstructured N-terminal domain (NTD) that is conserved from yeast to humans (Extended Data Fig. 9c). Although required for mRNA export[28], the molecular function of the NTD is unclear. In the UAP56–TREX-2M structure, the UAP56 NTD binds between the GANP and PCID2 winged-helix (WH) domains (through UAP56 residues 10–15, NTD interface I) and along the GANP Sac3 domain (through UAP56 residues 39–44, NTD interface II; Fig. 4b). Consistent with the structure, deletion of UAP56 residues 1–28, which includes NTD interface I, resulted in an approximately 2.5-fold reduction in UAP56–TREX-2M affinity (KD = 0.15 ± 0.02 µM) compared with full-length UAP56 (KD = 0.07 ± 0.01 µM) (Fig. 4c and Extended Data Fig. 9d). UAP56 lacking the entire NTD (residues 1–43, UAP56ΔNTD) displayed a more than tenfold reduced affinity (KD = 0.95 ± 0.05 µM) (Fig. 4c and Extended Data Fig. 9e). Moreover, the isolated UAP56 NTD peptide (residues 1–21) was sufficient to bind to TREX-2M in vitro and in nuclear cell extracts, whereas mutated NTD peptides were not (Extended Data Fig. 9f–h). Furthermore, mutations affecting conserved residues in the WH domains of GANP and PCID2 have been shown to lead to mRNA export defects in yeast in vivo[50]. These TREX-2M mutations would critically contribute to the newly identified interface between TREX-2M and the negatively charged UAP56 NTD (Fig. 4b).

Four experimental assays demonstrate that the UAP56–TREX-2 interfaces are functionally relevant in cells: first, we examined the impact of different UAP56 mutations in a cell-based RNA export tethering assay. Aptamer-mediated tethering of UAP56 to a reporter pre-mRNA promoted its export to a degree comparable to the tethering of the mRNA export factor NXF1 (refs. 54,55) (Fig. 4d and Extended Data Fig. 9i,j), consistent with UAP56 promoting mRNP export after TREX disassembly. The combined mutation of critical residues in the UAP56 NTD interface I (L10S, L11S, D12K, Y13S) reduced the export-promoting effect compared to WT UAP56, while removal of the entire UAP56 NTD strongly reduced its export-promoting ability (Fig. 4d and Extended Data Fig. 9i,j), in agreement with our in vitro results (Fig. 4c). This reduction in the export-promoting effect was comparable to mutations of UAP56 RecA1 residues that face PCID2 in our UAP56–TREX-2M structure (D49A, L51W, Q78A, L81K) (Fig. 4d and Extended Data Fig. 9i). As expression levels and nuclear import of the different λN-tagged UAP56 constructs were unaffected (Extended Data Fig. 9j), the observed export defects are most likely due to an impaired UAP56–TREX-2 interaction. Tethering of the TREX-2 subunits PCID2, CETN3 or ENY2 to the reporter pre-mRNA did not promote export (Fig. 4d), presumably because the human TREX-2 complex is constitutively anchored to the NPC basket[56]. In a second experiment, we truncated the UAP56 NTD in a CRISPR–Cas9 knockout–rescue assay, leading to a severe growth defect in human K562 cells (Extended Data Fig. 4d,e). Third, we probed the UAP56 NTD–PCID2 interface by mutating PCID2. We generated a human K562 cell line to acutely deplete PCID2 using the dTAG system (Extended Data Fig. 10a,b). While the ectopic expression of WT PCID2 fully rescued PCID2–dTAG depletion, expression of the PCID2 mutant (K374D and K388D) in the UAP56 NTD interface was lethal (Extended Data Fig. 10c,d). This mutant PCID2 protein was also impaired in binding cellular UAP56 (Extended Data Fig. 10e). Fourth, we carried out poly(A) RNA FISH in human cells using the PCID2–dTAG cell line (Fig. 4e and Extended Data Fig. 11). Nuclear poly(A) RNA FISH signal accumulated after PCID2–dTAG depletion, consistent with a block in mRNA nuclear export. This effect was of a comparable magnitude to the independent GANP–dTAG depletion (Fig. 4e and Extended Data Fig. 11). The ectopic expression of WT PCID2 could fully rescue the poly(A) RNA signal after PCID2–dTAG depletion, but a PCID2 mutant in the UAP56 NTD interface could not.

Collectively, these data suggest that the interaction of UAP56 with the NPC-anchored TREX-2 complex is important for mRNA nuclear export. In cells, the efficient docking of mRNPs at the NPC may be further enhanced by multivalent interactions between multiple UAP56 molecules of the mRNP and multiple TREX-2 complexes at the NPC, owing to the NPC’s eightfold symmetry.

TREX-2 triggers RNA release from UAP56

For export across the NPC, mRNPs must eventually dissociate from TREX-2, which is anchored to the NPC’s basket (Fig. 4a). A clue as to how this might happen came from our UAP56–TREX-2M structure. Although we prepared the UAP56–TREX-2M cryo-EM sample in the presence of RNA and non-hydrolysable AMP-PNP, UAP56 is not RNA-clamped in the structure (Figs. 3e and 4b). Instead, the UAP56 RecA1 lobe is sandwiched between PCID2 and a highly conserved loop within GANP (residues 674–686) (Figs. 3e and 4b and Extended Data Figs. 9b and 10f). This GANP loop, which we named the wedge, is visible only in the UAP56–TREX-2M structure, and not in the isolated human TREX-2M (Fig. 3e) or in a published yeast TREX-2M cryo-EM structure[53] (Extended Data Fig. 9a). In our UAP56–TREX-2M structure, the GANP wedge adopts a position near the UAP56 RecA1 lobe, which would be occupied by the RecA2 lobe in RNA-clamped UAP56[19]. Notably, the UAP56–TREX-2M complex contains the AMP-PNP nucleotide, which is bound between UAP56 RecA1 residue F65 and the evolutionarily invariant GANP wedge residue R678 (Fig. 4b and Extended Data Fig. 10f,g). At this location, GANP R678 substitutes for F381 of UAP56 RecA2, which would coordinate the nucleotide in RNA-clamped UAP56 (refs. 19,33) (Fig. 4b and Extended Data Fig. 10g). These data suggest that the GANP wedge could promote the release of RNA from UAP56, consistent with a previous observation implicating TREX-2 in the removal of UAP56 from yeast mRNPs[44].

As the release of RNA from DExD-box ATPases is coupled to ADP and Pi release, we investigated whether TREX-2M affects the apparent ATPase activity of UAP56. Using an in vitro ATPase assay (Fig. 4f and Extended Data Fig. 10h), we observed that recombinant TREX-2M stimulates the ATPase activity of UAP56 by more than fiftyfold in the presence of RNA (Fig. 4f and Extended Data Fig. 10h). A single point mutation of the GANP wedge residue R678 to alanine reduced the stimulatory effect of TREX-2M approximately tenfold (Fig. 4f and Extended Data Fig. 10h), without affecting UAP56–mutant TREX-2M binding (Extended Data Fig. 9e). As RNA-clamped UAP56 complexes contain ADP and Pi (Fig. 1h), TREX-2 would probably stimulate UAP56 by dissociating RNA, ADP and Pi from UAP56, rather than promoting ATP hydrolysis itself. To test this, we immobilized UAP56–ADP-Pi–RNA complexes through RNA on beads and incubated these with either TREX-2M, the TREX-2M GANP wedge mutant (R678A) or with the THO complex (Fig. 4g and Extended Data Fig. 10i). While TREX-2M unclamped all UAP56 from the RNA, the TREX-2M GANP mutant was less efficient, consistent with the ATPase assay (Fig. 4f). By contrast, the THO complex had no measurable effect on the unclamping of UAP56–ADP-Pi–RNA complexes, consistent with our GCI data (Fig. 1g and Extended Data Fig. 2e) and the proposed role of THO in the loading, but not unloading of UAP56 from RNA.

Taken together, these data suggest that TREX-2 may function not only as the nuclear docking site for UAP56–mRNPs at the NPC, but also as the site at which UAP56 dissociates from mRNPs.

A general model for mRNA export

The data presented in this study offer a framework for understanding mRNA export (Fig. 5a). Central to this model is the ATPase UAP56, which orchestrates a linear process that guides mRNAs through distinct molecular complexes, from the completion of mRNP biogenesis to mRNP docking and remodelling at the NPC before export. Synthesizing previous insights, we propose a five-step pathway for the sequential events governing mRNA export (Fig. 5a).

Fig. 5: A general model for mRNA nuclear export.

a, The RNA clampase UAP56 acts as a molecular switch to direct human mRNAs to (1) assemble and (2) disassemble TREX–mRNPs; (3) form UAP56–mRNPs aided by SARNP; (4) dock; and (5) release at the NPC through TREX-2. Loading of the mRNA export factor NXF1–NXT1 onto mRNPs may occur in the nucleoplasm[73] or at the NPC74,75, initiating mRNP nuclear export. These illustrated steps may occur co-transcriptionally. b, Immunoprecipitations followed by quantitative MS analysis of WT or three UAP56 protein mutants probe the mRNA export model. Left, experiment schematic. The heat map at the top (greyscale) shows log_2-transformed fold changes in protein enrichment of WT UAP56 versus a control (ctrl). The bottom heat maps (blue–white–red scale) show the fold changes in three UAP56 mutants versus WT UAP56. The enrichment of GANP in UAP56 mutant M2 is likely due to the binding of free nuclear UAP56 mutant M2 protein to TREX-2. Experiment outcomes are discussed in detail in Extended Data Fig. 12a.

First, during mRNA transcription and maturation, ALYREF and other mRNA export adaptors18,55 bind to the newly made mRNP through specific protein marks, which initiates the selective packaging of mRNA into mRNP globules through low affinity and multivalent protein–protein and protein–mRNA interactions1,2,3,4,57.

Second, these mRNPs acquire a high density of N-UBMs and C-UBMs on their surface, which recruit the tetrameric THO complex through four UAP56 molecules, assembling TREX on the mRNP surface[2]. TREX thereby aids further mRNP compaction and chaperones the mRNA, preventing the formation of harmful R-loops.

Third, THO dissociates from these multivalent TREX–mRNPs when UAP56 clamps onto mRNA together with ATP. SARNP may bind together with ALYREF in the resulting UAP56–ADP-Pi–mRNP complexes to stabilize RNA-clamped UAP56 and prevent UAP56 from reassociating with THO, thereby increasing the efficiency of TREX disassembly.

Fourth, these remodelled UAP56–mRNPs would diffuse in the nucleus36,37 before docking at the NPC-anchored TREX-2 complex through UAP56. Once docked, UAP56–mRNPs could bind to the mRNA export factor NXF1–NXT1 that is enriched at the NPC by several FG repeat-containing proteins58,59, including the TREX-2 subunit GANP14,60.

Fifth, TREX-2 unclamps UAP56 from mRNA, releasing these mRNPs for their export through the NPC through the mRNA export factor NXF1–NXT1. Consistent with this model, overexpression of the isolated GANP Sac3 domain in yeast leads to an mRNA export defect[14], presumably because nucleoplasmic TREX-2 prematurely releases UAP56 from mRNPs. In cells, these five steps might occur during or after transcription.

This general mRNA export model relies on UAP56 as the central molecule, which would functionally and sequentially connect TREX and TREX-2 complexes. This predicts that the interactions of UAP56 with THO or TREX-2 differentially regulate UAP56 binding to mRNPs. To test this, we designed three UAP56 mutants (M1–M3): UAP56 mutant M1 (D49R/L51D) impairs binding to TREX-2; mutant M2 (F336E/R339D) impairs binding to THO; and mutant M3 (M1 + M2, D49R/L51D/F336E/R339D) impairs binding to both THO and TREX-2. As the THO- and TREX-2-binding surfaces of UAP56 partially overlap, we confirmed the expected binding specificities of each mutant in vitro (Extended Data Fig. 10j,k). We then expressed WT or mutant UAP56 proteins in human K562 cells and analysed their protein interactomes by quantitative MS (Fig. 5b, Extended Data Figs. 10l and 12a and Supplementary Table 4). For UAP56 mutant M1, which is defective in TREX-2 binding, SARNP and mRNP proteins were enriched. By contrast, for UAP56 mutants M2 and M3, which are defective in THO- or THO- and TREX-2-binding, SARNP and mRNP proteins were depleted. These results support that (1) the THO complex promotes the binding of UAP56 to mRNPs; (2) SARNP associates with UAP56–mRNPs downstream of THO but upstream of TREX-2; and (3) TREX-2 removes UAP56 from mRNPs. While we do not exclude that UAP56 molecules or mRNAs could bypass individual steps in the proposed model (Fig. 5a), our data support the sequential actions of the THO and TREX-2 complexes on UAP56.

Discussion

Here we describe a general model for mRNA nuclear export involving a conserved set of factors, which depends on a series of regulated protein–protein and protein–mRNA interactions. Notably, the in silico UAP56 protein interaction screen identified additional UBM-containing and UCM-containing proteins, including a protein of viral origin (Extended Data Fig. 6i,j and Supplementary Table 3). Thus, while SARNP appears less important for mRNA nuclear export than other pathway factors (UAP56, THO, ALYREF, TREX-2)[33], its function may be partially redundant with other UCM- or non-UCM-containing factors or might in some cases be bypassed entirely. Taken together, we speculate that the mRNA export pathway provides additional levels of regulation for mRNA biogenesis and quality control that remain to be identified.

Our mechanistic insights into UAP56 as an RNA clampase show parallels to its close DExD-box ATPase homologue, EIF4A3—a member of the splicing-dependent EJC. Both ATPases bind to a cognate MIF4G-containing protein for their loading onto mRNA (here and previously19,61), both can form stable ATPase–ADP-Pi–RNA complexes (here and previously[26]) and both are mRNA-bound for prolonged periods (here and previously62,63). UAP56 would clamp onto mRNA for minutes, owing to the high rates of mRNA nuclear export37,64,65,66, aided by ALYREF, SARNP or other proteins18,67. EIF4A3 would clamp onto mRNA, in some cases for days[62], until the first round of translation, helped by other proteins and the two EJC subunits, MAGOH and Y1416,68. Other DExD-box ATPase–MIF4G systems may be regulated by related mechanisms to control other RNA processes.

The UAP56–TREX-2 interaction also provides insights into ‘gene gating’. By generating chromatin-tethered UAP56–mRNPs, transcribed genes could enrich at the NPC-tethered TREX-2 complex10,13, enhancing gene expression efficiency42,43,48,69,70. Supporting this model, mutations in yeast GANP that affect gene gating[51] map onto the UAP56–TREX-2 interface in our (Extended Data Figs. 9b and 10f) and other UAP56–TREX-2 complex structures, reported while this Article was under review71,72.

In conclusion, we reveal a mechanistic framework for the selective and efficient nuclear export of mRNA and the molecular functions of conserved proteins and complexes that control individual steps. At the core of this pathway lies the protein UAP56, which orchestrates the nuclear export of mRNA as an ATP-gated molecular switch.

Methods

Vectors and sequences

All vectors and sequences are described in Supplementary Table 5.

Protein purification

THO complex, EJC and ALYREF

Recombinant THO complex tetramer (THOC1, THOC2 residues 1–1203, THOC3, THOC5, THOC6 and THOC7), dimer (same as tetramer but lacking THOC6) and monomer (THOC1, THOC2 residues 1–1203, THOC3, THOC5 residues 1–224, THOC7 residues 1–159) as well as the EJC subunits eIF4A3, MAGOH–Y14 and ALYREFN (residues 1–182), ALYREFC (residues 106–257) and full-length ALYREF were purified as described previously2,20.

UAP56 and UAP56 fusion proteins

6×His-TwinSTREPII-3C-UAP56, 6×His-MBP-3C-UAP56, 6×His-3C-UAP56ΔNTD (residues 44–428) and 10×His-3C-UAP56 WT and mutant constructs were purified as described previously for UAP56[20]. The fusion proteins 10×His-UAP56–UCM-1, 10×His-UAP56–N-UBM and 10×His-UAP56–UCM-1–N-UBM were expressed in Escherichia coli BL21 DE3 RIL cells grown in LB medium, induced at an optical density at 600 nm (OD_600) of 1.0 with 0.5 mM IPTG and incubated at 37 °C for 3 h. Cells were resuspended in lysis buffer (25 mM HEPES pH 7.9, 5% (v/v) glycerol, 300 mM NaCl, 20 mM imidazole, 0.05% Tween-20 and cOmplete EDTA-free protease inhibitor cocktail) and lysed by sonication. The lysate was clarified by centrifugation and the supernatant was filtered through 1 µm and 0.45 µm filters and applied to a HisTrap HP 5 ml column (Cytiva) pre-equilibrated with buffer A (25 mM HEPES pH 7.9, 5% (v/v) glycerol, 300 mM NaCl, 20 mM imidazole). The column was washed with buffer A containing 44 mM imidazole and proteins were eluted with a linear gradient from 50 mM to 300 mM imidazole. The peak fractions were diluted with buffer C (25 mM HEPES pH 7.9, 5% (v/v) glycerol, 1 mM DTT) to 100 mM NaCl and further purified by anion-exchange chromatography using a HiTrapQ 5 ml column (Cytiva), pre-equilibrated in buffer C. The column was washed with buffer C containing 100 mM NaCl and eluted using a linear gradient from 200 mM to 400 mM of NaCl. Peak fractions were concentrated and loaded on a HiLoad 16/600 Superdex 200 pg column (Cytiva) equilibrated using buffer D (25 mM HEPES pH 7.9, 5% (v/v) glycerol, 250 mM NaCl, 1 mM DTT). The purified proteins were concentrated, flash-frozen and stored at −80 °C.

SARNP UCM-1 and ALYREF N-UBM

10×His-SUMO-3V5-tagged ALYREF N-UBM, SARNP UCM-1, UCM-1–N-UBM and UCM-1(R106D)–N-UBM were expressed in E. coli BL21 DE3 RIL cells. UCM-1, N-UBM and UCM-1 (R106D) were expressed in LB medium at 37 °C for 3 h after induction with 0.5 mM IPTG at an OD_600 of 1.0. UCM-1–N-UBM and UCM-1(R106D)–N-UBM were incubated at 18 °C overnight after induction. Cell pellets were resuspended in lysis buffer and lysed by sonication. Lysates were clarified by centrifugation, filtered through 1 µm and 0.45 µm filters and loaded onto a HisTrap HP 5 ml column equilibrated in buffer A. The column was washed with buffer A and proteins were eluted at 350 mM imidazole. The peak fractions were diluted to 50 mM NaCl with buffer C and loaded onto the HiTrapQ HP 5 ml column equilibrated in buffer C. The column was washed with buffer C supplemented with 50 mM NaCl and eluted using a linear gradient from 50 mM to 500 mM NaCl. The peak fractions were concentrated and applied to a HiLoad 16-600 Superdex 75 pg column (Cytiva) equilibrated using buffer E (10 mM HEPES pH 7.9, 500 mM NaCl, 10% (v/v) glycerol, 1 mM DTT, 20 mM imidazole). The peak fractions were concentrated again, flash-frozen in liquid nitrogen and stored at −80 °C. Buffer A and B for the purification of UCM-1–N-UBM contained 500 mM NaCl.

The UCM-1 R106D mutant was purified using a similar strategy with the following exceptions: two wash steps were performed during HisTrap using buffer A including a high-salt wash (25 mM HEPES pH 7.9, 5% (v/v) glycerol, 1 M NaCl) and buffer A supplemented with 50 mM imidazole. During the anion-exchange step, the column was washed with 100 mM NaCl and eluted by a linear gradient from 100 mM to 400 mM NaCl.

SARNP

SARNP-6×His or SARNP5xRtoD-6×His were expressed in E. coli BL21 DE3 RIL cells grown in LB medium overnight at 18 °C after induction with 0.5 mM IPTG at OD_600 = 1.0. MBP-SARNP47–210-3C-3V5-SUMO-10×His was expressed in E. coli BL21 DE3 RIL cells grown in LB medium for 3 h at 37 °C after induction with 0.5 mM IPTG at OD_600 = 1.0. Cell pellets were resuspended in lysis buffer (50 mM HEPES pH 7.9, 500 mM NaCl, 10% (v/v) glycerol, 20 mM imidazole, 1 mM DTT, 0.5 mM PMSF, cOmplete EDTA-free protease inhibitor cocktail and 0.1% Tween-20), lysed by sonication and centrifuged. The supernatant was filtered through a 0.4-µm filter and loaded onto a HisTrap HP 5 ml column equilibrated using buffer E. The column was washed with 15 mM imidazole and SARNP eluted using a linear gradient from 15 to 350 mM imidazole. The peak fractions were diluted to 100 mM NaCl using buffer F (25 mM HEPES pH 7.9, 10% (v/v) glycerol and 2.5 mM DTT) and applied to a HiTrapQ HP 5 ml column equilibrated using buffer F (200 mM NaCl). The column was washed and bound protein was eluted over a linear gradient from 100 mM to 800 mM NaCl. The peak fractions were concentrated and applied to the HiLoad 16-600 Superdex 200 pg column, pre-equilibrated using buffer D containing 2.5 mM DTT and 250 mM salt. The purified protein was concentrated, flash-frozen and stored at −80 °C.

TREX-2M and TREX-2M (R678A)

TREX-2M and TREX-2M (R678A) were expressed in E. coli BL21 DE3 RIL cells grown in LB medium at 37 °C until OD_600 at 1.0. Expression was induced by addition of 0.5 mM IPTG and cells were incubated at 18 °C overnight. Cells were collected by centrifugation and resuspended in lysis buffer (containing 500 mM NaCl and no Tween-20 for the TREX-2M purification). Cells were lysed by sonication and lysates were centrifuged. The supernatant was filtered through 1-μm and 0.45-μm filters and applied to a HisTrap HP 5 ml column equilibrated with buffer A, washed with buffer A (50 mM NaCl) and eluted over a linear gradient to 350 mM imidazole. The complex was diluted in buffer C to 100 mM NaCl and loaded on a HiTrapQ HP 5 ml column equilibrated with buffer C containing 100 mM NaCl. After a wash step with the buffer C containing 100 mM NaCl, the complex was eluted from the HiTrapQ column using a linear gradient to 800 mM NaCl (500 mM NaCl for TREX-2M (R678A)). The peak fractions were concentrated and applied to a HiLoad 16-600 Superdex 200 pg column equilibrated with buffer D. The purified complex was concentrated, flash-frozen and stored at −80 °C.

MBP–GANP and PCID2–UAP56–UCM-1–N-UBM

MBP–GANP (residues 582–1004) and 10×His–PCID2–UAP56–UCM-1–N-UBM – SEM1 were expressed in E. coli BL21 DE RIL cells. MBP–GANP was expressed in LB medium at 18 °C overnight after induction with 0.5 mM IPTG at OD_600 at 1.0 and 10×His-PCID2–UAP56–UCM-1–N-UBM – SEM1 was expressed in autoinduction medium at 30 °C. Bacterial cell pellets for MBP–GANP were lysed in buffer A containing 500 mM NaCl by sonication and centrifuged. The supernatant was loaded on a HisTrap HP 5 ml column equilibrated using buffer A containing 500 mM NaCl and 50 mM imidazole. The column was washed with this buffer A and eluted over a linear gradient to 350 mM imidazole using buffer B contained 500 mM NaCl. Peak fractions were diluted to 100 mM NaCl using buffer C, applied to a HiTrapQ HP 5 ml column and washed with buffer C containing 100 mM NaCl. The proteins were eluted using a linear gradient to 800 mM NaCl. The flow-through of the anion-exchange step was concentrated and loaded on a HiLoad 16-600 Superdex 200 pg column equilibrated with buffer E. Peak fractions were concentrated, flash-frozen and stored at −80 °C.

10×His-PCID2–UAP56–UCM-1–N-UBM – SEM1 was purified using a similar strategy with the following changes: buffers A and B contained 300 mM NaCl and the co-expressed complex was eluted from HisTrap using a linear gradient from 50 to 300 mM imidazole. Moreover, the column was washed with buffer C containing 160 mM NaCl during the anion-exchange and eluted over a linear gradient from 160 to 400 mM NaCl.

MBP–MCP

MBP–MCP was expressed in E. coli Rosetta2 pLysS cells, grown in LB medium at 37 °C until OD_600 at 0.7, induced by addition of 0.5 mM IPTG and incubated at 37 °C for 3 h. Cells were resuspended in lysis buffer (20 mM HEPES pH 7.9, 200 mM KCl, 1 mM EDTA, 0.5 mM PMSF) and lysed by sonication. The lysate was centrifuged, filtered through a 0.45-µm filter and loaded on a MBP Trap HP column (Cytiva) equilibrated with buffer G (20 mM HEPES pH 7.9, 200 mM KCl, 1 mM EDTA). The column was washed first with buffer G and then with buffer H (20 mM HEPES pH 7.9, 20 mM KCl, 1 mM EDTA) and the protein was eluted using buffer H containing 10 mM maltose. The protein was further purified using a HiTrap Heparin HP 5 ml column and washed with buffer H (no EDTA). The protein was eluted over a linear gradient to 400 mM KCl. Peak fractions were flash-frozen in storage buffer (10 mM HEPES pH 7.9, 57 mM KCl, 1 mM EDTA, 10% (v/v) glycerol) and stored at −80 °C.

Pull-down experiments using recombinant proteins

In vitro THO–UAP56 disassembly assay

Recombinant MBP–UAP56 (6.75 μg per reaction) was combined with a twofold molar excess of monomeric THO complex (10 μg per reaction) in buffer I (20 mM HEPES pH 7.9, 50 mM KCl, 1 mM MgCl_2, 5% glycerol, 0.1% Igepal CA-630) and incubated with 30 μl of amylose resin (E8021S, NEB), pre-equilibrated in buffer I, for 30 min at room temperature. The resin was then separated from the supernatant by centrifugation, washed three times with buffer I, resuspended in 40 μl of buffer I per reaction and split into individual tubes for each THO–UAP56 disassembly reaction. Components for the release reaction were prepared in a final volume of 40 μl buffer I (200 μM 15 U RNA, 0.1 mM ATP, and 55, 55 and 60 μg of Sumo-V5–N-UBM, Sumo-V5–UCM-1 and Sumo-V5–UCM-1–N-UBM, respectively), combined with the amylose resin with immobilized UAP56–THO complex and incubated for 60 min at room temperature. After four washes with buffer I, the bead-retained complexes were then eluted in buffer I supplemented with 100 mM maltose for 20 min at room temperature. Elutions and input samples of the individual recombinant proteins were separated on 4–12% gradient SDS–PAGE gels and visualized by Coomassie staining. The amount of bead-retained THO complex in each reaction was analysed in Fiji[76]. The intensity of the THOC2 band was measured, normalized to the intensity of the MBP–UAP56 band, and the background subtracted; THOC2 in the reaction incubated with buffer I without supplements was set to 100%.

RNA-clamping assay

In step 1, for each reaction 1 μg of in vitro transcribed 450 nucleotides AdML RNA and 5.1 μg of MBP–MS2 (equimolar with the RNA) in buffer J (20 mM HEPES pH 7.9, 100 mM KCl, 2 mM MgCl_2, 5% glycerol, 0.1% Igepal CA-630) were incubated with 20 μl of amylose resin (E8021S, NEB), pre-equilibrated in buffer J for 30 min at room temperature. The resin was then collected by centrifugation, the supernatant containing unbound components was removed, and three washes with buffer J were conducted before the resin with immobilized MBP–MS2–RNA was resuspended in 40 μl buffer J supplemented with 1 mM AMP-PNP and split into the desired number of reactions. Components to be tested for RNA binding in step 2 (23 μg UAP56 or UAP56–N-UBM, UAP56–UCM, UAP56–UCM-1–N-UBM (twofold molar excess over the RNA), 24 μg SARNP (twofold molar excess over UAP56)) were prepared in buffer J containing 1 mM AMP-PNP, combined with the resin prepared in step 1 and incubated for 90 min at room temperature. The resin was then again collected by centrifugation, washed three times with buffer J and incubated with 40 μl buffer J containing 0.4 μg benzonase to elute RNA-bound proteins. Elutions and input samples of the individual recombinant proteins were separated on 4–12% gradient SDS–PAGE gels and visualized by Coomassie staining. To assess the amount of RNA-bound UAP56 in Fiji[76] we measured the intensity of the UAP56 band, subtracted the background and normalized to UAP56 in the presence of SARNP set to 100%.

