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This Week

- Studies linking diet with health must get a whole lot better

- A big chance for science at the heart of global policymaking

- Why I think ending article-processing charges will save open access

- Had COVID? A delayed booster might lead to a better response

EDITORIAL 11 October 2022

Studies linking diet with health must get a whole lot better

A research-rating system has identified gaps in studies that assess the connection between diet and various health risks.

Researchers have created a star-based metric that rates the quality of the evidence for a link between a given behaviour — such as eating red meat — and a particular health outcome.Credit: Education Images/Universal Images Group/Getty

Does eating red meat reduce lifespan? Some researchers certainly think so. Work such as the Global Burden of Diseases, Injuries, and Risk Factors Study[1] has led the World Health Organization and the US Department of Agriculture to advise that people limit consumption of unprocessed red meat, to protect themselves from diseases such as type 2 diabetes and various cancers.

Other researchers are less sure. Targets for red-meat consumption, set by public-health officials and expert panels, vary widely, with some advising that people eat no more than 14 grams per day and others not stating a recommended limit. This sends a confusing message, which in itself is not good for public health.

It’s not just red meat: the evidence base surrounding much nutritional and wider health advice is similarly disputed. Now, a new approach could help health policymakers to better evaluate the quality of studies assessing potential health risks. A team at the Institute for Health Metrics and Evaluation (IHME) at the University of Washington in Seattle has created a star-based metric that rates the quality of the evidence for a link between a given behaviour — such as eating red meat or smoking — and a particular health outcome[2]. A five-star score means that the link is clearly established; one star means that either there’s no association between the two factors or that the evidence is too weak to draw a firm conclusion.

What the researchers call ‘burden of proof’ analysis does not, of itself, clear up vexing issues such as the risks of red meat or the benefits of vegetables. But as a judgement on the quality of available research, it can help to flag, to research funders, areas in which better evidence is needed for firmer conclusions.

How is the star rating constructed? What are its parameters — and can the methodology itself be considered to be rigorous research? The IHME team did several things to try to quantify the effects of various biases in the studies being assessed. An epidemiological study, for example, might be biased in different ways to a study testing the outcomes of health interventions. The researchers also did away with what can be a common source of bias in research, namely, the assumption that health risks increase exponentially with the parameter being studied, for example blood pressure or consumption of unprocessed red meat. And they attempted to account for the bias that can arise when sample sizes are small.

Applying this framework to studies assessing a total of 180 questions, produced results that are mostly unsurprising. Studies assessing an association between smoking and a variety of cancers, for example, earn a five-star rating[3]. Similarly, high systolic blood pressure — the force exerted by the heart to pump blood — has a five-star association with the narrowing of the blood vessels called ischaemic heart disease[4].

Studies assessing diet and its health outcomes get notably lower star-ratings. The IHME’s analysis, for example, finds only weak evidence of an association between eating unprocessed red meat and outcomes such as colorectal cancer, type 2 diabetes and ischaemic heart disease[5]. It finds no relationship in studies that explore whether eating unprocessed red meat leads to two kinds of strokes. There is stronger, but not overwhelming, evidence that eating vegetables reduces the risk of strokes and ischaemic heart disease[6].

In some cases, the lower star-ratings could be due to effect size: for example, any health risks from red-meat consumption are likely to be small relative to the huge toll that smoking takes on the body. Above all, the lower-rated findings demonstrate that studies in these areas need to get better if they are to yield convincing results.

Teasing out the effect of a single dietary component from those of the complex variety of exposures over a person’s lifetime is difficult. It would need larger studies, with a diverse pool of participants and strict control over their daily diet. Such studies will entail collaboration between research groups with different expertise, and access to participants in different environmental settings — a move that funders must encourage. This is an undertaking worth prioritizing. A small risk for an individual does not mean a small impact on public health: a low-risk behaviour can have a large population-level impact if it is very common.

The literature in the field of responsible research and innovation highlights how metrics in science must always be interrogated for robustness and rigour. There needs to be wide consultation and, as much as possible, the unintended consequences of using metrics must be anticipated, as initiatives such as the San Francisco Declaration on Research Assessment and the Leiden Manifesto show. This work must come sooner rather than later.

We have evidence that underpowered clinical studies, lacking necessary controls to make sense of the data, are not helping. If funders do not target their efforts at producing quality data, the public will remain confused, weary, distrustful and deprived of the information they need to make informed health and lifestyle choices.

Nature 610, 231 (2022)

doi: https://doi.org/10.1038/d41586-022-03199-1

References

1. GBD 2019 Risk Factors Collaborators Lancet 396, 1223–1249 (2020).

2. Zheng, P. et al. Nature Med. https://doi.org/10.1038/s41591-022-01973-2 (2022).

3. Dai, X. et al. Nature Med. https://doi.org/10.1038/s41591-022-01978-x (2022).

4. Razo, C. et al. Nature Med. https://doi.org/10.1038/s41591-022-01974-1 (2022).

5. Lescinsky, H. et al. Nature Med. https://doi.org/10.1038/s41591-022-01968-z (2022).

6. Stanaway, J. D. et al. Nature Med. https://doi.org/10.1038/s41591-022-01970-5 (2022).

The Burden of Proof Studies

Let’s move beyond the rhetoric: it’s time to change how we judge research

EDITORIAL 11 October 2022

A big chance for science at the heart of global policymaking

The UN’s top leadership is reaching out to the scientific community to help inform decision making — a welcome move in a highly uncertain world.

UN secretary-general António Guterres (right) and General Assembly president Csaba Kőrösi (left) must work constructively to boost research and evidence in the UN’s policymaking.Credit: Luiz Rampelotto/EuropaNewswire/DPA/Alamy, Lev Radin/Pacific Press/LightRocket/Getty

The new president of the United Nations General Assembly, Csaba Kőrösi, last month opened the body’s 77th session with a speech that signalled that science might come to play a more prominent part in the UN’s policymaking organ, at least during his tenure. Together with an earlier and more substantive move by the UN’s top diplomat, secretary--general António Guterres, this is a welcome chance for more research-informed policymaking.

First things first. Many parts of the UN system already rely heavily on scientific advice. Specialized agencies such as the World Health Organization and the UN Environment Programme employ, contract with or consult researchers at all levels. Individual UN agreements, such as the biodiversity and climate conventions, are informed by bodies that issue science advice, such as the Intergovernmental Panel on Climate Change. Worldwide, hundreds, if not thousands, of researchers are involved in studying, advising on or monitoring the Sustainable Development Goals (SDGs) — the UN’s plan for the world to cut poverty and achieve sustainability.

But science has a more limited role in the UN’s central policymaking, both in the executive arm and the legislature. A year ago, Guterres, who heads the UN’s executive branch, based in New York City, set out to change that. His agenda for his second term (2022–26) includes many science-based issues, not least the climate, a global green economy and the SDGs. His office has looked at how science-to-policy advice works in more than 90 countries and institutions, with a view to establishing a structure that fills gaps in existing scientific advice, translates science to policy effectively, and is truly relevant to decision-making.

This won’t be the first time that someone in Guterres’s role has established an official science-advisory mechanism. Several models have been tried over the years. Guterres inherited a science advisory board set up by his predecessor, Ban Ki-moon. The secretary-general’s office evaluated these efforts and found that they didn’t influence decision-making or policy. The office says the next step will be to consult leaders and experts across the UN, before settling on how to shape a network of scientific advice that the secretary-general can tap on a regular basis.

Kőrösi said he wants scientists to help the General Assembly with “knowledge from microscopes to microphones”. One way to make that happen could be to establish an office or a mechanism through which assembly members can access expertise to inform their decisions, just as science offices support many national parliaments around the world. It is early days for Kőrösi’s move, and we need to see more detail — such as the names of the countries that are backing it. But there’s no doubt it will be a first for the UN’s legislature, if it can happen.

The General Assembly is akin to a parliament of countries. It sets the UN’s budget and debates and agrees resolutions on crises and conflicts. Crucially, it is separate from the executive branch (Guterres and his colleagues). It is sometimes derided as an expensive talking shop — often by people in powerful nations — but it is the one part of the UN that gives every country a voice. And this level of inclusion pays off. In 2015, it was the General Assembly (rather than a specialized agency) that signed off on the SDGs. This endorsement ensured that the goals became part of the institutional architecture of policy, business and civil society in member states.

Kőrösi’s is not a new idea. Innovation-studies scholar Calestous Juma, one of the first executive secretaries of the UN Convention on Biological Diversity, recommended it in a landmark report 20 years ago, called Knowledge and Diplomacy: Science Advice in the United Nations System. Juma, originally from Kenya, suggested it as a way that representatives from all UN member countries could access the best available evidence ahead of their deliberations.

A key question is, who will pay? If the General Assembly sets the UN budget, could it vote to fund such an office? Often, it’s the UN’s more powerful nations that fund science. If all countries want such an office, they need to find ways to pay for it.

Some observers are concerned about duplicating the science advice that already exists in the UN, and potential clashes with Guterres’s efforts. It is undeniable that the wings of the UN system often struggle to work in concert. The two offices will need to have a constructive relationship, but they do have different roles. When the UK Parliament established a Parliamentary Office of Science and Technology, few made the argument that it would duplicate existing advice given to the UK government’s executive agencies.

For any science-advice system to be effective, both the executive and the legislative arms need to be included, says Chris Tyler, director of research and policy at University College London, and former director of the Parliamentary Office of Science and Technology. “If you want to change the weather, you deal with the executive, but if you want to change the climate, you need to talk to the legislature.”

Ideally, Guterres and Kőrösi’s initiatives will reinforce each other to create a strong, relevant voice for science at the heart of the UN’s policymaking. That will be not just welcome, but timely. The pandemic has shown the value of science and innovation. As the world grapples with food security, climate change and other pressing challenges, a strong voice is needed more than ever.

Nature 610, 232 (2022)

doi: https://doi.org/10.1038/d41586-022-03200-x

The UN must get on with appointing its new science board

The sustainability movement is 50. Why are world leaders ignoring it?

WORLD VIEW 12 October 2022

Why I think ending article-processing charges will save open access

The way that the global north pays for publishing hampers public, scholar-led efforts in Latin America.

Juan Pablo Alperin

When the Public Library of Science, a non-profit organization based in San -Francisco, California, and other publishers popularized article-processing charges (APCs) in the mid-2000s, scholarly publishing in Latin America was already embracing open access (OA) using a different model: instead of charging authors, academic institutions published journals edited by faculty members. The approach is a type of ‘diamond OA’, which works without fees for readers or authors.

Over the same time period, APCs have become ubiquitous in the global north, embraced by for-profit journals and encouraged by many leading European and US funders. The vibrant publishing ecosystem in Latin America (and elsewhere in the global south) will not be left unscathed.

I know this ecosystem is vibrant and diverse because I’ve spent 15 years working at the Public Knowledge Project (PKP) — based at Simon Fraser University in Vancouver, Canada — and I am now co-scientific director of this initiative to make research publicly available. I have met hundreds of journal editors who work hard, often in challenging conditions, to bring the knowledge discovered by their communities to the rest of the world. An incredibly diverse set of journals now uses Open Journal Systems, free software developed by PKP, to manage, publish and index their work (S. Khanna et al. Preprint at SciELO preprints https://doi.org/jgbz; 2022). My and my colleagues’ work shows that many non-academics frequently access this content. Many titles focus on locally relevant issues, such as rural development, local histories and Indigenous cultures. There are, of course, journals of broad global interest as well.

Yet many Latin American scholars still seek to publish in well-funded journals in Europe and North America, which are seen as more prestigious and get more international attention. And many research-evaluation systems favour journals indexed in databases such as Scopus and Web of Science, which contain a tiny fraction of the journals found in the global south. As more journals charge APCs, more Latin American institutions are pressured to pay them.

In Colombia alone, APC payments are estimated to have grown by 18-fold since 2019. The amount is expected to increase after some five dozen institutions signed Latin America’s first ‘transformative agreement’ (a contract to pay APCs to subscription-based journals that are changing business models) late last year. At least 120 journals in Latin America have begun charging APCs in the past 5 years, although this model inherently links publication to authors’ (or their funders’ or institutions’) ability to pay.

APC waivers cannot solve this problem. They require authors to proactively ask for charity (even as Latin American institutions shoulder diamond OA costs), and rarely apply to countries in the region, most of which are above income thresholds set by publishers. As institutions pay more APCs, they will feel pressure to forgo investments in the Latin American diamond OA ecosystem.

There is some movement towards diamond OA in the rest of the world. In September, I participated in the first Diamond Open Access Conference, hosted online and in Zadar, Croatia. This year, the European Commission has awarded funding to two initiatives to support institutional OA publishing. In Canada, national funders continue to support the PKP and its partnership with the dissemination platform Érudit to build open, non-commercial infrastructure for sharing research. In August, Redalyc, an OA initiative at the Autonomous University of Mexico State in Toluca, was awarded a grant from Arcadia, a charitable fund in London. Signatories of the Action Plan for Diamond Open Access (see go.nature.com/3cghq) include more than 130 research-supporting institutions across the world. This support is welcome, but more is needed.

Of course, the model is not perfect. OA funding mechanisms are needed that protect the diversity that exists in the system, lower the burdens of production and raise the quality of journals. Reliance on volunteers and institutional support (such as access to servers, software and IT staff) make some diamond OA journals precarious. However, in my view, this model is much less problematic than one in which journals gain more revenue for publishing more papers and that relies on publishing papers from fields and communities that are well resourced.

APCs beget APCs. The more funds that are available to pay them, and the more researchers who have the ability to do so, the more journals will feel pressured to charge them.

If governments, funders and institutions — including those in Latin America — do not want to be responsible for dismantling this diverse and global scholarly OA ecosystem, they should stop supporting APCs altogether. Funds that are allocated to APCs should be invested in shared infrastructure, tools and services that can support multiple journals simultaneously.

Such an embrace of diamond OA could lead to virtuous cycles in which journals can lower operating costs, raise their quality and elevate their place in research assessment.

This future might not seem possible to those ensconced in an APC-world dominated by large, commercial publishers. I have worked alongside editors, scholarly publishing experts and OA advocates long enough to know that it is — but only if APCs stop before the two models collide and we get thrown off the rails.

Nature 610, 233 (2022)

doi: https://doi.org/10.1038/d41586-022-03201-w

An open-access history: the world according to Smits

Open science, done wrong, will compound inequities

RESEARCH HIGHLIGHT 07 October 2022

Had COVID? A delayed booster might lead to a better response

People vaccinated not long after being infected with SARS-CoV-2 mount a weaker immune response than do those whose infections are well behind them.

A memory B cell (artificially coloured). These antibody-making immune cells might benefit from a longer interval between SARS-CoV-2 infection and vaccination. Credit: Dr Gopal Murti/SPL

COVID-19 vaccines don’t pack their usual punch in people who get boosted shortly after an infection[1].

People who catch SARS-CoV-2 before vaccination develop especially strong immune defences against the virus. To learn whether this ‘hybrid immunity’ depends on the timing between infection and boosting, Clarisa Buckner at the National Institute of Allergy and Infectious Diseases in Bethesda, Maryland, and her colleagues tracked 66 people, who had a variety of histories of vaccination and infection, after they received a third dose of an mRNA vaccine based on the virus’s spike protein.

In the 60 days after boosting, most participants’ antibody responses against variants old and new grew stronger. But participants who had been infected less than 180 days before their boost had a weaker antibody response to the third dose than did those with infections more than 180 days in the past. The latter had responses as good as those of people who hadn’t been infected before the boost.

Analysis of participants’ antibody-making B cells suggested that a short window between infection and boosting did not allow enough time for some of these cells to return to an ideal state for another tussle with the spike protein.

Nature 610, 234 (2022)

doi: https://doi.org/10.1038/d41586-022-03118-4

References

1. Buckner, C. M. et al. Cell https://doi.org/10.1016/j.cell.2022.09.032 (2022).

News in Focus

- AI mathematician, tumour fungi and Africa’s coronavirus genomes

- Will there be a COVID winter wave? What scientists say

- ‘Spooky’ quantum-entanglement experiments win physics Nobel

- Chemists who invented revolutionary ‘click’ reactions win Nobel

- ‘Bit of panic’: Astronomers forced to rethink early Webb telescope findings

- What Italy’s far-right election victory means for science

- COVID jabs for kids: they’re safe and they work — so why is uptake so patchy?

- What does the future look like for monkeypox?

NEWS ROUND-UP 12 October 2022

AI mathematician, tumour fungi and Africa’s coronavirus genomes

The latest science news, in brief.

AlphaTensor was designed to perform matrix multiplications, but the same approach could be used to tackle other mathematical challenges.Credit: DeepMind

Deepmind AI masters matrix mathematics

An artificial intelligence (AI) developed by machine-learning company DeepMind in London has tackled a type of calculation called matrix multiplication.

The system — called AlphaTensor — leverages the skills that DeepMind’s game-playing AIs use to beat human players at games such as Go and chess. Matrix multiplication is a widely used mathematical technique that involves multiplying numbers arranged in grids, or matrices, that might represent sets of pixels in images, air conditions in a weather model or the internal workings of an artificial neural network.

AlphaTensor broke ground by finding shortcuts to solve these problems with fewer steps (A. Fawzi et al. Nature 610, 47–53; 2022). The same general approach could have applications in other kinds of mathematical operation, its developers say, such as decomposing complex waves or other mathematical objects into simpler ones.

“It is very impressive,” says Martina Seidl, a computer scientist at Johannes Kepler University in Linz, Austria. “This work demonstrates the potential of using machine learning for solving hard mathematical problems.”

Africa’s pandemic revealed in 100,000 viral genomes

Analysis of more than 100,000 SARS-CoV-2 genomes collected in Africa reveals that most variants were imported into Africa more often than they were exported from the continent (H. Tegally et al. Science 378, eabq5358; 2022).

The collection of genomes enabled the study’s authors to map when and where variants were introduced into Africa. The researchers found, for example, that although the Omicron subvariant BA.1 was exported from Africa at least 54 times, it was imported at least 69 times from Europe and 102 times from North America (see ‘Virus on the go’). These import events brought the variant to African countries outside of southern Africa, says study co-author Eduan Wilkinson, a bioinformatician at Stellenbosch University in South Africa.

Source: H. Tegally et al. Science 378, eabq5358; 2022

Overall, the team’s analyses indicated that most SARS-CoV-2 variants were introduced into Africa from other parts of the world more often than they were transported out. “The ironic part was that Africa was punished a few times from discovery of variants,” says study co-author Tulio de Oliveira, a bioinformatician also at Stellenbosch University. “But a great majority of the variants, including most of the introductions of Omicron, did not come from Africa.”

The fungus Candida — shown here growing under laboratory conditions — has been found in some tumour samples.Credit: Nicolas Armer/dpa/Alamy

Fungi inside cancers might affect growth

For years, evidence has been mounting that bacteria are linked to cancer. Now, researchers have found a similar connection with another type of microorganism: fungi.

Tumours of various cancers contain different species of microscopic fungus, and investigating the species that are present might one day be useful for diagnosing cancer or predicting its course.

Researchers catalogued fungal populations in more than 17,000 tissue and blood samples representing 35 types of cancer. They found that some fungal species were linked to different outcomes, depending on the cancer. For example, the presence of Malassezia globosa was linked to significantly reduced survival rates in breast cancer (L. Narunsky-Haziza et al. Cell 185, 3789–3806; 2022).

Another study looked at gastrointestinal, lung and breast tumours, and found that they tended to contain Candida, Blastomyces and Malassezia fungi, respectively (A. B. Dohlman et al. Cell 185, 3807–3822; 2022).

Although the studies suggest an association between fungal species and certain cancers, they do not show whether or not the fungi are directly responsible for cancer progression.

Nature 610, 237 (2022)

doi: https://doi.org/10.1038/d41586-022-03202-9

NEWS EXPLAINER 03 October 2022

Will there be a COVID winter wave? What scientists say

Emerging variants and waning immunity are likely to push infection rates higher in the Northern Hemisphere as influenza also makes a comeback.

Ewen Callaway

Social dynamics are back to pre-pandemic norms in many parts of the world.Credit: Raj K Raj/Hindustan Times/Getty

Evidence is building that the Northern Hemisphere is on course for a surge of COVID-19 cases this autumn and winter. New immune-evading strains of the Omicron variant of SARS-CoV-2, behaviour changes and waning immunity mean that many countries could soon see large numbers of COVID-19 infections — and potentially of hospitalizations — say scientists.

Nature explores the factors that might drive a COVID-19 wave — and what countries can do to blunt the effects with the new generation of vaccines that target Omicron.

Will there be a COVID-19 wave this autumn and winter?

In mid-August, an effort called the COVID-19 Scenario Modeling Hub laid out several possible scenarios for the United States over the coming months. After surges caused by the BA.5 Omicron variant — resulting in high immunity in the population — the United States could be in for a relatively quiet COVID-19 season, the models suggested, as long as vaccine-booster campaigns began quickly and new variants didn’t emerge. Even with a new variant, a big surge in cases wasn’t certain.

More than a month on, hospitalizations are declining in line with projections, says Justin Lessler, an infectious-disease epidemiologist at the University of North Carolina at Chapel Hill who leads the modelling effort. But other factors on the horizon could spell trouble. The roll-out of ‘bivalent’ boosters that target both the original SARS-CoV-2 strain and Omicron “has been a little bit slow”, says Lessler. And there are now subtle signs that Omicron is evolving and spawning a new generation of immunity-dodging variants. “It could lead to some upswings as we go into the fall and winter months,” Lessler adds. Some US states are already beginning to see an uptick in cases, notes epidemiologist Jennifer Nuzzo at Brown University in Providence, Rhode Island.

The United Kingdom’s weekly population survey of SARS-CoV-2 infections, a gold standard in COVID-19 data, has documented an increase in COVID-19 prevalence in England and Wales in its past two reports. The number of people hospitalized after testing positive for SARS-CoV-2 is rising quickly — although from low levels — in Britain and other European countries.

In the background, a slew of immunity-dodging variants are emerging globally, and researchers think these will fuel an autumn–winter wave.

Are new variants behind rising case numbers?

Probably not yet, says Tom Wenseleers, an evolutionary biologist at the Catholic University of Leuven in Belgium. The current rise in SARS-CoV-2 infections is probably largely because in most people, immunity generated by vaccines or previous infections is waning. People are also mixing more than they did earlier in the pandemic. In many countries, including the United Kingdom, social dynamics are nearly back to pre-pandemic levels, say health officials. Factors that cause other respiratory viruses to thrive in cooler months — including extra time spent indoors — could also be at play.

Will we see a new Omicron strain this autumn?

We might see three or more. The Omicron subvariants that drove past waves — BA.2, BA.4 and BA.5 — are subsiding, but their descendants are gaining mutations that seem to be helping them to spread.

SARS-CoV-2-watchers are tracking an unprecedented menagerie of variants from a number of branches of the Omicron family tree, says Tom Peacock, a virologist at Imperial College London. Despite these variants’ distinct ancestries, they carry many of the same mutations to the SARS-CoV-2 spike protein (the part of the virus that immune systems target). “Clearly, there’s an optimal way for a variant to look going into this season,” says Peacock.

Researchers are keeping a close eye on certain sublineages. The United Kingdom and some other European countries, for instance, are seeing the swift ascent of BQ.1 (a descendant of BA.5 with several key changes). In India, spawn of the BA.2.75 variant that drove an infection wave several months ago are now outcompeting all others, says microbiologist Rajesh Karyakarte, based in Pune, who coordinates SARS-CoV-2 genetic sequencing in the state of Maharashtra. In samples his team sequenced in late September, a subvariant called BA.2.75.2 was the most common, followed by a close relative. Another BA.2 offshoot, BA.2.3.20, is growing quickly in Singapore, and has turned up in Denmark and Australia.

“I’m fairly confident that at least one of these variants or a combination of them will lead to a new infection wave,” says Wenseleers. And, because they all seem to be behaving similarly, “at the end of the day, it’s not that important which of these becomes the next big thing”.

The Omicron variant: evolutionary descendants of this lineage of the virus SARS-CoV-2 (yellow and green dots) are gaining mutations that seem to be helping them spread.Credit: Steve Gschmeissner/SPL

Why are these variants on the rise?

Two words: immune evasion. All the variants that researchers are tracking contain multiple overlapping changes to a portion of the spike protein called the receptor binding domain, which is targeted by potent infection-blocking, or neutralizing, antibodies. That numerous viruses are independently developing the same spike mutations suggests that these changes provide a big advantage to the viruses’ ability to spread, says Yunlong Richard Cao, an immunologist at Peking University in Beijing.

In a September preprint[1], Cao and his colleagues evaluated the capacity of the new crop of variants to evade neutralizing antibodies from vaccination and previous infection with other variants. They found that BQ.1.1 (a member of the BQ.1 family with one extra spike change) and BA.2.75.2 were the most immune evasive, even able to dodge most neutralizing antibodies elicited by infection with BA.5. Two antibody drugs were still effective against BA.2 and BA.5, but they are likely to lose much of their potency against many of the emerging Omicron subvariants, the study suggests. Another team[2], including Peacock, came to similar conclusions about BA.2.75.2. “The degree of immune escape and evasion is amazing right now, crazy,” says Cao.

How big will autumn–winter waves be?

On the basis of initial estimates, Wenseleers thinks that autumn–winter waves will be similar in size to BA.5 surges, at least as far as infection numbers go. What’s harder to predict is the effect on hospitalizations. The build-up of population immunity from vaccination and previous infection is likely to keep admissions lower than during past COVID-19 waves, say researchers, but how low is unclear. “While a completely different game than it would have been in 2020 or 2021, a surge still would probably be associated with an increase in deaths and an increase in hospitalizations,” says Lessler.

But even a relatively muted COVID-19 wave could put strain on hospitals, which are facing backlogs and other conditions that put a heavy burden on health systems in the winter. Influenza, which has barely registered over the past two winters, is likely to come back with a vengeance in the Northern Hemisphere this season, stoking fears of a ‘twindemic’ of influenza and COVID-19. “In a bad flu year, hospital systems get pretty stressed,” says Lessler.

What about the new vaccines?

Boosters, including bivalent vaccines, are likely to offer some protection against infection with emerging variants. But this might not last long, say scientists. One part of the bivalent vaccines is based on an Omicron subvariant — BA.1 in UK-approved vaccines and BA.5 in the United States. But there are signs[3] that the vaccines tend to stimulate the production of neutralizing antibodies that best recognize not Omicron, but the ancestral virus on which the first vaccines were based. A second dose of the boosters might be needed to generate high levels of Omicron-specific neutralizing antibodies, says Cao.

Fortunately, all evidence suggests that COVID-19 vaccines old and new remain highly effective at preventing severe disease, which Nuzzo argues should be the main goal of autumn and winter booster programmes. This means concentrating booster campaigns on those at the highest risk of severe disease, including older people and people with underlying health conditions, who will benefit the most from the added protection. “We need a laser focus on protection against severe illness,” she says.

Nature 610, 239-241 (2022)

doi: https://doi.org/10.1038/d41586-022-03157-x

References

1. Cao, Y. et al. Preprint at bioRxiv https://doi.org/10.1101/2022.09.15.507787 (2022).

2. Sheward, D. J. et al. Preprint at bioRxiv https://doi.org/10.1101/2022.09.16.508299 (2022).

3. Alsoussi, W. B. et al. Preprint at bioRxiv https://doi.org/10.1101/2022.09.22.509040 (2022).

New Omicron-specific vaccines offer similar protection to existing boosters

NEWS 04 October 2022 Correction 05 October 2022

‘Spooky’ quantum-entanglement experiments win physics Nobel

Award goes to three experimental physicists whose pioneering research has laid the groundwork for quantum information science.

Davide Castelvecchi &

Elizabeth Gibney

John Clauser (left), Anton Zeilinger and Alain Aspect have won this year’s Nobel physics prize for their research on quantum entanglement.Credit: J. Clauser (CC BY-SA 4.0), Matthias Röder/dpa/Alamy, The Royal Society (CC BY-SA 3.0)

Three quantum physicists have won the 2022 Nobel Prize in Physics for their experiments with entangled photons, in which particles of light become inextricably linked. Such experiments have laid the foundations for an abundance of quantum technologies, including quantum computers and communications.

Alain Aspect, John Clauser and Anton Zeilinger will each share one-third of the 10-million-Swedish-kronor (US$915,000) prize.

“I was actually very surprised to get the call,” said Zeilinger, a physicist at the University of Vienna, at the press conference announcing the award. “This prize would not be possible without the work of more than 100 young people over the years.”

Aspect, a physicist at the University of Paris-Saclay, received the call during a committee meeting. “I happened to be sitting near Aspect this morning when he got the call,” says Serge Haroche, an experimental physicist at the Collège de France in Paris who shared the 2012 Nobel Prize in Physics for work in quantum physics. When he left the room, Haroche added, Aspect’s colleagues guessed correctly that it was Stockholm calling.

The trio’s experiments proved that connections between quantum particles were not down to local ‘hidden variables’, unknown factors that invisibly tie the two outcomes together. Instead, the phenomenon comes from a genuine association in which manipulating one quantum object affects another far away. German physicist Albert Einstein famously called the phenomenon ‘spooky action at a distance’ — it is now known as quantum entanglement.

All three winners are pioneers of the fields of quantum information and quantum communications, says Pan Jianwei, a physicist at the University of Science and Technology of China in Hefei who participated in some of Zeilinger’s landmark experiments as a graduate student in the 1990s. The recognition was long overdue, Pan says. “We have been waiting for this for a very, very long time.”

The win is “beautiful news” agrees Chiara Marletto, a theoretical physicist at the University of Oxford, UK. The modern versions of the experiments pioneered by the three winners could be central to one of the great open questions of physics today, she says — how to reconcile quantum mechanics with Albert Einstein’s general theory of relativity.

Particle pairs

Because of the effects of quantum entanglement, measuring the property of one particle in an entangled pair immediately affects the results of measurements on the other. It is what enables quantum computers to function: these machines, which seek to harness quantum particles’ ability to exist in more than one state at once, carry out calculations that would be impossible on a conventional computer. Today, physicists are using entanglement to develop quantum encryption and a quantum internet that would allow for ultrasecure communications and new kinds of sensors and telescopes.

But whether particles could be fundamentally linked in this way — such that measuring one determines the properties of another, rather than just revealing a predetermined state — had been a topic of debate since physicists laid the foundations of quantum mechanics in the 1920s.

In the 1960s, physicist John Bell proposed a mathematical test known as Bell’s inequality. This said that experimental results that seemed to be correlated beyond a particular value would be possible only through quantum entanglement, rather than being due to certain kinds of hidden variable. Quantum mechanics predicts a higher degree of correlation than would be possible in classical, or pre-quantum, physics.

In 1972, Clauser — now a physicist at J.F. Clauser & Associates in Walnut Creek, California — and his colleagues developed these ideas into a practical experiment that violated the Bell inequality, supporting the theories of quantum mechanics.

David Kaiser, a quantum physicist and historian of science at the Massachusetts Institute of Technology in Cambridge, says that Clauser had come across Bell’s work by chance while browsing in the library at Columbia University in New York City, where he was a PhD student. Clauser was captivated, and he wrote to Bell to ask him whether anyone had tried testing his inequalities experimentally. Bell replied that no one had — and encouraged him to do so. The reaction from the rest of the community wasn’t so warm, however. “People would say, in writing, that this isn’t real physics — that the topic isn’t worthy,” says Kaiser.

Loopholes and teleportation

Despite Clauser’s success, experimental loopholes remained that left room for hidden variables to create the illusion of quantum entanglement. It was these loopholes that Aspect set out to close in the 1980s. His experiments used a changing set-up that meant that experimental decisions could not be said to be predetermining the results.

And in 1997, Zeilinger and his colleagues at the University of Vienna used the phenomenon of entanglement to demonstrate quantum teleportation, in which a quantum state gets transmitted from one location to another. Quantum systems cannot be detected and reconstituted somewhere else, because measurement destroys their delicate quantum properties. But a state can be transferred between two particles at a distance, if each is entangled with half of a previously entangled pair.

Teleportation allows for supersecure communications, because any eavesdropping would cause particles to lose their delicate quantum states. It might also enable future quantum computers to transfer information. Since Zeilinger’s initial experiments, physicists have succeeded in teleporting electrons, as well as atoms and superconducting circuits.

In more recent experiments, Zeilinger, together with Kaiser and other collaborators, has sought to seal further loopholes in tests of Bell’s inequality by using properties of starlight emitted billions of years ago to define experimental settings.

Although the physics is now the basis of a budding industry, these kinds of experiment could continue to provide insights into fundamental physics. One hope, says Marletto, is that they will show whether two particles can become entangled through a gravitational interaction. General relativity is apparently incompatible with quantum mechanics, and such experiments could provide hints on how to develop a quantum theory of gravity to replace it. “Gravity is the elephant in the room,” says Marletto.

Zeilinger “often anticipated the strangest and most counter-intuitive phenomena” in quantum physics, says Gabriela Barreto Lemos, a physicist at the Federal University of Rio de Janeiro in Brazil, who recalls warmly her time as a postdoctoral researcher in Zeilinger’s lab. “Whenever we presented him with new ideas, he would challenge us to go further, think more outside the box, be more imaginative,” she says.

Kaiser credits the three Nobel recipients with having had the persistence and ingenuity to probe what seem like “fantastical” phenomena, and to ask: “Can the world really work like this?”

“At the time, it was just blue-sky research, with no applications in view,” says Haroche. “It’s a wonderful example of the connection between basic science and application,” he adds. “A demonstration of the usefulness of useless knowledge.”

Nature 610, 241-242 (2022)

doi: https://doi.org/10.1038/d41586-022-03088-7

This is a breaking news story that will be updated throughout the day.

Updates & Corrections

- Correction 05 October 2022: An earlier version of this articles said that Clauser first came across Bell’s work when he was a postdoctoral researcher. This has now been corrected.

Quantum ‘spookiness’ passes toughest test yet

First quantum computer to pack 100 qubits enters crowded race

Quantum ‘spookiness’ passes toughest test yet

NEWS 05 October 2022

Chemists who invented revolutionary ‘click’ reactions win Nobel

Researchers honoured for game-changing methods to couple molecules, including reactions that can be run in living cells.

Davide Castelvecchi &

Heidi Ledford

Carolyn Bertozzi, Morten Meldal and Barry Sharpless (left to right) developed ways of joining molecules quickly and without unwanted by-products.Credit: James Tensuan/The New York Times/Redux/eyevine; University of Copenhagen; K.C. Alfred/San Diego Union-Tribune via ZUMA/Alamy

Three chemists who pioneered a useful technique called click chemistry to join molecules together efficiently have won this year’s Nobel Prize in Chemistry.

Barry Sharpless at Scripps Research in La Jolla, California, and Morten Meldal at the University of Copenhagen laid the foundation for click chemistry, and both independently discovered a pivotal reaction that could link two molecules — an azide and an alkyne — with relative ease[1],[2],[3]. This reaction has been used to develop a host of molecules, including plastics and potential pharmaceuticals.

The third winner, Carolyn Bertozzi at Stanford University in California, used click chemistry to map the complex sugar-based polymers called glycans on the surface of living cells without disturbing cell function[4]. To do this, she developed processes called bioorthogonal reactions, which are now being used to aid the development of cancer drugs.

Speaking by telephone during this morning’s Stockholm press conference, Bertozzi said she had been “absolutely stunned” by the early-morning call telling her she had won. “I’m still not entirely positive that it’s real, but it’s getting realer by the minute,” she said.

Click chemistry has been applied to DNA-sequencing technologies and materials science, as well as aiding basic research into cell function and the discovery of biomolecules.

But that’s only the beginning, Bertozzi told reporters. “I think the field of click chemistry is still in its early stages,” she said during the prize announcement. “There’s probably many new reactions to be discovered.” The value of the technique is its simplicity, she added: it is highly efficient and produces relatively little waste.

“In Carolyn Bertozzi’s, Barry Sharpless’s and my groups we changed some concepts in chemistry which allow you to do things that were never possible before,” Meldal tells Nature.

This is a second Nobel prize for Sharpless, who won a share of the chemistry Nobel in 2001 for developing catalysts based on chiral molecules — those with non-superimposable mirror images. The same year, Sharpless co-wrote a review[1] for the journal Angewandte Chemie in which he argued that chemistry needed to move towards simpler reactions, with less waste in the form of unwanted by-products. “Just a handful of good reactions are needed to assemble vast numbers of highly diverse organic molecules,” he and his co-authors wrote.

Downhill reactions

The authors envisioned a way to design pairs of molecules that react only with each other, and in an irreversible way. This means forming a strong, covalent bond, in contrast to the selective but weaker lock-and-key interactions typical of many biomolecules, says Per-Ola Norrby, a computational chemist at pharmaceutical company AstraZeneca in Gothenburg, Sweden, who worked in Sharpless’s group in the 1990s. A click reaction “is so much downhill in terms of energy, that it never goes back”, he adds. Such reactions should also occur in mild solvents, such as water, and produce only harmless by-products.

“It’s extremely difficult to do one kind of chemistry in the presence of another kind,” says Meldal. “We did something which is completely orthogonal to all existing chemistries.” Orthogonal, which in geometry means perpendicular, refers in chemistry to reactions that can proceed independently in the same medium without affecting each other. Bertozzi coined the term bioorthogonal to refer to reactions that occur without interfering with the chemistry of a living cell.

In 2001, Meldal and his collaborator Christian Tornøe discovered the azide–alkyne reaction[2] — accidentally, says Meldal — while working at the Carlsberg Laboratory in Valby, Denmark. Happenstance, he says, is how many major discoveries occur. “You are doing something you cannot imagine.” An azide is a type of molecule that contains three nitrogen atoms bound in a straight line; an alkyne contains a triple bond between carbons.

The azide–alkyne reaction, which Sharpless and his colleagues also discovered[3], independently, in the same period, became known as the prototypical example of click chemistry.

Chemists have since managed to make click chemistry work between molecules of increasing size and complexity. “I am particularly fond of making bridges in peptides and protein structures to stabilize their properties,” Meldal says.

Knud Jensen, a chemical biologist at the University of Copenhagen who was Meldal’s first student, says the technique spread rapidly across all areas of chemistry, and even in materials science. “Everybody does the click.”

Bioorthogonal breakthrough

Around the same time, Bertozzi’s group had been working on understanding glycans, and searching for ways to label the sugars on cells without damaging the cells themselves. Click chemistry seemed to offer a possibility, but the copper used to catalyse the reactions could be toxic to cells.

In 2003, Bertozzi returned from a conference with an idea for dispensing with copper by using alkynes that have been constrained into a ring, says Nicholas Agard, a senior scientist at biotechnology company Genentech in South San Francisco, California, who was then a graduate student in Bertozzi’s group. The ring bends the alkyne out of its normal linear form, placing it under strain. “The question was: is that energy releasable in an effective way?” he says.

Agard spent hours digging around in the library looking for a precedent for the idea that ring strain could be harnessed for a non-toxic form of click chemistry. He eventually found a 40-year-old paper[5] in German that seemed to have some relevance. “I could understand three things in it: ‘phenyl azide’, ‘cyclooctyne’ and ‘explosion’,” he says.

But Agard and his colleagues were able to find a nonexplosive way to harness ring strain for click chemistry[4], and the laboratory set about applying the technique to label cells in zebrafish and mice. It was an exciting time, says Pamela Chang, a chemical biologist who earned her PhD in Bertozzi’s lab in 2010. “We knew we were part of something special,” she says. “It made for a really amazing environment for all of us.”

By then, Bertozzi was busy travelling to give talks and running a large research group, but made time to have meetings with lab members and help them to edit their papers. She is an advocate for women in science and, in particular, a role model for female chemists, says Chang, who notes that at one point, 50% of the lab’s members were women — an unusually high proportion in the chemistry department at the time.

“She gave us a lot of freedom,” says Chang, who now uses click-chemistry approaches in her own research on bacterial enzymes and metabolism in gut microbiomes at Cornell University in Ithaca, New York. “If you needed advice or help, she would drop everything and make time for you.”

Sharpless, too, is known for being approachable, says Norrby: “You don’t notice that he has a Nobel.” He was also not the kind of boss who required his staff to work 80-hour weeks, Norrby says, something he appreciated as an early-career researcher with family obligations. Sharpless “was totally cool with that, and that’s not common in the high echelons of research”.

Meldal hopes his prize will encourage young people to pursue careers in chemistry. The field is crucial for many of society’s challenges and, together with physics, offers “a full description of what is around us”, he says.

Nature 610, 242-243 (2022)

doi: https://doi.org/10.1038/d41586-022-03087-8

References

1. Kolb, H. C., Finn, M. G. & Sharpless, K. B. Angew. Chem. Int. Edn 40, 2004–2021 (2001).

2. Tornøe, C. W. & Meldal, M. In Peptides 2001 Proc. Am. Pept. Symp. 263–264 (Kluwer Academic Publishers, 2001).

3. Rostovtsev, V. V., Green, L. G., Fokin, V. V. & Sharpless, K. B. Angew. Chem. Int. Edn 41, 2596–2599 (2002).

4. Agard, N. J., Prescher, J. A. & Bertozzi, C. R. J. Am. Chem. Soc. 126, 15046–15047 (2004).

5. Wittig, G. & Krebs, A. Chem. Ber. 94, 3260–3275 (1961).

NEWS 28 September 2022

‘Bit of panic’: Astronomers forced to rethink early Webb telescope findings

Revised instrument calibrations are bedevilling work on the distant Universe.

Alexandra Witze

Astronomers have been poring over early data from the James Webb Space Telescope.Credit: Nolan Zunk/University of Texas at Austin

Astronomers have been so keen to use the new James Webb Space Telescope that some have got a little ahead of themselves. Many started analysing Webb data right after the first batch was released, on 14 July, and quickly posted their results on preprint servers — but are now having to revise them. The telescope’s detectors had not been calibrated thoroughly when the first data were made available, and that fact slipped past some astronomers in their excitement.

The revisions don’t so far appear to substantially change many of the exciting early results, such as the discovery of a number of candidates for the most distant galaxy ever spotted. But the ongoing calibration process is forcing astronomers to reckon with the limitations of early data from Webb.

Figuring out how to redo the work is “thorny and annoying”, says Marco Castellano, an astronomer at the Italian National Institute of Astrophysics in Rome. “There’s been a lot of frustration,” says Garth Illingworth, an astronomer at the University of California, Santa Cruz. “I don’t think anybody really expected this to be as big of an issue as it’s becoming,” adds Guido Roberts-Borsani, an astronomer at the University of California, Los Angeles.

Calibration is particularly challenging for projects that require precise measurements of the brightness of astronomical objects, such as faint, faraway galaxies. For several weeks, some astronomers have been cobbling together workarounds so that they can continue their analyses[1]. The next official round of updates to Webb’s calibrations are expected in the coming weeks from the Space Telescope Science Institute (STScI) in Baltimore, Maryland, which operates the telescope. Those updates should shrink the error bars on the telescope’s calibrations from the tens of percentage points that have been bedevilling astronomers in some areas, down to just a few percentage points. And data accuracy will continue to improve as calibration efforts proceed over the coming months.

This is the first scientific image released from the Webb telescope publicly, on 11 July, showing a deep-field look at the sky that includes a number of distant galaxies.Credit: NASA, ESA, CSA, STScI

The STScI made it clear that the initial calibrations to the telescope were rough, says Jane Rigby, operations project scientist for Webb at NASA’s Goddard Space Flight Center in Greenbelt, Maryland. Much of the issue stems from the fact that Webb, which launched in December 2021, is a new telescope whose details are still being worked out. “It’s been a long time since the community has had a brand-new telescope in space — a big one with these amazingly transformative powers,” Rigby says.

“We knew it wasn’t going to be perfect right out of the box,” says Martha Boyer, an astronomer at the STScI who is helping to lead the calibration efforts[2].

Calibration controversy

All telescopes need to be calibrated. This is usually done by observing a well-understood star such as Vega, a prominent star in the night sky. Astronomers look at the data being collected by the telescope’s various instruments — such as the brightness of the star in different wavelengths of light — and compare them with measurements of the same star from other telescopes and of laboratory standards.

Working with Webb data involves several types of calibration, but the current controversy is around one of the telescope’s main instruments, its Near Infrared Camera (NIRCam). In the six months after Webb launched, STScI researchers worked to calibrate NIRCam. But given the demands on Webb, they had only enough time to point it at one or two calibration stars, and to take data using just one of NIRCam’s ten detectors. They then estimated the calibrations for the other nine detectors. “That’s where there was a problem,” Boyer says. “Each detector will be a little bit different.”

Within days of the first Webb data release, non-peer-reviewed papers began appearing on the arXiv preprint server, reporting multiple candidates for the most distant galaxy ever recorded. These studies relied on the brightness of distant objects, measured with Webb at various wavelengths. Then, on 29 July, the STScI released an updated set of calibrations that were substantially different from what astronomers had been working with.

“This caused a little bit of panic,” says Nathan Adams, an astronomer at the University of Manchester, UK, who, along with his colleagues, pointed out the problem in a 9 August update to a preprint they had posted in late July[3]. “For those including myself who had written a paper within the first two weeks, it was a bit of — ‘Oh no, is everything that we’ve done wrong, does it all need to go in the bin?’”

A young observatory

To try to standardize all the measurements, the STScI is working through a detailed plan to point Webb at several types of well-understood star, and observe them with every detector in every mode for every instrument on the telescope[4]. “It just takes a while,” says Karl Gordon, an astronomer at the STScI who helps lead the effort.

In the meantime, astronomers have been reworking manuscripts that describe distant galaxies on the basis of Webb data. “Everyone’s gone back over and had a second look, and it’s not as bad as we thought,” Adams says. Many of the most exciting distant-galaxy candidates still seem to be at or near the distance originally estimated. But other preliminary studies, such as those that draw conclusions about the early Universe by comparing large numbers of faint galaxies, might not stand the test of time. Other fields of research, such as planetary studies, are not affected as much because they depend less on these preliminary brightness measurements.

“We’ve come to realize how much this data processing is an ongoing and developing situation, just because the observatory is so new and so young,” says Gabriel Brammer, an astronomer at the University of Copenhagen who has been developing Webb calibrations independent of the STScI.

In the long run, astronomers are sure to sort out the calibration and become more confident in their conclusions. But for now, Boyer says, “I would tell people to proceed with caution — whatever results they might be getting today might not be quite right in six months, when we have more information. It’s just sort of, ‘Proceed at your own risk.’”

Nature 610, 243-244 (2022)

doi: https://doi.org/10.1038/d41586-022-03059-y

References

1. Nardiello, D. et al. Preprint at https://arxiv.org/abs/2209.06547 (2022).

2. Boyer, M. L. et al. Preprint at https://arxiv.org/abs/2209.03348 (2022).

3. Adams, N. J. et al. Preprint at https://arxiv.org/abs/2207.11217 (2022).

4. Gordon, K. D. et al. Preprint at https://arxiv.org/abs/2204.06500 (2022).

Four revelations from the Webb telescope about distant galaxies

Webb telescope spots CO2 on exoplanet for first time: what it means for finding alien life

Link: Webb telescope wows with first image of an exoplanet

NEWS 30 September 2022

What Italy’s far-right election victory means for science

Researchers fear further cuts to funding and a lack of action on climate.

Giorgia Guglielmi

Giorgia Meloni is expected to become Italy’s next prime minister.Credit: Nicolò Campo/LightRocket/Getty

Last week’s general election in Italy resulted in a clear victory for the right-wing coalition, setting up far-right leader Giorgia Meloni to become the first female prime minister of a country where leading political figures have overwhelmingly been male.

But researchers have little hope that the new government will boost Italy’s underfunded research system. And some worry that issues such as climate change won’t get the attention they deserve.

“I don’t have high hopes for the foreseeable future,” says Federico Ronchetti, a physicist at the National Institute for Nuclear Physics in Frascati, who in 2020 launched a petition calling for Italy’s public investment in research to double.

Science featured little in the electoral campaigns, including that of the winning coalition. “This doesn’t mean, however — or at least I hope so — that this absence will be reflected in the action of the government to come,” says senator-for-life Elena Cattaneo, a stem-cell scientist at the University of Milan.

However, as the right-wing coalition sets out to form a new government, no name has yet been suggested for leader of the Ministry of University and Research — Italy’s key research funding agency. That, Cattaneo says, is “a sign of the lack of interest” in science.

Meloni and her team did not respond to requests for comment from Nature.

Money problems

With spending on research and development standing at about 1.5% of gross domestic product (GDP), Italy lags behind the roughly 2.2% average of European Union countries. Most research funding comes from industry, with public investment accounting for only about 0.5% of Italy’s GDP. Some 14,000 Italian researchers left the country between 2008 and 2019 — a trend that can be partly explained by cuts to research budgets.

Some researchers now worry that under the new government, funding for public research will be slashed further. “Past right-wing governments have significantly cut spending for science,” says Mario Pianta, an economist at the Scuola Normale Superiore in Florence. And Meloni’s government, he says, will face extra challenges: Italy’s economy is being battered by high energy costs, a sudden burst of inflation and rising public debt. In this context, Pianta says, “research-spending cuts are easier to achieve politically than other public-spending cuts”.

The previous government, led by technocrat Mario Draghi, sought to use some of the pandemic-recovery funds from the European Union to help boost science in Italy. The investment plan allocated around €11 billion (US$11 billion) to research.

During her election campaign, Meloni said she intends to “update” that plan, amid rising prices exacerbated by the war in Ukraine. Now, researchers worry that part of the research budget could be shifted elsewhere — perhaps to ensure a stable energy supply or towards industries such as tourism.

Some are also concerned about Meloni’s attitude towards scientists. Although containment and social distancing have proved crucial to limiting transmission of COVID-19 and reducing hospitalizations, she this month criticized the restrictions introduced in Italy.

Climate concerns

Ahead of the elections, all the major Italian political parties, including Meloni’s Brothers of Italy, agreed to establish an advisory council to assess climate and environment matters and advise the new government.

But these issues don’t seem to be a top priority for Meloni’s party. In an independent analysis of the parties’ commitments to climate and the environment, Brothers of Italy ranked joint last. Its promises concerning practical responses to the climate crisis were “insufficient”, says Stefano Caserini, a climate scientist at the Polytechnic University of Milan who led the analysis. “The need to move away from fossil fuels wasn’t clear,” he adds.

Meloni’s election manifesto did mention incentives for agricultural innovation. Agricultural geneticists hope that genome-editing techniques, which can help to make plants resilient to diseases and changing environmental conditions, will be exempted from existing laws that have limited their use, in Italy and eventually in Europe more widely. These techniques could make agriculture more productive and sustainable, says Enrico Pè, a plant geneticist at Sant’Anna School of Advanced Studies in Pisa. However, he says, Meloni’s plans don’t offer concrete proposals. “I’m not sure what to expect.”

A task force set up by the previous government developed a basic-research plan that recommended increasing public funding and changing how money is allocated by creating an agency that evaluates project proposals and “operates according to international standards”. So far, it’s unclear whether the new government will restructure the many agencies that evaluate universities and public research institutions, but Meloni has promised a ten-year research-funding strategy. This would be a welcome change, because science funding in Italy is erratic and tends to lack long-term planning, says Alberto Baccini, an economist at the University of Siena. But without increased funding, he adds, “long-term planning won’t make much of a difference”.

Nature 610, 245 (2022)

doi: https://doi.org/10.1038/d41586-022-03127-3

Lockdown in Italy: personal stories of doing science during the COVID-19 quarantine

Italy’s rise in research impact pinned on ‘citation doping’

NEWS FEATURE 11 October 2022

COVID jabs for kids: they’re safe and they work — so why is uptake so patchy?

Some countries are now offering COVID vaccines for children as young as six months. Nature looks at how effective they are and why more kids haven’t had them.

Smriti Mallapaty

A girl receives a COVID-19 vaccination at a mall in the Philippines in February, on the first day of eligibility for children aged 5–11. Credit: Rouelle Umali/Xinhua/eyevine

It was more than a year after adults began receiving COVID-19 vaccinations that regulators approved the jabs for children. The gap left some parents anxious to protect their kids, and others wondering about the vaccines’ safety and effectiveness.

About 120 countries have now authorized COVID-19 vaccines for use in children, reaching hundreds of millions of arms. The majority of vaccines have been approved for children aged five and older, but a dozen or so countries have also started to administer them to infants from six months old. The most popular shot is the mRNA vaccine developed by Pfizer and BioNTech, now approved in more than 100 countries.

Data are trickling out on how they fare against the coronavirus SARS-CoV-2, and in particular the potent Omicron variant. Information is patchy because of the variant’s late emergence, and the millions of kids who had already been exposed to the virus by the time roll-outs started. Many governments also cut back on regular testing for COVID-19, meaning that the period of rich data on infections and vaccines is effectively over. “For us, it’s an end of an era,” says Ran Balicer, an epidemiologist at the health-care provider Clalit Health Services in Tel Aviv.

Nature spoke to researchers about how safe and effective the vaccines are, how popular they have been and whether parents should vaccinate their kids if offered the chance.

How safe and effective are the vaccines in children?

By far the most widely used vaccines in kids are ones based on mRNA, offered in close to 90% of the countries and regions that have approved vaccines for kids, according to an analysis prepared for Nature by the health-analytics firm Airfinity. The mRNA vaccines “are really safe vaccines for everybody, including children”, says Kawsar Talaat, an infectious-disease physician and vaccine scientist at the Johns Hopkins Bloomberg School of Public Health in Baltimore, Maryland. Given to billions of adults before being used in children, they have an “unparalleled” safety record, she says.

Some individuals, especially boys and men aged 16–24, develop inflammation of the heart muscle and its outer lining — conditions known as myocarditis and pericarditis — after receiving the mRNA vaccines. But those cases are rare, generally mild and resolve on their own. Cases in kids aged 5–11 are extremely rare — around one in every million children vaccinated. In fact, side effects from the vaccines, such as headaches and fever, have mostly been mild in young children[1].

So the vaccines are extremely safe, but how effective they are is a trickier question to answer — especially with Omicron complicating the picture. The vaccines were designed using the original SARS-CoV-2 virus, and many of the trials in children were conducted when earlier, less immune-evasive and less contagious variants such as Delta were circulating.

The data, which relate largely to the mRNA shots developed by Pfizer and BioNTech, and by Moderna, reveal that in the face of Omicron, the vaccines are good at preventing severe disease, but are less effective at limiting infection — and this protection wanes rapidly. These findings largely correspond with what happens in adults.

Studies from Singapore[2], the United States[3] and Italy[4] find that two shots of the Pfizer–BioNTech vaccine offer moderate to good protection against hospitalization in kids aged 5–11 and in adolescents, reducing the risk by between 40 and 83%. Estimates of protection levels vary widely by country and region, depending on factors such as the time elapsed since participants’ vaccination, testing intensity and previous waves of infection.

Some countries have also begun offering children, particularly adolescents, a third dose, and these boosters seem to be effective, according to US data[5]: five months after adolescents received a second dose of the Pfizer vaccine, when their protection against visits to emergency-care units had fallen to zero, a booster restored that protection to 81%.

Vaccines also protect against a rare but serious complication of COVID-19 known as multisystem inflammatory syndrome in children (MIS-C). It’s not clear, however, whether vaccines can help to reduce the paediatric incidence of long COVID, a condition in which people experience symptoms months after being infected. Several studies have shown that children who test positive can develop lasting symptoms, but estimates of the prevalence in kids vary widely[6],[7] — from as low as 2% to as high as 66% — owing to differences in study design and how researchers define the condition.

However good they are at stemming severe disease, the vaccines do not seem to offer kids much protection from infection in the face of Omicron. One study from Qatar, posted online in July without peer review[8], found that administration of two doses of the Pfizer vaccine was 26% effective at preventing infection in children aged 5–11, and that protection waned to negligible levels three months after the second dose. Adolescents had higher levels of protection and it waned more slowly.

This is probably because adolescents received a larger vaccine dose, says study co-author Hiam Chemaitelly, an infectious- diseases epidemiologist at Weill Cornell Medicine–Qatar in Doha. Those aged 12 and above get 30 micrograms of vaccine, but younger children receive 10 micrograms, which “was no match for the immune evasion of Omicron”, Chemaitelly says. In each age bracket, the Qatar data showed that the vaccines were more effective at the younger end — at which kids get a higher dose relative to their size.

Children aged six months to four years are the most recent age group for which vaccines — mostly mRNA shots — have been approved. Data on how well the shots work in this group is scarce; the jabs were approved on the basis of small trials involving a few thousand children, which were designed primarily to assess safety and dosing. “A lot of the data for paediatric vaccines is inadequate and requires us to connect the dots,” says Peter Hotez, a paediatric vaccine scientist at Texas Children’s Hospital in Houston. Since June, when the United States approved vaccines for use in this age group, some 8% of kids under 5 there have had at least one dose, but weekly vaccination rates have been dropping.

The vaccines seem to work just as well at younger ages, according to preliminary data from trials. In August, Pfizer reported that three doses of its vaccine had an efficacy of 76% in preventing COVID-19 in kids aged 6 months to 2 years and 72% in those aged 2–4 years, at a time when the BA.2 Omicron variant was circulating (see ‘Vaccine versus Omicron’).

Sources: Pfizer/Ref. 3

About two dozen countries have approved two Chinese inactivated-virus vaccines — one produced by Beijing-based company Sinovac, and another produced by state-owned Sinopharm, also based in Beijing — for children as young as 6 months. Data from Argentina, Brazil and Chile show that administration of two doses of these vaccines in kids aged three and older is moderately effective at preventing COVID-19, but does a better job of protecting against hospitalization.

Information on the other half-dozen vaccines is even more scarce. For example, India has approved four vaccines for use in people aged five and older, two of which have been rolled out, both in adolescents. Post-trial data on their effectiveness in kids is lacking. “We don’t have effectiveness data on adults, so it is very unlikely that we will get it in kids,” says Gagandeep Kang, a virologist at the Christian Medical College in Vellore, India.

What is the uptake in children?

Where vaccines are available, take-up in children has varied widely. Close to 90% of those aged 3–17 are fully immunized in Chile, compared with 28% of kids aged 5–11 in New Zealand and 3% of the same age group in the Netherlands (see ‘Patchy uptake’).

Source: Airfinity

“It’s been terrible,” says Yvonne Maldonado, a paediatrician and infectious-disease specialist at Stanford University School of Medicine in California, and the younger the kids, the lower the rates. One reason for the slow uptake could be the delay in getting vaccines to kids. Many parents probably wondered why they needed to vaccinate their children who had already been infected and recovered, says Fiona Russell, a paediatrician and infectious-diseases epidemiologist at the University of Melbourne, Australia.

News that Omicron was milder than previous variants also quickly spread, and hospitals weren’t as overrun as with earlier variants, owing to mass adult vaccination and access to better treatments. In some countries, such as Israel, the vaccination campaign for those aged 5–11 launched at a time when “the disease was no longer considered such a threat by the public”, says Balicer.

But that perception is incorrect, says Hotez. Public health agencies have failed to communicate the risks of paediatric COVID-19, he says. “The case has not been adequately made about the significant number of deaths and hospitalizations among kids.” And anti-vaccine groups in the United States have been especially outspoken against childhood vaccinations, he adds.

Ximena Aguilera, a public-health researcher at the University of Development in Santiago, attributes Chile’s success to the extensive network of vaccination sites, including mobile clinics at schools and neighbourhoods. Belief in the benefits of vaccination remains stronger than rumours spread by anti-vaccine groups, she adds.

Should countries be rolling out vaccines for children?

Researchers agree that vaccinating children will protect them from severe disease and death. The majority of deaths during the pandemic have occurred in people 65 and older, but more than 16,000 children under the age of 20 have lost their lives to COVID-19, and that figure “may be several times higher”, says Hotez.

In the United States, COVID-19 has killed close to 1,500 people aged 18 and under. These are “avoidable deaths”, says Maldonado. “If we can prevent deaths in children, we should do that.” And vaccines don’t just help to avert deaths, she says, they also keep kids out of hospitals. Vaccines could also better prepare children for emerging variants and the unknown long-term risks of multiple infections, says Paul Licciardi, an immunologist at Murdoch Children’s Research Institute in Melbourne.

An 8-month-old girl receives a COVID-19 vaccine in Pennsylvania in June 2022.Credit: Hannah Beier/The New York Times/Redux/eyevine

For a parent deciding whether to vaccinate their child, the costs and benefits look a little different from those for a health authority determining whether to roll out a mass vaccination programme — or to allocate precious funding elsewhere. Some researchers question the need for national vaccination programmes for healthy kids.

For many countries, access to vaccines remains a major hurdle, but in those that have approved them for children, the patchwork of regulations reflects a lack of global consensus. Data from Airfinity show that about a dozen countries, including the United States, Canada and Israel, have cleared vaccination for children from their first year of life. The majority of countries that have approved childhood vaccinations are offering them to those aged two to six, and about two dozen to adolescents only (see ‘Approvals by age’). Some nations, such as Denmark, are not offering COVID-19 vaccines to healthy children.

Source: Airfinity

Once again, “Omicron changed the game”, says Shamez Ladhani, a paediatric infectious-diseases physician at St George’s, University of London. It meant the vaccines were less potent at preventing infection and onward spread, and any protection waned rapidly. They do offer protection against severe disease, but the risk is lower to begin with, especially in younger children, says Ladhani. For example, a US study of kids aged 5–11, conducted when Omicron predominated, estimated that vaccination reduced the risk of hospitalizations from 19 to 9 admissions per 100,000 infections[9]. “The numbers are so tiny that you lose precision,” says Ladhani.

The large number of kids who have already had COVID-19 also “changes the calculus as to the benefit of vaccines”, says Shabir Madhi, a vaccinologist at the University of the Witwatersrand in Johannesburg, South Africa, because these children have gained some immunity to the virus, and infections are expected to be less severe the second or third time around.

Some 59 million young people under the age of 20 are reported to have had COVID-19 at some point since the pandemic began. And many more infections have gone undetected, as studies that test for the presence of antibodies against SARS-CoV-2 — a marker of past infection — have shown. Data from South Africa[10] suggest that, by early 2022, 84% of children under 12 had been infected.

But vaccines can provide an advantage even to those who have been infected. Studies in adults have shown that those who were vaccinated and contracted COVID-19 were best protected, and studies in children support those findings. “Hybrid immunity is still the best immunity you can get,” says Ladhani. He and his colleagues found that adolescents in England who had been vaccinated and had been previously infected with Omicron had almost complete protection against a second Omicron infection, as described in a preprint posted on 22 August[11].

But Madhi says that it still isn’t clear what extra benefit against severe disease a healthy child who has previously been infected with SARS-CoV-2 would get from being vaccinated. And researchers agree that vaccinating kids has a small and short-lived effect on reducing community transmission.

Children with pre-existing conditions or compromised immune systems should still get the jab, according to a recommendation from the World Health Organization’s Strategic Advisory Group of Experts on Immunization (SAGE) in August. “We do not recommend, still, the wider use of the vaccine in younger groups, since these are not the priority,” said Alejandro Cravioto, SAGE chair, at a virtual press conference. Countries need to decide for themselves where their priorities lie, they said.

Reinforcing these sentiments is the fact that mass-vaccination programmes are expensive, and governments with limited resources have to choose which diseases to vaccinate their kids against. The Pan American Health Organization, headquartered in Washington DC, conducted an unpublished analysis for a hypothetical country of 50 million people, and estimated that vaccinating the entire population against COVID-19 would cost more than US$1 billion — 12 times the annual routine childhood vaccination budget of US$89 million. Vaccination against COVID-19 “can’t be coming at the cost of children dying of other diseases”, says Madhi.

In 2021, pandemic disruptions meant that some 25 million children missed out on routine vaccines, such as for measles, diphtheria, tetanus and whooping cough. Measles cases in Africa were fourfold higher in the first three months of 2022 compared with the same period in 2021, and other vaccine-preventable diseases are also on the rise, from polio to yellow fever.

But even a low risk of COVID-19 is still a risk. “Because this pandemic is really being driven by adults, it’s hard to remember that kids exist,” says Maldonado. But “we need to remember that all populations are affected, and that as much as possible, we build the infrastructure to support all age groups”.

Nature 610, 246-248 (2022)

doi: https://doi.org/10.1038/d41586-022-03203-8

References

1. Walter, E. B. N. Engl. J. Med. 386, 35–46 (2022).

2. Tan, S. H. X. et al. N. Engl. J. Med. 387, 525–532 (2022).

3. Price, A. M. et al. N. Engl. J. Med. 386, 1899–1909 (2022).

4. Sacco, C. et al. Lancet 400, 97–103 (2002).

5. Klein, N. P. et al. MMWR Morb. Mortal. Wkly Rep. 71, 352–358 (2022).

6. Molteni, E. et al. Lancet Child. Adolesc. Health 5, 708–718 (2021).

7. Berg, S. K. et al. Lancet Child. Adolesc. Health 6, 614–623 (2022).

8. Chemaitelly, H. et al. Preprint at medRxiv https://doi.org/10.1101/2022.07.26.22278045 (2022).

9. Shi, D. S. et al. MMWR Morb. Mortal. Wkly Rep. 71, 574–581 (2022).

10. Madhi, S. A. et al. Preprint at medRxiv https://doi.org/10.1101/2022.07.13.22277575 (2022).

11. Powell, A. A. et al. Preprint at medRxiv https://doi.org/10.1101/2022.08.19.22278987 (2022).

FDA authorizes COVID vaccines for the littlest kids: what the data say

Kids and COVID: why young immune systems are still on top

What COVID vaccines for young kids could mean for the pandemic

NEWS FEATURE 12 October 2022

What does the future look like for monkeypox?

With cases declining in the United States and Europe, Nature examines scenarios of how the outbreak might play out.

Sara Reardon

A public-health sign about monkeypox in Toronto, Canada. Credit: Steve Russell/Toronto Star/Getty

On 29 April, a person in Nigeria developed an unusual rash and then travelled to the United Kingdom —carrying monkeypox with them. Since then, the virus has reached more than 70,000 people in over 100 countries. That has surprised health-care specialists around the world, because the sustained spread doesn’t resemble the sporadic pattern of previous monkeypox outbreaks in people, caused by a virus that lives in animals in Africa.

Almost six months after the virus started to spread, however, vaccination efforts and behavioural changes seem to be containing the current strain — at least in the United States and Europe (see ‘Monkeypox cases’). But the situation could still play out in several ways, say researchers. At best, the outbreak might fizzle out over the next few months or years. At worst, the virus could become endemic outside Africa by reaching new animal reservoirs, making it nearly impossible to eradicate. “There are so many factors at play that are working in opposing directions,” says Jessica Justman, an infectious-disease physician at Columbia University in New York City.

Source: WHO

Specialists don’t expect that this year’s outbreak will cause the kind of worldwide disruption seen with COVID-19. The monkeypox virus doesn’t seem to be airborne like COVID-19, highly transmissible like smallpox or long-lasting in the body like HIV. It spreads mostly through sexual contact, and has been diagnosed mainly in men who have sex with men, particularly those with multiple sexual partners or who have anonymous sex. And although it causes severe, painful rashes, it is rarely fatal; the outbreak is a strain descended from the milder ‘clade 2’ monkeypox virus in West Africa. (A more deadly ‘clade 1’ virus is found in Central Africa.) It is not clear that the strain causing the current outbreak is any more intrinsically transmissible than its clade 2 ancestors; rather, a form of the virus could have reached a population whose behaviours led it to spread more rapidly, says Elliot Lefkowitz, a bioinformatician at the University of Alabama at Birmingham.

“I think we are in a good position to control this epidemic, but it will mostly rely on the behaviour of the population,” says Gerardo Chowell-Puente, an epidemiologist at Georgia State University in Atlanta.

With so much unknown about the latest monkeypox strain and so much contingent on how people respond, it is difficult to predict future trends. Still, researchers have developed scenarios to help plan for the different ways the outbreak might progress. Here are some of the big questions about monkeypox’s future.

What are the current trends?

In the United States and Europe, confirmed monkeypox infections have been declining since mid-August.

Public-health experts credit behavioural change: a study[1] by the US Centers for Disease Control and Prevention (CDC) that surveyed men who have sex with men found that around half had been curbing risky sexual activity because of concerns about the virus. Also probably helpful were public-health campaigns that told people what symptoms to look for and encouraged them to report cases, as well as vaccines that were offered to people at high risk (although the vaccines’ efficacy at reducing infection or spread is unclear).

Still, the monkeypox case numbers have serious caveats, Justman says. Many people — and their physicians — probably do not recognize the symptoms or might be afraid to report an infection because of the stigma. “I have no confidence that all the people who need to be tested are being tested,” she says.

And some countries in South America and Africa are seeing the opposite trend. “It’s too early to say we have defeated it,” says infectious-disease physician Dimie Ogoina at Niger Delta University in Wilberforce Island, Nigeria. In that country, where the current outbreak is likely to have started, cases continue to rise, with a weekly record of 56 reported in September. The Nigeria Centre for Disease Control in Abuja says the country has seen more than 400 cases. That is a small number compared with the United States’ 26,000 cases, but is almost certainly a vast undercount; it is likely that many more are being missed in Nigeria than in the United States, Ogoina says. Nigeria does not have a strong disease-surveillance programme, and Ogoina expects that many people who catch the virus might not recognize the symptoms or go to physicians. Reported case numbers will always be undercounts, especially in areas that don’t have good surveillance programmes, agrees Rosamund Lewis, technical lead for monkeypox at the World Health Organization (WHO) in Geneva, Switzerland.

The WHO hopes to acquire 60,000 test kits to ship to Africa, but it is a logistical challenge to get people tested and diagnosed. It is also unclear whether the apparent rise in African cases reflects a true outbreak there or is the result of more thorough testing, Lewis says. She suspects that both factors contribute.

What do models project?

Because so little is known about how monkeypox spreads and how different factors could affect that, modelling more than a few weeks in advance is unlikely to produce an accurate result. The WHO does not release long-term monkeypox forecasts. And although the CDC releases a monthly technical report on the outbreak with a set of potential scenarios, the agency says it has only moderate confidence in its predictions. Its latest report, on 29 September, said that US cases are most likely to plateau or fall over the next month, but it is also possible they could increase exponentially (see go.nature.com/3sxrbmv).

Chowell-Puente releases a weekly monkeypox forecast online, and follows trends in the United States and several countries in Europe. He uses several scenarios to produce an overall three-week forecast that has reflected real trends fairly accurately so far. As of 3 October, his forecast predicts that cases will either plateau or decline in the countries he models. Chowell-Puente says that although these trends could change if there is a new public-health policy, an alteration in the public’s behaviour or a mutation in the virus, they are unlikely to do so quickly. He hasn’t modelled cases in Africa, but says he might in the future.

Other models are more detailed, although not necessarily more accurate. One system, from researchers at RTI International (a non-profit global research institute headquartered in Research Triangle Park, North Carolina), used information from previous outbreaks of the monkeypox virus — specifically the clade 2 outbreaks — to project what could happen with the current strain. Previous notable clade 2 outbreaks include a short-lived one in the United States in 2003, which infected more than 70 people, and an outbreak in Nigeria that was spotted in 2017 and led to 146 suspected cases. From analyses of viral genomes, this seems to be the strain that led to the current situation.

Using information from those outbreaks — and assuming that the virus has not significantly mutated — Donal Bisanzio, an RTI epidemiologist who is based in Nottingham, UK, estimated viral transmissibility. He and his team modelled how the virus would spread over the course of weeks in a virtual high-income country of 50 million people. In as-yet-unpublished work, the model predicts that if the virtual country did nothing to combat the virus, it could expect around 6,000 cases (that is, infecting 0.01% of the population) before the outbreak fizzled out. This is largely because the virus is not very transmissible and because the model assumes (as is the case in countries such as the United States) that most older people have been vaccinated against smallpox, a related virus.

But the researchers found that the number of infections would drop significantly if infected people isolated themselves for three weeks (to wait out monkeypox’s possible incubation period), and if men who have sex with men curbed their sexual activity until the end of the outbreak. Vaccinating an infected person’s contacts could reduce infections slightly further.

Wouldn’t vaccination quash the outbreak?

Hundreds of thousands of doses have been distributed in Europe and the United States to help vaccinate people at high risk, such as men who have sex with men and people who have been in contact with someone exposed to the virus. Vaccinating everyone isn’t an option: the United States anticipates that fewer than two million doses of the most widely used vaccine — a repurposed smallpox jab — will be available this year.

But it is unclear how much this has contributed to the slowdown in case numbers. One preprint[2], for instance, has suggested that, in people who have never had a smallpox inoculation, the vaccine does not seem to greatly boost levels of antibodies that can neutralize the monkeypox virus, and which are one component of an immune response. By contrast, the CDC has released crude case-rate data from US jurisdictions suggesting that monkeypox incidence — among those people recommended to receive the vaccine — has been more than ten times higher among unvaccinated than vaccinated individuals (see go.nature.com/3yifurf). (These data, however, were not controlled for age, underlying conditions, behaviour or other differences between the two groups). Several clinical trials testing vaccine effectiveness are under way.

It is also unclear how long vaccine effects will last. In an unpublished paper, Ogoina reports finding one unvaccinated person who was reinfected just nine months after recovering from his initial infection, suggesting that immunity might wane more quickly than scientists had anticipated.

But specialists don’t expect that the vaccine will become useless. The smallpox vaccine was invented some 180 years before the disease was eradicated, but in that time, the variola virus that causes smallpox — a relative of the monkeypox virus — never evolved to resist the jab, Lefkowitz says. And although there are no currently approved treatments for monkeypox, a few antiviral drugs used for smallpox are currently being tested for their efficacy against monkeypox.

What could cause cases to increase?

The US and European trends are encouraging, but if people sense that the danger has passed, Justman says, an increase in risky behaviour might cause a resurgence of the virus. She is particularly concerned about the virus’s potential to spread on university campuses, where students live in close quarters and might play sports that involve extended physical contact. “I don’t think things will stand still,” she says. “They will evolve, and our guidance will evolve.”

Physician Dimie Ogoina works in Nigeria, where monkeypox cases are rising.Credit: KC Nwakalor/The New York Times/Redux/eyevine

In Nigeria, meanwhile, Ogoina is concerned that the virus could spread quickly among people who are HIV-positive — nearly two million people in Nigeria alone. Although evidence is scarce, records from the 2017 outbreak there suggest that monkeypox is much more deadly in many of these individuals, who often have weakened immune systems.

What if the virus mutates?

Unlike RNA viruses such as SARS-CoV-2 or HIV, the monkeypox virus’s genome is composed of DNA, which tends to accrue mutations more slowly than RNA because it is more stable. A June paper in Nature Medicine[3] surprised researchers when it reported that the virus that was spreading through Europe had picked up single-letter mutations much faster than previous poxviruses had. But these mutations seem to have had little effect; they are probably markers of where human antiviral enzymes have snipped at the virus in attempts to deactivate it, the researchers said. Scientists have also found areas of deletions or rearrangements in some monkeypox genomes; these are common in poxviruses and haven’t yet been linked to a change in function.

It is difficult to estimate the chances of the virus becoming more transmissible in the future, Lefkowitz and others say, although that can’t be ruled out. One analysis (not yet peer reviewed) of the mutations in monkeypox DNA caused by human enzyme activity has inferred that the current strain might have first reached humans in 2016, before the outbreak was identified in Nigeria in 2017 (see go.nature.com/3stezeu). But researchers don’t know whether the virus has been continuously transmitting between humans undetected since then, or whether it hopped back into animals for a few years before crossing back into humans more recently, perhaps aided by a particular mutation. “There’s no one particular mutation in the current virus that is a smoking gun,” Lefkowitz says, in terms of making people more ill or more likely to spread the infection.

“We don’t understand transmission any better than we understand the pathogen,” he adds. Still, he says, the chance of a worrisome mutation arising increases the longer the outbreak goes on.

What if the virus finds a new reservoir?

Researchers still don’t know what animal in Africa serves as the most important monkeypox reservoir, carrying the virus and spreading it to humans. Rodents are a likely candidate: the 2003 US outbreak happened when rodents imported from Ghana infected pet prairie dogs. But the virus has also been found in numerous other mammals, including monkeys and anteaters.

In August, researchers found a dog in France that had contracted the virus from its owners, although it is unclear whether the animal could transmit it back to humans. The US CDC updated its guidance to discourage people with monkeypox from interacting with animals. But Chowell-Puente thinks it’s unlikely that monkeypox will find a permanent home among animals outside Africa, because the virus requires time to adapt to a new species and transmit. The current strain seems to prefer humans.

What would it take to eliminate monkeypox altogether?

In areas where animal-to-human transmission occurs, it will be impossible to eliminate the virus completely without a vaccine for people and (eventually) animals. Yet despite the risk that the virus might spread out of Africa again, Ogoina says that African countries have not yet received any vaccines. That’s because wealthy nations have not yet donated any doses to countries that cannot afford them.

Even if vaccines do arrive, behavioural changes will be needed to curb monkeypox, especially given the unanswered questions about vaccine effectiveness, says Adesola Yinka-Ogunleye, a London-based epidemiologist at the Nigeria Centre for Disease Control. “If we do not control monkeypox in endemic areas, then no matter the efforts put into non-endemic countries, we know we’re not going to achieve control,” she says.

Nature 610, 250-252 (2022)

doi: https://doi.org/10.1038/d41586-022-03204-7

References

1. Delaney, K. P. et al. MMWR Morb. Mortal. Wkly Rep. 71, 1126–1130 (2022).

2. Zaeck, L. M. et al. Preprint at medRxiv https://doi.org/10.1101/2022.08.31.22279414 (2022).

3. Isidro, J. et al. Nature Med. 28, 1569–1572 (2022).

The monkeypox virus is mutating. Are scientists worried?

How deadly is monkeypox? What scientists know

Why scientists fear monkeypox spreading in wild animals

Monkeypox: how universities are preventing outbreaks on campus

Opinion

- Young people need experiences that boost their mental health

COMMENT 10 October 2022

Young people need experiences that boost their mental health

More policymakers and practitioners should encourage exploration and discovery during youth, to prevent adolescents from reaching crisis.

Andrew J. Fuligni &

Adriana Galván

Taking part in activities outside of school can provide a healthy way for adolescents to explore their world.Credit: Jeffrey Greenberg/Universal Images Group via Getty

As co-directors of the UCLA Center for the Developing Adolescent at the University of California, Los Angeles, we are often contacted by concerned community members and reporters searching for an explanation for troubling increases in mental-health problems among adolescents. (Our centre disseminates the science of adolescent development to policymakers and practitioners, to inform policies and programmes for those aged between 10 and 25.)

Over the past 15 years or so, the prevalence of depression and suicidality (which encompasses suicidal thoughts, plans or attempts) in people aged around 10–24 has risen — at least among those for whom data are available (see ‘Trends in adolescent mental health’). Invariably, those who contact us want to talk about the presumed culprit: the rise of social media.

Yet three decades of research, predominantly in neuroscience and developmental psychology, suggests that there might be a better way to help young people than focusing on single-cause explanations for their mental-health issues: ensuring that they are exposed to conditions and experiences that are known to help adolescents thrive.

More policymakers and practitioners — from parents and educators to psychologists and paediatricians — should be drawing on the body of research that shows how adolescence can be supported as a key developmental window of exploration and discovery. Specifically, this means providing young people with safe ways to try new things; giving them opportunities to contribute to other people’s lives; and helping them to foster healthy relationships with parents and other caring adults. It also means helping young people to get enough sleep to enable the discovery and learning that is so important at this time of life.

Trends in adolescent mental health

Depression and suicidality has risen among adolescents worldwide in recent decades.

The prevalence estimates for mental-health problems in young people represent less than 7% of adolescents globally. With this caveat in mind, according to the World Health Organization, one in seven of those aged 10–19 worldwide are currently experiencing a mental-health disorder, including anxiety and depression. And among those aged 15–19, suicide is the fourth leading cause of death globally.

In countries for which detailed data are available, the figures suggest that substantial proportions of adolescents experience distress. According to an April report by the US Centers for Disease Control and Prevention, for example, 44% of students in grades 9–12 (ages 14–18) felt persistently sad or hopeless during the previous 12 months (see go.nature.com/3srxtsp).

There is no clear single explanation for recent trends. A meta-analysis of 29 studies, covering nearly 81,000 children aged 18 or younger, indicates that the COVID-19 pandemic has had a negative impact on adolescent mental health globally^26. Yet multiple indicators — adolescents’ own reports of sadness and suicidality, the number of psychiatric visits to emergency departments and suicide rates — suggest that mental-health challenges were increasing in countries such as the United States well before the pandemic.

Social media is often blamed for this rise. Most experts acknowledge that using digital media in a way that interferes with sleep or in-person interactions and other healthy behaviours is not conducive to good mental health. However, most meta-analyses, cohort studies involving hundreds of participants, and other rigorous, well-designed studies suggest that associations between the use of digital media and mental health are relatively small and probably of little clinical significance^27.

Other potential single-cause explanations are similarly unconvincing. Adolescents, parents or other carers being more willing to discuss issues with each other and with health-care providers, for example, might contribute to increased reports of sadness and negative mood from adolescents. But this in itself is unlikely to be driving the rising rates of suicidality.

Support the age of discovery

In recent years, mental-health professionals, parents and educators have been demanding immediate action to address the mental-health needs of today’s youth.

In 2019, the World Health Organization called for investments in mental-health care around the world, and for special priority to be given to children and adolescents (see Supplementary information for further reading). In 2021, the American Academy of Pediatrics and other key US children’s health organizations declared a national emergency. In the same year, US surgeon general Vivek Murthy, the leading spokesperson in the federal government on matters of public health, called for a “whole-of-society effort” to address youth mental health.

Such calls are important for addressing the needs of those already in distress. But, in our view, much more could be done to prevent mental-health crises from developing in young people in the first place. All sorts of findings — admittedly from studies conducted largely in North America or Europe — suggest that adolescence is a time of opportunity, as well as a time of risk-taking.

Numerous studies indicate that brain development during adolescence supports a crucial period of learning and discovery that involves — appropriately — more risk-taking and greater sensitivity to certain external cues.

Brain imaging has shown, for instance, that limbic regions important for learning and motivation are more active in youth than in adults and younger children[1]. (The age of participants classed as youth or adolescents can differ between studies.) Neuroscientists have also linked greater connectivity between limbic regions and prefrontal neural networks in youth to enhanced performance in various cognitive skills, including decision-making and working memory[2].

Experiments involving psychological tasks have shown that adolescents are more tolerant of uncertainty than are adults. In a 2012 study, for example, adolescents were more willing than adults to keep playing a game in which the likelihood of winning or losing money was uncertain[3]. They are more likely than younger or older groups to actively explore new solutions in learning tasks. And they outperform adults both in updating their previous knowledge when they make errors or encounter new environments[4], and in decision-making tasks that require them to estimate uncertainty and change in the environment[5].

In the context of these findings, adolescents’ greater willingness to take risks than other age groups can be understood as an adaptive, essential part of exploring the world around them. Research in rodents shows that the developing brain has more of the molecular machinery — dopamine and dopamine receptors — needed to perceive new experiences as rewarding than do adult animals[6]. Likewise, adolescents’ greater reactivity to stress[7] — as shown by experimental studies and observational work involving them reporting their day-to-day experiences — reflects the fact that they are in a particularly sensitive, responsive stage in their development.

More policymakers, educators, paediatricians, psychiatrists, parents, schools and other institutions should be using this pivotal window in development to influence the mental health and life trajectories of young people. Three experiences in particular seem to be key during this period.

A young volunteer teaches children in a makeshift school at a camp near Mogadishu, Somalia, for people displaced by famine.Credit: Mohamed Abdiwahab/AFP via Getty

First, youth need healthy ways to channel their motivation to explore their world. These could be school-based extracurricular activities, special-interest clubs and sports or community-based activities, such as volunteering in social-service agencies. But to have the greatest impact, programmes must be designed thoughtfully. A 2014 meta-analysis[8] showed, for example, that community service positively affected all sorts of measures in young people aged 12–20. These ranged from participants’ thoughts about themselves to their level of motivation in school. But this happened only if participants were also given an opportunity to process their experiences, such as through keeping a journal or in group discussions.

Second, young people need opportunities to contribute to the lives of others. Both survey work and experiments in developmental psychology have shown that adolescents become increasingly attuned to their position and role in the world as they age. This might manifest as a greater concern about their status among peers, or as an increased awareness of how factors such as ethnicity and economic background shape their standing in society. They also increasingly explore different ways to play a part in society through their jobs, families and activities.

And various studies indicate that, whether young people are helping their families with chores or through financial contributions, providing emotional support to friends or volunteering in their communities, they seem to be primed to contribute in many ways[9]–[11].

Experimental interventions and surveys have also shown that opportunities to contribute to others’ lives — either informally or through volunteering, youth leadership programmes and community engagement — can have multiple effects on adolescent well-being.

In a 2013 clinical trial, adolescents who spent 2 months volunteering with children aged 5–11 had lower levels of the pro-inflammatory cytokine interleukin-6 and cholesterol, and were less likely to be overweight[12] compared with a control group. (Both body weight and biological markers of inflammation have been linked to depression and other mental-health problems.) In correlational work in behavioural psychology, contributing to others has been linked to adolescents having a greater sense of meaning and purpose — which can, in turn, promote better mental health, especially for youth from marginalized groups[13].

Third, adolescents need healthy relationships with parents and other adults: these are essential for young people’s mental health and well-being.

Data from questionnaires, for instance, show that adolescents who have secure and supportive relationships with their parents or other carers have lower levels of depression and a stronger sense of identity than do those with insecure relationships[14]. Caring, affectionate and validating parenting behaviours — collectively known as positive parenting — have also been linked to the maturation of certain brain regions that are associated with the regulation of emotions, such as the amygdala[15].

Many studies have shown that interventions to improve relationships in families, introduced by public-health and psychology researchers over the past three decades, can reduce the use of substances and improve mental health in youth[16]. (Interventions include the use of educational tools to increase parental or carer involvement in adolescents’ daily lives, guidance on how to improve communication between adolescents and their carers, and linking carers to external support services.)

Other studies, largely from behavioural psychology and education research, have shown that relationships with caring adults outside the family home can also be important in shaping the lives of young people.

Sports and other extracurricular activities can help to introduce youth to adult mentors. And various studies examining the importance of role models suggest that formal mentoring programmes, such as those involving a young adult in the community spending time with an adolescent, can positively affect the mental health of youth[17]. Mentoring seems to be particularly important for adolescents with unstable home environments, such as those who experience homelessness or are in the foster-care system.

Prioritize healthy sleep

What could be crucial to the effectiveness of these experiences, however, is ensuring that adolescents get enough sleep.

Sufficient sleep is likely to be pivotal to enabling the exploration and discovery that is so important at this time of life. Both correlational and experimental studies (mainly in adults) have shown that regular and sufficient amounts of sleep enhance many types of learning. Research has shown, for example, that sleep-deprived people are more likely to have lapses in attention, deficits in working memory, decreased memory encoding and compromised reinforcement learning[18] than are control groups. In someone who is sleep-deprived, the amygdala — which is involved in emotional reactivity to a stressor — is more easily activated[19] and the brain is less capable of accurately coding incremental increases in the value of a reward[20].

What’s more, epidemiological studies in US school students aged 14–18 have shown two trends in recent years: declines in mental health (see go.nature.com/3e8apts) and reductions in the amount of sleep[21] — although it is hard to show a causal link between these historical changes (see ‘Paying for late nights?’). Various sociocultural factors could be contributing to this drop-off in sleep. These include the use of digital media before bed, pressures around schoolwork and early-morning or late-evening extracurricular activities.

Sources: Ref. 21/US CDC YRBSS

Also, studies indicate that mental health during adolescence is particularly sensitive to sleep. There is a consistent link between sleep problems and most of the psychiatric disorders that are evident during this period, including attention deficit hyperactivity disorder, anxiety and depression[22].

Delaying school start times, reducing homework and introducing family-based interventions that promote healthy sleep habits (such as quiet times before bed or the removal of phones at night) have shown promise. When the Seattle School District in Washington state delayed high-school start times by nearly one hour in 2016–17, for instance, students’ sleep increased by about 34 minutes each night and grades improved by 4.5%[23]. Other studies have shown similar effects of later school start times[24].

Understand today’s youth

It is not only policymakers and practitioners who should give more attention to the experiences and conditions that help adolescents to thrive — although our call is directed mainly towards them.

Scientists could dig deeper into what kinds of intervention are most effective for supporting adolescents — ideally using best principles and practices for involving young people as partners in discovery, rather than just as participants. Researchers should also try to improve their understanding of the unique challenges facing adolescents today – as well as the diversity of concerns that emerge in countries and settings around the world.

Much of the existing research on youth development and mental health is based on adolescents from high-income, predominantly Western nations. Moreover, researchers often study only those countries’ majority populations. To determine which specific mental-health needs could be supported worldwide, more neuroscientists, psychologists and psychiatrists need to study adolescents in the global south and in minority groups, and attend to the local circumstances of each country and community.

Some concerns for contemporary youth cross national borders. In such cases, international collaborations could help to improve collective understanding.

For instance, a 2021 literature review searched studies published since 2016 for terms such as climate anxiety and climate mental health. The review was conducted by the See Change Institute, a California-based non-profit organization that studies the role of human behaviour in social and environmental change, in collaboration with others. According to its results, climate change is an existential concern for many US adolescents and young adults, who will have to contend with the increasingly concerning environmental, social and economic effects of a warming world in their lifetimes (see go.nature.com/3ejph7t).

Similar findings have emerged from global surveys. In a 2021 study of 10,000 youth aged 16–25 in 10 countries, 84% of respondents were at least moderately worried about climate change; 59% were very or extremely worried[25].

Today’s youth are the future leaders, innovators and citizens who will confront profoundly challenging issues such as climate change and rising social inequalities. It is thus not enough to help them to manage their mental health once they are in crisis. Enabling adolescents to discover their place in the world begins with adults having a better appreciation of young people’s unique strengths, and supporting their ability to thrive.

Nature 610, 253-256 (2022)

doi: https://doi.org/10.1038/d41586-022-03172-y

References

1. DePasque, S. & Galván, A. Neurobiol. Learning Memory 143, 1–7 (2017).

2. van den Bos, W., Cohen, M. X., Kahnt, T. & Crone, E. A. Cereb. Cortex 22, 1247–1255 (2012).

3. Tymula, A. et al. Proc. Natl Acad. Sci. USA 109, 17135–17140 (2012).

4. Hauser, T. U., Iannaccone, R., Walitza, S., Brandeis, D. & Brem, S. NeuroImage 104, 347–354 (2015).

5. Eckstein, M. K., Master, S. L., Dahl, R. E., Wilbrecht, L. & Collins, A. G. E. Dev. Cogn. Neurosci. 55, 101106 (2022).

6. Laviola, G., Pascucci, T. & Pieretti, S. Pharmacol. Biochem. Behav. 68, 115–124 (2001).

7. Eiland, L. & Romeo, R. D. Neuroscience 249, 162–171 (2013).

8. van Goethem, A., van Hoof, A., Orobio de Castro, B., van Aken, M. & Hart, D. Child Dev. 85, 2114–2130 (2014).

9. Fuligni, A. J. Perspect. Psychol. Sci. 14, 331–343 (2019).

10. Damon, W. The Path to Purpose: Helping Our Children Find Their Calling in Life (Free Press, 2009).

11. Karan, M. et al. Develop. Cogn. Neurosci. 56, 101128 (2022).

12. Schreier, H. M. C., Schonert-Reichl, K. A. & Chen, E. JAMA Pediatr. 167, 327–332 (2013).

13. Sumner, R., Burrow, A. L. & Hill, P. L. Am. Psychol. 73, 740–752 (2018).

14. Bond, L., Toumbourou, J. W., Thomas, L., Catalano, R. F. & Patton, G. Prev. Sci. 6, 73–88 (2005).

15. Whittle, S. et al. Develop. Cogn. Neurosci. 8, 7–17 (2014).

16. Kim, H. K. & Leve, L. D. J. Consult. Clin. Psychol. 79, 740–750 (2011).

17. McQuillin, S. D., Hagler, M. A., Werntz, A. & Rhodes, J. E. Am. J. Community Psychol. 69, 201–220 (2022).

18. Krause, A. J. et al. Nature Rev. Neurosci. 18, 404–418 (2017).

19. Yoo, S.-S., Gujar, N., Hu, P., Jolesz, F. A. & Walker, M. P. Curr. Biol. 17, R877–R878 (2007).

20. Venkatraman, V., Chuah, Y. M. L., Huettel, S. A. & Chee, M. W. L. Sleep 30, 603–609 (2007).

21. Twenge, J. M., Krizan, Z. & Hisler, G. Sleep Med. 39, 47–53 (2017).

22. Tarokh, L., Saletin, J. M. & Carskadon, M. A. Neurosci. Biobehav. Rev. 70, 182–188 (2016).

23. Dunster, G. P. et al. Sci. Adv. 4, eaau6200 (2018).

24. Goldin, A. P., Sigman, M., Braier, G., Golombek, D. A. & Leone, M. J. Nature Hum. Behav. 4, 387–396 (2020).

25. Hickman, C. et al. Lancet Planet. Health 5, e863–e873 (2021).

26. Racine, N. et al. JAMA Pediatr. 175, 1142–1150 (2021).

27. Odgers, C. L. & Jensen, M. R. J. Child Psychol. Psychiatr. 61, 336–348 (2020).

Supplementary Information

1. Further reading list

Competing Interests

The authors declare no competing interests.

Collection: Coming of age: the emerging science of adolescence

Young people’s mental health is finally getting the attention it needs

Tackling the mental-health crisis in young people

Work

- Yay to stipend rise, say UK postgrads — but don’t stop there

- Mental anguish and mistreatment are rampant in marine science

- Corralling photons to solve problems in seconds

CAREER NEWS 27 September 2022

Yay to stipend rise, say UK postgrads — but don’t stop there

PhD students call for further reforms after country’s largest research funder responds to campaigns with 10% increase.

Natasha Gilbert

The stipend increase comes on top of a previously announced rise of 2.9%, which many postgrads had seen as a real-terms cut, given rising inflation and living costs.Credit: Getty

UK graduate-student researchers and at least one university association are welcoming an increase in PhD stipends for this academic year from UK Research and Innovation (UKRI), the nation’s largest public research funder. But the researchers are also calling for further reforms, such as better leave policies for those who are ill or are new parents, and for long-term improvements in salaries and working rights.

On 2 September, UKRI said that it would increase the minimum PhD stipend of £15,609 (US$18,700) by an additional 10% for the 2022–23 academic year, on top of the 2.9% rise previously announced. This increase brings the total to £17,668. The boost comes after widespread complaints from postgraduate researchers that the original stipend rise was effectively a funding cut, in view of rising inflation and living costs across the nation.

The increase, which takes effect on 1 October, will give PhD students around £2,000 more in this academic year. A UKRI spokesperson says that the agency will provide universities with the funds to finance the stipend increase, rather than expecting them to come up with it themselves. In a statement, the UKRI said that it decided to increase the stipend after considering complaints from PhD students and the research community, and reviewing policies supporting UK postgraduate research. “The response from the community made it clear that the work on stipends had to be prioritized,” said Melanie Welham, the agency’s executive champion for people, culture and talent, in the statement.

The stipend review is part of a UKRI initiative known as the New Deal for Postgraduate Research, which aims to improve conditions for postgraduate researchers and make the United Kingdom more attractive to a diverse range of PhD applicants.

Welham said in the statement that UKRI would continue to review its funding for PhD students, and will examine how the agency determines stipend-funding levels.

Ansh Bhatnagar, who campaigned for the stipend boost, says that it will make a significant difference to UK postgraduate researchers. Bhatnagar, who is about to start the second year of a PhD in theoretical physics at Durham University, co-drafted an open letter to the UKRI with more than 300 UK postgraduate researchers, seeking a funding boost. The letter has more than 14,800 signatories. He says that some postgraduate researchers considered dropping out of their programme because they could not afford to live on the previous stipend.

A continuing struggle

Yet some postgraduate researchers say that the additional 10% increase is not enough. The current annual rate of inflation in the United Kingdom exceeds 9% and the annual growth in the Retail Price Index (RPI), a measure of the cost of retail goods, is approaching 12%.

Emma Francis, a fourth-year PhD student in psychiatric research at University College London, and Hannah Franklin, a fourth-year PhD student at University College London and the Francis Crick Institute in London who uses stem cells to investigate motor neuron disease, had sought a stipend boost for around 400 fellow students. “They’ve increased the stipend, which is great,” says Franklin. “Is it enough? No.” She says that PhD students continue to struggle, and that cost-of-living increases, including in energy outlays, render the increase insufficient.

UKRI told Nature that it has no further comment.

In a statement, the Russell Group, which represents 24 research-intensive universities in the United Kingdom, praised the funder for the stipend boost. But it said that the UK government and other funders should help students in need by offering hardship funding.

Warwick University in Coventry, as well as Durham and Leicester, are among institutions to have announced that they will offer students who receive university-funded stipends the increased minimum payment. Franklin and Francis are calling on all universities to follow suit.

Bhatnagar says, however, that universities must implement reforms that go beyond just higher stipends, if they are to attract and retain PhD students. He says that he plans to continue campaigning for these changes, which include better arrangements for sick pay and parental leave.

The UKRI stipend rise, Bhatnagar says, “must be a first step in a broader package of support that postgraduate researchers need”.

Nature 610, 409 (2022)

doi: https://doi.org/10.1038/d41586-022-03069-w

Collection: Young scientists

PhD students face cash crisis with wages that don’t cover living costs

CAREER NEWS 23 September 2022

Mental anguish and mistreatment are rampant in marine science

Survey finds that students and postdocs report verbal and sexual abuse, unpaid labour and dangerous work conditions.

Virginia Gewin

Early-career researchers in marine science face grim conditions in their field.Credit: Getty

Unpaid labour, mental-health disorders, discrimination and abuse are rampant concerns in marine science, according to a survey of students and early-career researchers in the field[1].

Of the 492 respondents to the survey, conducted globally between 1 April and 5 May 2020, nearly three-quarters said they had experienced mockery, discrimination and/or abuse in their place of work or education. Nearly one-fifth of respondents reported sexual abuse, and more than one-third reported verbal abuse.

Many participants, particularly those who identified as female or non-binary, reported experiencing anxiety, depression or burnout. More than 60% of respondents felt that their mental health had worsened because of their work. And more than one-third reported working in unpaid positions under risky conditions, including situations in which they had to use malfunctioning or unsanitary equipment, or had insufficient protection from chemicals. The survey did not provide more information on working conditions.

While attending the 2019 World Marine Mammal Science Conference in Barcelona, Spain, the study’s lead author — Anna Osiecka, a marine researcher at the University of Gdańsk in Poland — says she had heard an unusually high number of anecdotes about unpaid work and mistreatment from graduate students. To explore the extent of these experiences, she and her colleagues launched the online survey, aimed at early-career researchers from undergraduate students to postdoctoral researchers in ocean and marine science.

Limited diversity

Most of the survey-takers were between the ages of 22 and 35, and 82% were women. Fewer than half of the respondents held a paid job in ocean science or conservation, yet 49% had a graduate degree in the field. Most were white and from the global north — a possible artefact of the survey methods, which included outreach on social media and professional mailing lists, says Osiecka. She notes that the high number of white respondents could be the reason that she and her co-authors could not tease out the influence of ethnicity on respondents’ experiences as they had hoped to.

Still, the lack of responses from people of colour highlights the lack of diversity that has long plagued ocean science. In the United States, for example, approximately 35% of the population identifies as Black, Latinx and/or Indigenous, yet Latinx students earn less than 10% of Earth, atmospheric and ocean sciences graduate degrees, and Black students earn less than 3%, according to 2019 data from the US National Center for Science and Engineering Statistics.

Casandra Newkirk, a marine biologist at the University of North Carolina at Charlotte, says the survey results resonate with her as the only Black postdoc in her department. “I’ve had situations where I felt minimized,” says Newkirk, who was not aware of the survey. “Which is why I don’t even really like to speak up anymore,” she says, adding that she doesn’t want the stress that comes with academia. “I don’t want to be a faculty member,” she says.

Harassment and poor working conditions are prevalent

Fewer than one-quarter of survey participants had told superiors about some of the abuse that they had experienced — ranging from discriminatory exclusion from work to performing tasks that put their health, life or safety at risk — and just under 3% said they had reported all of it. These respondents said they feared losing future career or work opportunities in their desired field if they reported the abuse. Osiecka says leaders in marine science and ocean conservation must create mechanisms to improve safety by, for example, crafting a code of conduct so that harassers can be removed from workplaces and professional meetings, or developing a way for junior researchers to safely and anonymously report abuse.

Respondents painted a picture of widespread unpaid labour. Only about 60% of all of the participants’ reported work time was paid; the rest was either unpaid or was compensated by food and board. Survey results also indicated that respondents spent, on average, more than US$6,000 to cover the costs of work-related travel, visas or insurance. And just over half received professional references for their unpaid work.

Marine science has long struggled to improve racial justice and gender equity, and to create a welcoming environment for people from all socio-economic strata, says Eddie Love, programme manager and chair of the diversity, equity, inclusion and justice initiative at the Ocean Foundation, an international non-profit conservation organization based in Washington DC. But that struggle is beginning to pay off, he says, in part because junior researchers are challenging leaders across the discipline to be held accountable for increasing the diversity of employees, addressing harassment and establishing a healthy workplace culture. “There’s been progress, but there is still a lot of work to do,” he says.

To make the discipline more inclusive, says Osiecka, its culture must be changed. For example, she says, researchers in ocean and marine sciences who are familiar with yachts, know how to sail or can afford expensive diving certificates are more competitive candidates for available jobs because of their privileged circumstances. Furthermore, she notes, those applicants are usually the ones who can afford to take unpaid internships. “We’re expected to have a lot of unpaid experience to even deserve to be paid in the first place, which is completely ridiculous,” she says. “People should be paid for any work that they do.”

Intertwined with the issue of pay for work is the problem of huge workloads and a steep learning curve for junior scientists, which inevitably leads to burnout, says Mibu Fischer. A Brisbane-based marine ethnoecologist at the Commonwealth Scientific and Industrial Research Organisation, the Australian government’s research agency, Fischer says a lack of workplace diversity — particularly amid the country’s push to reconcile relations between Indigenous and non-Indigenous Australians — leaves her feeling overworked and isolated.

Fischer, an Aboriginal woman in Queensland, works to create avenues to integrate Indigenous knowledge with academic science. She is concerned that efforts to stamp out unpaid labour in the field leave limited funding for completing projects. That, she says, leads to small teams who are tasked with a great deal of work — and often limits important participation from Indigenous groups.

Osiecka hopes that the survey results help to demonstrate the scale of workplace abuse and discrimination across the discipline. “It’s not just single people being ridiculous, overreacting or being overly sensitive,” she says. “There is a massive issue with how we’re all being treated.”

Nature 610, 410 (2022)

doi: https://doi.org/10.1038/d41586-022-03031-w

References

1. Osiecka, A. N., Wróbel, A., Hendricks, I.-W. & Osiecka-Brzeska, K. Front. Mar. Sci. 9, 835692 (2022).

Collection: Diversity and scientific careers

WHERE I WORK 10 October 2022

Corralling photons to solve problems in seconds

Quantum physicist Chao-Yang Lu develops superfast computers that rely on the curious collisions of single particles of light.

James Mitchell Crow

Chao-Yang Lu is a quantum physicist at the University of Science and Technology of China in Hefei, China. Credit: Dave Tacon for Nature

In my laboratory, we develop quantum computers based on single photons, the fundamental particles of light. In 2020, our computer was the first worldwide to demonstrate ‘quantum advantage’: it completed a calculation in 200 seconds that would take a conventional supercomputer more than 2 billion years.

Today’s computers and mobile phones perform calculations using a binary code of 1s and 0s. Their silicon transistors can be only ever in either the 1 or the 0 state: on or off. But if we use fundamental particles such as photons to perform calculations, quantum effects come into play. In the quantum world, where a wave-like photon can be in two places at once, you can have 1 and 0 simultaneously. Quantum computers can take advantage of this ‘superposition’ to solve certain problems exponentially faster than classical computation can.

Here, I am looking through the control electronics part of our quantum computer. The control electronics ‘phase lock’ our photons so that they arrive in the computer together, with 15-nanometre precision. The machine performs calculations on the basis of the photons’ interactions.

When I accepted a professorship at the University of Science and Technology of China, my first quantum machine could control only six single photons. By 2020, my team had a machine that could control up to 76, and demonstrate quantum advantage. We are now up to 130 photons.

Quantum advantage used to be called quantum ‘supremacy’. It is very good that the new terminology has been adopted.

The problem that our computer solved to show quantum advantage is very abstract, a mathematical proof. My next steps are to scale the computer to control more photons — maybe 200 in the near term — and to reconfigure it for real-world applications, such as accelerating drug development by accurately predicting the interactions between candidate drugs and their targets.

Nature 610, 412 (2022)

doi: https://doi.org/10.1038/d41586-022-03205-6

Research

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- Structure of the Ebola virus polymerase complex

- Structural basis for directional chitin biosynthesis

Article Open Access Published: 12 October 2022

Radiation-driven acceleration in the expanding WR140 dust shell

Yinuo Han,

Peter G. Tuthill,

Ryan M. Lau &

…

Anthony Soulain

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Nature volume 610, pages 269–272 (2022)

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Abstract

The Wolf–Rayet (WR) binary system WR140 is a close (0.9–16.7 mas; ref. [1]) binary star consisting of an O5 primary and WC7 companion[2] and is known as the archetype of episodic dust-producing WRs. Dust in WR binaries is known to form in a confined stream originating from the collision of the two stellar winds, with orbital motion of the binary sculpting the large-scale dust structure into arcs as dust is swept radially outwards. It is understood that sensitive conditions required for dust production in WR140 are only met around periastron when the two stars are sufficiently close2,3,4. Here we present multiepoch imagery of the circumstellar dust shell of WR140. We constructed geometric models that closely trace the expansion of the intricately structured dust plume, showing that complex effects induced by orbital modulation may result in a ‘Goldilocks zone’ for dust production. We find that the expansion of the dust plume cannot be reproduced under the assumption of a simple uniform-speed outflow, finding instead the dust to be accelerating. This constitutes a direct kinematic record of dust motion under acceleration by radiation pressure and further highlights the complexity of the physical conditions in colliding-wind binaries.

Main

The WR140 was observed on six occasions between 2001 and 2017 with the near-infrared cameras NIRC and NIRC2 at the Keck Observatory. The near-infrared imagery, displayed in Fig. 1, spans orbital phases between ϕ = 0.043 and 0.592, clearly showing an expanding dust shell with an evolving apparent morphology. Although the images span two orbital cycles, features appear to be consistent, implying a high degree of cycle-to-cycle replication of the same underlying morphology. Prominent structures at earlier orbital phases (≤0.111) include an eastern and western dust arc. As the dust shell expands, these structures give rise to an ‘eastern arm’ and a prominent ‘southern bar’ at later orbital phases, following the nomenclature of structures identified in previous imaging[4].

Fig. 1: Near-infrared imagery of WR140’s expanding circumstellar dust structure.

The observing bands (top left), instrument (top right) and orbital phase ϕ (bottom right) are labelled in each panel. Coronagraphic (cor.) and aperture masking (mask) images are labelled in parentheses after the observing bands. All other images were observed with the full aperture. The scale bar in each panel indicates 0.3 arcsec (corresponding to 501 AU at a distance of 1.67 kpc). The images are stretched linearly by the following amounts to accentuate the dust structures that are otherwise faint relative to the bright stellar core: ϕ = 0.077 Kp-band image stretched between 0 and 0.8 relative to the brightest pixel; ϕ = 0.111 Kp-band image between 0 and 0.5; ϕ = 0.111 H-band image between 0 and 0.8; ϕ = 0.592 Lp-band image between 0 and 1.5 × 10−3; ϕ = 0.592 Ms-band image between 1 × 10−4 and 5 × 10−3. All other images are not stretched. An observing log is presented in Extended Data Table 1.

The well-resolved detailed form of the plume motivates the construction of a geometric model to explain the structural variation and expansion over time. We modelled the geometry of the dust plume as a linearly expanding spiral based on the ‘pinwheel mechanism’5,6. We assumed that dust production turns on and off episodically as the binary approaches, and departs the region near periastron. The wind-collision region can be approximated as the surface of a cone at large distances from the stars where the velocity of the compressed wind has reached its asymptotic value[7]. Dust assumed to form on this conical interface between the WR star and the O-star primary subsequently expands radially outwards as the binary continues in its orbit. Originally modelled on WR104 (ref. [5]), such pinwheel models have been shown to accurately reproduce dust structures in several Wolf–Rayet (WR) binaries including Apep[8] and WR112 (ref. [9]).

The orbital parameters of WR140 are well constrained1,10 and form the basis for generation of the geometric dust plume model. By fitting to the location and geometry of dust structures across the multiple epochs of observations, our model suggests that the half-opening angle of the conical shock front is θw = 40 ± 5°. This value is in close agreement with that estimated by Fahed et al.[11] (42 ± 3°), which is derived by fitting a cone model[12] to the 5696A C iii emission line, and Williams et al.[13] (34 ± 1°) by modelling the 10830 He i subpeak. The θw value estimated in this study implies a momentum ratio between the O and WR star of \(\eta =0.04{3}_{-0.015}^{+0.021}\) assuming radiative postshock conditions7,14, which is a larger value than that expected from the mass-loss rate and wind speed of the stars (Extended Data Table 2). Upon fitting to the turn-on and turn-off values independently, we find that dust production occurs over a period of \({0.7}_{-0.1}^{+0.3}\) yr centred at the periastron passage, with key parameters of the dust production thresholds displayed in Extended Data Table 3.

An image simulated under this geometric model at ϕ = 0.592 is displayed in Fig. 2a. When comparing with the corresponding observations (Fig. 1), this model successfully reproduces a number of prominent features, with very accurate registration between the structural edges in the model and the data. The eastern arm represents a segment of the ellipse that corresponds to the earliest dust produced in the dust production episode (when dust production turned on), whereas the southern bar corresponds to the most recently produced dust in the episode (just before dust production turned off).

Fig. 2: Geometric model of WR140’s circumstellar dust structure.

a–d, Model images at ϕ = 0.592 simulated under four different variants of the geometric model. The four models assume that the dust production rate is uniform (a), orbitally modulated (b), azimuthally asymmetric (c) and both orbitally modulated and azimuthally asymmetric (d). An animation showing the evolution of the model in time is available in Supplementary Video 1. e–h, The outline of the ‘orbital + azimuthal’ model overlaid on the observations at three different epochs, in which e–g show models at orbital phases of 0.592, 0.111 and 0.043 adjusted for dust acceleration, whereas h shows a model at an orbital phase of 0.043 that is not adjusted for acceleration, resulting in a clear misfit to the data. The scale bar in each panel indicates 0.3 arcsec.

However, not all predicted features are apparent in the data. Although the geometric model is primarily designed to reproduce structural features, its physical interpretation implies a pinwheel system producing isothermal, optically thin dust at a constant rate. The clear absence of structures predicted by the geometric model, most noticeably dust features to the north and west (an outer-western arc) and below the southern bar, cannot be explained by simple density variations due to orbital velocity, and deeper astrophysical insight into the plume generative model is required.

We found that two modifications to the geometric model enable it to account for the lack of the north-western outer dust arcs, both of which have physical motivation in the mechanics of the system. First, as dust nucleation and condensation sensitively depend on the physical conditions mediated by stellar winds[3], a change in the shock structure will occur over the orbit, resulting in a dust production rate that varies continuously within the dust-producing segment of the orbit. We found that when the dust production rate is smoothly reduced to a local minimum at periastron in the model, we obtain a significantly improved fit that removes the western dust arc from the image, in accordance with observation.

Second, asymmetries introduced at the wind base can be amplified into asymmetric large-scale structures. Williams et al.[4] found that an increased dust density at the trailing edge of the shock cone relative to the orbital direction produced a better fit than a uniform model. This asymmetry could be introduced by the fast orbital motion near periastron, which results in an intrinsic ‘headwind’ against the instantaneous direction of orbit. By allowing dust to preferentially form on the trailing edge of the conical shock front and smoothly decrease in density towards the leading edge, we again obtained an improved fit with the relative brightness of the northern dust structures reduced. A schematic diagram illustrating the two effects is displayed in Fig. 3a.

Fig. 3: A model for dust production and acceleration in WR140.

a, The ‘orbital’ and ‘azimuthal’ directions referred to in this paper relative to the orbit of a binary consisting of a WR star and an O or B star. A dust production rate that varies in the both the orbital and azimuthal directions is found to reproduce the observed structures. b, The acceleration regimes near the binary. No dust is produced interior to the dust nucleation radius, where material flows at a constant rate. Upon nucleation, dust forms an optically thick shell accelerated at a uniform rate before the acceleration decreases as the dust becomes optically thin. c, The best-fit acceleration (dashed) and resulting dust expansion speed (solid) and the corresponding 1σ interval of models drawn from the posterior distribution fitted to the data. The theoretically expected acceleration via radiation pressure in the optically thin regime assuming a dust-to-gas mass ratio of 0.02 is overplotted (dotted).

Figure 2b,c displays model images under the two modifications to the original model (see also Extended Data Fig. 1). We find that a very close fit to the data is achieved when both the orbital and azimuthal modulation of dust production are combined (Fig. 2d–f). Overlaying the outline of the model on the corresponding ϕ = 0.592 Lp-band observation shows that the geometry predicted by the model excellently reproduces the dust structures and their location (Fig. 2e).

Previous interpretations of dusty WR binaries have focused on identifying the upper limit on the binary separation that enables sufficiently high densities in the colliding winds to form dust. As gas compression is derived from wind–wind collision, it is natural to expect an upper distance threshold above which a sufficiently high density cannot be reached to facilitate dust nucleation. However, our model appears to suggest that there may also exist a lower limit, implying that dust is only formed within a Goldilocks zone where just the right conditions of density and temperature are met.

Observationally, several other WR binaries with infrared-imaged dust plumes, such as WR112 (ref. [9]), WR98a15,16 and WR104 (refs. 5,6), appear to be continuous dust producers that do not show apparent signs of such a threshold effect. The Apep system appears to oscillate in and out of a single threshold, with dust production occurring near periastron[8]. If dust production in WR140 indeed reaches two maxima over a single orbit as suggested by our model, it would be the first WR binary known to exhibit the full range of this Goldilocks effect.

Previous work by Usov[3] has shown that the degree of WR wind compression and cooling is sensitively dependent on the wind velocity, and so it is plausible that, at very short distances, the WR wind is significantly slowed by its binary companion via radiative braking, thereby reducing the rate of dust production. Lau et al.[17] proposed that this mechanism may be responsible for impeding dust formation in the Gamma Velorum system. Alternatively, near periastron, the wind of the O star may not have accelerated to its terminal velocity before reaching the shock front.

With all other parameters fixed, the final parameter of interest in the model is the dust expansion speed. Dust grains are expected to form in a population of postshock gas where the two stellar winds of the binary collide. This dust may then be accelerated by stellar radiation pressure until reaching the terminal dust drift velocity. The assumption of a constant expansion speed similar to the stellar wind speed has been shown to accurately reproduce the expanding dust shell in similar systems such as WR112 (ref. [9]).

However, the multiepoch observations of WR140 suggest that a uniformly expanding dust shell cannot simultaneously reproduce the time-evolving spatial extent of the dust shell observed in all epochs. Fitting to the ϕ = 0.592 epoch, which shows the best-resolved dust structures, the model suggests a dust expansion speed of 2,400 ± 100 km s−1, which is broadly consistent with a streaming velocity of 2,170 ± 100 km s−1 along the shock cone determined by Fahed et al.[11]. However, extending this model to earlier epochs shows a clear misfit to the location of the dust shell. Figure 2h shows the outline of such a uniform expansion model overlaid on the earliest epoch of imagery at ϕ = 0.043. The dust shell predicted by uniform expansion fails to fit, being significantly larger than that observed, implying that the average expansion speed up to ϕ = 0.043 must be lower than in subsequent epochs.

Dropping the assumption of uniform expansion, the locations of the dust structures were fitted independently to each epoch. The resulting expansion speeds yielded a clear accelerating trend, with most of the impulse imparted onto the dust by the first two epochs. Given the uncertainties associated with fitting the location of dust features, direct derivation of the magnitude of acceleration as a function of distance from the star was not possible. To constrain the basic physics, we therefore posited a simple model based on expectations for radiatively accelerated dust as illustrated in Fig. 3b. Dust is not expected to form very close to the star owing to high temperatures and harsh ultraviolet radiation, and so the postshock gas is assumed to originate at a constant drift velocity, v_0. At a distance of rnuc, dust nucleates and condenses to form an optically thick sheet, which experiences maximal radiation pressure with a constant acceleration, \({a}_{\max }\). The dust continues to expand and eventually becomes optically thin at rt, at which stage the acceleration decreases as 1/r^2.

Under these working assumptions, we find that dust grains form at a speed of \({v}_{0}=1,\,81{0}_{-170}^{+140}\) km s−1, which is then accelerated in the optically thick regime by \({a}_{\max }=90{0}_{-400}^{+700}\) km s−1 yr−1 up to \({r}_{{\rm{t}}}=22{0}_{-80}^{+150}\) AU before becoming optically thin. The dust nucleation radius is fitted to be rnuc = 50 ± 30 AU, although this value is not well constrained.

Figure 3c shows the acceleration and velocity as a function of distance resulting from the best-fit model. Radiation pressure has long been suggested to play a major role in accelerating material near WR stars[18]. We find that the best-fit acceleration in the optically thin regime may be reproduced by radiation-driven acceleration expected from the grain and stellar properties of the system (Methods). It is therefore plausible, if not expected, for radiation pressure to drive dust expansion at the acceleration derived from the observations.

The acceleration of matter by radiation pressure is ubiquitous throughout astrophysics, and is central to a range of astrophysical theatres ranging from the clearing of primordial material in protoplanetary disks around young stars[19] to the formation of planetary nebulae towards the end of stellar evolution[20]. These multiepoch observations of WR140 constitute the first direct detection of dust structures undergoing acceleration in the circumstellar radiation field: more commonly proper motions recorded are under the influence of gravity. Acceleration due to interactions other than gravity, however, are rarely witnessed because typically radiative acceleration zones are very short and terminal velocity rapidly attained. However, as we have discovered, WR binaries produce postshock material that reaches a significant distance from the photosphere before dust condensation, providing a rare laboratory in which the acceleration zone may be probed. This motivates further high angular resolution observations of such dust structures close to their nucleation point to test theories of radiatively driven acceleration physics in the circumstellar environment.

Methods

Image reduction

Imagery from Keck’s NIRC[21] were taken using sparse-aperture interferometry techniques as described in Tuthill et al.[22], with an observing log describing specific configurations given in Extended Data Table 1. After data reduction of sequences of short-exposure data cubes, interleaved with observations of a point-source reference star, our codes deliver calibrated standard interferometric observables (visibilities and closure phases). Image recovery was performed with the BSMEM algorithm[23] at a pixel scale of 10 mas pixel−1. The BSMEM algorithm reconstructs a model-independent image iteratively, taking into account both the difference between the model and data (χ^2) to assess the goodness of fit, and the entropy function (H) to minimize information and avoid overfitting as the normalized χ^2 converges towards unity. The balance between the two terms during the iterative minimization is set by the regularization weight (α), which is adaptively determined by the algorithm. The value of the regularization weight is chosen such that it maximizes the Bayesian evidence (that is, the probability of obtaining the data given the regularization weight) given a Jeffreys prior[24].

Data analysis for the NIRC2 instrument, also operational at the Keck observatory, proceeded by centring and stacking data frames common to the same epoch, observing band and aperture configuration (full pupil or coronagraph) and interpolating outlying pixels in the stacked image. For the ϕ = 0.592 images that showed the faintest dust structure, we subtracted the sky background determined using the dithered data frames.

Geometric model

Original model

Our model assumes that dust is produced on a thin conical shock front, after which it expands radially away from the binary, generating a spiral form as a consequence of rotation of the nose of the shock with the orientation of the orbiting binary. Noting WR140’s well-constrained orbital parameters, we adopted values from Monnier et al.[1] presented in Extended Data Table 2, which were derived using a combination of knowledge from radial velocity[11] and optical interferometry. The model is available at github.com/yinuohan/WR140 (ref. [8]).

Parameters additional to the orbit that are required to define the plume model are the half-opening angle of the conical wind-shock interface (θw), the terminal dust velocity and the range of orbital phases over which dust production occurs. We constrained these parameters by fitting the geometry generated by the model to the observed dust structures across the multiepoch images. The images generated by the geometric model assume isothermal dust. This is a reasonable assumption given that the temperature gradient in the orbital direction is expected to be small; however, a more exact model of the emission properties would require further radiative transfer simulations. As the primary objective is to reproduce the geometric structure of the dust plume, the features of interest are the shape and location of dust structures. These properties are difficult to programmatically extract from the observations, thereby hindering the construction of a metric and subsequently using an automated sampling algorithm to explore the parameter space, as found by Han et al.[8] in the case of the Apep system. We therefore fitted the structures manually with an understanding of the physical effects of parameters and visual inspection of the model overlaid on the observations.

Orbital modulation

Even though the abrupt turn-on and turn-off are able to closely reproduce corresponding features such as the southern bar and the southern segment of the eastern arm, the orbital modulation of dust production rate once dust production begins appears to require a gradual variation, as suggested by the gradual dimming of the eastern arm towards the north. We modelled the variation of the dust production rate over the orbit as the superposition of two distinct peaks, each of which is modelled as a half-Gaussian function varying over the true anomaly. The turning on and off of dust production corresponds to the peaks of the two half-Gaussians, with dust production gradually suppressed near periastron as shown in Extended Data Fig. 2. We find that a reasonable fit is obtained when the half-Gaussians peak at the same turn-on and turn-off orbital phase as previously fitted, each with a standard deviation of \({40}_{-10}^{+\mathrm{30^\circ }}\) in true anomaly. This results in an interval between half powers of \(0.1{5}_{-0.09}^{+0.05}\) yr along the orbit.

Azimuthal asymmetry

We assumed that the density of dust being produced at the conical shock front varies over the azimuthal angle about the WR–OB axis as a Gaussian function (with periodic boundary condition). Fixing the peak dust density at the trailing side of the shock cone (where the shock cone intersects the orbital plane), the model was best fitted when the dust density distribution exhibited a standard deviation of 80 ± 20° in the azimuthal angle measured from the trailing edge. This results in a density contrast of \(1{3}_{-8}^{+77}\) between the trailing and leading sides of the dust plume.

Expansion speed fitting

The image of the spiral dust plume is a projection of its true three-dimensional structure, and so measuring the true displacement in three-dimensional space relies on the geometric model.

Different dust features in the same observation are produced at different points in time. The effect of this time offset is particularly important towards earlier orbital phases. Although the construction of the geometric model assumes uniform expansion across the entire dust plume, it is still possible to measure the displacement of specific features within an observation by fitting only to the location of that feature. We therefore measured the displacement of the eastern arm and southern bar independently by fitting to their location separately for each epoch of observation, thereby dropping the assumption ofuniform expansion speed. Extended Data Fig. 3 displays a series of model outlines overplotted on the corresponding data for models with and without accounting for acceleration. A misfit between the model-predicted location of features and their observed location in the absence of acceleration, which is particularly prominent at earlier epochs, implies that the inclusion of acceleration is required to explain their motion.

The geometric model predicts the range of orbital phases over which dust is produced, and so it is possible to predict the time when the two dust features were produced for each epoch. Here we assumed that the eastern arm and southern bar are produced at the very beginning and end of a dust production episode, respectively. Subtracting this time point from the time of observation gives the total time since a given feature was produced, giving two sets of displacements as a function of time, one for the eastern arm and one for the southern bar. We then combined the two sets of measurements so that they share the same time axis and used the combined data and a point at the origin for subsequent fitting.