SARNP UCM-1 and ALYREF N-UBM–UAP56 pull-down

To assess the interaction of UAP56 and the SARNP UCM-1 or the ALYREF N-UBM 7.5 μg of Sumo-V5-3C-tagged UCM-1, N-UBM or UCM-1–N-UBM were combined with a fourfold molar excess of UAP56 in buffer K (25 mM HEPES pH 7.9, 40 mM KCl, 5% glycerol, 0.01% Igepal CA630, 1 mM MgCl_2, 1 mM TCEP) in the presence of 50 μM 15 U RNA and 1 mM AMP-PNP, and incubated for 1 h at 4 °C before being added to 10 µl magnetic V5 beads (v5tma, Chromotek), pre-equilibrated in buffer K. After incubation for another hour rotating at 4 °C, the beads were centrifuged briefly to recover beads from the lid (1,300g, 2 min, 4 °C) and washed three times with buffer K on a magnetic rack. The samples were eluted using 30 μl 200 mM glycine (pH 2.52) for 5 min at room temperature. Eluates were neutralized using 2.5 μl 1 M Tris pH 10.4, and, together with input samples of the individual recombinant proteins, separated on 4–12% gradient SDS–PAGE gels and visualized by Coomassie staining.

UAP56–TREX-2M pull-down

To analyse the interaction of TREX-2M and UAP56, TREX-2M with an MBP-tag on the GANP subunit, was combined with a fourfold molar excess of UAP56 and a tenfold molar excess of UCM–UBM fusion peptide in buffer K, with or without 50 μM 15 U RNA and 1 mM AMP-PNP, and incubated rotating for 1 h at 4 °C. The samples were added to 30 μl pre-equilibrated amylose resin (E8021S, NEB), and incubated for another hour with rotating at 4 °C. Beads were centrifuged (1,300g, 2 min, 4 °C) to remove the unbound fraction, washed three times with buffer K, and bead-bound complexes were eluted for 1 h at room temperature in 30 μl buffer K supplemented with 100 mM maltose. Elutions and input samples of the individual recombinant proteins were separated on 4–12% gradient SDS–PAGE gels and visualized by Coomassie staining.

UAP56 NTD –TREX-2M pull-down

To test the interaction of the isolated UAP56 NTD and TREX-2M, 30 μl of Pierce High Capacity NeutrAvidin Agarose beads (29202, Thermo Fisher Scientific) were pre-equilibrated with buffer K and incubated with or without 30 μg of biotinylated UAP56 NTD peptide (residues 1–24, WT or mutant, with biotin on the C terminus) in buffer K for 1 h at room temperature. The beads were then washed three times to remove unbound peptide and incubated with protein samples (set up in a 50 µl reaction containing 50 µM 15U RNA and 1 mM AMP-PNP and, as applicable: 7.5 µg TREX-2M with or without a fourfold molar excess of UAP56; 7.5 µg GANP(582–1004) with a 2.5-fold molar excess of the PCID2–UAP56–UCM–N-UBM – SEM1). After an incubation of 1 h rotating at 4 °C, the beads were again collected by centrifugation, washed three times with buffer K and bead-bound material eluted for 5 min at room temperature in 30 μl of 200 mM glycine pH 2.52. The elutions were neutralized with 100 mM Tris pH 10.4, separated alongside input samples of isolated recombinant proteins on 4–12% gradient SDS–PAGE gels and visualized by Coomassie staining.

RNA-unclamping assay

Biotinylated 15U RNA (33 µM), recombinant UAP56 (10 µM) and 1 mM ATP were incubated in buffer A2 (20 mM HEPES pH 7.9, 40 mM KCl, 2 mM MgCl_2, 5% glycerol, 0.1% Igepal CA630) with 20 µl Pierce High Capacity NeutrAvidin Agarose beads (29202, Thermo Fisher Scientific), pre-equilibrated in buffer A2, for 30 min at room temperature. After three washes with buffer A2 to remove unclamped UAP56 and excess ATP, the beads were resuspended in buffer A2 and split into the desired number of reactions. Next, 2.2 µM/0.44 µM WT or GANP R678A TREX-2M or 5 µM THO complex monomer were added in buffer A2 and the reactions incubated for 10 min at room temperature. Unbound proteins were then removed through washes twice in buffer (20 mM HEPES pH 7.9, 500 mM KCl, 2 mM MgCl_2, 5% glycerol, 0.1% Igepal CA630) and twice in buffer A2 before elution of RNA bound proteins (0.4 μg benzonase in buffer A2) for 10 min at room temperature. Elutions were then analysed by Coomassie-stained SDS–PAGE and the amount of remaining RNA clamped UAP56 quantified in Fiji.

UCM/UBM–UAP56 and –UAP56–TREX-2 pull-down

Biotinylated peptides were immobilized in buffer A2 on 20 µl Pierce High Capacity NeutrAvidin Agarose beads (29202, Thermo Fisher Scientific), pre-equilibrated in buffer A2. The beads were washed three times to remove excess peptide and resuspended in buffer A2 before adding 20 µM UAP56 or 3.2 µM UAP56 with 6.4 µM TREX-2M and incubating at room temperature for 30 min (for UAP56 alone) or at 4 °C for 1 h (for UAP56—TREX-2M). Unbound UAP56 was then removed, the beads washed three times and bead-bound UAP56 was eluted in low-pH buffer for 10 min at room temperature. The elutions were neutralized and analysed by Coomassie-stained SDS–PAGE.

UAP56–SARNP pull-down

Magnetic anti-Flag M2 Beads (Merck, M8823; 20 µl per reaction) were equilibrated in buffer A2. Flag-tagged UAP56 (5 µM) and WT or mutant SARNP (20 µM) were added to the beads and incubated for 1 h at room temperature. Subsequently, unbound protein was removed, beads were washed three times and bead-bound complexes were eluted in low-pH buffer for 10 min at room temperature. The elutions were neutralized and analysed by SDS–PAGE and Coomassie staining.

ALYREF–UAP56–SARNP pull-downs

MBP tagged full-length or truncated ALYREF (2.5 µM) was immobilized in buffer A2 on 20 µl buffer-equilibrated amylose resin (NEB, E8021) with, as applicable, 12.5 µM UAP56, UCM-1, N-UBM or C-UBM or 6 µM full-length SARNP, and with or without 1 mM ATP and 200 µM 15 U RNA for 1 h at room temperature. Unbound protein was removed, beads were washed twice and bound complexes were eluted by incubating the beads for 5 min in SDS sample buffer at 95 °C before analysing the elutions using Coomassie-stained SDS–PAGE.

GCI analysis

For GCI[77] experiments, the analyte is immobilized on a microfluidic chip and a putative ligand is flown in at increasing concentrations (association) and subsequently washed out with buffer (dissociation) (Fig. 1f). Binding is recorded as a change in the refractive index, yielding sensograms, which are fitted with a 1-to-1 binding kinetic model. GCI experiments were performed on a Creoptix WAVE system (Creoptix) using 4PCP WAVEchips (quasi-planar polycarboxylate surface; Creoptix). Chips were conditioned with borate buffer (100 mM sodium borate pH 9.0, 1 M NaCl), and either streptavidin (10 μg ml−1 in 10 mM sodium acetate pH 5.0) or a monoclonal anti-V5 antibody (R960252, Invitrogen; 2 μg ml−1 in 10 mM sodium acetate pH 5.0) immobilized using a standard amine coupling protocol, followed by passivation of the surface with BSA (0.5% in 10 mM sodium acetate pH 5.0) and final quenching with 1 M ethanolamine pH 8.0. Biotinylated 15 U RNA, UCM or UBM peptides or V5-tagged THO or TREX-2M complexes were captured on the prepared chip until the desired density was reached. UAP56 was injected in a 1:2 dilution series, starting from a highest concentration of 5 μM with or without 200 μM 15 U RNA, in 25 mM HEPES pH 7.9, 50 mM KCl, 1 mM MgCl_2, 1 mM TCEP, with and without 1 mM ATP at 25 °C. Blank injections were used for double referencing and a DMSO calibration curve was used for bulk correction. Analysis and correction were performed using the Creoptix WAVEcontrol software (applied corrections: x and y offset, DMSO calibration, double referencing) using a one-to-one binding model. The data and fitted models were plotted in R.

UAP56—ATP cross-linking

Recombinant UAP56 with an N-terminal 10×His-2×Strep-3C tag (8 μM) was incubated in a total reaction volume of 15 μl in buffer A3 (25 mM HEPES pH 7.9, 50 mM KCl, 2 mM MgCl_2) including 0.025 μM radioactive [α^32P]ATP or [γ^32P]ATP (around 3,000 Ci mmol−1), 5 μl of magnetic nickel particles (Promega, V8560) and with or without 120 μM 15 U RNA for 30 min at room temperature. Unbound UAP56 and excess ATP and RNA were removed and the beads washed three times before being resuspended in 15 μl of buffer A3 and crosslinked for 2 min at a distance of 7 cm in a Stratagene Stratalinker UV1800 at λ = 254 nm. After the cross-linking reaction 5 μl of 5× SDS loading dye was added and the beads boiled for 2 min at 92 °C. The samples were then analysed on homemade 10% SDS–PAGE gels, stained with Coomassie-stain and the radioactive signal visualized using storage phosphor-screens and an Amersham Typhoon laser scanner.

In this experiment the ATP base is cross-linked to UAP56. Using [α^32P]ATP radioactive signal can be observed for UAP56-cross-linked nucleotide independently of whether or not ATP is hydrolysed in the UAP56–RNA complex, because the radioactive α^32P is present in both ADP and ATP. By contrast, when using [γ^32P]ATP, radioactive signal would only be observed for the UAP56–RNA complex if intact ATP had been crosslinked. If, as observed here, ATP is hydrolysed in the UAP56–RNA complex, the radioactive γ^32P is lost after the denaturation of the complex and no radioactive signal is observed.

Size-exclusion chromatography

UAP56 (10His-Twinstrep-3C tagged, 20 μM) was incubated in a 500 μl reaction in buffer X3 (25 mM HEPES pH 7.9, 100 mM KCl, 1 mM MgCl_2, 1 mM TCEP) with 1 mM ATP or ADP and with or without 120 μM 15 U RNA for 1 h at room temperature. The samples were then analysed on a Superdex 200 increase 10/300 GL size-exclusion chromatography column, equilibrated in buffer X3, with monitoring of the UV absorption at 260 nm and 280 nm.

IP experiments

GFP–UAP56 IP for MS analysis

GFP IPs were performed in triplicates from nuclei of GFP–UAP56 or WT K562 cells. For each replicate, 200 million cells were fractionated into nuclei and cytoplasm as previously described2,78, nuclei were lysed in buffer L (50 mM Tris pH 7.5, 100 mM KCl, 3 mM MgCl_2, 0.25% Triton X-100, 0.25% Igepal CA630, 10% glycerol, 1× protease inhibitor cocktail, 1 mM DTT) supplemented with 1 μg ml−1 benzonase and 0.1% deoxycholate and the lysates were incubated for 15 min at 4 °C on a rotating wheel followed by a centrifugation step to pellet chromatin (21,000g, 10 min, 4 °C). The supernatant was then incubated with 20 μl magnetic GFP-Trap MA-Agarose beads (Chromotek), pre-equilibrated in buffer L and incubated on a rotating wheel for 4 h at 4 °C. The beads were then collected on a magnetic rack, washed four times in 1 ml buffer L, and four times with 20 mM Tris pH 7.5, 100 mM KCl. After the final wash step, all buffer was removed and the beads were snap-frozen in liquid nitrogen. The samples were analysed by MS starting from an on-bead digest of bound protein complexes.

Flag–UAP56 IP for western blot and quantitative MS

V5-flag-TurboID-tagged UAP56 constructs (WT and mutants M1, M2 and M3) as well as V5-flag-TurboID-eGFP-NLS as a control were cloned under the TRE-tight promoter into the PiggyBac system ePB vector backbone, featuring in addition the expression of rtTA-Advanced-P2A-mScarlet under the human UbC promoter. Plasmids were electroporated into WT human K562 cells together with a plasmid encoding a PiggyBac transposase. mScarlet expression was used to identify a transgene harbouring cell population by FACS, and transgene expression was induced by the addition of 0.2 µg ml−1 doxycycline 2 days before collecting the cells.

Flag IPs were performed in triplicates from 60 million nuclei per replicate as described above for GFP–UAP56 IP, but with 20 μl magnetic anti-flag M2 magnetic beads (Millipore, M8823) and 10% of the beads were analysed by western blot using anti-THOC2 (ab129485, Abcam, 1:1,000), anti-UAP56 (ab181059, Abcam, 1:1,000), anti-histone H3 (ab1791, Abcam, 1:1,000) and goat-anti-rabbit antibody coupled to HRP (Thermo Fisher Scientific, 31466, 1:5,000) antibodies.

UAP56 IP western blotting

UAP56 IP experiments were performed as outlined above with the following changes: UAP56 was precipitated with anti-UAP56 antibody (Cell Signaling Technology, 47258) coupled to magnetic protein G beads (Thermo Fisher Scientific, 88802, the control reaction was performed with protein G beads without antibody) from 1.5 million K562 cell nuclei. To analyse the UAP56 interactome after SARNP depletion (Extended Data Fig. 5g,h) we used a SARNP-FKBP12F36V cell line. dTAG-V1 was added 6 h before collecting the cells to deplete SARNP. Elutions were analysed by standard western blot procedures and probed with anti-GANP (ab113295, Abcam, 1:1,000), anti-THOC2 (ab129485, Abcam, 1:1,000), anti-UAP56 (ab181059, Abcam, 1:1,000), anti-SARNP (PA5-56585, Invitrogen, 1:1,000), anti-ALYREF (ab202894, Abcam, 1:1,000) and goat-anti-rabbit antibody coupled to HRP (Thermo Fisher Scientific, 31466, 1:5,000).

UAP56 NTD IP and analysis

UAP56 NTD peptide (residues 1–24, biotin on the C terminus, WT or scrambled control, 75 μg per experiment) was immobilized on 30 μl of Pierce Strepdavidin magnetic beads (88817, Thermo Fisher Scientific, pre-equilibrated in PBS + 0.1% Igepal CA630) for 10 min at room temperature. Beads were then washed three times in buffer K and added to K562 nuclear lysate (see below). For the K562 nuclear lysate, 70 million K562 cells were fractionated in nuclei and cytoplasm (see above). Nuclei were resuspended in 700 μl buffer L supplemented with 0.1% deoxycholate and incubated on a rotating wheel for 1 h at 4 °C. The lysates were then briefly sonicated and centrifuged for 5 min at 3,000g and 4 °C. The supernatant was united with the peptide-bound beads and incubated with rotation for 2 h at 4 °C, after which the beads were essentially washed and analysed as described above (GFP–UAP56 IP), with the difference that 10% of the beads were used for western blotting and probed with an anti-GANP (ab113295, Abcam, 1:1,000) antibody and goat-anti-rabbit antibody coupled to HRP (Thermo Fisher Scientific, 31466, 1:5,000). Any specific interactor of the UAP56 NTD is expected to also interact with full-length UAP56, based on which we intersected the MS results of the UAP56 NTD peptide IP with all proteins enriched above a log_2[FC] cut-off of 0.5 in the GFP–UAP56 IP before further analysis.

V5-PCID2 IP western blotting

V5-PCID2 IP experiments were performed as outlined above with the following changes: V5-PCID2 was precipitated from 1.5 million K562 cells using magnetic V5-Trap beads (v5tma, Chromotek). We used cells containing a FKBP12F36V tag79,80 on endogenous PCID2, allowing for the rapid depletion of the endogenous protein after addition of the dTAG-V1 compound 6 h before collecting the cells, and which expressed dox-inducible mScarlet-V5-PCID2 WT or mutant proteins. Elutions were analysed using standard western blotting procedures and probed with anti-GANP (ab113295, Abcam, 1:1,000), anti-V5 (2F11F7, Invitrogen, 1:1,000), anti-UAP56 (ab181059, Abcam, 1:1,000), anti-histone H3 (17168-1-AP, Proteintech, 1:1,000) antibodies, and goat-anti-rabbit antibody coupled to HRP (Thermo Fisher Scientific, 31466, 1:5,000) and goat-anti-mouse antibody coupled to HRP (Thermo Fisher Scientific, G-21040, 1:5,000).

Endogenous TREX-disassembly assay

Nuclear extracts from a THOC1-3C-GFP overexpressing K562 cell line were prepared as previously described[20]. In total, 3.6 ml of nuclear extracts was supplemented with protease inhibitor cocktail and incubated with GFP-Trap Agarose resin (Chromotek), pre-equilibrated with buffer M (20 mM HEPES pH 7.9, 100 mM KCl, 2 mM MgCl_2, 8% glycerol, 0.05% (v/v) Igepal CA-630, 0.5 mM TCEP) for 3 h at 4 °C. The beads with immobilized endogenous TREX-mRNPs were then washed five times with 1.5 ml buffer M, aliquoted in 12 individual reactions and collected by centrifugation for 1 min at 1,000g to remove the supernatant. Meanwhile, 10×His-Sumo-3V5-3C-UBM, 10×His-Sumo-3V5-3C-UCM or 10×His-Sumo-3V5-3C-UCM–N-UBM were prepared at a final concentration of 19 µM in buffer G (25 mM HEPES pH 7.9, 200 mM NaCl, 10 mM MgCl_2, 10% glycerol, 5 mM ATP, 1 mM TCEP) and incubated at room temperature for 30 min. Next, the beads with immobilized endogenous TREX-mRNPs were incubated either with buffer G or supplemented with UCM and/or UBM peptide in buffer G, as described above, for 1 h at room temperature with rotation. After addition of a final concentration of 50 µg ml−1 benzonase and a further 30 min incubation, the beads were centrifuged for 1 min at 1,000g and washed twice with buffer M. Complexes remaining on the beads were eluted by boiling in 2× SDS sample buffer, loaded onto an SDS–PAGE gel and run for 3 min at 180 V in 1× MOPS buffer. The gels were stained with Coomassie blue, and the bands containing bead-retained protein were excised for MS analysis.

An aliquot of the elutions was analysed by western blotting according to standard protocols. We used anti-GFP (CAS A11122, Thermo Fisher Scientific, 1:1,000), anti-UAP56 (AB181059, Abcam, 1:1,000) and anti-EIF4A3 (AB180519, Abcam, 1:1,000). Primary antibodies were incubated overnight at 4 °C. For detection, we used a secondary goat-anti-rabbit antibody coupled to HRP (CAS 31466, 1:5,000).

Cryo-EM sample preparation, imaging and analysis

Model building for the endogenous human TREX complex including the ALYREF N-UBM

The structure of the human endogenous TREX complex (PDB: 7ZNK)[2] was analysed together with the THO monomer 2B map (Electron Microscopy Data Bank (EMDB): EMDB-14806)[2]. Manual inspection revealed additional density on the UAP56 RecA2 lobe, which we hypothesized to be the ALYREF N-UBM. The ALYREF N-UBM was modelled in Coot based on the superposition of an AlphaFold2 Multimer prediction model of a UAP56–ALYREF complex on UAP56 chain p. All structural figures were prepared using UCSF Chimera X81,82.

TREX–EJC–RNA complex reconstitution and sample preparation

TREX–EJC–RNA complexes were reconstituted as described previously[2] with small modifications. We used a 15 U ssRNA to assemble the ALYREFN–EJC–RNA and ATPγS was omitted from buffer U. The eluted sample was loaded onto a 15–40% sucrose density gradient and centrifuged at 23,000 rpm for 16 h in a SW60Ti rotor. We collected fractions and analysed every other fraction using SDS–PAGE stained with Coomassie blue.

For Cryo-EM sample preparation, we followed the described methodology described previously[2] with the following variations: the 15–40% sucrose density gradient was supplemented with a glutaraldehyde gradient from 0 to 0.05% to stabilize the complexes and it was centrifuged at 23,000 rpm for 16 h in a SW60Ti rotor, and we applied the sample to glow discharged Quantifoil Cu 200 2/1 grids.

Cryo-EM data acquisition of TREX–EJC–RNA complex reconstituted on 15 U RNA

Data were collected at IST Austria on the Thermo Fisher Titan Krios G3i system operated at 300 keV, equipped with a Gatan K3 direct electron detector operated in counting mode and a BioQuantum post-column energy filter set to a slit width of 10 eV. The objective aperture was retracted and a 50 μm C2 aperture was inserted. Data were collected at pixel size of 0.84 Å px–1, a total dose of 60 e− fractionated over 40 frames and a defocus range of −0.75 to –1.25 μm using EPU. The dataset was collected at a dose rate of 33.914 e− px–1 s–1. We acquired 5 images per hole and collected a total of 10,510 micrographs.

Data were preprocessed using Warp (v.1.09)[83]. CTF parameters were estimated with a spatial resolution of 6 × 4 and motion correction was performed with a spatial resolution of 6 × 4. We picked 470,103 particles in Warp using a custom BoxNet model and extracted them in RELION (v.3.1)[84] in a box size of 672 Å. For initial classification, particles were binned to 3.42 Å pixel–1.

3D classification with six classes was performed on the extracted particles using a reference volume of a TREX–EJC–RNA on 15U reconstruction from a dataset collected on a Glacios TEM microscope low-pass filtered to 60 Å and a spherical mask of 550 Å diameter. Class 5 was selected with 84,300 octamer particles. To increase dataset size, we separately extracted four THO–UAP56 dimers from each octamer, yielding a total of 329,826 dimers after removal of duplicates and re-extraction in CryoSPARC (box size, 436 ; 1.24 Å, pixel–1). After another round of heterogeneous refinement with three classes the 204,147 particles of the best class were further refined through (1) a local refinement and non-uniform refinement using a TREX complex mask, yielding the 5.89 Å TREX complex Map A and (2) a local refinement using a mask including THO monomer 1A and UAP56, yielding the 4.12 Å THO–UAP56–ALYREF–N-UBM complex map B.

Model building for the THO–UAP56–ALYREF–N-UBM complex

The structure of the human THO–UAP56 complex (PDB: 7ZNL)[2] was docked into the THO–UAP56–ALYREF–N-UBM complex MapB. For UAP56, both RecA lobes were individually rigid-body fitted into the new map in Coot85,86. The ALYREF N-UBM was fitted into the density based on an AlphaFold2 Multimer prediction of a UAP56–ALYREF complex and manually adjusted in COOT and the resulting structure was refined in phenix87,88 using the phenix.real_space_refine routine with secondary structure and rotamer restraints.

UAP56–UCM-1–N-UBM–TREX-2M complex reconstitution and sample preparation

A PCID2–UAP56–UCM-1–N-UBM fusion protein in complex with SEM1 was combined with a 1.2× molar excess of MBP–GANP(582–1004) in buffer N (25 mM HEPES pH 7.9, 5% glycerol, 1 mM MgCl_2, 1 mM TCEP, 200 μM 15 U RNA, 1 mM AMP-PNP) and incubated on ice for 1 h. The sample was then centrifuged (21,130g, 15 min, 4 °C) and applied to a Superdex 200 increase 10/300 size-exclusion column, pre-equilibrated in buffer N, to separate the PCID2-UAP56–UCM-1–N-UBM – MBP–GANP(582–1004) complex from isolated components. The peak fractions were analysed by SDS–PAGE and Coomassie staining to confirm stochiometric complex formation of the three proteins. The peak fraction was then diluted with buffer N to 0.8 mg ml−1 and cryo-EM grids were prepared by applying 4 µl of the sample to glow-discharged Cu R1.2/1.3 300-mesh holey carbon grids (Quantifoil). The grids were prepared, blotted at 8 °C under 90% humidity and plunged into liquid ethane using a Leica EM GP2.

Cryo-EM data acquisition of a UAP56–UCM-1–N-UBM–TREX-2M complex

Two datasets were collected on a 300 kV Titan Krios G4 equipped with a cold field-emission gun, a post-column Selectris energy filter (Thermo Fisher Scientific) with a 5 eV slit width and a Falcon 4i direct electron detector (Thermo Fisher Scientific). The objective aperture was retracted and a 50 μm C2 aperture was inserted. For dataset 1, we collected 6,839 micrographs using EPU in the .eer format, with five images per hole, a pixel size of 0.749 Å px−1, a total dose of 50 e− Å−2 and a defocus range of −1 to −2.5 μm. Dataset 2 consists of 9,374 micrographs collected at a tilt angle of 20° and otherwise identical settings.

We performed on-the-fly preprocessing (patch motion correction and CTF estimation) using the CryoSPARC[89] live routine. For dataset 1, we initially picked 3.8 million particles in CryoSPARC live, extracted them with a 225 Å box and binned to 1.755 Å px−1 and performed 2D classification. We then generated ab initio models for TREX-2M and UAP56–TREX-2M, which were further refined through heterogeneous refinements and non-uniform refinements. These models were used as the initial reference maps for three rounds of heterogeneous refinement of 1.67 million particles picked in WARP and extracted with a 225 Å box and binned to 1.755 Å px−1, yielding 199,358 UAP56–TREX-2M particles in the best class. These were re-extracted with a 225 Å box and binned to 0.877 Å px−1. Further heterogeneous refinement and 3D variability analysis yielded 19,188 UAP56–TREX-2M particles in the best two classes.

For dataset 2 we picked 2.4 million particles in WARP and extracted them with a 225 Å box and binned to 1.755 Å px−1. After 2D classification, we obtained 660,903 TREX-2M and UAP56–TREX-2M particles. Two rounds of heterogeneous refinement yielded 316,490 TREX-2M and 120,526 UAP56–TREX-2M particles.

The 316,490 TREX-2M particles were re-extracted with a 225 Å box and binned to 0.877 Å pixel−1, and subjected to a non-uniform refinement followed by 3D variability analysis. Then, 57,499 particles from the best two clusters were refined through a local CTF refinement and a final local refinement with TREX-2M mask yielded the 3.5 Å TREX-2M complex Map C.

The UAP56–TREX-2M particles were re-extracted with a 225 Å box and binned to 0.877 Å pixel−1 and subjected to another round of heterogeneous refinement, a non-uniform refinement, and a 3D variability analysis, resulting in 18,304 UAP56–TREX-2M particles, which were combined with the UAP56–TREX-2M particles from dataset 1. The combined 37,692 particles were subjected to a local CTF refinement and a final local refinement, leading to the 3.5 Å UAP56–TREX-2M complex map D. A further 3D classification with 20 classes and a GANP–UAP56-RecA2 mask, followed by local refinement of a class with 7,741 particles with bound UAP56-RecA2 lobe yielded the 4.22 Å UAP56–TREX-2M complex map E.

Model building for the TREX-2M complex and the UAP56-UCM-1-N-UBM–TREX-2M complex

An Alphafold2 Multimer prediction of TREX-2M or UAP56–TREX-2M was used as an initial model and docked into map C and map D densities, respectively. Model building was then manually adjusted in COOT and refined in phenix87,88 using the phenix.real_space_refine routine with secondary structure and rotamer restraints. The model of the UAP56 RecA2 lobe was obtained from an AlphaFold2 Multimer prediction and manually fitted into the UAP56 RecA2 density in map E.

ATPase assay

Steady-state UAP56 ATPase activity was measured using a NADH-coupled ATPase assay20,90, with final concentrations of 5 U ml−1 rabbit muscle pyruvate kinase, type III (Sigma-Aldrich), 5 U ml−1 rabbit muscle l-lactic dehydrogenase, type XI (Sigma-Aldrich), 500 µM phosphoenolpyruvate and 50 µM NADH. The reactions were prepared in a final volume of 10 μl in a 1,536-well plate and in buffer O (25 mM HEPES pH 7.9, 40 mM KCl, 0.5 mM MgCl_2, 5% (w/v) glycerol, 0.5 mM ATP) with 0.5/2 µM UAP56 (when measured with TREX-2M or in isolation), 2 µM TREX-2M (WT or R678A mutant), 200 µM 15 U RNA. The NADH emission signal decay was monitored over time at 37 °C in a PHERAstar FS (BMG LABTECH), with a 0.03–100 µM NADH dilution series as a calibration standard. UAP56 ATPase rates were determined by linear regression of the NADH decay, corrected for ATP decay, as hydrolysed molecules of ATP per s per enzyme. Input samples of the individual reactions were separated on 4–12% gradient SDS–PAGE gels and visualized by Coomassie staining.