We fitted the model for acceleration driven by radiation pressure described in the main text using a Markov chain Monte Carlo, solving for the differential equations

$$a(r)=\frac{{{\rm{d}}}^{2}r}{{\rm{d}}{t}^{2}}=\left\{\begin{array}{ll}0 & {\rm{if}}\,0\le r < {r}_{{\rm{nuc}}}\\ {a}_{\max } & {\rm{if}}\,{r}_{{\rm{nuc}}}\le r < {r}_{{\rm{t}}}\\ \frac{{a}_{\max }{r}_{{\rm{t}}}^{2}}{{r}^{2}} & {\rm{if}}\,{r}_{{\rm{t}}}\le r\end{array}\right.$$

(1)

at each iteration to obtain r(t), which is then compared with the data. The reported fitted results are derived from the median of the marginalized posterior distributions, with uncertainties estimated using the 16th and 84th percentiles. Extended Data Fig. 4 compares the measured location of dust with the best-fit model to these measurements with and without accounting for acceleration. The systematic offset in residuals for the model, assuming that the radial distance of dust is proportional to time since production, suggests that uniform expansion is not a suitable model.

We note that acceleration fitting relies on knowledge of the time of production of the dust features being fitted to, which in turn is derived from the orbital phase of the earliest and most recently produced dust, which carries uncertainties. Even though the displacement–time measurements at most orbital phases appear to be approximately linear, and even if we were to drop the assumption that displacement is strictly proportional to time in a model without acceleration, a best-fit straight line fitted through these points does not intercept the origin, implying the presence of acceleration at least at very early stages of the expansion. Such an offset from the origin arises from the combined measurements of both the eastern arm and southern bar, and any systematic errors resulting in such an offset across both sets of measurements is unlikely.

Acceleration from radiation pressure

To estimate the expected effect of radiation pressure, we assumed stellar and grain properties adopted by the dust grain model by Lau et al. (manuscript in preparation), which includes a combined stellar luminosity of Lbin = 1.43 × 10^6L⊙, a grain size of ag = 0.04 μm, a grain density of ρg = 1.6 g cm−3 (ref. [25]) and a radiation-pressure efficiency of Qpr = 1 as appropriate for hot stellar sources given the relatively large grain size[26]. We calculated the radiation force experienced by an individual dust grain as

$${F}_{{\rm{rad}}}={Q}_{{\rm{pr}}}{\sigma }_{{\rm{g}}}\frac{{L}_{{\rm{bin}}}}{4{\rm{\pi }}{r}^{2}c}$$

(2)

where σg is the cross-sectional area of a dust grain, r is the distance of the dust grain from the star and c is the speed of light. Assuming that any gas is comoving with the dust as it accelerates, we found that a dust-to-gas ratio of 0.019 reproduces the best-fit acceleration model derived from the observations.

Lau et al.[17] estimated a mean dust-to-gas ratio of approximately 4 × 10−5 in the system by comparing the total dust mass derived from observations with the stellar mass-loss rate; however, the larger dust-to-gas ratio found in this study is not surprising given that dust production exhibits significant spatial and temporal variation. As the geometric model suggests, dust is only produced over a fraction of the orbit, and further orbital modulation implies that the dust features (the eastern arm; the southern bar) probed in this study correspond to only a very small fraction of the orbit. Furthermore, dust production occurs only in a confined stream. The two effects together imply that dust in the system is expected to be highly concentrated in the two structures probed in this study, which may account for the more than two orders of magnitude increase in dust density in these structures relative to the mean density, assuming a uniform dust distribution across the system.

Additional contributions to the acceleration could theoretically include further wind collision downstream, which is not modelled here. However, the two stellar winds rapidly become parallel farther away from the apex of the shock front and so this effect is not expected to be a major contribution given the geometry.

The interval of constant acceleration (\({a}_{\max }\)) models a scenario in which the dust is initially optically thick, harvesting all photon momentum available over the area that it spans. The extent to which this assumption may hold true motivates an important point of observational follow-up, such as by monitoring variations in the spectral energy distribution of newly formed dust over time.

Instrumental and optical depth effects

Critical constraints on the accelerating motion of the dust plume are provided by data at early orbital phases, which were observed with aperture masking interferometry. It is therefore important to ensure that the plate scale of images recovered with this technique is not adversely affected by systematic biases, for example due to different observing bandpasses. Aperture masking observations of reference binary stars of known configuration spanning a diverse range of filters and bandwidths at each epoch (Alp UMi with CH_4 and Beta Del with KCont in June 2001; HD 2150123 with H, K in July 2001; Zeta Aqr with H, K, Beta Del with KCont and HD 2150123 with H, K in July 2002) all recovered separations within errors of anticipated locations, which suggest that bandwidth effects do not adversely affect proper motion tracking as performed in this study.

Furthermore, the same apparatus and pipelines have also been applied as part of a relatively well-tested and mature experiment that has been benchmarked, delivering numerous multiepoch astrophysical studies of similar resolved structures in other contexts. Notable science targets whose structures were registered and tracked over these observing epochs include IRC+10216 (ref. [27]) and LkHa 101 (refs. 28,29). In particular, IRC+10216 had outflow motions tracked by tagging structures in recovered images over many epochs with both broad and narrow filters, with the resulting motions consistent with a physically plausible, monotonically expanding flow, and no biases introduced with bandwidth or other settings[27]. In the WR140 dataset presented in this study, the close agreement between features taken with different filters at the same epoch also argue against plate scale biases being a significant effect.

It is also possible that optical depth effects can alter the apparent spatial extent of structures, as inner dust could potentially shadow outer dust at earlier orbital phases if the dust is optically thick, making the structure appear smaller. However, this is unlikely to alter the observed spatial extent by an amount that could explain the apparent acceleration. If shadowing were to be a prominent effect, the thickness of the dust plume required would result in a significantly different structure at earlier orbital phases that resemble a much smaller half-opening angle (approximately 10° smaller than the best-fit value), and at later orbital phases this would produce thick and fuzzy structures that deviate from the fine structures observed. These observations therefore imply that flux gradients and/or shadowing within a thick dust plume are not large enough to bias our kinematic analysis of WR140.

Data availability

The NIRC and NIRC2 data underlying this article are publicly available on the Keck Observatory Archive (koa.ipac.caltech.edu/cgi-bin/KOA/nph-KOAlogin) under programme IDs U59N, U2N, U45N, U14N2, U044 and Z273. Source data are provided with this paper.

Code availability

The dust plume model used in this study is available at github.com/yinuohan/WR140.

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Acknowledgements

Y.H., P.G.T. and A.S. acknowledge the traditional owners of the land, the Gadigal people of the Eora Nation, on which the University of Sydney is built and this work was carried out. Y.H. acknowledges funding from a Gates Cambridge Scholarship enabled by Grant No. OPP1144 from the Bill and Melinda Gates Foundation. P.G.T. acknowledges the longstanding support for innovative imaging at the W.M. Keck observatory, and development of the techniques particularly from J. Monnier. The data presented herein were obtained at the W. M. Keck Observatory, which is operated as a scientific partnership among the California Institute of Technology, the University of California and the National Aeronautics and Space Administration. The Observatory was made possible by the financial support of the W. M. Keck Foundation. The authors recognize and acknowledge the significant cultural role and reverence that the summit of Maunakea has always had within the indigenous Hawaiian community. This research made use of NASA’s Astrophysics Data System; the emcee package[30]; NUMPY[31]; MATPLOTLIB[32]; and Astropy, a community-developed core Python package for Astronomy[33].

Author information

Authors and Affiliations

1. Institute of Astronomy, University of Cambridge, Cambridge, UK

Yinuo Han

2. Sydney Institute for Astronomy, School of Physics, The University of Sydney, Sydney, New South Wales, Australia

Yinuo Han, Peter G. Tuthill & Anthony Soulain

3. NSF’s NOIR Lab, Tucson, AZ, USA

Ryan M. Lau

4. Institute of Space & Astronautical Science, Japan Aerospace Exploration Agency, Sagamihara, Japan

Ryan M. Lau

5. Université Grenoble Alpes, CNRS, IPAG, Grenoble, France

Anthony Soulain

Contributions

P.G.T. prepared and performed several sets of observations and reduced NIRC data. Y.H. and P.G.T. devised and fitted the geometric model. Y.H. reduced NIRC2 data, fitted the plume motion and, with input from P.G.T., R.M.L. and A.S., wrote the manuscript. R.M.L. analysed dust properties and interpreted the interaction between dust and stellar winds. A.S. analysed possible optical thickness variations for different grain sizes. All authors contributed to analysing the data and synthesizing interpretations of the physics.

Corresponding author

Correspondence to Yinuo Han.

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The authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Face-on view of the geometric model for WR140.

The four panels correspond to model images at ϕ = 0.592 simulated under the assumption that the dust production rate is uniform (original), orbitally modulated (orbital), azimuthally asymmetric (azimuthal) and both orbitally modulated and azimuthally asymmetric (orbital + azimuthal), respectively.

Extended Data Fig. 2 Orbital and azimuthal variation of dust production rate in the geometric model.

(a) Dust production rate as a function of orbital phase near periastron. Dust is not produced at all other orbital phases. (b) Dust production rate as a function of azimuthal angle (about the WR–O star axis) relative to the trailing edge. The 1σ uncertainty regions in both plots are shaded.

Extended Data Fig. 3 Comparison between the model-predicted and observed location of the Eastern Arm for models with and without acceleration.

All observations in which the Eastern Arm is visible are reproduced twice, one of which is overplotted with the outline of the model with acceleration and fitted to the location of the Eastern Arm (left panel of each pair) and the other without acceleration (right panel of each pair). The model without acceleration is fitted to the final epoch of observations. Lack of acceleration results in a misfit at earlier epochs.

Extended Data Fig. 4 Comparison between observed and model-predicted location of dust features.

(a) The data points correspond to the location of the Eastern Arm and Southern Bar obtained by fitting the geometric model to these features at epochs where the features are visible. The error bars correspond to 1σ uncertainties, which were estimated in an Markov chain Monte Carlo fit under the model with acceleration assuming independently and identically distributed Gaussian errors. The solid lines show 100 models randomly drawn from the posterior distribution. The dotted line shows the best-fit model assuming instead that the radial distance of dust features is proportional to time (that is, no acceleration). (b) residuals of the models with and without acceleration. The model without acceleration resulted in a systematic bias in the residuals.

Source data

Extended Data Table 1 A log of NIRC and NIRC2 observations of WR140 presented in this study

Extended Data Table 2 Properties of the WR140 system

Extended Data Table 3 Dust production thresholds in the original model

Supplementary information

Peer Review File

Geometric model animation

This animation shows the geometric model of WR140 evolving in time. The offset value refers to the time since periastron passage. The expansion speed of the dust plume is fixed at 2,400 km s−1. The model takes into account variations in the dust production rate along the orbital and azimuthal directions.

Source data

Source Data Extended Data Fig. 4

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Han, Y., Tuthill, P.G., Lau, R.M. et al. Radiation-driven acceleration in the expanding WR140 dust shell. Nature 610, 269–272 (2022). https://doi.org/10.1038/s41586-022-05155-5

- Received: 15 December 2021

- Accepted: 27 July 2022

- Published: 12 October 2022

- Issue Date: 13 October 2022

- DOI: https://doi.org/10.1038/s41586-022-05155-5

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Nested dust shells around the Wolf–Rayet binary WR 140 observed with JWST

- Ryan M. Lau

- Matthew J. Hankins

- Ryodai Yamaguchi

Nature Astronomy Article 12 Oct 2022

Article Open Access Published: 12 October 2022

Personalizing exoskeleton assistance while walking in the real world

Patrick Slade,

Mykel J. Kochenderfer,

Scott L. Delp &

…

Steven H. Collins

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Nature volume 610, pages 277–282 (2022)

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Abstract

Personalized exoskeleton assistance provides users with the largest improvements in walking speed[1] and energy economy2,3,4 but requires lengthy tests under unnatural laboratory conditions. Here we show that exoskeleton optimization can be performed rapidly and under real-world conditions. We designed a portable ankle exoskeleton based on insights from tests with a versatile laboratory testbed. We developed a data-driven method for optimizing exoskeleton assistance outdoors using wearable sensors and found that it was equally effective as laboratory methods, but identified optimal parameters four times faster. We performed real-world optimization using data collected during many short bouts of walking at varying speeds. Assistance optimized during one hour of naturalistic walking in a public setting increased self-selected speed by 9 ± 4% and reduced the energy used to travel a given distance by 17 ± 5% compared with normal shoes. This assistance reduced metabolic energy consumption by 23 ± 8% when participants walked on a treadmill at a standard speed of 1.5 m s−1. Human movements encode information that can be used to personalize assistive devices and enhance performance.

Main

Exoskeletons that assist leg movement show promise for enhancing personal mobility but have yet to provide real-world benefits. Millions of people have mobility impairments that make walking slower[5] and more fatiguing[6], while millions more people have occupations that require strenuous locomotion[7]. In research laboratories, exoskeletons can increase walking speed1,8,9 and reduce the energy required to walk2,3,4,10,11,12,13,14,15,16, but these benefits have not yet translated to real-world conditions[17]. Providing beneficial assistance in the real world is difficult for several reasons: the specialized equipment used to personalize assistance is not available outside the laboratory; unlike walking on a treadmill, everyday walking occurs in many bouts of varying speed and duration; and devices must be self-contained and easy to use. In this study, we addressed each of these challenges to demonstrate effective exoskeleton assistance under naturalistic conditions.

Maximizing the benefits of exoskeleton assistance requires personalization to individual needs, which is challenging outside of a laboratory. The largest improvements in human walking performance have been achieved by individualizing assistance using human-in-the-loop optimization1,2,3,4, a process in which device control is systematically tuned to improve human performance while a person uses a device. Measuring important aspects of performance, including metabolic rate[16], has required expensive laboratory equipment and long periods of steady treadmill walking[18]. Individualizing consumer or medical devices in this way would require several long visits to a specialized clinic, which would be costly and impractical. If human performance could instead be estimated quickly, using low-cost wearable sensors, optimization could be performed as people moved naturally through their daily lives. This might be possible using musculoskeletal modelling[19], but such simulations are computationally intensive[20] and require individualization. Data-driven models may be able to capture important features of human performance more simply21,22,23,24,25.

We developed a data-driven model that relates human motion during exoskeleton-assisted walking to metabolic energy consumption and can be used outside the laboratory. Human movement arises from the interaction between the inertia of our body segments and forces from the environment and our muscles. We hypothesized that careful analysis could extract meaningful information about muscular energy expenditure from subtle changes in motion. In a previous experiment[4], participants walked with exoskeleton assistance in about 3,600 different conditions while data were recorded from both laboratory equipment that measure biomechanical outcomes and low-cost, portable sensors on the exoskeleton. We trained a logistic regression model using this previous dataset (Extended Data Fig. 1). The data-driven classification model compared sensor data from two different patterns of exoskeleton assistance, each defined by a ‘control law’, and classified which control law provided a larger benefit. The model inputs were ankle angle and ankle velocity, segmented by gait cycle, and the torque parameters for each control law. The model then estimated the likelihood that the first control law resulted in lower metabolic energy expenditure. In essence, the classifier favoured later, larger exoskeleton torques and smooth, well timed movements that led to increased ankle extension at toe-off. During optimization, the user experienced a set of control laws, the data-driven model compared all possible pairs of control laws, the control laws were ranked, and an optimization algorithm[26] updated the estimate of the optimal parameters and generated a new set of control laws to evaluate (Fig. 1). This process was repeated until convergence criteria were met.

Fig. 1: Data-driven exoskeleton optimization.

We used data from laboratory tests to train a model that can perform optimization in real-time outside the laboratory. a, During optimization, the participant walks with the exoskeleton and experiences a sequence of k control laws, each defining a pattern of exoskeleton torque. The optimizer’s goal is to identify the torque pattern that maximizes performance. b, Ankle angle (θ) and ankle velocity (\(\dot{\theta }\)) for each stride are recorded from sensors on the exoskeleton. c, All possible pairs of control laws are then compared (C). For each pair, differences in segmented motion data (Δ) are calculated by subtraction. d, Differences in motion are multiplied with classifier model weights (W), using a dot product operation, to obtain the pair coefficient (wij). e, A logistic function uses the pair coefficient to compute the probability (pij) that the first control law is more beneficial than the second. f, The score (S) for each control law (n) is computed by summing the probabilities of all pairs that include that control law. g, Control laws are then ranked by score and used to update an optimizer. h, The optimizer selects a set of k new control laws, consisting of d parameters, to evaluate. This optimization process is repeated until convergence criteria are satisfied, in this case a set number of evaluations having been completed. During real-world experiments, optimization was performed on the exoskeleton’s microcontroller.

Data-driven optimization can use the information embedded in our movements to identify exoskeleton assistance patterns that are as effective as those found with laboratory-based methods, but in one-quarter of the time. We conducted experiments to optimize assistance with a tethered exoskeleton emulator (Fig. 2a). The data-driven optimization evaluated eight sets of control laws in 32 min, four times faster than the state-of-the-art approach using indirect respirometry to measure metabolic rate[2] (Fig. 2b). Data-driven and metabolic optimization approaches identified the same participant-specific adjustments to assistance (Fig. 2c). Data-driven Optimized assistance and Metabolic Optimized assistance resulted in similar metabolic cost, which was significantly lower than the metabolic cost of walking with the exoskeleton in a Zero Torque mode (Fig. 2d). The average of the Data-driven Optimized parameters matched those of Generic assistance, which were taken from the best previous study[4], but Data-driven Optimized assistance provided a larger benefit. This demonstrates the importance of individualization; even subtle changes in torque can lead to substantial performance enhancements. To test the generality of the data-driven model, we conducted experiments at a range of additional speeds and inclines with a subset of participants. The Data-driven Optimized assistance and Metabolic Optimized assistance resulted in similar torque profiles and metabolic cost reductions across these conditions (Fig. 2e). This shows that the data-driven classification model captured a fundamental relationship between exoskeleton torque, ankle movement and whole-body walking effort. The model approximates this biological relationship, precluding statistical guarantees of optimality. Nevertheless, our results demonstrate that human movement encodes information related to underlying physiological processes, and that data-driven methods can extract this information without laboratory equipment or complex multi-scale models.

Fig. 2: Data-driven optimization results.

a, Exoskeleton assistance was applied using a tethered ankle exoskeleton emulator[43]. b, Assistance parameters optimized using the data-driven method converged to within 5% of the parameters identified using metabolic optimization, but in one-quarter of the time (n = 9). The error bars represent the standard deviation. c, Individual participants had unique Data-driven Optimized parameters, centred around the Generic assistance parameters. d, Data-driven Optimized assistance and Metabolic Optimized assistance resulted in similar metabolic costs of walking, significantly lower than with Zero Torque, Normal Shoes or Generic assistance, when walking at 1.25 m s−1 (ANOVA, n = 9, *P ≤ 2.7 × 10−8, **P ≤ 2.4 × 10−5, ***P ≤ 0.047). The boxes extend from the lower to upper quartile values of the data, with a line at the median and a dot at the mean. The whiskers extend between the minimum and maximum of the data values. e, Optimized torque patterns varied with walking condition, with similar changes in Data-driven Optimized and Metabolic Optimized parameters. Data-driven Optimized assistance and Metabolic Optimized assistance led to similar reductions in metabolic rate when walking at 0.75 m s−1 (slow), 1.25 m s−1 (normal) and 1.75 m s−1 (fast), and on a 10° incline at 1.25 m s−1.

Source data

We developed a speed-adaptive controller to adjust assistance based on natural variations in walking speed. People vary their walking speed widely during the day[27] in response to changes in context[28] and constraints[29]. Variations in speed complicate exoskeleton control and may help explain why assistive devices that reduce walking effort during steady walking on a treadmill have not provided similar benefits during use under more natural conditions[17]. The speed-adaptive controller we developed (Extended Data Fig. 2) interpolated between assistance parameter values previously optimized at different walking speeds (Fig. 3a) based on the estimated speed of each step (Fig. 3b). We tested Speed-adaptive assistance on a subset of participants as they walked on a treadmill with sinusoidally varying speeds. Speed-adaptive assistance reduced the energetic cost of walking more than Generic assistance with constant parameters (Fig. 3c). Adjusting exoskeleton assistance based on speed is an effective strategy for handling speed variations that occur during normal walking.

Fig. 3: Speed-adaptive control.

a, The Speed-adaptive controller interpolated between previously optimized assistance parameters to estimate the optimal parameters for each step based on walking speed on the previous step. Optimized values are normalized to the allowable range for each parameter. b, Ground truth and estimated walking speed for a representative participant. Speed was estimated on each step using a model that took stride period as an input (Extended Data Fig. 2) with a root-mean-square error (RMSE) of 0.06 m s−1. The shaded region represents the mean ± one standard deviation. c, When participants (n = 3) walked on a treadmill that varied speed sinusoidally between 0.75 m s−1 and 1.75 m s−1, Speed-adaptive assistance reduced the metabolic cost of walking more than the fixed Generic assistance.

Source data

We created an untethered exoskeleton for real-world assistance using a design approach based on emulation and optimization. Wearable robotic devices are typically designed using models or intuition, built as specialized prototypes, and then tested. However, humans are highly complex and diverse, making it difficult to predict the range of characteristics that will be optimal across a population. As a result, most devices designed this way are unable to provide optimal assistance and often provide no benefit at all. To develop the untethered exoskeleton used in this study, we first performed experiments with versatile exoskeleton emulators[30]. These laboratory-based, tethered hardware systems allowed us to perform a wide range of control and optimization experiments (Figs. 1–3) and identify the electromechanical characteristics that our untethered device would need. Using these design guidelines, we built a specialized, untethered device that provides predictable, meaningful benefits. This emulation and optimization design paradigm can reduce the cost and time required to develop new wearable robots.

On the basis of the results of our emulator experiments, we designed a specialized, untethered ankle exoskeleton. The system consisted of an exoskeleton worn on each ankle and a battery pack at the waist (Fig. 4a and Supplementary Video 1). The exoskeleton was designed to apply the range of optimal torque profiles identified in the tethered optimization study (Fig. 2) while having low mass (1.2 kg per ankle). A brushless motor and custom drum transmission applied torque about the ankle joint, while portable electronics sensed the user’s motion and performed real-time control and optimization (Fig. 4b and Extended Data Fig. 3). The exoskeleton provided a peak torque of 54 Nm (Fig. 4c), which was about 50% to 75% of the biological ankle torque of participants in this study[31]. Torque was controlled using a mixture of classical feedback control and iterative learning[32], with a tracking error of less than 1% of the peak torque. Maximum assistance could be applied continually without overheating the motor (Fig. 4d). The battery weighed 0.3 kg and powered the exoskeleton for at least 30 min on a single charge. While the energy cost of carrying mass near a distal joint is high[33], locating motors and electronics near the assisted joint results in more efficient power transmission, a simpler design and lower total weight, which can yield large net benefits.

Fig. 4: Untethered ankle exoskeleton.

a, A participant walking in a community setting wearing the exoskeleton. b, The exoskeleton consists of (1) a battery pack worn on the waist, (2) a motor, drum and rope transmission to produce assistive torques, (3) electronics to receive sensor data, command the motor and perform optimization, (4) a carbon fibre and aluminium frame to transmit forces, and (5) a shoe and (6) a calf strap to transfer forces to the body. c, The motor can apply a peak torque of 54 Nm when walking at 1.5 m s−1, sufficient to match the optimized assistance parameters identified in emulator experiments. Torques were tracked accurately; the shaded region represents the mean ± one standard deviation. d, The motor temperature during 30 min of walking with maximum assistance remained well below the 75 °C thermal limit. An exponential fit indicated a steady-state temperature of 35.4 °C.

Source data

We used the information encoded in a single walking step to optimize exoskeleton assistance while people walked naturally in short bouts of varying speed. People take thousands of steps per day, but real-world walking occurs in many separate bouts, most of which are short, with 90% being less than 100 steps in duration[34]. Speed is relatively consistent within each bout, but varies across bouts[27]. This fragmentation presents a challenge for collecting optimization data and efficiently fine-tuning assistance. Our data-driven optimization method addresses the problem of gathering useful data from short walking bouts by using kinematic data collected with every step. In pilot tests, we found that we could accurately compare control laws based on just 44 continuous steps, opportunistically captured during natural bouts, allowing our system to accumulate data from about 77% of steps in a typical day[34]. We addressed variations in speed by defining speed bins based on observed human behaviour, associating collected data with the appropriate bin, noting when sufficient data for any one speed bin had been accumulated, applying the data-driven classifier to rank assistance parameters and using these rankings to update the optimal parameter estimates for all speed bins (Extended Data Fig. 4).

Real-world optimization quickly improved assistance during natural walking conditions. We conducted experiments in which participants performed one hour of walking in short bouts with exoskeleton assistance (Fig. 5a) on a public sidewalk (Fig. 5b and Supplementary Video 2). Participants were given ecologically relevant[35] audio prompts[28] that caused them to self-select walking speeds that matched a ground-truth distribution[27] (Fig. 5c). Prompts were provided in random order and at specific intervals to obtain bout durations that also matched a ground-truth distribution[34] (Fig. 5d). The optimizer steadily converged throughout the experiment (Fig. 5e), indicating steadily decreasing uncertainty as to which exoskeleton parameters would result in optimal performance according to the data-driven model. Post hoc analysis showed that the optimizer did not reach steady state, suggesting that additional time could have provided a better estimate of the optimal parameters. Peak torques optimized during naturalistic walking were larger than those from treadmill-based experiments (Fig. 5f). Participants may have felt more stable during outdoor walking[36], allowing them to benefit from larger torques, consistent with observations from other comparisons of outdoor and treadmill walking with exoskeleton assistance[37].

Fig. 5: Real-world optimization of exoskeleton assistance.

a, Participant walking on the public validation course. b, Map of the 566-m course used for optimization and validation. Participants walked the course repeatedly during optimization. c,d, Distribution of self-selected walking speeds (c) and walking bout durations (d) during optimization and validation, compared with previously recorded ground-truth distributions of real-world walking data27,34. e, As assistance was optimized over one hour of naturalistic bouts of walking, the convergence parameter (σ) continually improved. The error band represents one standard deviation. f, Optimized parameters for each participant were unique. The red squares depict the Generic Speed-adaptive assistance parameters, consisting of constant values for peak torque normalized to body mass (Nm kg−1) and rise time (percent gait cycle). For ease of comparison, we show the Generic Speed-adaptive peak torque in Nm, averaged across all participants. Peak torque values in this figure are not normalized to emphasize that several participants reached the maximum peak torque that the untethered exoskeleton could provide. The inset torque profiles indicate how each torque parameter affected the assistance profile. g, During validation under naturalistic walking conditions on the public course, Real-world Optimized assistance substantially reduced the energy cost of transport and increased walking speed compared with Normal Shoes (ANOVA, n = 10, *P ≤ 0.039). h, Real-world Optimized assistance also substantially reduced the metabolic cost of walking compared with Normal Shoes during benchmark treadmill conditions (ANOVA, n = 10, *P ≤ 0.023). Boxes extend from the lower to upper quartiles, with a line at the median and a dot at the mean. Whiskers extend between the minimum and maximum values.

Source data

Real-world Optimized assistance increased self-selected walking speed and reduced the metabolic energy expended per distance travelled during naturalistic walking. In a separate validation experiment, participants performed a fixed set of outdoor walking bouts with varying durations and speeds, while ground-truth metabolic rate and speed were measured (Supplementary Video 3). Condition order was randomized (Extended Data Table 1). With Real-world Optimized assistance, the energetic cost of transport was reduced by 17 ± 5% (analysis of variance (ANOVA), n = 10, P = 0.039) and walking speed was increased by 9 ± 4% (ANOVA, n = 10, P = 0.031) compared with Normal Shoes (Fig. 5g). These energy savings are equivalent to removing a 9.2 kg backpack[38], and the increase in walking speed of 0.12 m s−1 is similarly meaningful[39]. Real-world Optimized assistance provided roughly twice the benefits of Generic Speed-adaptive assistance, indicating that personalization was an important contributor to these benefits. Generic Speed-adaptive assistance may have provided a larger benefit if it had used the average of the torque parameters optimized during outdoor walking, rather than treadmill walking. These results demonstrate that lower-limb exoskeletons can provide meaningful benefits under naturalistic walking conditions and provide benchmarks for assessing the real-world benefits of future devices. Assistance can be personalized automatically in a natural setting, seamlessly improving human–robot interaction over time.

Assistance optimized under real-world conditions produced even larger benefits under standard treadmill conditions. After performing optimization in a public setting, we tested our untethered exoskeleton during standardized laboratory walking conditions to directly compare with previous devices[16]. Real-world Optimized assistance reduced the energy cost of treadmill walking by 16% at 1.25 m s−1, 23% at 1.5 m s−1, and 18% when walking up a 10° incline (ANOVA, n = 10, P < 0.023) compared with Normal Shoes (Fig. 5h and Extended Data Table 2), approximately twice the benefits of the previous devices with the best performance for these conditions (Extended Data Fig. 5). The energy savings during inclined walking were equivalent to removing a 15.2 kg backpack[40]. Pilot results suggest that the device provides similar benefits under other conditions, including walking on a 5° incline, loaded walking and stair climbing (Extended Data Fig. 6). Emulator-informed hardware design coupled with opportunistic, data-driven optimization led to exceptional performance enhancements across walking conditions.

Participants reported that the untethered exoskeleton was easy to use and relatively comfortable. Wearable robotic devices should be usable, comfortable and functional for everyday activities to be adopted by users[41]. Participants reported that the exoskeleton was relatively easy to use (Extended Data Table 3), ranking it in the 65th percentile of previously surveyed consumer devices[42]. Participants found that the exoskeleton did not interfere with their clothing and had a manageable weight, but were neutral as to whether it would be comfortable to wear throughout the day (Extended Data Table 4). Participants reported that it was easy to put on and take off the exoskeleton, stand while wearing the exoskeleton, and walk indoors and outdoors for extended periods with the exoskeleton (Extended Data Table 5). Six of the ten participants reported that they would prefer using the exoskeleton rather than normal shoes if they were to walk the public course again. The device we tested is a research prototype and not a refined product; substantial improvements would be required to allow reliable, unsupervised use during typical daily activities. The survey results suggest that it may be possible to create mobility-enhancing products that are easy to use, comfortable and reliable, and that many people may opt to use them.

These approaches to real-world personalization, adaptive assistance and specialized exoskeleton design could potentially be extended to address the needs of workers with physically demanding jobs and people with mobility impairments. A similar overall development approach could be used to address the most important outcomes for each biomechanically and neurologically similar group. Assistance could aid a variety of tasks, such as stair climbing or lifting, and improve other aspects of performance, such as balance or joint pain. In each case, additional training data could be collected in the laboratory and used to train new data-driven models, illuminating the information contained within the body’s movements for each task. With each training dataset, the learned models could be made more capable, progressively building more general relationships between movement and performance outcomes. Data from laboratory-based emulation and optimization experiments could simultaneously provide design guidelines for products. When used regularly, we expect devices like this to become finely tuned to the needs of each individual, resulting in larger performance enhancements than observed in this study. Longitudinal experiments will be needed to understand how such assistance affects behaviour and quality of life; as moving becomes easier, we hope to find that people will be more active, helping them to lead healthier lives.

Methods

Experimental design

The research objective was to personalize exoskeleton assistance during real-world walking. To achieve this objective, we proposed a method of data-driven optimization, which uses portable sensors on the exoskeleton to personalize assistance for each participant. We hypothesized that Data-driven Optimized assistance would provide larger reductions in metabolic rate than Generic assistance. We conducted a power analysis based on previous laboratory-based optimization experiments and found that a sample size of eight participants was necessary for the planned validation experiments. This analysis used a power of 1 − β = 0.8, a significance level of α = 0.05, the difference in mean metabolic rate between optimized (1.44 W kg−1) and generic (1.64 W kg−1) assistance from a previous experiment[4], and the variability in metabolic rate (standard deviation 0.15 W kg−1) from the same experiment[4]. In this context, β is the probability of incorrectly accepting the null hypothesis. We collected data from nine participants for the tethered exoskeleton experiments and ten participants for the untethered exoskeleton experiments. We tested more participants than the minimum number determined by the power analysis to provide a factor of safety in case data from any participants were found to be unusable during later analysis. All participants that were recruited completed the corresponding experiment, and all data from all participants were included in each corresponding analysis. All participants had at least 8 h of experience walking with assistance from powered ankle exoskeletons, minimizing the effects of training that can occur while participants learn to walk with an exoskeleton[4]. All participants were volunteers and provided written informed consent before completing the protocol (IRB-48749), which was approved by the Stanford University institutional review board. Consent was obtained for publication of identifiable images of research participants. The experiments consisted of human participant testing in both laboratory and outdoor settings. Participants wore bilateral ankle exoskeletons and walked under a series of assistance conditions in a randomized order. Each of the experiments is described in the following sections. We used a one-way ANOVA to determine whether differences in the metabolic cost of walking across assistance conditions were different from zero.

Measuring the metabolic cost of walking

The metabolic cost of walking was computed with measurements from respirometry equipment. Respirometry equipment was used to measure the volume of carbon dioxide and oxygen exchanged on each breath. A standard equation was used to compute metabolic energy expenditure in watts for each breath[44]. Metabolics measurements during the real-world walking experiment and validation were collected with portable respirometry equipment worn using a vest on the participant’s torso (K5, COSMED). Metabolics measurements during other exoskeleton experiments were collected with tethered respirometry equipment (Quark CPET, COSMED). Metabolics data were recorded during a quiet standing condition at the beginning of each day of experiments. This quiet standing value was removed from subsequent measurements to isolate the energy cost associated with walking and remove any absolute error associated with respirometry system calibration. The change in metabolic rate as a percentage of a baseline condition, measured within the same experiment, is reported as the primary outcome to account for differences in respirometry equipment calibration coefficients between data collections. Participants refrained from all food and drink except for water for at least 3 h before experiments that included respirometry measurements to avoid confounds from the thermal effect of food. Steady-state metabolic cost was computed by averaging data from the last 3 min of each 6-min condition. Cumulative metabolic cost was computed as the total energy expended during the condition[45], including the metabolic cost beyond that of quiet standing for 3 min following completion of the condition, following methods from previous studies of non-steady gait23,29. Excess oxygen consumption and carbon dioxide production during the return to steady state in quiet standing reflect delays between instantaneous energy use at muscles and expired gas measurements that arise owing to mitochondrial, transport and respiratory dynamics[18]. Including respiratory data from the period following activity enables more accurate measurement of the energy actually expended during short bouts of walking[46]. The energetic cost of transport was calculated as the cumulative metabolic cost divided by the total distance walked.

Exoskeleton assistance conditions

A variety of exoskeleton assistance conditions were evaluated to determine the benefits that they provided to the user. These assistance conditions included walking in Normal Shoes and walking with the exoskeletons while they applied Zero Torque, Generic assistance, Speed-adaptive assistance, Generic Speed-adaptive assistance, Metabolic Optimized assistance, Data-driven Optimized assistance and Real-world Optimized assistance.

We tested walking in Normal Shoes, without the exoskeleton, as a baseline condition for the untethered exoskeleton experiments. Ideally, assistance from an untethered exoskeleton would lead to a lower metabolic cost than walking in Normal Shoes, providing a net benefit to the user. Separate pairs of the same type of Nike running shoe, weighing 0.3 kg per shoe, were used for the Normal Shoes condition and incorporated into the tethered exoskeleton and the untethered exoskeleton.

The Zero Torque mode was an exoskeleton condition in which the exoskeleton provided no assistive torques. During this mode, the exoskeleton maintained a small amount of slack in the cable transmission so that virtually no torque was applied to the ankle. This condition was used as a baseline for experiments with the tethered exoskeleton (but not for experiments with the untethered exoskeleton) because it allowed us to isolate the benefits of exoskeleton assistance from the energetic costs of wearing the emulator, which were expected to differ from those of an untethered device specialized to provide the same assistance.

The Generic assistance condition used a fixed set of assistance parameters identified from a previous optimization experiment. Generic assistance patterns have been found to reduce the metabolic cost of walking less than assistance personalized to each individual2,3,4. The tethered exoskeleton experiments in this study used Generic assistance computed by averaging the optimized parameters from a group of participants in a previous experiment using the same tethered ankle exoskeleton that had provided the largest energetic benefits owing to exoskeleton assistance so far[4]. The Generic assistance pattern allowed us to estimate the contributions of personalization through data-driven optimization in tests with the tethered emulator.

During Speed-adaptive assistance, the exoskeleton used estimates of walking speed to select assistance parameters expected to be more effective at that speed. The speed-adaptive control approach is described in detail in ‘Speed-adaptive controller and validation experiment’. In the Speed-adaptive condition, the controller interpolated between separate sets of assistance parameters that had been optimized for the same participant at walking speeds of 0.75 m s−1, 1.25 m s−1 and 1.75 m s−1. This condition was used to test the efficacy of the speed-adaptive control approach for handling speed variations during treadmill walking with the tethered exoskeleton emulator.

In the Generic Speed-adaptive assistance condition, the speed-adaptive controller selected assistance parameters expected to be more effective at that speed for an average participant. The controller interpolated between separate sets of Generic assistance parameters for each walking speed, computed by averaging the optimized assistance profiles from the tethered exoskeleton experiments in this study (Fig. 2). The generic parameters for 0.75 m s−1 and 1.75 m s−1 were computed by averaging across three participants’ optimized parameters, whereas the generic parameters for walking at 1.25 m s−1 were computed by averaging ten participant’s optimized parameters. The Generic Speed-adaptive assistance condition was compared with Real-world Optimized assistance during overground walking with the untethered exoskeleton. Ideally, the Generic Speed-adaptive parameters would have been taken as the average values from real-world optimization, but those values were not yet known at the time the experiment was conducted. The Generic Speed-adaptive condition provided the best available comparator for isolating the benefits of personalization during overground experiments with the untethered exoskeleton.

Metabolic Optimized assistance was personalized based on metabolic measurements using a previously established optimization method[2] for identifying the exoskeleton control parameters that minimize the metabolic cost of walking for a specific person. To perform metabolic optimization, a participant walked on a treadmill while receiving exoskeleton assistance. The same assistance profile, or ‘control law’, was provided to the participant for 2 min while respirometry measurements were recorded and the steady-state metabolic cost of walking for that control law was estimated[18]. Participants repeated this process of walking on a treadmill for 2 min per control law until a fixed number of control laws had been evaluated. We refer to the set of control laws to be evaluated as one ‘generation’ of control laws—the terminology used in evolution-inspired optimization strategies such as the covariance matrix adaptation evolutionary strategy (CMA-ES)[26]. Each generation of control laws must be tested before updating the estimate of the optimal control law and generating a new generation of control laws to test. On the basis of heuristics relating generation size to the number of parameters to be optimized[26], the lab-based experiments with four optimization parameters had participants complete eight control laws per generation. After each generation, an optimizer (CMA-ES) ranked the control laws in order of metabolic cost, updated the optimization parameters and selected a new set of promising control laws to evaluate. This optimization approach was established in previous experiments that demonstrated large improvements in the metabolic energy cost of walking and running with exoskeleton assistance2,4,47. This optimizer was selected because it is sample efficient, meaning that it requires relatively few evaluations to reach a reliable estimate of the optimal parameters. In the context of exoskeleton optimization, that means fewer exoskeleton control modes to be experimentally tested on the human participant, which is important both to study design and to real-world use of devices that personalize assistance. Metabolic Optimized assistance was used to validate the data-driven optimization approach in the first experiments with the tethered exoskeleton emulator.

Data-driven Optimized assistance was personalized using data-driven optimization. The data-driven optimization used the same optimization framework as the metabolic optimization, except that it used the data-driven classifier, rather than indirect respirometry measurements, to perform the ranking step. The classifier was trained on data from a previous laboratory experiment[4] and compared control laws based on the exoskeleton torque parameters applied and the resulting ankle angle and ankle angular velocity (Fig. 1 and Extended Data Fig. 1). The data-driven optimization condition was applied in tests of the data-driven optimization approach using the tethered exoskeleton emulator.

Real-world Optimized assistance used speed-adaptive control with parameters that were personalized using the data-driven optimization approach while walking with the untethered ankle exoskeleton under naturalistic conditions. The controller used the same speed estimation and adaptation approach as with the Generic Speed-adaptive condition, except that the parameters for each speed were personalized to the individual participant using opportunistic optimization. The Real-world Optimized parameters were computed using the approach detailed in ‘Opportunistic optimization approach’. The Real-world Optimized condition was applied in outdoor and treadmill tests with the untethered exoskeleton.

Data-driven optimization

Data-driven optimization personalized assistance using a data-driven classification model to determine which exoskeleton control parameters provided the largest benefits for each person. The participant walked while receiving a sequence of different patterns of exoskeleton assistance, each defined by the corresponding control law. During laboratory-based experiments, participants walked on a treadmill for 30 s for each control law. During real-world experiments, participants walked overground for 44 continuous steps for each control law. A fixed number of exoskeleton control laws, comprising one generation of the evolution-inspired optimizer, were then ranked using the data-driven classifier. The optimizer then updated its estimate of the optimal parameters and generated a new set of control laws to evaluate. The following paragraphs detail what type of data were collected, how the data were processed, how the data-driven classification model evaluated control laws, how the optimizer was updated based on the data-driven classifications and how a new set of control laws was selected for evaluation in the next generation of optimization (Fig. 1).

Exoskeleton torque control parameters, defined by the control law, were fixed within each evaluation period. The person experienced several control laws before the data-driven model processed data and the optimizer updated its estimate of the optimal assistance parameters and generated a new set of control laws to evaluate.

The data-driven model input consisted of carefully processed portable sensor data, including ankle angle and ankle velocity measurements and the control law parameters that set the pattern of exoskeleton torque. The angle and velocity measurements were sampled using an absolute rotary encoder at the ankle joint of the exoskeleton worn on the left leg. The control law parameters consisted of four values: peak torque magnitude, peak time, rise time and fall time[2].

Portable sensor data were processed by segmenting the ankle angle and velocity measurements by gait cycle and then discretizing the data for each gait cycle into a discrete number of bins. The gait cycles were segmented whenever a heel strike was detected by the pressure-sensing insoles. The first six gait cycles of data were discarded to avoid confounds from fast adaptation[48] by the person in response to the new assistance pattern—in pilot tests, we found that data from these first six steps exhibited substantial changes in ankle kinematics, while subsequent strides were more consistent. The remaining gait cycles were discretized by averaging the measurements within each of 30 discrete bins and then averaging each bin across the gait cycles for that control law. The processed data were reshaped into a single vector with 64 values: 30 binned values for the ankle angle across the gait cycle, and 30 binned values for the ankle velocity across the gait cycle, and 4 values for the torque parameters. The model input consisted of the vector of data for one control law subtracted from the vector of data for a different control law, which also comprised 64 values. This difference in the sensor measurements provided the model with information about how the person’s movements and exoskeleton torque differed between the two control laws. The choice to segment data by gait cycle follows our previous findings that data-driven models can more accurately estimate metabolic energy expenditure from sensors worn by unassisted humans when the data are formatted in this way[23].

The data-driven classification model was trained to compare two control laws at a time, determining which control law was estimated to have provided a larger reduction in the metabolic cost of walking. The data-driven classification model was a logistic regression model. To train the data-driven classifier, we input previously collected data[4] that included portable sensor data, in the form of exoskeleton joint angles and velocities, and ground-truth labels, in the form of metabolic measurements, for many exoskeleton control laws. The sensor data were taken as input into the model to estimate the likelihood that the first of the compared control laws resulted in a lower metabolic cost of walking compared with the second control law. The resulting probability was a continuous value from 0 to 1, with 1 indicating the highest likelihood that the first control law reduced the metabolic cost of walking more than the second control law. The ground-truth labels were computed by subtracting the measured metabolic costs, estimated from 2 min of respirometry data, for the two control laws. A label with a negative value indicated that the first control law was more beneficial, meaning that it reduced the metabolic cost of walking more than the second control law. A positive-valued label indicated that the second control law was more beneficial. The previously collected training data were from an experiment in which 10 participants walked under approximately 3,600 different exoskeleton control laws[4]. When training the data-driven classifier, we used regularization, a technique that encourages simpler models and avoids overfitting to training data, to improve model estimates for new data points that were not in the training set. In this case, we used a lasso regularization term that penalized the absolute value of the model weights multiplied by a regularization parameter with a value of 1.

The data-driven classification model was trained to capture a relationship between leg movement, exoskeleton torque parameters and the metabolic cost of walking with assistance. The linear weights used by the data-driven classifier are visualized using a colour code in Extended Data Fig. 1. To aid interpretation, we also overlay the mean difference for each model input as a black line. This was calculated as the value from the control law resulting in lower metabolic rate minus the value from the control law resulting in higher metabolic rate, averaged across all pair-wise comparisons, such that the sign of the mean difference is meaningful. We also provide the cumulative contributions of each term in the model to classification over the entire training set. The percent contributions are calculated as the absolute value of the product of the model weight and the difference input, summed over all pair-wise comparisons, divided by the sum over all model terms. Even linear data-driven models can be difficult to interpret because of the complex interactions between model terms through the dynamics of the underlying system, which can be nonlinear and coupled. In this case, the underlying system is a human walking with an exoskeleton, and we expect strong interactions between exoskeleton torques, joint velocities and joint angles, and between states at different times in the gait cycle. The model may be capturing aspects of these interactions in non-obvious ways. Nevertheless, we can gain some intuition about the relationships that the model may have identified if we consider the effects of key model terms independently.

The model weights associated with differences in ankle kinematics suggest that a lower metabolic rate was associated with increased ankle plantarflexion at toe-off, while guarding against premature onset of push-off, excessive plantarflexion velocity and reduced dorsiflexion mid-stance. The largest single contributor to classification based on ankle kinematics, constituting about 10% of the total, favoured a larger plantarflexion angle at 62% stride, the time of toe-off during normal walking. A large negative weight on the difference in ankle velocity at 48% stride seemed to penalize conditions that resulted in premature onset of ankle push-off. A sequence of negative weights on ankle plantarflexion velocity during push-off seemed to favour slower, smoother movement during that phase. Taken together, these velocity regulation terms constituted about 16% of the total classification. Smaller negative weights on ankle angle at 38% stride and ankle velocity before the onset of push-off suggest a preference for conditions with greater mid-stance dorsiflexion. The model terms associated with ankle angle and velocity were most informative during late stance, when the concentric contractions of the plantarflexor muscles are less efficient and exoskeleton torque may have the most capacity to reduce metabolic cost49,50. During the leg swing phase, model terms contributed little to the total classification, consistent with expectations for an ankle exoskeleton that produced no torque when the foot was off the ground. The sum of all model weights associated with ankle angle and velocity were 10% and 23% of the total, respectively.

The model weights on differences in torque parameter values indicated that lower metabolic rate was associated with a later time of peak torque and, to a lesser extent, a larger peak torque magnitude. Exoskeleton assistance was governed by a torque pattern defined by four parameters: peak torque magnitude, peak time, rise time and fall time[2]. These four parameters had allowable ranges of 0 to 1 Nm kg−1, 40% to 55% stride, 20% to 40% stride, and 10% to 20% stride, respectively. Fall time was further constrained to be at most equal to the difference between peak time and the time of toe-off, which prevented application of torque during the swing phase. The largest single contributor to classification based on exoskeleton torque, constituting about 60% of the total, favoured applying peak torque at a later time in the gait cycle. As the peak time was constrained, this term had the effect of maintaining a peak time close to the upper limit of 55% stride. Consequently, fall time was effectively constrained to its lower bound of 10% stride. The large model weight on peak time is consistent with previous observations that the timing of ankle exoskeleton assistance is important[51], and that later onset of torque assistance can correspond to larger improvements in metabolic rate[52]. The data-driven model also favoured larger peak torque magnitudes, with the associated term contributing about 4% to the total classification. Interestingly, peak torque was not driven to its upper limit for most participants or conditions, and the classification contribution of this term was about ten times less than the sum of contributions from ankle kinematics. This suggests that how a person reacts to exoskeleton assistance is more important for determining metabolic rate than the magnitude of the torque and power provided by the exoskeleton.

Exoskeleton control laws were ranked using the probability values estimated by the data-driven classifier. Each pair of control laws that were passed to the data-driven classification model yielded one probability value, defining whether the first control law was expected to have provided a larger benefit than the second control law. All possible pairs of control laws were classified with the data-driven model to obtain a complete set of probability values. Each control law was scored by summing the probabilities from all pairs that included that control law. The control laws were ranked by the magnitudes of their scores, with a larger value indicating that the control law was more likely to provide a larger reduction in the metabolic cost of walking (Fig. 1). This ranking step replaced the previous approach based on metabolic measurements from indirect respirometry equipment, allowing optimization to take place outside the laboratory using inexpensive sensors and a microcontroller on the exoskeleton. Using the data-driven ranking, the optimizer then updated its internal parameters and generated a new set of control laws to evaluate. The new estimate of the optimal control law was equal to the weighted average of the best-performing control laws. New control laws were selected from a distribution around this estimate of the optimum, with the shape of the distribution set by the covariance matrix and the spread of the distribution set by the convergence parameter.

The data-driven optimization process can be better understood by working through these steps using example data. Imagine that we have three control laws, labelled 1, 2, and 3, which happen to be in order of increasing metabolic cost and decreasing peak torque. Imagine that these control laws had identical torque timing parameters and resulted in identical ankle angles and ankle velocities. When performing the three pair-wise comparisons (Fig. 1c), the differences between ankle angle and ankle velocity would be zero. The differences between torque parameters (ΔC_12, ΔC_13 and ΔC_23) would each be a vector with one positive value followed by three zeros. When taking the dot product of the parameter differences with the model weights on control parameters, which are all positive (Fig. 1d), the pair coefficients (w_12, w_13, and w_23) would all be positive scalars. For each of these pair coefficients, the logistic function (Fig. 1e) would return a probability greater than 0.5, indicating that the first control law in the pair is likely to have a lower metabolic cost than the second control law. Let us imagine that each of the probability values (p_12, p_13, and p_23) was 0.9. When performing the control law scoring step (Fig. 1f), the score for the first control law (s_1) would be the sum of p_12 and p_13, or 1.8. The score for the second control law (s_2) would be the sum of p_21 and p_23. As p_21 is the complement of p_12, p_21 = 1 – p_12 = 0.1. Thus, s_2 would equate to 1. The score for the third control law (s_3) would be the sum of p_31 and p_32, or 0.2. Thus, the scores would correctly rank the control laws in terms of metabolic cost. The optimizer would then use this ranking to perform an update, estimating that the optimal torque parameters were close to those of control law 1, but slightly offset towards control law 2. The optimizer would then select new control laws to evaluate, drawn from a distribution around the new estimate of the optimal parameters (Fig. 1g).

Tethered optimization experiments

To compare the efficacy of data-driven optimization to a range of other assistance conditions, we conducted tethered exoskeleton experiments in an indoor laboratory setting. Participants wore tethered bilateral ankle exoskeleton emulators[43]. Exoskeleton assistance was governed by a torque pattern characterized by four parameters: peak torque magnitude, peak time, rise time and fall time[2]. The exoskeleton control loop ran at 1,000 Hz on a real-time computer (Speedgoat). Exoskeleton sensor measurements were recorded at a rate of 2,000 Hz, including pressure values from shoe insoles, commanded torque parameters, measured torque, ankle angle, and ankle velocity. Measurements were used to estimate time within the gait cycle as a percentage of the total gait cycle time, which was used to calculate the desired torque. Torque tracking was accomplished using a combination of classical feedback control and iterative learning, which accounted for errors that consistently occurred at the same point in the gait cycle on each step[30].

Two tethered exoskeleton experiments were used to evaluate the effectiveness of various assistance conditions. The first experiment compared assistance conditions while participants walked at 1.25 m s−1, a normal walking speed previously used for metabolic optimization experiments2,4. Healthy young adults (n = 9, 5 men and 4 women; age, 24.8 ± 1.8 yr; body mass, 65.3 ± 8.0 kg; height, 1.73 ± 0.07 m) completed a two-day experimental protocol. On the first day, participants performed experiments to personalize assistance parameters with metabolic optimization and data-driven optimization, in a randomized order. Participants completed eight generations of optimization for each approach. Each generation consisted of eight control laws. The optimizations were initialized with the Generic assistance parameters, corresponding to the average of the optimized parameters identified for a previous group of expert participants[4]. The optimizations were initialized with the covariance matrix set to the identity matrix and a scaling factor that corresponded to 20% of the range of the normalized assistance parameters (a sigma value of 0.1). The metabolic optimization control laws lasted 2 min, which allowed steady-state metabolic cost to be estimated from respirometry data with a good balance between the time required for each control law and estimation accuracy[2]. This led to a total evaluation time of 128 min of walking. During data-driven optimization, each control law was evaluated for 30 s, sufficient to obtain an accurate estimate of participant motions, which were nearly steady following the rapid adaptation phase[23]. This required a total evaluation time of 32 min of walking. For each participant, the parameters identified using data-driven and metabolic approaches were similar. For example, optimized peak torque values were well correlated across methods (R^2 = 0.76, P = 1.4 × 10−4, n = 9). On the second day, participants performed a standing rest condition followed by assistance conditions including Normal Shoes and walking with the exoskeletons under Zero Torque, Generic assistance, Metabolic Optimized assistance and Data-driven Optimized assistance. The assistance conditions for these validation tests were randomized and presented in a double-reversal order, as ABCDDCBA, to mitigate the effects of trial order related to within-day adaptation and fatigue. Each condition lasted for 6 min and included metabolic measurements.

The second experiment was used to evaluate the same set of assistance conditions at additional speeds and treadmill grades. A subset of healthy adult participants from the first experiment (n = 3, 3 men; age, 24.0 ± 2.0 yr; body mass, 66.0 ± 8.0 kg; height, 1.76 ± 0.05 m) completed the experiment. Participants completed the same experimental protocol used in the first tethered exoskeleton experiment for three additional walking conditions: walking at a slow speed of 0.75 m s−1, a fast speed of 1.75 m s−1, and on a 10° incline at 1.25 m s−1.

Speed-adaptive controller and validation experiment

We developed a speed-adaptive controller that adjusted exoskeleton assistance based on walking speed. During real-world walking, people naturally vary their speed[27]. We hypothesized that adjusting exoskeleton assistance based on walking speed would provide larger metabolic reductions than a constant pattern of assistance. We estimated the walking speed of each step using a linear model, relating measured stride durations to measured walking speeds (Extended Data Fig. 2). Walking speed estimates from each step were used to interpolate exoskeleton assistance parameters from those optimized at a range of fixed speeds (Fig. 3a).

During speed-adaptive control, walking speed from one step was used to select the assistance parameters for the following step. We expect this approach to perform well when changes in walking speed occur slowly, or when there are rapid changes in speed but they constitute a small portion of total steps, as in natural human gait. Our experimental data are consistent with the observation that most acceleration and deceleration occurs within a few steps at the start and end of each walking bout. The expected stance duration was also adjusted based on speed estimates, following an approach established in previous research[53]. In future studies, the speed-adaptive controller could be improved to deliver more effective assistance during rapid changes in gait speed by incorporating instantaneous estimates of walking speed54,55 and stance duration. Acceleration regimes could also be considered, with a binning approach analogous to the one used for speeds in this study, to allow optimization of assistance specific to acceleration and deceleration phases. Other approaches, such as those using phase-based control[55] or adjusting assistance based on changes in joint kinematics rather than walking speed[56], may be beneficial for generalizing to a large set of activities.

We conducted a third tethered exoskeleton experiment to evaluate whether adapting assistance to variations in walking speed could provide larger reductions in metabolic cost than a fixed generic assistance profile. Healthy young adults (n = 3, 3 men; age, 24.0 ± 2.0 yr; body mass, 66.0 ± 8.0 kg; height, 1.76 ± 0.05 m) completed the experiment. These participants had previously completed the first two tethered exoskeleton experiments, providing Data-driven Optimized parameters for walking speeds of 0.75 m s−1, 1.25 m s−1, and 1.75 m s−1. Participants walked on a treadmill while the speed varied sinusoidally from 0.75 m s−1 to 1.75 m s−1 with a period of 30 s. Participants completed assistance conditions including walking in Normal Shoes and walking with the exoskeletons under Zero Torque, Generic assistance (which did not change in response to changes in speed) and Speed-adaptive assistance (using the optimized control parameters previously identified for each participant). The validation tests were randomized and presented in a double-reversal ABCDDCBA order to mitigate the effects of noise in the metabolics measurements and trial order.

Untethered exoskeleton design

The untethered exoskeleton was designed to provide the optimized assistance parameters from the tethered exoskeleton experiments under real-world conditions. The maximum peak torque magnitude for the optimized assistance during the tethered exoskeleton study was 54 Nm when walking at a moderately fast speed of 1.5 m s−1. The motor and power transmission elements were designed to robustly provide this level of assistance. A portable battery was selected to allow 30 min of continuous walking on a single charge. The device was designed to be lightweight to reduce the metabolic power required to carry the exoskeleton.

The untethered exoskeleton had a mass of 1.2 kg for each ankle. Many of the mechanical elements were the same as in the tethered exoskeleton, including the frame, shoe and pressure-sensor insole. New elements included the portable motor, drum-and-cable transmission, electronics, and battery (Extended Data Fig. 3). A set of computer-aided design files and a bill of materials are provided as Supplementary Data 2.

The brushless motor (AK80-9, CubeMars) contained a single stage 9:1 gear ratio and internal motor driver electronics. This gearmotor has a rated peak torque of 18 Nm, a no-load speed of 25 rad s−1, and a mass of 0.5 kg. We selected this motor based on simulations with a simplified model that predicted it would be capable of applying the patterns of ankle torque and velocity that corresponded to optimized assistance in the tethered exoskeleton experiments, assuming an additional 5:1 gear ratio from the drum to the heel spur.

The custom drum was machined from 7075 aluminium, with a radius of 0.020 m. A cable connected the heel spur to the motor drum. The heel spur had a maximum lever arm (the distance from the centre of the ankle joint to the rope tie-off point) of 0.115 m. The lever arm decreased as the ankle plantarflexion angle increased, with a singularity at a maximum plantarflexion angle of 55° ensuring that no ankle torque could be applied to hyperextend the ankle joint. The torque assistance profile of the exoskeleton was not impacted by changes in the lever arm because torque was measured directly at the ankle; strain gauges on the superior and inferior surfaces of the heel lever directly sensed bending moment independent of cable force. This allowed for accurate torque control without explicitly correcting for joint angle. When the motor applied torque to the drum, a force was generated in the cable, which then transmitted this force to the heel lever, creating a torque about the ankle joint of the exoskeleton. The drum-and-cable transmission had the added benefit of being backdrivable, avoiding the possibility of force spikes that can be produced by classically stiff actuators57,58. The cable could also be driven to a slack state to allow the person to move freely when desired, an important capability that prevents interference when not providing assistance[59].

The untethered exoskeleton electronics consisted of a microcontroller, portable sensing elements, a motor driver integrated into the motor and a rechargeable battery. The untethered exoskeleton used a Raspberry Pi 4b microcontroller to read sensor data and perform real-time control and optimization at a rate of 200 Hz. A breakout board enabled sensors to interface with the microcontroller. A step-down voltage converter enabled the electronics to be safely powered by a portable battery. The portable sensing elements included a rotary encoder in the ankle joint that measured ankle angle and velocity, a pressure-sensing insole in the shoe, a set of strain gauges in a full Wheatstone bridge configuration applied to the heel spur to measure torque, and an amplifier (IAA100, Futek) to allow measurement of strain-gauge signals. The pressure-sensing insole had pressure sensors located at the heel, fifth metatarsal, distal phalanx of the great toe and the first metatarsal. Fusing information from these different sensors enabled robust estimation of stance and stride period while providing measurements to extract information for optimizing assistance. This choice of sensors was guided by the design heuristic that multiple modes of sensing are important for effective exoskeleton control[60]. Muscle electrical activity could have provided additional information for control, but with the added challenge of handling noise from sensors placed on the skin[61]. The total weight of electronics was 0.15 kg.

The entire system was powered by a lithium polymer battery with a nominal voltage of 24 V, a capacity of 1,300 mAh, and a weight of 0.3 kg. Battery life was experimentally evaluated under the most demanding assistance pattern, characterized by a peak torque of 54 Nm and late timing of peak torque. Tests were conducted while walking on a treadmill at a speed of 1.5 m s−1. The battery was initially charged to a maximum voltage of 25.2 V and the battery life experiment was stopped once the battery voltage reached 21.6 V, corresponding to a cell voltage of 3.6 V, the minimum safe level recommended for discharging a lithium polymer battery. During testing, cell voltage was monitored by a safety regulator and an audio alarm was played once the cell voltage reached 3.6 V. We found that the 0.3-kg battery used in real-world tests allowed 36.3 min of operation under these conditions.

The design of the untethered device was guided by previous laboratory-based ankle exoskeletons, incorporating design elements that allowed for large assistive torques while maintaining comfortable forces on the body[43]. The shoe, carbon fibre struts and calf spacers were designed to be interchangeable to fit different participants, following best practices for fitting[62]. The motor-and-drum transmission and heel spur were designed to be one size fits all, with interchangeable shoes and spacers accommodating differences in foot size and mediolateral dimensions of participants’ legs. It might at first appear that the force applied by the cable between the drum and heel spur would pull the exoskeleton down the leg, but the rigid exoskeleton frame allows the axial component of this force to be reacted out at the exoskeleton joint rather than as shear on the person’s skin[43]. Thus, only a normal force is applied to the shank of the leg, which allows for more comfortable application of high torques[63]. The carbon fibre frame of the exoskeleton used stiff material and a cross-section with a high-area moment of inertia to prevent meaningful deflection during loading. As the system regulated exoskeleton joint torque, rather than motor current or velocity, and as torque was measured directly at the joint, compliance and dissipation in the transmission, exoskeleton frame and human–exoskeleton interface did not affect the accuracy or consistency of the applied torque.

The design of the untethered exoskeleton required several trade-offs. The highest design priority was providing a peak torque of 54 Nm during walking at 1.5 m s−1, specified from previous optimization experiments, with the least mass possible. We considered several factors to ensure that the motor would provide 54 Nm during operation. We simulated the torque needed to provide the desired assistance, overcome transmission inefficiencies, and accelerate the mass of the motor rotor and drum as required to track ankle movements during walking at 1.5 m s−1. The motor had to operate at a safe steady-state temperature to prevent damage to the windings. A brushless motor was selected for its relatively high efficiency and peak torque. This untethered exoskeleton was designed for the optimized parameters of our experimental participant group, and other participants may require a different device with different balance between torque and weight to provide the same reductions in the metabolic cost of walking.