Human K562 cell line experiments

Generation of an endogenously tagged GFP–3C–UAP56 cell line

Human K562 cells (DSMZ) were edited to express an eGFP–3C–DDX39B fusion protein using a modification of a previously described CRISPR–Cas9 knockin protocol[91]. In brief, the gRNA was designed using the Benchling.com CRISPR gRNA design tool (Benchling; AAACTAACTGGGCCGGCAGGGGAAC) and cloned into the plasmid pLCG (hU6-sgRNA-EFSSpCas9-P2A-mCherry)[92], a gift from J. Zuber. The 500 bp sequences flanking the DDX39B start codon were obtained by PCR on genomic K562 cDNA (using 5′ homology genomic primers: ATCCTCAAGTAAGGGGGTACCAGGACTCTACTTGTCATCTCCATTTTCC, GAGATGTTGAAGGTCTTCATAACTGGGCCAGCAGGGGA; and 3′ homology genomic primers: AGGGCCCGGGTGGAGGTTCCGCTGGAGCAGAGAACGATGTGGACAATG, ATCCCCCCTTTTCTTTTAAAGAATTCTGATCTAGCCTTAAGTATAAACCC) and subcloned into the pLPG vector[92], a gift from J. Zuber, digested with MluI using Gibson Assembly (NEB), yielding the final vector pLPG-GFP-AID (5′-BlastR-P2A-eGFP-AID-3C).

K562 cells were grown in RPMI medium supplemented with 10% FBS (Sigma-Aldrich), 2% l-glutamine (Gibco), 1% sodium pyruvate (Sigma-Aldrich) and 1% penicillin–streptomycin (Sigma-Aldrich) and transfected with the HDR donor and the Cas9 plasmids using Neon electroporation device (Invitrogen) according to user guide manual (for suspension cells). Then, 6 days after transfection, after several passages, cells were subjected to FACS using the BD FACSAria III (BD Biosciences) system. Cells expressing the eGFP-tag were sorted into 96-well plates. After approximately 2 weeks, wells with homogeneous fluorescence were genotyped (primers: TGCTAATTACACAAGGCTT, ACCTGCCACAGACCACTTCT), homozygous clones were further analysed by western blotting for homozygous knockin of the tag using anti-UAP56 (ab181059, Abcam, 1:1,000), anti-GFP (A11122, Invitrogen, 1:1,000), goat-anti-rabbit antibody coupled to HRP (Thermo Fisher Scientific, 31466, 1:5,000) and goat-anti-mouse antibody coupled to HRP (Thermo Scientific G-21040, 1:5,000).

Generation of an endogenously tagged PCID2 cell line

K562 cells with PCID2 endogenously tagged with an N-terminal eGFP–FKBP12F36V–3C tag79,80 were generated as outlined above for UAP56 with the following changes: the gRNA (TCCGTTCGGCGGCGCTCCCA) was designed using the CHOPCHOP web tool and gRNA ordered as a crRNA from IDT. Cas9–gRNA ribonuclein particles were generated according to the manufacturer’s instructions (https://eu.idtdna.com). Repair template DNA molecules with 50-bp-long homology arms (HA) were generated by PCR using 5′-end biotin modified oligonucleotides (Sigma-Aldrich) (5′-HA primer (mutated to eliminate the PAM in the modified locus): TGACGCCAGCTGGCCCGCTTGAGGCGTAGGGGGTGGCGCTCTCCGTTGCGCGGCGCTCCCATGAAGACCTTCAACATCTCTCAGCAGGAC, and biotin-TGACGCCAGCTGGCCCGCTT; 3′-HA primer: GCGCGCTCCCCGGCTAGGACCCACCTGCTGCAGGTACTGGTTAATGGTAATGTGCGCCATGGAACCTCCACCCGGGCCCTGAAA; and biotin-GCGCGCTCCCCGGCTAGGA). K562 cells were transfected with the repair templates and ribonuclein complexes using a MaxCyte ATx electroporator. Cells expressing the eGFP-tag were sorted into 96-well plates by FACS and, after approximately 2 weeks, cell clones with homogeneous fluorescence were genotyped (primers, GAGGGGACACACGGAACA and CCGAACACACAATCAGAGCC) and further analysed by western blotting for homozygous tagging and for degradation efficiency upon the addition of dTAG-V1.

Generation of an endogenously tagged SARNP cell line

SARNP was endogenously tagged with a C-terminal 3C–FKBP12F36V–eGFP tag as outlined for PCID2 with the following reagents: gRNA: AGTATCAGGAACTTTTCATC; homology arm PCR: 5′-HA primer CTTCTTTACAGGCAAAGAAGAGGAAAAGAGCAGAGCGCTTTGGGATTGCCCTGGAAGTTCTGTTTCAGGGCC and biotin-CTTCTTTACAGGCAAAGAAGAGGAAAAG, 3′-HA primer AGAAGGAGAGAAATGGAAAACACTGGAGAACAGAAAGTATCAGGAACTTTTCAGCACGGGCTTGCG and biotin-AGAAGGAGAGAAATGGAAAACACTGG; genotyping primers: AACCCAGGCAACTATTGTCTTC and CAGCAATAAGTCAAACTGCTGC.

Generation of an endogenously tagged GANP cell line

GANP was endogenously tagged with an internal FKBP12F36V–eGFP tag as outlined for PCID2 with the following reagents: gRNA: CGTGCCCATGTACTCTGACG; homology arm PCR: 5′-HA primer CTTCCAGCTGTCTGTGCAGCCTGAACCACCGCCTCCAGAGCCCGTGCCCGGAGGTGGATCGATGGGAGT and biotin- CTTCCAGCTGTCTGTGCAG, 3′-HA primer CTTCCCAGAGTCCAGACCTAGAAAAAAAGAGTCCCTACCTCGTCAGAGTAGGAACCTCCACCCTTGTACAG and biotin-CTTCCCAGAGTCCAGACCTAGA; genotyping primers: TGCAGCTATGTTTT GTCCTGT and TGGGGTGATGACTAAGGACG.

Inducible UAP56/DDX39A CRISPR knockout cell line

Dual sgRNAs were designed against both UAP56 (GGACATCCATTCCCAGAA and GAACAGCTGGAGCCAGTTACT) and DDX39A (GCTGGCCTTCCAGATCAGCA and GCATGTCGTGGTGGGGACCCC) and cloned into modified Dual-sgRNA_hU6-mU6 vectors[93] (gift from J. Zuber) also expressing eBFP2 (for UAP56 sgRNAs) or iRFP670 (for DDX39A sgRNAs). Both Dual-sgRNA expression vectors were packaged in lentiviruses as previously described[94]. Lentiviruses were then used to infect K562 cells, which allow for the doxycycline-inducible expression of Cas9[93] (gift from J. Zuber), and a cell population containing both Dual-sgRNA constructs was selected by FACS sorting for eBFP- and iRFP670-positive cells.

Expression of rescue constructs using the PiggyBac system

Rescue constructs for UAP56 and PCID2 were generated by cloning of the CDSs under the TRE-tight promoter into the PiggyBac system ePB vector backbone, featuring in addition the expression of rtTA-Advanced-T2A-puromycin resistance under the human UbC promoter. UAP56 was fused at the C terminus to a P2A site and mScarlet to monitor transgene expression. PCID2 was expressed with an N-terminal mScarlet-3×V5-3C tag. Rescue constructs were electroporated into UAP56/DDX39A inducible CRISPR KO cells for UAP56 and into FKBP12F36V-PCID2 cells for PCID2 together with a plasmid encoding a PiggyBac transposase. Transgene expression was induced by the addition of 0.2 µg ml−1 doxycycline.

Cell growth competition experiments

The depletion of essential genes, such as UAP56 or PCID2[95], leads to a severe growth phenotype, enabling cell growth competition experiments. Knockout of the DDX39A and DDX39B genes and the expression of DDX39B rescue constructs together with mScarlet were induced 6 days after electroporation of the rescue constructs with 0.2 µg ml−1 doxycycline, followed by a cell sorting for the presence of all four fluorophores (GFP for inducible Cas9, BFP and iRFP for the expression of the dual gRNAs, mScarlet for the rescue construct) one day after doxycycline induction. The quadruple-positive cells (and the respective controls) were mixed with WT K562 competitor cells on the second day after doxycycline induction and the ratio of BFP-positive to BFP-negative cells was determined using the BD Fortessa cytometer on several days until the BFP positive cells perished in the control sample without rescue protein. Cell loss was normalized to the mean of the samples containing the untreated maternal line.

For PCID2, the expression of rescue proteins fused to mScarlet was induced 6 days after the electroporation of the rescue construct with 0.2 µg ml−1, followed by a cell sorting for mScarlet (rescue construct) and eGFP (for endogenous eGFP–FKBP12F36V–3C–PCID2) 1 day after doxycycline induction. The double-positive cells (and the respective controls) were mixed with a BFP-expressing competitor cell line followed by degradation of the endogenous eGFP–FKBP12F36V–3C–PCID2 protein after dTAG-V1 treatment (0.25 µM dTAG-V1). The ratio of BFP-positive to BFP-negative cells was determined using the BD Fortessa cytometer on several days until the BFP-negative cells perished in the control sample without rescue protein. Cell loss was normalized to the mean of the samples containing the untreated maternal line.

Generation of the K562 export reporter cell line

The full reporter sequence, consisting of the mCherry coding sequence (CDS) with a single intron containing ten boxB sites, an IRES and the GFP-puromycin resistance ORF (mCherry1/2-5′SS-10×boxB-IRES-GFP-PuroR-3′SS-mCherry2/2), was synthesized (Genewiz) and cloned into a lentiviral vector backbone[96] (pRRL SFFV d20GFP.T2A.mTagBFP Donor was a gift from A, Scharenberg; Addgene plasmid, 31485), yielding the plasmid containing pRRL-SFFV-reporter plasmid. Viral particles were generated by polyethylenimine transfection (Polysciences) of the pRRL-SFFV-reporter plasmid, together with the helper plasmids pCMVR8.74 (a gift from D. Trono (Addgene plasmid, 22036) and pCMV-VSV-G[97] (a gift from B. Weinberg; Addgene plasmid, 8454) into LentiX-cells (Takara) according to standard procedures. K562 (DMSZ) cells were infected at limiting dilutions and mCherry-positive single cells were isolated using the FACSAria III cell sorter (BD Biosciences). Viral integration of the entire reporter sequence was assessed by genotyping PCR. LentiX and K562 cells were maintained at 37 °C under 5% CO_2 and tested negative for mycoplasma.

Plasmid transfection into K562 export reporter cell line for λN-mediated tethering

The CDS of a protein of interest was cloned into an acceptor plasmid containing the λN-BC2-Flag tag, a P2A site and the BFP CDS (plasmid nLV-Ef1a, a gift from S. Ameres) using Gibson assembly[98]. For each protein of interest that promoted export, a control plasmid lacking the λN-tag was created (Supplementary Table 5). Plasmids were transfected into the K562 reporter cell line using the Neon Transfection System 10 μl Kit (Invitrogen, MPK1025) according to the manual with 3 µg plasmid per 2 × 10^6 cells (pulse voltage (V) = 1,450, pulse width (ms) = 10 and pulse number = 3) in three replicates on different days. Then, 48 h after transfection, cells were analysed using an iQue Screener Plus (Sartoriuos). Flow cytometry data were filtered for good events using FlowAI[99], transfected K562 cells were selected by gating for BFP-positive cells, and their GFP intensity extracted and plotted using GraphPad Prism (v.8).

To control for the expression and nuclear localization of λN-UAP56 and λN-UAP56 ΔNTD aliquots of one million cells were fractionated as previously described2,78 and analysed by western blotting using anti-UAP56 (ab181059, Abcam, 1:1,000), anti-histone H3-HRP (5192S, Cell Signalling Technologies, 1:1,000) and goat-anti-rabbit antibody coupled to HRP (Thermo Fisher Scientific, 31466, 1:5,000).

Poly(A) RNA FISH

For poly(A) RNA FISH experiments we used the cell lines generated for the PCID2-dTAG depletion-rescue experiment as well as a GANP-dTAG cell line (as described above). Specifically, we used the PCID2-FKBP-GFP clonal cell line and populations expressing the respective WT and mutant rescue constructs in the PCID2-FKBP-GFP cell line background. Expression of the rescue constructs was induced 7 days before the experiment, whereby depletion of endogenous PCID2-FKBP-GFP was induced by the addition of dTAG-V1 for 16 h.

Cells were added to 8-well slides (µ-Slide 8 Well high, 80806, Ibidi) precoated with 0.5 µg ml−1 concanavalin A and allowed to adhere for 1 h before fixation in 4% PFA for 10 min at room temperature. The slides were then washed in PBS and incubated in 70% ethanol for 1 h at 4 °C. Subsequently, cells were washed with 5× SSC-T (5× SSC, 0.1% Tween-20) and then incubated first in hybridization buffer (30% formamide, 5× SSC, 1× Denhardt’s solution, 50 µg ml−1 heparin, 0.1% Tween-20, 10% dextran sulfate) for 30 min at 37 °C and then for with hybridization buffer supplemented with 100 nM oligo-dT FISH probe for 2 h at 37 °C. The slides were then washed three times in wash buffer (30% formamide, 5× SSC, 0.1% Tween-20), twice with 5× SSC-T and then incubated for 15 min at room temperature with 5× SSC-T containing 200 ng ml−1 DAPI. After three additional washes slides were imaged on an Olympus IX83 based spinning disc confocal microscope with a ×40 air objective (for image quantification, examples are shown in Extended Data Fig. 11b) or a ×100 oil-immersion objective (to record representative images, examples are shown in Fig. 4e).

For each sample, we prepared four replicates and collected five images each, which were analysed using a Python pipeline using Stardist[100] and Cellpose[101] for image segmentation of the nucleus and cytoplasm. The PCID2 rescue constructs were expressed in populations consisting of PCID2-FKBP-GFP cells that either did or did not express the rescue construct. The expression of the rescue constructs can be distinguished due to the mScarlet fused to PCID2 in the rescue constructs (Extended Data Fig. 11b). This enables us to analyse the effect of the rescue construct compared with no rescue construct directly within the same image by grouping cells according to mScarlet levels for the analysis. Data were analysed using R v.4.0.

AlphaFold2 Multimer screening

Protein interaction prediction screening was performed using a custom pipeline (HT-Colabfold) based on Colabfold, which uses AlphaFold2 Multimer102,103,104. This pipeline was used to predict interactions between UAP56 and 696 proteins that were designated putative UAP56 interactors based on their at least twofold enrichment over a WT control in UAP56–GFP immunoprecipitates. HT-Colabfold manages the pairing, scheduling and data collection for large-scale structure prediction and interaction screens. The pipeline executes pairwise predictions utilizing MMseqs (git@92deb92) for local multiple sequence alignment generation (CPU-node) and Colabfold (git@7227d4c) for structure prediction (GPU-nodes). Each prediction involved the generation of five models, omitting structure relaxation. Predictions with an average iPTM score of >0.5 were considered to be putative hits and diagnostic plots (PAE plot, pLDDT plot and sequence coverage) as well as the generated structures were manually inspected.

Reproducibility

All experiments, except for cryo-EM data collection and processing, have been repeated at least three times with similar results.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

3D cryo-EM density maps of the TREX–EJC–ALYREF complex, TREX-2M and UAP56–TREX-2M have been deposited into the Electron Microscopy Data Bank under accession numbers EMD-18980 (map A) and EMD-18979 (map B), EMD-18977 (map C), EMD-18978 (map D) and EMD-18981 (map E). The coordinate files of the TREX–EJC–ALYREF, TREX-2M and UAP56–TREX-2M have been deposited at the Protein Data Bank under the accession numbers 8R7L, 8R7J and 8R7K. The coordinate file of the TREX–mRNA complex was updated in the Protein Data Bank under the accession number 7ZNK. Proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE[105] partner repository under the accession number PXD069399.

Code availability

HT-Colabfold is free open-source software (MIT) and is available at GitHub (https://gitlab.com/BrenneckeLab/ht-colabfold).

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Acknowledgements

We thank our colleagues in the Brennecke and Plaschka groups for their support and discussions; the staff at the Protein Technologies Facility at the Vienna BioCenter Core Facilities (VBCF), a member of the Vienna BioCenter (VBC), for assistance with protein production; the staff at the VBCF Electron Microscopy Facility, in particular T. Heuser and H. Kotisch, for support, data collection and maintaining facilities; V.-V. Hodirnau at the Institute of Science and Technology Austria EM facility for cryo-EM data collection; staff at the VBCF Proteomics facility, in particular O. Hudecz, for MS analysis; R. Zimmermann and his team as well as staff at the CLIP cluster (https://clip.science) for computational support; J. Ahel for local installation of AlphaFold2; M. Madalinski for peptide synthesis; staff at the in-house Molecular Biology Service for reagents; S. Ameres, J. Zuber and J. Santiago Cuellar for sharing reagents; S. Ameres and U. Elling for help with export reporter design; R. Kalis and J. Zuber for advice on CRISPR knockout generation; M. Vorländer for help with data analysis; C. Bernecky, L. Lorenzo-Orts and I. Patten for discussions; and A. Anderson, C. Bernecky, T. Heick-Jensen, L. Lorenzo-Orts, A. Stark and the members of the Brennecke and Plaschka groups for feedback on the manuscript. U.H. was supported by a Marie Skłodowska-Curie fellowship (896416) and an EMBO long-term fellowship (ALTF_1175-2019); D.R.-B. and R.F. by Marie Sklodowska-Curie fellowships (101028744 and 101150910); J.B. by the Austrian Academy of Sciences and Austrian Science Fund (W1207). Research in the laboratory of C.P. is supported by Boehringer Ingelheim, the European Research Council under the Horizon 2020 Research and Innovation Programme (ERC-2020-STG 949081 RNApaxport) and by the Austrian Science Fund (FWF) doc.funds program DOC177-B (RNA@core: Molecular mechanisms in RNA biology). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. For the purpose of open access, the author has applied for a CC BY public copyright license to any author accepted manuscript version arising from this submission.

Funding

Open access funding provided by Research Institute of Molecular Pathology (IMP)/Institute of Molecular Biotechnology (IMBA)/Gregor Mendel Institute of Molecular Plant Biology (GMI).

Author information

Author notes

1. Ulrich Hohmann

Present address: Institute of Molecular Biology (IMB), Mainz, Germany

Authors and Affiliations

1. Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Vienna, Austria

Ulrich Hohmann, Max Graf, Belén Pacheco-Fiallos, Ulla Schellhaas, Laura Fin, Alexander W. Phillips, Daria Riabov-Bassat, Rupert Faraway, Thomas Pühringer, Michael-Florian Szalay, Elisabeth Roitinger & Clemens Plaschka

2. Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna BioCenter (VBC), Vienna, Austria

Ulrich Hohmann, László Tirián, Dominik Handler, Elisabeth Roitinger & Julius Brennecke

3. Vienna BioCenter PhD Program, Doctoral School of the University of Vienna and Medical University of Vienna, Vienna, Austria

Max Graf, Belén Pacheco-Fiallos & Ulla Schellhaas

4. Max Perutz Labs, Vienna BioCenter (VBC), Vienna, Austria

Rupert Faraway

5. University of Vienna, Max Perutz Labs, Department of Biochemistry and Cell Biology, Vienna, Austria

Rupert Faraway

Contributions

U.H. designed research and, with M.G., L.F. and T.P., purified recombinant proteins. U.H. and M.G. performed biochemical experiments and collected TREX-2 cryo-EM data. B.P.-F. performed the native TREX disassembly assay and collected TREX–EJC–ALYREF cryo-EM data. U.H. and C.P. carried out cryo-EM data analysis and structure determination. U.S. performed the export tethering assay, for which M.-F.S. engineered and M.-F.S. and C.P. designed the cell line. L.T., A.W.P. and D.R.-B. engineered and grew mammalian cell lines. L.T. carried out growth competition experiments. L.T., R.F. and U.H. performed poly(A) RNA FISH experiments. U.H. performed structural predictions with an AlphaFold2 pipeline implemented by D.H.; U.H., J.B. and C.P. prepared the manuscript with input from all of the authors. E.R. supervised MS analysis. J.B. supervised L.T. and D.H. and designed research. J.B. and C.P. supervised U.H.; C.P. supervised M.G., U.S., B.P.-F., L.F., D.R.-B., R.F., T.P. and M.-F.S., designed research and initiated the mRNA export project.

Corresponding authors

Correspondence to Ulrich Hohmann, Julius Brennecke or Clemens Plaschka.

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Competing interests

The authors declare no competing interests.

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Nature thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Identification of the ALYREF N-UBM binding site in UAP56.

a, Multiple sequence alignment of the ALYREF N-UBM (left) and C-UBM (right) from H. sapiens (Hs, Uniprot ID Q86V81), M. musculus (Mm, O08583), D. rerio (Dr, F1Q9D1), D. melanogaster (Dm, Q9V3E7), C. elegans (Ce, Q21559), A. thaliana (At, Q8L773), and S. cerevisiae (Sc, Q12159) and S. pombe (Sp, Q09330), coloured by conservation (purple background, invariant residue). Purple stars mark key interface residues with UAP56. b, Re-analysis of a human endogenous TREX–mRNP structure reveals an unidentified binding site for a N-UBM on UAP56. Front (left) and top views (middle) of UAP56 bound to THO and mRNPs with the THOC2 MIF4G and Stern domains (green), UAP56 (pink) and the ALYREF C-UBM (purple) (PDB ID 7ZNK), with cryo-EM density around UAP56 and ALYREF shown (EMDB-14806). An additional density at low resolution on UAP56’s RecA2 lobe fits with the AlphaFold2 predicted binding site of ALYREF’s N-UBM (purple). c, Details of UAP56–N-UBM (top) and UAP56–C-UBM (bottom) interfaces with key residues labelled and shown as sticks. Colours as in a. d-g, Diagnostic plots for the AlphaFold2 Multimer prediction of ALYREF and UAP56. The N- and C-UBM are predicted with high confidence to bind distinct binding sites: the C-UBM is predicted in the previously experimentally determined binding site on the RecA1 lobe, the N-UBM on a novel binding site on RecA2. Shown are d, the PAE plot, e, the pLDDT plot, f, the structure of the top ranked model in Cα trace coloured by pLDDT (shown are only the ordered and interacting elements: UAP56 RecA1 and RecA2, residues 40-428; ALYREF N-UBM and C-UBM, residues 1-24 and 236-257), and g, a superposition of the structures of all five models, rank 1-5, as in f, but coloured for rank 1 with UAP56 in pink and ALYREF in purple, and in shades of grey for rank 2-5.

Extended Data Fig. 2 TREX–EJC–RNA complex reconstitution and cryo-EM analysis.

a, Complex reconstitution scheme for TREX (= THO–UAP56–ALYREFN)–EJC–RNA from recombinant proteins. Complex components were mixed and separated on a 15-40% sucrose gradient with or without 0.05% glutaraldehyde, fractionated and analysed by Coomassie-stained SDS-PAGE. Shown are peak lanes of the crosslinked complex used for cryo-EM sample preparation (right) and the corresponding fraction from the non-crosslinked gradient (left). b, Denoised cryo-EM micrograph of TREX–EJC–RNA. Scale bar, 400 Å. c, Three-dimensional image classification tree, with representative 2D classes shown below. The dataset contains 10,510 micrographs, of which 470,103 particles (THO octamer) were picked with WARP and processed in RELION and cryoSPARC. The final particle stack contained 204,147 particles and was refined to 5.89 Å for the entire TREX complex (Map A) and to 4.12 Å for a THO monomer (Map B). d, Gold-standard Fourier shell correlation (FSC = 0.143) of the TREX complex (Map A) and THO-monomer–UAP56–N-UBM (Map B) cryo-EM maps. e, Orientation distribution plots, as visualized in cryoSPARC, for all particles contributing to the TREX complex (Map A) and THO-monomer–UAP56–N-UBM (Map B) cryo-EM maps. f, TREX complex (Map A) and THO-monomer–UAP56–N-UBM (Map B) cryo-EM maps coloured by local resolution, alongside the same maps coloured by subunit as in Fig. 1b. g, Structure of the THO-monomer–UAP56–N-UBM complex (left, top view; middle and right, front view), with the THOC2 MIF4G and Stern domains (green), UAP56 (pink) and the N-UBM (purple) shown together with the Map B density (inset on the right). The structure reveals density for the ALYREF N-UBM at the newly identified binding site on the UAP56 RecA2 lobe.

Extended Data Fig. 3 UAP56–ALYREF interfaces are important in vivo, ATP and RNA alone do not dissociate the THO–UAP56 complex, and prediction of a UAP56–SARNP complex.

a, Biochemical evidence that the N-UBM binds a binding site on UAP56 distinct from the C-UBM. GCI-derived direct binding kinetics for UBM peptides as the analyte and UAP56 as the ligand (see Fig. 1f). Sensograms (pink), fitted traces (black), and summary tables are shown. UAP56 binds N- and C-UBM peptides with mid-micromolar affinity (top row). Mutating key residues in the UAP56 C-UBM binding site (R208S, Q212A, R216S, K241S; RQRK) prevents C-UBM binding but leaves N-UBM binding unaffected (middle row). Mutating key residues in the newly identified N-UBM binding site (R276E, A302R, E309S) abolishes N-UBM binding without affecting C-UBM binding (bottom row). b, Depletion efficiency of DDX39A and DDX39B after their dox-induced CRISPR-knockout in a human K562 cell line. Shown is a western blot probing for DDX39A/B in wildtype and CRISPR knockout cells at zero, one, or two days after induction of the knockout. A histone H3 western blot loading control is shown alongside. c, mScarlet levels, as determined by FACS, assess UAP56 rescue construct expression for the experiment in panel d, at two days after the induction of the DDX39A/B knockout and the expression of UAP56-P2A-mScarlet rescue constructs; n = 3 samples. d, Human K562 cell growth competition assays to probe UAP56–ALYREF interfaces and the UAP56 ATPase activity. Wild type or mutant UAP56 (DDX39B) is ectopically expressed from a doxycycline (dox)-inducible promoter in human K562 cells, which also express dual CRISPR guide RNAs against UAP56 and DDX39A and carry a dox-inducible Cas9. Addition of dox leads to the simultaneous CRISPR-Cas9 knockout of UAP56 and DDX39A and initiates the ectopic expression of the respective rescue construct. See Methods for UAP56–N-UBM, and –C-UBM mutation details. To probe UAP56’s ATPase activity we mutated the DECD-box motif to AACD. Dox-induced cells are mixed at a 1:1 ratio with wild type K562 cells and cell growth is monitored by FACS (see Methods for details); error bars, mean ± s.d. from n = 3 independent samples. e, GCI-derived direct binding kinetics for a THO dimer or monomer as the analyte and UAP56 as the ligand (compare to Fig. 1f, g). Sensograms (pink), fitted traces (black), and summary tables are shown. f, As in e, but probing THO tetramer (left), dimer (middle) or monomer (right) as the analyte and UAP56 with either only RNA (top) or only ATP (bottom) as the ligand. Because experiments using UAP56 + ATP or UAP56 + RNA were done on separate GCI chips, the numerical and absolute response levels differ between these conditions. Sensograms (pink), fitted traces (black), and summary tables are shown. g, Size exclusion chromatography traces of wild type UAP56 with ADP and a 15 nucleotide poly-Uridine RNA (15U RNA), or of the ATPase mutant UAP56 AACD (D196A, E197A) with ATP and with or without the 15U RNA. Shown are the UV-traces measured at 280 nm (black) and 260 nm (grey). h, ATP and RNA lead to the partial dissociation of THO from UAP56 in vitro. Experimental setup (left) and Coomassie stained SDS-PAGE gel of a representative experiment (right). i-l, Diagnostic plots for the AlphaFold2 Multimer prediction of SARNP–UAP56. The UCM-1 is predicted with high confidence to bind the UAP56’s RecA2 lobe. Shown are i, the PAE plot, j, the pLDDT plot, k, the structure of the top ranked model in Cα trace coloured by pLDDT (shown are only the ordered and interacting elements: UAP56 RecA1 and RecA2, residues 40-428; SARNP UCM-1 region, residues 84-110), and l, a superposition of the structures of all five models, rank 1-5, as in k, but coloured for rank 1 with UAP56 in pink and SARNP in yellow, and in shades of grey for rank 2-5. The predicted complex is highly similar to a recent crystal structure of a human UAP56–yeast SARNP complex[22], exhibiting an RMSD of 0.957 Å across 391 atom pairs.