Another important decision was whether to place the motor and electronics near the assisted joint or closer to the torso. The energy cost of carrying mass at distal joints is high[33], suggesting a relocated drive approach with heavy motors carried more proximal to the centre of mass of the body. We considered mounting the motor and electronics at the hip and using a Bowden cable to transmit forces to the ankle joint. Bowden cables have an inner cable that moves relative to an outer conduit like a bicycle brake. This introduces complex transmission dynamics, including stick–slip friction, history dependence and a dependence on leg posture, making torque control more challenging, reducing control bandwidth and decreasing energy efficiency. The cables and additional electrical wires also add to the weight of the system. For these reasons, we selected a drum-and-cable transmission located on the shank of the leg. Locating motors and electronics near the assisted joint resulted in more efficient power transmission, lower transmission compliance, better control bandwidth and less total weight.

Our untethered exoskeleton was designed to allow tests of real-world personalization and resulting mobility benefits during naturalistic walking in a community setting. A significant amount of additional engineering would be required to make this device ready for everyday use by consumers. Everyday use would require easier donning and doffing, a more comfortable interface, more robust electronics hardware and more intuitive, independent control, for example, utilizing a smartphone app. In addition, the exoskeleton would have to be tested to ensure functionality during additional common activities such as navigating stairs, and to ensure that it did not interfere with common activities such as sitting and driving. While we did not directly evaluate descending stairs in this study, we did notice that the long heel spur required participants to walk carefully to avoid hitting the previous step. This design choice was made for convenience, allowing us to use as many elements from our previous tethered exoskeleton design as possible. A less obtrusive transmission would be needed for a consumer device. The commercially available Dephy ExoBoot[64] provides an example of a more streamlined design; it has no spur behind the heel of the shoe, has simple donning and doffing features, and has minimal structure on the medial side of the leg, making it a good candidate for extended use in a large range of activities. Other autonomous ankle exoskeletons10,17 demonstrate complementary ways of designing hardware that is more compatible with everyday use. With increased torque capacity, more accurate torque control and real-world personalization using the approach described here, we expect commercial devices could achieve similar reductions in metabolic rate.

Opportunistic optimization approach

We overcame the challenges of optimizing assistance during short bouts of walking at varying speeds by opportunistically accumulating data across many bouts and binning by speed. This opportunistic optimization approach used the same data-driven classification model and optimization method that were validated in the tethered experiments, with the addition of a check that sufficient consecutive steps had been collected for each control law and a method for addressing a wide range of speeds (Extended Data Fig. 4).

The opportunistic optimization method checked that sufficient steps had been collected before moving on to the next control law. We chose the requirement of 44 steps to approximate the durations used in the tethered data-driven optimization experiments. If sufficient continuous steps were not collected before the end of the walking bout, the optimizer would start over with the same controller on the next bout. Once sufficient strides were collected, the next control law was applied for that speed bin. As with the tethered experiments, the first six strides of data were discarded to avoid confounds related to rapid adaptation to a new exoskeleton control law.

The same data-driven classification model used in the tethered exoskeleton experiments was used for the real-world optimization, but a different set of assistance torque parameters were optimized. The torque parameters for peak time and fall time were fixed to the average values of the Data-driven Optimized parameters from the first tethered exoskeleton experiment (54.6% of the gait cycle and 10.0% of the gait cycle). We fixed the values of peak time and fall time because the optimized values changed little across speeds and participants, indicating that fixed values may be sufficient. The optimized values of peak torque and rise time varied substantially across speeds and participants, and so these parameters were optimized in untethered exoskeleton experiments. Optimizing two, rather than four, torque parameters reduced the dimensionality of the optimization, requiring only six, rather than eight, control laws to be collected for each generation of optimization. Reducing the number of control laws to be evaluated per generation allowed for more generations to be completed within a set experiment time, providing more frequent optimization updates and a better estimate of the optimal values. This may have come at the cost of suboptimal assistance timing parameters for some participants.

Once data for all the control laws in a generation were collected, the data-driven classification model ranked the control laws. The optimizer used this ranking to update its estimate of the optimal parameters and to adjust internal parameters, such as the convergence parameter (σ) that set the spread of the distribution from which to draw parameters for the next generation. Optimizations were performed for three bins of walking speed: less than 1.22 m s−1, between 1.22 m s−1 and 1.38 m s−1, and greater than 1.38 m s−1. These speeds were chosen based on the 33rd and 66th percentile of real-world walking speed distributions[27], resulting in an equal expected likelihood for the participant to walk in each bin. Speed-adaptive control interpolated assistance based on the speed of each individual step (Extended Data Fig. 2). When a sufficient number of steps were collected for one control law, the estimated walking speeds for all steps during that control law were averaged, the corresponding speed bin was selected, and data were stored for the optimization process. When a complete generation of control laws were collected for a speed bin, control laws for that bin were ranked and the optimization parameters for that bin were updated. The estimate of the optimal assistance parameters for the other speed bins were also adjusted by a lesser amount, with the magnitude of the adjustment being proportional to the value of the convergence parameter, σ, for that bin (Extended Data Fig. 4). This allowed parameters in all speed bins to update more quickly at the beginning of the optimization, with decreased across-speed influences as the optimizations within each speed bin converged.

We chose to optimize a set of assistance parameters for each of three bins of walking speed, but it is possible to formulate this optimization in different ways. The data-driven classifier requires comparisons of control laws at similar walking speeds. A larger number of bins of walking speeds could be used to provide more granular speed-based adaptation, at the expense of additional time to optimize a larger number of assistance parameters. It may also be possible to simultaneously solve for a larger set of control parameters that fully define the speed-adaptive controller, but this would introduce challenges related to the larger parameter space, interaction effects between parameters, and poorly conditioned maps between parameters that have a strong effect on assistance at one speed and little effect on assistance at different speeds. Instead, we opted for a small set of speed bins, with a relatively simple approach to updating the optimal parameter estimates.

Real-world optimization experiments

In the real-world optimization experiments, we used the untethered exoskeleton to optimize assistance during naturalistic bouts of walking and then evaluated the optimized assistance profiles under real-world and treadmill conditions.

Healthy adult participants (n = 10, 6 men and 4 women; age, 24.2 ± 1.8 yr; body mass, 67.0 ± 8.2 kg; height, 1.72 ± 0.07 m) completed a two-day protocol. On the first day, participants walked outside in a public setting along a path consisting of concrete, asphalt and brick sidewalks (Fig. 5b) for approximately 1 h while the untethered exoskeleton provided assistance and performed data-driven optimization. To emulate natural walking, the participants received audio cues to tell them to start and stop walking bouts. The durations of these bouts were randomly drawn from a preselected distribution (Fig. 5d) that matched naturally occurring bout durations[34]. Participants stood at rest between bouts for a randomized duration of 5 s to 10 s. To encourage a normal range of speeds, we provided participants with audio prompts, such as “Walk as if you were walking to catch a bus” and “Walk as if you were walking a small dog”, at the start of each bout. A previous study[28] demonstrated that these prompts were associated with different self-selected walking speeds, and we expected that participants would adopt similar speeds. We randomly sampled from a distribution of speeds (Fig. 5c) that mimicked natural walking patterns measured in a previous study[27].

On the second day, participants performed outdoor and treadmill validation tests to evaluate the benefits provided by Real-world Optimized assistance. For the outdoor validation, participants walked along a 566-m path in the same public setting with a fixed ordering of bouts of specific distances and corresponding speed prompt commands that were selected to match real-world distributions27,34. Distances were set using cones to mark stopping locations, which ensured consistent distances for each bout. Participants completed this outdoor course once for each condition, including Real-world Optimized assistance, Generic Speed-adaptive assistance and Normal Shoes. The ordering of the conditions was randomized to minimize effects of testing order (Extended Data Table 1). The double-reversal protocol, used in the first three laboratory experiments, was not used because the outdoor experiments took significantly more time owing to the longer trial time, varying self-selected walking speeds, short bouts of walking, and rest periods between bouts and conditions. Each real-world condition required about 15 min, compared with about 8 min for each treadmill condition. Outdoor and indoor tests of Real-world Optimized assistance were conducted on the same day to avoid confounding effects from differing respirometry system calibrations. The total walking time for these two experiments was about 1.5 h, and we found that participants were not able to complete the additional 1.5 h of walking that would have been required for a double-reversal approach without experiencing fatigue. For the 3 min following completion of the path, participants stood at rest while respirometry data were collected to capture the total metabolic cost of completing the course. The duration of walking for each bout was timed with a stopwatch. Walking speed for each bout was computed by dividing the fixed distance for that bout by the time spent walking during that bout. Walking speed for each condition was calculated as the total distance travelled divided by the total time spent walking while navigating the course.

The indoor validation consisted of a standing rest condition followed by six treadmill conditions, each lasting 6 min. Participants walked on a treadmill at 1.25 m s−1, at 1.5 m s−1, and on an incline of 10° at 1.25 m s−1. Participants completed each treadmill speed and grade twice, once with Real-world Optimized assistance, as identified during the outdoor optimization period, and once with Normal Shoes. The ordering of conditions was randomized, with a constraint that the exoskeleton would only be donned and doffed one time to reduce experiment time (Extended Data Table 2). We did not use the double-reversal protocol in these tests because we found that participants could not reliably complete the additional 1.5 h of walking that would have been required without experiencing fatigue, and so instead used the more typical approach of single presentations with randomized order.

One pilot participant completed additional indoor conditions, walking at 1.25 m s−1 with an incline of 5°, walking at 1.25 m s−1 with a load of 20% of their body weight carried in a weight vest, and stair climbing on a stairmill at 50 steps per minute. The results (Extended Data Fig. 6) were used to test the generality of the approach. Owing to the small sample size (n = 1), this figure and the numerical results for change in metabolic rate are not included in the main text.

We performed a naturalistic overground experiment in an outdoor, suburban community setting. People require assistance in many different settings and for a variety of additional activities, and future work should extend the approaches presented in this study to optimize assistance and evaluate assistive device benefits for a wider range of tasks. For example, future devices could sense, adapt to and optimize assistance for various grades[55], during stair navigation[17] and over rough terrain[54]. These future studies will provide additional translational impact for daily mobility.

Comparison with other untethered exoskeletons

We compared the benefits of Real-world Optimized assistance with the untethered exoskeleton to the best results of comparable previous studies10,11,12,13,14,15,16. To allow direct comparison, we considered only studies that tested untethered devices, report data for normal walking, tested similar walking conditions, tested sufficient participants and used standard data-processing techniques. For untethered exoskeletons, the most relevant outcome is the percent change in the energy cost of walking with exoskeleton assistance to walking in normal shoes without the exoskeleton. Changes in walking conditions can affect outcomes, so we considered studies conducted at within 10% of the speeds and inclines that we tested. Before conducting our final experiment, we selected the speeds (1.25 m s−1 and 1.5 m s−1) and inclines (10°) that captured the largest percent reductions in metabolic rate that had previously been observed for any exoskeleton study in the literature. We compared with previous exoskeleton studies with at least five participants, because studies reporting data from fewer tests are difficult to interpret owing to measurement noise and inter-participant variability. We compared with previous studies in which the metabolic cost of walking was calculated using standard techniques, by averaging respirometry measurements during the last 2 min or 3 min of a 5-min or 6-min steady-state treadmill condition. One previous exoskeleton study[65] was excluded because steady-state metabolic cost was computed by taking the median of respirometry measurements. We found that using the median rather than the mean to compute metabolic rate in our untethered exoskeleton study increased the magnitude of the reductions in metabolic cost by an average of 7% across participants. This is a large amount compared with the total improvement of 23%, indicating that the median and mean measurements are not equivalent. We were not able to obtain the data from the previous study that would have allowed computation of the mean percent change in metabolic rate.

To keep Extended Data Fig. 5 legible, we only depict studies reporting results within a 5% change in metabolic rate of the best previous value for that condition category. There are several other untethered exoskeletons that have provided some reduction in metabolic rate under conditions similar to those tested in this study. For example, the Dephy ExoBoot, the commercially available exoskeleton with the most similar features to the prototype tested in this study, can provide a 5.2% reduction in metabolic energy consumption compared with walking with Normal Shoes while walking on a treadmill with time-varying speed[64]. Another technologically mature untethered exoskeleton, the MyoSuit Beta, has shown that hip assistance during outdoor uphill walking can reduce metabolic rate compared with wearing the exoskeleton in Zero Torque mode[37]. Sufficient data are not yet available to estimate the benefits compared with walking without the exoskeleton. In the interests of clarity, we did not include the results of all previous exoskeleton experiments in Extended Data Fig. 5.

We compared the results of this study against all types of lower-limb exoskeleton, including devices that assist the knees and hips, to provide the clearest understanding of the relative benefits of this design and personalization approach. Considering instead only ankle exoskeletons would allow for a more mechanistic comparison of system components and biomechanics outcomes, at the cost of reduced generality of the high-level findings. As exoskeleton technologies mature and address more tasks and populations, joint-specific benefits or restrictions related to specific conditions may make it more sensible to apply joint-specific comparisons in some contexts.

Our untethered exoskeleton provided the largest reductions in the metabolic cost of walking primarily owing to the way it personalized assistance to individual users, but hardware design differences may also have contributed to its efficacy. Design differences between the untethered exoskeleton and some previous devices include: directly measuring joint torque, rather than inferring it from motor current; providing slack in the transmission to avoid interference during leg swing and Zero Torque mode; and larger peak torque capabilities, such that benefits were limited more by the user’s ability to accept assistance than by limitations in the hardware. Directly measuring joint torque requires additional electronics hardware for sensing and signal processing but enables more precise control of applied torques, which eliminates errors owing to model mismatch and power losses in the transmission and interface with the body. This helps provide users with a consistent assistance pattern. Placing slack in the transmission cable during periods when zero torque is desired prevents the inadvertent application of the small damping torques needed for linear feedback control. Although they may seem small, these damping torques can substantially increase user effort. Allowing for larger peak torques, in this case approximately twice the value of previous untethered ankle exoskeletons10,17,64,66, allowed for a larger range of possible assistance parameters. This makes it more likely that the global optimum for a given participant and walking speed lie within the range of hardware-feasible control. Larger torques require a rigid frame to react out transmission forces in the exoskeleton joint[43], rather than through shear on the skin[63], to maintain user comfort. The present results would therefore seem to favour devices that can apply higher torques to achieve greater benefits from assistance, at the cost of greater worn mass. This relationship, however, will be sensitive to the populations and tasks that are assisted. The above design decisions enabled the untethered ankle exoskeleton in this study to provide accurate, reliable and substantial assistance to the user, which enabled participants to obtain large net benefits from real-world personalized assistance.

Participant surveys on exoskeleton usability

Participants completed a series of surveys to evaluate the ease of use, comfort and functionality of the untethered exoskeleton after completion of all the experiments. Participants completed a System Usability Scale survey[67] to determine how easy it was to operate the untethered exoskeleton. Users reported that the exoskeleton was relatively easy to use, with an overall score of 72.5 (Extended Data Table 3), placing it in the 65th percentile of 5,000 devices previously surveyed[42]. Participants also completed surveys adapted from the Orthotics and Prosthetics Users’ Survey[68], which acts as a self-report instrument for evaluating the outcomes of prosthetics and orthotics services in a clinically useful manner. Among comfort-related outcomes, participants were most likely to agree that the weight of the device was manageable, that it was easy to put on and that their clothes were free of wear (Extended Data Table 4). Participants were more likely to be neutral or to disagree that the exoskeleton would be comfortable throughout the day. Among outcomes related to functionality, participants found standing, walking indoors and outdoors, and donning and doffing the exoskeleton to be easy or very easy (Extended Data Table 5). Participants found picking objects up from the ground and walking up steep ramps to be slightly difficult. When asked whether they would prefer to use the exoskeleton or normal shoes if they had to complete the outdoor walking course again, six out of the ten participants reported that they would prefer to use the exoskeleton.

Reporting summary

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

Data availability

All study data necessary to replicate this work are available in the Source Data included with the paper. Computer-aided design files and a bill of materials for the untethered ankle exoskeleton are provided in Supplementary Data 2. Source data are provided with this paper.

Code availability

Optimization code samples are provided in Supplementary Data 1. This code uses Python version 3.6.1. The required python packages are numpy (1.17.4), scikit-learn (0.21.3), scipy (1.3.2) and matplotlib (2.0.2).

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Acknowledgements

We thank G. R. Tan, D. Miller and V. Chiu for assistance with exoskeleton hardware; K. Poggensee for assistance with accessing previous experimental data; W. Peisch for assistance with mechanical design; and W. Gu, O. Chaudhuri, A. Okamura, B. Roth, S. Slade and L. Lau for editorial suggestions. Funding: National Science Foundation Graduate Research Fellowship DGE-1656518 (P.S.), Stanford Graduate Fellowship (P.S.), Wu Tsai Human Performance Alliance Postdoctoral Fellowship (P.S.), NIH Grant P41EB027060 (S.L.D.) and National Science Foundation Grant No. CMMI-1734449 (S.H.C.).

Author information

Authors and Affiliations

1. Department of Mechanical Engineering, Stanford University, Stanford, CA, USA

Patrick Slade, Scott L. Delp & Steven H. Collins

2. Department of Bioengineering, Stanford University, Stanford, CA, USA

Patrick Slade & Scott L. Delp

3. Department of Aeronautics and Astronautics, Stanford University, Stanford, CA, USA

Mykel J. Kochenderfer

Contributions

P.S. contributed to conceptualization, methodology, investigation, visualization and writing. M.J.K. contributed to conceptualization and writing. S.L.D. contributed to conceptualization and writing. S.H.C. contributed to conceptualization, methodology, visualization, and writing.

Corresponding author

Correspondence to Steven H. Collins.

Ethics declarations

Competing interests

S.H.C. is an inventor on a patent application (US patent no. 10,537,283) that covers the emulator systems used in this study.

Peer review

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Nature thanks Carlos Rodriguez Guerrero, Zachary Lerner 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 Weights, inputs, and effects for the data-driven classification model.

The data-driven classifier decoded latent information from human movement that was not otherwise interpretable, allowing exoskeleton assistance to be optimized without laboratory-based measurement equipment. Top row: Model weights and mean inputs. The model compares data from two control laws at a time and associates inputs with higher or lower metabolic rate to estimate which control law resulted in a lower metabolic rate. Inputs comprised differences in ankle angle and ankle angular velocity at 30 different points in the gait cycle and differences in the four control law parameters of peak torque magnitude, peak time, rise time, and fall time. The data-driven model weights that multiply these differences are shown as a background colour of blue or red. Blue indicates that a positive difference is associated with lower metabolic rate, while red indicates that a positive difference is associated with higher metabolic rate. Darker colours indicate greater influence. Black lines depict the average, across all training data, of the differences in inputs. To generate this average, we ordered each pair-wise comparison by metabolic rate, such that inputs from the control law with a higher metabolic rate were always subtracted from those with a lower metabolic rate. Typical values of the model inputs differ, in part because of differences in units, and so the magnitudes of model weights do not correspond well to the contributions of those terms to the classification overall. Bottom row: The classification contributions of each term in the model, averaged over the entire training set. The percent contribution is calculated as the absolute value of the product of the model weight and the input difference, summed over all pair-wise comparisons, divided by the sum over all model terms. For the x-axes, 0% and 100% of the gait cycle refer to the instant of heel strike of the assisted limb at the beginning and end of one stride. Toe-off occurs at about 62% of the gait cycle. For a discussion of the intuitive meaning of the weights and contributions, please see the Methods subsection “Data-driven optimization”.

Source data

Extended Data Fig. 2 Speed-adaptive control approach.

a, To calibrate the walking speed estimator, data are collected while the participant walks at several prescribed speeds, each within the range of speeds associated with a set of assistance parameters to be optimized. The measured stride durations and ground-truth speed measurements from those tests are used to fit an affine equation with linear regression. b, The resulting model can then be used to estimate walking speed based on measurements of stride duration alone. c, The speed-adaptive controller relates estimated walking speed to exoskeleton assistance parameters by interpolating between assistance parameters specified at a set of chosen speeds. In this case, there are three sets of optimized parameters corresponding to three different walking speeds.

Extended Data Fig. 3 Diagram of untethered exoskeleton electromechanical hardware.

These computer-aided design drawings depict the hardware elements of the untethered exoskeleton. The primary components are labelled. An image of the entire device, including textile components, can be found in Fig. 4b. A running shoe (not pictured) is attached to the toe strut with pins that extend from the tip of the toe strut into a carbon fiber plate embedded in the sole of the shoe. The heel of the running shoe is attached to the heel spur by a rope (not pictured) tied into holes on either side of the heel spur and passing through a plastic tube embedded in the sole of the shoe. A Vectran transmission cable (not pictured) transmits force from the drum to the tip of the heel spur. At the top of the calf strut, Velcro straps (not pictured) are connected to the strut through slots. These straps adhere to a separate Velcro strap (not pictured) worn on the shank of the leg, just below the knee. A complete bill of materials and set of computer-aided design files for this untethered exoskeleton assembly is included as Supplementary Data 2.

Extended Data Fig. 4 Optimizing assistance during real-world walking.

The exoskeleton applied speed-adaptive control, which adjusted exoskeleton assistance parameters on each step. Stride duration (tstride) was used to estimate walking speed (v) as described in Fig. 3. While the participant walked, portable sensor data (d) were collected, which included ankle angle (θ), ankle velocity (\(\dot{{\boldsymbol{\theta }}}\)), and the control law defining exoskeleton assistance torque (C). If sufficient continuous strides (z) were not collected before the bout finished, the data were discarded and evaluation of the same control law began anew on the next walking bout. If sufficient continuous strides were collected, then data were stored for the associated control law number (n) and walking speed bin (b), selected based on the average walking speed for the collected strides. The control law number was incremented and the next control law was applied to the user. After six control laws had been applied for a given walking speed bin, forming one generation for the optimizer, the stored data were used to update the optimization parameters associated with that speed bin. When any bin performed an update, the estimate of the optimal parameter values (μ) for the other bins were also updated. Bins that were closer to convergence, indicated by a small value of the convergence parameter (σ) for that bin, were adjusted less. This approach allowed the optimizer to rapidly adapt to the participant early in the optimization, then to fine-tune the speed-specific parameters as the optimization progressed. Following the update, the optimizer selected a promising set of new control laws to be sequentially evaluated in the next generation for the associated walking speed bin.

Extended Data Fig. 5 Exoskeleton comparison for standardized walking conditions.

We compared the benefits of Real-world Optimized assistance from the untethered exoskeleton under standardized laboratory conditions to those of prior untethered exoskeletons10,11,12,13,14,15,16. We considered only the results of tests that: compared exoskeleton-assisted outcomes to walking in normal shoes without an exoskeleton; used standard indirect respirometry procedures; had sufficient sample sizes; and applied walking conditions within 10% of the chosen walking speeds and inclines in this study, which were chosen to allow comparison to the largest prior improvements in metabolic rate. For legibility, in this figure we depict only results within a 5% reduction in net metabolic cost of the best prior results for each category. Please see the Methods subsection “Comparison to other untethered exoskeletons” for a complete explanation of the methods used to select amongst prior exoskeleton experiments. Real-world Optimized assistance from the untethered exoskeleton resulted in large improvements in energy cost.

Source data

Extended Data Fig. 6 Additional untethered exoskeleton treadmill condition evaluations.

For one pilot participant (n = 1), walking with Real-world Optimized assistance reduced the metabolic cost of walking compared to Normal Shoes during several additional treadmill conditions. These results suggest that Real-world Optimized assistance may perform well during a wide range of common walking activities. The conditions were walking at 1.25 m s−1 on a 5° incline, walking at 1.5 m s−1 while wearing a vest weighing approximately 20% body weight, and climbing stairs at a rate of 50 steps per minute. These results are not included in the main text due to their preliminary nature compared to the primary study outcomes.

Source data

Extended Data Table 1 Randomized condition ordering for the outdoor evaluations in the real-world exoskeleton optimization experiment

Extended Data Table 2 Randomized condition ordering for the indoor evaluations in the real-world exoskeleton optimization experiment

Extended Data Table 3 Usability survey results for exoskeleton participants

Extended Data Table 4 Survey results on the comfort of the untethered exoskeleton

Extended Data Table 5 Survey results on the functionality of the untethered exoskeleton

Supplementary information

Reporting Summary

Supplementary Data 1

Sample code consisting of functions used with the opportunistic optimization approach. This simulates an optimization of the exoskeleton torque parameters over multiple generations. This code uses Python version 3.6.1. The required python packages are numpy (1.17.4), scikit-learn (0.21.3), scipy (1.3.2) and matplotlib (2.0.2).

Supplementary Data 2

The computer-aided design files and bill of materials needed to render and replicate the untethered exoskeleton. These computer-aided design files depict the individual hardware elements of the untethered exoskeleton and a complete assembled structure. These files can be used to manufacture and assemble a replica of the system for full reproduction of our untethered ankle exoskeleton.

Supplementary Video 1

A close-up of the untethered exoskeleton assisting a person walking in a public setting. The video is slowed down by a factor of four to allow better visualization of the motor-and-drum transmission applying torque about the ankle joint to assist the person as they extend their ankle and push off of the ground with their toes.

Supplementary Video 2

Real-world optimization of exoskeleton assistance during one hour of walking. Every 44 steps, a new set of assistance parameters were provided to the person. This allowed the optimizer to quickly evaluate many possible assistance parameters. Audio cues provided to the participant through an earpiece prompted them to self-select a naturalistic range of walking speeds. The prompts for each bout of walking are shown as text. Participants also received audio cues to stop walking, the timing of which were chosen to produce a naturalistic distribution of bout durations. Playback is at 15 times the actual speed.

Supplementary Video 3

Validation of Real-world Optimized exoskeleton assistance. The video shows 7 of the 15 outdoor walking bouts used to determine the real-world benefits exoskeleton assistance. Participants completed all walking bouts under three walking conditions: Real-world Optimized exoskeleton assistance, Generic Speed-adaptive exoskeleton assistance and Normal Shoes. Participants walked between yellow cones whose separations were chosen to provide a natural distribution of bout lengths. Different verbal prompts were given to the participant to elicit a naturalistic range of walking speeds. A portable respirometry system measured the ground-truth energy expenditure during movement. The humming of the respirometry air sampling system is audible in the video.

Source data

Source Data Figs. 2–5 and Extended Data Figs. 1, 5 and 6

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Slade, P., Kochenderfer, M.J., Delp, S.L. et al. Personalizing exoskeleton assistance while walking in the real world. Nature 610, 277–282 (2022). https://doi.org/10.1038/s41586-022-05191-1

- Received: 23 February 2022

- Accepted: 04 August 2022

- Published: 12 October 2022

- Issue Date: 13 October 2022

- DOI: https://doi.org/10.1038/s41586-022-05191-1

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Article Open Access Published: 26 September 2022

Layered subsurface in Utopia Basin of Mars revealed by Zhurong rover radar

Chao Li,

Yikang Zheng,

Xin Wang,

Jinhai Zhang,

Yibo Wang,

Ling Chen,

Lei Zhang,

Pan Zhao,

Yike Liu,

Wenmin Lv,

Yang Liu,

Xu Zhao,

Jinlai Hao,

Weijia Sun,

Xiaofeng Liu,

Bojun Jia,

Juan Li,

Haiqiang Lan,

Wenzhe Fa,

Yongxin Pan &

…

Fuyuan Wu

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Nature volume 610, pages 308–312 (2022)

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Abstract

Exploring the subsurface structure and stratification of Mars advances our understanding of Martian geology, hydrological evolution and palaeoclimatic changes, and has been a main task for past and continuing Mars exploration missions1,2,3,4,5,6,7,8,9,10. Utopia Planitia, the smooth plains of volcanic and sedimentary strata that infilled the Utopia impact crater, has been a prime target for such exploration as it is inferred to have hosted an ancient ocean on Mars11,12,13. However, 45 years have passed since Viking-2 provided ground-based detection results. Here we report an in situ ground-penetrating radar survey of Martian subsurface structure in a southern marginal area of Utopia Planitia conducted by the Zhurong rover of the Tianwen-1 mission. A detailed subsurface image profile is constructed along the roughly 1,171 m traverse of the rover, showing an approximately 70-m-thick, multi-layered structure below a less than 10-m-thick regolith. Although alternative models deserve further scrutiny, the new radar image suggests the occurrence of episodic hydraulic flooding sedimentation that is interpreted to represent the basin infilling of Utopia Planitia during the Late Hesperian to Amazonian. While no direct evidence for the existence of liquid water was found within the radar detection depth range, we cannot rule out the presence of saline ice in the subsurface of the landing area.

Main

Subsurface stratification on Mars preserves key records to decipher the geological evolution, the hydrological cycle and the palaeoclimatic and palaeoenvironmental changes of the planet1,2,3,4,5,6,7,8. One efficient tool to investigate the shallow structure of a planet is ground-penetrating radar (GPR) equipped on an exploration rover. As exemplified by recent studies on the Moon14,15,16,17, GPR is capable of imaging the subsurface up to a depth of several hundreds of metres with metre-scale resolution. On Mars, rover GPR is available from two continuing missions, Perseverance[9] and Tianwen-1 (ref. [10]). Such missions with roving GPR capability aim to probe the detailed subsurface structure of the landing areas and to establish the geological framework, as well as find critical components that may constitute a habitable environment on Mars, either presently or in its past.

On 15 May 2021, China’s first Mars mission, Tianwen-1, successfully deployed the Zhurong rover in southern Utopia Planitia, a topographically transitional region between the southern highlands and the northern lowlands of the Martian crustal dichotomy[11] (Fig. 1a). The landing area is mapped as the Late Hesperian lowland unit[18] (Fig. 1b), constituting predominantly the Vastitas Borealis Formation (VBF)[19] that was formed as flood-related outflow-channel sediments reworked by near-surface, volatile-driven processes[13] or as a sublimation residue of a vanished ocean[12]. Not only the origin but also the stratification of the VBF, as well as its postformation history, at present lack observational constraints. These ambiguities hinder a deep understanding of the sedimentation history and associated geological processes of the northern lowlands of Mars.

Fig. 1: Region around the Zhurong rover landing site.

a, Topographic map showing the landing site of Zhurong (red star), as well as the landing sites of the Phoenix, InSight, Curiosity, Perseverance and Viking-2 landers/rovers (orange squares). The purple solid and dashed lines show the locations of Martian palaeoshorelines of ref. [11], roughly delineating the depositional contact of the VBF in the northern plains. b, Simplified geological map near the Zhurong landing site with data from ref. [18]. Scale bar, 200 km. c, Geomorphic map of the Zhurong landing area with data from ref. [22]. Scale bar, 15 km. d, Traverse of the Zhurong rover from 25 May (Sol 11) to 6 September (Sol 113) 2021 on the basemap of a Tianwen-1 High Resolution Imaging Camera image (Sol 19, 2 June 2021). The red star marks the landing site (25.066° N, 109.925° E) and the red line shows the track of the rover. Scale bar, 100 m. Relative distances to the landing site are marked alongside the track.

Utopia Planitia is also characterized by distinctive geomorphic features including giant polygons, pitted cones and layered-ejecta craters that indicate a large body of water/ice might have existed there in the past20,21,22,23,24,25 (Fig. 1c). However, it remains uncertain the extent to which hydraulic sediments are found in the subsurface and whether or not water is still present at depth in this region. East of Utopia Planitia, the volcanic eruption of Elysium Mons (Fig. 1a) during the Late Hesperian to Amazonian resulted in volcanic flows and associated debris flow deposits overlying the central and southeastern Utopia Basin26,27,28, thus representing an episode of widespread resurfacing in Utopia Planitia. However, because of possible subsequent reworking, it is unclear whether or not the volcanic flows of the Elysium eruption or unrecognized late-stage volcanism on Mars has affected the vast plains far from Elysium Mons including the Zhurong landing area where the closest volcanic outcrops are located several hundreds of kilometres to the north (Fig. 1b). Recent geomorphological and chronological studies of the Zhurong landing site suggest that resurfacing probably occurred in this area during the Middle to Late Amazonian epochs23,25, but the nature of such resurfacing events has been poorly constrained.

Multi-layered subsurface structure

The GPR onboard the Zhurong rover, the Rover Penetrating Radar (RoPeR), is equipped with a high-frequency channel (450–2,150 MHz) and a low-frequency channel (15–95 MHz), capable of penetrating 3–10 m and up to roughly 100 m, respectively, below the Martian surface, depending on the dielectric properties of subsurface materials[10]. Between 25 May (Sol 11) and 6 September 2021 (Sol 113), RoPeR acquired radar-sounding data over a distance of roughly 1,171 m with an approximately 8 m increase in elevation southwards from the landing site (Figs. 1d and 2a). In this study, we used the data from the RoPeR low-frequency channel to image with unprecedented high resolution the subsurface structure down to roughly 80 m depth along the traverse of the Zhurong rover, thus providing observational constraints for understanding the sedimentary history and hydrological evolution of Utopia Planitia.

Fig. 2: Imaging result and interpretation of the low-frequency radar data.

a, The low-frequency radar imaging profile, with the uppermost thick black line denoting the topography relative to the landing site. The dashed line above 10 m denotes the estimated bottom of the top layer presumably containing mainly regolith. The two solid lines at depths of around 30 and 80 m represent the contacts between the second and third layers and the base of the third layer, respectively. The two dashed lines at around 10 and 40 m deep roughly separate finer- and coarser-grained rocky blocks within the second and third layers, respectively. b, The interpreted lithologic stratigraphy based on radar imaging. c, The variation of dielectric permittivity with depth. The red line is the averaged 1D dielectric permittivity profile and the bounding grey band denotes the variations around the average dielectric permittivity at each depth. Dielectric permittivity below roughly 80 m is not well constrained (see text for details).

We analysed the data from the RoPeR low-frequency channel (Extended Data Fig. 1) and constructed the radar reflection profile with a series of processing procedures, including preprocessing, noise attenuation, migration and topographic correction with optimal parameters (Methods and Extended Data Figs. 2–5). The resultant radar profile shows depth-varying reflection characteristics within the depth range of 10–80 m (Fig. 2a and Extended Data Fig. 5), which is the focus of our analysis and interpretation. We also estimated the dielectric permittivity (without considering dielectric loss) in the depth range of 0–80 m on the basis of diffraction analysis (Methods and Extended Data Fig. 6).

According to the pattern of reflection characteristics and the estimates of dielectric permittivity, we divide the subsurface structure into four layers (Fig. 2a,b). The first layer is no thicker than 10 m, with an average dielectric permittivity ranging from 3–4 (Fig. 2c). However, the top part of the low-frequency radar profile is highly contaminated by strong artefacts (Fig. 2a and Extended Data Fig. 5), probably associated with multiple reflections between the rover and the ground surface. Thus, it is hard to determine the depth of the base of this top layer and separate it from the underlying materials. The second layer, extending from 10 to 30 m, in which the radar reflections are discontinuous and distributed unevenly (that is, matrix supported), nonetheless shows a general weak-to-strong change with depth accompanied by an increase in average dielectric permittivity to 4–6 (Fig. 2c). No sharp interface is observed within this layer (Fig. 2a). These features suggest that the second layer contains rocky blocks, the clast sizes of which increase with depth.

The third layer is within the depth range of 30–80 m, which has a similar weak-to-strong reflection variation pattern to the second layer, but with stronger reflections and higher values of average dielectric permittivity (ranging from 6–7, Fig. 2c), suggesting that there are larger rocky blocks distributed more evenly (that is, clast supported) at greater depth than in the overlying layer. The third layer also shows no obvious interface within it, indicating a relatively gradual change in clast size with depth between the upper and the lower parts of the layer. The bottom of this third layer is not imaged without ambiguity, either because there is no sharp stratigraphic contact or the energy of radar reflections gradually decays at depths of roughly 70–80 m (Fig. 2a). In the basal layer below approximately 80 m, the fourth layer, radar reflections are too weak and too diffuse to identify any coherent structure, suggesting that this layer is either out of the detection range of the low-frequency channel or characterized by weak internal reflections. Such ambiguity precludes further interpretation of the basal layer. Our numerical simulation results of the expected radar response of rocky blocks of various sizes are broadly consistent with the observed low-frequency data (Extended Data Figs. 7 and 8), which supports the stratigraphic interpretation of the radar reflections in terms of both the variations in grain/clast size and the spatial distribution of rocky blocks.

Basin infilling and resurfacing

The data from the RoPeR low-frequency channel show a multi-layered subsurface structure beneath the Zhurong landing area in southern Utopia Planitia, the first of its kind identified on Mars. The uppermost layer with a thickness of less than 10 m is interpreted as the Martian regolith. The second and third layers are taken to represent two fining-upwards sequences. The upper sequence is roughly 20 m thick and probably constitutes small boulders and cobbles in its lower portion. The lower sequence is much thicker, up to around 50 m thick, and the observed enhanced radar reflections, in combination with the synthetic modelling results, indicate the existence of metre-scale boulders in its lower part (Fig. 2 and Extended Data Figs. 7 and 8).

In addition to this layering, an important structural feature is the smooth transitions between layers (Fig. 2a and Extended Data Fig. 8a,b), in contrast to the subsurface structure at the InSight landing site in western Elysium Planitia where sharp interfaces are imaged in between basaltic and sedimentary strata[29]. Such a difference, combined with the geological and geomorphic observations (Fig. 1b), indicates that, in the top 80 m beneath the Zhurong landing area, any intact layer of consolidated lava flows from the Elysium eruption or unrecognized late-stage volcanism may have been either absent or too thin to survive subsequent reworking. Otherwise, there would be a strong reflection interface at the base of this layer resulting from the large dielectric contrast between basalt and sedimentary rock. This interpretation is also supported by the average dielectric permittivity of 3–7 (Fig. 2c), which differs greatly from that of the Amazonian Elysium volcanic unit (around 9), but agrees with the value of the VBF (around 5) estimated by the Mars Advanced Radar for Subsurface and Ionosphere Sounding within the similar depth range[30].

The thin upper sequence at depths of 10–30 m (Fig. 2) could reflect Amazonian resurfacing in the Zhurong landing area23,25. On the basis of the crater diameter-rim height relation for fresh craters[31], a reduction in the crater population with diameters less than 1.1 km because of a Middle Amazonian resurfacing event at around 1.6 billion years ago (Ga)[23] corresponds to a thickness of roughly 40 m for the infilling materials of the craters, suggesting that the Middle Amazonian resurfacing could account for subsurface materials to more than 30 m deep. Thus, it is reasonable to consider the upper sequence to be the result of resurfacing since around 1.6 Ga. Long-term weathering and repeated impacts are two surface processes possibly involved in the Amazonian resurfacing and potentially responsible for the upper fining-upwards sequence32,33 (Fig. 3b). Either of these two processes, or a combination, have been proposed for the formation of similar near-surface fining-upwards sequences on Mars[34] and the Moon[17]. Alternatively, aqueous processes involving sedimentation to account for the Amazonian resurfacing (Fig. 3b) also need consideration. In the Zhurong landing area, the widespread presence of Amazonian-aged layered-ejecta craters[35] and pitted cones that potentially have a mud-volcano origin[24] (Fig. 1c) indicate the possible occurrence of cryosphere-fracturing-induced transient floods, especially during a period of high obliquity in the Amazonian36,37, which may have led to the deposition of the upper sequence.

Fig. 3: Conceptual model of the ancient resurfacing of southern Utopia Planitia of Mars.

a, During the Late Hesperian to Early Amazonian a catastrophic flood event occurred, leading to the formation of a fining-upwards sequence of conglomerate deposits as the flood discharge subsided, corresponding to the upper VBF. Scale bar, 20 m. b, A resurfacing event probably associated with a transient flood, or long-term reworking by weathering or repeated impacts, occurred in the Amazonian, resulting in a fining-upwards sequence with relatively smaller boulders and cobbles atop the consolidated older sediments. c, The subsequent loss of water to high latitudes resulting from the modern high obliquity of Mars led to the formation of the dry near-surface regolith and dominantly aeolian deposition/erosion processes at present.

The thicker lower sequence in the depth range of 30–80 m (Fig. 2) may represent an older, probably more substantial resurfacing event of the Zhurong landing site. This resurfacing could be Late Hesperian–Early Amazonian in age, given the consistent 3.5–3.2 Ga ages from the crater size–frequency distribution over various spatial ranges in southern Utopia Planitia21,23,24. Both the predominance of the Late Hesperian VBF around the landing area13,19,21 and the relatively low dielectric permittivity (Fig. 2c) similar to that of the VBF[30] suggest that the lower sequence may represents an upper portion of the VBF deposits, which could have a thickness of up to roughly 270 m at the landing area[23]. In this scenario, the fining-upwards nature of the sequence indicates that the deposition of the VBF may have been related to the rapid catastrophic flooding of southern Utopia Planitia[21] (Fig. 3a).

Possible existence of subsurface ice

One of the primary goals of RoPeR is to probe whether there is subsurface water/ice in southern Utopia Planitia, particularly as distinctive geomorphic features in this region suggest that a substantial amount of water/ice might have existed in the geologic past. Our low-frequency radar imaging profile shows radar signals within the depth range of 0–80 m (Fig. 2a), precluding the existence of a water-rich layer within this depth range as the existence of water would strongly attenuate the radar signals and diminish the visibility of deeper reflections. The estimated low (less than 9) dielectric permittivity (Fig. 2c) further supports the absence of a water-rich layer as water-bearing materials typically have high (greater than 15) dielectric permittivity[7]. We further tested this assessment with thermal considerations by conducting a heat conduction simulation based on available thermal parameters estimated from previous studies (Methods). Our thermal simulation results (Extended Data Fig. 9d) show that the Zhurong landing area has an annual average temperature of around 220 K in the RoPeR detection depth range, which is much lower than the freezing point of pure water (273 K), and also lower than the eutectic temperatures of typical sulfate and carbonate brines, but slightly above those of perchlorate brine systems[38]. This observation suggests that the shallow subsurface of the Zhurong landing area could not stably contain liquid water nor sulfate or carbonate brines, consistent with the radar imaging result.

The combination of our temperature estimates (Extended Data Fig. 9) and radar image (Fig. 2a) suggests that the presence of perchlorate brine is possible, but might only occur deeper than roughly 80 m. At this stage, we cannot rule out the existence of saline ice in the presence of sulfate or carbonate, as the dielectric permittivity of these materials (2.5–8) is indistinguishable from rocky materials (Fig. 2c). Liquid water is proposed to exist under the polar ice caps of Mars7,39, and water ice is also reported to be present at shallow depths of low-to-mid-latitude regions40,41,42. Our results from southern Utopia Planitia do not provide evidence for the presence of water in the upper roughly 80 m. Liquid water and/or brines, if they exist, may have been buried at greater depths (Fig. 3c), mostly beyond the penetrating depth of RoPeR.

Methods

RoPeR data processing

The RoPeR is equipped with two channels, including a high-frequency channel with an operating frequency range of 450–2,150 MHz and a low-frequency channel with a bandwidth ranging from 15 to 95 MHz. Note that the so-called high-frequency channel and low-frequency channel were defined specifically for the RoPeR onboard the Zhurong rover[10], and therefore the same terms are used here. The high-frequency channel has a penetrating depth of about 3–10 m with a vertical resolution of a few centimetres, and the low-frequency channel can penetrate down to roughly 80 m deep with a metre-scale vertical resolution, depending on the dielectric properties of subsurface materials[10]. In this study, we processed the data from the low-frequency channel of RoPeR (Extended Data Fig. 1) to image the subsurface structure. The data processing for the RoPeR low-frequency channel consists of the following steps.

1. (1)
Self-test trace removal. The self-test traces from the RoPeR low-frequency channel are used specifically for checking the status of the RoPeR module. These traces therefore contain no effective subsurface information and should be excluded before processing. All the self-test traces were identified by the character string ‘Self_Test’ in the corresponding data label file and then removed from the raw data. The RoPeR low-frequency channel has a total of 2,863 traces after removing self-test traces (Extended Data Fig. 2a).

2. (2)
Trace-spacing regularization. The low-frequency channel of RoPeR took measurements every 25–50 cm, depending on the operation parameter. After 17 August 2021, the trace spacing of the low-frequency channel changed from 50 to 25 cm. We regularized the trace spacing by downsampling the data after 17 August 2021. The regularized data have 2,289 traces with an average trace spacing of 50 cm (Extended Data Fig. 2b).

3. (3)
Direct current shift removal. The direct current shift was estimated as the average of the samples before time zero and was subtracted from each trace (Extended Data Fig. 2c).

4. (4)
Time zero correction. The time zero of the low-frequency channel is 212.5 ns according to the Ground Research and Application System (GRAS) of China’s Lunar and Planetary Exploration Programme. Thus, the data before time zero were removed[10] (Extended Data Fig. 2c).

5. (5)
Background removal. This step, essentially subtraction of the mean related to background, was achieved by subtracting the average value of each segment of the traces (Extended Data Fig. 3a).

6. (6)
Band-pass filtering. On the basis of the operating frequencies of low-frequency channel[10], we applied band-pass filtering between 15 and 95 MHz to enhance the signal-to-noise ratio. This step, however, leads to less noticeable changes in the data compared with the previous step.

7. (7)
Automatic gain control. With increasing propagation depth, the energy of radar echoes decreases gradually. Thus, we systematically applied automatic gain control to boost the energy of radar echoes from deep reflectors (Extended Data Fig. 3b).

8. (8)
Random noise attenuation. Random noise was suppressed to enhance the visibility of the whole profile[43] (Extended Data Fig. 3c). We adopted a streaming orthogonal prediction filtering method for denoising, which has proved to be capable of effectively eliminating random noise while preserving real signals[43].

9. (9)
Migration. Using the velocity model estimated by diffraction separation and focusing analysis (see Dielectric permittivity estimation by diffraction analysis), the radar profile was migrated and further converted from the time domain to the depth domain to recover both shapes and depths of reflectors16,17 (Extended Data Fig. 4).

10. (10)
Topographic correction. We corrected the topography of the migrated radar profile using the relative elevation information provided by GRAS (Extended Data Fig. 5).

Dielectric permittivity estimation by diffraction analysis

Diffractions are echoes from small-scale subsurface anomalies, such as small blocks and fractures, which carry abundant subsurface velocity information that is crucial for estimating the dielectric permittivity for GPR44,45,46,47. First, we used the plane-wave destruction method48,49 to separate diffractions from reflections. Then, we used those separated diffractions to construct the subsurface macro-velocity model by focusing analysis44,45. Finally, we converted the velocity model into a dielectric permittivity model using the following equation:

$${\varepsilon }_{r}={\left(\frac{c}{v}\right)}^{2},$$

where \({\varepsilon }_{r}\) is the dielectric permittivity, c is the speed of light in vacuum and v is the subsurface velocity.

As shown in Extended Data Fig. 6, from 0 to 80 m depth, there are mainly three layers according to the values of dielectric permittivity, consistent with the reflection pattern shown in Fig. 2a in the main text. Beneath around 80 m deep, the dielectric permittivity is not well constrained because the number of effective radar echoes is insufficient. To better illustrate the depth-dependence of dielectric permittivity, we derived the averaged one-dimensional (1D) dielectric permittivity profile (Fig. 2c) from the two-dimensional (2D) dielectric permittivity image.

Numerical simulation of GPR

We performed numerical simulation of the GPR on the Zhurong rover to verify the validity of our stratigraphic interpretation. According to the radar imaging results (Fig. 2), we designed a numerical model of dielectric permittivity by assuming different layers containing rock clasts of varying size and abundance in a sandy matrix (Extended Data Fig. 7). Such a method has been applied previously for verifying the imaging results of lunar penetrating radar data16,17,50,51. In our interpretation of the radar profile, we focus on the reflection pattern in the depth range in which radar signals are visible. Previous radar simulation results with frequency contents comparable to the RoPeR low-frequency data show that the presence of weak dielectric losses does not noticeably affect the reflection pattern in which radar signals dominate over noise[52]. As there is little liquid water detected within the penetration depth range (0–80 m) of the RoPeR low-frequency data at the Zhurong landing site, the corresponding dielectric loss is expected to be weak and thus not to affect the simulated reflection pattern. Therefore, we neglected dielectric loss in the numerical model. We applied the finite-difference method to solve the Helmholtz equation in the numerical simulation16,17,50,51. We compared the simulation results with the observation and adjusted the sizes and spatial distribution of rocks (Extended Data Fig. 7) until the synthetics and the data show similar depth-dependent variations in both the reflection pattern and average strength envelope, as well as dielectric permittivity (Extended Data Fig. 8).

Thermal simulation for the Zhurong and Phoenix landing sites

To investigate the possibility of the presence of liquid water or brine in the subsurface, we calculated the three-phase diagrams (gas–liquid–solid) of briny water under the thermal and lithostatic conditions at the Zhurong landing site. For comparison, we also conducted the heat conduction simulation for NASA’s Phoenix landing site, which is located further north (roughly 68° N, Fig. 1a), where ground ice has been detected[39]. The simulation process is provided below and results are presented in Extended Data Fig. 9.

1. (1)
Temperature estimation. In the subsurface where conduction dominates, we can get the heat conduction equation as

$$\frac{\partial T}{\partial t}=\kappa \left(\frac{{\partial }^{2}T}{{\partial }^{2}z}\right),$$

where \(T(z,t)\) is temperature as a function of depth \(z\) and time t, \(\kappa \) is thermal diffusivity and is approximately set to 1 × 10−6 m^2 s−1 according to the thermal diffusivity of ice[53] or sandstone[54]. On the ground surface (z = 0), T(0,t) can be described in the form of sine series expansion:

$$T\left(0,t\right)={T}_{0}+\mathop{\sum }\limits_{i=1}^{n}{A}_{0i}\sin \left(\frac{2i{\rm{\pi }}}{P}t+{\phi }_{0i}\right),$$

where P is the duration (that is, 1 year in our calculation), T_0 is the annual average ground temperature, A_0 is the amplitude, φ_0 is the initial phase and i is the order of expansion. Thus, the heat conduction equation can be written as[53]

$$T\left(z,t\right)={T}_{0}+\gamma z+\mathop{\sum }\limits_{i=1}^{n}{A}_{0i}{{\rm{e}}}^{-\sqrt{\frac{i{\rm{\pi }}}{\kappa P}}z}\,\sin \left(\frac{2i{\rm{\pi }}}{P}t+{\phi }_{0i}-\sqrt{\frac{i{\rm{\pi }}}{\kappa P}}z\right),$$

where γ is a constant to describe the thermal gradient \(\partial T/\,\partial z\), which can be determined by Fourier’s law:

$$\gamma =\frac{\partial T}{\partial z}=\frac{{Q}_{0}}{{k}_{0}},$$

where the average thermal conductivity k_0 of Martian subsurface up to 140 m was set to be 0.8 Wm−1 K−1 (ref. [55]); an average heat flux (Q_0) of 18 mW m−2 was selected from the present-day heat flow model of Mars[56].

The annual surface temperatures (z = 0) at the Zhurong and Phoenix landing sites (Extended Data Fig. 9a,b) were calculated using the Mars Climate Database[57]. Given that the RoPeR data used in this study were obtained from 25 May to 6 September 2021 (UTC), when the solar longitude Ls varied from 50 to 95 and the solar day from Sol 11 to Sol 113, we specifically analysed the temperature–pressure crossplots (Extended Data Fig. 9d) at Ls = 50–95 (Sol 11–Sol 113) for the Zhurong landing site. As we consider the seasonal changes of temperature here, the temperature in the shallowest 1 m, where diurnal temperature change dominates, is not presented in Extended Data Fig. 9d.

2. (2)
Lithostatic pressure calculation. Lithostatic pressure PL was calculated by \({P}_{L}=\rho {gz}\), where g is the gravitational acceleration on Mars, which is 3.693 m s−2. Density, \(\rho \), was set to linearly increase from 1,211 to 1,500 kg m−3 with depth on the basis of the parameters given in refs. 58,59.

3. (3)
Three phases of water and eutectic points of brines. The triple point of water is located at 273 K, 612 Pa. The eutectic points of possible brines38,60,61 are plotted for comparison (Extended Data Fig. 9d).

Data availability

The Tianwen-1 data including the Mars Rover Penetrating Radar (RoPeR) data and the High Resolution Imaging Camera image used in this study are processed and produced by the GRAS of China’s Lunar and Planetary Exploration Programme, and provided by CNSA at https://clpds.bao.ac.cn/web/enmanager/mars1. The dataset containing the imaging result of the RoPeR low-frequency data, the estimated 2D dielectric permittivity model and the averaged 1D dielectric permittivity profile can be accessed at the World Data Center (WDC) for Geophysics, Beijing (https://doi.org/10.12197/2022GA018). Source data are provided with this paper. Other datasets generated and analysed in this study are available from the corresponding author upon reasonable request.

Code availability

The codes used in this study are available to interested researchers upon request.

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Acknowledgements

We acknowledge the China National Space Administration (CNSA), China’s first Mars exploration mission (Tianwen-1) team, the GRAS, and the payload team of GPR. We are grateful to R. Mitchell for constructive comments and editing the manuscript. We thank Y. Lin, H. Lin, S. Hu, B. Zhou, Y. Su, J. Liu, S. Dai, Y. Li, F. Zhang, C. Xu, X. Liang, L. Chen and Z. Zhang for their helpful discussions during this work. This study is supported by the Institute of Geology and Geophysics, Chinese Academy of Sciences (grant no. IGGCAS-202102), the Key Research Programme of the Chinese Academy of Sciences (grant no. ZDBS-SSW-TLC001) and the National Natural Science Foundation of China (grants 42288201 and 41941002).

Author information

Author notes

1. These authors contributed equally: Chao Li, Yikang Zheng, Xin Wang, Jinhai Zhang

Authors and Affiliations

1. Key Laboratory of Earth and Planetary Physics, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China

Chao Li, Xin Wang, Jinhai Zhang, Lei Zhang, Wenmin Lv, Xu Zhao, Jinlai Hao, Weijia Sun, Juan Li & Yongxin Pan

2. Key Laboratory of Petroleum Resource Research, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China

Yikang Zheng, Yibo Wang & Yike Liu

3. College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, China

Yibo Wang, Ling Chen, Juan Li, Yongxin Pan & Fuyuan Wu

4. State Key Laboratory of Lithospheric Evolution, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China

Ling Chen, Pan Zhao, Haiqiang Lan & Fuyuan Wu

5. State Key Laboratory of Space Weather, National Space Science Center, Chinese Academy of Sciences, Beijing, China

Yang Liu

6. Center for Excellence in Comparative Planetology, Chinese Academy of Sciences, Hefei, China

Yang Liu

7. Institute of Remote Sensing and Geographical Information System, School of Earth and Space Sciences, Peking University, Beijing, China

Xiaofeng Liu, Bojun Jia & Wenzhe Fa

Contributions

L.C. and J.Z. designed the research. C.L., Y.Z., X.W., J.Z., Y.W., L.Z., Y.L., W.L., X.Z., J.H., W.S., X.L., B.J., J.L., H.L., W.F. and Y.P. conducted the data analysis. L.Z., L.C. and J.Z. performed the thermal simulation. W.L, J.Z., L.Z., Y.Z. and Y.W. performed the numerical simulation of the GPR data. L.C., P.Z., J.Z., X.W., Y.L., Y.P. and F.W. contributed to the interpretation of the observations. L.C., X.W., P.Z., J.Z., C.L. and L.Z. wrote the manuscript. All coauthors discussed the results and commented on the manuscript.

Corresponding author

Correspondence to Ling Chen.

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Extended data figures and tables

Extended Data Fig. 1 Low-frequency radar profile of the raw data.

The raw data have a total of 2,945 traces, including self-test traces.

Extended Data Fig. 2 Low-frequency radar profiles after applying a series of data regulation processing.

a, Self-test trace removal. b, Trace spacing regularization. c, Direct-current (DC) shift removal and time zero correction.

Extended Data Fig. 3 Low-frequency radar profiles after applying a series of signal enhancement processing.

a, Background removal. b, Band-pass filtering and automatic gain control (AGC). c, Random noise attenuation.

Extended Data Fig. 4 Low-frequency radar profile after applying migration and time-to-depth conversion.

The velocity model used was constructed by plane-wave destruction and focusing analysis.

Extended Data Fig. 5 Low-frequency radar profile after topographic correction.

The topographic corrections range from ~0.3 m to ~7.9 m.

Extended Data Fig. 6 Dielectric permittivity model.

This model was converted from the velocity model used in migration and time-to-depth conversion.

Extended Data Fig. 7 Numerical model of dielectric permittivity.

This model was constructed according to the stratigraphic interpretation of the radar profile shown in Fig. 2.

Extended Data Fig. 8 Comparison between the observed ground-penetrating radar (GPR) data and synthetic data.

a, Part of the Rover Penetrating Radar (RoPeR) low-frequency data (track distance from 320–420 m). b, The corresponding normalized average strength envelope of a. c, Simulated low-frequency radar data using the model shown in Extended Data Fig. 7. d, The corresponding normalized average strength envelope of c. The peak amplitudes of the strength envelope in d were clipped by 20% to boost the deep record. Both datasets were processed with the same processing procedures as those shown in Extended Data Figs. 2 and 3.

Extended Data Fig. 9 Thermal simulations for the Zhurong and Phoenix landing sites.

a, Temperature distribution based on the heat conduction simulation for the Zhurong landing site. b, The same as a, but for the Phoenix landing site. c, Temperature variation with depth, obtained from the results in a and b. d, Crossplots between temperature and depth/pressure for different solar longitudes (Ls). Phase curves of water (solid lines) and the eutectic points of possible brines (dashed vertical black lines) are presented. The pink dot denotes the triple point of water (273 K, 612 Pa). For the Zhurong radar data used in this paper, Ls 50 (Sol 11) and Ls 95 (Sol 113) represent the starting and the ending solar longitude (solar day), respectively.

Source data

Supplementary information

Peer Review File.

Source data

Source Data Extended Data Fig. 9.

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Li, C., Zheng, Y., Wang, X. et al. Layered subsurface in Utopia Basin of Mars revealed by Zhurong rover radar. Nature 610, 308–312 (2022). https://doi.org/10.1038/s41586-022-05147-5

- Received: 29 November 2021

- Accepted: 26 July 2022

- Published: 26 September 2022

- Issue Date: 13 October 2022

- DOI: https://doi.org/10.1038/s41586-022-05147-5

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Article Open Access Published: 12 October 2022

Maturation and circuit integration of transplanted human cortical organoids

Omer Revah,

Felicity Gore,

Kevin W. Kelley,

Jimena Andersen,

Noriaki Sakai,

Xiaoyu Chen,

Min-Yin Li,

Fikri Birey,

Xiao Yang,

Nay L. Saw,

Samuel W. Baker,

Neal D. Amin,

Shravanti Kulkarni,

Rachana Mudipalli,

Bianxiao Cui,

Seiji Nishino,

Gerald A. Grant,

Juliet K. Knowles,

Mehrdad Shamloo,

John R. Huguenard,

Karl Deisseroth &

…

Sergiu P. Pașca

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Nature volume 610, pages 319–326 (2022)

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Abstract

Self-organizing neural organoids represent a promising in vitro platform with which to model human development and disease1,2,3,4,5. However, organoids lack the connectivity that exists in vivo, which limits maturation and makes integration with other circuits that control behaviour impossible. Here we show that human stem cell-derived cortical organoids transplanted into the somatosensory cortex of newborn athymic rats develop mature cell types that integrate into sensory and motivation-related circuits. MRI reveals post-transplantation organoid growth across multiple stem cell lines and animals, whereas single-nucleus profiling shows progression of corticogenesis and the emergence of activity-dependent transcriptional programs. Indeed, transplanted cortical neurons display more complex morphological, synaptic and intrinsic membrane properties than their in vitro counterparts, which enables the discovery of defects in neurons derived from individuals with Timothy syndrome. Anatomical and functional tracings show that transplanted organoids receive thalamocortical and corticocortical inputs, and in vivo recordings of neural activity demonstrate that these inputs can produce sensory responses in human cells. Finally, cortical organoids extend axons throughout the rat brain and their optogenetic activation can drive reward-seeking behaviour. Thus, transplanted human cortical neurons mature and engage host circuits that control behaviour. We anticipate that this approach will be useful for detecting circuit-level phenotypes in patient-derived cells that cannot otherwise be uncovered.

Main

Human brain development is a remarkable self-organizing process in which cells proliferate, differentiate, migrate and wire to form functioning neural circuits that are subsequently refined by sensory experience[1]. A critical challenge for understanding human brain development, particularly in the context of disease, is a lack of access to brain tissue. By applying instructive signals to human induced pluripotent stem (hiPS) cells grown in three-dimensional (3D) cultures, self-organizing organoids resembling specific regions of the nervous system, including human cortical organoids (hCO; also known as human cortical spheroids) can be generated2,3,4,5,6. However, there are several limitations that restrict their broader applications in understanding neural circuit development and function. Specifically, it is unclear whether maturation of hCO is constrained by the lack of certain microenvironments and sensory inputs that exist in vivo. Moreover, as hCO are not integrated into circuits that can generate behavioural outputs, their utility in modelling genetically complex and behaviourally defined neuropsychiatric diseases is currently limited.

Transplantation of hCO into intact living brains has the potential to overcome these limitations. Previous studies have demonstrated that human neurons transplanted into the rodent cortex survive, project and make connections with rodent cells7,8,9,10,11,12. However, these experiments have typically been performed in adult animals, which probably limits synaptic and axonal integration. Here we introduce a transplantation paradigm in which we transplanted 3D hCO derived from hiPS cells into the primary somatosensory cortex (S1) of immunodeficient rats at an early, plastic developmental stage[13]. Neurons from transplanted hCO (t-hCO) undergo substantial maturation, receive thalamocortical and corticocortical inputs that are capable of evoking sensory responses and extend axonal projections into the rat brain that can drive reward-seeking behaviours. Advanced maturation in t-hCO reveals defects in neurons derived from patients with Timothy syndrome (TS), a severe genetic disease caused by a mutation in the L-type voltage-sensitive calcium channel CaV1.2 (encoded by CACNA1C)[14].

To study human-derived cortical neurons within in vivo circuits, we stereotactically transplanted intact, 3D hCO into the S1 of early postnatal athymic rats (postnatal days 3–7) (Fig. 1a and Extended Data Fig. 1a–c). At this time point, thalamocortical and corticocortical axonal projections have not yet completed their innervation of the S1 (ref. [13]). This approach therefore aims to maximize integration of t-hCO while minimally compromising endogenous circuitry. To visualize the location of t-hCO in living animals, we performed T2-weighted MRI reconstructions of the rat brain at 2–3 months post-transplantation (Fig. 1b and Extended Data Fig. 1d). t-hCO were readily observed and volume measurements of t-hCO were similar to those calculated from fixed slices (Extended Data Fig. 1d,e; P > 0.05). We identified t-hCO in 81% of engrafted animals at approximately 2 months post-transplantation (n = 72 animals; hCO from 10 hiPS cell lines; hiPS cell lines are listed in Supplementary Table 1). Of these, 87% were located in the cerebral cortex (Fig. 1c). By performing consecutive MRI scans at multiple time points in the same transplanted rats, we found that t-hCO increased ninefold in volume over 3 months (Fig. 1d and Extended Data Fig. 1f). The survival rate of transplanted animals was high 12 months after transplantation (74%) (Extended Data Fig. 1g and Supplementary Table 2), and no discernible locomotor or memory deficits, gliosis or electroencephalogram (EEG) abnormalities were detected (Extended Data Figs. 1h–m and 3e).

Fig. 1: Transplantation of human cortical organoids in the developing rat cortex.

a, Schematic of the experimental design. hCO generated from hiPS cells are transplanted at days 30–60 of differentiation into the S1 of newborn athymic rats. b, Coronal and horizontal view T2-weighted MRI images showing t-hCO in the S1 at 2 months post-transplantation. Scale bar, 2 mm. c, Quantification of the success rate of transplantations shown per hiPS cell line (n = 108, numbers inside bars indicate number of t-hCO per hIPS cell line) and cortical or subcortical position (n = 88). d, Coronal MRI images (left; scale bar, 3 mm) and corresponding 3D volume reconstructions (scale bar, 3 mm) showing t-hCO growth over 3 months. e, Overview of example t-hCOs in the rat cortex. Scale bar, 1 mm. f, Representative immunocytochemistry images of t-hCO showing from top left to right (at time in differentiation): PPP1R17 (4 months), NeuN (8 months), SOX9 and GFAP (8 months), PDGFRα; (8 months), MAP2 (8 months) and IBA1 (8 months). Scale bars, 20 μm. Co-expression of HNA indicates cells of human origin. g, snRNA-seq: uniform manifold approximation and projection (UMAP) dimensional reduction visualization of all clustered high-quality t-hCO nuclei after Seurat integration (n = 3 t-hCO samples, n = 2 hiPS cell lines). Astroglia, astrocyte lineage cells; cyc prog, cycling progenitor; GluN DL, deep layer glutamatergic neuron; GluN DL/SP, deep layer and subplate glutamatergic neuron; GluN UL, upper layer glutamatergic neuron; oligo, oligodendrocyte; OPC, oligodendrocyte progenitor cell; RELN, reelin neurons. h, Gene Ontology (GO) term enrichment analysis of genes significantly upregulated (adjusted P < 0.05, fold change > 2, expressed in at least 10% of nuclei) in t-hCO glutamatergic neurons compared with hCO glutamatergic neurons. The dashed line denotes a q value of 0.05. i, UMAP visualization of GluN cell types of t-hCO using label transfer from the adult human motor cortex[22] snRNA-seq reference dataset. CT, corticothalamic cell; ET, extratelencephalic cell; IT, intratelencephalic cell; NP, near-projecting.

We next assessed the cytoarchitecture and gross cellular composition of t-hCO. Antibody staining for rat endothelia revealed vascularization of t-hCO, whereas staining for IBA1 revealed the presence of rat microglia throughout the graft (Fig. 1f and Extended Data Fig. 3c,d). Immuno-stainings identified human nuclear antigen (HNA)-positive cells that co-expressed PPP1R17 (cortical progenitors), NeuN (neurons), SOX9 and GFAP (glial lineage cells) or PDGFRα (oligodendrocyte progenitor cells) (Fig. 1f). To explore the cellular composition of t-hCO with single-cell resolution, we performed single-nucleus RNA sequencing (snRNA-seq) at approximately 8 months of differentiation. Quality filtering and removal of rat nuclei yielded 21,500 high-quality human single-nucleus profiles (Fig. 1g and Extended Data Fig. 4a,b). Expression patterns of canonical cell-type markers identified clusters of major cortical cell classes, including both deep and superficial layer glutamatergic neurons, cycling progenitors, oligodendrocytes and astrocyte lineage cells (Fig. 1g, Extended Data Fig. 4c and Supplementary Table 3). Immunostainings for SATB2 and CTIP2 revealed that, despite the presence of cortical layer subtypes, t-hCO displayed no obvious anatomical lamination (Extended Data Fig. 3a). snRNA-seq of stage-matched hCO yielded broadly similar cell classes with some exceptions, including a lack of oligodendrocytes and the presence of GABAergic neurons, which may reflect in vitro conditions that favour the generation of some ventral progenitors in long-term cultures, as previously reported[15] (Extended Data Fig. 4f–i and Supplementary Table 4). Differential gene expression analysis highlighted substantial differences in glutamatergic neurons between t-hCO and hCO (Supplementary Table 5), including upregulation of gene sets associated with neuronal maturation, such as synaptic signalling, dendrite localization and voltage-gated channel activity (Fig. 1h and Supplementary Table 6). Thus, t-hCO cortical glutamatergic neurons display advanced transcriptional maturation.

To examine whether these transcriptional changes in t-hCO are associated with morphological differences between in vitro hCO and in vivo t-hCO, we reconstructed stage-matched biocytin-filled hCO and t-hCO neurons in acute slices at 7–8 months of differentiation (Fig. 2a). t-hCO neurons were considerably larger with the soma 1.5-fold larger in diameter, twofold more dendrites and, overall, a sixfold increase in total dendrite length than hCO in vitro (Fig. 2b). Moreover, we observed significantly higher dendritic spine density in t-hCO neurons than in hCO neurons (Fig. 2c). This suggests that t-hCO neurons undergo extensive dendritic extension and arborization, which in combination with ongoing cellular proliferation probably contributes to the extensive growth of t-hCO following transplantation (Fig. 1d and Extended Data Fig. 1f). This prompted us to investigate electrophysiological properties. The membrane capacitance was eightfold higher (Extended Data Fig. 8d), the resting membrane potential was more hyperpolarized (by approximately 20 mV) and current injections elicited higher maximal firing rates in t-hCO neurons than in hCO neurons in vitro (Fig. 2d,e), consistent with the larger and more complex morphological features of t-hCO. Furthermore, the rate of spontaneous excitatory postsynaptic current events (EPSCs) in t-hCO neurons was significantly higher (Fig. 2f), indicating that the increase in dendritic spine density observed in t-hCO neurons is associated with an increase in the number of functional excitatory synapses. We confirmed the immature profile of hCO neurons in vitro by  recording from labelled glutamatergic neurons (Extended Data Fig. 6a–c).

Fig. 2: t-hCO neurons undergo advanced maturation.

a, 3D reconstruction of biocytin-filled hCO and t-hCO neurons at 8 months of differentiation. b, Quantification of morphological features (n = 8 hCO neurons, n = 6 t-hCO neurons; **P = 0.0084, *P = 0.0179 and ***P < 0.0001). c, 3D-reconstructed dendritic branches of hCO and t-hCO at 8 months of differentiation. The red asterisks indicate putative dendritic spines. Quantification of dendritic spine density (n = 8 hCO neurons, n = 6 t-hCO neurons; **P = 0.0092). d, Quantification of the resting membrane potential (n = 25 hCO neurons, n = 16 t-hCO neurons; ***P < 0.0001). e, Repetitive action potential firing in hCO and t-hCO induced by increasing current injections, and quantification of the maximal firing rate (n = 25 hCO neurons, n = 16 t-hCO neurons; ***P < 0.0001). f, Spontaneous EPSCs (sEPSCs) in hCO and t-hCO neurons at 8 months of differentiation, and quantification of the frequency of synaptic events (n = 25 hCO neurons, n = 17 t-hCO neurons; ***P < 0.0001). For b–f, hCO and t-hCO from line 1208-2 are taken from the same differentiation batch maintained in parallel. g, Gene set enrichment analysis (one-sided Fisher’s exact test) of genes significantly upregulated (adjusted P < 0.05, fold change > 2, expressed in at least 10% of nuclei) in t-hCO glutamatergic neurons compared with hCO glutamatergic neurons with gene sets of both early-response (ERG) and late-response (LRG) activity-dependent genes identified from an in vivo mouse study[16] and human-specific LRGs from in vitro neurons[17]. The dashed line denotes Bonferroni-corrected P value of 0.05. h, GluN gene expression (pseudobulk and scaled for each gene) across snRNA-seq replicates of LRG genes significantly upregulated in t-hCO glutamatergic neurons. i, Immunostaining showing SCG2 expression in t-hCO (top) and hCO (bottom) neurons. White arrowheads indicate SCG2+ cells. Scale bar, 25 µm. Data are presented as mean ± s.e.m.

In accordance with the increased activity observed in t-hCO in ex vivo slices, snRNA-seq revealed upregulation of activity-dependent gene transcripts in t-hCO compared with hCO in vitro. t-hCO glutamatergic neurons expressed higher levels of late-response activity-regulated genes (Fig. 2g,h) found in previous studies of mouse and human neurons16,17. For example, BDNF[18], SCG2 and OSTN, a primate-specific activity-regulated gene[17], showed increased expression in t-hCO neurons compared with hCO neurons (Fig. 2g–i). Therefore, across transcriptional, morphological and functional analyses, t-hCO neurons displayed features of enhanced maturation compared with hCO neurons.

To further assess how maturation of t-hCO relates to the developing human brain, we performed transcriptomic comparisons with human fetal19,20 and adult21,22 cortical cell types, as well as with developing bulk cortical gene expression data[23] (Extended Data Fig. 5). Consistent with previous work[24], the global transcriptomic maturation state of both hCO and t-hCO at 7–8 months of differentiation broadly matched in vivo developmental timing and was most equivalent to the late fetal period (Extended Data Fig. 5a). Of note, we observed increased transcriptomic maturation of t-hCO compared with age-matched hCO, as well as upregulation of transcripts associated with synaptogenesis, astrogenesis and myelination (Extended Data Fig. 5b–d). At the cell-class level, we found evidence for more refined cortical layer subtypes in t-hCO, with glutamatergic neuron cluster overlap to adult L2/3, L5 and L6 neuronal subclasses (Fig. 1i). By contrast, there was more limited cluster overlap between t-hCO glutamatergic neurons and fetal cortical neurons from the second trimester (Extended Data Fig. 5e–j). To determine whether t-hCO neurons functionally resemble postnatal human neocortical neurons, we performed electrophysiological recordings and anatomical reconstructions of human cortical L2/3 pyramidal neurons in acute slices from postnatal human cortex (Extended Data Fig. 7a). The electrophysiological properties of L2/3 pyramidal neurons were similar to those of t-hCO pyramidal neurons (Extended Data Fig. 7e). Morphologically, L2/3 neurons from postnatal human samples were much more similar to t-hCO than to hCO, although L2/3 cells were longer, contained more branches overall and had higher spine density (Fig. 3g and Extended Data Fig. 7b–d).

Fig. 3: Advanced neuronal features in t-hCO reveal activity-dependent disease phenotypes in human cortical neurons.

a, Transplantation of hCO generated from control and TS hiPS cell lines into newborn rats. b, 3D reconstruction of biocytin-filled t-hCO neurons at 8 months of differentiation. c, Quantification of mean dendrite length (n = 19 control neurons, n = 21 TS neurons; **P = 0.0041). d, 3D-reconstructed dendritic branches from control and TS t-hCO at 8 months of differentiation, and quantification of dendritic spine density (n = 16 control neurons, n = 21 TS neurons, ***P < 0.0001). Red asterisks indicate putative dendritic spines. e, Spontaneous EPSCs in control and TS t-hCO neurons at 8 months of differentiation. f, Cumulative frequency plots and quantification of synaptic event frequency and amplitude (n = 32 control neurons, n = 26 TS neurons; **P = 0.0076 and P = 0.8102). g, Sholl analysis of TS and control neurons in hCO and t-hCO. The dashed line shows postnatal human L2/3 pyramidal neurons for comparison (n = 24 control t-hCO neurons, n = 21 TS t-hCO neurons, n = 8 control hCO neurons and n = 7 TS hCO neurons). Data are presented as mean ± s.e.m.

The ability of t-hCO to recapitulate advanced morphological and functional features in human cortical neurons prompted us to verify whether t-hCO can be used to uncover disease phenotypes. We focused on TS, a severe neurodevelopmental disease, caused by a gain-of-function mutation in the gene encoding CaV1.2, which initiates activity-dependent gene transcription in neurons[14]. We generated hCO from three patients with TS that carry the most common substitution (p.G406R) and from three control participants (Fig. 3a). Following transplantation, we found that TS neurons had an altered dendritic morphology compared with controls (Fig. 3b and Extended Data Fig. 8a,b), with a twofold increase in the number of primary dendrites and an overall reduction in mean and total dendrite length (Fig. 3c and Extended Data Fig. 8c). This was associated with an increase in synaptic spine density and higher frequency of spontaneous EPSCs in TS compared with control neurons (Fig. 3d–f and Extended Data Fig. 8g). Further analysis revealed an abnormal dendritic branching pattern in TS t-hCO versus control, but not in TS hCO in vitro at a similar differentiation stage (Fig. 3g). This is consistent with an activity-dependent dendritic retraction in TS that we previously reported[25] and highlights the ability of this transplantation platform to reveal disease phenotypes in an in vivo context.

We next asked to what extent t-hCO cells functionally integrate into the rat S1. The S1 in rodents receives robust synaptic input from the ipsilateral ventrobasal nucleus and the posterior nucleus of the thalamus, as well as from the ipsilateral motor and secondary somatosensory cortices and contralateral S1 (Fig. 4a). To reconstruct innervation patterns, we infected hCO with rabies-dG-GFP/AAV-G and, after 3 days, hCO were transplanted into the rat S1. At 7–14 days post-transplantation, we observed dense GFP expression in neurons in the ipsilateral S1 and ventrobasal nucleus (Fig. 4b,c). In addition, antibody staining for the thalamic marker netrin G1, revealed the presence of thalamic terminals in t-hCO (Fig. 4d,e). To assess whether these afferent projections were capable of evoking synaptic responses in t-hCO cells, we performed whole-cell recordings from human cells in acute thalamocortical slices[26]. Electrical stimulation of the nearby fibres in the rat S1, internal capsule, white matter, t-hCO or optogenetic activation of opsin-expressing thalamic terminals in t-hCO evoked short-latency EPSCs in t-hCO neurons, which were blocked by application of the AMPA receptor antagonist NBQX (Fig. 4f,g and Extended Data Fig. 9a–g). These data demonstrate that t-hCO become anatomically integrated into the rat brain, and are capable of being activated by host rat tissue.

Fig. 4: Transplanted hCO receive sensory-related inputs.

a, Schematic of the rabies-tracing experiment. b, GFP and human-specific STEM121 expression between t-hCO and the rat cortex (top). GFP expression in the rat ipsilateral ventrobasal (VB) nucleus (bottom left) and the ipsilateral S1 (bottom right) is also shown. Scale bars, 50 μm. Red squares indicate the region of the brain from which the image is taken. c, Quantification of GFP-expressing cells (n = 4 rats). d,e, Netrin G1+ thalamic terminals in t-hCO. In d, a coronal section containing t-hCO and the VB nucleus is shown. Scale bar, 2 mm. In e, Netrin G1 expression and STEM121 expression in t-hCO (left) and VB neurons (right) are shown. Scale bars, 50 µm. Orange dashed line indicates border of t-hCO. f,g, Current traces from t-hCO neurons following electrical stimulation in the rat S1 (f) or the internal capsule (g), with (purple) or without (black) NBQX (left). EPSC amplitude with or without NBQX (n = 6 S1 neurons, *P = 0.0119; and n = 6 internal capsule neurons, **P = 0.0022) (middle). Percentage of t-hCO neurons that displayed EPSCs in response to electrical stimulation of the rat S1 (f) or internal capsule (g) (right). aCSF, artificial cerebrospinal fluid. h, Schematic of the 2P-imaging experiment (left). GCaMP6s expression in t-hCO (middle). Scale bar, 100 μm. Timelapse of GCaMP6s fluorescence (right). i, Z-scored fluorescence of spontaneous activity. j, Schematic of whisker stimulation. k, Single-trial z-scored 2P fluorescence traces aligned to whisker deflection at time zero (dashed line) in an example cell. l, Population-averaged z-scored responses of all cells aligned to whisker deflection at time zero (dashed line) (red) or randomly generated timestamps (grey). m, Schematic of the optotagging experiment. n, Raw voltage traces from an example t-hCO unit during blue laser stimulation or whisker deflection. The red arrowheads indicate the first light-evoked (top) or whisker deflection-evoked (bottom) spike. Grey shading indicates period of whisker deflection. o, Spike waveforms of light and whisker deflection responses. p, Single-trial spiking aligned to whisker deflection in an example cell. 0 indicates whisker deflection (dashed line). q, Population-averaged z-scored firing rates of all light-responsive units aligned to whisker deflection at time zero (dashed line) (red) or randomly generated timestamps (grey). r, Proportion of light-responsive units significantly modulated by whisker deflection (n = 3 rats) (left). Latency to peak z-score (n = 3 rats; n = 5 (light green), n = 4 (dark green) and n = 4 (cyan) whisker deflection-modulated units per rat) (right). Data are presented as mean ± s.e.m.

We next asked whether t-hCO could be activated by sensory stimuli within an in vivo context. We transplanted hCO expressing the genetically encoded calcium indicator GCaMP6s into the rat S1. After 150 days, we conducted fibre photometry or two-photon calcium imaging (Fig. 4h and Extended Data Fig. 10a). We found that t-hCO cells exhibited synchronous, rhythmic activity (Fig. 4i, Extended Data Fig. 10b and Supplementary Video 1). To characterize the spiking activity of t-hCO, we performed extracellular electrophysiological recordings in anaesthetized, transplanted rats (Extended Data Fig. 10c–f). We generated stereotactic coordinates based on images acquired from MRI; these recorded units thus represent putative human neurons, although electrophysiology alone does not permit species-of-origin identification. We observed synchronous bursts of activity (Extended Data Fig. 10d). Spiking bursts were approximately 460 ms in length and were separated by approximately 2-s-long silent periods (Extended Data Fig. 10d,e). Individual units fired on average approximately three spikes per burst, and each burst recruited approximately 73% of recorded units. The activity of individual units was highly correlated, and these correlations were higher than those observed in units identified in non-transplanted animals recorded under the same conditions (Extended Data Fig. 10f). To further characterize the spiking responses of neurons of definitive human origin, we performed optotagging experiments in anaesthetized rats transplanted with hCO expressing the light-sensitive cation channel channelrhodopsin-2 (hChR2), in which t-hCO neurons were identified by their short-latency (less than 10 ms) response to blue light stimulation (Fig. 4m–o). t-hCO neurons displayed spontaneous bursts of activity of similar frequency to those observed with calcium imaging, as well as to electrophysiological recordings performed within t-hCO without optotagging (Extended Data Fig. 10c–g). Spontaneous activity was not observed in stage-matched hCO recorded in vitro. To assess whether t-hCO could be activated by sensory stimuli, we briefly deflected the rat whiskers contralateral to t-hCO (Fig. 4j,m and Extended Data Fig. 10h,k). In accordance with previous studies8,10, a subset of t-hCO cells displayed increases in activity in response to whisker deflection that were not observed when data were aligned to randomized timestamps (Fig. 4k–q and Extended Data Fig. 10h–q). Indeed, approximately 54% of optotagged single units displayed significant increases in firing rates following whisker stimulation that peaked after approximately 650 ms (Fig. 4r). Together, these data suggest that t-hCO receive appropriate functional inputs and can be activated by environmental stimuli.

We next investigated whether t-hCO can engage rat circuits to drive behaviour. We first examined whether t-hCO neurons send axonal projections into surrounding rat tissue. We infected hCO with a lentivirus encoding hChR2 fused to EYFP (hChR2–EYFP). We observed EYFP expression in ipsilateral cortical regions including auditory, motor and somatosensory cortices, as well as in subcortical regions including the striatum, hippocampus and thalamus, 110 days later (Fig. 5a). To assess whether these efferent projections were capable of evoking synaptic responses in rat cells, we optically activated hChR2–EYFP-expressing t-hCO cells while recording from cortical rat cells in acute brain slices. Activation of t-hCO axons with blue light evoked short-latency EPSCs in rat pyramidal cortical neurons, which were blocked by NBQX (Fig. 5b–g). Moreover, these responses could be blocked by application of tetrodotoxin (TTX) and recovered by 4-amino-pyridine (4-AP), indicating that they were evoked by monosynaptic connections[27] (Fig. 5e).

Fig. 5: Transplanted hCO make functional connections onto rat neurons and modulate behaviour.

a, Schematic of axon tracing (left). t-hCO EYFP expression (right). Scale bar, 100 μm. A1, auditory cortex; ACC, anterior cingulate cortex; d. striatum, dorsal striatum; HPC, hippocampus; l. septum, lateral septum; mPFC, medial prefrontal cortex; piri, piriform cortex; v. striatum, ventral striatum; VPM, ventral posteromedial nucleus of thalamus; VTA, ventral tegmental area. Red squares indicate the region of the brain from which the image is taken. b, Schematic of the stimulation experiment. c,d, Example blue light-evoked photocurrents (top) and voltage responses (bottom) in human EYFP+ t-hCO (c) or rat EYFP– cells (d). e,f, Current traces from rat neurons following blue light stimulation of t-hCO axons with TTX and 4-AP (green), TTX (grey) or in aCSF (black) (e), or with (purple) or without (black) NBQX (f). g, Latency of blue light-evoked responses in rat cells (n = 16 cells); horizontal bar indicates mean latency (7.13 ms) (left). Amplitude of light-evoked EPSCs recorded with or without NBQX (n = 7 cells; ***P < 0.0001) (middle). Percentage of rat cells that demonstrated EPSCs in response to blue light (right). h, Schematic of the behavioural task. d0, day 0. i, Performance of an example animal on day 1 (left) or day 15 (right) of training. The mean number of licks performed on day 1 (left) or day 15 (right centre) (n = 150 blue light trials, n = 150 red light trials; ***P < 0.0001). Cumulative lick count across red and blue light trials on day 1 (left centre) or day 15 (right). NS, not significant. j,k, Behavioural performance of all animals transplanted with t-hCO expressing hChR2–EYFP (j) or control fluorophore (k) on day 1 or day 15 (hChR2–EYFP: n = 9 rats, **P = 0.0049; control: n = 9, P = 0.1497). l, Evolution of preference score (n = 9 hChR2, n = 9 control; **P < 0.001, ***P < 0.0001). m, FOS expression in response to optogenetic activation of t-hCO in the S1. Images of FOS expression (left), and quantification (n = 3 per group; *P < 0.05, **P < 0.01 and ***P < 0.001) (right) are shown. Scale bar, 100 μm. Data are presented as mean ± s.e.m. BLA, basolateral amygdala; MDT, mediodorsal nucleus of the thalamus; PAG, periaqueductal grey.

We finally asked whether t-hCO were capable of modulating rat behaviour. To test this, we transplanted hCO expressing hChR2–EYFP into the S1, and 90 days later, we implanted an optical fibre into t-hCO for light delivery. We then trained rats on a modified operant conditioning paradigm (Fig. 5h). We placed animals into a behavioural testing chamber and applied randomly interleaved presentations of 5-s-long blue (473 nm) and red (635 nm) laser stimulations. Animals received a water reward if they licked during the blue light stimulation, but not if they licked during the red light stimulation. On the first day of training, animals showed no difference in their licking behaviour during either blue or red light stimulation. However, on day 15, animals transplanted with hCO expressing hChR2–EYFP showed increased licking during blue light stimulation compared with red light stimulation. These changes in licking behaviour were not observed in control animals transplanted with hCO expressing a control fluorophore (learning success rate: hChR2 89%, EYFP 0%, Fig. 5i–l and Supplementary Video 2). These data suggest that t-hCO cells can activate rat neurons to drive reward-seeking behaviours. To explore what rat neural circuits might be engaged by t-hCO to drive these changes in behaviour, we optogenetically activated t-hCO in trained animals and collected tissue after 90 min. Immunohistochemistry revealed expression of the activity-dependent protein FOS in several brain regions implicated in motivated behaviours, including the medial prefrontal cortex, the mediodorsal thalamus and the periaqueductal grey, and this was not observed in unstimulated control animals or in stimulated control animals that did not express an opsin (Fig. 5m). Together, these data demonstrate that t-hCO can modulate the activity of rat neurons to drive behaviour.

Discussion

Neural organoids represent a promising system to explore human development and disease in vitro, but they are limited by the lack of circuit connectivity that exists in vivo. We developed a novel platform in which we transplanted hCO into the S1 of early-postnatal immunocompromised rats to examine human cell development and function in vivo. We have demonstrated that t-hCO develop mature cell types[28] that are not seen in vitro, and that t-hCO integrate both anatomically and functionally into the rodent brain. Integration of t-hCO into rodent neural circuits allowed us to establish links between the activity of human cells and learned animal behaviour, showing that t-hCO neurons can modulate rat neuron activity to drive behavioural responses.

The platform that we have described has several advantages in comparison to previous studies that have transplanted human cells into the rodent brain7,8,9,10,11,12. First, we transplanted hCO into the developing cerebral cortex of early-postnatal rats, which probably favours anatomical and functional integration. Second, MRI monitoring of t-hCO allowed us to examine graft position and growth in living animals, enabling us to perform long-term studies across multiple animals and establish reliability across multiple hiPS cell lines. Finally, we engrafted intact organoids, rather than a dissociated single-cell suspension, which is less disruptive to human cells and probably facilitates integration and the generation of a unit of human cortical neural cells in the rat brain.

We recognize that despite the advances that this platform offers, there are temporospatial and cross-species limitations that preclude the formation of human neural circuits with high fidelity even after transplantation at early stages in development. For example, it is currently unclear whether the spontaneous activity observed in t-hCO represents a developmental phenotype, similar to rhythmic activity observed during cortical development[29], or is related to the lack of inhibitory cell types present in t-hCO. Similarly, it is uncertain to what extent the lack of lamination in t-hCO might influence circuit connectivity[30]. Future work will be aimed at incorporating other cell types such as human microglia, human endothelial cells and various proportions of GABAergic interneurons, as has been shown in vitro using assembloids[6], as well as understanding how neural integration and processing might be altered in patient-derived t-hCO at the transcriptional, synaptic and behavioural levels.

Overall, this in vivo platform represents a powerful resource to complement in vitro studies of human brain development and disease. We anticipate that this platform will allow us to uncover new circuit-level phenotypes in patient-derived cells that have otherwise been elusive and to test novel therapeutic strategies.

Methods

hCO generation

We generated hCO from hiPS cells as previously described2,5. To initiate the generation of hCO from hiPS cells cultured on feeders, intact hiPS cell colonies were lifted from the plates using dispase (0.35 mg ml−1) and transferred to ultra-low attachment plastic dishes (Corning) in hiPS cell medium supplemented with the two SMAD inhibitors dorsomorphin (5 μM; P5499, Sigma-Aldrich) and SB-431542 (10 μM; 1614, Tocris) and the ROCK inhibitor Y-27632 (10 μM; S1049, Selleckchem). For the first 5 days, the hiPS cell medium was changed every day and supplemented with dorsomorphin and SB-431542. On the sixth day in suspension, neural spheroids were transferred to neural medium containing neurobasal-A (10888, Life Technologies), B-27 supplement without vitamin A (12587, Life Technologies), GlutaMax (1:100, Life Technologies), penicillin and streptomycin (1:100, Life Technologies) and supplemented with the epidermal growth factor (EGF; 20 ng ml−1; R&D Systems) and fibroblast growth factor 2 (FGF2; 20 ng ml−1; R&D Systems) until day 24. From day 25 to day 42, the medium was supplemented with brain-derived neurotrophic factor (BDNF; 20 ng ml−1, Peprotech) and neurotrophin 3 (NT3; 20 ng ml−1; Peprotech) with medium changes every other day. From day 43 onward, hCO were maintained in unsupplemented neurobasal-A medium (NM; 1088022, Thermo Fisher) with medium changes every 4–6 days. For the generation of hCO from hiPS cells cultured on feeder-free conditions, hiPS cells were incubated with accutase (AT-104, Innovate Cell Technologies) at 37 °C for 7 min, dissociated into single cells and seeded into AggreWell 800 plates (34815, STEMCELL Technologies) at a density of 3 × 10^6 single cells per well in Essential 8 medium supplemented with the ROCK inhibitor Y-27632 (10 μM; S1049, Selleckchem). After 24 h, spheroids were collected from each microwell by pipetting medium in the well up and down and transferring it into ultra-low attachment plastic dishes (3262, Corning) containing Essential 6 medium (A1516401, Life Technologies) supplemented with dorsomorphin (2.5 μM; P5499, Sigma-Aldrich) and SB-431542 (10 μM; 1614, Tocris). From day 2 to day 6, Essential 6 medium was changed every day and supplemented with dorsomorphin and SB-431542. From the sixth day in suspension, neural spheroids were transferred to neurobasal medium and maintained as described above.

Transplantation into athymic newborn rats

All animal procedures followed animal care guidelines approved by Stanford University’s Administrative Panel on Laboratory Animal Care (APLAC). Pregnant RNU euthymic (rnu/+) rats were purchased (Charles River Laboratories) or bred in house. Animals were maintained under a 12-h light–dark cycle and provided food and water ad libitum. Three-to-seven-day-old athymic (FOXN1–/–) rat pups were identified by immature whisker growth before culling. Pups (male and female) were anaesthetized with 2–3% isoflurane and mounted on a stereotaxic frame. A craniotomy, about 2–3 mm in diameter, was performed above the S1, preserving the dura intact. Next, the dura mater was punctured using a 30-G needle (approximately 0.3 mm) close to the lateral side of the craniotomy. A hCO was next moved onto a thin 3 × 3-cm parafilm and excess medium was removed. Using a Hamilton syringe connected to a 23 G, 45° needle, the hCO was gently pulled into the most distal tip of the needle. The syringe was next mounted on a syringe pump connected to the stereotaxic device. The sharp tip of the needle was next positioned above the 0.3-mm-wide pre-made puncture in the dura mater (z = 0 mm), the syringe was reduced 1–2 mm (z = approximately –1.5 mm), and until a tight seal between the needle and the dura mater was formed. Next, the syringe was elevated to the centre of the cortical surface at z = –0.5 mm, and the hCO was ejected at a speed of 1–2 µl per minute. After injection of hCO was completed, the needle was retracted at a rate of 0.2–0.5 mm per minute, the skin was closed, and the pups were immediately placed on a warmed heat pad until complete recovery. Some animals were engrafted bilaterally.

MRI of transplanted rats

All animal procedures followed animal care guidelines approved by Stanford University’s APLAC. Rats (more than 60 days post-transplantation) were anaesthetized with 5% isoflurane for induction and 1–3% isoflurane during imaging. For imaging, an actively shielded Bruker 7 Tesla horizontal bore scanner (Bruker Corp.) with International Electric Company (IECO) gradient drivers, a 120-mm inner diameter shielded gradient insert (600 mT/m, 1,000 T/m/s), AVANCE III electronics, eight-channel multi-coil radiofrequency and multinuclear capabilities, and the supporting Paravision 6.0.1 platform were used. Acquisitions were performed with an 86 mm inner diameter actively de-couplable volume radiofrequency coil with a four-channel cryo-cooled receive-only radiofrequency coil. Axial 2D Turbo-RARE (repetition time = 2,500 ms, echo time = 33 ms, 2 averages) 16 slice acquisitions were performed with 0.6–0.8-mm slice thickness, with 256 × 256 samples. Signal was received with a 2-cm inner diameter quadrature transmit–receive volume radiofrequency coil (Rapid MR International, LLC). Finally, 3D volume rendering and analysis were performed using Imaris (BitPlane) built-in surface estimation functions. Successful transplantations were defined as transplantations that resulted in a continuous area of T2-weighted MRI signal in the transplanted hemisphere. Failed transplantations were defined as transplantations that did not result in a continuous area of T2-weighted MRI signal in the transplanted hemisphere. Subcortical t-hCO were excluded from subsequent analyses.

Lentivirus labelling and G-deleted rabies infections

To stably express GCaMP6s in hCO for two-photon calcium imaging, hiPS cells were infected with pLV[Exp]-EF1a::GcaMP6s-WPRE-Puro followed by antibiotic selection. In brief, the cells were dissociated with EDTA and suspended at a density of approximately 300,000 cells in 1 ml in Essential 8 medium in the presence of polybrene (5 µg ml−1) and 15 µl of virus. Cells were then incubated in suspension for 60 min and plated at a density of 50,000 cells per well. Once confluent, cells were treated with 5–10 µg ml−1 puromycin for 5–10 days or until stable colonies appeared. Acute infections of hCO were performed as previously described[5] with a few modifications. In brief, day 30–45 hCO were transferred to 1.5-ml microcentrifuge Eppendorf tubes containing 100 µl neural medium. Next, approximately 90 µl of medium was removed and 3–6 µl of high-titre lentivirus (0.5 × 10^8 to 1.2 × 10^9) was added to the tube, and the hCO were moved to the incubator for 30 min. Next, 90–100 µl medium was added to each tube and the tubes were returned to the incubator overnight. The next day, hCO were transferred to fresh neural medium in low-attachment plates. After 7 days, hCO were moved to glass-bottom 24-well plates for imaging and infection quality assessment. pLV[Exp]-SYN1::EYFP-WPRE and pLV[Exp]-SYN1::hChR2-EYFP-WPRE were generated by VectorBuilder. Lentivirus was used for most experiments as it is incorporated into the host genome permitting reporter expression in the infected cell lineage. For rabies tracing, day 30–45 hCO were co-infected with rabies-ΔG-eGFP and AAV-DJ-EF1a-CVS-G-WPRE-pGHpA (plasmid #67528, Addgene), thoroughly washed over the course of 3 days, transplanted into the rat S1 and maintained in vivo for 7–14 days.

Tissue preparation, immunohistochemistry and quantification

For immunocytochemistry, animals were anaesthetized and transcardially perfused with PBS followed by 4% paraformaldehyde (PFA in PBS; Electron Microscopy Sciences). Brains were post-fixed with 4% PFA for either 2 h or overnight at 4 °C, cryopreserved in 30% sucrose in PBS for 48–72 h, embedded in 1:1, 30% sucrose: OCT (Tissue-Tek OCT Compound 4583, Sakura Finetek), and sectioned coronally at 30 μm using a cryostat (Leica). For immunohistochemistry in thick sections, animals were perfused with PBS, brains were then dissected and sectioned coronally at 300–400 μm using a vibratome (Leica), and sections were then fixed with 4% PFA for 30 min. Cryosections or thick sections were then washed with PBS, blocked for 1 h at room temperature (10% normal donkey serum (NDS) and 0.3% Triton X-100 diluted in PBS), and incubated at 4 °C with primary antibodies in blocking solution. Cryosections were incubated overnight, whereas thick sections were incubated for 5 days. Primary antibodies used were: anti-NeuN (mouse, 1:500; ab104224, abcam) anti-CTIP2 (rat, 1:300; ab18465, abcam), anti-GFAP (rabbit, 1:1,000; Z0334, Dako), anti-GFP (chicken, 1:1,000; GTX13970, GeneTex), anti-HNA (mouse, 1:200; ab191181, abcam), anti-NeuN (rabbit, 1:500; ABN78, Millipore), anti-PDGFRA (rabbit, 1:200; sc-338, Santa Cruz), anti-PPP1R17 (rabbit, 1:200; HPA047819, Atlas Antibodies), anti-RECA-1 (mouse, 1:50; ab9774, abcam), anti-SCG2 (rabbit, 1:100; 20357-1-AP, Proteintech), anti-SOX9 (goat, 1:500; AF3075, R&D Systems), Netrin G1a (goat, 1:100; AF1166, R&D Systems), anti-STEM121 (mouse, 1:200; Y40410, Takara Bio), anti-SATB2 (mouse, 1:50; ab51502, abcam), anti-GAD65/67 (rabbit, 1:400; ABN904, Millipore) and anti-IBA1 (goat, 1:100; ab5076, abcam). Sections were then washed with PBS and incubated with secondary antibodies for either 1 h at room temperature (cryosections) or overnight at 4 °C (thick sections). Alexa Fluor secondary antibodies (Life Technologies) diluted in blocking solution at 1:1,000 were used. Following washes with PBS, nuclei were visualized with Hoechst 33258 (Life Technologies). Finally, slides were mounted for microscopy with cover glasses (Fisher Scientific) using Aquamount (Polysciences) and imaged on a Keyence fluorescence (BZ-X analyzer) microscope or Leica TCS SP8 (Las-X) confocal microscope. Images were processed in ImageJ (Fiji). To quantify the fraction of human neurons in t-hCO and the rat cortex, 387.5-µm-wide rectangular images were taken at the t-hCO centre, edge or from the adjacent rat cortex. The edge of the graft was determined by assessing changes in tissue transparency, the presence of HNA+ nuclei and/or tissue autofluorescence. In each image, the total number of NeuN+ and HNA+ cells was divided by the total number of NeuN+ cells within the same area. To ensure that only cells with nuclei in the imaging plane were counted, only cells that were also Hoechst+ were included in the calculation. Two images, taken at least 1 mm apart, were averaged to reduce statistical error.

Single-nuclei dissociation and gene expression

One week before sample collection, animals transplanted with hCO (approximately 8 months of differentiation) were housed in a dark room and whiskers were trimmed to minimize sensory stimulation. Nuclei isolation was performed as previously described[31] with some modifications. In brief, t-hCO and hCO were disrupted using the detergent-mechanical cell lysis method with a 2-ml glass tissue grinder (D8938, Sigma-Aldrich/KIMBLE). Crude nuclei were then filtered using a 40-μm filter and centrifuged at 320g for 10 min at 4 °C before performing a sucrose density gradient. After a centrifugation step (320g for 20 min at 4 °C), samples were resuspended in 0.04% BSA/PBS supplemented with 0.2 U μl−1 RNAse inhibitor (40 U μl−1, AM2682, Ambion) and passed through a 40-μm flowmi filter. Dissociated nuclei were then resuspended in PBS containing 0.02% BSA and loaded onto a Chromium Single cell 3′ chip (with an estimated recovery of 8,000 cells per channel). snRNA-seq libraries were prepared with the Chromium Single cell 3′ GEM, Library & Gel Bead Kit v3 (10x Genomics). Libraries from different samples were pooled and sequenced by Admera Health on a NovaSeq S4 (Illumina).

Single-nuclei expression analysis

Gene expression levels were quantified for each putative nuclei barcode using the 10x Genomics CellRanger analysis software suite (version 6.1.2). Specifically, reads were mapped to a combined human (GRCh38, Ensemble release 98) and rat (Rnor_6.0, Ensemble release 100) reference genome created using the mkref command and quantified using the count command with –include-introns=TRUE to include reads mapping to intronic regions. For t-hCO samples, human nuclei were identified based on a conservative requirement of at least 95% of total mapped reads aligning to the human genome. All subsequent analyses were performed on the filtered barcode matrices outputted from CellRanger using the R (version 4.1.2) package Seurat (version 4.1.1)[32].

To ensure that only high-quality nuclei were included for downstream analyses, an iterative filtering process was implemented for each sample. First, low-quality nuclei with less than 1,000 unique genes detected and with mitochondrial counts accounting for greater than 20% of the total counts were identified and removed. Subsequently, raw gene count matrices were normalized by regularized negative binomial regression using the sctransform function (vst.flavor=”v2”), which also identified the top 3,000 highly variable genes using default parameters. Dimensionality reduction using principal component analysis (PCA) on the top variable genes was performed, and clusters of nuclei were identified in PCA space by shared nearest-neighbour graph construction and modularity detection implemented by the FindNeighbors and FindClusters functions using a dataset dimension of 30 (dims = 30 chosen based on visual inspection of elbow plot and used for all samples and integration analyses) with default parameters. We subsequently performed iterative rounds of clustering (resolution = 1) to identify and remove clusters of putative low-quality cells based on outlier low gene counts (median below the 10th percentile), outlier high-fraction mitochondrial genes (median above the 95th percentile), and/or high proportions of putative doublets identified by the DoubletFinder package[33] (median DoubletFinder score above the 95th percentile). t-hCO samples (n = 3) and hCO samples (n = 3) were each separately integrated using the IntegrateData function with the above parameters. Another round of quality filtering was subsequently performed on the integrated datasets as described above.

Following low-quality nuclei removal, integrated datasets were clustered (resolution = 0.5) and embedded for visualization purposes with UMAP[34]. Marker genes for each cluster were determined using the FindMarkers function with default parameters, calculated on the normalized gene expression data. We identified and categorized major cell classes through a combination of marker19,20,21,35 gene expression and annotation via integration with reference human fetal and adult cortical datasets. Specifically, cycling progenitors were identified by the expression of MKI67 and TOP2A. The Progenitor clusters were defined by absence of mitotic transcripts, high cluster overlap to the multipotent glial progenitor cell cluster described in the late midfetal cortex[20], and expression of EGFR and OLIG1. We used the term astroglia to encompass multiple states of astrocyte differentiation, from late radial glia through to astrocyte maturation. Astroglia clusters expressed high levels of SLC1A3 and AQP4 and demonstrated mapping to fetal radial glia subtypes and/or adult astrocytes. OPCs expressed PDGFRA and SOX10, whereas oligodendrocytes expressed markers of myelination (MOG and MYRF). Glutamatergic neurons were identified by the presence of neuronal transcripts (SYT1 and SNAP25), absence of GABAergic markers (GAD2) and expression of NEUROD6, SLC17A7, BCL11B or SATB2. GluN neurons were further classified into upper layer (expression of SATB2 and absence of BCL11B) and deep layer (expression of BCL11B) subclasses. Putative subplate (SP) neurons expressed known SP markers[18] such as ST18 and SORCS1, in addition to deep layer GluN markers. Choroid plexus-like cells were defined by expression of TTR and meningeal-like cells expressed fibroblast-associated genes and mapped to leptomeningeal/vascular cells of reference datasets.

Differential gene expression analysis between t-hCO and hCO subclasses was performed using a recently developed pseudobulk approach[36] across sample replicates, implemented with the Libra R package (version 1.0.0). Specifically, the edgeR (version 3.36.0, R package) log-likelihood ratio test was performed between groups on gene counts summed across cells for a given cell class for each sample replicate. For heat map visualizations, counts per million (CPM) normalized expression values were calculated using edgeR (cpm() function) and scaled (to achieve mean = 0, standard deviation = 1). Gene Ontology (GO) enrichment analyses among significantly upregulated t-hCO GluN genes (Benjamini–Hochberg-adjusted P values less than 0.05, expressed in at least 10% of t-hCO GluN cells, and fold change increase of at least 2) were performed using the ToppGene Suite (https://toppgene.cchmc.org/)[37]. We used the ToppFun application with default parameters and reported Benjamini–Hochberg-adjusted P values calculated from hypergeometric tests from GO annotations.

To map our snRNA-seq clusters to annotated cell clusters from reference primary adult single-cell RNA-seq or snRNA-seq studies19,20,21,22, we performed a pairwise dataset integration approach. We used the SCTransform normalization (v2) workflow in Seurat to integrate and compare cluster overlap between datasets (using identical parameters described above). Individual datasets were randomly subsetted to have a maximum of 500 cells or nuclei per original cluster to improve computational efficiency. Using an analogous approach previously described[22], cluster overlap was defined as the proportion of cells or nuclei in each integrated cluster that overlapped with the reference cluster labels. To further classify GluN, we utilized the TransferData workflow in Seurat on GluN subsetted data, to assign reference dataset labels to our GluN cells.

To assess the global transcriptomic maturation state of t-hCO and hCO samples, we compared our pseudobulk samples with BrainSpan/psychENCODE[23], which consists of bulk RNA-seq spanning human brain development. We performed PCA on the combined sample normalized gene expression matrix from cortical samples of 10 postconception weeks and older, subsetted to 5,567 genes (shared with our data) that were previously identified to be developmentally regulated across BrainSpan cortical samples (defined as greater than 50% variance explained by age using a cubic model)[38]. We further obtained genes associated with major neurodevelopmental transcriptomic signatures through non-negative matrix factorization, as previously described[38]. Sample weights calculated from the non-negative matrix factorization procedure are plotted in Extended Data Fig. 5b for each of the five signatures described by Zhu et al.[38]. Similarly, activity-dependent transcriptional markers were obtained from previous published studies. Specifically, significantly upregulated ERGs and LRGs from glutamatergic neurons identified by snRNA-seq collection of the mouse visual cortex after visual stimulation were obtained from Supplementary Table 3 of Hrvatin et al.[16]. Human-enriched LRGs were obtained from human fetal brain cultures activated with KCl and collected 6 h after stimulation, which were filtered for genes significantly upregulated in humans but not rodents[17] (Supplementary Table 4). Gene set enrichment analyses using these gene sets were performed using a one-sided Fisher’s exact test.

Ex vivo t-hCO slice electrophysiology

Rats were anaesthetized with isoflurane and brains were removed and placed in cold (approximately 4 °C) oxygenated (95% O_2 and 5% CO_2) sucrose slicing solution containing: 234 mM sucrose, 11 mM glucose, 26 mM NaHCO_3, 2.5 mM KCl, 1.25 mM NaH_2PO_4, 10 mM MgSO_4 and 0.5 mM CaCl_2 (approximately 310 mOsm). Coronal rat brain slices (300–400 µm), containing t-hCO, were sectioned using a Leica VT1200 vibratome as previously described[39]. Slices were then moved to a continuously oxygenated slice chamber at room temperature, which contained aCSF made with: 10 mM glucose, 26 mM NaHCO_3, 2.5 mM KCl, 1.25 mM NaHPO_4, 1 mM MgSO_4, 2 mM CaCl_2 and 126 mM NaCl (298 mOsm) for at least 45 min before recording. Slice recordings were performed in a submerged chamber where they were continuously perfused with aCSF (bubbled with 95% O_2 and 5% CO_2). All data were recorded at room temperature. t-hCO neurons were patched with a borosilicate glass pipette filled with an internal solution containing 127 mM potassium gluconate, 8 mM NaCl, 4 mM magnesium ATP, 0.3 mM sodium GTP, 10 mM HEPES and 0.6 mM EGTA, pH 7.2, adjusted with KOH (290 mOsm). For reconstruction purposes, biocytin (0.2%) was added to the recording solution.

Data were acquired with a MultiClamp 700B Amplifier (Molecular Devices) and a Digidata 1550B Digitizer (Molecular Devices), low-pass filtered at 2 kHz, digitized at 20 kHz and analysed using Clampfit (Molecular Devices), Origin (OriginPro 2021b, OriginLab) and custom MATLAB (Mathworks) functions. The liquid junction potential was calculated using JPCalc, and recordings were corrected with an estimated −14 mV. The I–V manipulations were constructed from a series of current steps in 10–25 pA increments from –250 to 750 pA.

Electrical stimulations of thalamic, white matter and S1 afferents during patch clamp recordings of hCO neurons were performed in thalamocortical slices, as previously described[25]. In brief, the brain was situated on a 3D-printed stage tilted at 10° and the frontal brain was cut with a 35° angle. The brain was then glued onto the cut surface, and slices, which preserve the thalamocortical projection axons, were generated. Bipolar tungsten electrodes (0.5 MΩ) were mounted on a second micromanipulator and were strategically positioned to stimulate four regions per each cell (internal capsule, white matter, S1 and hCO). Synaptic responses were recorded following a 300-µA phasic stimulation at 0.03–0.1 Hz.

hChR2-expressing hCO neurons were activated with 480 nm, LED-generated (Prizmatix) light pulses delivered via a ×40 objective (0.9 NA; Olympus) onto hChR2-expressing processes next to the recorded cell. The illumination field was approximately 0.5 mm in diameter at a total intensity of 10–20 mW. Pulse width was set at 10 ms, which corresponds to the pulse delivered during behavioural training experiments. Multiple stimulation frequencies were applied, from 1 to 20 Hz, but for quantification, only the first pulse of the train was used. The inter-train interval was typically more than 30 s long, to minimally affect synaptic depression or facilitation pathways. To test whether hChR2 responses are monosynaptic, we applied TTX (1 µM) to the bath until the EPSC response was eliminated, which was followed by application of 4-amino-pyridine (4-AP; 100 µM). Typically, the response was recovered within several minutes, with a slightly longer delay between LED onset and EPSC generation. NBQX (10 µM) was applied to test whether the responses were driven by AMPA receptors.

Slice preparation and patch clamp recordings of in vitro hCO

Acute hCO slices were generated as previously described[5]. In brief, hCO slices were embedded in 4% agarose and transferred to an aCSF containing 126 mM NaCl, 2.5 mM KCl, 1.25 mM NaH_2PO_4, 1 mM MgSO_4, 2 mM CaCl_2, 26 mM NaHCO_3 and 10 mM d-(+)-glucose. Slices were cut at 200–300 µm at room temperature using a Leica VT1200 vibratome and maintained in aCSF at room temperature. Whole-cell patch clamp recordings from hCO slices were then performed under an upright SliceScope microscope (Scientifica). Slices were perfused with aCSF (bubbled with 95% O_2 and 5% CO_2), and signals from cells were recorded at room temperature. hCO neurons were patched with a borosilicate glass pipette filled with an internal solution containing 127 mM potassium gluconate, 8 mM NaCl, 4 mM magnesium ATP, 0.3 mM sodium GTP, 10 mM HEPES and 0.6 mM EGTA, pH 7.2, adjusted with KOH (290 mOsm). For reconstruction purposes, 0.2% biocytin was added to the internal solution.

Data were acquired with Clampex (Clampex 11.1, Molecular Devices) using a MultiClamp 700B Amplifier (Molecular Devices) and a Digidata 1550B Digitizer (Molecular Devices), low-pass filtered at 2 kHz, digitized at 20 kHz and analysed using Clampfit (version 10.6, Molecular Devices) and custom MATLAB (MATLAB 2019b, Mathworks) functions. The liquid junction potential was calculated using JPCalc, and recordings were corrected with an estimated −14 mV liquid junction potential. The I–V manipulations were constructed from a series of current steps in 5–10-pA increments from –50 to 250 pA.

Streptavidin staining and neuron tracing

For morphological reconstruction of patch-clamped neurons, 0.2% biocytin (Sigma-Aldrich) was added to the internal solution. Cells were filled for at least 15 min after break-in. Pipettes were then retracted slowly, over 1–2 min, until the recorded membrane fully resealed. Following slice physiology procedures, slices were post-fixed in 4% PFA overnight at 4 °C and then washed with PBS X3 before being incubated with streptavidin-conjugated DyLight 549 or DyLight 405 (Vector Labs) at 1:1,000 dilution for 2 h at room temperature to label cells that were filled with biocytin (2%; Sigma-Aldrich) during patch clamp recordings. Slices were then mounted for microscopy on glass slides using Aquamount (Thermo Scientific) and imaged the next day on Leica TCS SP8 confocal microscope, using a ×40 1.3 NA, oil immersion objective, at ×0.9–1.0 zoom with an xy sampling frequency of approximately 7 pixels per micrometre. Z stacks at 1-μm intervals were serially obtained and z-stack tiling and Leica-based automated stitching were performed to cover the entire dendritic tree of each neuron. The neurons were subsequently semi-manually traced using the neuTube interface[40] and SWC files were generated. The files were next loaded into Fiji (ImageJ, version 2.1.0; NIH) plugin SimpleNeuriteTracer[41].

Primary human sample collection

Human cerebral cortical tissue was obtained with informed consent under a protocol approved by the Stanford University Institutional Review Board. The two postnatal human tissue samples (3 and 18 years of age) were obtained from resection of the frontal lobe cortex (middle frontal gyrus) as part of surgeries for treating medically refractory epilepsy. After resection, tissue was collected in ice-cold NMDG-aCSF containing: 92 mM NMDG, 2.5 mM KCl, 1.25 mM NaH_2PO_4, 30 mM NaHCO_3, 20 mM HEPES, 25 mM glucose, 2 mM thiourea, 5 mM Na-ascorbate, 3 mM Na-pyruvate, 0.5 mM CaCl_2·4H_2O and 10 mM MgSO_4·7H_2O. The pH was titred to 7.3–7.4 with concentrated hydrochloric acid. The tissue was transferred to the laboratory within 30 min and coronal sections were made per the procedure described above.

Fibre implantation for fibre photometry

All animal procedures followed animal care guidelines approved by Stanford University’s APLAC. Rats (more than 140 days after transplantation) were anaesthetized with 5% isoflurane for induction and 1–3% isoflurane during surgery. Animals were placed into a stereotactic frame (Kopf) and Buprenorphine Sustained-Release (SR) was administered subcutaneously. The skull was exposed, cleaned and 3–5 bone screws were inserted. To target the t-hCO, we generated stereotactic coordinates based on the images acquired with MRI. A burr hole was drilled at the site of interest, and a fibre (400 μm in diameter, 0.48 NA, Doric) was lowered to 100 µm below the surface of the hCO and affixed to the skull with UV-cured dental cement (Relyx).

Fibre photometry recordings

Fibre photometry recordings were performed as previously described[42]. For recordings of spontaneous activity, rats were placed into a clean homecage and a 400-µm diameter fibre-optic patch cord (Doric) coupled to the fibre photometry acquisition system was connected to the implanted optical fibre. Animals were free to explore the homecage during a 10-min recording of spontaneous activity. For recordings of evoked activity, rats (more than 140 days after transplantation) were anaesthetized with 5% isoflurane for induction and 1–3% isoflurane for maintenance. Animals were placed into a stereotactic frame (Kopf), and whiskers contralateral to the t-hCO were trimmed to approximately 2 cm and threaded through mesh that was coupled to a piezo-electric actuator (PI). A 400-µm diameter fibre-optic patch cord (Doric) coupled to the acquisition system was connected to the implanted optical fibre. Fifty deflections (2 mm at 20 Hz for 2 s per presentation) were then made to the whiskers contralateral to the t-hCO using a piezo-electric actuator at random times during a 20-min recording. Deflection timing was controlled using custom MATLAB code using the MATLAB Support Package for Arduino. Events were synchronized with the acquisition software using transistor-transistor logic (TTL) pulses.

Cranial window surgery

Rats (more than 140 days after transplantation) were anaesthetized with 5% isoflurane for induction and 1–3% isoflurane during surgery. Animals were placed into a stereotactic frame (Kopf), and Buprenorphine SR and dexamethasone were administered subcutaneously. The skull was exposed, cleaned and 3–5 bone screws were inserted. To target t-hCO, we generated stereotactic coordinates based on the images acquired with MRI. A circular craniotomy (approximately 1 cm in diameter) was made with a high-speed drill directly above the transplanted hCO. Once the bone was as thin as possible, but before drilling all the way through the bone, the remaining intact bone disk was removed using forceps to reveal the underlying t-hCO. The craniotomy was filled with sterile saline, and a glass coverslip and custom headbar were affixed to the skull with UV-cured dental cement (Relyx).

Acute in vivo two-photon calcium imaging

Two-photon imaging was performed using a Bruker multiphoton microscope using Nikon LWD objective (×16, 0.8 NA). Imaging of GCaMP6 was conducted at 920 nm, ×1.4 zoom in a single z-plane, with 8× frame averaging at 7.5 frames per second. Rats were anaesthetized with 5% isoflurane for induction and maintained with 1–3% isoflurane. Rats were placed into a custom head-fixed apparatus and positioned beneath the objective. A 3-min baseline recording of spontaneous activity was obtained. Fifty airpuffs (100-ms duration per presentation) were then delivered to the whisker pad contralateral to the t-hCO using a picospritzer at random times during a 20-min recording. Airpuff timing was controlled using custom MATLAB code using the MATLAB Support Package for Arduino. Events were synchronized with the acquisition software (PrairieView 5.5) using TTL pulses. For analysis, images were corrected for x–y motion using affine corrections in MoCo, running in Fiji. Fluorescence traces from individual cells were extracted using CNMF-E[43]. Fluorescence from each region of interest was extracted, converted to a dF/F trace, and then converted into a z-score.

Acute in vivo extracellular electrophysiology

Rats (more than 140 days after transplantation) were anaesthetized with 5% isoflurane for induction and 1–3% isoflurane during surgery. Animals were placed into a stereotactic frame (Kopf) and Buprenorphine SR and dexamethasone were administered subcutaneously. Whiskers contralateral to the t-hCO were trimmed to approximately 2 cm and threaded through mesh that was coupled to a piezo-electric actuator. The skull was exposed and cleaned. A stainless-steel ground screw was fastened to the skull. To target the t-hCO, we generated stereotactic coordinates based on the images acquired with MRI. A circular craniotomy (approximately 1 cm in diameter) was made with a high-speed drill directly above the t-hCO. Once the bone was as thin as possible, but before drilling all the way through the bone, the remaining intact bone disk was removed using forceps to reveal the underlying t-hCO. Single units were recorded using either 32-channel or 64-channel high-density silicon probes (Cambridge Neurotech) grounded to the ground screw and pre-amplified with an RHD amplifier (Intan). Electrodes were lowered through the craniotomy into the target site using a manipulator and the craniotomy was filled with sterile saline. Data acquisition was performed at 30 kHz with an Open Ephys acquisition system. Recordings only proceeded if we identified more than ten channels with highly correlated, rhythmic spontaneous activity, suggesting that electrodes were positioned in the graft (based on two-photon calcium imaging data). A 10-min baseline recording of spontaneous activity was obtained. Fifty deflections (2 mm at 20 Hz for 2 s per presentation) were then made to the whiskers contralateral to the t-hCO using a piezo-electric actuator at random times during a 20-min recording. Deflection timing was controlled using custom MATLAB code using the MATLAB Support Package for Arduino (MATLAB 2019b). Events were synchronized with the acquisition software using TTL pulses.

For optotagging experiments, a 200-µm diameter fibre-optic patch cord (Doric), coupled to a 473-nm laser (Omicron), was connected to a 200-µm diameter optical fibre that was positioned above the craniotomy. Immediately before this, the power output from the patch cord was adjusted to 20 mW. Electrodes were lowered through the craniotomy into the target site using a manipulator and the craniotomy was filled with sterile saline. Ten pulses of 473-nm light (at 2 Hz with a 10-ms pulse width) were delivered at the start of the recording. Light-responsive units were defined as units that displayed a spiking response within 10 ms of light onset on 70% or more of trials.

For analysis, spikes were sorted using Kilosort2 and were manually curated using Phy2 (ref. [44]). Firing rates were computed using 200-ms bins, with a sliding window of 100 ms and converted into a z-score. A hidden Markov model with two states was used to label ‘on’ and ‘off’ states in the population activity. On states were considered to represent bursts, and off states were considered to represent inter-burst intervals. The emission transition parameters of the model were fit using the Baum–Welch algorithm (MATLAB hmmtrain with a convergence threshold of 1 × 10–6 and initial guesses of transition matrix: [0.95, 0.05; 0.05, 0.96] and emission: [0.5, 0.5; 0.1, 0.99]), and the state assignment at each time point was then estimated using the Viterbi algorithm. To assess responses to whisker deflection, a Wilcoxon signed-rank test was performed to compare firing rates in the 1 s following the onset of whisker deflection to the 1 s before whisker deflection with a significance threshold of P < 0.05. Latencies were computed as the time to reach peak z-score in the 2 s following whisker deflection. The power spectral density was calculated using Welch’s method (pwelch() in MATLAB), with a window slide of 10 × fs, where fs is the sampling rate of the signal.

Fibre implantation for optogenetic manipulations

Rats (more than 90 days after transplantation) were anaesthetized with 5% isoflurane for induction and 1–3% isoflurane during surgery. Animals were placed into a stereotactic frame (Kopf) and Buprenorphine SR was administered subcutaneously. The skull was exposed, cleaned and 3–5 bone screws were inserted. To target the t-hCO, we generated stereotactic coordinates based on the images acquired with MRI. A burr hole was drilled at the site of interest, and a fibre (200 μm in diameter, 0.48 NA; Thorlabs) was lowered to 100 µm below the surface of the hCO and affixed to the skull with UV-cured dental cement (Relyx).

Optogenetic behavioural assay

Water scheduled rats (1 h of water per day) were placed into a custom operant chamber containing a nosepoke portal equipped with a lick spout for water reward delivery. Entries into the nosepoke portal were detected by the breakage of an infrared beam, and licks were detected using a capacitive touch sensor. All events were controlled and recorded using custom MATLAB code using the MATLAB Support Package for Arduino (MATLAB 2019b). At least 1 week after surgery, animals began pre-training. On day 1 of pre-training, animals received small water rewards at the reward spout at randomized delays for 1 h. On days 2 and 3 of pre-training, animals received small water rewards only after performing increasing numbers of licks to the lick spouts for 1 h. All animals readily performed this behaviour. After pre-training, animals were trained to associate optogenetic stimulation of the transplanted hCO with reward delivery. Animals were placed into the operant chamber and a 200-µm diameter fibre-optic patch cord (Doric), coupled to both a 473-nm (Omicron) and a 635-nm (CNI) laser outside of the operant chamber, was connected to the implanted optical fibre. Immediately before this, the power output from the patch cord was adjusted to 20 mW. Laser timing was controlled by a Master-8 pulse generator (AMPI). One second after entering the nosepoke portal, animals received random presentations of either 473-nm or 635-nm stimulation (10 Hz with 10-ms pulse width for 5-s total stimulation). If animals performed one or more licks during the 473-nm stimulation, a small water reward was dispensed at the reward spout after stimulation was complete. The next trial was initiated after collection of this reward. If animals performed one or more licks during the 635-nm stimulation, there was no consequence. Trials were separated by a variable interval of 5–10 s. Animals received daily training sessions that concluded after 150 473-nm and 150 635-nm trials had been completed or after 150 min, whichever came first, for a total of 15 days. Behavioural performance was quantified by calculating a preference index for each training session: (number of licks during 473-nm stimulation − number of licks during 635-nm stimulation)/(number of licks during 473-nm stimulation + number of licks during 635-nm stimulation). The learning success rate was defined as the percentage of trained animals that achieved a preference score >0.2 on at least 2 consecutive training days.