Extended Data Fig. 4 SARNP UCMs bind UAP56 and UAP56 can simultaneously bind the ALYREF N-UBM, C-UBM and SARNP UCM-1.

a, Structural modelling of a UAP56–RNA–SARNP-UCM-1–ALYREF-N-UBM complex (see Fig. 2d) superimposed on THO–UAP56 reveals a clash between the SARNP UCM-1 and the MIF4G domain in THOC2. UAP56, pink; RNA, black; SARNP, yellow; ALYREF UBMs, purple; THOC2 MIF4G and Stern in green and, except for the clashing THOC2 α-helix 29, transparent. b, Details of the UAP56–SARNP UCM-1 model with key interface residues shown as sticks (top). Shown below is the same view of UAP56 in the THO–UAP56 complex (THOC2, green)[20], revealing a steric clash between SARNP and THOC2. Colours as in panel a. c, UAP56 interacts with a SARNP UCM. Recombinant SUMO-V5 tagged SARNP UCM-1 (residues 82-115) is immobilized on magnetic V5 beads and incubated with recombinant full length UAP56. After washing out unbound UAP56, bead-bound complexes are eluted at low pH and analysed by SDS-PAGE followed by Coomassie staining, revealing that UAP56 interacts with the UCM-1 (lane 5). Mutating key residues in the predicted interface (SARNP UCM-1 R106D or UAP56 D283R) prevents the interaction almost completely (lanes 6 and 7). UAP56 shows little background binding (lanes 8 and 9). d, mScarlet levels, as determined by FACS, assess UAP56 rescue construct expression for the experiment in panel e, at two days after the induction of the DDX39A/B knockout and the expression of UAP56-P2A-mScarlet rescue constructs. Samples for ‘−dox’, ‘+ dox’ and ‘+dox, wildtype rescue’ are reproduced from Extended Data Fig. 3c, d for a direct comparison; n = 3 independent experiments. e, Human K562 cell growth competition assays to probe UAP56–SARNP and –TREX-2 interfaces. Assay as carried out in Extended Data Fig. 3d, but for the UAP56–UCM mutant or UAP56 truncated for its N-terminal domain (ΔNTD). The growth curves for the ‘−dox’, ‘+dox’ and ‘+dox, wildtype rescue’ are reproduced from Extended Data Fig. 3d; error bars, mean ± s.d. from n = 3 independent samples. f, Flag-immunoprecipitation coupled to a western blot experiment of wildtype or UCM-binding mutant (D283R) V5-flag-TurboID-tagged UAP56. V5-flag-TurboID-UAP56 was immunoprecipitated using magnetic FLAG-beads from the nuclear extract of human K562 cells, two days after the expression of the exogenous UAP56 construct was induced by the addition of doxycycline. After on-bead washes, UAP56 and co-precipitating proteins were eluted from the beads by boiling in SDS-PAGE sample buffer and were probed by western blotting for UAP56 and SARNP (right). Input samples are shown alongside (left). g, UAP56 binds the five UCMs of human SARNP. Biotinylated UCM peptides are immobilized on neutravidin agarose beads and incubated with recombinant UAP56. Unbound UAP56 is removed through washes and bound UAP56 eluted at low pH before analysis on a Coomassie-stained SDS-PAGE gel (lanes 2-6). Input UAP56 is shown in lane 1. UAP56 shows low background binding (lane 7). h, Multiple sequence alignment of the SARNP UCMs from H. sapiens (Uniprot ID P82979), M. musculus (Mm, Q9D1J3), D. rerio (Dr, Q504C3), D. melanogaster (Dm, Q9VHC8), C. elegans (Ce, Q9N3G0), A. thaliana (At, Q9LZ08), S. pombe (Sp, O74871) and S. cerevisiae (Sc, P40040) (bottom), coloured by conservation (orange background, invariant residue). SARNP is multivalent in all eukaryotes, with multiple UCMs connected by low-complexity linkers (Fig. 2c). We speculate that UCM-multivalency may have evolved to disassemble multivalent tetrameric THO–UAP56 complexes in native mRNPs and/or to increase the efficiency of SARNP binding to UAP56 in native mRNPs. i, The SARNP UCMs are required for UAP56 interaction, but not the SARNP SAP domain. In vitro pulldown where flag-tagged UAP56 is immobilized on M2 anti-flag resin and incubated with recombinant SARNP constructs. Unbound protein is removed through washes and bead-bound complexes are eluted at low pH prior to analysis on a Coomassie-stained SDS-PAGE gel. Recombinant full length SARNP interacts with UAP56 (lane 5), as does SARNP lacking the N-terminal SAP domain (MBP-SARNP residues 47-210, lane 7). Mutating a key arginine (R) in all five SARNP UCMs abolishes binding to UAP56 (SARNP 5x RtoD, lane 6). Protein inputs (lane 1-4) and background binding controls are shown (lanes 8-10). The asterisks mark the M2 flag antibody heavy and light chains. j-l, UAP56 can simultaneously bind the ALYREF N-UBM, C-UBM and the SARNP UCM. In vitro pulldown experiments, where recombinant MBP-tagged ALYREF is immobilized on amylose beads and incubated with recombinant UAP56 and SARNP, UCM or N- or C-UBM containing constructs. Unbound proteins are removed through washes before bead-bound complexes are eluted by boiling in SDS sample buffer and analysed on Coomassie-stained SDS-PAGE gels. Experiments are performed in the absence (left) or presence (right) of ATP and 15 nucleotide poly-Uridine RNA (15U RNA). j, Full length ALYREF interacts with UAP56 (lane 4) or UAP56–SARNP (lane 5). SARNP does not bind ALYREF (lane 6), and neither UAP56 nor SARNP show background binding to the beads (lane 7). Protein inputs are shown in lanes 1-3. k, ALYREF harbouring only a C-UBM (residues 106-257) interacts with UAP56 (lane 5), and with an UAP56–UCM-1 complex (lane 6), an UAP56–N-UBM complex (lane 7), or an UAP56–UCM−1–N-UBM complex (lane 8). UCM-1 and N-UBM do not interact with ALYREF (lane 9) and show no background binding (lane 10). Protein inputs are shown in lanes 1-4. l, as in k, but using an ALYREF construct harbouring only an N-UBM (residues 1-182) and using isolated ALYREF N-UBM instead of C-UBM constructs.

Extended Data Fig. 5 ALYREF N-UBM and SARNP UCM cooperatively bind UAP56 and SARNP assists TREX disassembly and stabilizes mRNA-clamped UAP56.

a, GCI-derived binding kinetics for SARNP UCM peptides as the analyte and UAP56 as the ligand. UAP56 binds the SARNP UCM-1 (top row) and UCM-4 (middle row) with similar affinities (KDs of 10 to 20 μM), and binding is not affected by the addition of the non-hydrolysable ATP analogue AMP-PNP or by AMP-PNP and a 15 nucleotide poly-Uridine RNA. The mutation R106D in UCM-1 prevents UAP56 binding (bottom). Sensograms (pink), fitted traces (black), and summary tables are shown. b, The SARNP UCM-1 and ALYREF N-UBM bind UAP56 simultaneously in a UCM-1–N-UBM fusion protein. SUMO-V5-3C–N-UBM, SUMO-V5-3C–UCM, or a SUMO-V5-3C–UCM–N-UBM fusion (with or without the SARNP R106D) mutation are immobilized on magnetic anti-V5 beads, incubated with UAP56 and washed, eluted and analysed as by Coomassie-stained SDS-PAGE. UAP56 binds the isolated UCM-1 with apparent higher affinity than the N-UBM (lanes 6 and 7). The binding to the UCM-1–N-UBM fusion is greater than for individual peptides (lane 8 vs. lanes 6 and 7), suggesting that both peptides can bind UAP56 simultaneously. Mutating R106 in the UCM–N-UBM fusion reduces UAP56 binding to the level of the N-UBM alone (lane 9), and UAP56 shows little background binding (lane 10). c, The SARNP UCM-1 and ALYREF N-UBM bind their cognate binding sites in UAP56 fusion proteins. SUMO-V5-3C tagged N-UBM or UCM-1 are immobilized on magnetic V5 beads and incubated with UAP56 or UAP56–N-UBM, UAP56–UCM or UAP56–UCM–N-UBM fusion proteins. Beads were washed, eluted, and analysed as in panel b. UAP56 binds both N-UBM and UCM (lanes 7 and 11). The UAP56–N-UBM fusion binds the UCM like wild type UAP56, but not the N-UBM (lanes 12 and 8). UAP56–UCM does not bind to immobilized UCM and binds the N-UBM with apparent higher affinity than UAP56 alone (lanes 13 and 9), suggesting that N-UBM and UCM bind synergistically. We note that the synergistic binding of the UAP56–N-UBM to the UCM was not apparent, which may be specific to this condition because of the lower affinity of the isolated N-UBM versus UCM-1 to UAP56. The UAP56–UCM–N-UBM fusion binds neither immobilized N-UBM nor UCM (lanes 10 and 14), suggesting that both peptides are bound to their cognate binding site in the fusion protein. None of the UAP56 proteins exhibits relevant background binding (lanes 15-18). d, THO–UAP56 disassembly assay with recombinant proteins. Experiment schematic (top) and SDS-PAGE analysis of the results are shown (bottom, Coomassie stain). The amount of bead-retained THO complex is quantified underneath from three independent experiments. The addition of ATP and RNA alone or together with either an ALYREF N-UBM peptide or a SARNP UCM-1 peptide resulted in the comparable, but only partial, dissociation of THO from UAP56. The THO–UAP56 complex disassembled more efficiently when we added the ALYREF N-UBM together with the SARNP UCM-1 peptide or when we used full-length SARNP with its five UCMs. When we instead added a peptide comprising the SARNP UCM fused to the ALYREF N-UBM, the THO–UAP56 complex disassembled almost completely. To account for variations in MBP-UAP56 immobilization, the band intensities of each lane are normalized by the MBP-UAP56 levels prior to THO quantification. e-f, Western blot analysis for endogenous TREX disassembly experiments. Shown are western blots for the experiment in Fig. 2e in e., and for a separate experiment using full-length recombinant SARNP, with or without the ALYREF N-UBM, or non-fused N-UBM and SARNP UCM-1 peptides in f. We blotted for the EJC subunit EIF4A3 as a proxy for mRNPs, owing to its high abundance in purified human TREX–mRNPs according to mass spectrometry[2]. g, Western blot analysis of SARNP levels in the SARNP-FKBP-GFP cell line after 0, 1, 2, 3, or 4 h of adding dTAG-V1. SARNP is homozygously tagged and depletes rapidly upon the addition of dTAG-V1. A histone H3 western blot is shown alongside as a loading control. h, UAP56 is immunoprecipitated with or without the rapid, dTAG-V1-dependent depletion of cellular SARNP. Human K562 wild type or dTAG-degron containing SARNP cells are harvested after 8 h of DMSO or dTAG-V1 treatment. The obtained cells are fractionated into nuclei and cytoplasm and analysed as in panel h, with western blotting for the THO subunit THOC2, UAP56, ALYREF, and SARNP. Notably, no increase in THO levels is observed in the UAP56 interactome upon SARNP depletion. This may be explained by the previous observation that nearly all THO complexes are engaged with mRNPs at steady-state and are therefore UAP56-bound[2]. Consistently, the cellular concentration of UAP56 exceeds THO complex levels by around 40-fold[106]. Thus, perturbing TREX disassembly would not increase the levels of UAP56-bound THO complexes. i, UAP56 is stabilized on RNA by SARNP. A 450 nucleotide long MS2-loop containing AdML RNA is immobilized on amylose beads through an MBP-MCP fusion protein. The RNA is incubated in the presence of the non-hydrolysable ATP analogue AMP-PNP with UAP56, UAP56 and full length SARNP or with UAP56–N-UBM, UAP56–UCM or UAP56–UCM–N-UBM fusion proteins. Unbound proteins are washed out, and RNA-bound proteins are eluted through RNase (benzonase) digestion of the RNA and visualized on Coomassie stained SDS-PAGE gels, with the quantification of bound UAP56 shown alongside (UAP56 with SARNP is set to 100% bound). UAP56 alone shows little RNA binding. The presence of a N-UBM or UCM increases the RNA bound fraction, and the presence of the UCM–N-UBM fusion or full length SARNP leads to maximum RNA binding. j, GCI-derived binding kinetics for a 15 nucleotide poly-Uridine RNA (immobilized) probed with UAP56, UAP56–UCM-1, UAP56–N-UBM or an UAP56–UCM-1–N-UBM fusion, with ATP in all buffers. Sensograms (pink line), the fitted model (black), and a binding kinetics summary table are shown, revealing that N-UBM and UCM-1 increase the affinity of UAP56–ATP to RNA approximately 6-fold. Thus, SARNP and UAP56 mRNA-clamping do not only promote THO release but also stabilize UAP56–mRNP complexes. This is consistent with cellular data for a function of SARNP downstream of TREX–mRNPs (see also panels g-i and Fig. 5b), and the THO-dependent association of yeast SARNP (Tho1) with nascent RNA[29].

Extended Data Fig. 6 AlphaFold2 Multimer identifies putative UAP56 interactors.

a-h, Diagnostic plots for the AlphaFold2 Multimer prediction of the paralogs RBM26 a-d, or RBM27 e-h, and UAP56. A putative C-UBM is predicted for RBM26 and RBM27 with high confidence to bind the characterized C-UBM binding site on the RecA1 lobe of UAP56. RBM26 and RBM27 have been implicated in RNA decay[41]. Shown are a,e, the PAE plots, b,f, the pLDDT plots, c,g, the structures of the top ranked model in Cα trace coloured by pLDDT (shown are only the ordered and interacting elements: UAP56 RecA1 and RecA2, residues 40-428; RBM26/27 C-UBM region residues 803-852/891-940), and d,h, a superposition of the structures of all five models, rank 1-5, as in c,g, but coloured for rank 1 with UAP56 in pink and RBM26/27 in purple, and in shades of grey for rank 2-5. i, Multiple sequence alignment of known and novel human C-UBM containing proteins (HsALYREF, Uniprot ID Q86V81, HsCHTOP Q9Y3Y2, HsPHAX Q9H814, HsRBM26 Q5T8P6, HsRBM27 Q9P2N5), coloured by conservation (purple underground = invariant residue). j, Multiple sequence alignment of putative UCM motifs in human SARNP, LENG8, NCBP3 and the Human cytomegalovirus protein pUL69 (HsSARNP, Uniprot ID P82979, HsLENG8 Q96PV6, HsNCBP3 Q53F19, pUL69 P16749), coloured by conservation (orange underground = invariant residue). k-n, Diagnostic plots for the AlphaFold2 Multimer prediction of NCBP3 and UAP56. A UCM containing region is predicted with high confidence to bind to the RecA2 lobe of UAP56. NCBP3 has been implicated in mRNA biogenesis[40]. Shown are k, the PAE plot, l, the pLDDT plot, m, the structure of the top ranked model in Cα trace coloured by pLDDT (shown are only the ordered and interacting elements: UAP56 RecA1 and RecA2, residues 40-428; NCBP3, residues 59-184 and 231-295), and n, a superposition of the structures of all five models, rank 1-5, as in m, but coloured for rank 1 with UAP56 in pink and NCBP3 in yellow, and in shades of grey for rank 2-5.

Extended Data Fig. 7 TREX-2[M] and UAP56–TREX-2[M] complex cryo-EM analysis.

a, In vitro pulldown assay probing the UAP56–TREX-2M interaction. Recombinant TREX-2M complex (MBP-GANP residues 582-1004, PCID2, SEM1) is immobilized on amylose beads through the MBP on the GANP subunit and incubated with UAP56 in the presence or absence of UCM-1–N-UBM peptide and/or 15 U RNA and AMP-PNP. After washes the bead bound proteins are eluted with a maltose containing buffer and visualized in Coomassie stained SDS-PAGE gels. UAP56 forms a near stochiometric complex with TREX-2M, and complex formation is compatible with binding of the UCM-1–N-UBM peptide and the presence of 15 U RNA and AMP-PNP. Lane 6 is additionally shown in Fig. 3d. b, UAP56 can interact with the N-UBM, C-UBM or UCM-1 when bound to the TREX-2M complex. Biotinylated N-UBM, C-UBM or UCM-1 peptide is immobilized on neutravidin agarose beads and incubated with recombinant TREX-2M and UAP56. After several washes, bead-bound complexes are eluted using low pH and analysed by SDS-PAGE followed by Coomassie staining. UAP56 in the UAP56–TREX-2 complex can bind the N-UBM, C-UBM and UCM-1 (lanes 3-5), and this interaction occurs through UAP56, since isolated TREX-2M does not bind these peptides (lanes 6-8). UAP56 and TREX-2M do not bind unspecifically to the beads (lane 9). c, Complex reconstitution scheme for the UAP56–TREX-2M complex for cryo-EM. MBP-GANP residues 582-1004 was incubated with a complex consisting of the PCID2–UAP56–UCM–N-UBM fusion protein and SEM1, 15 poly-Uridine RNA and the non-hydrolysable ATP analogue AMP-PNP and the formed complex separated by size exclusion chromatography, with a peak fraction shown on a Coomassie-stained SDS-PAGE gel. d, Denoised cryo-EM micrograph of the UAP56–TREX-2M sample. Scale bar, 100 Å. e, Three-dimensional image classification tree, with representative 2D classes shown below. From two datasets, containing 6,839 and 9,374 (collected at 20-degree stage tilt) micrographs, 1,673,226 and 2,378,841 particles were picked using a custom trained BoxNet in WARP and processed in cryoSPARC. The final particle stacks contained 57,499 particles for TREX-2M (Map C) and 37,692 particles for UAP56–TREX-2M (Map D) and were each refined to 3.5 Å resolution. The Map D particle stack was further classified using a GANP–UAP56 RecA2 mask, yielding a particle stack of 7,741 particles with observable RecA2 density which was refined to 4.22 Å resolution (Map E). f, Gold-standard Fourier shell correlation (FSC = 0.143) of the TREX-2M complex (Map C), UAP56 RecA1–TREX-2M complex (Map D), and the UAP56 RecA1-RecA2–TREX-2M complex (Map E) cryo-EM maps. g, Orientation distribution plots, as visualized in cryoSPARC, for all particles contributing to the TREX-2M complex (Map C), the UAP56 RecA1–TREX-2M complex (Map D), and the UAP56 RecA1-RecA2–TREX-2M complex (Map E) cryo-EM maps.

Extended Data Fig. 8 TREX-2[M] and UAP56–TREX-2[M] complex cryo-EM analysis.

a, TREX-2M complex (Map C) cryo-EM density, shown in left, front, and right side views and coloured by subunit (GANP, light blue; PCID2, dark blue; SEM1, dodger blue). Shown alongside on the very right is the superposition of the TREX-2M model, in cartoon representation and again coloured by subunit, superimposed on the cryo-EM Map C. b, UAP56 RecA1–TREX-2M complex (Map D) cryo-EM density, shown in left, front, and right side views and coloured by subunit (UAP56 RecA1, pink; GANP, light blue; PCID2, dark blue; SEM1, blue). Shown alongside on the very right is the superposition of the UAP56 RecA1–TREX-2M model, in cartoon representation and again coloured by subunit, superimposed on the cryo-EM Map D. c, Representative segments of GANP, PCID2, SEM1, and UAP56 NTD and RecA1 from Map D superimposed on the respective cryo-EM densities. d, Superposition of the UAP56 RecA1 RecA2 model, in cartoon representation and coloured in pink, on the cryo-EM Map E. e, TREX-2M complex (Map C), UAP56 RecA1–TREX-2M complex (Map D), and UAP56 RecA1 RecA2–TREX-2M complex (Map E) cryo-EM maps coloured by local resolution.

Extended Data Fig. 9 Structural analysis of TREX-2[M] and UAP56–TREX-2[M], UAP56 NTD interactome and controls for the RNA export tethering assay.

a, The human TREX-2M complex cryo-EM structure is highly similar to previous crystal structures and a cryo-EM structure of the yeast TREX-2M complex. Shown are superpositions of the human TREX-2M model determined in this study, in front view and as Cα trace coloured by subunit (GANP, light blue; PCID2, dark blue; SEM1, dodger blue), on previous structures of the yeast TREX-2M complex (in grey) determined through crystallography (left, PDB ID 3T5V; middle left, PDB ID 4TRQ; middle right, PDB ID 5UBP) or cryo-EM (right, PDB ID 5G5P)50,51,52,53. b, Structure of the UAP56–TREX-2M complex, with UAP56 in pink and cartoon representation and PCID2, SEM1 and GANP in surface representation and coloured by sequence conservation (maroon, conserved; cyan, variable). A front view of the entire complex is shown in the centre, flanked by front and side views of PCID2–SEM1 (left) or GANP (right). Surfaces patches involved in complex formation, such as the UAP56 RecA1 proximal patch in PCID2, the UAP56 NTD binding site, the GANP wedge or the PCID2–GANP interface, show a high degree of sequence conservation. c, Multiple sequence alignment of the UAP56 N-terminal domain (NTD) from H. sapiens (Hs, Uniprot ID Q13838), M. musculus (Mm, Q9Z1N5), D. rerio (Dr, Q803W0), D. melanogaster (Dm, Q27268), C. elegans (Ce, Q18212), A. thaliana (AtRH56, Q9LFN6), S. pombe (SpSub2, O13792) and S. cerevisiae (ScSub2, Q07478). Residues invariant or conserved among these species are highlighted in pink or light pink, respectively. d, GCI-derived binding kinetics for TREX-2M (immobilized) probed with UAP56ΔNTD1 (residues 28-428). Sensogram (pink line), the fitted model (black), and a summary table of the binding kinetics is shown. Related to Fig. 4c. e, In vitro pulldown assay as in Extended Data Fig. 7a, but using wild type TREX-2M or the wedge mutant TREX-2M (containing GANP R678A) and wild type UAP56 or UAP56 ΔNTD (residues 44-428). A UAP56–TREX-2M complex is formed with the wedge loop mutant TREX-2M, while the deletion of the UAP56 NTD abolishes complex formation. f, In vitro pulldown assay probing TREX-2M binding of the isolated UAP56 NTD. Biotinylated UAP56 NTD peptides (residues 1-21, wild type or mutant E9K, L10S, L11A, D12K, Y13S) are immobilized on streptavidin beads and incubated with TREX-2M in the presence or absence of UAP56, or with TREX-2M with UAP56 fused to the C-terminus of PCID2. Protein complexes are eluted and visualized on Coomassie stained SDS-PAGE gels. Wild type, but not mutant, NTD peptide forms a complex with TREX-2M (compare lane 8 to 5), and complex formation is abolished in the presence of full length UAP56. TREX-2M with UAP56 fused to the PCID2 C-terminus also does not form a complex with NTD peptide, suggesting that UAP56 is TREX-2M-bound in the fusion construct. g, UAP56 NTD protein interactome. A C-terminally biotinylated UAP56 NTD peptide (residues 1-21) was immobilized on strepdavidin beads, with a scrambled peptide serving as the control. Beads were incubated with nuclear K562 lysate, washed and the NTD peptide’s interactome analysed by western blot, probing for the TREX-2 subunit GANP, or by mass spectrometry (see panel h). h, Volcano plot showing the log2 fold-changes of the protein interactome of wildtype versus scrambled UAP56 NTD. Proteins with a log2 fold-change over two and a -log10 p-value over one are labelled. SRFBP1 is a nucleolar protein required for ribosome biogenesis with no known function in mRNA biogenesis. Data (n = 3) were analysed using a two-sided Welch t-test with FDR correction for multiple testing. i, RNA export tethering assay, related to Fig. 4d with λN-BFP, λN-NXF1 and λN-UAP56 from Fig. 4d shown for comparison. We split an mCherry open reading frame in two halves (exon 1, exon 2) by inserting an intron containing ten BoxB RNA aptamers, an IRES, and a GFP open reading frame (top). λN-tagged proteins are transiently expressed and bind the reporter RNA through the RNA aptamers. Export of the reporter RNA allows GFP production, which is quantified through Fluorescence-activated cell sorting (FACS). While the direct UAP56-tethering to the reporter RNA bypasses preceding pathway steps and does not replicate UAP56’s native mRNA binding dynamics, this assay allowed us to investigate the export-promoting features of UAP56. Mutations interfering with UAP56’s ATPase activity (D196A, E197A, ‘AACD’) do not substantially alter the export promoting effect, while mutating key residues in the UAP56 NTD (Y13S or L10S, L11S, D12K, Y13S, ’10-SSKS-13’) leads to a reduced effect. As additional controls, we also tethered the THO complex via THOC1 or THOC5, or SARNP, which showed no effect, as well as ALYREF, which modestly stimulated reporter pre-mRNA export. Boxplots include the median (centre), interquartile range (25th–75th percentiles) as the height of the box, and whiskers extending to the 5th and 95th percentiles. n = min. 40,000 cells examined over three independent experiments. j, λN-UAP56 and λN-UAP56 ΔNTD are expressed and imported to the nucleus at similar levels. Two replicates of λN-UAP56 or λN-UAP56 ΔNTD expressing K562 cells are fractionated into nucleus and cytoplasm, proteins separated by SDS-PAGE and analysed by western blotting, probing for UAP56 (top) and Histone H3 (bottom, fractionation control) on the same membrane; the full membrane is shown. Both constructs are expressed to similar levels (as judged from the whole cell extract, compared to endogenous UAP56 to showcase equal loading). Equal amounts of each construct are important into the nucleus, and nuclear levels of the λN-tagged proteins are comparable to levels of endogenous nuclear UAP56.

Extended Data Fig. 10 In vivo probing of PCID2 interfaces, GANP wedge loop analysis, controls for UAP56 ATPase assays and analysis of UAP56 THO/TREX-2 binding mutants.

a-b, Analysis of the PCID2-FKBP-GFP cell line, showing by western blot that tagged PCID2 is efficiently depleted upon the addition of dTAG-V1 (a) and by PCR that PCID2 is tagged homozygously (b). het, heterozygous population; hom, homozygous clonal cell line. c-d, Human K562 cell growth competition assays to probe the interaction of the UAP56 NTD with the TREX-2 subunit PCID2 and between PCID2 and the TREX-2 subunit GANP. Wild type or mutant mSarlet-V5-PCID2 is ectopically expressed from a doxycycline (dox)-inducible promoter in human K562 cells, where endogenous PCID2 is tagged with the dTAG-degron (FKBP12F36V). Addition of the dTAG-V1 compound leads to the rapid depletion of endogenous PCID2. Addition of dox initiates the ectopic expression of the respective rescue construct, as monitored by mScarlet levels two days after induction (c). Dox and dTAG-V1 or control treated cells are mixed at a 1:1 ratio with wild type K562 cells at day 0. Cell growth is monitored by FACS (d, see Methods for details); error bars, mean ± s.d. from n = 3 independent samples. e, PCID2 wild type or mutant immunoprecipitation after the rapid depletion of endogenous dTAG-PCID2 using the dTAG-V1 compound. Human K562 cells, as used in panel c,d, were harvested 8 h after the treatment with dox and dTAG-V1 or dox and DMSO. Cells were fractionated into nuclei and cytoplasm, and PCID2 was immunoprecipitated using anti-V5 magnetic beads. After washes, bead-bound proteins were eluted by boiling in SDS-PAGE and western blotted for TREX-2 (GANP, PCID2), UAP56, and Histone H3. GANP protein levels are reduced upon the ectopic expression of the PCID2 NTD-binding mutant for unclear reasons. Input samples are shown (left). f, Multiple sequence alignment of the GANP wedge loop region of GANP proteins from H. sapiens (Hs, Uniprot ID O60318), M. musculus (Mm, Q9WUU9), D. rerio (Dr, F1Q712), D. melanogaster (Dm, Q9U3V9), C. elegans (Ce, Q19643), A. thaliana (At, F4JAU2), S. pombe (Sp, O74889) and S. cerevisiae (Sc, P46674), coloured by conservation (blue background, invariant residue), and with secondary structure elements depicted on top. Highlighted are the wedge loop with the key and invariant residue R678 (numbering according to human GANP), as well as the invariant residues R692 and R728 which might stabilize the wedge loop and UAP56 RecA1 interactions and are implicated in gene gating[51]. g, The GANP wedge binding mimics the ATP-binding pocket in clamped UAP56. Shown are the UAP56-bound nucleotide and nucleotide base stacking residues in sticks representation, coloured by heteroatom, and with UAP56 in pink, GANP in light blue and the nucleotide in pink for the UAP56–TREX-2M structure (left, superimposed on the cryo-EM density), and in grey for the clamped human UAP56 structure (right, PDB ID 8ENK)[33]. In the UAP56–TREX-2M structure the adenine moiety of the non-hydrolyzable ATP analogue forms stacking interactions with F65 in the UAP56 RecA1 lobe and the GANP wedge residue R678 (left). In clamped UAP56, the adenine base of bound ADP stacks with F65 of UAP56’s RecA1 lobe and F381 of the RecA2 lobe (right). Superposition of both structures reveals that the GANP wedge residue R678 substitutes the RecA2 lobe residue F381 (bottom). h, Protein samples used in the UAP56 ATPase assay in Fig. 4f. An aliquot of each reaction was separated by SDS-PAGE and visualized by Coomassie-staining. i, UAP56–RNA unclamping assay. UAP56 is incubated with bead-immobilized 15 nucleotide poly-Uridine RNA (15U RNA) in the presence of ATP, to form UAP56–ADP-Pi–RNA complexes. After removing unbound UAP56 and excess ATP, TREX-2M complexes containing GANP wild type or the ‘wedge’ mutant (R678A) are added. UAP56–ADP-Pi–RNA complexes remaining on the beads after incubation are eluted by digestion of the RNA (benzonase) and analysed by Coomassie-stained SDS-PAGE. j, GCI-derived binding kinetics of the THO tetramer (immobilized) probed with wildtype or UAP56 mutants M1, M2, or M3. Sensogram (pink line), the fitted model (black), and a summary table of the binding kinetics are shown. k, In vitro pulldown assay probing the interaction of TREX-2M with UAP56 mutants M1, M2 or M3. TREX-2M complex (MBP-GANP residues 582-1004, PCID2, SEM1), immobilized on amylose beads through the MBP on the GANP, is incubated with wildtype or mutant UAP56. Bound proteins are eluted with a maltose containing buffer and visualized in Coomassie stained SDS-PAGE gels. l, Western blot analysis of the UAP56 wildtype and mutant IP samples that were analysed by mass spectrometry in Fig. 5b. GANP is not shown as it was not detected in the western blot. wt, wildtype; M1, UAP56 TREX-2 binding mutant; M2, UAP56 THO-binding mutant; M3, combined UAP56 THO- & TREX-2-binding mutant. See Extended Data Fig. 11a for a detailed description of the experiment outcomes.