Optogenetic stimulation and FOS staining

Rats were placed into a clean rat cage, and a 200-µm diameter fibre-optic patch cord (Doric), coupled to a 473-nm (Omicron) laser, was connected to the implanted optical fibre. Immediately before this, the power output from the patch cord was adjusted to 20 mW. Stimulated animals received 10 Hz, 10-ms pulse width and 473-nm stimulation for 10 min before being returned to their homecage. Unstimulated animals received no stimulation for 10 min before being returned to their homecage. Rats were euthanized by transcardial perfusion with 150 ml PBS, followed by 100 ml 4% PFA 90 min after optogenetic stimulation. Brains were extracted and 100-μm sections were cut on a vibratome. The slices were labelled with goat anti-GFP (abcam) and rabbit anti-FOS (abcam) primary antibodies, Alexa 488 donkey anti-goat (Invitrogen) and Alexa 594 donkey anti-rabbit (Invitrogen) secondary antibodies, and DAPI. Images were acquired using a confocal microscope (Zeiss) with a ×20 objective and overlaid with images from the Paxinos, George and Watson rat brain atlas for blinded manual counting of FOS-positive cells in specified brain regions.

EEG recordings

FOXN1−/− rats were anaesthetized with isoflurane and stereotactically implanted with stainless steel wires (791400, A-M Systems) over the bilateral somatosensory cortices and the bilateral motor cortices. A reference wire was positioned over the cerebellum, and implants were secured with dental cement (Metabond, S399, S371 and S398; Jet Set4 Liquid, Lang Dental, 3802X6). The following stereotactic coordinates were used, relative to Bregma: primary somatosensory cortex (S1BF), anterior–posterior −1.3 mm and lateral 3.3 mm; and primary motor cortex (M1), anterior–posterior +2.5 mm, lateral 2.5 mm. The wires of the implant were secured onto custom-made Mill-Max headpiece adapters (ED90267-ND, Digi-Key Electronics). To initiate the recording, the adapters were connected to the head stage, consisting of a digitizer and amplifier board (C3334, Intan Technologies). Awake, freely behaving animals were tethered to an acquisition board (Open Ephys) with lightweight SPI interface cables (C3206, Intan Technologies). Continuous real-time EEG was recorded with Open Ephys software (version 0.4.4.1; https://open-ephys.org). Data were sampled at 2 kHz and bandpass filtered between 1 and 300 Hz.

Assessment of locomotor behaviour in the open field arena

Rats (more than 3 months old, more than 90 days post-transplantation) were handled for 3 min on 5 consecutive days before behavioural testing began. Rats were placed into the corner of an open field activity arena (43 cm × 43 cm × 30 cm) containing three planes of infrared detectors within a sound attenuating chamber (Med Associates). Rats were allowed to explore the arena for 10 min, and the distance moved was computed with Med Associates software. The arena was cleaned with a 1% Virkon solution at the end of each session.

Novel object recognition

Two different objects (green tower and white bottle) were used in this test. The objects had similar heights and volumes, but differed in their shape, texture and appearance. On day 1, rats were placed into a black square plastic area (50 cm × 50 cm × 45 cm) and allowed to explore the arena with two habituation objects (15-ml centrifuge tubes) for 5 min. On day 2, rats first underwent a training session. In this session, rats were placed into the arena and allowed to explore the arena for 5 min with two identical objects, which were placed in diagonally opposite corners 15 cm away from the corner. Rats were then returned to their homecage for 5 min. For the testing session, rats were placed back into the arena for 5 min and one of the two objects was replaced with a novel object. Rats were tracked with an automated tracking system (Noldus Information Technology), and time spent interacting with each object was manually scored by an experimenter who was blind to the experimental groups. Interaction was defined as the rat pointing its nose towards the object within 2 cm of it. Objects for training and testing and the location of these objects were pseudorandomized. Objects and the arena were cleaned with a 1% Virkon solution at the end of each training and testing session. A discrimination index was calculated as (time spent interacting with novel object – time spent interacting with familiar object)/(time spent interacting with novel object + time spent interacting with familiar object) during the testing session.

Fear conditioning

The experiment consisted of 1 day of training, 1 day of contextual fear testing and 1 day of cued fear testing. The same context was used for both training and contextual testing. This context had aluminium walls, a grey metal grid for a floor, yellow house lights and the scent of mint extract, and was cleaned with 10% simple green solution between rats (Coulbourn Instruments). The cued fear testing context was circular with plastic walls and floor, blue house lights and the scent of vanilla extract, and was cleaned with 70% ethanol between rats. On day 1 (training), rats were placed individually into the training chamber for 200 s. A tone (20 s, 80 dB, 2 kHz) was presented followed by an electrical shock (0.5 mA for a duration of 2 s) 18 s after the end of the tone. This procedure was repeated a total of three times with 60-s intervals between the end of the shock and the start of the subsequent tone. Rats were removed from the chamber and returned to their homecage 60 s after the last shock. On day 2, rats were placed back in the training context without any tone or shock for 5 min for contextual memory testing. On day 3, the rats were placed in the cued fear context. After 200 s of habituation, rats were presented with tones (20 s, 80 dB, 2 kHz) three times at 80-s intervals. Stimulus presentation was controlled using FreezeFrame software. An overhead camera was used to record behaviour. Freezing behaviour was scored manually by an experimenter who was blind to the experimental groups. Freezing was defined as the absence of all movements except those caused by respiration.

Statistics and reproducibility

Data are presented as mean ± s.e.m., unless otherwise indicated. Distribution of the raw data was tested for normality of distribution; statistical analyses were performed using the Student’s t-test, Kolmogorov–Smirnov test, Wilcoxon signed-rank test or analysis of variance with Bonferroni correction for multiple comparisons. Statistical analyses were performed in Prism (GraphPad) and OriginPro (OriginLab). Full statistical details for each figure panel are included in Supplementary Table 7. Data shown from representative experiments were repeated with similar results in at least three independent biological replicates, unless otherwise noted. Sample sizes were estimated empirically based on previous studies. hCO were randomly assigned to different experiments. Behavioural experiments were performed by researchers who were blinded to the experimental conditions.

Ethics statement

All experiments involving human cells complied with all relevant guidelines and regulations. Human donors in this study consented to the use of their cells to generate hiPS cells and derived cells. The source of the cells and their institutional approvals are listed in Supplementary Table 1. This study also benefitted from a consultation with the Stanford Center for Law and the Biosciences on the ethical aspects of the work as part of the Stanford Big Idea Project on Brain Organogenesis (Stanford Wu Tsai Neurosciences Institute). Approval for transplantation of hCO into rats was obtained from the Stanford Laboratory Animal Care (APLAC) Research Compliance Office. No discernible locomotor or memory deficits were detected in transplanted animals and their wellbeing was monitored throughout. Surgical neural tissue samples were obtained with approval from the Stanford University Institutional Review Board.

Reporting summary

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

Data availability

Single-cell gene expression data are available under the Gene Expression Omnibus accession number GSE190815. The data that support the findings of this study are available on request from the corresponding author. The following public datasets were used for snRNA-seq analysis: human genome sequence information from Ensembl (http://ftp.ensembl.org/pub/release-98/fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna.primary_assembly.fa.gz) and human gene annotation from GENCODE (http://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_32/gencode.v32.primary_assembly.annotation.gtf.gz); information on the rat genome sequence is from Ensembl (ftp://ftp.ensembl.org/pub/release-100/fasta/rattus_norvegicus/dna/Rattus_norvegicus.Rnor_6.0.dna.toplevel.fa.gz); the Allen Brain Institute generated human adult snRNA-seq data from the medial temporal gyrus[21] and the primary motor cortex[22] (https://portal.brain-map.org/atlases-and-data/rnaseq; accessed May 2022); human fetal cortical single-cell RNA-seq data19,20 (Polioudakis et al.[19] data were obtained from http://solo.bmap.ucla.edu/shiny/webapp/ on April 2022; Trevino et al.[20] data were downloaded from the Gene Expression Omnibus with the accession number GSE162170) and bulk RNA-seq data from the developing human cortex were generated by psychENCODE[38] (http://development.psychencode.org/; accessed April 2022).

Code availability

Data and code used for data processing and analysis are available on request from the corresponding author. The code used to analyse snRNA-seq data is available for download from https://github.com/kkelley85/Transplant_organoid_snRNAseq.

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Acknowledgements

We thank L. D. Pisani, and members of the Pasca, Deisseroth and Huguenard laboratories including M. Thete for insightful discussions and technical support. This work was supported by the Stanford Big Idea Project on Brain Organogenesis (Wu Tsai Neuroscience Institute) (to S.P.P., K.D. and B.C.), the National Institute of Mental Health (R01 MH115012) (to S.P.P.), the Kwan Funds (to S.P.P.), the Senkut Funds (to S.P.P.), the Coates Foundation (to S.P.P.), the Ludwig Family Foundation (to S.P.P.), the Alfred E. Mann Foundation (to S.P.P.), the Stanford Maternal & Child Health Research Institute (MCHRI) Postdoctoral Fellowship (to F.B. and O.R.), the Walter V. and Idun Berry Postdoctoral Fellowship (to F.G. and J.A.), the NARSAD Young Investigator Award (to F.G.) and an NIH NIDA K99/R00 (K99 DA050662) (to F.G.). S.P.P. is a New York Stem Cell Foundation (NYSCF) Robertson Stem Cell Investigator and a CZI Ben Barres Investigator. We thank the Stanford Center for Innovation in In vivo Imaging (SCi 3)—Small Animal Imaging Center, which is supported by the NIH S10 Shared Instrumentation grant (S10RR026917-01); and the Stanford Behavioral and Functional Neuroscience Laboratory, which is supported by a NIH S10 Shared Instrumentation for Animal Research grant (1S10OD030452-01).

Author information

Author notes

1. These authors contributed equally: Omer Revah, Felicity Gore, Kevin W. Kelley

Authors and Affiliations

1. Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA

Omer Revah, Felicity Gore, Kevin W. Kelley, Jimena Andersen, Noriaki Sakai, Xiaoyu Chen, Min-Yin Li, Fikri Birey, Xiao Yang, Neal D. Amin, Shravanti Kulkarni, Rachana Mudipalli, Seiji Nishino, Karl Deisseroth & Sergiu P. Pașca

2. Stanford Brain Organogenesis, Wu Tsai Neurosciences Institute and Bio-X, Stanford University, Stanford, CA, USA

Omer Revah, Kevin W. Kelley, Jimena Andersen, Xiaoyu Chen, Min-Yin Li, Fikri Birey, Xiao Yang, Neal D. Amin, Shravanti Kulkarni & Sergiu P. Pașca

3. Department of Bioengineering, Stanford University, Stanford, CA, USA

Felicity Gore, Rachana Mudipalli & Karl Deisseroth

4. Department of Chemistry, Stanford University, Stanford, CA, USA

Xiao Yang & Bianxiao Cui

5. Stanford Behavioral and Functional Neuroscience Laboratory, Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA

Nay L. Saw & Mehrdad Shamloo

6. Department of Comparative Medicine, Stanford University, Stanford, CA, USA

Samuel W. Baker

7. Department of Neurosurgery, Stanford University, Stanford, CA, USA

Gerald A. Grant & Mehrdad Shamloo

8. Department of Neurology and Neurological Sciences, Stanford, CA, USA

Juliet K. Knowles & John R. Huguenard

9. Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA

Karl Deisseroth

Contributions

O.R., N.S. and S.N. designed and performed the transplantation experiments. J.A. and X.C. prepared the single-nucleus profiling experiments. K.W.K. performed the single-cell RNA-seq analysis. J.A., N.D.A., S.K. and O.R. performed immunocytochemistry and imaging. F.B., J.A., X.C. and O.R. maintained and differentiated hiPS cells. F.B. generated the GCaMP6s-expressing hiPS cell line. O.R. performed MRI. X.Y. and B.C. contributed to characterizing the morphology of the graft. O.R., M.-Y.L. and J.R.H. conducted and analysed the electrophysiological properties of neurons by patch clamp. O.R. and J.K.K. performed and analysed EEG recordings. G.A.G. collected primary human samples. N.S. monitored animal wellbeing. O.R. and M.-Y.L. reconstructed neuronal morphology. O.R. performed rabies tracing. F.G. performed and analysed two-photon imaging, fibre photometry, extracellular in vivo electrophysiological recordings, optogenetic manipulations and immunohistochemistry, and axonal tracing. S.W.B. assisted with optogenetic behavioural experiments. R.M. assisted with calcium imaging analysis. N.L.S. and M.S. performed and supervised the behavioural testing. O.R., F.G., S.P.P. and K.D. conceived the project and designed the experiments. O.R., F.G., K.W.K. and S.P.P. wrote the manuscript with input from all authors. S.P.P. supervised all aspects of the work.

Corresponding author

Correspondence to Sergiu P. Pașca.

Ethics declarations

Competing interests

Stanford University holds patents for the generation of cortical organoids/spheroids (listing S.P.P. as an inventor) and a provisional patent application for transplantation of organoids (listing S.P.P., O.R., F.G., K.D. and K.W.K. as inventors).

Peer review

Peer review information

Nature thanks the anonymous reviewers for their contribution to the peer review of this work.

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 Schematic of the transplantation procedure and effects on animal behaviour.

a–c. Schematics of surgical approach. d. Left, t-hCO visualized with T2-weighted MRI. Right, t-hCO visualized with DAPI. e. Reconstructed volumes of the same t-hCO calculated from MRI or histological slices (n = 3, P = 0.9160 t-hCO). f. Quantification of MRI volume reconstruction over time (n = 6 t-hCO, *P = 0.0239). g. Quantification of survival in transplanted animals over time. h–j. Animals transplanted with t-hCO do not show behavioral deficits or seizures. h. Distance traveled in open field arena by transplanted (orange) or non-transplanted control animals (grey) for each minute of a 10-minute testing session (left) and across the entire 10-minute testing period (right) (n = 11 non-transplanted control rats, 9 transplanted rats, P = 0.4082). i. Discrimination index ((time spent interacting with novel object - time spent interacting with familiar object)/(time spent interacting with novel object + time spent interacting with familiar object)) during novel object test calculated for transplanted (orange) or non-transplanted control animals (grey) (n = 11 non-transplanted control rats, 9 transplanted rats, P = 0.8756). j. Freezing behaviour during fear conditioning training, contextual fear memory test, and cued fear memory test for transplanted (orange) or non-transplanted control animals (grey) (n = 11 non-transplanted control rats, 9 transplanted rats, P = 0.9599). k. Representative EEG recordings from the frontal and somatosensory cortices of non-transplanted (left) and transplanted (right) rats. l. Power spectral density plots of EEG activity recorded in the somatosensory cortex of non-transplanted control and transplanted rats (n = 3 transplanted rats, 3 non-transplanted rats). m. Power spectral density plots of somatosensory cortex EEG activity recorded simultaneously in the non-transplanted and transplanted hemisphere of individual rats (n = 3 transplanted rats). Data are presented as mean ± SEM.

Extended Data Fig. 2 Immunohistochemical characterization of t-hCO.

a–b. NeuN expression in t-hCO and the surrounding rat brain. c. Quantification of the overlap of HNA and NeuN expression in t-hCO and rat cortex (n = 5 t-hCO from 4 hiPS lines, 1–2 t-hCO per line). d. Representative images of GAD65/67 expression in t-hCO-rat cortex border. e. Example image of a rarely observed HNA+GAD65/67+ neuron in t-hCO. All images were acquired 8 months after transplantation unless otherwise stated.

Extended Data Fig. 3 Immunohistochemical characterization of t-hCO continued.

a. Representative images of SATB2 and CTIP2 expression in t-hCO. b. Example images of SOX2 and NeuN expression in t-hCO. c. Representative images of rat-edothelial-marker-1 (RECA1) and IBA1 expression in t-hCO and nearby rat cortex 3 months after transplantation. d. HNA and IBA1 expression in t-hCO. Absence of overlap between HNA and IBA1 suggests microglia originate from rat. e. GFAP and IBA1 expression in t-hCO and rat cortex. All images were acquired 7–9 months in differentiation unless otherwise stated.

Extended Data Fig. 4 Data quality of single nucleus RNA-seq samples and hCO analysis.

a. The number of snRNA-seq read counts aligned to rat and human genome for each nucleus split by sample. Human nuclei were defined as nuclei with >95% of total reads aligning to the human genome. b. snRNA-seq quality metrics showing the distribution of the number of counts, number of genes, and mitochondrial (MT) gene fraction per cell in each sample. MT gene fraction plotted as boxplots (horizontal line denotes median; lower and upper hinges correspond to the first and third quartiles; whiskers extend 1.5 times the interquartile range with outliers shown outside this range). Lines denote nuclei quality thresholds. c. Gene expression violin plots for selected marker genes in t-hCO. d. Same integrated UMAP as shown in Fig. 1g, colored by t-hCO sample. e. Cell type proportions across t-hCO samples colored by clusters. f. UMAP dimensional reduction visualization of all clustered high-quality hCO nuclei after Seurat integration (n = 3 t-hCO samples from 3 hiPS lines). g. Gene expression violin plots for selected marker genes. h. Same integrated UMAP as shown in panel f, colored by hCO sample. i. Cell type proportions across hCO samples colored by clusters. hCO and t-hCO from 2242-1 at day 227 are taken from the same differentiation batch maintained in parallel. Cyc. prog., cycling progenitor; Astroglia, astrocyte lineage cell; IPC, intermediate progenitor cell; GluN_UL, upper layer glutamatergic neuron; GluN_DL, deep layer glutamatergic neuron; GluN_DL/SP, deep layer and subplate glutamatergic neurons; RELN, Reelin neurons; IN, GABAergic neurons; Choroid, choroid plexus-like cells; Mening., meningeal-like cells.

Extended Data Fig. 5 Transplanted hCO RNA-seq comparisons to fetal and adult human cortex.

a. Left: The first principal component (PC1) calculated on gene expression (log base 2 RPKM, reads per kilobase of exon per million reads mapped) from human cortical BrainSpan samples[23] and pseudobulk (Methods) snRNA-seq samples using previously defined developmentally regulated genes[38]. Right: PC1 values for t-hCO and hCO samples. b. The sample weights (Methods) of five neurodevelopmental transcriptomic signatures identified by Zhu et al.[38] across human cortical BrainSpan samples. Dashed lines denote fitted curves by LOESS regression. c. Gene set enrichment analysis (one-sided Fisher’s exact test) using the top 200 genes from each neurodevelopmental signature with significantly up-regulated (adjusted P-value < 0.05) t-hCO genes from pseudobulk GluN clusters (neurogenesis, neuronal differentiation, and synaptogenesis signatures), Astroglia clusters (astrogenesis signature), and from pseudobulk of all clusters (myelination-related signature). Line denotes Bonferroni corrected P-value of 0.05. d. Gene expression (pseudobulk and scaled) of the top 50 significantly upregulated t-hCO genes (ranked by differential expression p-value significance) for each signature. e–f. Heat maps of t-hCO (e) and hCO (f) cluster overlap by RNA-seq integration with primary human fetal cortical cell clusters19,20. Cell cluster labels are from original studies. RG, radial glia; Cyc.prog, cycling progenitors; tRG, truncated radial glia, mGPC, multipotent glial progenitor cell; OPC/Oligo, oligodendrocyte progenitor cell/oligodendrocyte; nIPC, neuronal intermediate progenitor cell; GluN, glutamatergic neuron; CGE IN, caudal ganglionic eminence interneuron; MGE IN, medial ganglionic eminence interneuron; EC, endothelial cell; MG, microglia; Peric., Pericytes; PgG2M and PgS, cycling progenitors; IP, intermediate progenitor; oRG, outer radial glia; End, endothelial cell; Per, pericyte; vRG, ventricular radial glia; ExM, maturing excitatory neuron; ExN, excitatory neuron; ExM.U, maturing upper layer excitatory neuron; ExDp, excitatory deep layer neuron; In, interneuron. g–h. UMAP visualization of GluN cell type classification of t-hCO (g) and hCO (h) using label transfer (methods) from adult human cortical single nuclei RNA-seq reference datasets. Transfer labels from dissected cortical layers of medial temporal gyrus (MTG)[21] shown in g and to the left in h. Right (h): transfer labels from annotated GluN subclasses from motor cortex (M1)[22]. I–j. Heat maps of t-hCO (i) and hCO (j) cluster overlap by RNA-seq integration with primary human adult cortical cell clusters21,22. Cell cluster labels are from original studies. Exc, excitatory neuron; Inh, inhibitory neuron; Astro, astrocyte; Endo, endothelial cells; CT, corticothalamic cell; ET, extratelencephalic cell; IT, intratelencephalic cell; micro, microglia; NP, near-projecting; oligo, oligodendrocyte; OPC, oligodendrocyte precursor; PVM, perivascular macrophage; VLMC, vascular and leptomeningeal cells.

Extended Data Fig. 6 Electrophysiological and morphological properties of glutamatergic hCO neurons.

a. Example 3D-reconstruction of biocytin-filled Camk2α:eYFP-expressing hCO neurons at 8 months. b. Morphological properties of 3D reconstructed Camk2α+ hCO neurons (n = 8). c. Electrophysiological properties of Camk2α+ hCO neurons (n = 16 neurons). Data are presented as mean ± SEM.

Extended Data Fig. 7 Electrophysiological and morphological properties of cortical neurons from postnatal human cerebral cortex.

a. Schematics showing the location of resected specimens and recording conditions. b. 3D-reconstruction of biocytin-filled human L2/3 pyramidal neurons. c. Quantification of the soma diameter, number of primary branches, total number of dendrites, total length and spine density (n = 12 L2/3 neurons from 2 specimens). d. Sholl analysis comparison of the dendritic complexity of L2/3 neurons (n = 7 L2/3 neurons from sample 1 and n = 8 neurons from sample 2). e. Quantification of membrane capacitance, resting membrane potential, maximal firing rate, spike amplitude, spike half-width and spike threshold of L2/3 pyramidal neurons (n = 22 L2/3 neurons from 2 specimens). Dotted lines show t-hCO for comparison. Data are presented as mean ± SEM.

Extended Data Fig. 8 Morphological and electrophysiological properties of control and TS t-hCO neurons.

a–c. Morphological properties of control and TS t-hCO neurons. a–b. Examples of 3D-reconstructed t-hCO neurons derived from control Line identities of filled cells from left to right, Control: 1208-2; 2242-1; 8119-1; 2242-1; 2242-1; 1208-2; and TS: 8303-S3; 8303-S3; 7643-6; 7643-6; 9862-2; 8303-S3; 8303-S3; 8303-S3; 8303-S3; 9862-2; 8303-S3; 7643-6. c. Quantification of the soma diameter, number of primary branches, total number of dendrites and total length (control: n = 19 neurons, TS: n = 23 neurons, P = 0.592, *P = 0.025; control: n = 19 neurons, TS: n = 21 neurons P = 0.7627, * P = 0.0112). d. Comparison of membrane capacitance in hCO and t-hCO (hCO: n = 25 neurons, t-hCO: n = 18 neurons, ***P < 0.0001). e–g. Electrophysiological properties of control and TS t-hCO neurons. e. Example traces of a single AP firing in control and TS t-hCO neurons, showing differences in AP height (black arrows) and threshold (gray dashed line). f. Comparison of electrophysiological properties in control and TS t-hCO neurons; membrane capacitance (Control: n = 30 neurons, TS: n = 36 neurons, P = 0.0848), resting membrane potential (Control: n = 31 neurons, TS: n = 33 neurons, **P < 0.0044), maximal firing rate (Control: n = 29 neurons, TS: n = 36 neurons, ***P < 0.0001), spike amplitude (Control: n = 31 neurons, TS: n = 35 neurons, ***P < 0.0001), spike half-width (Control: n = 31 neurons, TS: n = 34 neurons, P = 0.0984) and spike threshold (Control: n = 31 neurons, TS: n = 34 neurons, ***P < 0.0001). g. Cumulative frequency plots and quantification of spontaneous EPSCs decay time and charge (Control: n = 32 neurons; TS: n = 26 neurons, P = 0.0744, P = 0.4812). Data are presented as mean ± SEM.

Extended Data Fig. 9 Electrophysiological characterization of inputs onto t-hCO.

a. Schematic of experimental preparation for electrically activating rat tissue while performing whole cell recordings from t-hCO neurons. b. Left, current traces from a representative t-hCO neuron following electrical stimulation in white matter with (purple) or without (black) NBQX. Middle, quantification of EPSC amplitude with or without NBQX (paired t-test, *P = 0.0295). Right, percentage of t-hCO neurons that displayed EPSCs in response to electrical stimulation in white matter. c. Left, Current traces from a representative t-hCO neuron following electrical stimulation of t-hCO with (purple) or without (black) NBQX. Middle, quantification of EPSC amplitude with or without NBQX (Wilcoxon test, **P = 0.0022). Right, percentage of t-hCO neurons that displayed EPSCs in response to electrical stimulation of t-hCO. d. Latency to EPSC in t-hCO neurons following electrical stimulation of somatosensory cortex (S1, n = 13 neurons), internal capsule (IC, n = 7 neurons), white matter (WM, n = 8 neurons), or t-hCO (n = 11 neurons). e. Schematic of experimental preparation for optogenetically activating rat thalamic terminals in t-hCO while performing whole cell recordings from t-hCO neurons. f. Top, example recorded t-hCO neuron. Bottom, current traces from a representative t-hCO neuron following optogenetic activation of rat thalamic terminals in t-hCO with (purple) or without (black) NBQX. g, Left, latency to EPSC in t-hCO neurons following optogenetic activation of rat thalamic terminals in t-hCO (n = 7 neurons, 2 animals). Right, percentage of t-hCO neurons that displayed EPSCs in response to optogenetic activation of rat thalamic terminals in t-hCO. Data are presented as mean ± SEM.

Extended Data Fig. 10 Characterization of t-hCO activity in vivo.

a–g. Characterization of spontaneous activity in t-hCO in vivo. a–c. Fiber photometry recordings of spontaneous t-hCO activity in vivo. a. Top, Schematic of experimental preparation. Bottom, representative image of GcaMP6s expression in t-hCO. Scale bar, 100 μm. b. Example z-scored fluorescence traces from awake recording of spontaneous activity. c–f. Extracellular recordings of spontaneous t-hCO activity in vivo. c. Top, Schematic of experimental preparation. Bottom, average waveforms of putative t-hCO units. d. Hidden Markov model used to identify ‘on’ and ‘off’ periods of population activity. Top, raster plot of spontaneous activity of simultaneously recorded units. Bottom, population averaged z-scored activity with ‘on’ states identified by Hidden Markov model overlaid in red. e. Quantification of spontaneous spiking activity. Left, number of spikes each unit contributed to each burst (n = 4 rats, 10 (dark green), 12 (light green), 19 (cyan), and 14 (blue) units per rat). Left center, proportion of recorded units that were engaged in each burst (n = 4 rats). Right center, ON period duration (n = 4 rats). Right, OFF period duration (n = 4 rats). f. Histogram of median correlations of each unit with all other simultaneously recorded units recorded in transplanted rats (putative human units, blue, n = 48 units from 4 rats) or non-transplanted rats (rat units, red, n = 56 units from 3 rats, P < 0.0001). g. Peak power spectral density frequency of spontaneous activity across all recording modalities used (two photon n = 1 rat, fiber photometry n = 3 rats, extracellular electrophysiology n = 4 rats, optotag n = 3 rats). h–q. Characterization of evoked activity in t-hCO in vivo. h–j. Fiber photometry recordings of t-hCO activity in response to whisker deflection. h. Schematic of experimental preparation for fiber photometry recording of t-hCO neurons in response to whisker deflection. i. Z-scored responses to whisker deflection at time zero (red) or randomly generated timestamps (grey) averaged across animals (n = 3 rats). j. Quantification of mean z-score following whisker stimulation compared to baseline (n = 3 rats, *P = 0.0430). k–n. Extracellular electrophysiological recordings of t-hCO activity in response to whisker deflection. k. Schematic of experimental preparation for extracellular electrophysiological recordings of t-hCO activity in response to whisker deflection. l. Single trial responses to whisker stimulation from a representative example single unit. Top, raster plot of single trial spiking activity aligned to whisker deflection. Bottom, trial-averaged z-scored firing rate. m. Population-averaged z-scored firing rates of all cells aligned to whisker deflection at time zero (red) or randomly generated timestamps (grey). n. Quantification of evoked spiking activity. Left, proportion of units significantly modulated by whisker deflection (n = 4 rats). Right, latency to peak z-score (n = 4 rats, n = 8 (dark green), 11 (light green), 12 (cyan), and 11 (blue) whisker deflection-modulated units per rat). o–q. Heatmaps of trial-averaged responses of all identified putative t-hCO cells across different recording modalities. o. Top, heatmap of trial-averaged z-scored fluorescence traces from all t-hCO cells recorded with two-photon calcium imaging aligned to whisker deflection (left) or randomly generated timestamps (right). Bottom, population-averaged z-scored fluorescence traces (n = 14 cells from 1 rat). p. Top, heatmap of trial-averaged z-scored firing rates from all t-hCO units recorded with extracellular electrophysiology aligned to whisker deflection (left) or randomly generated timestamps (right). Bottom, population-averaged z-scored firing rates (n = 42 units from 4 rats). q. Top, heatmap of trial-averaged z-scored firing rates from all opto-tagged t-hCO units recorded with extracellular electrophysiology aligned to whisker deflection (left) or randomly generated timestamps (right). Bottom, population-averaged z-scored firing rates (n = 31 units from 3 rats). Data are presented as mean ± SEM.

Supplementary information

Reporting Summary

Supplementary Table 1

hiPS cell lines used for experiments. hiPS cell lines used in various experiments

Supplementary Table 2

Morbidity reports submitted by veterinary staff for transplanted and non-transplanted animals over a 12-month period.

Supplementary Table 3

Transplanted hCO cell type cluster marker genes. Marker genes for each annotated cell cluster form the integrated t-hCO snRNA-seq determined using the Seurat FindMarkers function with default parameters (two-sided Wilcoxon Rank Sum test with Bonferroni correction).

Supplementary Table 4

hCO cell type cluster marker genes. Marker genes for each annotated cell cluster form the hCO snRNA-seq dataset determined using the Seurat FindMarkers function with default parameters (two-sided Wilcoxon Rank Sum test with Bonferroni correction).

Supplementary Table 5

Glutamatergic neuron differentially expressed genes. Significantly increased differentially expressed t-hCO GluN genes (EdgeR log likelihood ratio test with Benjamini-Hochberg adjustment, adjusted P value < 0.05, expressed in at least 10% of t-hCO GluN cells) with absolute fold change difference of at least 2 between t-hCO and hCO glutamatergic neurons.

Supplementary Table 6

Gene ontology (GO) term enrichment for up-regulated genes in t-hCO glutamatergic neurons. GO term enrichment P values (hypergeometric test) of up-regulated genes in t-hCO glutamatergic neurons compared to hCO glutamatergic neurons (Supplementary Table 5)

Supplementary Table 7

Statistical details for Figures 1–5, Extended Data Figures 1–10.

Supplementary Video 1

In vivo two-photon calcium imaging of spontaneous activity of t-hCO.

Supplementary Video 2

Optogenetic activation of t-hCO drives reward-seeking behaviour.

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Revah, O., Gore, F., Kelley, K.W. et al. Maturation and circuit integration of transplanted human cortical organoids. Nature 610, 319–326 (2022). https://doi.org/10.1038/s41586-022-05277-w

- Received: 16 December 2021

- Accepted: 25 August 2022

- Published: 12 October 2022

- Issue Date: 13 October 2022

- DOI: https://doi.org/10.1038/s41586-022-05277-w

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SARS-CoV-2 disrupts host epigenetic regulation via histone mimicry

John Kee,

Samuel Thudium,

David M. Renner,

Karl Glastad,

Katherine Palozola,

Zhen Zhang,

Yize Li,

Yemin Lan,

Joseph Cesare,

Andrey Poleshko,

Anna A. Kiseleva,

Rachel Truitt,

Fabian L. Cardenas-Diaz,

Xianwen Zhang,

Xuping Xie,

Darrell N. Kotton,

Konstantinos D. Alysandratos,

Johnathan A. Epstein,

Pei-Yong Shi,

Wenli Yang,

Edward Morrisey,

Benjamin A. Garcia,

Shelley L. Berger,

Susan R. Weiss &

…

Erica Korb

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Nature volume 610, pages 381–388 (2022)

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Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged at the end of 2019 and caused the devastating global pandemic of coronavirus disease 2019 (COVID-19), in part because of its ability to effectively suppress host cell responses1,2,3. In rare cases, viral proteins dampen antiviral responses by mimicking critical regions of human histone proteins4,5,6,7,8, particularly those containing post-translational modifications required for transcriptional regulation9,10,11. Recent work has demonstrated that SARS-CoV-2 markedly disrupts host cell epigenetic regulation12,13,14. However, how SARS-CoV-2 controls the host cell epigenome and whether it uses histone mimicry to do so remain unclear. Here we show that the SARS-CoV-2 protein encoded by ORF8 (ORF8) functions as a histone mimic of the ARKS motifs in histone H3 to disrupt host cell epigenetic regulation. ORF8 is associated with chromatin, disrupts regulation of critical histone post-translational modifications and promotes chromatin compaction. Deletion of either the ORF8 gene or the histone mimic site attenuates the ability of SARS-CoV-2 to disrupt host cell chromatin, affects the transcriptional response to infection and attenuates viral genome copy number. These findings demonstrate a new function of ORF8 and a mechanism through which SARS-CoV-2 disrupts host cell epigenetic regulation. Further, this work provides a molecular basis for the finding that SARS-CoV-2 lacking ORF8 is associated with decreased severity of COVID-19.

Main

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has proven to be a highly virulent virus resulting in a devastating and global pandemic. While recent findings have suggested that SARS-CoV-2 infection disrupts epigenetic regulation12,13,14 and suppresses the innate antiviral host cell response1,2,3, it is unclear how this occurs. In rare cases, other highly virulent viruses interfere with host cell epigenetic regulation through mimicry of host cell proteins15,16,17, particularly histones4,5,6,7,8. Histones function by wrapping DNA into complex structures and, in doing so, control access to the genome. Histone proteins are modified by a wide range of post-translational modifications (PTMs) that are dynamically regulated to control gene expression9,10,11. Histone mimicry allows viruses to disrupt the host cell’s ability to regulate gene expression and respond to infection effectively. However, no validated cases of histone mimicry have previously been reported within coronaviruses. Although SARS-CoV-2 probably uses many mechanisms to interfere with host cell functions, we examined whether it uses histone mimicry to disrupt chromatin regulation and the transcriptional response to infection.

ORF8 contains a histone H3 mimic

To determine whether histone mimicry is used by SARS-CoV-2, we first performed a bioinformatic comparison of all SARS-CoV-2 viral proteins[18] with all human histone proteins (Extended Data Fig. 1a,b). Most SARS-CoV-2 proteins are highly similar to those in the coronavirus strain that caused the previous major SARS-CoV outbreak with the notable exception of the proteins encoded by ORF3b and ORF8, of which ORF8 is the most divergent in SARS-CoV-2 (refs 19,20). Notably, we detected an identical match between amino acids 50–55 of the protein encoded by ORF8 and critical regions within the histone H3 N-terminal tail (Fig. 1a). Furthermore, ORF8 aligns to a longer sequential set of amino acids (six residues) than in any previously described and validated case of histone mimicry4,5,6,7,21 or a putative histone mimic in the SARS-CoV-2 envelope protein22,23 (Extended Data Fig. 1c,d). On the basis of a crystal structure of ORF8, these residues are located in a disordered region on the surface of the protein in an ORF8 monomer[24]. Most compellingly, the motif contains the ‘ARKS’ sequence, which is found at two distinct sites in the histone H3 tail (Fig. 1a) and is well established as one of the most critical regulatory regions within H3. Both H3 ARKS sites are modified with multiple PTMs, including mono-, di- and trimethylation and acetylation at H3 lysine 9 (H3K9me and H3K9ac) and at H3 lysine 27 (H3K27me and H3K27ac). This amino acid stretch is absent from the previous SARS-CoV ORF8-encoded protein both before and after a deletion generated ORF8a and ORF8b[25] but is present in bat SARS-CoV-2 and variants of concern (Extended Data Fig. 1e,f). ORF8 is highly expressed during infection26,27, with ORF8 transcript expressed at higher levels than histone H3 and ORF8 protein expressed at over 20% above the level of the most abundant histone H3 protein within 24 h of infection[28] (Extended Data Fig. 1g,h). Finally, proteomic characterization of SARS-CoV-2 protein binding partners indicates that ORF8 binds DNA methyltransferase 1 (DNMT1)22,29.

Fig. 1: ORF8 associates with chromatin.

a, ORF8 contains an ARKS motif at amino acid 50 that matches the histone H3 tail. b, Lamin A/C staining of HEK293T cells transfected to express Strep–ORF8. c, ORF8 and lamin A/C staining of SARS-CoV-2-infected A549ACE2 cells at MOI = 1, 48 h after infection. d, Sequential salt extraction of HEK293T cells expressing ORF8 or ORF8ΔARKSAP. e, Gene tracks for ORF8 ChIP–seq normalized to input controls. f, Targeted mass spectrometry analysis of trypsin-digested ORF8 showing that ORF8 is acetylated at lysine 52. The intact peptide or precursor at 879.9508 m/z with a 2+ charge was isolated and fragmented. Tandem mass spectrometry spectra show unfragmented precursor (green) with matching product ions within a mass error of 10 ppm. Fragment intensity is relative to that for the ion with the highest intensity across the m/z range. The colour, letter and number for each fragment indicate the sequence that fragment contains within the larger peptide (top). y (red) and b (blue) fragments indicate C- and N-terminus-matched fragments, respectively. g, ORF8 expression results in decreased levels of KAT2A. Scale bars, 10 μm. For gel source data, see Supplementary Fig. 1b,l.

To determine whether ORF8 functions as a histone mimic, we began by examining its intracellular localization. Although ORF8 does not have a well-defined nuclear localization sequence, it is 15 kDa in size and thus small enough to diffuse into the nucleus. We transfected HEK293T cells with a construct encoding Strep-tagged ORF8 and visualized ORF8 with a Strep-Tactin-conjugated fluorescent probe. Although ORF8 localization was variable in appearance, ORF8 was typically located in the cytoplasm and at the periphery of the nucleus when using immunofluorescence (Fig. 1b), as previously reported[30], and in both the cytoplasm and nucleus when using cell fractionation (Extended Data Fig. 2a). Given the observed expression pattern, we next asked whether ORF8 colocalizes with lamin proteins. We found that ORF8 colocalized with lamin B1 and lamin A/C in cells transfected to express ORF8 (Fig. 1b and Extended Data Fig. 2b,c). Next, we infected an A549 lung epithelial-derived cell line expressing the ACE2 receptor (A549ACE2) with SARS-CoV-2, stained cells with an antiserum specific to ORF8 (Extended Data Fig. 2d,e) and confirmed a similar expression pattern in infected cells (Fig. 1c). Notably, while other functions have been proposed for ORF8 (refs 30,31,32,33,34,35,36,37), a potential role for ORF8 in the nucleus of host cells and specifically in regulating chromatin has not been explored.

We next tested whether ORF8 is associated with chromatin by using increasing salt concentrations to examine chromatin binding. We found that ORF8 dissociated from the chromatin fraction at salt concentrations similar to those at which lamin and histone proteins dissociate (Fig. 1d). By contrast, ORF8 with a deletion of the ARKSAP motif (ORF8ΔARKSAP) dissociated at lower salt concentrations and was present at lower levels in the chromatin fraction in comparison to ORF8 with this motif (Fig. 1d and Extended Data Fig. 2f,g), indicating that the putative histone mimic site affects the strength of ORF8’s association with chromatin. We next performed chromatin immunoprecipitation with sequencing (ChIP–seq) for ORF8 to determine whether and where ORF8 associates with genomic DNA. Although ORF8 did not have clearly defined peaks, ORF8 immunoprecipitation showed enrichment over input (Fig. 1e) and ORF8 was enriched within specific genomic regions, particularly those associated with H3K27me3 (Extended Data Fig. 2h–k).

On the basis of the localization of ORF8 to the periphery of the nucleus and its association with chromatin (observed using both biochemical and sequencing approaches), we further tested whether ORF8 associates with lamin-complex proteins. We found that ORF8 co-immunoprecipitated with lamin B1, histone H3 and HP1α, a protein associated with both lamin proteins and histones (Extended Data Fig. 3a). Reciprocal co-immunoprecipitation for lamin B1 and histone H3 confirmed ORF8 binding (Extended Data Fig. 3b). Next, we tested whether ORF8 also co-immunoprecipitates with the histone-modifying enzymes that target the ARKS motif within histone H3. We found that ORF8 was associated with the histone acetyltransferase KAT2A (also known as GCN5), which targets H3K9 (Fig. 1f). Although both ORF8 and ORF8ΔARKSAP immunoprecipitated with a previously established cytoplasmic binding partner, HLA-A2 (ref. [30]), we did not detect ORF8ΔARKSAP association with chromatin proteins, indicating that the ARKSAP motif strengthens ORF8’s association with chromatin proteins (Extended Data Fig. 3c,d). Further, ORF8 did not bind to BRD4, which preferentially binds acetylated histone H4 (Extended Data Fig. 3e). Finally, we used mass spectrometry to identify additional binding partners beyond those found through a candidate approach focused on chromatin modifiers (Supplementary Table 1). Whole-cell lysate that was largely depleted of chromatin proteins was used in a complementary approach in which mainly cytoplasmic proteins were therefore identified. However, the transcription factor SP2 was detected and confirmed to bind to ORF8 by co-immunoprecipitation (Extended Data Fig. 3f).

On the basis of the observation that ORF8 associates with KAT2A, we used targeted mass spectrometry to determine whether the proposed ORF8 histone mimic site is modified similarly to histones. Using a bottom–up approach, ORF8 was purified from cells, reduced, alkylated and digested. Separation with liquid chromatography was followed by parallel reaction monitoring mass spectrometry (LC–PRM-MS) targeting possible unmodified and modified forms of ORF8 commonly found for histones, including serine phosphorylation and lysine monomethylation, dimethylation, trimethylation and acetylation. Of these targets, unmodified and acetylated lysine were identified. The acetylated peptide contained a mass shift of +42 Da and demonstrated almost complete coverage of all possible product ions. High-resolution mass spectrometry differentiated the precursor from the trimethylated peptide and matched all product ions within a mass error of 10 ppm (Fig. 1f and Extended Data Fig. 3g). This demonstrates that ORF8 is acetylated on the lysine within the proposed ARKS histone mimic site, similarly to histone H3. Notably, presence of acetylated lysine within the ARKSAP motif is probably incompatible with dimerization of ORF8, which involves a hydrogen-bond interaction at this residue[24], and thus suggests that ORF8 can exist as a monomer within cells. Finally, given that ORF8 promotes lysosomal degradation of another binding partner30,38, we examined whether ORF8 similarly affects chromatin-associated proteins. ORF8 expression resulted in a marked decrease in the abundance of KAT2A (Fig. 1g), whereas levels of nuclear lamina proteins and lamina-associated heterochromatin were unchanged or slightly increased (Extended Data Fig. 3h–l). These findings suggest that not only does ORF8 associate with proteins such as acetyltransferases, but it probably also is modified by them similarly to histone H3 and induces their degradation. Taken together, these findings demonstrate that ORF8 is well positioned to act as a histone mimic on the basis of its association with chromatin and chromatin-modifying enzymes and its ability to deplete the histone acetyltransferase KAT2A.

ORF8 disrupts chromatin regulation

We next examined whether ORF8 expression disrupts histone PTMs using an unbiased mass spectrometry approach. HEK293T cells were transfected with a control plasmid encoding GFP or with a plasmid encoding ORF8 with a Strep tag. Transfected cells, identified by GFP fluorescence or by a Strep-Tactin-conjugated fluorescent probe, were isolated using fluorescence-activated cell sorting (FACS). Histones were purified through acid extraction, and bottom–up unbiased mass spectrometry was performed to quantify all detected histone PTMs. Notably, histone modifications associated with transcriptional repression were increased while numerous histone modifications associated with active gene expression were depleted in cells expressing ORF8 (Fig. 2a). In particular, modifications within the H3 ARKS motifs were highly disrupted. For example, the peptides containing methylated H3K9 and H3K27, which are associated with transcriptional repression, showed robustly increased abundance in response to ORF8 expression. Conversely, the peptide containing both H3K9ac and H3K14ac, both of which have a well-established link to active gene expression, showed decreased abundance in response to ORF8 expression. These data support a role for ORF8 as a putative histone mimic and demonstrate that it is capable of disrupting histone PTM regulation at numerous critical sites within histones.

Fig. 2: ORF8 function in histone PTM regulation.

a, Mass spectrometry analysis of histone PTMs in control (GFP-expressing) or ORF8-expressing HEK293T cells isolated by FACS. The z score and fold change are shown for modifications that were significantly changed in response to ORF8 expression, were detected in over 1% of the total peptide abundance and have well-established functions (full results shown in Supplementary Table 2). b–g, Immunofluorescence analysis of HEK293T cells transfected to express GFP or Strep–ORF8 showing that ORF8 expression increases H3K9me3 (b,c) and H3K27me3 (d,e) while decreasing H3K9ac (f,g). Conversely, ORF8 with deletion of the histone mimic site ARKSAP (ORF8ΔARKSAP) does not affect these histone PTMs. n = 614 (GFP), 497 (ORF8) and 170 (ORF8ΔARKSAP) cells for H3K9me3; 616, 550 and 154 cells for H3K27me3; and 666, 568 and 170 cells for H3K9ac compiled from three independent transfections. One-way ANOVA with post hoc two-sided t test and Bonferroni correction. h, Western blot analysis of histones isolated from FACS-sorted transfected cells. i, ATAC-seq of HEK293T cells expressing GFP, ORF8 or ORF8ΔARKSAP isolated by FACS. Reads per million mapped reads surrounding the transcription start site (TSS) of all expressed genes were averaged. n = 2 independent replicates. Original blots shown in Supplementary Fig. 1. Scale bars, 10 μm. The FACS gating strategy and cell numbers isolated are shown in Supplementary Fig. 2. For gel source data, see Supplementary Fig. 1e. Box plots are centred on the median with bounds at the 25th and 75th percentile, the minimum and maximum defined as the median ± 1.5× the interquartile range and whiskers extending to the lowest and highest values in the range.

Source data

To confirm the mass spectrometry findings, we used immunofluorescence imaging to measure methylated and acetylated H3K9 and H3K27. We found that cells expressing ORF8 exhibited increased H3K9me3 and H3K27me3 and decreased H3K9ac staining compared with those transfected with control plasmid (Fig. 2b–g). ORF8 expression did not significantly disrupt H3K27ac, global acetylation, H3S10 phosphorylation, H3K9me2 or lamin B (Extended Data Fig. 4a,b). Although ORF8ΔARKSAP was expressed at similar levels to ORF8 (Extended Data Fig. 4c), it did not increase H3K9me3 or H3K27me3 and had a non-significant intermediate effect on H3K9ac (Fig. 2b–g). Next, we examined an acquired mutation in ORF8 commonly found in SARS-CoV-2 strains encoding an S84L substitution (ORF8S84L). This site is unlikely to affect protein stability31,39 and lies outside the histone mimic region, and the substitution is thus not expected to affect the ability of ORF8 to regulate histone PTMs. Expression of ORF8S84L also increased H3K9me3 and H3K27me3 levels while decreasing H3K9ac (Extended Data Fig. 4d–f), indicating that, as predicted, this common variant does not alter the histone mimic function of ORF8. Similarly, a six-residue deletion in another unstructured region of ORF8 with similar amino acid make-up but a different sequence (AGSKSP) as the histone mimic site did not affect the ability of ORF8 to disrupt histone regulation (Extended Data Fig. 4g).

We next sought to confirm these findings using independent methods. To ensure equal levels of expression of ORF8 and ORF8ΔARKSAP, we isolated transfected cells by FACS (Extended Data Fig. 5a). We then isolated histones through acid extraction and confirmed that ORF8 increased H3K9me3 and H3K27me3 and deceased H3K9ac in an ARKSAP-dependent manner by western blot analysis (Fig. 2h). Similarly, CUT&Tag sequencing of H3K9ac demonstrated that ORF8, but not ORF8ΔARKSAP, deceased H3K9ac (Extended Data Fig. 5b,c). Finally, assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) demonstrated that ORF8, but not ORF8ΔARKSAP, decreased chromatin accessibility (Extended Data Fig. 5d and Fig. 2i). The changes in both H3K9ac and chromatin accessibility were largely global but were particularly evident for genes with intermediate to high expression (Extended Data Fig. 5e–h).

To determine how these chromatin disruptions affect gene expression, we used RNA sequencing (RNA-seq) to define differentially expressed genes in transfected cells (Extended Data Fig. 6a–c). While ORF8 and ORF8ΔARKSAP shared a subset of differentially expressed genes, the presence of the histone mimic motif resulted in less dynamic gene expression changes. Distinct gene groups were also differentially expressed between ORF8 and ORF8ΔARKSAP, with ORF8 decreasing gene expression relative to ORF8ΔARKSAP, particularly highly expressed genes (Extended Data Fig. 6d–i and Supplementary Table 3). Genes that were downregulated in response to ORF8 expression relative to ORF8ΔARKSAP also had higher basal levels of H3K9ac and greater accessibility than genes that were upregulated (Extended Data Fig. 6j,k), suggesting that they may be more sensitive to depletion of H3K9ac. Together, these results support a model in which ORF8 has multiple functions as previously proposed30,31,32,33,40 and activates a number of gene expression pathways, particularly in the absence of the ARKSAP motif. However, presence of the ARKSAP motif dampens the host cell transcriptional response and decreases expression of genes with high accessibility and H3K9ac. Together, these data define a role for ORF8 in disruption of host cell histone PTMs through a new case of histone mimicry of the ARKS motifs in histone H3.

SARS-CoV-2 disrupts chromatin regulation

Having shown that ORF8 alone is sufficient to disrupt chromatin regulation, we next examined the effect of ORF8 on histone PTM regulation in the context of viral infection. We generated a recombinant mutant SARS-CoV-2 virus with a deletion of ORF8 (SARS-CoV-2ΔORF8) using a cDNA reverse genetics system41,42. We infected A549ACE2 cells with SARS-CoV-2 or SARS-CoV-2ΔORF8 and compared the levels of the viral genomes and infectious virus production in the presence and absence of ORF8. Because of their overexpression of the ACE2 receptor, these cells are readily and rapidly infected by SARS-CoV-2 and thus provide an ideal system in which to compare the cellular responses to mutant forms of the virus without the confounding factor of different rates of infection. No differences in genome copy number or viral titre were detected at 24 h, and only subtle differences were observed at 48 h (Extended Data Fig. 7a,b and Fig. 3a,b), allowing for direct comparison of these two viruses at these early time points after infection.We therefore infected A549ACE cells with SARS-CoV-2 or SARS-CoV-2ΔORF8 and used ChIP–seq with spike-in normalization (ChIP-RX) to allow for the detection of global changes in histone PTMs. We found that SARS-CoV-2 infection resulted in robust increases in H3K9me3 and H3K27me3 compared with mock-infected cells (Extended Data Fig. 7c–e), mirroring the effects of ORF8 expression. However, deletion of ORF8 substantially attenuated this effect, indicating that the effect of SARS-CoV-2 on repressive histone modifications is partly due to ORF8 expression. Similarly, ATAC-seq demonstrated that infection with wild-type SARS-CoV-2 resulted in substantial chromatin condensation whereas infection with SARS-CoV-2ΔORF8 resulted in an intermediate phenotype. Finally, ChIP-RX indicated that SARS-CoV-2 infection resulted in decreased H3K9ac, and this effect was again attenuated in cells infected with SARS-CoV-2ΔORF8 (Fig. 3d,e). These data demonstrate that ORF8 contributes to the effects of SARS-CoV-2 infection on chromatin accessibility and histone modifications in host cells.

Fig. 3: SARS-CoV-2 infection affects histone PTMs.

a,b, Reverse transcription with quantitative PCR (qRT–PCR) analysis of expression of the SARS-CoV-2 gene RDRP (a) and plaque assay analysis of viral titre (b) in A549ACE cells 48 h after infection with wild-type SARS-CoV-2 (SARS-CoV-2WT), SARS-CoV-2ΔARKSAP or SARS-CoV-2ΔORF8 at MOI = 1. Two-way ANOVA with Dunnett’s multiple-comparison test (additional time points shown in Supplementary Table 4). Representative of two independent infections. PFU, plaque-forming units. c,d, ATAC-seq (c) and H3K9ac ChIP-RX (d) of A549ACE cells with SARS-CoV-2WT, SARS-CoV-2ΔARKSAP, SARS-CoV-2ΔORF8 or mock infection 48 h after infection. MOI = 1. n = 3 for ATAC-seq except n = 2 for SARS-CoV-2ΔARKSAP. n = 3 for ChIP-RX except n = 2 for SARS-CoV-2WT. RPM, reads per million. e, ChIP–seq and ATAC-seq gene tracks of genes in signalling pathways relevant to viral response. f, Western blot analysis of KAT2A in A549ACE cells following infection with wild-type or mutant SARS-CoV-2 viruses. g, Post-mortem lung tissue from patients with COVID-19 stained for H3K9me3 and nucleocapsid protein to identify SARS-CoV-2-infected cells. Arrows indicate infected cells. h, Quantification of H3K9me3 in infected cells compared with neighbouring cells and with control tissue. n = 12 infected cells and 131 uninfected neighbouring cells from three patients with COVID-19 and 60 cells from three control individuals. One-way ANOVA with post hoc two-sided t test and Bonferroni correction. Scale bars, 10 μm. For gel source data, see Supplementary Fig. 1o. Box plots are centred on the median with bounds at the 25th and 75th percentiles, the minimum and maximum defined as the median ± 1.5× the interquartile range and whiskers extending to the lowest and highest values in the range.

Source data

Because it is likely that ORF8 has multiple effects on cellular function, on the basis of both recent puplications30,31,32,33,40 and our mechanistic data, we also sought to determine whether these effects were specifically due to the histone mimic motif. To do this, we generated a mutant form of SARS-CoV-2 with a deletion of only the ARKSAP motif (SARS-CoV-2ΔARKSAP). In A549ACE2 cells, SARS-CoV-2ΔARKSAP replicated similarly to wild-type virus (Fig. 3a,b) but substantially alleviated the effect of infection on chromatin accessibly and H3K9ac, matching the effects of ORF8 deletion (Fig. 3c–e). Given the robust effects of SARS-CoV-2 on H3K9ac and the ability of ORF8 to deplete KAT2A (Fig. 1g), we also examined the effect of infection on KAT2A levels. Wild-type SARS-CoV-2 infection reduced KAT2A expression, whereas infection with SARS-CoV-2ΔORF8 or SARS-CoV-2ΔARKSAP did not (Fig. 3f). These data indicate that ORF8, and specifically the ARKSAP motif within ORF8, contributes to the effects of SARS-CoV-2 on the host cell epigenome.

To ensure that the differences observed in host cell chromatin regulation following SARS-CoV-2 and SARS-CoV-2ΔORF8 infection are not due to any subtle difference in rates of infection between viruses, we sought to further confirm these finding using an approach that is independent of the number of cells infected. We used immunocytochemistry to stain for histone modifications of interest, using staining for double-stranded RNA (dsRNA) to identify and specifically examine infected cells. At 24 h after infection, cells infected with SARS-CoV-2 had increased H3K9me3 and H3K27me3 and decreased H3K9ac compared with either mock-infected cells or uninfected neighbouring cells (Extended Data Fig. 8a–f). As observed in ChIP–seq data, this effect was largely lost with deletion of ORF8.

To determine whether similar effects also occur in the context of a patient population, we obtained post-mortem lung tissue samples from three patients with coronavirus disease 2019 (COVID-19) and matched controls. We stained tissue for H3K9me3 as well as for SARS-CoV-2 nucleocapsid protein to identify infected cells. We found that, in all patient samples, infected cells showed increased H3K9me3 staining compared with neighbouring cells within the same tissue, as well as compared with control tissue (Fig. 3g,h and Extended Data Fig. 8g). While sample availability limits the conclusions that can be drawn from this assay, this finding indicates that histone PTMs are also disrupted in patients with severe COVID-19 disease. In summary, we found that the effects of SARS-CoV-2 infection on histone PTMs and chromatin compaction require ORF8 expression and mirror the ARKSAP-dependent effects of ORF8.

SARS-CoV-2 effects on transcription

Next, we examined how the changes in histone PTMs detected through ChIP–seq relate to gene expression using RNA-seq. All viruses contained similar numbers of reads, and the only difference in SARS-CoV-2 transcript expression was for ORF8 in SARS-CoV-2ΔORF8 (Extended Data Fig. 9a–d). However, in wild-type virus, ORF8 transcript was highly expressed and more abundant than histone H3-encoding transcripts (Extended Data Fig. 9e). Interestingly, early in infection, the three viruses tested each disrupted a distinct set of genes, indicating that presence of the histone mimic motif changes the transcriptional response to infection (Fig. 4a–c). By 48 h after infection, all three viruses made up the vast majority of the mapped reads and resulted in robust changes in gene expression compared with mock-infected cells (Extended Data Fig. 9c,f,g). The functional groups of genes most induced by infection also differed among the three viruses, indicating distinct host cell responses at early time points (Fig. 4d and Extended Data Fig. 10a). This is notable given that wild-type SARS-CoV-2 and SARS-CoV-2ΔARKSAP had nearly identical copy numbers and replication rates in A549ACE2 cells (Fig. 3a,b), and thus the different transcriptional responses are unlikely to be due to differences in the number of cells infected or the viral load within infected cells. Interestingly, direct comparison of SARS-CoV-2ΔORF8 and SARS-CoV-2ΔARKSAP also showed distinct gene expression changes and functional group enrichment (Extended Data Fig. 10b,c), indicating again that ORF8 probably has multiple functions beyond those mediated by the ARKSAP domain. In addition, gene expression changes in response to infection were correlated with changes in H3K9ac (Extended Data Fig. 10d–f). Notably, these data further support recent findings indicating that SARS-CoV-2 results in a limited early transcriptional response1,2,43 and demonstrate that the ORF8 ARKSAP domain is linked to changes in gene expression.

Fig. 4: ORF8 affects gene expression and viral replication during SARS-CoV-2 infection.

a, Differential gene expression analysis by RNA-seq of A549ACE2 cells 24 h after infection with SARS-CoV-2WT, SARS-CoV-2ΔORF8 or SARS-CoV-2ΔARKSAP, compared with mock infection. MOI = 1. Significantly differentially expressed genes are shown in blue (downregulated) and red (upregulated). n = 3. Significance based on DESeq2 analysis with multiple-comparison correction. b, Overlap of differentially expressed genes in response to infection with SARS-CoV-2WT, SARS-CoV-2ΔORF8 or SARS-CoV-2ΔARKSAP. c, Gene tracks of genes in signalling pathways relevant to viral response. d, Top gene ontology (GO) terms for genes upregulated by SARS-CoV-2WT and SARS-CoV-2ΔARKSAP infection. Significance based on clusterProfiler analysis with Benjamini–Hochberg-adjusted P values. e,f, qRT–PCR analysis of expression of the SARS-CoV-2 gene RDRP (e) and plaque assay analysis of viral titre (f) in iAT2 pulmonary cells at 48 h after infection with SARS-CoV-2WT, SARS-CoV-2ΔORF8 or SARS-CoV-2ΔARKSAP at MOI = 1. n = 3 replicates. One-way (e) or two-way (f) ANOVA with Dunnett’s multiple-comparison test (additional time points for f and all replicates shown in Supplementary Table 4). Bar plots indicate mean ± s.e.m.

Given the robust effects of ORF8 deletion on host cell chromatin regulation and the transcriptional response to infection, we sought to test whether ORF8 mediates the replication of SARS-CoV-2 using a physiologically relevant cell type. Induced human pluripotent stem cell-derived lung alveolar type II (iAT2) pulmonary cells[44] were infected with SARS-CoV-2, SARS-CoV-2ΔORF8 or SARS-CoV-2ΔARKSAP (multiplicity of infection (MOI) = 1). Notably, we observed that both mutant viruses had decreased genome copy numbers at 48 h after infection in most replicates (Fig. 4e and Supplementary Table 4), suggesting that ORF8, and specifically the ARKSAP domain, affects SARS-CoV-2 genome replication in a host cell. However, viral titres measured through plaque assays demonstrated that SARS-CoV-2ΔORF8 generated fewer infectious particles than wild-type SARS-CoV-2 while SARS-CoV-2ΔARKSAP appeared similar to wild-type virus and in some cases even showed more plaque formation (Fig. 4f and Supplementary Table 4). Fitting with previous work indicating that ORF8 affects endoplasmic reticulum (ER) stress pathways[32], this suggests that ORF8 probably has an ARKSAP-independent function that may promote viral particle formation. Taken together, this work presents a link between a specific SARS-CoV-2 protein and the epigenetic disruptions that occur in response to infection and provides a mechanistic explanation for mounting evidence12,13,45 that epigenetic disruptions contribute to the severity of COVID-19.

Discussion

The work described here identifies a new case of histone mimicry during infection by SARS-CoV-2 and defines a mechanism through which SARS-CoV-2 acts to disrupt host cell chromatin regulation. We found that the protein encoded by the SARS-CoV-2 ORF8 gene contains an ARKS motif and that ORF8 expression disrupts histone PTM regulation. ORF8 is associated with chromatin-associated proteins, histones and the nuclear lamina and is itself acetylated within the histone mimic motif similarly to histones. ORF8 expression disrupts multiple critical histone PTMs and promotes chromatin compaction, whereas ORF8 lacking the histone mimic motif does not. Further, SARS-CoV-2 infection in human cell lines and post-mortem patient lung tissue causes similar global disruptions to chromatin acting in part through the histone mimic. In addition, deletion of the ORF8 gene or the sequence encoding the histone mimic affects the host cell transcriptional response to SARS-CoV-2 infection. Finally, loss of ORF8 decreases the replication of SARS-CoV-2 in human induced pluripotent stem cell-derived iAT2 pulmonary cells while loss of the histone mimic motif specifically affects viral genome copy number.

Notably, the role of ORF8 in chromatin disruption early in infection is not inconsistent with other proposed roles for ORF8 in other cellular compartments or at later stages of infection30,31,32,34,46 and does not preclude other proposed mechanisms of transcriptional disruption in response to SARS-CoV-2 (ref. [23]). In fact, our data point towards a model in which ORF8 has multiple functions, including acting as a histone mimic motif. The effects of deletion of accessory proteins from SARS-CoV-2 in a transgenic mouse model appear complex, with ORF8 loss causing decreases in replication and viral load but having limited effects on survival[47]. However, data from patients with COVID-19 were used to examine a rare 382-nucleotide deletion variation in SARS-CoV-2 isolated in Singapore that results in the loss of a small portion of ORF7B and the majority of the ORF8 gene. This work found that this SARS-CoV-2 variant is associated with a milder infection in patients with COVID-19 and an improved interferon response48,49. Our findings in human iAT2 pulmonary cells point towards the loss of ORF8 as a possible cause for these differences and provide an epigenetic mechanism underlying the role of ORF8 in promoting SARS-CoV-2 virulence within the patient population. Finally, the work described here has critical implications for understanding emerging viral strains carrying deletions and mutations in the ORF8 gene[50] and COVID-19 pathogenesis in patients.

Methods

A549ACE cells

ACE2-expressing A549 cells were generated as previously described[3]. A549ACE2 cells were grown in RPMI-1640 with 10% FBS and 1% penicillin-streptomycin and were maintained free of mycoplasma. Cells were infected at an MOI of 1 and fixed or lysed at 24 or 48 h after infection.

HEK293T cells

HEK293T cells were obtained from the American Type Culture Collection (ATCC), cultured in DMEM (with 4.5 g L–1 glucose, l-glutamine and sodium pyruvate) supplemented with 10% FBS (Sigma-Aldrich, F2442-500ML) and 1% penicillin-streptomycin (Gibco, 15140122) and maintained free of mycoplasma. Calcium phosphate transfection was used to introduce plasmid DNA encoding GFP, ORF8 and mutant ORF8 into HEK293T cells. For immunocytochemistry experiments, cells were plated on poly(d-lysine)-coated coverslips. Cells were washed 24 h after transfection with culture medium and fixed or pelleted and flash frozen 48 h after transfection. Cells were fixed using 4% paraformaldehyde (PFA) in PBS for 8 min. To pellet cells, cells were detached from the culture plate using TrypLE Express (Gibco, 12605010) dissociation reagent, spun down for 5 min at 180 g and flash frozen in liquid nitrogen.

iAT2 cells

Generation of human-derived induced alveolar epithelial type II-like (iAT2) cells was performed as described[44]. To maintain a stable and pure culture of the iAT2 cell line, SFTPCtdTomato+ cells were sorted and serially passaged every 14 d. Cells were grown in organoid format using 90% Matrigel with a cell density of 400 cells per µl. Cells were fed using CK+DCI medium + Rock inhibitor for the first 48 h after splitting and then changed to K+DCI medium for 5 d followed by CK+DCI medium for 7 d. Every 14 d, alveolosphere organoids were passaged, organoids were released from Matrigel using 2 mg ml–1 Dispase for 1 h at 37 °C and single cells were then generated using 0.05% trypsin for 15 min at 37 °C. Cell number and viability were assessed using Trypan blue, and cells were finally passaged to new Matrigel drops left to polymerize for 30 min at 37 °C in a 5% CO_2 incubator, after which cells in solidified Matrigel were fed according to plate format.

For the generation of two-dimensional (2D) alveolar cells for virus infection, when alveolosphere organoids were passaged, cells were plated on precoated 1:30 Matrigel plates at a cell density of 125,000 cells per cm^2 using CK+DCI medium + Rock inhibitor for the first 48 h, and the medium was then changed to CK+DCI medium. Seventy-two hours after cell plating, cells were infected with SARS-CoV-2 virus using an MOI of 1 for 48 h.

Cell line validation and testing

Cell lines were authenticated as previously described[3]. HEK293T and Vero E6 cells were obtained from ATCC at the onset of this project. All cell lines used were confirmed to be negative for mycoplasma and are retested twice annually.

ORF8 constructs

The ORF8 expression plasmid was obtained from Addgene, pLVX-EF1alpha-SARS-CoV-2-orf8-2xStrep-IRES-Puro (Addgene plasmid 141390). ORF8 deletion constructs were produced on the ORF8 backbone using Pfu Turbo HotStart DNA polymerase (Agilent, 600322-51), and primers were created using the DNA-based primer design feature of the online PrimerX tool. Constructs were verified by Sanger sequencing.

SARS-CoV-2 infection

Virus generation

SARS-CoV-2 (USA-WA1/2020 strain) was obtained from BEI and propagated in Vero E6 cells. The genome RNA was sequenced and found to be identical to GenBank MN985325.1. Mutant viruses were generated using the cDNA reverse genetics system as previously described[42].

Infections

Cells were infected with wild-type or mutant SARS-CoV-2 at an MOI of 1 PFU per cell (A549ACE2) or 5 PFU per cell (iAT2) as previously described[3]. Virus was added to cells for 1 h at 37 °C and was then removed and replaced with medium. Cells were lysed at 48 h after infection and RNA was isolated. All infections and virus manipulations were conducted in a Biosafety Level 3 (BSL3) laboratory using appropriate protective equipment and protocols.

Viral growth kinetics and plaque assays

Growth kinetics analysis and plaque assays were performed as previously described[3]. In brief, at the indicated time points, 200 µl of supernatant was collected from cells and stored at −80 °C for titration of infectious virus. Samples were diluted in serum-free DMEM and adsorbed onto Vero E6 cells at 37 °C for 1 h before a liquid overlay was added (DMEM with 2% FBS, 1× sodium pyruvate and 0.1% agarose). After 3 d, the overlay was removed and cells were fixed with 4% PFA and stained with crystal violet for plaque visualization and counting. All plaque assays were performed in biological triplicate and technical duplicate.

Viral genome quantification by qRT–PCR

RNA collection, qRT–PCR and viral genome quantification were performed as previously described[3]. In brief, at the indicated time points, infected cells were lysed using RLT Plus Buffer, genomic DNA was removed and RNA was extracted using the Qiagen RNeasy Mini kit (Qiagen, 74134). cDNA was generated using a High-Capacity cDNA Reverse Transcriptase kit (Applied Biosystems, 4368814). cDNA was amplified using specific qRT–PCR primers targeting viral NSP12 (forward, 5′-GGTAACTGGTATGATTTCG-3′; reverse, 5′-CTGGTCAAGGTTAATATAGG-3′), iQ SYBR Green Supermix (Bio-Rad, 1708880) and the QuantStudio 3 PCR system (Thermo Fisher). Quantification of SARS-CoV-2 genome copies was performed using a standard curve generated by serially diluting a known concentration of the pcDNA6B-nCoV NSP12-FLAG construct encoding the RDRP gene (a gift from G. Stark, Cleveland Clinic) after digestion with XhoI. Genome copy numbers were determined using standard curve analysis in QuantStudio 3 software, and copy numbers per microgram of RNA were calculated using the cDNA reaction volumes and input RNA for the cDNA reactions.

Cell fractionation

Pelleted cells were briefly thawed on ice. Buffer 1 (15 mM Tris-HCl (pH 7.5), 60 mM KCl, 15 mM NaCl, 5 mM MgCl_2, 1 mM CaCl_2 and 0.25 M sucrose with 1 mM PMSF, 1 mM DTT and a Complete Protease Inhibitor cocktail tablet added immediately before use) was added to the pellet at roughly five times the volume of the pellet and gently pipetted up and down to dissociate the pellet. Samples were incubated on ice for 5 min, followed by addition of an equal volume of buffer 1 with 0.4% NP-40 to the sample. Samples were then mixed by inversion for 5 min at 4 °C. Samples were spun at 200 g for 10 min in a prechilled centrifuge to pellet nuclei. The supernatant (cytoplasmic fraction) was transferred to a new tube. Pellets were resuspended gently in 0.5 ml buffer 1 to wash the nuclei and then pelleted again with the supernatant discarded. Nuclear pellet solubilization buffer (150 mM NaCl, 50 mM Tris-HCl (pH 8.0), 1% NP-40 and 5 mM MgCl_2 with 1 mM PMSF, 1 mM DTT and Benzonase enzyme at 250 U µl–1 added shortly before use) was added to the pellet at half the volume of buffer 1 used. Samples were then incubated at room temperature in a thermoshaker until the pellet was fully dissolved. The amount of Benzonase enzyme was doubled in samples with undissolved material left after 20 min. Samples were then centrifuged at 13,000 r.p.m. for 20 min at 4 °C. Supernatant (nuclei fraction) was collected. Sample concentrations were determined by BCA assay, and samples were boiled in a western loading buffer for 10 min before analysis by western blotting.

Chromatin sequential salt extraction

Salt extractions were performed as described[51]. In brief, a 2× RIPA solution was made (100 mM Tris (pH 8.0), 2% NP-40 and 0.5% sodium deoxycholate) and mixed with varying concentrations of a 5 M NaCl solution to generate RIPA containing 0, 100, 200, 300, 400 and 500 mM NaCl. Pelleted cells were resuspended in buffer A with protease inhibitors (0.3 M sucrose, 60 mM KCl, 60 mM Tris (pH 8.0), 2 mM EDTA and 0.5% NP-40) and rotated at 4 °C for 10 min. Nuclei were pelleted by centrifugation at 6,000 g for 5 min at 4 °C. Supernatant was removed and saved, and 200 µl of RIPA with 0 mM NaCl and protease inhibitors was added to the sample. Samples were mixed by pipetting 15 times and incubated on ice for 3 min before centrifuging at 6,500 g for 3 min at 4 °C. Supernatant was saved and the RIPA steps were repeated for all NaCl concentrations. Samples were then boiled and sonicated before analysis by western blotting.

ATAC-seq

HEK293T cells were stained and sorted to isolate transfected cells using the same method as described below. Sorted cells were resuspended in cold lysis buffer (10 µl per 10,000 cells; 10 mM Tris-Cl (pH 7.5), 10 mM NaCl, 3 mM MgCl_2, 0.1% (vol/vol) NP-40, 0.1% (vol/vol) Tween-20 and 0.01% (vol/vol) digitonin) and washed in wash buffer (10 mM Tris-Cl (pH 7.5), 10 mM NaCl, 3 mM MgCl_2 and 0.1% (vol/vol) Tween-20). Transposition was performed with Tagment DNA TDE1 (Illumina, 15027865). Transposition reactions were cleaned with AMPure XP beads (Beckman, A63880), and libraries were generated by PCR with NEBNext High-Fidelity 2× PCR Master Mix (NEB, M0541). Library size was confirmed on a Bioanalyzer before sequencing on the NextSeq 550 platform (40-bp read length, paired end).

Infected A549ACE cells were fixed before collection for ATAC-seq. The protocol was performed as above except with 0.05% Igepal CA-630 added to the lysis buffer. In addition, after the transposase reaction, a reverse cross-linking solution was added (with a final concentration of 50 mM Tris-Cl, 1 mM EDTA, 1% SDS, 0.2 M NaCl and 5 ng ml–1 proteinase K) up to 200 μl. The mixture was incubated at 65 °C with shaking at 1,000 r.p.m. in a heat block overnight and then purified as above.

For ATAC-seq analysis, alignments were performed with Bowtie2 (2.1.0)[52] using the hg38 genome with the pipeline at https://github.com/shenlab-sinai/chip-seq_preprocess. Reads were mapped using NGS plot. For HEK293T cell ATAC-seq, genes with high, intermediate, low and no expression were defined by DESeq2 normalized basemean values from HEK293T cell RNA-seq data with under 2 basemean as non-expressing genes and the remaining genes binned into three groups for low, intermediate and high expression. For A549ACE cell ATAC-seq, three biological replicates each with 2–3 technical replicates were performed. Ten million reads from each individual technical replicate were subsetted (SAMtools v1.9, seed 1) and merged, and each condition was then merged across biological replicates. For average profile plots, each condition was downsampled to 40 million reads and plotted against all genes identified by DESeq2 as expressed over 1 from A549ACE RNA-seq data.

ChIP–seq

For ORF8 ChIP–seq, 2 d after transfection, cells were fixed for 5 min with 1% PFA in PBS and the reaction was then quenched with 2.5 M glycine. Cells were washed twice, collected in PBS with protease and phosphatase inhibitors and then pelleted at 1,200 r.p.m. for 5 min. Cells were then rotated in lysis buffer 1 (50 mM HEPES-KOH (pH 7.5), 140 mM NaCl, 1 mM EDTA, 10% glycerol, 0.5% NP-40 and 0.25% Triton X-100) for 10 min at 4 °C and spun at 1,350 g for 5 min at 4 °C to isolate nuclei. Supernatant was discarded and cells were resuspended in lysis buffer 2 (10 mM Tris-HCl (pH 8), 200 mM NaCl, 1 mM EDTA and 0.5 mM EGTA) to lyse nuclei. Samples were rotated for 10 min at room temperature and were spun again at 1,350 g for 5 min at 4 °C. The supernatant was discarded and the pellet was resuspended in lysis buffer 3 (10 mM Tris-HCl (pH 8), 100 mM NaCl, 1 mM EDTA, 0.5 mM EGTA, 0.1% EDTA and 0.5% N-lauroylsarcosine). Lysates were sonicated on a Covaris sonicator for 40 min (200 cycles per burst). Triton X-100 was added to reach a final concentration of 1%, and lysates were spun at 20,000 g for 10 min at 4 °C. Strep-Tactin magnetic beads (MagStrep type 3 XT beads; IBA, 2-4090-002) were added to the lysates overnight with rotation at 4 °C. Beads were then washed with a low-salt buffer (0.1% SDS, 1% Triton X-100, 2 mM EDTA, 20 mM Tris (pH 8) and 150 mM NaCl), a high-salt buffer (0.1% SDS, 1% Triton X-100, 2 mM EDTA, 20 mM Tris (pH 8) and 500 mM NaCl), a LiCl wash buffer (150 mM LiCl, 1% NP-40, 1% sodium deoxycholate, 1 mM EDTA and 10 mM Tris (pH 8)) and then TE with 50 mM NaCl. Chromatin was eluted from beads for 30 min with shaking at room temperature in 55 µl BXT elution buffer (IBA, 2-1042-025) followed by the addition of 150 µl elution buffer (50 mM Tris-HCl (pH 8.0), 10 mM EDTA and 1% SDS) for 30 min at 65 °C. Samples were removed from beads and cross-linking was reversed by further incubating chromatin overnight at 65 °C. RNA was digested with RNase for 1 h at 37 °C, and protein was digested with proteinase K for 30 min at 55 °C. DNA was then purified with the Zymo PCR purification kit. The Illumina TruSeq ChIP purification kit was used to prepare samples for sequencing on an Illumina NextSeq 500 instrument (42-bp read length, paired end).

For ORF8 ChIP–seq analysis, alignments were performed with Bowtie2 (2.1.0)[52] using the hg38 genome with a ChIP–seq pipeline (https://github.com/shenlab-sinai/chip-seq_preprocess). ORF8 reads were mapped using NGS plot. For comparison with histone modification ChIP–seq datasets, ENCODE and 4D nucleome data were used for H3K9ac (experiment ENCSR000ASV), lamin (4DN experiment set 4DNES24XA7U8), H3K9me3 (experiments ENCSR000FCJ and ENCSR179BUC), H3K9me2 (experiment ENCSR55LYM) and H3K27me3 (experiment ENCSR000AKD). To define ORF8-enriched regions, HiddenDomains was used for each of two ORF8 ChIP–seq experiments normalized to input. Output files were merged with bedtools (v2.18.1) intersect to select the subset of enriched regions found in both replicates. DiffBind (3.4.11) was used to examine H3K27me3 enrichment within ORF8-enriched regions. The Deeptools (3.3.0) plotEnrichment tool was used to count percentages of reads of histone modification ENCODE ChIP–seq datasets that were within ORF8-enriched regions. ngs.plot.r (2.63) was used to generate plots of ORF8 enrichment within genomic regions of interest.

For histone PTM ChIP–seq, 4–10 million cells were resuspended in 1 ml of lysis buffer 1 (50 mM HEPES-KOH (pH 7.5), 140 mM NaCl, 1 mM EDTA, 10% glycerol, 0.5% NP-40 and 0.25% Triton X-100) and rotated at 4 °C for 10 min, followed by centrifugation and removal of supernatant. Cells were then resuspended in 1 ml of lysis buffer 2 (10 mM Tris-HCl (pH 8.0), 200 mM NaCl, 1 mM EDTA and 0.5 mM EGTA) and rotated for 10 min at 4 °C, followed by centrifugation and removal of supernatant. Cells were then resuspended in 1 ml of lysis buffer 3 (10 mM Tris-HCl (pH 8.0), 100 mM NaCl, 1 mM EDTA, 0.5 mM EGTA, 0.1% sodium deoxycholate and 0.5% N-lauroylsarcosine) and rotated again for 10 min at 4 °C. Cells were then sonicated with a Covaris S220 sonicator for 35 min (peak incident power, 140; duty factor, 5%; cycles per burst, 200). This was followed by addition of 110 µl Triton X-100 and centrifugation at maximum speed (20,000 g) for 15 min at 4 °C to clear the lysate. The lysate chromatin concentration was then equalized according to DNA content (as measured with a Qubit fluorometer). Following this, 5% of equivalently treated chromatin from Camponotus floridanus pupae was added to all samples according to chromatin concentration, and 50 µl of lysate was saved as input shearing control. Then, 250 µl of equalized lysate was added to washed, antibody-conjugated Protein A/G Dynabeads (2 µg of antibody conjugated to 15 µl of Protein A/G Dynabeads, resuspended in 50 µl per immunoprecipitation), and immunoprecipitations were rotated overnight at 4 °C in a final volume of 300 µl. The following day, immunoprecipitations were washed five times in RIPA wash buffer (50 mM HEPES-KOH (pH 7.5), 500 mM LiCl, 1 mM EDTA, 1% NP-40 and 0.7% sodium deoxycholate) and once in TE (pH 8.0). Washes were followed by two elutions into 75 µl of elution buffer (50 mM Tris-HCl (pH 8.0), 10 mM EDTA and 1% SDS) at 65 °C for 45 min with shaking (1,100 r.p.m.). DNA was purified by phenol:chloroform:isoamyl alcohol (25:24:1) extraction followed by ethanol precipitation. Pelleted DNA was resuspended in 25 µl TE. Libraries for sequencing were prepared using the NEBNext Ultra II DNA Library Prep Kit for Illumina (NEB, E7645), as described by the manufacturer but using half the volume for all reagents and starting material. For PCR amplification, the optimal number of PCR cycles was determined using a qPCR side reaction with 10% of the adaptor-ligated, size-selected DNA. Seven to ten cycles of PCR were used for histone PTM libraries and 5 cycles were used for input controls. Samples were sequenced on a NextSeq 500 instrument (42-bp read length, paired end).

For analysis of histone PTM ChIP–seq data, reads were demultiplexed using bcl2fastq2 (Illumina) with the options ‘--mask-short-adapter-reads 20 --minimum-trimmed-read-length 20 --no-lane-splitting --barcode-mismatches 0’. Reads were trimmed using TRIMMOMATIC[53] with the options ‘ILLUMINACLIP:[adapter.fa]:2:30:10 LEADING:5 TRAILING:5 SLIDINGWINDOW:4:15 MINLEN:15’ and aligned to a hybrid hg38 + C. floridanus (v7.5, RefSeq) genome assembly using bowtie2 (v2.2.6)[52] with the option ‘--sensitive-local’. Alignments with a mapping quality below 5 (using SAMtools) and duplicated reads were removed. Peaks were called using MACS2 (v2.1.1.20160309)[54] with the options ‘--call-summits --nomodel --B’. Differential ChIP peaks were called using DiffBind[55] with the options ‘bFullLibrarySize=FALSE, bSubControl=TRUE, bTagwise=FALSE’ for dba.analyze(). For DiffBind testing, the DESeq2 algorithm with blocking was used, and ChIP replicate was used as the blocking factor while testing for differences between mock and infected samples. For ChIP signal tracks, individual replicate tracks were produced for RPM and fold enrichment over input control, merged and averaged.

To account for potential global differences in histone PTM abundance that would otherwise be missed by more standard quantile normalization-type approaches, high-quality deduplicated read counts were produced for both human- and C. floridanus-mapping reads, resulting in proportions of reads mapping to the exogenous genome for each histone PTM. Input controls were also treated in this way to account for potential differences in initial spike-in addition between samples. For each histone PTM, the proportion of spike-in reads was normalized by the appropriate input control value. Because spike-ins should be inversely proportional to target chromatin concentration, a ratio of SARS-CoV-2/mock values was produced for each histone PTM × replicate, and for SARS-CoV-2 samples resulting signal values were divided by this ratio. This resulted in per-base-pair signal values adjusted by the degree of global difference in a given histone PTM’s level between sample types.

All antibodies are described in Supplementary Table 6.

RNA-seq

RNA was extracted using a Qiagen RNA purification kit. Samples were prepared for sequencing using the Illumina TruSeq purification kit and sequenced on an Illumina NextSeq 500 instrument (75-bp read length, single read). Library size was confirmed on a Bioanalyzer before sequencing on the NextSeq 550 platform (single end, 75 cycles).

For RNA-seq analysis for SARS-CoV-2 infection experiments, a reference genome for alignment was built by concatenating the human (GRCh38 assembly) and SARS-CoV-2 (WA-CDC-WA1/2020 assembly; MN985325.1) genomes. For RNA-seq analysis for HEK293T cell experiments, the GRCh38 assembly was used. For all RNA-seq, reads were aligned using STAR (v2.6.1a) with default parameters and only uniquely mapped reads were retained for downstream analysis. TDF files were generated using IGVtools. Reads were counted towards human genes (GENCODE v35) and SARS-CoV-2 genes (WA-CDC-WA1/2020 assembly; MN985325.1) using featureCounts (v1.6.2). Low-count genes were filtered out so that only genes with counts per million (CPM) values greater than 1 in at least three samples were used. Data normalization and differential gene expression analysis were performed using the DESeq2 R package (v1.26.0). We defined genes as significant using a false discovery rate (FDR) cut-off of 0.05 and 1.5× fold change. GO enrichment analysis for differentially expressed genes was implemented with the clusterProfiler R package (v3.14.3), using the human genome annotation record in the org.Hs.eg.db R package (v3.10.0) and a Benjamini–Hochberg-adjusted P value of 0.05 as the cut-off.

Immunoprecipitation

Anti-Strep tag affinity purification, whole-cell lysate and cytoplasmic HLA-A2 co-immunoprecipitation

Protein and binding partners were purified with affinity Strep tag purification. For ORF8 PTM analysis and mass spectrometry binding partner analysis, whole-cell lysates were prepared as described below. Frozen cell pellets were thawed briefly and suspended in lysis buffer (immunoprecipitation (IP) buffer (50 mM Tris-HCl (pH 7.5) at 4 °C, 150 mM NaCl, 1 mM EDTA and 10 mM sodium butyrate) supplemented with 0.5% Nonidet P 40 Substitute (NP-40; Fluka Analytical) and cOmplete mini EDTA-free protease and PhosSTOP phosphatase inhibitor cocktails (Roche)). Samples were incubated on a tube rotator for 30 min at 4 °C. Debris was pelleted by centrifugation at 13,000 g for 15 min at 4 °C. Lysates were then incubated with Strep-Tactin magnetic beads (40 µl; MagStrep type 3 XT beads; IBA, 2-4090-002) for 2 h with rotation at 4 °C. Beads were washed three times with 1 ml wash buffer (IP buffer supplemented with 0.05% NP-40) and then once with 1 ml IP buffer. Strep-tagged ORF8 complexes were eluted from beads in BXT buffer (IBA, 2-1042-025) with shaking at 1,100 r.p.m. for 30 min.

Anti-Strep tag affinity purification for chromatin binding partners

Cells were rotated in lysis buffer 1 (50 mM HEPES-KOH (pH 7.5), 140 mM NaCl, 10 mM sodium butyrate, 1 mM EDTA, 10% glycerol, 0.5% NP-40 and 0.25% Triton X-100) supplemented with 0.5% Nonidet P 40 Substitute (NP-40; Fluka Analytical) and cOmplete mini EDTA-free protease and PhosSTOP phosphatase inhibitor cocktails (Roche) for 10 min at 4 °C and spun at 1,350 g for 5 min at 4 °C to isolate nuclei. Supernatant was discarded and cells were resuspended in lysis buffer 2 (10 mM Tris-HCl (pH 8), 200 mM NaCl, 10 mM sodium butyrate, 1 mM EDTA and 0.5 mM EGTA) to lyse nuclei. Cells were rotated for 10 min at room temperature and were spun again at 1,350 g for 5 min at 4 °C. The supernatant was discarded and the chromatin pellet was resuspended in lysis buffer 3 (10 mM Tris-HCl (pH 8), 100 mM NaCl, 10 mM sodium butyrate, 1 mM EDTA, 0.5 mM EGTA, 0.1% EDTA and 0.5% N-lauroylsarcosine). Lysates were sonicated using a tip sonicator with three 5-s bursts at 50% power with chilling on ice between bursts. After sonication, lysates were brought to a concentration of 1% Triton X-100 to disrupt lamina protein interactions. Debris was pelleted by centrifugation at 16,000 g at 4 °C, and the supernatant was incubated with Strep-Tactin magnetic beads (40 µl; MagStrep type 3 XT beads; Iba, 2-4090-002) for 2 h with rotation at 4 °C. Beads were washed three times with 1 ml wash buffer (IP buffer supplemented with 0.05% NP-40) and then once with 1 ml IP buffer. Strep-tagged ORF8 complexes were eluted from beads in BXT buffer (IBA, 2-1042-025) with shaking at 1,100 r.p.m. for 30 min. To analyse relative ORF8 construct levels in cytoplasmic versus chromatin fractions by western blotting, samples were taken from lysis buffer 1 and lysis buffer 3, respectively.

Reverse immunoprecipitation

Chromatin pellet lysate was obtained as described above for chromatin protein immunoprecipitation. Lysates were combined with antibody-conjugated Protein A Dynabeads (15 µg of antibody conjugated to 100 µl of Dynabeads) and rotated overnight at 4 °C. The following day, beads were washed three times with 1 ml wash buffer (IP buffer supplemented with 0.05% NP-40) and then once with 1 ml IP buffer. Chromatin protein complexes were eluted from beads in elution buffer (50 mM Tris-HCl (pH 8.0), 10 mM EDTA and 1% SDS) for 30 min with shaking at 65 °C.

All antibodies are described in Supplementary Table 6.

Immunocytochemistry

Fluorescence immunocytochemistry of HEK293T cells and A549ACE2 cells

Cells were fixed in 4% PFA for 10 min and washed with PBS. Fixed cells were permeabilized using 0.5% Triton X-100 in PBS for 20 min. Cells were blocked in blocking solution (PBS with 3% BSA, 2% serum and 0.1% Triton X-100) for at least 1 h and stained with designated primary antibody overnight at 4 °C. The following day, cell coverslips were washed with PBS and incubated with secondary antibody for 1 h at room temperature. For detection of Strep-tagged ORF8, Strep-Tactin DY-488 (IBA, 2-1562-050; 1:500) was added to the secondary antibody solution. Nuclei were stained with DAPI (1:1,000 in PBS) for 10 min with washing in PBS. Coverslips were mounted onto microscope slides using ProLong Gold antifade reagent (Thermo Fisher).

Fluorescence immunocytochemistry analysis of lamin B1, lamin A/C and H3K9me2

HEK293T cells were fixed with 2% PFA (Electron Microscopy Sciences, 15710) for 8 min at room temperature and washed three times with DPBS (Gibco, 14190-136). Cells were permeabilized with 0.25% Triton X-100 (Thermo Fisher, 28314) for 10 min, washed three times with DPBS for 5 min each wash and blocked in 1% BSA (Sigma, A4503) in PBST (DPBS with 0.05% Tween-20, pH 7.4 (Thermo Fisher, 28320)) for 60 min. Cells were incubated with primary antibody diluted in blocking buffer for 1 h, washed three times with PBST for 5 min each wash and incubated with secondary antibody diluted in blocking buffer for 60 min. Cells were washed twice with PBST and once with PBS for 5 min each wash and were then mounted on a slide using Duolink In Situ Mounting Medium with DAPI (Sigma, DUO82040-5ML). All procedures were performed at room temperature.

Immunohistological staining of patient lung tissue

Formalin-fixed, paraffin-embedded slides were obtained from Penn’s Pathology Clinical Service Center. Slides were deparaffinized and rehydrated as follows: incubation for 10 min with xylene (twice), 10 min with 100% ethanol (twice), 5 min with 95% ethanol, 5 min with 70% ethanol, 5 min with 50% ethanol and then running distilled water. Slides were then processed using heat-induced epitope retrieval (HIER). Slides were incubated in hot sodium citrate buffer (10 mM sodium citrate and 0.05% Tween-20, pH 6.0), placed in a pressure cooker and heated in a water bath for 25 min with high pressure settings. Slides were cooled at room temperature and washed twice in TBS. Membranes were permeabilized in TBS with 0.4% Triton X-100 for 20 min. Slides were then incubated in blocking solution (TBS with 10% goat serum, 1% BSA and 0.025% Triton X-100) for 2 h. Slides were incubated in mouse primary antibody solution containing anti-SARS-CoV-2 nucleocapsid and rabbit anti-H3K9me3 antibody solution overnight at 4 °C. The following day, slides were washed with TBS and incubated in secondary antibody solution. Nuclei were stained with DAPI (5 µg ml–1) in TBS for 10 min followed by washing with TBS. Coverslips were mounted with ProLong Gold antifade reagent (Thermo Fisher). All antibodies are described in Supplementary Table 6.

Image acquisition

Fluorescence immunocytochemistry of ORF8 and histone PTMs

Cells were imaged on an upright Leica DM 6000, TCS SP8 laser scanning confocal microscope with 405-nm, 488-nm, 552-nm and 638-nm lasers. The microscope uses two HyD detectors and three PMT detectors. The objective used was a ×63 HC PL APO CS2 oil objective with an NA of 1.40. Type F immersion liquid (Leica) was used for oil objectives. Images were 175.91 × 171.91 µm^2, 1,024 × 1,024 pixels and 16 bits per pixel. For PTM quantification, HEK293T cells and human lung tissue were imaged at a single z plane and A549 cells were imaged with a z stack through the nucleus.

Fluorescence immunocytochemistry analysis of lamin B1, lamin A/C and H3K9me2

All confocal immunofluorescence images were acquired using a Leica SP8 laser scanning confocal system with a ×63/1.40-NA HC PL APO CS2 objective and HyD detectors in standard mode with 100% gain. For comparison of lamin A/C and lamin B1 signal intensities between mock and ORF8-positive cells, single-plane confocal images were acquired. All images were acquired with the same microscope settings (zoom, laser power, gain, etc.). For analysis of the organization of H3K9me2-marked chromatin at the nuclear lamina, three-dimensional (3D) images of the middle z plane of the nucleus were taken as z stacks using 0.1-μm intervals with a range of 1 μm per nucleus. Confocal 3D images were deconvoluted with Huygens Professional software using the microscope parameters, standard PSF and automatic settings for background estimation.

Image analysis

Images were analysed using ImageJ software (version 2.0.0-rc-69/1.52p, build 269a0ad53f). Single-z-plane images of HEK293T cells and human lung tissue and summed z stacks through A549 nuclei were used for PTM quantification. Regions of interest (ROIs) of in-focus nuclei were semi-automatically defined using the DAPI channel and the ‘analyze particles’ functionality with manual corrections. HEK293T histone PTMs were quantified in transfected cells and non-transfected neighbouring cells using mean grey values. Signal for Strep-tagged ORF8 constructs (Strep-Tactin-488) and GFP was used to define transfected cells, and the HEK293T histone PTM levels in transfected cells were relativized to the histone PTM levels in non-transfected neighbouring cells. Histone PTMs were quantified in A549 cells and human lung tissue using integrated density values. dsRNA and SARS-CoV-2 nucleocapsid signal was used to define infected A549 cells and human lung cells, respectively. The total fluorescence intensity of the lamin A/C and lamin B1 signal was measured from the whole nuclei of mock and ORF8-positive cells. Analysis of the peripheral heterochromatin organization was performed as a comparison of a fraction of H3K9me2-marked chromatin at the nuclear lamina/periphery of mock and ORF8-positive cells. A fraction of H3K9me2 signal at the nuclear lamina/periphery was measured using lamin B signal as a mask or DAPI signal to create a mask of a 0.6-μm-thick nuclear peripheral zone.

Protein alignment

To identify potential histone mimicry, SARS-CoV-2 protein sequences were aligned to human histone protein sequences (H2A, H2B, H3.1, H3.2, H4, H2A.X, H2A.Z, macroH2A and H3.3) using Multiple Sequence Comparison by Log-Expectation (MUSCLE) with default settings. SARS-CoV-2 protein sequences were obtained from protein sequences published for the first Wuhan isolate[56].

FACS

HEK293T cell pellets were gently resuspended in 1 ml FACS buffer (Ca2+/Mg2+-free PBS with 2% BSA) and pelleted at 500 g for 5 min at 4 °C; the supernatant was removed. Cells transfected with ORF8 construct and non-transfected control cells were then gently resuspended in 1 ml FACS buffer with a 1:500 dilution of Strep-Tactin DY-488 and rotated at 4 °C for 1 h, protected from light. Cells were then washed twice in 1 ml FACS buffer, resuspended in 1 ml FACS buffer and filtered through a 35-µm mesh into FACS tubes. A BD Influx cell sorter was used to analyse cells. Strep-Tactin DY-488 and GFP were excited with a 488-nm laser and signal was collected with a 530/40-nm detector. Excluding doublets and cell debris, cells were gated on the Strep-Tactin DY-488 signal, where thresholds were set using non-transfected control cells such that <1% of control cells were considered positive for Strep-Tactin DY-488. Strep-Tactin DY-488-positives cells were collected in FACS buffer and pelleted for subsequent experiments. The FACS gating strategy and cell numbers isolated are shown in Supplementary Fig. 2.

Histone extraction

Transfected cells were isolated by FACS as described above. Sorted cells were pelleted, resuspended in 1 ml cold H_2SO_4 and rotated overnight at 4 °C. Following the overnight incubation, cells were pelleted at maximum speed and the supernatant was transferred to a fresh tube. Trichloroacetic acid was added to 25% by volume, and the cells were left on ice at 4 °C overnight. Cells were again pelleted at maximum speed, and the supernatant was discarded. Prechilled acetone was then used to gently wash the pellet twice. Following the second wash, the tubes were left to air dry before the pellet was resuspended in water. Samples were then broken up by alternating 10 min of sonication and 30 min of shaking at 50 °C until pellets were fully dissolved.

Mass spectrometry

Histone PTM analysis by quantitative mass spectrometry

Purification of histones was validated by SDS–PAGE followed by Coomassie staining demonstrating sufficient enrichment. A BCA assay (Thermo Fisher) was performed for protein estimation using the manufacturer’s instructions, and 20 µg of histone was used for chemical derivatization and digestion as described previously[57]. In brief, unmodified lysines were derivatized twice with a 1:3 ratio of acetonitrile to proprionic anhydride. Histones were then digested with trypsin in a 1:20 enzyme to protein ratio at 37 °C overnight. Digested histones with newly formed N termini were derivatized twice as done previously. Finally, histones were dried with a vacuum concentrator. The dried samples were reconstituted in 0.1% trifluoroacetic acid (TFA) and desalted with the C18 micro spin column (Harvard Apparatus). The column was prepared with 200 μl of 100% acetonitrile and equilibrated with 200 μl of loading buffer with 0.1% TFA. Peptides were loaded onto the column, washed with loading buffer and eluted with 200 μl of 70% acetonitrile in 0.1% formic acid. All steps for loading, washing and elution were carried out with benchtop centrifugation (300 g for 2 min). The eluted peptides were then dried in a centrifugal vacuum concentrator.

Dried histone peptides were reconstituted in 0.1% formic acid. A synthetic library of 93 heavy labelled and derivatized peptides containing commonly measured histone PTMs[58] was spiked into the endogenous samples to a final concentration of approximately 100 ng µl–1 for endogenous peptides and 100 fmol µl–1 for each heavy labelled synthetic analyte. For each analysis, 1 µl of sample was injected onto the column for data-independent analysis on a Q-Exactive HF instrument (Thermo Scientific) attached to an Ultimate 3000 nano-UPLC system and Nanospray Flex ion source (Thermo Scientific). Using aqueous solution of 0.1% formic acid as buffer A and organic solution of 80% acetonitrile and 0.1% formic acid as buffer B, peptides were separated on a 63-min gradient at 400 nl min–1 starting at 4% buffer B and increasing to 32% buffer B over 58 min and then increasing to 98% buffer B over 5 min. The column was then washed at 98% buffer B over 5 min and equilibrated to 3% buffer B. Data-independent acquisition was performed with the following settings. A full MS^1 scan from 300 to 950 m/z was acquired with a resolution of 60,000, an automatic gain control (AGC) target of 3 × 10^6 and a maximum injection time of 55 ms. Then, a series of 25 MS^2 scans was acquired across the same mass range with sequential isolation windows of 24 m/z with a collision energy of 28, a resolution of 30,000, an AGC target of 1 × 10^6 and a maximum injection time of 55 ms. Data analysis and manual inspection using the synthetic library as a reference were performed with Skyline (MacCoss Lab). Ratios were generated using R Studio and statistical analysis was carried out in Excel as in previous histone analysis.

Trypsin and chymotrypsin digestion of ORF8 for identification of ORF8 modifications

The gel band containing ORF8 was destained with 50 mM ammonium bicarbonate with 50% acetonitrile. The band was then reduced in 10 mM DTT in 50 mM ammonium bicarbonate for 30 min at 55 °C. Next, the band was alkylated with 100 mM iodoacetamide in 50 mM ammonium bicarbonate at room temperature for 30 min in the dark. Protein was then digested by incubation with chymotrypsin or trypsin at an approximately 1:20 enzyme to protein ratio at 37 °C overnight. Following digestion, the supernatant was collected. To extract additional peptides from the gel, 150 μl of 50% acetonitrile and 1% TFA was added and samples were incubated with constant shaking for 30 min. The supernatant was collected and 100 μl of acetonitrile was added followed by incubation with constant shaking for 10 min. The final supernatant was collected. All three supernatants were combined and dried. The dried samples were then desalted as described above.

ORF8 versus control immunoprecipitation for identification of binding partners

ORF8 immunoprecipitation elutants were reduced and alkylated as described above. Proteins were then digested and desalted with mini S-Trap (Protifi) following the manufacturer’s instructions. In brief, 25 μl of elutant was combined with 25 μl of 10% SDS to a final SDS concentration of 5% after alkylation. Samples were then acidified with phosphoric acid and precipitated by adding 90% methanol in 100 mM triethylammonium bicarbonate (TEAB) in a 6:1 (vol/vol) ratio. Protein was then added to the trap with benchtop centrifugation (4,000 g for 1 min), washed and digested with trypsin at a 1:10 enzyme to protein ratio at 37 °C overnight. Following digestion, peptides were eluted from the trap with 40 μl of 100 mM TEAB, 40 μl of 0.2% formic acid and 40 μl of 50% acetonitrile in 0.2% formic acid. Combined elutant volumes were then dried.

Chymotrypsin LC–MS/MS and LC–PRM-MS analysis

Dried peptides were reconstituted with 0.1% formic acid, and 2 µg of each sample was injected. Chymotrypsin-digested ORF8 samples were analysed on a Q-Exactive (Thermo Scientific) coupled to an Easy nLC 1000 UHPLC system and Nanospray Flex ion source (Thermo Scientific). The LC instrument was equipped with a 75 µm × 20 cm column packed in house using Reprosil-Pur C18 AQ (2.4 µm; Dr. Maisch). Using the same column and buffer conditions as described previously, peptides were separated on an 85-min gradient at 400 nl min–1 starting at 3% buffer B and increasing to 32% buffer B over 79 min and then increasing to 50% buffer B over 5 min and finally increasing to 90% buffer B over 1 min. The column was then washed at 90% buffer B over 5 min and equilibrated to 3% buffer B. Data-dependent acquisition was performed with dynamic exclusion of 40 s. A full MS^1 scan from 350 to 1,200 m/z was acquired with a resolution of 70,000, an AGC target of 1 × 10^6 and a maximum injection time of 50 ms. Then, a series of MS^2 scans was acquired for the top 15 precursors with a charge state of 2–7, a collision energy of 28 and an isolation window of 2.0 m/z. Each MS^2 scan was acquired with a resolution of 17,500, an AGC target of 2 × 10^5 and a maximum injection time of 50 ms. A database search was performed using the human SwissProt sequence and ORF8 sequence with Proteome Discoverer 2.3 or 2.4 (Thermo Scientific) using the following search criteria: carboxyamidomethylation at cysteine residues as a fixed modification; oxidation at methionine and acetylation at lysine as variable modifications; two maximum allowed missed cleavages; precursor MS tolerance of 10 ppm; a 0.02-Da MS/MS. An unscheduled parallel reaction-monitoring method[59] was developed to identify or validate 45 possible modified and unmodified peptide targets of ORF8. Peptides were separated with the same LC gradient conditions. A full MS^1 scan from 300 to 900 m/z was acquired with a resolution of 70,000, an AGC target of 1 × 10^6 and a maximum injection time of 50 ms. Then, a series of MS^2 scans was acquired with a loop count of 23 precursors, a collision energy of 28 and an isolation window of 1.2 m/z. Each MS^2 scan was acquired with a resolution of 17,500, an AGC target of 1 × 10^6 and a maximum injection time of 100 ms. Data analysis and manual inspection were performed with Skyline[60] (MacCoss Lab) and IPSA[61].

Trypsin ORF8 LC–MS/MS and LC–PRM/MS analysis and IP LC–MS/MS analysis

Dried peptides were reconstituted with 0.1% formic acid, and 2 µg of each sample was injected. Data-dependent acquisition runs were analysed on a Q-Exactive HF or HF-X (Thermo Scientific) attached to an Ultimate 3000 nano UPLC system and Nanospray Flex Ion Source (Thermo Scientific). Using the same column and buffer conditions as described above, peptides were separated on a 112-min gradient at 400 nl min–1 starting at 5% buffer B, increasing to 35% buffer B over 104 min and then increasing to 60% buffer B over 8 min. The column was then washed at 95% buffer B for 5 min and equilibrated to 5% buffer B. Data-dependent acquisition was performed with dynamic exclusion of 45 s. A full MS^1 scan from 380 to 1,200 m/z was acquired with a resolution of 120,000, an AGC target of 3 × 10^6 and a maximum injection time of 32 ms. Then, a series of MS^2 scans were acquired for the top 20 precursors with a charge state of 2–5, a collision energy of 28 and an isolation window of 1.2 m/z. Each MS^2 scan was acquired with a resolution of 30,000, an AGC target of 1 × 10^6 and a maximum injection time of 32 ms (HF) or 55 ms (HFX). A database search was performed using the human SwissProt sequence and ORF8 sequence with Proteome Discoverer 2.3 or 2.4 (Thermo Scientific) with the following search criteria: carboxyamidomethylation at cysteine residues as a fixed modification; oxidation at methionine and acetylation at lysine as variable modifications; two maximum allowed missed cleavages; precursor MS^1 tolerance of 10 ppm; a 0.02-Da MS^2 tolerance. An unscheduled parallel reaction-monitoring method[59] was developed to identify 16 possible modified and unmodified peptide targets of ORF8. Peptides were separated with the same LC gradient conditions. A full MS^1 scan from 350 to 950 m/z was acquired with a resolution of 120,000, an AGC target of 3 × 10^6 and a maximum injection time of 100 ms. Then, a series of MS^2 scans were acquired with a loop count of 16 precursors, a collision energy of 28 and an isolation window of 1.2 m/z. Each MS^2 scan was acquired with a resolution of 30,000, an AGC target of 1 × 10^6 and a maximum injection time of 100 ms. Data analysis and manual inspection were performed with Skyline[60] (MacCoss Lab) and IPSA[61].

Statistics and reproducibility

Box-and-whisker plots show the median as the centre line, box limits for upper and lower quartiles, whiskers for 1.5× the interquartile range and points for outliers. ANOVA testing was performed and plots were generated with R. Bonferroni corrections were applied for multiple comparisons. Fiji was used for image analysis. Imaging and analysis were performed with the experimenter blinded to the experimental condition whenever possible. In some instances, such as for patient tissue imaging, analysis required targeted selection, imaging and analysis of infected cells compared with uninfected cells. This required the experimenter to be aware of cell infection status while imaging. However, in these cases, the measurement of interest (such as staining for a histone modification) was not viewed before choosing fields to avoid biasing selection.

Images are representative of multiple replicates as follows:

Figure 1b: >5 independent experiments.

Figure 1c: two independent experiments.

Figure 1d: three independent experiments.

Figure 1g: five independent samples from two separate runs of FACS sorting.

Figure 2b,d,f: exact cell numbers and replicates described in Fig. 2c,d,g.

Figure 2h: two shown of four independent samples from one FACS sort.

Figure 3f: three independent samples per condition from one infection.

Figure 3g: exact cell numbers and replicates described in Fig. 3h.

Extended Data Fig. 2a: three independent experiments.

Extended Data Fig. 2b,c: >5 independent experiments.

Extended Data Fig. 2d: two independent experiments.

Extended Data Fig. 2e: two independent experiments.

Extended Data Fig. 3b: two independent experiments.

Extended Data Fig. 4a: lamin and histone H3, three independent experiments; HP1α and KAT2A, two independent experiments.

Extended Data Fig. 4b: two independent experiments.

Extended Data Fig. 4c: two independent experiments.

Extended Data Fig. 4d: one independent experiment, repeating previously published data.

Extended Data Fig. 4e: two independent experiments.

Extended Data Fig. 4f: two independent experiments.

Extended Data Fig. 5a: exact cell numbers and replicates described in Extended Data Fig. 5b.

Extended Data Fig. 5c: exact cell numbers and replicates described in Extended Data Fig. 5d.

Extended Data Fig. 5e: same images as in Extended Data Fig. 5c.

Extended Data Fig. 6b: three shown of five independent samples from two runs of FACS sorting.

Extended Data Fig. 10a: exact cell numbers and replicates described in Extended D ata Fig. 10b.

Extended Data Fig. 10c: exact cell numbers and replicates described in Extended Data Fig.  10d.

Extended Data Fig. 10e: exact cell numbers and replicates described in Extended Data Fig. 10f.

Reporting summary

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

Data availability

All genome-wide sequencing data are available under accession number GSE186628. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository[62] with the dataset identifier PXD034379. Publicly available data used for analysis include ENCODE data ENCSR000ASV, ENCSR000FCJ, ENCSR179BUC, ENCSR55LYM and ENCSR000AKD and 4D nucleome dataset 4DNES24XA7U8.

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Acknowledgements

We thank and acknowledge M. Weitzman for feedback and suggestions, A. Stout for microscopy support, C. Comar for viral infections, R. Jain and P. Shaw for protocols, M. Feldman and K. Montone for providing patient samples, and S. Wolf and P. Korb for supervision and support. J.K., S.T., E.K. and research were supported by the Penn Center for Coronavirus and Other Emerging Pathogens and the Dean’s Innovation Fund from the Perelman School of Medicine at the University of Pennsylvania. E.K. was supported by the Sloan Research Fellowship, the Klingenstein-Simons Fellowship, the NARSAD Young Investigator Award and NIH grants R00MH111836 and 1DP2MH129985. Viral work was supported by NIH grant RO1AI140442 supplement for SARS-CoV-2 and funds from the Penn Center for Coronavirus Research and Other Emerging Pathogens. D.M.R. was supported in part by T32AI055400. A.P., A.A.K. and J.A.E. were supported by NIH grant R35HL140018. R.T. and W.Y. were supported in part by institutional funds from the University of Pennsylvania Perelman School of Medicine to the iPSC Core. iPSC cell line generation and sharing were supported by NIH grants NO175N92020C00005 and U01TR001810. Mass spectrometry was supported by AI118891. P.-Y.S. was supported by NIH grants HHSN272201600013C, U01AI151801 and U19AI171413 and by awards from the Sealy Smith Foundation, the Kleberg Foundation, the John S. Dunn Foundation, the Amon G. Carter Foundation, the Gillson Longenbaugh Foundation and the Summerfield Robert Foundation.

Author information

Author notes

1. These authors contributed equally: Samuel Thudium, David M. Renner, Karl Glastad

Authors and Affiliations

1. Department of Genetics at the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA

John Kee, Samuel Thudium, Katherine Palozola & Erica Korb

2. Epigenetics Institute at the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA

John Kee, Samuel Thudium, Karl Glastad, Katherine Palozola, Zhen Zhang, Yemin Lan, Joseph Cesare, Benjamin A. Garcia, Shelley L. Berger & Erica Korb

3. Department of Microbiology at the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA

David M. Renner, Yize Li & Susan R. Weiss

4. Penn Center for Research on Coronaviruses and Other Emerging Pathogens at the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA

David M. Renner, Yize Li & Susan R. Weiss

5. Department of Cell and Developmental Biology at the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA

Karl Glastad, Zhen Zhang, Andrey Poleshko, Anna A. Kiseleva, Johnathan A. Epstein & Shelley L. Berger

6. Department of Biochemistry and Biophysics at the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA

Joseph Cesare & Benjamin A. Garcia

7. Penn Cardiovascular Institute at the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA

Anna A. Kiseleva & Johnathan A. Epstein

8. Institute for Regenerative Medicine at the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA

Anna A. Kiseleva & Johnathan A. Epstein

9. Department of Medicine at the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA

Rachel Truitt, Fabian L. Cardenas-Diaz, Johnathan A. Epstein, Wenli Yang & Edward Morrisey

10. Penn-CHOP Lung Biology Institute at the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA

Fabian L. Cardenas-Diaz & Edward Morrisey

11. Department of Biochemistry and Molecular Biology, University of Texas Medical Branch, Galveston, TX, USA

Xianwen Zhang, Xuping Xie & Pei-Yong Shi

12. Center for Regenerative Medicine, Boston University and Boston Medical Center, Boston, MA, USA

Darrell N. Kotton & Konstantinos D. Alysandratos

13. The Pulmonary Center and Department of Medicine, Boston University School of Medicine, Boston, MA, USA

Darrell N. Kotton & Konstantinos D. Alysandratos

14. Department of Biology at the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA

Shelley L. Berger

Contributions

J.K. designed, performed and analysed the results for the majority of the experiments. S.T. generated cells, samples and DNA constructs and performed analysis. D.M.R. and Y. Li performed the SARS-CoV-2 viral infections and analysis of viral infections. K.G. and Z.Z. performed and analysed the results of ChIP–seq experiments. K.P. performed ATAC-seq and generated samples for histone PTM analysis. J.C. performed and analysed the results of mass spectrometry experiments with guidance from B.A.G. X.Z., X.X. and P.-Y.S. generated the deletion viruses. Y. Lan provided bioinformatic analysis. F.L.C.-D. and R.T. generated iAT2 cells with guidance from W.Y. and E.M. D.N.K. and K.D.A. provided stem cell lines. A.P., A.A.K. and J.A.E. performed ORF8 and nuclear lamina imaging and analysis. S.L.B. provided input and expertise and led ChIP–seq studies. S.R.W. provided input and expertise and led viral work. E.K. led the project and wrote the manuscript.

Corresponding author

Correspondence to Erica Korb.

Ethics declarations

Competing interests

The laboratory of P.-Y.S. has received funding support in sponsored research agreements from Pfizer, Gilead, Novartis, Merck, GSK, IGM Biosciences and Atea Pharmaceuticals. P.-Y.S. is a member of the scientific advisory boards of AbImmune and is founder of FlaviTech. S.R.W. is on the scientific advisory board of Ocugen and Immunome.

Peer review

Peer review information

Nature thanks the anonymous reviewers for their contribution to the peer review of this work.

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 Orf8 is a putative histone mimic.

(a) Alignments performed to identify putative histone mimic sites within the SARS-CoV-2 genome. (b) The number of exact sequential overlapping amino acids found between SARS-CoV-2 proteins and histone proteins. (c) The overlap of Orf8 and H3 compared to other proposed cases of histone mimicry. Exact amino acid overlap shown in dark blue and structurally similar amino acid overlap shown in light blue. NS1 is from Influenza A H3N2. Protein E is a previously proposed mimic in SARS-CoV-2. G9a is a human protein that mimics H3. (d) Previously proposed histone H3 mimic in SARS-CoV-2 protein E. (e) Overlap of SARS-CoV proteins with H3. (f) The ARKS motif is present in Bat SARS-CoV Orf8 and variants of concern but is not found with SARS-CoV Orb8a/b or the SARS-CoV precursor Orf8 before a mutation generated two distinct proteins. (g-h) Orf8 transcript (g) and protein (h) expression in SARS-CoV-2 infected Caco-2 cells from published datasets. MOI = 1. Plots indicate mean ± SEM.

Extended Data Fig. 2 Orf8 localization.

(a). Subcellular fractionation of HEK293T cells transfected with Strep-Orf8 indicates Orf8 is present in the cytoplasm and nucleus. (b) HEK293T cells expressing Orf8 co-stained with Lamin B and streptactin to detect Orf8. (c) Rotation of z-stacks (right and bottom panel for each stain) indicate colocalization of Orf8 and Lamin B. (d) Orf8 antiserum specifically detects Strep-tagged Orf8 by western blot In HEK293T cells transfected with Strep-Orf8. (e) Orf8 antiserum specifically stains infected A549ACE cells 48 h after SARS-CoV-2 infection with no staining observed in mock infection. (f) Deletion construct used to test effects of ARKSAP motif in Orf8. (g) Cellular fractions generated for use in subsequent chromatin-immunoprecipitation for ChIP-sequencing shows lower ratio of Orf8ΔARKSAP present in chromatin fraction then Orf8. (h) Gene tracks for Orf8 ChIP-sequencing normalized to input controls. Control indicates negative control IgG ChIP-sequencing. (i) Orf8 is enriched in genomic regions with high H3K27me3 and H3K9me2 relative to input controls with lower enrichment at regions with high H3K9me3. (j) Percentage of reads within Orf8-enriched regions from ENCODE datasets. N = 4 samples from ENCSR000FCJ and ENCSR179BUC for H3K9me3, N = 2 samples from ENCSR55LYM for H3K9me2, and N = 4 samples from ENCSR000AKD for H3K27me3. (k) Diffbind analysis of H3K27me3 vs input controls in Orf8 enriched regions. Scale bar = 5μM in a-b, 10μM in d. For gel source data, see Supplementary Fig. 1a, f, k. Bar plots indicate mean ± SEM.

Extended Data Fig. 3 Orf8 protein interactions and modifications.

(a) Orf8 co-immunoprecipitates with Lamin complex-associated proteins including LaminB, HP1α, and H3, and KAT2A. ‘-‘ indicates cells that are not expressing Orf8 for negative control IPs performed in parallel. (b) Reciprocal co-immunoprecipitation for Lamin and H3 confirm Orf8 binding. (c) Orf8ΔARKSAP co-immunoprecipitation with LaminB, HP1α, and H3, and KAT2A is not detected. (d) Orf8 and Orf8ΔARKSAP co-immunoprecipitates with MHC1 complex protein HLA-A2. (e) Orf8 co-immunoprecipitation with Brd4 is not detected. (f) Confirmation of mass spectrometry analysis for transcription factor SP2. Orf8 was immunoprecipitated with Streptactin beads and resulting lysates were probed for SP2 and Streptactin. Proteins purified for mass spectrometry analysis were isolated using whole cell lysate conditions rather than through enrichment of the chromatin fraction. This approach (described in methods) captured predominantly cytoplasmic binding partners and a limited numbers of chromatin-associated proteins. (g) Targeted mass spectrometry analysis of trypsin-digested Orf8 of the peptide containing the proposed histone mimic in Orf8. Orf8 acetylation at K52 is detected in the 3+ charged peptide by targeted mass spectrometry. MS/MS spectra with matching product ions (b ions in blue, y ions in red, c ions in yellow) within 10ppm mass error. (h) Representative confocal images of HEK293T cells transfected with orf8 expression construct (green) and stained for Lamin A/C (cyan) and Lamin B1 (red). DAPI counterstain shown in blue. Scale bar 5μm. (i) Bar graphs show distributions of total Lamin A/C or Lamin B1 signal intensity per cell in Mock and Orf8-expressing cells. n = 129 and 86 cells per sample. Statistical analysis was performed using Mann-Whitney test. (j) Representative confocal images of HEK293T cells transfected with Orf8 expression construct (green) and stained for H3K9me2 (red) and Lamin B (cyan). DAPI counterstain shown in blue. Scale bar 5μm. (k) Bar graph show fraction of H3K9me2-marked chromatin at the nuclear lamina/periphery of control and Orf8-expressing cells. n ≥ 32 cells per sample. (l) Representative confocal images shown in j of HEK293T cells transfected with Orf8 expression construct (green) and stained for Lamin B1 (red) with Orf8 and Lamin B1 enrichment through the cell plotted along the dotted lines shown in image. Scale bar 5μm. Statistical analysis was performed using Welch 2-sided t test. Box plots show median, 25th and 75th percentiles. Whiskers show Tukey confidence intervals. For gel source data, see Supplementary Fig. 1c, d, g–j.

Extended Data Fig. 4 Orf8 effects on histone PTMs.

(a) Orf8 does not significantly affect H3K27ac. N = 616 (GFP), 550 (Orf8) from 3 independent transfections. (b) H3 serine 10 phosphorylation (H3S10ph), H3K9 dimethylation (H3K9me2), Lamin B and global acetylation (of histones or of non-histone proteins) in GFP or Orf8 expressing cells isolated by FACS. (c) Orf8 constructs tested show similar levels of expression in HEK293T cells. N = 137 (Orf8), 87 (Orf8ΔARKSAP), 120 (S84L) from 4 independent transfections. (d–f) Orf8 with a mutation commonly found in the SARS-CoV-2 genome, Orf8-S84L, shows the same effects on histone PTMs H3K9me3 (d), H3K27me3 (e), and H3K9ac (f). N = 332 (GFP), 237 (S84L) cells for H3K9me3; 186, 166 cells for H3K27me3; 332, 237 cells for H3K9ac from 2 independent transfections. (g) Orf8 with a 6 amino acid deletion outside of the ARKSAP motif shows the same effects on H3K9me3. N = 216 (GFP), 120 (Orf8), 88 (AGSKSP) cells from 2 independent transfections. (a,g) 1-way ANOVA with post-hoc 2-sided t-test and Bonferroni correction. (b,d,e,f) 2-sided t-test. Scale bar = 10μM. For gel source data, see Supplementary Fig. 1n-m.

Source data

Extended Data Fig. 5 Orf8 effects on histone PTMs and chromatin accessibility.

(a) Expression of WT Orf8 and Orf8ΔARKSAP from FAC sorted cells used for western blot analysis. (b) Expression of WT Orf8 and Orf8ΔARKSAP from FAC sorted cells used for H3K9ac CUT&TAG. (c) H3K9ac CUT&TAG sequencing average profile of all expressed genes. (d) Expression of WT Orf8 and Orf8ΔARKSAP from FAC sorted cells used for ATAC-seq. (e) H3K9ac CUT&TAG average profiles for high, mid, low, and non-expressing genes. (f) ATAC-seq average profiles for high, mid, low, and non-expressing genes. (g) H3K9ac CUT&TAG and ATAC-seq gene tracks of genes relevant to viral responses in HEK 293T cells expression a control plasmid, Orf8, or Orf8ΔARKSAP. (h) Gene track example with limited changes between Orf8 and Orf8ΔARKSAP in H3K9ac CUT&TAG and ATAC-seq. N = 2 biologically independent samples for a,b,d. Bar plots indicate mean values. FACS gating strategy and cell numbers isolated shown in Fig. S2.

Extended Data Fig. 6 Orf8 disrupts gene expression.

(a) Expression of Orf8WT and Orf8ΔARKSAP from FAC sorted cells used for RNA-seq. (b) Volcano plot of differential gene expression analysis of Orf8WT expressing cells compared to GFP expressing cells. (c) Volcano plot of differential gene expression analysis of Orf8ΔARKSAP expressing cells compared to GFP expressing cells. Red indicates significantly differentially expressed genes. (d) Overlap of genes down or upregulated by Orf8 and Orf8ΔARKSAP compared to GFP expressing cells. (e) Gene ontology analysis of genes that are downregulated by Orf8 compared to Orf8ΔARKSAP. (f-g) Volcano plot of differential gene expression analysis of Orf8WT expressing cells compared to Orf8ΔARKSAP expressing cells graphed by p value (f) and mean expression (g). (h) Gene tracks of genes that are induced by Orf8ΔARKSAP but show a dampened response to Orf8. (i) Gene tracks of a gene that is not disrupted by Orf8 expression. (j) H3K9ac CUT&Tag reads at down and upregulated DEGs in Orf8 verses Orf8ΔARKSAP. (k) ATAC-seq reads at down and upregulated DEGs in Orf8 verses Orf8ΔARKSAP. DEG: differentially expressed gene. Volcano plots show differentially expressed genes in DESeq2 analysis with multiple comparison corrections. Gene ontology significance based on clusterProfiler analysis with Benjamini-Hochberg adjusted p-values. For RNA-seq, N = 2 for GFP and Orf8ΔARKSAP and N = 3 for Orf8WT. FACS gating strategy and cell numbers isolated shown in Supplemental Fig. 2.