Extended Data Fig. 11 Poly(A) RNA FISH experiments and analysis.

a, Poly(A) RNA FISH sample preparation and image analysis workflow, with a representative image shown together with a segmented image used for analysis. b, Representative images for all analysed samples. Shown are the poly(A) RNA FISH channel, the mScarlet channel (indicating the expression of mScarlet-PCID2 rescue constructs), the GFP channel (indicating the effect of dTag V1 depletion of PCID2-FKBP-GFP (top) or GANP-FKBP-GFP (bottom)), the DAPI channel showing nuclei, and an overlay of the poly(A) RNA FISH and DAPI channels, illustrating the nuclear/cytoplasmic distribution of the poly(A) RNA FISH signal. Cells expressing mScarlet are highlighted with a white outline. Scale bar = 10 µm. c, Boxplots depicting the results of the poly(A) RNA FISH image analysis for each replicate. We obtained four replicates per condition, each with >70 cells quantified. For experiments, where the mScarlet-PCID2 rescue construct is expressed upon the depletion of the endogenous PCID2-FKBP-GFP protein, cells were classified into mScarlet positive or negative, to quantify ‘−’ and ‘+’ rescue construct expression conditions within the same field of view. Boxplots include the median (centre), interquartile range (25th–75th percentiles) as the height of the box, and whiskers extending to the 10th and 90th percentiles. d, Results of the poly(A) RNA FISH quantification, showing data from Fig. 4e alongside additional controls. Shown are the averages of each replicate experiment, with p-values indicating the significance of condition comparisons using two-sided Welch t-tests with FDR correction for multiple testing.

Extended Data Fig. 12 Predicted effects of UAP56 mutants and AlphaFold2 prediction of a UAP56–RBM33 complex.

a, Predicted effects of mutations M1-3 in UAP56 on the general model for mRNA nuclear export (related to Fig. 5b). When binding of UAP56 to TREX-2 is prevented (M1), UAP56–mRNP docking at the NPC and release of UAP56 from mRNPs is impaired. This would result in the accumulation of UAP56 M1 on mRNPs together with SARNP. Because the experiment is performed in a wildtype background, with the levels of the ectopically expressed mutant UAP56 being lower than endogenous UAP56 and finite, this would lead to a mild reduction of THO complex levels in the UAP56 M1 protein interactome: the more UAP56 M1 accumulates on mRNPs, the less UAP56 M1 would be available to bind the THO complex. For UAP56 M2, where THO binding is impaired, the pathway is blocked at step (I), which would lead to the depletion of the THO complex, mRNP proteins, and of SARNP in the UAP56 M2 protein interactome. This mutant would lead to a relatively high concentration of free UAP56 M2 protein, which could allow UAP56 M2 to engage TREX-2 with however no functional consequence. When UAP56 M1 and M2 mutations are combined in M3, the mRNA export pathway would again be blocked at step (I), but now the free UAP56 M3 protein would also be unable to engage TREX-2. Unlike in M1, mRNP proteins would not accumulate with UAP56 in M3, because the binding of UAP56 to mRNPs is THO-dependent. b-e, Diagnostic plots for the AlphaFold2 Multimer prediction of RBM33–UAP56. RBM33, which aids the export of intronless mRNAs[67], is predicted to bind UAP56 via two novel peptides. In this prediction, one of the two RBM33 peptides would bind to the UAP56 UCM-binding site, although it is distinct from the UCM. Shown are b, the PAE plot, c, the pLDDT plot, d, the structure of the top ranked model in Cα trace coloured by pLDDT (shown are only the ordered and interacting elements: UAP56 RecA1 and RecA2, residues 40-428; RBM33 interacting regions 1 and 2, residues 218-278 and 774-837), and e, a superposition of the structures of all five models, rank 1-5, as in d, but coloured for rank 1 with UAP56 in pink and RBM33 in yellow, and in shades of grey for rank 2-5.

Extended Data Table 1 Cryo-EM data collection and refinement statistics

Supplementary information

Supplementary Information

Guide to Supplementary Tables 1–5 and Supplementary Fig. 1 (the uncropped blots).

Reporting Summary

Supplementary Table 1

Proteomics data of native UAP56–GFP immunoprecipitation.

Supplementary Table 2

Proteomics data of native TREX–mRNP disassembly assay.

Supplementary Table 3

AlphaFold2 Multimer screen results for UAP56 and its putative interactors.

Supplementary Table 4

Proteomics data of Flag–UAP56 immunoprecipitations.

Supplementary Table 5

Vectors and sequences.

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Hohmann, U., Graf, M., Tirián, L. et al. An ATP-gated molecular switch orchestrates human mRNA export. Nature 649, 1042–1050 (2026). https://doi.org/10.1038/s41586-025-09832-z

- Received: 09 February 2024

- Accepted: 29 October 2025

- Published: 06 November 2025

- Version of record: 17 December 2025

- Issue date: 22 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09832-z

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ZAK activation at the collided ribosome

Vienna L. Huso,

Shuangshuang Niu,

Marco A. Catipovic,

James A. Saba,

Timo Denk,

Eugene Park,

Jingdong Cheng,

Otto Berninghausen,

Thomas Becker,

Rachel Green &

…

Roland Beckmann

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Abstract

Ribosome collisions activate the ribotoxic stress response mediated by the MAP3K ZAK, which in turn regulates cell-fate consequences through downstream phosphorylation of the MAPKs p38 and JNK[1]. Despite the critical role of ZAK during cellular stress, a mechanistic and structural understanding of ZAK–ribosome interactions and how these lead to activation remain elusive. Here we combine biochemistry and cryo-electron microscopy to discover distinct ZAK–ribosome interactions required for constitutive recruitment and for activation. We find that upon induction of ribosome collisions, interactions between ZAK and the ribosomal protein RACK1 enable its activation by dimerization of its SAM domains at the collision interface. Furthermore, we discover how this process is negatively regulated by the ribosome-binding protein SERBP1 to prevent constitutive ZAK activation. Characterization of novel SAM variants as well as a known pathogenic variant of the SAM domain of ZAK supports a key role of the SAM domain in regulating kinase activity on and off the ribosome, with some mutants bypassing the ribosome requirement for ZAK activation. Collectively, our data provide a mechanistic blueprint of the kinase activity of ZAK at the collided ribosome interface.

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Main

The ribosome translates mRNA into protein, often with multiple ribosomes on a given mRNA called polysomes. Ribosomes are also essential sensors of cellular stress and can alert the cell of nutrient deprivation[2], damage to mRNAs and chemical insults that directly target and damage ribosomes3,4,5. Such cellular stresses cause ribosomes to stall on problematic mRNA, resulting in the lagging ribosome colliding with the stalled ribosome. These ribosome collisions are a key signal to activate both quality control pathways and broad stress signalling responses4,6.

For one of these signalling pathways, the ribotoxic stress response (RSR), the mitogen-activated protein kinase kinase kinase (MAP3K) ZAKα (referred to hereafter as ZAK) has a central role in orchestrating the RSR7,8,9. Previous studies have demonstrated that ZAK interacts constitutively with ribosomes during unstressed conditions but becomes activated (via autophosphorylation) and is released from the ribosome upon cellular stresses that impair translation1,10. Although previous studies have argued that ribosome collisions are key determinants of ZAK activation[1], other studies have suggested that both colliding and individually stalled ribosomes may be potent triggers10,11. In either case, ZAK activates the stress-activated protein kinases (SAPKs) p38 and JNK, leading to cell cycle arrest and/or apoptosis12,13,14.

Kinase regulation often requires scaffold proteins[15], and ZAK has been shown to interact with 14-3-3 proteins off the ribosome in a canonical phosphorylation-dependent manner downstream of activation10,12. Another scaffold is the receptor for activated C-kinase (RACK1), a conserved eukaryotic ribosomal protein on the head of the 40S subunit, which was first characterized for PKC activation and later argued to be a signalling hub for kinases including JNK16,17,18,19. Of note, RACK1 resides exactly at the collision interface and has a critical role in collision-mediated quality control events20,21,22,23,24, hinting that RACK1 could scaffold ZAK on the ribosome.

Although much is known about ZAK signalling and its downstream consequences for cell fate12,13, the molecular determinants of the interaction of ZAK with the ribosome and an understanding of how these interactions mediate activation have remained enigmatic. Here we elucidate how ZAK is recruited to ribosomes, both in its basal state and in induced stress conditions, and show how collision-specific interactions organized on RACK1 mediate ZAK activation.

ZAK enrichment on ribosomes

A single mammalian cell contains approximately 5 million ribosomes, and the ratio of ZAK to ribosomes is estimated at approximately 1:100 based on copy numbers measured across various cell types25,26. Therefore, we turned to overexpression of N-terminally tagged ZAK in HEK293T cells to enrich ZAK-bound ribosomes. We characterized ribosome binding of wild-type (WT) ZAK and monitored its phosphorylation status by Phos-tag immunoblotting after sucrose gradient fractionation. Overexpressed WT ZAK was found in the top fractions of the gradient and migrated on western blot (Phos-tag) at a molecular weight of approximately 250 kDa, consistent with fully phosphorylated (P) ‘activated’ protein1,12 (Fig. 1a); in addition, JNK-P levels were increased under basal conditions (Extended Data Fig. 1a). These observations reflect activation of ZAK and the RSR upon ZAK overexpression.

Fig. 1: Cryo-EM structure of ZAK bound to a colliding disome.

a, Polysome profile (top) and immunoblots of a Phos-tag gel (IBs; bottom) from sucrose gradient fractions of HEK293T cells transfected with Strep-tagged WT and kinase inactive ZAK(T161A/S165A) expressed from a complete CMV promoter plasmid. Polysome profile from WT transfection is shown. b, Cut top view on the ZAK–disome model shown as low-pass-filtered surface, highlighting mRNA, tRNA and EDF1-binding sites. The boxed panels show a zoomed-in view on the ANS (red)-binding site of refined cryo-EM maps (transparent) of the stalled (right) and collided (left) 80S. c,d, Composite cryo-EM map (c) and molecular model (d) of the ZAK–disome complex. Structural features of ZAK are indicated as SAM domain (SAM), pin, RIH, RIM and RIH-p. e, Schematic of the ZAK domain architecture based on AlphaFold prediction: N-terminal kinase domain, leucine zipper (LZ) region, SAM, YLD, sensing region and the CTD. Residues relevant for ribosome interaction as identified in this study are depicted in blue boxes. Vertical lines with P (red) indicate previously published subset of RSR phosphorylation sites (cluster 2)[12]. The blots represent at least two independent replicates (see the section ‘Statistics and reproducibility’). See Supplementary Fig. 1 for source data.

We next overexpressed a kinase inactive ZAK with mutations in the activation loop: T161A/S165A[27]. Despite its strong overexpression, this variant remained deep in the polysome fractions and migrated on western blot (Phos-tag) at the expected molecular weight for unphosphorylated inactive ZAK (Fig. 1a). Neither this ZAK mutant (T161A/S165A) nor another variant with a mutated ATP-binding site (K45M) led to increased levels of JNK-P when overexpressed (Extended Data Fig. 1a). Because these variants are not released from ribosomes in response to collisions[12], we decided to use them as tools for structural studies targeting ribosome-bound ZAK.

ZAK binds to the collided ribosome interface

We performed native pull-downs of inactive tagged ZAK variants from Expi293F cells either without or with low-dose anisomycin (ANS) to induce ribosome collisions1,28 (Extended Data Fig. 1b). We found that ribosomes co-enriched with all ZAK variants and independent of ANS addition (Extended Data Fig. 1b, left panel). We performed single-particle cryo-electron microscopy (cryo-EM) analysis of treated and untreated samples (Extended Data Figs. 2 and 3a). Three-dimensional classification showed large classes of stable disomes in the ANS-treated sample that were absent in the untreated sample, with extra density for ZAK at the collision interface. The most abundant disome class was refined to generate a composite ZAK-bound disome map (Fig. 1b,c and Extended Data Fig. 2; see Methods for details). A molecular model was generated based on existing human 80S structures29,30 and guided by AlphaFold multimer predictions for different regions of ZAK31,32,33 (Fig. 1d, Extended Data Table 1 and Extended Data Figs. 4a–c and 5a–e).

The ZAK-bound disome was in a typical arrangement as described before for human disomes[34]. The stalled ribosome contained P/P-site and E/E-site tRNAs, and, as expected, ANS was visualized in the peptidyl-transferase centre. The collided ribosome contained hybrid A/P–P/E-state tRNAs but no density for ANS (Fig. 1b). EDF1 was positioned on the collided ribosome as described[21], with the N-terminal domain now resolved and reaching over to the stalled ribosome (similar to its homologue Mbf1 in yeast) to interact with ribosomal protein eS26 (ref. [35]) (Fig. 1b and Extended Data Fig. 5f,g).

ZAK contacts the disome at multiple sites

ZAK consists of an N-terminal kinase domain (residues 16–277), leucine zipper region (residues 280–326), sterile-α motif (SAM) (residues 339–416) and a computationally predicted YEATS-like domain (YLD; residues 433–551)[36] (Fig. 1e and Extended Data Fig. 4b). The C-terminal 100 amino acids (residues 700–800) are critical for ribosome binding and activation (ribosome-binding region (RBR)), with the last 27 amino acids enriched in positively charged residues (residues 774–800; C-terminal domain (CTD)) being of particular importance1,10 (Fig. 1e).

In the refined structure, we identified density for different parts of ZAK. The largest density forms a bridge connecting RACK1 of the stalled ribosome, RACK1(s), with RACK1 of the collided ribosome, RACK1(c), and represents a dimer formed by two ZAK SAM domains (Extended Data Fig. 4c–e). Each SAM domain binds RACK1 with a small motif immediately downstream of the SAM domain, termed the RACK1-interacting motif (RIM; residues 417–422; Fig. 1c–e and Extended Data Figs. 4b and 5a–e). In addition, further downstream, a short α-helix serves as a second RACK1 interaction motif, termed the RACK1-interacting helix (RIH; residues 611–617; Fig. 1c–e and Extended Data Figs. 4b and 5a–e). Of note, the RIH on RACK1(s) features an additional peptide that reaches across the collision interface to interact with the collided ribosome, termed the RIH-peptide (RIH-p; residues 618–630; Extended Data Fig. 4b,e). Finally, on each ribosome, another short peptide (residues 767–771) interacts with ribosomal protein eS27 (eS27(s) and eS27(c); Fig. 1c–e and Extended Data Figs. 4b and 5a–e), termed the eS27-pin (pin). Consurf analysis[37] showed high conservation of residues present in each motif (Extended Data Fig. 1c). Although the remaining regions of ZAK are dynamic and not resolved, additional globular density emerges from the SAM dimers at low contour levels and when low-pass filtered to approximately 30 Å matches in overall size and shape a dimer of the leucine-zipper and kinase domains (Extended Data Fig. 2).

Together, the structure reveals how the disome facilitates recognition and interaction between the SAM domains of ZAK. We hypothesize that this structure represents a pre-activation state with ZAK primed for activation. In the following sections, we investigated the functions of each ZAK structural unit observed on the colliding disome.

ZAK C terminus binds to the 40S subunit

We first focused on the C terminus of ZAK. The density for the pin (residues G767–V771) was visible on all ZAK-bound 80S ribosomes, regardless of treatment or collision state (Extended Data Fig. 5a–e). These classes comprised both translating (bound to tRNAs and mRNAs) and hibernating (bound to eEF2–SERBP1) 80S (Extended Data Figs. 2, 3a and 5b,c), indicating that ZAK is guided by the pin to ribosomes independent of translation state. In vitro binding assays further confirmed that GST-tagged RBR bound 40S subunits and 80S ribosomes but not 60S subunits (Extended Data Fig. 1d,e). Cryo-EM reconstructions of reconstituted GST–RBR–40S complexes revealed well-resolved (2.3 Å) extra density on eS27, validating the pin interaction observed in native ZAK immunoprecipitations as a general 40S–80S recruitment interaction and allowing interpretation at the molecular (side chain) level (Extended Data Fig. 5d, second row). The pin is anchored to eS27 via W768, which intercalates between R80 and K36 of eS27 with further contacts contributed by adjacent residues (T769, K770 and V771) of ZAK (Fig. 2b and Extended Data Figs. 5d and 6a).

Fig. 2: The C terminus of ZAK mediates ribosome binding and collision-specific rRNA interaction.

a, Top view on the molecular model of the ZAK–disome. The box highlights the site of the pin on the stalled ribosome, comprising the collision interface formed by ES7 and eS27 (both on stalled) and ES6c on the collided ribosome. b, Molecular model of the pin within the ZAK RBR, bound to eS27 on the stalled 80S (see also Extended Data Figs. 5a–e and 6a). c, Zoomed-in view on the collision interface as outlined in panel a. The dashed line indicates regions flanking the pin. d, Schematic of the ZAK domain architecture, with important residues in the RBR highlighted. Residues marked in red were targeted for mutational analysis. S, sensing domain. e, Immunoblots of sucrose gradient fractions collected from HEK293T ZAK-knockout cells transfected with partial CMV promoter plasmids expressing various N-terminal FLAG ZAK constructs at endogenous levels. Immunoblots were probed for FLAG to visualize ZAK. A representative eS24 blot is shown (Extended Data Fig. 3b). f, Immunoblots of total lysate from gradients shown in panel e. g, CLIP-seq of FLAG-tagged ZAK(T161A/S165A) expressed from the full CMV promoter in HEK293T ZAK-knockout cells and associated ribosomal RNA (black lines) in untreated and ANS-treated conditions. Sequence reads were mapped to 18S rRNA and normalized to the non-ribosome binding 1–649 amino acid C-terminal truncation control. The shading indicates standard error of the mean from two biological replicates. ZAK-knockout cells are shown as a grey line in the untreated blot. The horizontal dotted line represents twofold enrichment. Reads from CLIP-seq were mapped to the 18S rRNA (monosome for untreated; disome for ANS-treated) and matched to ES7 (purple) and ES6b/c (red). The position of the pin of ZAK is highlighted with blue spheres. The blots represent at least two independent replicates (see the section ‘Statistics and reproducibility’). See Supplementary Fig. 2 for source data. bk, 40S beak; ft, 40S foot; h, 40S head.

Of note, the pin places the highly charged CTD in close proximity to the 18S rRNA expansion segment ES7 (Fig. 2c, dashed line). Although not visualized, multimodal electrostatic interactions between the negatively charged rRNA backbone and the positively charged CTD probably contribute to ΖΑΚ binding. Furthermore, only in the disome structure the N-terminal region flanking the pin is in close contact to ES6c of the collided ribosome (Fig. 2c and Extended Data Fig. 6b).

We next generated a system to characterize the effects of ZAK mutations on ribosome binding and activation by expressing ZAK at endogenous levels using an expression plasmid with a partial CMV promoter (Extended Data Fig. 1a). ZAK-knockout cells carrying the WT ZAK expression construct revealed ZAK and JNK activation upon ANS treatment resembling endogenous ZAK behaviour (Extended Data Fig. 1a). We followed ZAK binding to ribosomes using analytical sucrose gradients. In WT cells, ZAK distributes across the free and polysome fractions and is increased at the top of the gradient upon ANS treatment, consistent with ribosome dissociation following activation (Extended Data Fig. 1f). These data match previous observations1,10, although more clearly reveal unbound ZAK. The observation of an equilibrium between ribosome-bound and unbound ZAK populations is consistent with a role in surveying ribosome complexes in the cell.

We next generated a point mutation of the eS27-interacting residue (W768A; pinmut; Fig. 2d) and observed substantially reduced ZAK binding to polysomes independent of ANS treatment (Fig. 2e). We also predicted residues probably bound to ES7 based on the location and charge and generated a four-alanine patch mutant (R774A, K775A, K776A and R779Α; ES7-patchmut), which also revealed substantial loss of ZAK binding to polysomes (Fig. 2d,e). Mutating all five residues together (pinmut + ES7-patchmut) resulted in near complete loss of ribosome binding (Fig. 2e), which correlated directly with losses in ZAK activation and JNK phosphorylation (Fig. 2f). Together, the C-terminal interactions of ZAK with eS27 and ES7 contribute to overall affinity of ZAK for ribosomes and serve as a prerequisite for activation upon collision.

Collision-specific rRNA interaction

We further explored the interaction of ZAK with 18S rRNA using crosslinking and immunoprecipitation sequencing (CLIP-seq). We transfected ZAK-knockout cells with overexpressed kinase inactive ZAK(T161A/S165A), a C-terminal truncation mutant (residues 1–649) defective for ribosome binding[1] or a mock transfection control (ZAK knockout; Fig. 2g). We compared all samples to the C-terminal truncation mutant. We observed greater than twofold enrichment of ZAK footprints mapping to ES7 (18S; bases 1117–1195) in both untreated conditions (Fig. 2g, top) and with ANS (Fig. 2g, bottom). These observations establish that the C terminus of ZAK indeed forms contacts with the ribosome via ES7 independent of collisions, indicative of a ‘sampling’ mode.

A second footprint region emerged for 18S rRNA bases 710–766 corresponding to ES6b and ES6c (ES6b/c; Fig. 2g, bottom) only upon treatment with ANS. These data are consistent with our structural observations (Extended Data Fig. 6b) and suggest that portions of the C terminus of ZAK engage ES6c (ES6-patch) on the collided ribosome (ES6c(c)). Additional CLIP experiments performed with WT and kinase inactive ZAK(T161A/S165A) expressed at endogenous levels showed similar strong enrichment for ES6b/c upon ANS treatment (Extended Data Fig. 6c). No read enrichment was observed near helix 14, a region previously suggested to interact with ZAK[10]. Similarly, none of the ZAK constructs revealed CLIP signal on the 28S rRNA of the 60S subunit relative to controls consistent with the ZAK–ribosome interaction being mediated by the 40S subunit (Extended Data Fig. 6d). Finally, metagene analysis revealed enrichment of ZAK CLIP reads in the open reading frames of mRNAs for all full-length ZAK samples. This observation probably reflects ZAK interactions with mRNA on the ribosome due to its proximity to mRNA entry and exit sites between 40S body and head (Extended Data Fig. 6e).

RIH mediates ribosome binding

Next, we focused on the RIH, a short α-helical motif (residues 611–617) anchoring ZAK at the disome interface between blade 5 and blade 6 of RACK1 (Fig. 3a and Extended Data Fig. 4f). Our well-resolved maps (below 3 Å in the RACK1 region) revealed Y611, pointing towards the backbone loop of RACK1 (residues 242–248), and V615, packing against a hydrophobic surface of RACK1 (L206, L218 and L261) as the main interacting RIH residues (Fig. 3c, top, and Extended Data Fig. 5, third row). Of note, although being present on both RACK1 proteins, only the RIH on the stalled 80S features an additional short peptide (residues 618–630; RIH-p) that extends towards the collided 40S. This peptide potentially interacts with the C terminus of uS3 and rRNA at the junction of helices h37 and h38 (h37–h38) on the colliding ribosome (Fig. 3b and Extended Data Fig. 4e). Similar to the pin, we observed density for the RIH in almost all subclasses of 80S ribosomes regardless of ANS treatment (Extended Data Fig. 5b,c), indicating that the RIH constitutes another general ribosome-binding motif.

Fig. 3: ZAK interaction with RACK1 via RIH mediates ribosome binding.

a, Zoomed-in view of the molecular model of the ZAK–disome focusing on the RACK1 collision interface. The boxes highlight the position of the RIH and the RIH-p. b, Zoomed-in view focusing on the RIH of the stalled 80S (RIH(s)) and the RIH-p extending towards the disome interface to form contacts with uS3 and h37–h38 of the collided 80S. c, Two views highlighting the interactions of RIH Y611 and V615 with RACK1. Additional contacts may be formed by R616 and R617 of ZAK with D220 and E262 of RACK1. d, Schematic of the ZAK domain architecture with RIH highlighted. Residues marked in red were targeted for mutational analysis. e, Immunoblots of sucrose gradient fractions collected from HEK293T ZAK-knockout cells transfected with partial CMV promoter plasmids expressing various N-terminal FLAG ZAK constructs at endogenous levels. Immunoblots were probed for FLAG to visualize ZAK (see Extended Data Fig. 3c for eS24 blots). f, Immunoblots of total lysate from gradients shown in panel e. g, Immunoblots of sucrose gradient fractions collected from HEK293T WT or RACK1-knockout cells. Immunoblots were probed for endogenous ZAK (993A antibody), and the eS24 blot is shown in Extended Data Fig. 3d. h, Immunoblots of total lysate from gradients shown in panel g. Blots represent at least two independent replicates (see the section ‘Statistics and reproducibility’). See Supplementary Figs. 3 and 4 for source data.

We generated a RIH ZAK mutant (Y611, V615 and R616A; RIHmut; Fig. 3d) and observed complete loss of binding to polysomes in untreated and ANS-treated conditions (Fig. 3e), and, in addition, ZAK activation by ANS was completely abolished (Fig. 3f). This loss of ribosome binding and activation was recapitulated in a RACK1-knockout HEK293T cell line, where endogenous ZAK migrates entirely in the free fractions (Fig. 3g) and ZAK (and JNK) phosphorylation upon ANS treatment were lost (Fig. 3h). From these data, we conclude that the RIH, similar to the pin, makes collision-independent contacts with RACK1 necessary for ZAK binding and activation.

FPxL motif mediates collision sensitivity

We next focused on the RIM located immediately downstream of the SAM domain. The RIM was exclusively observed on collided ribosomes (Fig. 4a) and consists of a short peptide motif (FPPLIK; residues 417–422) that stretches over RACK1 blades 2 and 3 (Extended Data Fig. 4f; identical in both the stalled and the colliding ribosome). As revealed by our sub-3 Å maps in this region, contacts of the RIM are primarily established by F417 that caps the α5 of SAM and binds into a groove clad by Q119 and N133 of RACK1 blade 3, and by L420 that inserts into a hydrophobic pocket formed by F77, L89 and F113 between RACK1 blades 2 and 3 (Fig. 4b,c and Extended Data Fig. 5, fourth row).

Fig. 4: RIM on ZAK mediates collision sensitivity and activation.

a, Zoomed-in view on the molecular model of the ZAK–disome as in Fig. 3a. The boxes highlight the position of the RIM. b, Zoomed-in view highlighting the interactions between RACK1 and the ZAK RIM–F417 that caps the α5 of SAM, which binds into a groove clad by Q119 and N133 of RACK1 blade 3; L420 inserts into a hydrophobic pocket formed by F77, L89 and F113 between RACK1 blades 2 and 3. In addition, K422 forms a salt bridge with T93 and a hydrogen bond to Q76 of RACK1 blade 2 and H416 of SAM α5 contacts K139 in blade 3 of RACK1. Beyond F417, the RIM merges into the last α-helix of the SAM domain (α5) that extends into the solvent space above the 40S head. c, A 180° rotated view of b with RACK1 shown as surface colour coded according to lipophilicity potential (scale bar shown). d, Schematic of the ZAK domain architecture with the RIM highlighted. Residues marked in red were targeted for mutational analysis. Predicted FPxL motif residues are underlined. e, Immunoblots of sucrose gradient fractions collected from HEK293T ZAK-knockout cells transfected with partial CMV promoter plasmids expressing various N-terminal FLAG ZAK constructs at endogenous levels. Immunoblots were probed for FLAG to visualize ZAK (see Extended Data Fig. 3e for eS24 blots). f, Immunoblots of total lysate from gradients shown in panel e. g, Zoomed-in view on the molecular model of the SERBP1 C terminus including the FPxL motif bound to RACK1. The bottom panel shows RACK1 coloured by lipophilicity potential. h, Comparison of the RACK1-bound ZAK RIM and the SERBP1 C terminus. The top panel shows the overall position. The bottom panel shows an overlay. i, Immunoblots of total lysate from HEK293T cells treated with non-targeting (Scr) or SERBP1-targeting (SERBP1) siRNA. The blots represent at least two independent replicates (see the section ‘Statistics and reproducibility’). See Supplementary Figs. 5 and 6 for source data.

Mutating the three RIM residues directly contacting RACK1 (F417, L420 and K422A; RIMmut1; Fig. 4d) did not disrupt ZAK binding to polysomes and RIMmut1 remained bound upon ANS treatment (Fig. 4e). We observed the same results when mutating these residues as well as two proximal prolines (F417, P418, P419, L420 and K422A; RIMmut2; Fig. 4d,e). These data indicate that unlike the constitutive binding activity of the pin and the RIH, the RIM does not have a role in the constitutive association of ZAK with ribosomes. However, the same RIM mutants abrogated ZAK and JNK activation (Fig. 4f) and exhibited persistent ribosome binding even upon treatment with ANS (Fig. 4e). Thus, although the RIM is not critical for ZAK binding, it is strictly required for ZAK activation on collided ribosomes.

SERBP1 negatively regulates ZAK

We observed SERBP1 density at the RIM interaction site of RACK1 in both hibernating and translating 80S classes from the ZAK pull-down (Fig. 4g). These monosome classes also revealed density for a single ZAK RIH (also on RACK1) and the pin (Extended Data Fig. 5b,c). SERBP1 is a ribosome-associated factor that binds to dormant ribosomes, usually together with eEF2 (refs. 38,39). SERBP1 has a well-characterized internal region that binds to the mRNA channel, and a recent in situ single-particle cryo-EM study on native ribosomes in human cells has revealed that the uncharacterized C terminus of SERBP1 (SERBP1-C) contacts RACK1 in dormant and translating ribosomes[40]. Our density matches those data (Extended Data Fig. 5b,c,e) and agrees with an AlphaFold prediction of a RACK1–SERBP1 complex. The RACK1-interacting patch of SERBP1 shows remarkable structural similarity to the ZAK–RIM and contains a consensus FPxL sequence where F404 and L407 of SERBP1 contact RACK1 (Fig. 4h). Upstream of F404, SERBP1 continues to loop over RACK1, forming additional contacts via P396 and V398 with RACK1 blades 3 and 4, respectively. Thus, our data show that ZAK and SERBP1 share a common motif for RACK1 binding, suggesting that SERBP1 could have a role in regulating ZAK activation through direct competition. We addressed this question by monitoring RSR activation under SERBP1 knockdown conditions (with short interfering RNAs (siRNAs)) and saw increased JNK phosphorylation both in basal and activating conditions (Fig. 4i). These data are consistent with the model that SERBP1 acts as a negative regulator of ZAK, and perhaps other factors with similar motifs (see Discussion), through competition for RACK1 binding.

RACK1 scaffolds SAM dimer on disome

The largest additional density in the ZAK–disome structure from the ANS-treated ZAK pull-down bridges the two RACK1 proteins directly adjacent to the two RIMs (Fig. 5a and Extended Data Fig. 4d,e). Although the local resolution decreased with the distance from the ribosomes, from below 3 Å close to RACK1 to about 5 Å and 8 Å at the periphery (Extended Data Fig. 2), we could position an AlphaFold model for a SAM domain dimer based on its characteristic five-helix bundle shape for each SAM domain (Fig. 5a and Extended Data Fig. 4c–e,h). The long helix α5 proceeds from the ZAK–RIM interaction like a leg scaffolding the four shorter α-helices.

Fig. 5: SAM dimerization of ZAK regulates its kinase activity.

a, Zoomed-in view on the molecular model of the ZAK–disome as in Fig. 3a. The boxes highlight the position of the hyperactive phenotype mutants (red dashed box) and SAM dimer interface (green dashed box) as shown in b. b, Location of hyperactive phenotype mutants within the SAM five-helix bundle (left) and zoomed-in view on amino acids involved in a network of salt bridges (highlighted by dashed lines) within the asymmetric head-to-tail interface of the SAM dimer (right). c, Schematic of ZAK SAM bridge interaction with characterized interface residues highlighted. d, Immunoblots of total lysate from HEK293T ZAK-knockout cells transfected with partial CMV promoter plasmids expressing various SAM mutant N-terminal FLAG ZAK constructs at endogenous levels. e, Zoomed-in view on low-pass-filtered cryo-EM maps (transparent) of the ZAK–disome (left) and ZAK(K394D)–disome (right), highlighting the RACK1 collision interface with molecular models for a RACK1-bound SAM dimer or SAM monomer (on RACK1(c)) and SERBP1 (on RACK1(s)) docked. f, Immunoblots of total lysate from HEK293T ZAK-knockout cells transfected with partial CMV promoter plasmids expressing various N-terminal FLAG ZAK constructs at endogenous levels. g, Model of ZAK activation at the collided ribosome. Under unstressed conditions, ZAK interacts with ribosomes via the pin, ES7-patch and RIH, whereas the FPxL motif of SERBP1 is bound to RACK1 (‘Ribosome binding’). Upon ribosome collisions, the RIMs (FPxL motifs) bind to the two proximal RACK1s, the SAM domains dimerize and ZAK becomes active and autophosphorylated (‘Activation on collision’). Phosphorylated ZAK is released and forms a complex with 14-3-3 scaffold proteins targeting downstream effectors in the RSR pathway (‘Signalling’). The 60S subunit is in grey, the stalled 40S(s) or collided 40S(c) subunit is in yellow, the stalled RACK1 is in light orange, the collided RACK1 is in dark orange, ZAK is in light or dark blue, SERBP1 is in pink and 14-3-3 proteins are in green. The blots represent at least two independent replicates (see the section ‘Statistics and reproducibility’). See Supplementary Fig. 7 for source data.

In the AlphaFold model, the two ZAK SAM domains form the common asymmetric head-to-tail interface[41] (Extended Data Fig. 4c) involving a network of salt bridges formed between K394 and K387 on the stalled ribosome with D388 and D385 on the collided ribosome (Fig. 5b, right). Of note, on the stalled ribosome, we observed a rod-like extra density packed against SAM(s) at the ribosome-facing side of the helical bundle, which we assigned to an α-helix formed by residues 569–583 of ZAK (‘helix’; Extended Data Fig. 4b,d,e). The two SAM domains are directly anchored to the two RACK1s via their tight RIM connection and the SAM dimer can only form when the two RACK1s are juxtaposed upon ribosomal collision. Therefore, the SAM domains could in principle monitor and read out the distance between stalled and collided ribosomes. We hypothesize that formation of the SAM dimer represents the hallmark of collision sensing by ZAK, which ultimately leads to conformational changes in the N-terminal domains of ZAK to license kinase activation, thus resembling the mechanism of other SAM-containing kinases42,43.

SAM interface modulates kinase activity

The known disease-associated mutation in the SAM domain (F368C) causes a hyperactive phenotype characterized by ZAK activation similar to another previously characterized constitutively active W347S mutant10,44. Neither mutation is located at the SAM–SAM interface but in the hydrophobic core of the domain (Fig. 5b, left). Co-immunoprecipitations with uniquely tagged ZAKs (FLAG and haemagglutinin) revealed dimerization of WT ZAK and of the W347S and F368C variants (Extended Data Fig. 7a) with modest enrichment upon ANS treatment. These data are consistent with ZAK activation producing a more stable kinase dimer and suggests that ZAK exists at least in part as a dimer in untreated conditions. We mutated residues at the SAM–SAM interface to either alanine (K/R/D→A) or reversed charge (K/R→D) and characterized ZAK activation (Fig. 5b, right, 5c). These mutations resulted in an array of activity phenotypes ranging from completely inactivating to constitutively activating, indicating that the SAM–SAM interaction has a key role in kinase activity regulation (Extended Data Fig. 7b).

We used the pathogenic F368C variant as a positive control for hyperactivity and compared it with our strongest hyperactive interface mutant (K387D) and our most inactive interface mutant (K394D; Fig. 5d). After 24 h of expression of the variants in untreated ZAK-knockout HEK293T cells, we observed a substantial decrease in overall ZAK levels for both F368C and K387D compared with WT (Fig. 5d, lanes 1–3), consistent with previous studies showing that ZAK is degraded downstream of activation via a phosphodegron mechanism10,12. By contrast, the inactive K394D mutant exhibited stable protein levels and migrated at a size of approximately 100 kDa, similar to unactivated WT ZAK (Fig. 5d, lanes 1 and 4). Treatment with the ZAK inhibitor nilotonib during the 24-h expression led to full stabilization of ZAK(F368C) and ZAK(K387D) (Fig. 5d, lanes 6 and 7). Treatment with MLN4924 to inhibit cullin-RING-mediated protein degradation[45] similarly stabilized ZAK levels during the 24-h expression period, allowing us to visualize the highly phosphorylated form of ZAK(F368C) and ZAK(K387D) in untreated cells (Fig. 5d, lanes 10 and 11). As expected, ANS treatment induced autophosphorylation of WT ZAK but did not change the extent of phosphorylation of F368C and K387D, which were already fully activated before ANS treatment (Fig. 5d, lanes 13–15). Finally, ANS treatment did not cause any change in the phosphorylation of K394D, supporting the hypothesis that this mutation abrogates ZAK kinase activity, probably by interfering with the SAM–SAM interface (Fig. 5d, lane 16).

Together, these data support a model in which the SAM domain has a central role in ZAK activity. We hypothesize that the ZAK SAM domains need to form a head-to-tail dimer on the colliding ribosomes that results in kinase activation and/or relief of kinase autoinhibition. We explored this hypothesis through cryo-EM analysis of a native pull-down of ZAK(K45M) harbouring the inactivating K394D mutation (Extended Data Fig. 8). The 3D reconstructions revealed ZAK bound to both stalled and collided ribosomes via the pin and RIH including the RIH-p (Extended Data Fig. 4g), as for all previous structures; however, we found that the RIM connected to its SAM domain only on RACK1(c) and no SAM dimer bridging stalled and collided ribosomes (Fig. 5e and Extended Data Fig. 4g). On the stalled ribosome, instead of the ZAK–RIM interaction, we observed SERBP1 bound to RACK1 and the second SAM domain completely missing.

Kinase regulation is ribosome independent

To ask whether hyperactive mutants can indeed bypass collision-dependent activation, we combined RIH and RIM mutations (which disrupt ZAK binding and activation; Figs. 3f and 4f) with the hyperactive F368C mutation. These compound mutants with F368C revealed constitutive activation of ZAK (independent of ANS treatment; Fig. 5f, lanes 2–5) and thus independent of collisions or any ribosomal interaction. These data suggest that WT ZAK relies on specific ribosome interactions to relieve autoinhibition and to promote kinase activation, but that this mechanism can be circumvented by directly altering the SAM domain. The crystal structure of ZAK kinase domains complexed with vemurafenib is a dimer consistent with the idea that proximity of the kinase domains is important[27]. AlphaFold predicts various alternative conformations of the SAM dimer (Extended Data Fig. 4i), but the asymmetric head-to-tail interface dimer observed in the disome structure was only predicted for WT SAM domains and not for any of the described mutants. We speculate that the SAM domains have a role in stabilizing the inactive state of ZAK and that structural rearrangements caused either by (pathogenic) mutations in the SAM domain or facilitated by dimer formation after binding to the disome promote activation[46].

Discussion

Our structural and biochemical data reveal that the ZAK-driven RSR is activated by the unique interface of a collided disome. Although we cannot exclude the possibility of individual stalled ribosomes being sufficient for ZAK activation, our data strongly support a model in which collisions have a key role. Through specific interactions between ZAK and ribosomal proteins (RACK1 and eS27) as well as rRNA (ES6c and ES7), the SAM domains of ZAK are oriented to dimerize and promote kinase activation. These data reveal how the ribosome acts as a scaffold, initially for global recruitment of ZAK and then for ZAK kinase activation upon collision. In the case of disome formation after translational stalling, RACK1 reveals itself as the key scaffold and the central player for collision sensing and ZAK activation. These observations are reminiscent of the critical role of RACK1 in ribosome-associated quality control20,47,48, although the structural specifics for recruitment and activation of the E3 ligase ZNF598 (Hel2 in yeast) remain unknown.

Previous biochemical and sequencing data have revealed the presence of ribosome collisions even under untreated basal conditions[49]. Multiple modes of regulation probably work together to prevent premature RSR signalling during unstressed conditions. First, the stoichiometry of ZAK to ribosomes (1:100) ensures that ZAK can only be bound to a small fraction of the ribosomes at a given time. This suggests a sampling model (Fig. 5g) in which ZAK (as a monomer or dimer) transiently binds to monosomes and disomes. We postulate that ZAK more stably binds to disomes due to a higher local concentration of binding sites and due to additional binding contacts only available at the disome interface, eventually leading to kinase activation. The RIH-p is one example of a region of ZAK that may provide this specificity by bridging the stalled and collided ribosome. Another example is the ES6-patch, specifically bridging to the collided ribosome (Fig. 2c and Extended Data Fig. 6b). In addition, under basal conditions, disomes may not be long lived or abundant and ribosome-associated quality control factors can resolve collisions before ZAK has sufficient time for activation.

Another moderating influence is that the ZAK RIM, which binds to stalled and collided ribosomes, must compete with the abundant factor SERBP1 at the FPxL-binding site of RACK1; this competition probably serves as a source of basal negative regulation (Fig. 4i). We hypothesize that this FPxL motif (present in ZAK and SERBP1) may have broad implications for other ribosome-associated quality control factors that may also rely on this RACK1 interaction, with LARP4 being one example[50] (Extended Data Fig. 9).

It is not clear from our structures how the reorientation and stable dimer formation of the SAM domains on collided ribosomes is communicated to the other regions of ZAK for activation and catalysis. We hypothesize that SAM dimer formation on the stalled and collided RACK1 proteins facilitates a conformational rearrangement, possibly communicated through the leucine zippers and/or the YLD, that promotes ZAK activation by an allosteric mechanism. We identified hyperactivating SAM dimer interface mutants that promote kinase activation (Fig. 5d) and might prove useful to understand the molecular principles of kinase activation in future studies. Together, this work provides mechanistic insights into how ZAK, a central switch for cell-fate regulation, senses stress directly on the translational machinery upon collisions and proposes a conclusive model for how this leads to ZAK kinase activation.

Methods

Generation of the knockout cell line (ZAK and RACK1 knockout)

HEK293T RACK1-knockout cells were generated by CRISPR–Cas9 (refs. 51,52). The single guide RNA (sgRNA; ZAK target sequence: TGTATGGTTATGGAACCGAG; RACK1 target sequence ACTGCGGGGTAGTAGCGATCTGG) was subcloned into the pX330 plasmid[51]. HEK293T cells (American Type Culture Collection CRL-3216) were transfected with 1.5 µg of plasmid using Lipofectamine 3000 (L3000075, Thermo) according to the manufacturer’s instructions. After 2 days, cells were collected and plated on 96-well plates (3603, Corning) by limiting dilution. Colonies were confirmed for ZAK or RACK1 deletion by immunoblotting and sequencing.

Tissue culture

HEK293T cells were maintained using Dulbecco’s modified eagle medium (DMEM; 11995073, Thermo) supplemented with 10% FBS (A3160502, Thermo Fisher) and passed using trypsin-EDTA (0.25%) and phenol red (25200114, Thermo). Cells were seeded at 2 × 10^6 (15 cm; CLS430599, Millipore Sigma), 1.5 × 10^6 (10 cm; CLS430167, Corning) or 3 × 10^5 cells per well (six well; 3516, Fisher). At 24 h, cells were transfected using Lipofectamine 3000 Transfection Reagent (L3000075, Thermo) according to the manufacturer’s instructions. Twenty-four hours post-transfection, cells were treated and lysed (approximately 70% confluency). Medium was changed 1–2 h before drug treatment and/or lysis. ANS (A9789, Sigma) was added directly to the media (untreated = DMSO (D12345, Thermo); ANS collision dose = 0.38 µM final concentration). ANS stock solutions were 94.2 mM (25 mg ml−1) in DMSO. Unless noted, all ANS treatments were done for 15 min. For the protein stabilization experiments, medium was supplemented with 2 µM MLN4924 (B1036, ApexBio) or 2 µM nilotonib (A8232, ApexBio) from the time of transfection to time of harvesting (24 h). To end any treatment, medium was aspirated and cells were quickly washed with ice-cold PBS (10010-049, Thermo; 8 ml for 10 cm; 2 ml for six well) and then the lysis buffer (50 mM HEPES pH 7.5, 100 mM KOAc, 5% glycerol (G33-4, Fisher), 0.5% Triton X-100 (T9284, Millipore Sigma), 15 mM Mg(OAc)2, 1× Halt protease + phosphatase inhibitor cocktail (78445, Thermo Fisher) and Turbo DNase I (80 units; AM2239, Thermo)) was directly added to plates and the cells were collected by scraping. Plates (10 cm) were lysed in 200 µl lysis buffer; six-well dishes were lysed in 100 µl lysis buffer. Lysates were kept on ice and clarified at 8,500g for 5 min. Clarified lysates were flash frozen in N_2 and stored at −80 °C.

RNA knockdowns

Cells were seeded at 1 × 10^6 cells (10 cm) and 7.5 × 10^4 (six well). After 24 h, cells were treated with siRNA (50 µM stocks, 50 nM final concentration) using Lipofectamine RNAiMAX Transfection Reagent (13778150, Thermo) according to the manufacturer’s instructions. After 24 h, the medium was changed. Seventy-two hours post-siRNA transfection, cells were treated and lysed according to the above lysis protocol.

Sucrose gradients

Preparative (12 ml) sucrose gradients were made using 10× gradient buffer (250 mM HEPES pH 7.5, 1 M KOAc and 50 mM Mg(OAc)2) to make final gradients with sucrose buffer (1× gradient buffer, 10% or 50% sucrose (60% sucrose stock), 1 mM TCEP (TCEP25, Gold-Bio) and SuperaseIN (200 units)). Approximately 25–50 µg RNA (quantified by qubit HS) was loaded on gradients in 200–300 µl final volume. The Beckman Coulter Ultracentrifuge and Beckman SW41 swinging bucket rotor were used for centrifugation. For regular gradients (10–50% sucrose), spins were done at 274,000g for 1 h 45 min at 4 °C. Ten fractions were collected and absorbance at 260 nm (A_260) was measured using the Biocomp Piston Gradient Fractionator. Trichloroacetic acid (TCA; T3699, Millipore Sigma) was added to each fraction (10% final concentration). Samples were frozen at −20 °C overnight. The TCA precipitation protocol followed.

Analytical (200 µl) gradients were made by stacking 40 µl of 50%, 40%, 30%, 20% and 10% sucrose buffer in 250 µl tubes (343775, Beckman). Approximately 1–2 µg RNA (quantified by qubit or normalized by bicinchoninic acid (BCA)) was loaded in 10 µl final volume on a 10–50% 200 µl gradient. The Beckman Coulter Tabletop Centrifuge (CTZ24D006, Optima MAX) and TLS55 rotor were used for centrifugation. Spins were done at 214,000g for 22 min at 4 °C. Ten 20 µl fractions were taken and added directly to 7 µl of 4× loading buffer. Of each fraction, 8 µl was run on 4–20% TGX 26-well gel (5671095, Bio-Rad).

Immunoblotting

Concentration-normalized samples were generated using total protein quantification (BCA assay; 23225, Thermo Fisher) and were then diluted in 4× loading buffer (8% sodium dodecyl sulfate (SDS), 40% glycerol, 0.4 mM bromophenol blue and 40 mM Tris-Cl pH 6.8) and boiled at 95 °C for 10 min. Approximately 5 µg of protein was loaded into 4–20% Criterion TGX polyacrylamide gels and run in 1× Tris-glycine running buffer (1610732, Bio-Rad) at 150 V for 1 h. Proteins were transferred to polyvinylidene fluoride (PVDF) membranes (Trans-Blot Turbo RTA Midi 0.2 µm PVDF Transfer Kit 1704273) for 10 min at 2.5 A. Membranes were blocked with 5% non-fat milk (blocking buffer; sc-2325, Santa Cruz Biotech) in Tris-buffered saline with Tween 20 (TBS-T) for 1 h at room temperature. All primary antibody incubations were done overnight at 4 °C in blocking buffer. After three 10-min TBS-T washes, secondary antibodies diluted in blocking buffer were incubated for 1 h at room temperature. Three 10-min washes with TBS-T followed secondary incubation. All incubations and washes were performed with gentle rocking. Blots were visualized using SuperSignal West Pico PLUS (34580×4, Thermo) and/or West Femto Maximum (34095, Thermo) chemiluminescent substrates and the Bio-Rad ChemiDoc imaging system.

Phos-tag SDS–PAGE

Concentration-normalized samples were diluted in 4× loading buffer and boiled as described for immunoblotting and then loaded on 8% SDS–PAGE (10.7 µM Phos-tag acrylamide (AAL-107S1, Wako) and 21.3 µM MnCl_2). Samples were run in 1× Tris, glycine and SDS running buffer (125 V for 2.5 h). An EDTA-free pre-stained protein marker (F4005, Apex Bio) was used. Two 10-min washes in 1× transfer buffer (25 mM Tris, 192 mM glycine and 10% v/v methanol) supplemented with 1 mM EDTA (AM9260G, Thermo) were followed by two 10-min washes in 1× transfer buffer without EDTA. Samples were transferred to PVDF membranes (1620177, Bio-Rad) overnight at 35 V (room temperature). Blocking and antibody incubation and visualization are described in the section ‘Immunoblotting’.

TCA precipitations

Sucrose fractions in 10% TCA were thawed on ice and centrifuged at 21,000g at 4 °C for 30 min. The sucrose and TCA were aspirated off and the remaining pellet was washed with 500 µl acetone (A18P-4, Thermo) and spun at 21,000g at 4 °C for 10 min. The wash step was repeated and then the pellets were dried using a vacuum evaporator for 5 min. The dried pellets were resuspended in 4× loading buffer (40–60 µl total; 10 µl loaded on gel).

Co-immunoprecipitation of ZAK dimers

ZAK-knockout HEK293T cells were transfected with either FLAG-tagged or haemagglutinin-tagged ZAK constructs (24-h expression) and then treated and lysed from six-well plates (see the section ‘Tissue culture’; lysis buffer: 50 mM HEPES pH 7.5, 100 mM KOAc, 5% glycerol, 1% digitonin (D-180-1, Gold Bio), 5 mM Mg(OAc)2, Halt protease + phosphatase inhibitor cocktail (100×) and Turbo DNase I (80 units)) according to the above tissue culture protocols (six well, 100 µl lysis buffer). After scraping, the cells were incubated on ice for 30 min before clarification and flash freezing. On the day of the experiment, cells were thawed on ice and normalized using A_260 as measured by nanodrop (13-400-529, Thermo Scientific). MNase buffer (0.2×; B0247S, NEB) with 1 mM CaCl_2 and 500 U MNase (M0247S, NEB) were added to normalized lysates and the reaction was incubated at 22 °C for 30 min. The samples were moved to ice and 2 mM EGTA (50-997-744, Fisher) was added. For the co-immunoprecipitations, 90 µl of sample was added to 2 µl washed anti-FLAG M2 magnetic affinity resin (M8823, Millipore) and incubated for 90 min with rotation at 4 °C. The samples were washed three times with 100 µl wash buffer (50 mM HEPES pH 7.5, 100 mM KOAc, 5% glycerol, 0.1% digitonin, 5 mM Mg(OAc)2 and Halt protease + phosphatase inhibitor (100×)) for 5-min incubation on nutator at 4 °C. The protein was eluted (elution buffer: wash buffer plus 400 μg ml−1 3×FLAG peptide) with two 10 µl elutions (30 min incubation on rocker at 4 °C). Samples were added to 4× loading buffer and immunoblots were performed according to the above described methods.

CLIP sample preparation

CLIP-seq samples were prepared as previously described[53]. Two 10-cm plates per construct of HEK293T ZAK-knockout cells (pcDNA3.1-FLAG-ZAK_T161/S165A, pFN24K_3×FLAG-ZAK, pFN24K_3×FLAG-ZAK_T161/S165A and pFN24K_3×FLAG-ZAK_1-649) were transfected in addition to two 10-cm mock no DNA control plates. After 24 h of expression and 15 min of ANS treatment, cells were crosslinked at 254 nM UV, lysed, clarified and RNAseI digested (AM2295, Invitrogen). Immunoprecipitation was performed overnight at 4 °C using 13 µl of anti-FLAG M2 affinity resin (A2220, Millipore). Following immunoprecipitation, samples were washed, dephosphorylated with FastAP enzyme (EF0654, Thermo) and T4 PNK (M0201L, NEB) and the 3′ RNA adapter was ligated (M0437M, NEB). A small amount of sample was run on a Criterion XT 4–12% Bis-Tris gel and transferred to PVDF to perform a diagnostic western blot probing for ZAK expression and size. The remaining sample was run on a Criterion XT 4–12% Bis-Tris gel (3450124, Bio-Rad) and transferred to a nitrocellulose membrane and a region was cut corresponding to the ZAK protein size to plus approximately 75 kDa. Membrane pieces were digested with proteinase K (P8107S, NEB). RNA was purified on a clean and concentrator column and reverse transcribed with SuperScript III (18080044, Thermo). The RNA and free primer were digested and the RT-DNA purified with MyOne Silane beads (37002D, Thermo) and the 5′ DNA adapter was ligated (M0437M, NEB). Following cleanup, a pilot quantitative PCR was performed. Samples were amplified for the determined number of PCR cycles, gel extracted for products corresponding to 175–35 bp and submitted for next-generation paired-end sequencing with 2 × 150-bp read length.

CLIP analysis

All code for CLIP-seq analysis has been published on GitHub (https://github.com/jakesaba/2025_ZAK). In brief, unique molecular identifiers were appended to each paired-end read using umi_tools extract[54] and trimmed using trim_galore (https://github.com/FelixKrueger/TrimGalore). Reads were aligned using STAR[55] to the GRCH38 genome containing a single ribosomal DNA (chrR), originally generated by the Paralkar laboratory[56]. Aligned reads were sorted and indexed using samtools[57] and deduplicated using umi_tools dedup.

For mapping coverage to 18S rRNA, bam files were imported into R, coverage was normalized to library size and then mean-scale normalized across the 18S region. Mean-scaled coverage over 18S was then normalized to the coverage of the ZAK_1–649 truncation[1] at each position. To avoid dividing by 0, a pseudocount corresponding to the 0.1 percentile signal was applied to the coverage of the ZAK_1–649 sample at all positions. To reduce noise, nucleotide positions corresponding to less than 3% of the cumulative CLIP-seq coverage signal were removed and their fold enrichment was set equal to 1. Plots were smoothed using a rolling average with a window size of 10.

For genome-wide analysis of CLIP-seq peaks, a similar approach was used with a few exceptions. First, no mean-scale normalization was applied and coverage was normalized to the ZAK-knockout sample. A global pseudocount of 5 was applied and cumulative signal less than 3% of the cumulative CLIP-seq coverage at each gene locus was again removed. CLIP peaks with average reads per million of more than 10 and satisfying a more than twofold enrichment over a window size of more than 20 compared with the ZAK knockout were called. Significance was determined using a one-sided Poisson test. For significant peaks, a false discovery rate was assigned using the Benjamini–Hochberg procedure. For each gene, only the canonical transcript was used.

For metagene analysis, we aligned CLIP-seq coverage data to standardized transcript regions (5′ untranslated region (UTR), the coding sequence (CDS) and 3′ UTR). For each gene, only the canonical transcript was used, and only transcripts with a CDS length of at least 300 nucleotides were retained. For each transcript, the 5′ UTR, CDS and 3′ UTR were separately scaled to 100 positions, and coverage values were linearly interpolated to create a fixed-length alignment across all genes. These were concatenated to produce a ‘metagene’ axis of 300 standardized positions (0–100 for 5′ UTR, 100–200 for CDS and 200–300 for 3′ UTR). To account for background signal, the metagene profile of each condition was normalized to a ZAK-knockout control profile, computed as the ratio of the mean signal to the ZAK-knockout signal at each metagene position. For visualization, smoothed profiles (rolling average, window size = 5) were plotted with region boundaries clearly marked.

Bacterial expression and purification of ZAK RBR

For ZAK RBR (C terminus 100 amino acids) protein expression, the sequence was cloned into pGEX backbone with a N-terminal GST tag. The plasmid was transformed into BL21-competent cells (C2527I, NEB) and allowed to outgrow overnight at 37 °C. The starter culture was added to a 1-l flask of 2× YT media (31GE58, Grainger), and at optical density at 600 nm of 0.6, protein expression was induced with 1 mM IPTG for 2 h. Bacterial pellets were collected (4,000g for 10 min), flash frozen and stored at −80 °C.

Pellets were thawed on ice in lysis buffer (50 mM Tris pH 8, 150 mM NaCl, 5% glycerol, 1 mM TCEP, 0.2 mM phenylmethylsulfonyl fluoride (PMSF; P7626-25G, Sigma), 1× EDTA-free cOmplete protease inhibitor tablet (5056489001, Sigma), pinch of DNase I (10104159001, Millipore Sigma) and pinch of lysozyme (L6876, Sigma)) to a final volume of 50 ml and dounced on ice until fully resuspended. The lysate was sonicated at 50% amplitude (3 s on; 10 s off; 1 min total) before clarification using the TI45 rotor and spinning at 186,000g for 30 min. After the spin, the supernatant was filtered using 0.45-µM filter (431220, Corning) and loaded onto GSTrap 5 ml column (17513102, Cytiva) using the Cytiva (GE Healthcare) AKTA Pure FPLC system. After binding, the column was washed with wash buffer 1 (50 mM Tris pH 8, 150 mM NaCl, 1 mM TCEP, 0.2 mM PMSF and 1 protease inhibitor pill) and high-salt wash buffer 2 (50 mM Tris pH 8 and 1 M NaCl). The protein was eluted (elution buffer: 50 mM Tris pH 8, 300 mM NaCl and 10 mM reduced glutathione) and the eluted fractions were pooled and concentrated using Pierce Protein Concentrators PES, 30 K MWCO (88522, Thermo) to 1 ml before size-exclusion chromatography (Cytiva Superdex 75) with SEC buffer (50 mM HEPES pH 7.5, 300 mM KOAc, 5 mM Mg(OAc)2, 5% glycerol and 1 mM TCEP). Protein samples were concentrated, flash frozen and stored at −80 °C until use.

Native pull-downs of ZAK-bound ribosomal complexes for cryo-EM

Expi293F cells (A14527, Thermo Fisher) transiently transfected with the pcDNA3.1-FLAG-ZAK-K45M construct were treated with ANS (0.38 μM) for 15 min and collected in lysis buffer (50 mM HEPES pH 7.5, 150 mM KOAc, 5 mM Mg(OAc)2, 1 mM dithiothreitol, 0.5% NP-40 and EDTA-free protease inhibitor cocktail (Roche)). Cells were homogenized using a dounce homogenizer (DWK Life Science) and clarified by centrifugation at 36,603g for 15 min at 4 °C. The supernatant was treated with Nuclease S7 (20 U ml−1; Sigma-Aldrich) for 15 min at 25 °C. Digested lysates were incubated with Anti-FLAG M2 agarose beads (Sigma-Aldrich) on a rotating wheel for 3 h at 4 °C. Beads were transferred to a 1-ml Mobicol column (MoBiTec) and washed twice with 10 ml of wash buffer (50 mM HEPES pH 7.5, 150 mM KOAc, 5 mM Mg(OAc)2, 1 mM dithiothreitol and 0.01% NP-40). Complexes were eluted in elution buffer (20 mM HEPES pH 7.5, 150 mM KOAc, 5 mM Mg(OAc)2, 1 mM dithiothreitol, 0.05% Nikkol and 300 ng µl−1 FLAG peptide (Sigma-Aldrich)) for 1 h at 4 °C.

The same purification protocol as described above was also used for the Strep–ZAK(T161A/165A) and FLAG–ZAK(K45M/K394D) pull-downs as well as for the FLAG–ZAK(K45M) pull-down performed without previous challenging cells with ANS (see also Extended Data Fig. 1b).

In vitro binding assays and reconstitutions of ZAK RBR–ribosome complexes

Human ribosomal subunits and 80S monosomes were purified from Expi293F cells. Cells were lysed in lysis buffer (50 mM HEPES (pH 7.5), 150 mM KOAc, 5 mM Mg(OAc)2, 0.5% NP-40, 1 mM dithiothreitol, 1 mM PMSF and EDTA-free protease inhibitors). Lysates were clarified by centrifugation at 36,603g for 15 min, loaded onto 10–50% sucrose gradients and spun at 284,600g for 3.5 h at 4 °C using a SW40Ti rotor (Beckman Coulter). Gradients were fractionated into 500-µl fractions to separate 40S and 60S ribosomal subunits from 80S monosomes. 40S, 60S and 80S fractions were pooled and pelleted through a sucrose cushion using a TLA110 rotor (Beckman Coulter) at 460,800g for 45 min at 4 °C, then resuspended in binding buffer (50 mM HEPES pH 7.5, 150 mM KOAc, 5 mM Mg(OAc)2, 1 mM dithiothreitol and 0.01% NP-40).

For the in vitro binding assay, the GST-3C-tagged ZAK RBR protein (see above) was incubated with either purified ribosomal subunits or monosomes (25 pmol each) for 60 min at 4 °C. Reactions were diluted with 360 µl binding buffer and transferred to 1-ml Mobicol columns (MoBiTec) containing 20 µl glutathione Sepharose 4 fast flow resin (Cytiva) and incubated for 60 min at 4 °C. Beads were washed three times with binding buffer (1 × 800 µl, 2 × 500 µl). Bound complexes were eluted with binding buffer containing 25 mM reduced L-glutathione (Sigma-Aldrich) for 60 min at 4 °C. Samples were analysed on a 12% polyacrylamide gel (Invitrogen) and stained with Der Blaue Jonas (German Research Products).

Electron microscopy and image processing

For all cryo-EM samples, grids were prepared and images were processed the same way. All samples were crosslinked with 0.02% (v/v) glutaraldehyde on ice for 20 min. Reactions were quenched by addition of Tris-OAc to a final concentration of 25 mM. Of each sample, 3.5 μl (approximately 4–8 A_260 per ml) was applied to Quantifoil R3/3 holey carbon grids with 2-nm continuous carbon coating, blotted for 3 s and then plunge frozen in liquid ethane using a Vitrobot Mark IV (Thermo Fisher Scientific). Data collections were performed at 300 keV using a Titan Krios microscope equipped with a Falcon 4i direct electron detector and a SelectrisX imaging filter using EPU software (3.7; all Thermo Fisher Scientific) at a pixel size of 0.727 Å. Dose-fractioned movies were collected in a defocus range from −0.5 to −3.5 μm and with a total dose of 40 e− Å−2, fractionated in 40 frames to obtain a total dose of 1 e− Å−2 per frame. Gain correction, movie alignment and summation of movie frames were performed using MotionCor2 (v1.4.0)[58]. Contrast transfer function parameters were estimated using CTFFIND4 (v4.1.13)[59].

Structures of ZAK–disome complexes were obtained from native pull-downs after treatment with ANS using either the FLAG-tagged ZAK(K45M) mutant or the ZAK(T161A/165A) mutant.

From the combined datasets, 2,246,220 particles were automatically picked from a total of 95,813 micrographs in RELION (v5.0 beta)[60]. After 2D classification in CryoSPARC (v4.6.0)[61], 1,347,732 80S ribosomal particles were selected and 3D classified in RELION. This yielded 80S classes with strong density for neighbours, indicative of stable disomes, or with no or only weak extra density for neighbours. 80S with no neighbour density represented either 80S with mRNA and tRNAs in hybrid state (A/P and P/E) or POST state (P/P and E/E), or 80S bound to eEF2 (and SERBP1). Disome classes (80S classes with strong neighbour density) occurred either with a neighbour at the mRNA exit site or at the mRNA entry site, defining them as stalled and collided 80S, respectively. Both stalled and collided 80S were found in POST and in hybrid states, and each of those disome classes displayed additional density at RACK1, accounting for the ZAK SAM dimer. The two most abundant classes (POST-state stalled 80S and hybrid-state collided 80S) were further processed. They were classified with a soft mask focusing on the RACK1 region where extra density for ZAK was found, revealing subclasses with the ZAK SAM dimer varying in flexibility. Classes displaying strong SAM density were joined and refined to an overall resolution of 2.3 Å for both the stalled 80S and the collided 80S, and then subjected to local refinement in CryoSPARC. Local resolution was determined for the RACK1 SAM regions to be between below 3 Å (for the RACK1–RIM interaction) and between 5 Å and 8 Å for the globular SAM domain. Finally, for a sub-dataset, two maps for the entire ZAK–disome complex were generated by extending the box size of both stalled and collided 80S, respectively, and centring the disome density. Both maps showed low-resolution extra density adjacent to the SAM dimer, probably accounting for the leucine zipper–kinase domain (LZ–KD) dimer. The disome obtained from extending the stalled 80S was locally refined focusing on the RACK1 SAM region, yielding a clear density for the SAM dimer at a local resolution between 6 Å and 11 Å and served as consensus disome map. The disome obtained from extending the colliding 80S was used for fitting the LZ–KD dimer.

The composite map for the ZAK–disome was assembled by first fitting the individually refined stalled and collided 80S and the locally refined maps into the 6 Å resolution disome consensus map and then using the ‘vop max’ tool in UCSF Chimera X (v1.9)[62] to join the individual maps (Extended Data Fig. 2).

The sample obtained from the native FLAG–ZAK(K45M) pull-down without ANS treatment was processed as described above. Here, however, 3D classification of 325,370 particles picked from a total of 13,154 micrographs showed no classes indicative for stable disomes. Instead, the main classes represented two classes of translating 80S, one with tRNAs in hybrid state (A/P and P/E) and one with tRNAs in POST state (P/P and E/E), as well as 80S with a tRNA in the E site and bound to eEF2 and SERBP1, indicative of hibernating ribosomes38,39. The hybrid state translating and the hibernating 80S classes were first refined followed by local refinement focusing on either the entire 40S subunit (2.3 Å for the translating and 2.7 Å for the hibernating 40S) or the RACK1 region (2.7 Å for the translating and 3.0 Å for the hibernating 40S head; Extended Data Fig. 3a).

For the sample obtained from the FLAG–ZAK(K45M/K394D) pull-down, 3D classification of 629,553 particles picked from a total of 56,746 micrographs yielded similar 80S classes as described for the pull-downs using kinase-inactive ZAK mutants described above. Among them were classes representing disomes as well as hibernating (with eEF2/SERBP1 and E tRNA) and translating (hybrid and POST state) 80S. The classes representing stalled (with P/P and E/E tRNAs) and collided (with A/P and P/E tRNAs) 80S were refined followed by local refinement focusing on the RACK1 SAM region. Local resolution was determined for this region to be below 3 Å close to RACK1 and to between 5 Å and 15 Å for peripheral regions. We observed density for one SAM globular domain emerging from RACK1 of the collided ribosome, whereas on RACK1 of the stalled one is occupied by SERBP1 and the ZAK–RIH (Extended Data Fig. 8).

For the reconstituted ZAK–RBR–40S sample (obtained from the in vitro binding assay), 873,389 particles were picked from a total of 19,851 micrographs. 3D classification yielded 629,046 particles of 40S that were further refined, followed by local refinement focusing on either the head (2.1 Å) or the eS27 (2.3 Å) region (Extended Data Fig. 5d).

Model building and refinement

The disome model was generated by rigid-body fitting known 80S monosome structures into the cryo-EM density. For the stalled 80S, the ribosome structure (Protein Data Bank (PDB) ID 8GLP)[30] representing a POST-state 80S with mRNA and P-site tRNA, bound ANS was used; for the collided 80S ribosome, the structure of the human ribosome in the hybrid PRE state (PDB ID 6Y57)[29] served as a template. E-site tRNA model from the human disome (PDB ID 7QVP)[34] was used and fitted into density on the stalled 80S map. The tips of ES6c (690–740) and ES6b (741–800) were built based on AlphaFold3 prediction of 18S rRNA fitting into low-pass-filtered density on the collided 80S and the stable disome reconstructions (Extended Data Fig. 2). The mRNA model on the collided 80S from PDB ID 6Y57 was changed from 46-UUU-48 to AUG and 49-UUU-51 to UUC.

For the EDF1 C-terminal part (residues 24–145) on the collided 80S, the existing model from (PDB 6ZVH)[21] was fitted. For the N-terminal part (residues 7–14) on the stalled 80S, the AlphaFold3 prediction for the interaction of EDF1 with eS26 was used for fitting (Extended Data Fig. 5f,g). A rod-like density near uS4 on the collided 80S was identified as the C terminus of eS1 (254–264) and was modelled based on AlphaFold3, predicting an interaction between eS1 residues 231–264 with uS4. These models were processed by manual real-space refinement in WinCoot (v0.9.8.93)[63] and merged into a disome model followed by real-space refinement in Phenix (v1.20.1-4487)[64].

AlphaFold2 multimer models of ZAK full length and RACK1 revealed two interacting regions: one is ZAK 611–617 (RIH) intercalating between blade 5 and blade 6 of RACK1, and the second is ZAK 417–422 (RIM) stretching across RACK1 blade 2 and blade 3. This model served as a template to match extra densities identified at the RACK1 (Extended Data Fig. 5a–e).

To adjust RIM and RIH, respectively, we then generated AlphaFold3 models of ZAK 325–425 and RACK1, and models of ZAK 600–631 and RACK1. The resulting models were fitted into the corresponding density with only minor adjustments in Coot (Extended Data Fig. 5a–e).

The main density emerging from both RACK1 proteins at the disome interface corresponds to two SAM (residues 328–416) domains extending from the N terminus of RIM, prompting us to model a SAM dimer using AlphaFold2 multimer. Among the predicted dimer models, only the one representing the asymmetric head-to-tail interface fitted our density as a rigid body. Here manual adjustment was required only for helix α5 (393–416) on both SAM domains to fit into the clearly resolved rod-like density extending from the RACK1-bound RIM peptide. An additional rod-like extra density packed against the SAM domain on the stalled 80S was interpreted as an α-helix formed by residues 568–583 of ZAK (‘helix’), based on the AlphaFold database (https://alphafold.ebi.ac.uk/). The pin was identified by running AlphaFold3 predictions of the RBR region (701–800) and eS27, and then fitted into the corresponding density followed by minor adjustment in Coot.

A model for the C terminus of SERBP1 (SERBP1-C; residues 393–408) bound to RACK1 together with the ZAK RIH was generated with AlphaFold3 and adjusted using the 2.9 Å resolution map of the locally refined RACK1 from the hybrid-state translating ribosome (Extended Data Fig. 3a) followed by Phenix refinement.

AlphaFold3 models for the LZ–KD dimer (1–330) were docked into an additional globular density on the entire ZAK–disome complex when low-pass filtered to approximately 30 Å (Extended Data Fig. 2).

The ZAK model and disome model were later merged and further refined in Phenix. Model statistics were calculated using the MOLPROBITY implementation in PHENIX[65], and can be found in Extended Data Table 1.

All structural figures were prepared using UCSF Chimera X (v1.9)[62].

Statistics and reproducibility

Unless otherwise noted, all biochemical experiments and cell-based assays were repeated a minimum of two times (in part or in whole) and the two independent replicates showed similar results.

Antibodies used in study

The primary antibodies used were: rabbit anti-eS24 (ab196652, Abcam; 1:1,000); mouse anti-FLAG (A8592, Sigma; 1:5,000); rabbit anti-haemagglutinin (3724, Cell Signaling; 1:1,000); mouse anti-JNK1 (3708, Cell Signaling; 1:1,000; ‘total JNK’); rabbit anti-phospho-SAPK/JNK (4668S, Cell Signaling; 1:1,000; ‘JNK-phospho’); rabbit anti-RACK1 (5432S, Cell Signaling; 1:1,000); rabbit anti-SERBP1 (NBP1-85660, Novus; 1:1,000); mouse anti-STREP (71591-3, Sigma; 1:5,000); mouse anti-vinculin (sc-73614, Santa Cruz; 1:2,000); and rabbit anti-ZAK (A301-993A, Fortis; 1:1,000).

The secondary antibodies used were: anti-mouse (7076S, Cell Signaling; 1:5,000) and anti-rabbit (7074S, Cell Signaling; 1:5,000).

Oligonucleotides used in study

Non-targeting (scramble) siRNA (D-001810-01-20, Horizon Dharmacon) and SERBP1-targeting siRNA (L-020528-01-0005, Horizon Dharmacon) were used.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

CLIP-seq data have been deposited in the Gene Expression Omnibus (GSE299329). The cryo-EM structural data generated in this study have been deposited in the Electron Microscopy Data Bank and the PDB, respectively, under the following accession codes: EMD-54172 for the composite ZAK–disome (obtained from kinase inactive ZAK pull-down with ANS treatment; PDB ID 9RPV); EMD-54140 for the stalled 80S and EMD-54141 for the collided 80S (related to the composite map); EMD-54148 for the locally refined ZAK–RACK1 region of the stalled 80S and EMD-54147 for the locally refined ZAK–RACK1 region of the collided 80S (both related to the composite map); EMD-54191 for the human disome with ZAK (related to the composite map); EMD-54149 for the hybrid-state translating 80S and EMD-54150 for the hibernating 80S (obtained from native FLAG–ZAK(K45M) pull-down); EMD-54236 for the locally refined RACK1 region of the hybrid-state translating 80S (PDB ID 9RSX); EMD-54165 for the in vitro reconstituted ZAK–RBR–40S complex; EMD-54166 for the stalled 80S and EMD-54167 for the collided 80S (obtained from FLAG–ZAK(K45M/K394D) pull-down with ANS treatment). The structures used for atomic model building of ZAK-bound disome complexes are available from Worldwide PDB (wwPDB) with the accession codes 6Y57, 6ZVH, 7QVP and 8GLP.

Code availability

All code for CLIP-seq analysis has been published on GitHub (https://github.com/jakesaba/2025_ZAK).

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Acknowledgements

We thank C. Ungewickell, S. Rieder, A. Gilmozzi, J. Musial and J. Brunelle for excellent technical assistance; A. Lentzch and N. Sinha for critical reading; and J. Thorner, S. Regot, L. Chitoiu, C. Hernandez Rosales and M. Thoms for valuable scientific input. Work was supported by the Howard Hughes Medical Institute to R.G., by the European Research Council (ADG 885711 Human-Ribogenesis) and the DFG (BE1814/20-1 and BE1814/22-1) to R.B., by grants from the National Key R&D Program of China (2023YFC2413204) and the National Natural Science Foundation of China (32371350) to J.C., by Graduate School of Quantitative and Molecular Biosciences Munich to S.N. and T.D., by the US National Institute of Health (NIH) BCMB 5T32GM007445 and the National Science Foundation Graduate Research Fellowship Program under grant no. (DGE2139757) to V.L.H., by NIH F30 CA260910 to J.A.S., by a Howard Hughes Medical Institute fellowship of the Damon Runyon Cancer Research Foundation (DRG-2451-21) to M.A.C., and by NIH Dermatology 5T32AR074920, a JHU Provost’s Postdoctoral Fellowship Program and the Dermatology Foundation’s Dermatologist Investigator Research Fellowship to E.P.

Funding

Open access funding provided by Ludwig-Maximilians-Universität München.

Author information

Author notes

1. These authors contributed equally: Vienna L. Huso, Shuangshuang Niu

Authors and Affiliations

1. Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine, Baltimore, MD, USA

Vienna L. Huso, Marco A. Catipovic, James A. Saba, Eugene Park & Rachel Green

2. Howard Hughes Medical Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA

Vienna L. Huso, Marco A. Catipovic, James A. Saba, Eugene Park & Rachel Green

3. Department of Biochemistry, Gene Center, University of Munich, Munich, Germany

Shuangshuang Niu, Timo Denk, Otto Berninghausen, Thomas Becker & Roland Beckmann

4. Minhang Hospital & Institutes of Biomedical Sciences, Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism, Fudan University, Shanghai, China

Jingdong Cheng

Contributions

V.L.H. and M.A.C. developed the cell-based and biochemistry assays. V.L.H. performed the cell-based and biochemical experiments with mentorship from M.A.C. V.L.H. and J.A.S. generated the CLIP samples. S.N. purified all ZAK-bound ribosomal complexes, generated all samples for cryo-EM and processed all the cryo-EM data. S.N. generated the molecular models with the help of J.C. and T.D. S.N., together with T.B. and R.B., analysed the structures. S.N. and T.D. performed the in vitro binding assays. M.A.C. contributed the co-immunoprecipitation experiment. J.A.S. completed the CLIP data analysis. V.L.H. and M.A.C. generated the ZAK-knockout cell line. E.P. generated the RACK1-knockout cell line and related the RACK1-knockout experimental data. O.B. performed the cryo-EM data collection. S.N., V.L.H., T.B., R.G. and R.B. wrote and edited the manuscript with input from all authors. V.L.H. and S.N. contributed equally to this work and reserve the right to list their names first in their resumes.

Corresponding authors

Correspondence to Rachel Green or Roland Beckmann.

Ethics declarations

Competing interests

R.G. is a member of the scientific advisory board of Alltrna, Initial Therapeutics and Arrakis Pharmaceuticals, and consulted for Bristol Myers Squibb (Celgene) and Monta Rosa Therapeutics. All other authors declare no competing interests.

Peer review

Peer review information

Nature thanks Ahmad Jomaa, who co-reviewed with Emir Maldosevic; Daniel Wilson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

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Extended data figures and tables

Extended Data Fig. 1 Conservation of ZAK and characterization of various in vivo and in vitro systems.

a, Immunoblots from wildtype (WT) HEK293Ts and ZAK knockout HEK293Ts cells without or after addition of anisomycin (−/+ ANS) and with different ZAK (mutant) constructs expressed under a partial or full CMV promoter. Plasmid DNA concentrations for transfection indicated. b, Coomassie stained SDS gels of elution fractions from native pull-down of tagged ZAK mutants expressed in Expi293 cells and used for cryo-EM. See Extended Data Fig. 2 for joined data from the ZAK-K45M pull-down (left panel, ANS) and the ZAK-T161/165 A pull-down (both after ANS addition), Extended Data Fig. 3a for the pull-down from untreated cells (left panel, (−)) and Extended Data Fig. 9 for the ZAK-K45M-K394D pull-down (right panel) c, Consurf evolutionary conservation profile of certain ZAK regions of interest with RIM, RIH, eS27-pin and ES7-patch highlighted. Residues with red * were targeted for mutational analysis. d, Coomassie stained SDS gel of purified GST-tagged ribosome binding region (RBR) of ZAK. e, Coomassie stained SDS gels of in vitro binding assays using purified subunits (40S and 60S) or 80S ribosomes and purified GST-RBR. In = input, FT = flowthrough, Elu = elution, Bds = Beads. f, Immunoblots of sucrose gradient fractions collected from WT HEK293T cells. Immunoblots were probed for ZAK using 993 A antibody to visualize endogenous ZAK. Immunoblot against ribosomal protein eS24 is shown below. Blots represent at least two independent replicates (see “Statistics and Reproducibility” section). See Supplementary Figs. 8 and 9 for source data.

Extended Data Fig. 2 Cryo-EM data analysis, classification and resolution of the ZAK-disome complex.

The sample was obtained from a native pull-down using kinase inactive (K45M or T161/165 A) ZAK mutants after ANS addition (see Extended Data Fig. 1b, left and middle panels). a, Processing scheme: from a total of 95,813 micrographs 1,373,448 80S ribosomal particles were selected after 2D classification, 3D refined and classified. 80S classes with strong density for neighbors, indicative of stable disomes, displayed additional density at RACK1 (indicated with a black arrow), accounting for the ZAK SAM dimer. The two most abundant classes were classified with a soft mask focusing on the RACK1 region (shown in pink), revealing sub-classes with the ZAK SAM dimer varying in flexibility. Classes displaying strong SAM density were joined and refined to an overall resolution of 2.3 Å for both the stalled 80S and the collided 80S, and then subjected to local refinement. Local resolution was determined for the RACK1-SAM regions ranging from below 3 Å (for the RACK1-RIM interaction) to 5-8 Å for the globular SAM domain, revealing the position of the ZAK RIM and ZAK RIH. From a sub-dataset, maps for the entire ZAK-disome complex were generated for both stalled and collided 80S, respectively. They are shown low-pass filtered to highlight extra density for the ZAK SAM and kinase domains (see panel with fitted AF model below). The disome obtained from extending the stalled 80S was refined to 6 Å average resolution and was used as consensus map for assembling the composite map. This was created by fitting individually refined stalled and collided 80S and the locally refined maps into the consensus disome map. Further, this disome map was locally refined focusing on the RACK1-SAM region yielding a clear density for the SAM dimer at a local resolution between 6–11 Å (see Extended Data Fig. 4e). b, c, Gold-standard Fourier Shell Correlation (GSFSC) curves (obtained from CryoSPARC) and cryo-EM maps of the stalled (b) and collided (c) colored according to local resolution.

Extended Data Fig. 3 Cryo-EM data analysis, classification and resolution of the ZAK-bound ribosomal complexes obtained without ANS addition and eS24 control blots.

a, The sample was obtained from a native pull-down using the kinase inactive (K45M) ZAK mutant without ANS addition (see Extended Data Fig. 1b, left panel). 3D classification of 325,370 particles picked from a total of 13,154 micrographs showed 80S classes representing translating 80S with tRNAs in hybrid state (A/P, P/E) or with tRNAs in POST state (P/P, E/E) state, as well as 80S with a tRNA in the E site and bound to eEF2 and SERBP1, indicative of hibernating ribosomes. No classes indicative for stable disomes were found. The hybrid state translating and the hibernating 80S classes were first globally refined to 2.2 Å and 2.6 Å, respectively, followed by local refinement focusing on either the entire 40S subunit (2.3 Å for the translating and 2.7 Å for the hibernating 40S) or the RACK1 region (2.7 Å for the translating and 3.0 Å for the hibernating 40S head). Both maps revealed the ZAK eS27-pin, the ZAK RIH bound to RACK1 and the RACK1-bound SERBP1 C-terminal region. b, eS24 blots corresponding to Fig. 2e. c, eS24 blots corresponding to Fig. 3e. d, eS24 blots corresponding to Fig. 3g. e, eS24 blots corresponding to Fig. 4e. Blots represent at least two independent replicates (see “Statistics and Reproducibility” section). See Supplementary Fig. 10 for source data.

Extended Data Fig. 4 AlphaFold models and density fits for ZAKα.

a, AlphaFold model of ZAKα, colored according to a per-model confidence score (pLDDT; from 0 to 100). Blue regions display a very high confidence (pLDDT > 90), red/orange region low confidence (pLDDT <50). b, AlphaFold model of ZAKα with individual domains color coded. Regions not visible in our cryo-EM maps are shown in transparent grey except for KD (red), LZ (yellow), RBR (pale blue) and CTD (plum). KD = kinase domain; LZ = leucine zipper; RBR = ribosome binding region; CTD = C-terminal domain; RIM = RACK1-interacting motif; RIH = RACK1-interacting helix; RIH-p = RIH-peptide; pin = eS27-pin; YLD = YEATS-like domain; SAM = sterile alpha motif; helix = α-helix formed by residues 568–583 of ZAK. c, AlphaFold model of a SAM dimer with asymmetric head-to-tail interface. α-helices α1-5 are indicated. d, Three views on the AlphaFold model for SAM(s) (top) and SAM(c) fitted into respective isolated densities. The densities (mesh) were extracted from the local refined stalled and collided 80S (see also Extended Data Fig. 2) and were Gaussian low-pass filtered with a standard deviation of 0.75 in ChimeraX 1.9. ED indicates low-resolution density visible on SAM(c) possibly representing the same helix or a sequence preceding the RIH on the stalled ribosome. e, Cryo-EM map (grey mesh) of stable disome the locally refined on the RACK1-SAM region (see also Extended Data Fig. 2) with model for the ZAK-disome fitted. Extra density for the ZAK RIH-p extends from RACK1(s) towards the disome interface. f, Model of RACK1 shown as surface with bound ZAK RIH/RIH-p and with ZAK RIM/SAM. g, Cryo-EM map of the stalled 80S derived from the native pull-down of kinase inactive K394D ZAK ribosomal complexes, locally refined around the RACK1-SAM region (see also Extended Data Fig. 9). Note that extra density was present accounting for only one SAM domain emerging from the collided 80S. RACK1(s) was bound to ZAK RIH with the RIH-p extending towards the collision interface and to the C-terminus of SERBP1 (SERBP1-C). h, comparison of the SAM dimer AlphaFold model (shown in grey in the overlay) with the cryo-EM derived model. i, Three different symmetric like-to-like models of a ZAK dimer predicted by AF.

Extended Data Fig. 5 Fitting of ZAK eS27 pin, RIH, RIM and SERBP1-C and model of EDF1 bound to the colliding disome.

a, Top rows show cryo-EM maps of the individually refined stalled and collided 80S (Extended Data Fig. 2) from the dataset obtained from the kinase inactive ZAK pull-down after ANS treatment (+ANS). Below a view focusing on the eS27-pin, two views on RACK1-bound RIH and one view on the RACK1-bound RIM (with SAM α5) are shown. b, c, same as (a) for the untreated (-ANS) kinase inactive ZAK pull-down. Shown are cryo-EM maps for the refined hibernating (b) and hybrid state translating (c) 80S (see also Extended Data Fig. 3a). Views focusing on the ZAK eS27-pin, RIH and SERBP1-C (at the same RACK1 binding site as the ZAK RIM) and are shown as in (a). d, Cryo-EM map and view focusing on the eS27-pin for the reconstituted ZAK-RBR-40S complex. e, same as (a) with cryo-EM maps obtained from the kinase inactive ZAK-K394D pull-down after ANS treatment dataset (Extended Data Fig. 9). For all density snapshots focusing on RACK1 the cryo-EM maps are derived from local refinements on the RACK1-SAM region (see also Extended Data Figs. 2, 3a and 9). For (a) and (e) composite disome maps are shown with hallmarks indicated (density for ZAK SAM domains omitted). All views show either the ZAK-disome model or the model containing RACK1, the ZAK RIH and SERBP1-C fitted into the maps (mesh); pin = eS27-pin; RIM = RACK1-interacting motif; RIH = RACK1-interacting helix; SAM = sterile alpha motif; RBR = ribosome binding region. f, g, Two views focusing on the EDF1 binding site. The molecular model for EDF1 is fitted into the isolated map, extracted from the composite map of the ZAK-disome complex. Thumbnails at the bottom indicate the view, boxes highlight the zoomed region.

Extended Data Fig. 6 C-terminus structural validation and CLIP-seq analysis.

a, Model of the eS27-pin fitted into the 2.3 Å cryo-EM map (mesh) of reconstituted ZAK-RBR-40S complex (see also Extended Data Fig. 5d). The main interaction with eS27 is mediated by W768, which intercalates between R80 and K36 of eS27. The following residues (T769, K770 and V771) are packed on top of the 3-stranded β-sheet of eS27 with T769 and V771 forming additional interactions with eS27. b, View focusing on the collision interface of the ZAK-disome complex surrounding the eS27-pin. The model for the ZAK-disome was fitted into the cryo-EM map of the entire stable ZAK-disome (Extended Data Fig. 2, right side). Dashed lines indicate regions flanking the eS27-pin. c, CLIP-seq of Flag tagged WT and T161A/S165A ZAK expressed from partial CMV promoter in HEK293T ZAK-KOs and its associated ribosomal RNA (black lines) in untreated and ANS treated conditions. Sequence reads were mapped to 18S rRNA and normalized to non-ribosome binding 1–649 aa ZAK truncation sample. The horizontal line represents 2-fold enrichment. Shading indicates standard error of the mean from two biological replicates. Reads from CLIP-seq were mapped to the 18S rRNA and matched to regions including ES6b/c (red). d, CLIP-seq of T161A/S165A ZAK expressed from full CMV promoter (Black = overexpression, untreated; red = overexpression, ANS treated) and the ZAK-KO with no transfection (Grey = ZAK-KO, untreated). Sequence reads were mapped to 28S rRNA and normalized to 1–649 aa C-terminal truncation control. The horizontal line represents 2-fold enrichment. Shading indicates standard error of the mean from two biological replicates. e, Metagene analysis of each CLIP-seq sample (normalized to ZAK-KO). CLIP-seq coverage data was aligned to 5’UTR, CDS, and 3’UTR for each mRNA. Only ENSEMBL canonical transcripts were used.

Extended Data Fig. 7 Characterization of ZAK interactions in vivo and in vitro.

a, Immunoblots of co-immunoprecipitation of FLAG tagged or HA tagged ZAK either in untreated or ANS treated conditions. b, Immunoblots of total lysate from HEK293T ZAK knockout cells transfected with partial CMV promoter plasmids expressing various SAM mutant N-terminal FLAG ZAK constructs at endogenous levels. Blots represent at least two independent replicates (see “Statistics and Reproducibility” section). See Supplementary Fig. 11 for source data.

Extended Data Fig. 8 Cryo-EM data analysis, classification and resolution of the ZAK-K394D-disome complex.

The sample was obtained from a native pull-down using kinase inactive (K45M) ZAK-K394D mutant after ANS addition (see Extended Data Fig. 1b, right panel). 3D classification of 629,553 particles picked from a total of 56,746 micrographs showed 80S classes as described for the pull-downs using kinase inactive ZAK mutants described above. Amongst them were classes representing disomes as well as hibernating (with eEF2/SERBP1 and E tRNA) and translating (hybrid and POST state) 80S. The classes representing stalled (with P/P and E/E tRNAs) and collided (with A/P and P/E tRNAs) 80S were refined followed by local refinement focusing on the RACK1-SAM region. Local resolution was determined for this region ranging from below 3 Å close to RACK1 to 5–15 Å for peripheral regions. We observed density for one SAM globular domain emerging from RACK1 of the collided ribosome, whereas the SERBP1 C-terminus (SERBP1-C) and the ZAK RIH occupy RACK1 of the stalled ribosome.

Extended Data Fig. 9 Comparison of RACK1-bound ZAK, SERBP1 and LARP4.

a, Overlay of AlphaFold models for RACK1-bound ZAK (RIH, RIM and SAM domain), SERBP1-C and LARP4. Important conserved residues (F, P and L for the RIM and Y for the RIH) are indicated. b, Consurf evolutionary conservation profile RIM and RIH of ZAK with conserved SERBP1-C and LARP4 sequences aligned. Residues with red * were targeted for mutational analysis. ZAK RIM, SERBP1-C and LARP4 share the FPxL sequence for binding RACK1 and ZAK and LARP4 share a conserved Y residue in the RIH pinning it to RACK1.

Extended Data Table 1 Cryo-EM data collection, refinement and model validation

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This file contains Supplementary Figs. 1–11 (Uncropped immunoblots from main figures and extended data figures).

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Huso, V.L., Niu, S., Catipovic, M.A. et al. ZAK activation at the collided ribosome. Nature 649, 1051–1060 (2026). https://doi.org/10.1038/s41586-025-09772-8

- Received: 01 July 2025

- Accepted: 17 October 2025

- Published: 19 November 2025

- Version of record: 19 November 2025

- Issue date: 22 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09772-8

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Amendments & Corrections

- Author Correction: Repulsions instruct synaptic partner matching in an olfactory circuit

- Author Correction: Rewiring an olfactory circuit by altering cell-surface combinatorial code

- Author Correction: Structural insights into BCDX2 complex function in homologous recombination

- Author Correction: Rolling back human pluripotent stem cells to an eight-cell embryo-like stage

Author Correction Open access Published: 04 January 2026

Author Correction: Repulsions instruct synaptic partner matching in an olfactory circuit

Zhuoran Li,

Cheng Lyu,

Chuanyun Xu,

Ying Hu,

David J. Luginbuhl,

Asaf B. Caspi-Lebovic,

Jessica M. Priest,

Engin Özkan &

…

Liqun Luo

Nature volume 649, page E10 (2026)

The Original Article was published on 19 November 2025

Correction to: Nature https://doi.org/10.1038/s41586-025-09768-4 Published online 19 November 2025

Since the version of the article initially published, in the “Generation of cDNA constructs and transgenic flies” section of the Methods and in Supplementary Table 1, the transgenic fly used for experiments in Fig. 3g and i for Toll2 overexpression has been corrected: the transgenic fly was generated using the plasmid pJFRC19-13XLexAop2-IVS-Toll2-Flag and carries the LexAop-Toll2-Flag transgene on the third chromosome (VK5 site). This correction has been made to the HTML and PDF versions of the article.

Author information

Author notes

1. These authors contributed equally: Zhuoran Li, Cheng Lyu

Authors and Affiliations

1. Department of Biology and Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA

Zhuoran Li, Cheng Lyu, Chuanyun Xu, Ying Hu, David J. Luginbuhl & Liqun Luo

2. Department of Biochemistry and Molecular Biology, The Neuroscience Institute and Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, USA

Asaf B. Caspi-Lebovic, Jessica M. Priest & Engin Özkan

Corresponding author

Correspondence to Liqun Luo.

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Li, Z., Lyu, C., Xu, C. et al. Author Correction: Repulsions instruct synaptic partner matching in an olfactory circuit. Nature 649, E10 (2026). https://doi.org/10.1038/s41586-025-10089-9

- Published: 04 January 2026

- Version of record: 04 January 2026

- Issue date: 22 January 2026

- DOI: https://doi.org/10.1038/s41586-025-10089-9

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Author Correction Open access Published: 04 January 2026

Author Correction: Rewiring an olfactory circuit by altering cell-surface combinatorial code

Cheng Lyu,

Zhuoran Li,

Chuanyun Xu,

Jordan Kalai &

…

Liqun Luo

Nature volume 649, page E11 (2026)

The Original Article was published on 19 November 2025

Correction to: Nature https://doi.org/10.1038/s41586-025-09769-3 Published online 19 November 2025

In the version of the article initially published, in Fig. 1h, the bottom three panels in the “klg RNAi” column (second from right) were duplicates of the images in Fig. 1c. Fig. 1h has now been updated with the correct figures in the HTML and PDF versions of the article.

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Authors and Affiliations

1. Department of Biology and Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA

Cheng Lyu, Zhuoran Li, Chuanyun Xu, Jordan Kalai & Liqun Luo

2. Biology Graduate Program, Stanford University, Stanford, CA, USA

Zhuoran Li, Chuanyun Xu & Jordan Kalai

Corresponding author

Correspondence to Liqun Luo.

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Lyu, C., Li, Z., Xu, C. et al. Author Correction: Rewiring an olfactory circuit by altering cell-surface combinatorial code. Nature 649, E11 (2026). https://doi.org/10.1038/s41586-025-10090-2

- Published: 04 January 2026

- Version of record: 04 January 2026

- Issue date: 22 January 2026

- DOI: https://doi.org/10.1038/s41586-025-10090-2

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Author Correction Published: 08 January 2026

Author Correction: Structural insights into BCDX2 complex function in homologous recombination

Yashpal Rawal,

Lijia Jia,

Aviv Meir,

Shuo Zhou,

Hardeep Kaur,

Eliza A. Ruben,

Youngho Kwon,

Kara A. Bernstein,

Maria Jasin,

Alexander B. Taylor,

Sandeep Burma,

Robert Hromas,

Alexander V. Mazin,

Weixing Zhao,

Daohong Zhou,

Elizabeth V. Wasmuth,

Eric C. Greene,

Patrick Sung &

…

Shaun K. Olsen

Nature volume 649, page E12 (2026)

The Original Article was published on 21 June 2023

Correction to: Nature https://doi.org/10.1038/s41586-023-06219-w Published online 21 June 2023

In the version of the article initially published, some of the kymographs shown in Figs. 4j and 6c, and Extended Data Fig. 9a,b, were inadvertently duplicated during figure preparation. These panels were initially inserted as placeholders and were mistakenly included in the final manuscript submission. Additionally, several columns in the Source Data were unintentionally duplicated during the transfer from the original Prism files to the submitted Excel file; these duplications affect the Source Data for Fig. 4 and Extended Data Fig. 9. All errors have been corrected in the HTML and PDF versions, and the Source Data files for those three figures have been more clearly labelled to assist readers in evaluating the data and corresponding statistical analysis. The original Figs. 4j and 6c and Extended Data Fig. 9a,b are available for comparison in the Supplementary information accompanying this amendment. The main results, conclusions, and our interpretations of the data remain unchanged.

Supplementary information is available in the online version of this amendment.

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Author notes

1. These authors contributed equally: Yashpal Rawal, Lijia Jia, Aviv Meir

Authors and Affiliations

1. Department of Biochemistry & Structural Biology and Greehey Children’s Cancer Research Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA

Yashpal Rawal, Lijia Jia, Shuo Zhou, Hardeep Kaur, Eliza A. Ruben, Youngho Kwon, Alexander B. Taylor, Sandeep Burma, Alexander V. Mazin, Weixing Zhao, Daohong Zhou, Elizabeth V. Wasmuth, Patrick Sung & Shaun K. Olsen

2. Department of Biochemistry and Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA

Aviv Meir & Eric C. Greene

3. Department of Biochemistry & Biophysics, University of Pennsylvania School of Medicine, Philadelphia, PA, USA

Kara A. Bernstein

4. Developmental Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

Maria Jasin

5. Department of Neurosurgery, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA

Sandeep Burma

6. Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA

Robert Hromas

7. Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, San Antonio, TX, USA

Shaun K. Olsen

Corresponding authors

Correspondence to Eric C. Greene, Patrick Sung or Shaun K. Olsen.

Supplementary information

Original, uncorrected Fig. 4, Fig. 6 and Extended Data Fig. 9

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Rawal, Y., Jia, L., Meir, A. et al. Author Correction: Structural insights into BCDX2 complex function in homologous recombination. Nature 649, E12 (2026). https://doi.org/10.1038/s41586-025-10081-3

- Published: 08 January 2026

- Version of record: 08 January 2026

- Issue date: 22 January 2026

- DOI: https://doi.org/10.1038/s41586-025-10081-3

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Author Correction Published: 23 December 2025

Author Correction: Rolling back human pluripotent stem cells to an eight-cell embryo-like stage

Md. Abdul Mazid,

Carl Ward,

Zhiwei Luo,

Chuanyu Liu,

Yunpan Li,

Yiwei Lai,

Liang Wu,

Jinxiu Li,

Wenqi Jia,

Yu Jiang,

Hao Liu,

Lixin Fu,

Yueli Yang,

David P. Ibañez,

Junjian Lai,

Xiaoyu Wei,

Juan An,

Pengcheng Guo,

Yue Yuan,

Qiuting Deng,

Yang Wang,

Ying Liu,

Fei Gao,

Junwen Wang,

Shahriar Zaman,

Baoming Qin,

Guangming Wu,

Patrick H. Maxwell,

Xun Xu,

Longqi Liu,

Wenjuan Li &

…

Miguel A. Esteban

Nature volume 649, page E13 (2026)

The Original Article was published on 21 March 2022

Correction to: Nature https://doi.org/10.1038/s41586-022-04625-0 Published online 21 March 2022

In the version of the article initially published, the “Animal study and ethics statement” section of the Methods read:

“All animal experiments were performed in accordance with the ethics guidelines of the Guangzhou Institutes of Biomedicine and Health. Human–mouse chimera and human blastoid experiments were approved and followed up by the Animal Care and Use Committee and Human Subject Research Ethics Committee under license numbers IACUC2016012 and GIBH-IRB2020-034, respectively, of the Guangzhou Institutes of Biomedicine and Health. These committees comprise experts in different fields (including scientists working on development and other disciplines, and non-scientists (doctors and lawyers)), and they evaluated the rationale of the experiment plan, origins and consent of human materials and the qualification of the investigators. Several of these experts followed the study until completion. Both experiments followed relevant international regulations, including the 2016 Guidelines for Stem Cell Research and Clinical Translation released by the International Society for Stem Cell Research (ISSCR). A specialized review process was activated in the human–mouse chimera experiments to assess whether the degree of functional integration was sufficiently high to raise concerns that the nature of the chimeric animal would be substantially changed. Human blastoid experiments were subjected to an embryo research oversight process. Several of these experts followed the study until completion. We have consent forms for all iPS cells generated in our laboratory. For the chimera experiments, we used the HN10-DsRed ES cell line, which were established by Hainan Medical University, China. We performed a TPRX1–EGFP knock-in into HN10-DsRed ES cells, as H9 ES cells cannot be used for interspecies chimeras. All human PSC lines were used anonymized”.

It has now been corrected in the HTML and PDF versions of the article to:

“All animal experiments were performed in accordance with the ethics guidelines of the Guangzhou Institutes of Biomedicine and Health. Teratoma experiments were approved by the Institutional Animal Care and Use Committee of the Guangzhou Institutes of Biomedicine and Health under license number IACUC:2021002. Human blastoid experiments were approved by the Institutional Review Board of the Guangzhou Institutes of Biomedicine and Health under license number GIBH-IRB2020-034. Human–mouse chimera experiments were part of work in a team grant of the Chinese Academy of Sciences (XDA16030502). The team in this grant had ethical clearance for work on human-mouse chimeras (license numbers IACUC:2019037 approved by Institutional Animal Care and Use Committee of the Guangzhou Institutes of Biomedicine and Health and GIBH-IRB2019-020 approved by the Institutional Review Board of the Guangzhou Institutes of Biomedicine and Health). The license number for performing embryo-complementation experiments at the facility of the Guangzhou Institutes of Biomedicine and Health was IACUC:2016012 (approved by Institutional Animal Care and Use Committee of the Guangzhou Institutes of Biomedicine). Our research work on human-mouse chimeras was very rigorously overseen on a quarterly basis by a large and independent panel of experts from different disciplines including non-scientific at the Chinese Academy of Sciences. Special care by the reviewing experts was put onto assessing whether the degree of functional integration was sufficiently high to raise concerns that the nature of the chimeric animal would be substantially changed. Several of these experts followed the study until completion. All experiments adhered to the relevant international regulations, including the 2016 Guidelines for Stem Cell Research and Clinical Translation released by the International Society for Stem Cell Research (ISSCR). Human blastoid experiments were subjected to an embryo research oversight process. Several of these experts followed the study until completion. We have consent forms for all iPSCs generated in our laboratory. For the chimera experiments, we used the HN10-DsRed ESC line, which were established by Hainan Medical University, China. We performed a TPRX1–EGFP knock-in into HN10-DsRed ESCs, as H9 and H1 ESCs cannot be used for interspecies chimeras. All human PSC lines were used anonymized”.

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Author notes

1. These authors contributed equally: Md. Abdul Mazid, Carl Ward, Zhiwei Luo, Chuanyu Liu

Authors and Affiliations

1. Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China

Md. Abdul Mazid, Carl Ward, Zhiwei Luo, Yunpan Li, Yiwei Lai, Wenqi Jia, Hao Liu, Lixin Fu, David P. Ibañez, Junjian Lai, Juan An, Wenjuan Li & Miguel A. Esteban

2. University of Chinese Academy of Sciences, Beijing, China

Md. Abdul Mazid, Liang Wu, Jinxiu Li, Wenqi Jia, Lixin Fu, David P. Ibañez, Xiaoyu Wei, Yue Yuan & Qiuting Deng

3. BGI-Shenzhen, Shenzhen, China

Chuanyu Liu, Yiwei Lai, Liang Wu, Jinxiu Li, Xiaoyu Wei, Yue Yuan, Qiuting Deng, Yang Wang, Ying Liu, Xun Xu, Longqi Liu & Miguel A. Esteban

4. Jilin Provincial Key Laboratory of Animal Embryo Engineering, Key Laboratory of Zoonoses Research, Ministry of Education, College of Veterinary Medicine, Jilin University, Changchun, China

Yu Jiang, Yueli Yang, Pengcheng Guo & Miguel A. Esteban

5. School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China

Juan An

6. Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China

Fei Gao

7. E-GENE, Shenzhen, China

Junwen Wang

8. Department of Genetic Engineering and Biotechnology, Faculty of Life and Earth Sciences, University of Rajshahi, Rajshahi, Bangladesh

Shahriar Zaman

9. Laboratory of Metabolism and Cell Fate, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China

Baoming Qin

10. Guangzhou Laboratory, Guangzhou, China

Guangming Wu

11. Cambridge Institute for Medical Research, Department of Medicine, University of Cambridge, Cambridge, UK

Patrick H. Maxwell

12. Guangdong Provincial Key Laboratory of Genome Read and Write, Shenzhen, China

Xun Xu

13. Institute of Stem Cells and Regeneration, Chinese Academy of Sciences, Beijing, China

Miguel A. Esteban

Corresponding authors

Correspondence to Md. Abdul Mazid, Longqi Liu, Wenjuan Li or Miguel A. Esteban.

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Mazid, M.A., Ward, C., Luo, Z. et al. Author Correction: Rolling back human pluripotent stem cells to an eight-cell embryo-like stage. Nature 649, E13 (2026). https://doi.org/10.1038/s41586-025-10044-8

- Published: 23 December 2025

- Version of record: 23 December 2025

- Issue date: 22 January 2026

- DOI: https://doi.org/10.1038/s41586-025-10044-8

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The symptoms of attention deficit hyperactivity disorder (ADHD) have been known for more than 100 years, although the condition was not formally recognized until the 1960s and did not receive its current name until 1980.

Globally, ADHD affects up to 8% of children and adolescents, and often persists into adulthood. Rates have been rising quickly over the past few decades, for reasons that are not entirely clear — a mystery that underscores how much we still have to learn about the condition.

That starts with its neurobiological cause. Dopamine signalling has long been a target for treatment, but researchers are still learning how exactly this neurotransmitter affects ADHD. The link is more nuanced than a simple deficit of one brain chemical, however.

Although ADHD affects both boys and girls, there is a big discrepancy in the rate of diagnosis, with boys being diagnosed two to three times more often. This underdiagnosis puts girls at a higher risk of bad outcomes and requires increased attention from the scientific community.

For decades, there has been one main, and highly effective, treatment for ADHD: oral stimulant drugs such as methylphenidate, commonly known as Ritalin. But stimulants can have problematic side effects, so researchers are working to develop alternatives, including non-stimulant drugs, talk therapy and even therapeutic video games. So far, however, nothing matches the efficacy of stimulants.

There is a big genetic component to ADHD, but environmental factors also have a role. One that made headlines in recent months is the potential link between prenatal exposure to paracetamol and neurological disorders such as ADHD and autism. This complex relationship needs scientific clarity that is currently lacking — and political fights have muddied the waters.

Most teens feel the addictive pull of social media, but those with ADHD are particularly vulnerable because of differences in the way their brains regulate attention and reward. This makes these youths more likely to use social media in risky ways. Getting to grips with this problem is tricky for parents, and requires thoughtful management.

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Seven technologies to watch in 2026

Nature’s round-up of innovations that are poised to make a splash in the year ahead.

- Michael Eisenstein

Technology Feature21 Jan 2026 Nature

Self-driving laboratories, advanced immunotherapies and five more technologies to watch in 2025

Sustainability and artificial intelligence dominate our seventh annual round-up of exciting innovations.

- Michael Eisenstein

Technology Feature20 Jan 2025 Nature

Seven technologies to watch in 2024

Advances in artificial intelligence are at the heart of many of this year’s most exciting areas of technological innovation

- Michael Eisenstein

Technology Feature22 Jan 2024 Nature

Seven technologies to watch in 2023

Nature’s pick of tools and techniques that are poised to have an outsized impact on science in the coming year.

- Michael Eisenstein

Technology Feature23 Jan 2023 Nature

Seven technologies to watch in 2022

Our fifth annual round-up of the tools that look set to shake up science this year.

- Michael Eisenstein

Technology Feature25 Jan 2022 Nature

Seven technologies to watch in 2021

COVID considerations unsurprisingly dominate the tech developments that could have a big impact in the coming year.

- Esther Landhuis

Technology Feature26 Jan 2021 Nature

Blood-based screening for aneurysms

Early-stage AI model links blood-based molecular signatures to aortic anatomy.

Partner Content21 Jan 2026

Table of Contents

Nature: 2026 Jan 22

This Week

AI and nuclear energy feature strongly in agenda-setting technologies for 2026

Making progress on global health will need high-quality evidence

News in Focus

US science in 2026: five themes that will dominate Trump’s second year

How to improve vaccine uptake: a huge study offers clues

Why is flu so bad this year? Highly mutated variant offers answers

Can’t get motivated? This brain circuit might explain why — and it can be turned off

AIs are biased towards some Indian castes — how can researchers fix this?

Do their ears hang low? The genetics of dogs’ adorable floppy ears

Ice age wolf pup’s stomach yields rare DNA from woolly rhino

US science after a year of Trump: what has been lost and what remains

Girls are starting puberty younger — why, and what are the risks?

Books & Arts

Forget formalism: mathematics was built on infighting and emotional turmoil

‘Greed is the iron cage of our times’ — why nationalism is here to stay

Opinion

Trump one year on: How six US researchers plan to protect science amid chaos and cuts

Work

‘Shattered’: US scientists speak out about how Trump policies disrupted their careers

Seven technologies to watch in 2026

Floating science stations: my month on a research vessel looking after buoys

Research

Probing quantum mechanics with nanoparticle matter-wave interferometry

3D-printed low-voltage-driven ciliary hydrogel microactuators

Global subsidence of river deltas

Early hominins from Morocco basal to the Homo sapiens lineage

Rare genetic variants confer a high risk of ADHD and implicate neuronal biology

Specificity, length and luck drive gene rankings in association studies

Mimicking opioid analgesia in cortical pain circuits

Astrocyte CCN1 stabilizes neural circuits in the adult brain

Lesion-remote astrocytes govern microglia-mediated white matter repair

The Microflora Danica atlas of Danish environmental microbiomes

Long-read metagenomics reveals phage dynamics in the human gut microbiome

iHALT unlocks liver functionality as a surrogate secondary lymphoid organ

Architecture of the neutrophil compartment

Fasting boosts breast cancer therapy efficacy via glucocorticoid activation

Cytosolic acetyl-coenzyme A is a signalling metabolite to control mitophagy

Inhibitors supercharge kinase turnover through native proteolytic circuits

An ATP-gated molecular switch orchestrates human mRNA export

ZAK activation at the collided ribosome

Amendments & Corrections

Author Correction: Repulsions instruct synaptic partner matching in an olfactory circuit

Author Correction: Rewiring an olfactory circuit by altering cell-surface combinatorial code

Author Correction: Structural insights into BCDX2 complex function in homologous recombination

Author Correction: Rolling back human pluripotent stem cells to an eight-cell embryo-like stage

Collections

ADHD

Technologies to Watch