Extended Data Fig. 7 Orf8 mediates SARS-CoV-2 effects on repressive histone PTMs.

(a) qRT-PCR analysis of expression of SARS-CoV-2 gene RdRp analysis of viral titer in A549ACE pulmonary cells at 24 and 48 h after infection with SARS-CoV-2WT or SARS-CoV-2ΔOrf8 at MOI = 1. (b) Plaque assay analysis of viral titer in A549ACE pulmonary cells at 24 and 48 h after infection with SARS-CoV-2WT or SARS-CoV-2ΔOrf8 at MOI = 1. N = 3 replicates from infection done in parallel. (c-d) ChIP-RX for H3K9me3 (c) and H3K27me3 (d) of A549ACE cells with SARS-CoV-2WT, SARS-CoV-2ΔOrf8, or mock infection at MOI = 1, 48 h after infection. N = 3. Peaks shown in right panels of c-d indicate SICER peak regions from merged ChIP-RX data. Grey lines indicate peak borders and within-peak distances anchored by each end of the peak. (e) ChIP-seq gene tracks of genes in signaling pathways relevant to viral response. P values indicate 2-way ANOVA with Šidák’s multiple comparisons correction. Bar plots indicate mean ± SEM. RPM, reads per million mapped reads.

Source data

Extended Data Fig. 8 Orf8 mediates SARS-CoV-2 effects on histone PTMs.

(a,c,e) H3K9me3 (a), H3K27me3 (c) or H3K9ac (e) staining of A549ACE cells 24 h after SARS-CoV-2WT, SARS-CoV-2ΔOrf8, or mock infection at MOI = 1. (b,d,f) Quantification of H3K9me3 (b), H3K27me3 (d) or H3K9ac (f). For H3K9me3 N = 39 (SARS-CoV-2), 35 (SARS-CoV-2ΔOrf8), for H3K27me3 N = 48 (SARS-CoV-2), 37 (SARS-CoV-2ΔOrf8), for H3K9ac N = 94 (SARS-CoV-2), 120 (SARS-CoV-2ΔOrf8) cells normalized to uninfected neighbor cells from 1-2 independent infections. Dotted line indicates relative signal in Mock infected condition. (g) Quantification of H3K9me3 in infected cells and neighboring cells from the same tissue slice for each individual patient sample shown separately, relative to control samples. For sample 1, N = 3 infected and 48 uninfected, sample 2, N = 7 infected and 125 uninfected, sample 3, N = 5 infected and 55 uninfected cells. (b,d,f) 1-way ANOVA with post-hoc 2-sided t-test and Bonferroni correction. Scale bar = 10μM. Box plots centered on median, bounds at 25th and 75th percentile, minimum and maximum defined as median ± 1.5x interquartile range, with whiskers extended to lowest/highest value in range.

Source data

Extended Data Fig. 9 RNA-sequencing read comparisons and 48 hour DEG.

(a) Reads aligned to the human and SARS-CoV-2 genomes 24 h after infection. (b) Levels of SARS-CoV-2 transcripts in cells infected with SARS-CoV-2WT, SARS-CoV-2ΔARKSAP, or SARS-CoV-2ΔOrf8 24 h after infection. (c) Reads aligned to the human and SARS-CoV-2 genomes 48 h after infection. (d) Levels of SARS-CoV-2 transcripts in cells infected with SARS-CoV-2WT, SARS-CoV-2ΔOrf8, or SARS-CoV-2ΔARKSAP 48 h after infection. (e) Normalized reads of SARS-CoV-2 Orf8 and human transcripts of histone H3 genes in cells infected with SARS-CoV-2WT or SARS-CoV-2ΔOrf8. (f) Overlap of differentially expressed genes in response to SARS-CoV-2WT, SARS-CoV-2ΔOrf8, or SARS-CoV-2ΔARKSAP compared to mock infection at MOI = 1, 48 h after infection. (g) Differential gene expression analysis by RNA-seq of A549ACE cells 48 h after SARS-CoV-2WT, SARS-CoV-2ΔOrf8, or SARS-CoV-2ΔARKSAP, compared to mock infection at MOI = 1. Significantly differentially expressed genes (relative to mock infection) are shown in blue (down) and red (up). N = 3. Volcano plots show differentially expressed genes in DESeq2 analysis with multiple comparison corrections. Bar plots indicate mean ± SEM.

Extended Data Fig. 10 Comparison of ChIP-sequencing and RNA-sequencing data in A549ACE cells.

(a) Gene ontology analysis of genes upregulated by SARS-CoV-2ΔOrf8 infection. (b) Genes expression changes between SARS-CoV-2ΔOrf8 and SARS-CoV-2ΔARKSAP. (c) Gene ontology analysis of genes upregulated by SARS-CoV-2ΔARKSAP compared to SARS-CoV-2ΔOrf8. (d) Mean peak difference for H3K9me3 in genes that are down, unchanged, or upregulated in responses to infection. (e) Mean peak difference for H3K27me3 in genes that are down, unchanged, or upregulated in responses to infection. (f) Mean peak difference for H3K9ac in genes that differentially expressed in responses to infection. Peak values determined by diffbind for the most significant peak intersecting the TSS of a gene. N = 3 ChIP-sequencing for each modification except N = 2 for H3K9ac wildtype SARS-CoV-2WT. P values indicate results from a Kruskal-Wallis test followed by a post-hoc Dunn’s test and Bonferroni correction. N.S. indicates genes that are not significant differentially expressed between groups. Box plots centered on median, bounds at 25th and 75th percentile, minimum and maximum defined as median ± 1.5x interquartile range, with whiskers extended to lowest/highest value in range.

Supplementary information

Supplementary Information

This file contains Supplementary Fig. 1 (original western blot images), Supplementary Fig. 2 (FACS gating strategy) and the legends for Supplementary Tables 1–6 (see separate Excel files for the tables).

Reporting Summary

Supplementary Table 1

ORF8 binding partners.

Supplementary Table 2

Gene ontology analysis of gene expression changes following ORF8 expression.

Supplementary Table 3

Histone PTM changes with ORF8 expression.

Supplementary Table 4

All infection data.

Supplementary Table 5

Gene ontology analysis of gene expression changes following SARS-CoV-2 infection of A549ACE cells.

Supplementary Table 6

Antibodies used in the manuscript.

Source data

Source Data Figs. 2 and 3 and Extended Data Figs. 4, 7 and 8.

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Kee, J., Thudium, S., Renner, D.M. et al. SARS-CoV-2 disrupts host epigenetic regulation via histone mimicry. Nature 610, 381–388 (2022). https://doi.org/10.1038/s41586-022-05282-z

- Received: 28 December 2020

- Accepted: 26 August 2022

- Published: 05 October 2022

- Issue Date: 13 October 2022

- DOI: https://doi.org/10.1038/s41586-022-05282-z

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SARS-CoV-2 mimics a host protein to bypass defences

- Lisa Thomann

- Volker Thiel

Nature News & Views 05 Oct 2022

Article Published: 28 September 2022

Structure of the Ebola virus polymerase complex

Bin Yuan,

Qi Peng,

Jinlong Cheng,

Min Wang,

Jin Zhong,

Jianxun Qi,

George F. Gao &

…

Yi Shi

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Nature volume 610, pages 394–401 (2022)

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Abstract

Filoviruses, including Ebola virus, pose an increasing threat to the public health. Although two therapeutic monoclonal antibodies have been approved to treat the Ebola virus disease1,2, there are no approved broadly reactive drugs to control diverse filovirus infection. Filovirus has a large polymerase (L) protein and the cofactor viral protein 35 (VP35), which constitute the basic functional unit responsible for virus genome RNA synthesis[3]. Owing to its conservation, the L–VP35 polymerase complex is a promising target for broadly reactive antiviral drugs. Here we determined the structure of Ebola virus L protein in complex with tetrameric VP35 using cryo-electron microscopy (state 1). Structural analysis revealed that Ebola virus L possesses a filovirus-specific insertion element that is essential for RNA synthesis, and that VP35 interacts extensively with the N-terminal region of L by three protomers of the VP35 tetramer. Notably, we captured the complex structure in a second conformation with the unambiguous priming loop and supporting helix away from polymerase active site (state 2). Moreover, we demonstrated that the century-old drug suramin could inhibit the activity of the Ebola virus polymerase in an enzymatic assay. The structure of the L–VP35–suramin complex reveals that suramin can bind at the highly conserved NTP entry channel to prevent substrates from entering the active site. These findings reveal the mechanism of Ebola virus replication and may guide the development of more powerful anti-filovirus drugs.

Main

Infections with filoviruses such as Ebola virus (EBOV) and Marburg virus can cause severe clinical symptoms, including haemorrhagic fever and multiorgan failure[4]. Before December 2013, 35 outbreaks of filovirus disease had been recorded in remote African regions with infrequent spillover from animals to humans[5]. Since December 2013, atypically extensive ebolavirus disease outbreaks, including an unprecedented outbreak from 2013 to 2016 in West African countries, an outbreak from 2018 to the present in the Democratic Republic of the Congo, and a more recent brief outbreak in Guinea, have a profound impact on public health systems. At least 14,000 fatalities from ebolavirus disease were reported between December 2013 and August 2020 (ref. [6]). The resurgence of EBOV in 2021 in Guinea suggests a persistent infection with reduced replication or a period of latency in humans[7]. Although filoviruses were initially thought to occur exclusively in Africa, recent studies have revealed that they are more widely distributed, including in Asia and Europe8,9. Moreover, previously unknown filovirus species have been reported[10], and the genetic diversity of filoviruses and other potential zoonotic viruses may be greater than previously recognized[11]. This presents a great challenge for the development of virus-targeting prophylactic and therapeutic countermeasures.

Filoviruses, including ebolavirus, are non-segmented negative-sense RNA viruses (nsNSV) with seven genes, and belong to the order Mononegavirales[12]. The viral RNA genome is encapsidated by the nucleoprotein (NP) and is further associated with the polymerase complex consisting of the large (L) protein, the cofactor viral protein 35 (VP35) and the transcription activator VP30, forming the ribonucleoprotein (RNP) complex[12]. In virions, the RNP complex interacts with the nucleocapsid-associated VP24, and is surrounded by the matrix protein VP40, which drives the morphogenesis and budding of virus particles[13]. Finally, the matrix layer is covered by the host cell-derived envelope in which the viral glycoprotein GP is embedded[12]. Filoviruses follow the typical life cycle of the cytoplasmically replicating nsNSVs, including multiple key processes such as cell entry, genome replication and transcription, morphogenesis and budding4,14.

In recent years, antiviral therapeutic approaches have targeted the different processes of ebolavirus life cycle. The most developed area of antiviral development is focused on the entry process, including monoclonal antibodies (ZMapp, mAb114 and REGN-EB3) and small molecule inhibitors1,2,15,16,17. In a randomized clinical trial, the overall mortality rates among patients who received REGN-EB3 and mAb114 were 33.5% and 35.1%, respectively, much lower than that for patients treated with ZMapp (49.7%)[18]. Moreover, the viral RNA synthesis machinery has been a promising target for the design of broadly reactive drugs against other viral diseases19,20. Nucleoside analogue drugs (remdesivir, favipiravir and BCX4430) and small interfering RNA (siRNA)-based degradation of viral mRNAs (TKM-Ebola) targeting the viral RNA synthesis machinery have been designed and tested to combat ebolavirus infection21,22,23,24. However, these small molecule inhibitors have displayed poor clinical outcomes. The mortality of remdesivir-administered patients was 53.1% in a randomized clinical trial, much higher than that of antibody-treated groups[18]. In addition, the nucleoside analogue drug favipiravir showed significant effectiveness in protecting mice from lethal EBOV challenge, but provided low protection in a clinical trial25,26. The mortality of patients receiving TKM-Ebola was 79%, and the drug did not improve survival compared with historic mortality[27]. Although REGN-EB3 and mAb114 have been approved by the US Food and Drug Administration, these antibodies only provide protection against Zaire ebolavirus. Therefore, there is an urgent demand for broadly reactive drugs to control infection with diverse filoviruses. However, the current knowledge gap in the understanding of the filovirus RNA synthesis machinery is hampering the development of such drugs.

The filovirus RNP complex is responsible for viral RNA synthesis, and the L–VP35 complex is the competent core unit for efficient RNA-dependent RNA polymerization from the viral RNP template[3]. The structures of L polymerase in complex with the cofactor phosphoprotein (P) from several mononegaviruses, including vesicular stomatitis virus (VSV), rabies virus, human metapneumovirus (HMPV), human respiratory syncytial virus (HRSV) and human parainfluenza virus (HPIV) have revealed two binding modes: P interacting with the N-terminal region of L polymerase or with the C-terminal region of L polymerase28,29,30,31,32. P serves as an indispensable cofactor for RNA synthesis, and it can tether L to the RNP complex and act as a chaperone to prevent the non-specific aggregation of nascent N protein with host RNA, preserving monomeric RNA-free N protein for RNP assembly among Mononegavirales[33]. The respiratory syncytial virus P protein also interacts with M2-1[34] and the cellular phosphatase PP1[35] to coordinate viral transcription. In addition to its P-like chaperone function, EBOV VP35 binds to double stranded RNA (dsRNA) and is crucial for host immune evasion[36]. However, little is known about the structural basis of the filovirus L–VP35 complex, which precludes the molecular understanding of filovirus RNA synthesis.

Overall structure of EBOV polymerase

We used the baculovirus expression system to co-express the EBOV L polymerase and VP35 proteins and obtained soluble complex protein that was suitable for structural and functional studies. We analysed the purified protein complex using size-exclusion chromatography, and SDS-PAGE and western blotting profiles showed that the L protein was easily degraded into two bands, with molecular weights of about 180 kDa and 150 kDa (Extended Data Fig. 1). A primer-extension assay using 11-mer RNA template and 4-mer RNA primer confirmed that the L–VP35 complex possessed RNA-dependent RNA polymerization activity. The RNA products were inhomogeneous with a small percentage of full-length product, and the majority were abortive products probably caused by early termination (Extended Data Fig. 1).

Using cryo-electron microscopy (cryo-EM), we determined structures of the L–VP35 complex and the VP35 oligomerization domain to resolutions of 3.0 Å and 3.4 Å, respectively (we refer to this as the state 1 conformation; Supplementary Figs. 1 and 2 and Supplementary Table 1). The final 3D reconstruction maps enabled us to build atomic models for residues 8–1383 of L and for the VP35 tetramer with different lengths (Fig. 1 and Supplementary Fig. 3). Although the L protein sequence identity is only about 25.5%, the overall ‘spoon-like’ structure of EBOV L–VP35 is similar to that of the HPIV5 L–P complex (Extended Data Fig. 2). The visible region of L polymerase contains an N-terminal domain (NTD), an RNA-dependent RNA polymerase (RdRp) domain and a GDP polyribonucleotidyltransferase (PRNTase) domain (Fig. 1). The RdRp domain of EBOV L folds into the canonical right-handed fingers–palm–thumb architecture observed in many RNA virus polymerase structures, containing six catalytic motifs (A–F) (Fig. 2a,b). The superposition of EBOV L and VSV L structures shows a root mean square deviation (r.m.s.d.) of 4.2 Å based on superimposition of Cα residues, illustrating the structural conservation of L polymerases during the evolution of Mononegavirales (Fig. 2c) and suggesting that RNA follows similar paths in nsNSV polymerases (Extended Data Fig. 3). However, we observed a filovirus-specific structural element consisting of a loop, which was absent in other mononegaviruses (Fig. 2d), owing to an insertion of ~30 residues in the NTD of L polymerase, roughly spanning residues 190 to 225 (Fig. 2e). Deletion of the insertion element abolishes the transcription activity of EBOV RNP (Extended Data Fig. 4), suggesting that this loop is essential for RNA synthesis. As in VSV L, the PRNTase domain of EBOV L has a large interface with the RdRp domain and is responsible for the 5′ capping of nascent viral mRNAs29,37. The structural organization of PRNTase domain highly resembles those of L proteins from other mononegaviruses and is supposed to have conserved sequence motifs (A′–E′). We used AlphaFold2 to predict the structure of the L protein and modelled the full-length EBOV L structure with bound VP35 tetramer (Extended Data Fig. 5). The putative model could be overlaid onto our structure, and showed the presence of a long loop (residues 1652–1761) between the connector domain and the methyltransferase domain, which may partially explain why the purified EBOV L protein tends to degrade.

Fig. 1: The overall structure of the EBOV L–VP35 complex.

a, Schematic diagram of the domain architecture of EBOV L and VP35. L protein can be divided into five regions. Structurally modelled domains are shown in colour: NTD, orange; fingers subdomain, blue; palm subdomain, red; thumb subdomain, green; PRNTase, cyan. The linkers, connector domain (CD), methyltransferase domain (MTase) and C-terminal domain (CTD) of L are not modelled in the structure, and are shown in white with dashed outline. The solved regions of the VP35 protomers vary in length and shown in different colours. The NTD of VP35 was not observed in all protomers owing to its flexibility. b,c, The cryo-EM density map (b) and atomic model (c) of the EBOV L–VP35 complex with coloured domains as depicted in a. The terminal residue numbers of the VP35 protomers are indicated.

Fig. 2: Structure of the EBOV L RdRp domain.

a, The RdRp domain is shown as cartoon, with the fingers subdomain in blue, the palm subdomain in red, the thumb subdomain in green, and the remaining NTD in orange. The active site is highlighted with a red asterisk. The partial supporting helix is observed in the fingers subdomain. b, The same view as in a, with catalytic motifs highlighted. c, The superposition of the RdRp domains of the EBOV (coloured by subdomains as in a) and VSV (grey) (Protein Data Bank (PDB) ID: 5A22) L proteins highlights the difference in the NTD. d, Close-up view of the specific insertion region of the EBOV L RdRp domain. e, Sequence alignment of the above specific insertion region spanning residues 190 to 225. The sequence is unique to filoviruses compared with other nsNSVs. MARV, Marburg virus; LLOV, Lloviu cuevavirus; RABV, rabies virus; RSV, respiratory syncytial virus.

Previous structures of L polymerases from VSV and pneumoviruses (HRSV and HMPV) have revealed two different conformations of the catalytic chamber: the initiation state observed in VSV L polymerase, in which the priming loop from the PRNTase domain and the supporting helix from the RdRp domain largely occlude the central RNA-binding cavity[37]; and a possible elongation state observed in HRSV and HMPV L polymerases, in which the priming loop retracts completely into a cavity in the PRNTase domain and the supporting helix is not visible, leaving ample space for the dsRNA intermediate duplex28,30. In our structure of the free L–VP35 complex, we found that the priming loop (also referred to as motif B′ in the PRNTase domain), motif D′ of the PRNTase domain and the partial supporting helix are disordered, suggesting that these structural elements are flexible (Fig. 3a,b).

Fig. 3: Structural comparison of EBOV L–VP35 complex in two states.

a–c, Overlay of EBOV L–VP35 complex structures in state 1 and state 2. a, The two structures could be superimposed with slight differences. For the L–VP35 complex in state 1, the motif B′ (also referred to as the priming loop) and D′ of the PRNTase domain, and the partial supporting helix of RdRp could not be modelled owing to their flexibilities; however, these regions are more stable and the main chain can be traced in state 2. b, Close-up view of the comparison of motifs B′ and D′, and the ends of unbuilt residues of motif B′ (magenta) and motif D′ (blue) are indicated by arrows related to the region outlined in a as indicated. c, Close-up view comparing the supporting helix and the ends of unmodelled residues of the supporting helix indicated by arrows, as circled in a. d, In state 2, the supporting helix flips outwards and moves away from the active site of RdRp, and three key residues of motif C in the RdRp domain form hydrogen bonds with the supporting helix to stabilize this conformation.

To further explore the elongation conformation of the L–VP35 complex, we incubated the free L–VP35 complex with template and primer RNAs in an enzyme reaction buffer and determined the complex structure to a resolution of 3.4 Å (we refer to this as the state 2 conformation; Supplementary Fig. 4). Although we do not observe clear density for the RNA in this L–VP35 complex structure, we were able to trace the complete priming loop (residues 1196–1216) and the supporting helix (residues from Ser610 to Thr623 are invisible in state 1 map) (Fig. 3a–d). The priming loop retracts completely into a cavity of the PRNTase domain, as seen in HRSV and HMPV L–P complex structures28,30 (Extended Data Fig. 6). The priming loop further stabilizes motif D′ (also referred to as the His–Arg (HR) motif), which consists of two catalytic residues—H1269 and R1270—critical for cap formation (Fig. 3a,b and Extended Data Fig. 6). This makes it possible for the flipping priming loop to approach the active site of the PRNTase domain, forming a compact conformation favourable for the capping function. The location of the supporting helix in the VSV L structure would clash with template-product dsRNA, indicating a necessary conformational change of this element during RNA elongation (Extended Data Fig. 7). In our EBOV L–VP35 structure, the supporting helix leaves from the central RNA-binding cavity and forms several hydrogen bonds with motif C to stabilize this conformation (Fig. 3d and Extended Data Fig. 7).

Interactions between L and VP35

The EBOV VP35 oligomerization domain can assemble into trimer or tetramer[38] structures (Extended Data Fig. 8). Our L–VP35 complex structure shows that tetrameric EBOV VP35 binds to the L polymerase (Fig. 1 and Extended Data Fig. 9a). Large variations in conformation were observed in each of the four VP35 monomers (VP35a, VP35b, VP35c and VP35d) (Extended Data Fig. 9b). Clear densities enabled the tracing of VP35a residues 82–149, which does not interact with L, as well as VP35b 80–146, VP35c 81–179 and VP35d 81–340, which do interact with L (Fig. 1, Extended Data Fig. 9b and Supplementary Table 3). The oligomerization region (residues 83–145) of VP35 forms a coiled-coil structure of four helices to stabilize the tetramer, which is further stabilized by antiparallel β-sheets formed by VP35c residues 145–148 and VP35d residues 174–177, and hydrogen bonds formed between VP35c Arg151 and VP35d Gln168 and Pro169, and between VP35c Glu160 and VP35d Arg151 (Extended Data Fig. 9c). The VP35–L interaction is defined mainly by hydrogen bonds, electrostatic interactions and van der Waals contacts, contributed minimally by VP35b and largely by VP35c and VP35d (Fig. 4, Extended Data Fig. 10 and Supplementary Table 3). Residues Thr127 and Ser130 of VP35b form two hydrogen bonds with the Asn434 from the fingers subdomain of L, and VP35b Arg133 forms a hydrogen bond with Asp432 of L (Fig. 4b and Supplementary Table 3). van der Waals contacts are also formed between Thr127 and Ser130 of VP35b and Asn434 of L (Supplementary Table 3). About 30 additional residues are observed after the oligomerization region in VP35c compared with VP35a and VP35b, which form a β-strand that interacts with an antiparallel β-strand in VP35d and a α-helices connected by the loops (Extended Data Fig. 9b,c). The major interactions between VP35c and L contain five hydrogen bonds and a number of van der Waals contacts. Among them, residues Met147 and Thr149 of VP35c interact with the main chain of Leu399 and Lys397 from the fingers subdomain through two hydrogen bonds, respectively (Fig. 4c and Supplementary Table 3). Moreover, the side chain of Thr153 of VP35c forms hydrogen bond with Glu643 of L to further stabilize the interaction (Fig. 4c and Supplementary Table 3). For VP35d, all the regions (residues 146–340) after the oligomerization domain were traced, consisting of an α-helix, a β-strand, two consecutive α-helices and the rigid C-terminal RNA-binding (or interferon-inhibitory) domain (RBD/IID) which can inhibit induction of type I α- or β-interferon by sequestering dsRNA byproducts of viral replication and by interacting with the members of innate receptor pathway[36]. VP35d contacts residues from the fingers subdomain, palm subdomain and NTD of L polymerase (Fig. 4, Extended Data Fig. 10 and Supplementary Table 3). The region between the oligomerization domain and the C-terminal RBD/IID of VP35d forms a long loop structure that lies on top of the hole through which NTPs enter the active site chamber (Fig. 4d and Extended Data Figs. 3 and 10). This long loop is further stabilized by three hydrogen bonds formed by Leu209, Glu211 and Gly215 of VP35d interacting with Lys778 from the palm subdomain and Arg315 and Gln322 from the NTD, respectively. The C-terminal RBD/IID of VP35d contacts the NTD via two α-helices and leaves its positively charged RNA-binding cavity exposed to the solvent (Figs. 1 and 4a).

Fig. 4: The interaction between EBOV L and VP35.

a, The overall structure of the L–VP35 complex in cartoon view. The L protein is coloured by subdomains as indicated. The VP35 protomers are shown in different colours. The interaction interfaces between EBOV L and VP35 can be distributed into four main regions, labelled I, II, III and IV. b–e, Close-up views of atomic interactions between L and VP35 in regions I (b), II (c), III (d) and IV (e). Key residues are shown as sticks. Hydrogen bonds are represented by yellow dashed lines.

Among the reported L–P complex structures, the EBOV L–VP35 structure most resembles the HPIV5 L–P complex. However, the resolution (4.3 Å) of the HPIV5 L–P structure is too low to accurately determine binding sites between L and P protein. Therefore, we analyse the conservation of these L protein recognition sites on the basis of the EBOV L–VP35 and HRSV L–P complex structures. Overlaying both structures, we find that the binding sites between EBOV L and VP35 are similar to those of HRSV L and P, involving the fingers, palm and NTD of L (Extended Data Fig. 10b). However, the critical residues contributing to the main interactions vary among the different nsNSV polymerase complexes (Extended Data Fig. 10c).

Inhibition of EBOV polymerase by suramin

Suramin is a potent inhibitor of Chikungunya virus and EBOV cell entry, but its mechanism of action remains largely unknown[39]. Suramin has also been reported to target the viral polymerases of norovirus and SARS-CoV-240,41. Here we show that suramin is also a potent inhibitor of the EBOV L–VP35 polymerase complex. Addition of 32 μM suramin completely abolished the polymerization activity of the EBOV L–VP35 complex in an in vitro enzymatic assay, with a half-maximal inhibition concentration (IC_50) of about 11 μM (Fig. 5a and Extended Data Fig. 11b). Furthermore, in a cell-based assay using a stable EBOV replicon cell line, suramin potently inhibited EBOV replication, with a half-maximal effective concentration (EC_50) of about 0.4 μM (Fig. 5b). The concentration of suramin required to reduce cell viability by 50% (CC_50) is over 200 μM (Extended Data Fig. 11c), indicating its relatively low cytotoxicity and high selectivity index (SI > 500).

Fig. 5: Mechanism of suramin inhibition of EBOV L–VP35.

a, Suramin inhibits the replication activity of the EBOV L–VP35 complex in an enzymatic assay, with an IC_50 value of 11.16 µM. b, Suramin inhibits EBOV RNP activity in EBOv-GLuc-Hyg replicon cells with an EC_50 value of 0.4 µM. Data in a,b are mean ± s.d. of three or four independent experiments. c, Overall structure of the L–VP35–suramin complex. The L–VP35 complex is shown in cartoon representation with the same colours as in Fig. 4. Suramin is shown as a stick model in purple with a dashed outline. Top left, close-up view of the suramin-binding site in surface representation, showing that suramin is located in the NTP entry channel. d–f, Atomic interactions between suramin and EBOV L. Key residues that are responsible for suramin binding are shown as sticks and coloured according to subdomain. Hydrogen bonds and salt bridges are shown as yellow and green dashed lines, respectively.

Source data

To uncover the structural basis for suramin inhibition of EBOV RNA polymerase, we determined the cryo-EM structure of the EBOV L–VP35–suramin complex at 3.3 Å resolution (Supplementary Fig. 5 and Supplementary Table 2). The overall structure of the L–VP35–suramin complex resembles that of the apo L–VP35 complex, but residues 1,000–1,400 could not be modelled owing to the weak density in this region, indicating that suramin binding may make the PRNTase domain more flexible. On the basis of the clear density, we built an atomic model of the head half and ring D′ of suramin, spanning the putative NTP entry channel in the RdRp domain (Fig. 5c and Supplementary Fig. 5).

The chemical structure of suramin (C_51H_40N_6O_23S_6) is a symmetric polysulfonated naphthylurea, which includes four benzene rings, two naphthalenes and six sulfonic acid groups with a urea linker at the centre (Extended Data Fig. 11a). In the electron microscopy density map, we could clearly see the head half of the suramin molecule occluding the NTP entry channel (Fig. 5c), and the key interactions include hydrogen bonds, electrostatic and hydrophobic interactions with conserved NTD and RdRp residues. The sulfonate at position 3 forms a salt bridge with the side chain of Lys392 and a hydrogen bond with the side chain of His392 from the fingers subdomain (Fig. 5d). The sulfonate at position 5 forms a salt bridge with the side chain of Lys293 from the NTD and a hydrogen bond with the main chain of Val559 from the fingers subdomain (Fig. 5e), whereas the sulfonate at position 1 has little interaction with the polymerase. A hydrophobic residue (Phe793) in palm subdomain undergoes substantial conformational change, and together with Phe319 and Met323 provides a stable hydrophobic cavity to accommodate benzene ring D of suramin, further tethering the suramin in the NTP entry channel (Fig. 5f). For the tail half of suramin, the density is only visible at a very low threshold, but we can model the full suramin molecule on the basis of the density and stereochemistry of suramin; the tail half of suramin occupies the space where the nascent RNA would go, where the surrounding basic residues may form electrostatic interactions with the negatively charged sulfonate groups of suramin (Extended Data Fig. 11d–g).

Discussion

The high-resolution structures of the EBOV polymerase complex presented here provide first structural characterization of EBOV L protein in complex with VP35. VP35 is functionally analogous to P proteins of other mononegaviruses, which not only acts as a cofactor of L protein but also facilitates nucleocapsid formation by binding the monomeric NP to prevent premature and non-specific assembly42,43. In addition, the binding of VP35 to L protein can prevent the self-aggregation of L protein caused by hydrophobic interactions, as there are several hydrophobic patches in the VP35-binding regions (Supplementary Table 3). Moreover, VP35 and VP24 are required for the proper condensation of filovirus nucleocapsid, which acts as a template for genome transcription and replication[44]. Structurally, VP35 is composed of an NTD, an oligomerization domain and C-terminal domain connecting with a linker. The NTD is responsible for interacting with free NP and the LC8 subunit of cellular dynein, which is also involved in viral RNA synthesis42,43,45, however, this region is not visible in our cryo-EM map, indicating that it is flexible and may adopt distinct conformations to bind different partners. In addition to functioning as an interferon antagonist[36], the C-terminal domain can also interact with the NP protein[46], and the L–VP35 complex structure presented here reveals that the three free C-terminal domains may function as anchors to underpin the L protein moving along the nucleocapsid during genome transcription and replication.

Mononegavirus L protein contains all the domains required for RNA synthesis, capping and methylation. The switch from initiation to elongation involves significant conformational changes of key elements, including the supporting helix and priming loop. Previous studies have revealed the different conformations of the priming loop28,30,32,37, but could not determine the conformation of the supporting helix in the non-initiation state. The outward state of the supporting helix that we captured in this study contributes towards the understanding of the conformational dynamics during the catalytic cycle. A filovirus-specific insertion element was observed in the NTD of EBOV L, which is essential for its transcription activity, as revealed by the replicon assay. A previous study reported that the NTD of the VSV L protein is essential for viral genome transcription[47]. Thus, we deduce that the NTD of the EBOV L protein might be involved in genome transcription. Notably, compared with other nsNSVs, filoviruses, including EBOV, have a unique cofactor protein VP30, which is crucial for transcription initiation; we therefore suspect that the insertion element in NTD may evolve with VP30 to regulate the transcription process.

The reoccurrence of EBOV in 2018–2020 in the Democratic Republic of the Congo and resurgence of EBOV in 2021 in Guinea is evidence of the need for economical and effective drugs to treat the disease. Suramin is a multifunctional drug with inhibition activity against parasites, viruses and cancers[48]. The L–VP35–suramin structure indicates that suramin can bind to the RdRp domain, blocking the NTP entry channel and occupying the space that the nascent RNA chain would occupy, thus hindering polymerization activity (Fig. 5 and Extended Data Fig. 11). Previous studies have shown that the suramin-binding sites in norovirus polymerase overlap the proposed NTP entry channel[40], and in SARS-CoV-2 RdRp, they occupy the chamber where the RNA template and primer go[41] (Extended Data Fig. 12). In both of the above structures, only half of a suramin molecule was seen, owing to the other half moiety remaining unrestricted by protein. In this study, we could trace the whole suramin molecule, with clear density at the head moiety and relatively weak density at the tail part, which could be stabilized by the surrounding positively charged residues. Our result suggests that the NTP entry channel would be an attractive target for the development of antiviral drugs.

Although suramin has been used to treat African sleeping sickness for about 100 years, we noted that the relatively large molecular weight and multiple negative charges could be shortcomings for clinical use. In addition, suramin also binds to cellular DNA polymerase, primase, helicases and transcription factors48,49,50, which might lead to off-target effects. The details of the interaction between suramin and L protein described here could guide optimization of the molecule to increase its affinity and specificity, such as by enhancing the hydrophobic interaction between ring D of suramin and the hydrophobic residues of L protein, or reducing the negative charge by deleting the sulfonate at position 1, which provided few interactions. In sum, the findings for suramin can be used as a proof of principle for the development of a broad-spectrum inhibitor.

In summary, we determine the structure of EBOV L protein in complex with tetrameric VP35 and capture a previously missing structural snapshot of mononegavirus polymerase in the non-initiation state, with a supporting helix and priming loop getting away from the active site. Of note, we demonstrate that suramin could inhibit EBOV L–VP35 activity and elucidate the structural basis of suramin binding to RdRp, suggesting the feasibility of developing non-nucleoside antiviral drugs to treat filovirus infection.

Methods

Protein expression and purification

The coding sequences of EBOV L (GenBank: AHX24663.1) and VP35 (GenBank: AHX24647.1) were synthesized and codon-optimized for Bac-to-Bac expression system using pFastBac Dual transfer vector. The sequences of EBOV L and VP35 were fused with an N-terminal 2×Strep tag and a C-terminal His tag, respectively. The purification process is performed as previously reported[51]. Insect SF9 cells (11496015; Invitrogen) and High Five cells (B85502; Invitrogen) were used to prepare the recombinant baculoviruses and express EBOV L–VP35 complex protein, respectively. Both cell lines are routinely maintained in our lab and tested negative for mycoplasma contamination. The High Five cells were harvested by centrifugation (3,000 rpm, 4 °C, 10 min) at 48 h post-infection and lysed by sonication in buffer A containing 25 mM HEPES, pH 7.8, 1 M NaCl, 5% glycerol, 1 mM Tris(2-carboxyethyl)phosphine (TCEP), 0.01% Tween 20, 4 mM MgCl_2 and 1 mM Phenylmethanesulfonyl fluoride (PMSF). Cell debris were discarded using super-centrifugation (30,000 rpm, 4 °C, 1 h) and 0.22 μm filter to collect supernatant. The protein solution was loaded into a HisTrap column (GE Healthcare, 5 ml) with equilibrium of buffer B containing 25 mM HEPES, pH 7.8, 500 mM NaCl, 5% glycerol, 1 mM TCEP, 4 mM MgCl_2, and the bound protein was eluted using 300 mM imidazole supplemented in buffer B. The eluted fractions were pooled and subjected to further purification using StrepTrap column (GE Healthcare, 5 ml). The EBOV L–VP35 complex protein was eluted using 2.5 mM d-desthiobiotin dissolved in buffer B. The protein was concentrated with a 100 kDa-cutoff Millipore Ultra centrifugal filter, and then loaded onto a size-exclusion chromatography (GE Healthcare, Superdex 200) with buffer B. The purified L–VP35 complex protein was confirmed by western blot using mouse monoclonal antibody against Strep tag (Easybio BE2038, dilution 1:3,000). The final products were collected and concentrated to ~5 mg ml−1, which was calculated by Nanodrop at 280 nm, and finally stored at −80 °C until to use.

RdRp enzymatic activity assay and its inhibition by suramin

To determine whether the purified L–VP35 protein is biologically active, we performed the primer-extension assay52,53. A mixture of 1.5 μM L–VP35 complex, 1 μM template RNA (5′-UUUGUUCGCGU-3′) and 200 μM primer RNA (5′-ACGC-3′) was added into a reaction buffer containing 20 mM Tris-HCl, pH 8.0, 20 mM NaCl, 4 mM MgCl_2, 1 mM TCEP, 0.01% Tween 20, 1 mM CTP/UTP/ATP and 0.12 μCi μl−1 [α-32P]GTP at 30 °C for 1 h. The reaction system was quenched with addition of formamide and boiled at 100 °C for 10 min. The RNA products were separated by 25% polyacrylamide gels containing 7 M urea in 0.5× TBE buffer. Images were taken by exposing the gels on a storage phosphor screen and read with a Typhoon scanner (GE Healthcare). For assays with inhibition by suramin, the setup was similar to the above, except that the suramin was added at final concentrations of 1, 2, 4, 8, 32 and 64 μM. The intensity of each band was quantified with ImageJ software.

Mini-replicon assay

Plasmids expressing EBOV RNP (pCAGGS-L or pCAGGS-L(196–225)GS (in which residues 196–225 were replaced with GS), pCAGGS-VP35, pCAGGS-VP30 and pCAGGS-NP) and T7 polymerase (pCAGGS-T7), and a reporter plasmid encoded GFP flanked by T7 promoter, 5′- and 3′-terminal untranslated region sequences, were co-transfected into HEK293T cells using Lipofectamine 3000 (Invitrogen)[54]. EBOV RNP activity was measured using the expression level of GFP calculated by CellVoyager CQ1 (Yokogawa) after 72 h. The expressions of VP35, NP and VP30 were detected using polyclonal antibodies (dilution 1:1,000) which were derived by immunizing rabbits (3–4 months old) with purified VP35, NP and VP30 proteins, respectively. Tubulin was used as a loading control and detected using mouse anti-tubulin (Easybio, BE0031, dilution 1:3,000). All rabbit experiments were performed according to the procedures approved by the Institute of Microbiology, Chinese Academy of Sciences and complied with all relevant ethical laws. Due to lack of antibody against L protein, the mRNA transcription levels of L (both wild type and L(196–225)GS mutant) were measured by quantitative real-time PCR (RT–qPCR). Total RNA was extracted using Cell Total RNA Isolation Kit (Foregene) and the concentration of RNA was measured by Nanodrop at 260 nm. About 5 μg total RNA was treated by Hifair III 1st Strand cDNA Synthesis SuperMix for quantitative PCR (qPCR) (purchased from YEASEN) to generate cDNA. RT–qPCR was performed using Hieff UNICON qPCR SYBR Green Master Mix (purchased from YEASEN) on the Bio-Rad CFX96 real-time PCR system. RT–qPCR primers for L mRNA were as follows: forward primer, 5′-GGACGAATCACAAAACTAGTCAATG-3′; reverse primer, 5′-CGGAAATAAACTCAGAAGCCCTG-3′. L mRNA copy numbers were calculated based on a standard curve generated with purified PCR products.

Cell-based assay for antiviral activity of suramin

Cell-based assay for inhibitory activity of suramin against EBOV polymerase was performed on the stable EBOV-GLuc-Hyg replicon cell line[55]. The cells were cultured overnight in Dulbecco’s modified Eagle medium (DMEM, Gibco) containing 100 μg ml−1 hygromycin and 10% fetal bovine serum (FBS, Gibco) at 37 °C, 5% CO_2. The medium was replaced with FBS-free substrate before the addition of drugs. The cells were incubated with different concentrations of suramin and further cultured in 96-well plate. After 72 h incubation, a 30 μl volume of supernatant from each well was pipetted after centrifugation and then added to a new 96-well white plate and mixed with same volume of Gluc assay solution (Gaussia Luciferase Assay Kit, GeneCopoeia) immediately. The values of luminescence were measured by Glmax Reader (Promega).

Cytotoxicity assay

The cytotoxicity of suramin against EBOV-Gluc-Hyg replicon cells were measured by CCK8 reagent (MCE). Briefly, the drug-treated cells were washed twice with PBS and mixed with reaction solution containing CCK8. After incubation at 37 °C for 1.5 h, the A_450 values were read by microplate reader (Thermo Fisher Scientific).

Statistical analysis

IC_50, EC_50 and CC_50 values were represented as mean ± s.d. from at least three independent experiments. All values were determined by the nonlinear regression analysis using GraphPad Prism software v9.0.0 (https://www.graphpad.com/).

Cryo-EM sample preparation and data collection

To prepare the cryo-EM sample of L–VP35, 3 μl protein solution (0.6 mg ml−1) was loaded into a cleaned Nitai grid (R1.2/1.3), which was blotted for 3 s with a humidity of 100% at 4 °C, and then plunged into liquid ethane using an FEI Vitrobot Mark IV. The well-prepared cryogenic specimens were transferred onto an FEI Titan Krios transmission electron microscope for data collection. Cryo-EM micrographs were automatically collected using Serial-EM software using beam-image shift imaging scheme. Images were recorded with a K3-subunit detector using the super-resolution counting mode at a calibrated magnification of 22,500, corresponding to a pixel size of 1.07 Å. The exposure was performed with a dose rate of 20 e−1 pixel−1 s−1 and an accumulative dose of 60 e−1 Å^2 for each micrograph, which fractionated into 32 frames. The defocus range of this dataset was roughly −1.5 to −2.6 μm.

For L–VP35 complex in the non-initiation state, the protein sample was diluted with enzymatic reaction buffer and then mixed with RNA at a molar ratio of protein:RNA of 1:2. After incubation for 1 h on ice, the complex was applied to a glow-discharged graphene coated grid (R1.2/1.3). The process of data collection was almost as same as above with a modified magnification of 29,000, yielding to a pixel size of 0.83 Å. Each image was exposed with a dose rate at 20 e−1 pixel−1 s−1 and total dose of 60 e−1 Å−2 and fractionated into a movie stack of 32 frames. The defocus range of this dataset was roughly −1.2 to −3.0 μm.

For the L–VP35–suramin complex, the purified protein was diluted to 0.4 mg ml−1 using salt-free buffer to reduce the concentration of NaCl to 300 mM and incubated with suramin at 4 °C for 1 h. An aliquot of 3 μl L–VP35–suramin complex was loaded onto glow-discharged Nitai grid (R1.2/1.3) following the same protocol for L–VP35. The micrographs were collected on a 300 kV Titan Krios transmission electron microscope equipped with a post-column GIF Quantum energy filter (Gantan) with a silt width of 20 eV. The images were recorded with K2 direct detection camara using the super-resolution counting mode with a calibrated pixel size of 1.04 Å on micrographs. Each stack was exposed with a dose rate at 10 e−1 pixel−1 s−1 and total dose of 60 e−1 Å−2 and fractionated into a movie stack of 32 frames. The defocus range of this dataset was roughly −0.9 to −2.4 μm.

Image processing

The movie frames were aligned using MotionCor2[56] and the contrast transfer function (CTF) values of each micrograph were determined using CTFFind4[57]. Fifty micrographs were selected for automatic particles picking using Laplacian-of-Gaussian bolb detection, and were subjected to two-dimensional (2D) classification to generate templates for autopicking against entire dataset. All subsequent classification and reconstruction procedures were performed using Relion-3.0[58]. For L–VP35 complex, a total of ~6,000,000 particles were selected from 4,432 micrographs, and were reduced to ~3,300,000 particles after three rounds of 2D classification. Due to lack of homologous structure, we generated the ab initio model (6 classes) using Relion-3.0 and selected the best class low-passed to 60 Å as the initial reference model for 3D classification. After two iterative rounds of 3D classification, a clean dataset of 1,083,293 particles from two classes with clear features of secondary-structure elements were subjected to 3D refinement, which yielded a reconstruction map at 3.5 Å. To further improve the map resolution, we performed dose weighting using MotionCor2 discarding the first two and last 14 frames in each stack to generate a reduced dataset with total dose of 30 e−1 Å−2. In addition, CTF refinement was performed to correct the local CTF values of each particle. After a final round of 3D refinement, we obtained a better map with a resolution at 3.0 Å. During above image processing, we noticed that extra densities belonging to VP35 oligomerization domain were cut off due to small box size. We extracted these particles with larger box size and re-centred them into the centre of whole particle not focused on L protein. Additional round of 3D classification was performed to discard bad particles among eight classes, and two of them showed obvious structural features. To avoid the density of L protein affecting the alignment, we performed particle subtraction that was subjected to the final round of 3D classification and 3D refinement with local angular search, and generated a reconstruction map at 3.4 Å for VP35 oligomerization domain (Supplementary Fig. 2). The processing of dataset for EBOV L–VP35 in the non-initiation state was quite straightforward. A total of 4,625 micrographs were collected, and ~5,000,000 particles were picked from the micrographs. After three rounds of 2D classification and two rounds of 3D classification, a clean dataset of 475,325 particles were subjected to 3D refinement, which yielded a final map at 3.4 Å resolution. Although we found a mass of density at C-terminal domain of VP35d, we were unable to reconstruct this region using local refinement or signal subtraction (Supplementary Fig. 4). The data processing for L–VP35–suramin complex was similar to the previous procedure. A total of 3,200,000 particles were auto-picked from 2,845 images, and after several rounds of 2D classification and three rounds of 3D classification, the remaining 193,982 particles were subjected to 3D refinement which yielded the density map at 3.3 Å resolution (Supplementary Fig. 5). The local resolution maps were evaluated by ResMap[59].

Model building and refinement

The low sequence identity between EBOV L and other polymerases prevented us using any reported structure as a starting model. The quality of map is good enough for us manually modelling ab initio. The density for VP35 C-termini was well fitted with the reported crystal structure (PDB ID: 3L26). However, the oligomerization domain could not be fitted satisfactorily and was built manually. The initial coordinates were refined against the corresponding maps using PHENIX[60] with secondary-structure restraints and Ramachandran restraints applied. And then, we performed manual model building to improve local fit using COOT[61]. The stereochemical quality of each model was assessed using MolProbity[62]. Structural figures were prepared with Pymol (https://pymol.org/) and CHIMERAX[63].

AlphaFold2 prediction of EBOV L

The whole structure of EBOV L protein was predicted by AlphaFold2[64] with default settings. We compared the top five ranked outputs with our solved structure and selected the most similar one to prepare the figures.

Reporting summary

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

Data availability

The cryo-EM density maps and atomic coordinates have been deposited to the Electron Microscopy Data Bank (EMDB) and the Protein Data Bank (PDB), respectively. The accession numbers are listed as follows: EBOV L–VP35 complex in state 1 (7YER, EMD-33775), EBOV L–VP35 complex in state 2 (7YES, EMD-33776), EBOV L–VP35-suramin complex (7YET, EMD-33777). All other data are available from the authors upon reasonable request.

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Acknowledgements

We thank all the staff members at the Center for Biological Imaging (CBI), Institute of Biophysics (IBP), Chinese Academy of Sciences (CAS) for assistance with data collection. We thank the staff of the State Key Laboratory of Membrane Biology, Institute of Zoology (IOZ), CAS for technical support of electron microscope operation. This study was supported by the National Key Research and Development Program of China (2021YFC2300700 to Y.S.), Strategic Priority Research Program of CAS (XDB29010000 to Y.S. and G.F.G.), and National Natural Science Foundation of China (NSFC) (81871658 and 32192452 to Y.S. and 32100119 to Q.P.). Y.S. is also partially supported by the Youth Innovation Promotion Association of CAS (Y201921).

Author information

Author notes

1. These authors contributed equally: Bin Yuan and Qi Peng

Authors and Affiliations

1. CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China

Bin Yuan, Qi Peng, Jinlong Cheng, Min Wang, Jianxun Qi, George F. Gao & Yi Shi

2. Savaid Medical School, University of Chinese Academy of Sciences, Beijing, China

Bin Yuan, Jin Zhong, George F. Gao & Yi Shi

3. CAS Key Laboratory of Molecular Virology and Immunology, Institut Pasteur of Shanghai, Chinese Academy of Sciences, Shanghai, China

Jin Zhong

4. Center for Influenza Research and Early-Warning (CASCIRE), CAS–TWAS Center of Excellence for Emerging Infectious Disease (CEEID), Chinese Academy of Sciences, Beijing, China

George F. Gao & Yi Shi

5. Research Unit of Adaptive Evolution and Control of Emerging Viruses, Chinese Academy of Medical Sciences, Beijing, China

George F. Gao & Yi Shi

Contributions

Y.S. and G.F.G. conceived the study. B.Y., J.C. and M.W. purified the protein samples and conducted biochemical and cellular experiments. B.Y. and Q.P. prepared the cryo-EM specimens and collected data. Q.P. conducted the image processing and reconstruction. J.Q. and Q.P. built the atomic models. Y.S., G.F.G., J.Q., Q.P. and B.Y. analysed the structure. Q.P., B.Y., J.Z., G.F.G. and Y.S. wrote the manuscript. All authors participated in the discussion and manuscript editing. B.Y. and Q.P. contributed equally to this work. Y.S. supervised all the work.

Corresponding authors

Correspondence to George F. Gao or Yi Shi.

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Extended data figures and tables

Extended Data Fig. 1 The purified EBOV L-VP35 complex has polymerization activity.

(a, b) Size-exclusion chromatography, SDS-PAGE and western blotting profiles of EBOV L-VP35 WT (a) and D742A mutant (b) proteins. Molecular weights (in kilodaltons, kDa) of ladder makers are shown in the left, and the L and VP35 bands are labeled on the right. (c, d) In vitro primer extension assay of L-VP35 complex. It demonstrates the purified complex protein possesses polymerase activity, and the product bands are inhomogeneous. The active site D742A mutant can abolish the production of RNA. Otherwise, a small percentage of full-length product (indicated by red arrow) can be clearly seen when the product bands are overexposed (d). The data shown above are representative results of at least three independent experiments using different protein preparations.

Extended Data Fig. 2 Structural comparison of EBOV L-VP35 and HPIV5 L-P complex.

Overall structure of EBOV L-VP35 (a) and HPIV5 L-P complex (PDB 6v85) (b). Overlay of the NTD (c), RdRp (d), PRNTase (e) domains and tetrameric P/VP35 protein (f) between EBOV L-VP35 and HPIV5 L-P complex shows a similar architecture, but with local differences.

Extended Data Fig. 3 The paths for RNA synthesis within EBOV L-VP35 structure.

(a) The EBOV L-VP35 structure is shown in surface representation to highlight the entry channel of NTP substrate which was indicated by a red cycle. (b) RNA elongation model of EBOV L-VP35 complex. Template RNA entry and exit channels are indicated as black arrows, and NTP entry and nascent RNA product exit tunnels are indicated by red and purple arrows. (c) Cutoff view of the L protein shown in electrostatic surface representation (blue, positive charge; red, negative charge). The paths are filled by the template and nascent RNA strands which are modeled based on the structure of rotavirus polymerase complex with in situ elongation conformation (PDB 6OGZ).

Extended Data Fig. 4 The critical role of the insertion element in transcription activity of EBOV RNP.

(a) Clear green fluorescence can be visualized with the wild type L protein. However, for the insertion-element-deletion L(196-225)GS construct (residues 196 to 225 consisting of the insertion loop structure were deleted and two ends were linked with GS residues), no green fluorescence was observed. As a negative control, the L gene was not transfected in the replicon system. (b) The expression levels of NP, VP35 and VP30 were measured using Western Blotting assay and the tubulin was used as loading control. The data shown are representative of three independent experiments. (c) The transcription levels of L mRNA were analyzed by RT-PCR. The data represent mean values (histograms) ± s.d. (error bars) from three independent experiments.

Source data

Extended Data Fig. 5 The predicted full-length structure of EBOV L by AlphaFold2.

(a) Superimposition of the predicted and solved structures of EBOV L protein, and they could be overlaid well. The predicted structure is colored in grey, while the solved structure is colored as depicted in Fig. 1. (b) The modeled full-length structure of EBOV L-VP35 complex. (c–e) Close-up view of the predicted structures of CD (purple), MTase (magenta) and CTD (black) domains.

Extended Data Fig. 6 Structural analysis of the EBOV L PRNTase domain.

(a) Overlay of the PRNTase domains of L proteins from the EBOV (colored by cyan) and RSV (grey). (b) The same view as in (a) but with catalytic motifs indicated by different colors (motif A’, red; motif B’, purple; motif C’, yellow; motif D’, blue; motif E’, orange). The Cα atoms of the conserved glycines in GxxT motif and motif A’ (Gly1129) are shown in sphere representation.

Extended Data Fig. 7 Conformational change of the priming loop and supporting helix.

The priming loop and supporting helix would clash with RNA duplex formed during the RNA elongation process (a), and would undergo conformational change to release adequate space for RNA elongation (b) with the priming loop retracting into PRNTase domain and the supporting helix moving outward. The RNA was modeled based on the structure of rotavirus polymerase complex with in situ elongation conformation (PDB 6OGZ), and the RdRp active site was indicated by a red star.

Extended Data Fig. 8 Structural comparison of filovirus VP35 ODs.

(a) The structure of tetrameric VP35 OD from EBOV L-VP35 complex. (b–d) The crystal structures of tetrameric REBOV VP35 OD (PDB 6GBQ), trimeric EBOV VP35 OD (PDB 6GBO) and trimeric MARV VP35 OD (PDB 5TOI). (e) The sequence alignment of VP35 OD regions from different filoviruses.

Extended Data Fig. 9 The entangled tetrameric VP35 wraps around L protein.

(a) EBOV L is shown in grey surface representation, and VP35 protomers are shown in cartoon representation with different colors. (b) Structural conformations of different VP35 protomers. (c) The major atomic interactions between VP35 protomers. The key interacting residues are shown in stick representation. Hydrogen bonds are presented in yellow dash lines, while the hydrogen bonds between the anti-parallel β strands are not shown (left side).

Extended Data Fig. 10 Binding interface between VP35 and EBOV L and its comparison with other nsNSV polymerase complexes.

(a) The binding footprints of VP35 protomers on EBOV L are indicated by black dash line. The interacting residues of L are colored and labelled according to the bound VP35 protomers, and the overlapping regions are shown in orange (VP35b/d) and pink (VP35c/d), respectively. (b) Comparison of the binding interfaces of EBOV L-VP35 and RSV L-P polymerase complexes. The L and P of RSV are colored in black and white, respectively. The L and VP35 of EBOV are colored as in Fig. 4. (c) Sequence alignment of critical interactive residues from L proteins among Mononegavirales. The L residues that formed hydrogen bonds with the residues from VP35 of EBOV and P of RSV are indicated by red and blue stars, respectively.

Extended Data Fig. 11 The inhibition mechanism of suramin against EBOV L-VP35.

(a) The chemical structure of suramin. Each benzene ring group is labeled by a unique symbol. (b) Inhibitory activity of suramin against EBOV L-VP35 complex was measured at enzymatic level. The RNA products were shown in urea-PAGE, and a series of concentration of suramin were added in the enzyme reaction system. The data shown are representative of three independent experiments using different protein preparations. (c) The 50% cytotoxicity concentration (CC_50) of suramin was determined with the stable replicon cell. Each data point indicates the mean value of three independent experiments and the error bars represent standard deviation. (d–e) The structures of EBOV L-VP35 (d) and L-VP35-suramin (e) complex are shown in surface representation, and the NTP entry channel is indicated by a dashed circle. The suramin is stuck in the NTP entry channel to prevent NTP substrates reaching active site of RdRp. (f-g) The suramin could also hinder the activity of RdRp by occupying the spaces for product RNA strand. Cutoff view of the L-VP35-suramin complex overlapped with the modeled RNA (f). The tail part of suramin molecule would clash with nascent RNA product strand (g). The template (golden) and product (black) RNA strands are modeled based on the structure of rotavirus polymerase with in situ elongation conformation (PDB 6OGZ).

Source data

Extended Data Fig. 12 Structural comparison of EBOV L-VP35-suramin complex with SARS-CoV-2 RdRp-suramin and Murine Noroviruses (MNV) RdRp-suramin complex.

(a-b) Superimposition of the EBOV L-VP35-suramin with SARS-CoV-2 RdRp-suramin complex (PDB 7D4F) and MNV RdRp-suramin complex (PDB 3UR0) was performed based on the RdRp domain. A close-up view of suramin within the catalytic chamber in the SARS-CoV-2 RdRp-suramin complex (c), EBOV L-VP35-suramin complex (d) and MNV RdRp-suramin complex (e).

Supplementary information

Supplementary Information

This file contains Supplementary Figs. 1-6 and Supplementary Tables 1-3. Supplementary Figs. 1-5 are the cryo-EM analysis processes of structures determined in this paper and Supplementary Fig. 6 shows the uncropped gels, western blotting images and autoradiographs used for preparing Extended Data Fig. 1, 4 and 11. Supplementary Tables 1-2 contain cryo-EM data processing and refinement statistics for L-VP35 complex in two states and L-VP35-suramin complex and Supplementary Table 3 shows interactions between EBOV L protein and VP35.

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Yuan, B., Peng, Q., Cheng, J. et al. Structure of the Ebola virus polymerase complex. Nature 610, 394–401 (2022). https://doi.org/10.1038/s41586-022-05271-2

- Received: 21 January 2022

- Accepted: 24 August 2022

- Published: 28 September 2022

- Issue Date: 13 October 2022

- DOI: https://doi.org/10.1038/s41586-022-05271-2

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Article Open Access Published: 21 September 2022

Structural basis for directional chitin biosynthesis

Wei Chen,

Peng Cao,

Yuansheng Liu,

Ailing Yu,

Dong Wang,

Lei Chen,

Rajamanikandan Sundarraj,

Zhiguang Yuchi,

Yong Gong

Hans Merzendorfer &

…

Qing Yang

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Nature volume 610, pages 402–408 (2022)

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Abstract

Chitin, the most abundant aminopolysaccharide in nature, is an extracellular polymer consisting of N-acetylglucosamine (GlcNAc) units[1]. The key reactions of chitin biosynthesis are catalysed by chitin synthase2,3,4, a membrane-integrated glycosyltransferase that transfers GlcNAc from UDP-GlcNAc to a growing chitin chain. However, the precise mechanism of this process has yet to be elucidated. Here we report five cryo-electron microscopy structures of a chitin synthase from the devastating soybean root rot pathogenic oomycete Phytophthora sojae (PsChs1). They represent the apo, GlcNAc-bound, nascent chitin oligomer-bound, UDP-bound (post-synthesis) and chitin synthase inhibitor nikkomycin Z-bound states of the enzyme, providing detailed views into the multiple steps of chitin biosynthesis and its competitive inhibition. The structures reveal the chitin synthesis reaction chamber that has the substrate-binding site, the catalytic centre and the entrance to the polymer-translocating channel that allows the product polymer to be discharged. This arrangement reflects consecutive key events in chitin biosynthesis from UDP-GlcNAc binding and polymer elongation to the release of the product. We identified a swinging loop within the chitin-translocating channel, which acts as a ‘gate lock’ that prevents the substrate from leaving while directing the product polymer into the translocating channel for discharge to the extracellular side of the cell membrane. This work reveals the directional multistep mechanism of chitin biosynthesis and provides a structural basis for inhibition of chitin synthesis.

Main

The biosynthesis of chitin is essential for the survival and reproduction of various organisms from different taxonomic groups, such as life-threatening fungi, agriculture-devastating oomycetes and insect pests. Therefore, it provides a preferred target for discovering antifungal agents or pesticides4,5,6.

The core of the chitin biosynthetic machinery is an integral membrane enzyme named chitin synthase (CHS) (EC 2.4.1.16)[3]. CHS belongs to glycosyltransferase family 2 (GT2), a large enzyme family that includes cellulose, alginate and hyaluronan synthases7,8. Chitin synthesis is proposed to involve three major steps: (1) the processive addition of GlcNAc from UDP-GlcNAc (donor substrate) to the terminal C4-hydroxyl group of the nascent chitin chain (acceptor substrate) by the catalytic domain of the enzyme facing the cytoplasmic side; (2) the release of the nascent chain to the extracellular space through a transmembrane channel within the enzyme; and (3) the spontaneous assembly of released nascent chains into nanofibrils[3]. CHS controls the first two steps of this process but may also participate in the formation of fibrils. Despite differing in the number of transmembrane helices and organization of the respective cytosolic domains, CHSs from various species share a conserved catalytic domain9,10 (Extended Data Fig. 1), thus allowing the development of competitive inhibitors with broad-spectrum activities. Because chitin is absent in plants and mammals, CHS might constitute one of the safest among the 30 currently used insecticidal and fungicidal targets for the control of fungal pathogens and insect pests[11]. Among the fungicidal agents that target CHS is nikkomycin Z (NikZ), which consists of a pyrimidine-nucleoside peptide backbone and is a first-generation broad-spectrum CHS inhibitor currently in phase II clinical trials12,13.

P. sojae is a pathogen that causes soybean (Glycine max L.) root and stem rot, which results in economic losses of more than US$1 billion per year14,15. Knockout of the P. sojae chitin synthase PsChs1 impairs mycelial growth, sporangial production and zoospore release, and thus greatly reduces the virulence of P. sojae[16]. PsChs1 serves as both an excellent antifungal target and a model system for CHS research. In this study, we report five cryo-electron microscopy (cryo-EM) structural snapshots of PsChs1, which provide not only a mechanistic understanding of chitin biosynthesis at the atomic level but also a structural basis for the rational design of CHS-targeting inhibitors.

Enzymatic activity

PsChs1 was mainly purified as a dimer in solution (Extended Data Fig. 2a–c). Activity of PsChs1 clearly depends on specific divalent ions, and EDTA completely blocked enzyme activity (Extended Data Fig. 2d). The addition of GlcNAc together with divalent ions significantly increased the activity of PsChs1 (Extended Data Fig. 2d). Enzyme kinetics revealed a Hill coefficient of 1, indicating that GlcNAc is not a positive effector of PsChs1 (Extended Data Fig. 2e). In line with previous data, which has shown that yeast chitin synthase Chs2 can use 2-acylamido analogues of GlcNAc as acceptors of GlcNAc derived from UDP-GlcNAc[17], this finding suggests that free GlcNAc may act as an acceptor to prime the reaction. Of note, the addition of (GlcNAc)2–5 did not affect enzyme activity (Extended Data Fig. 2f).

The sugar polymer produced by PsChs1 could be degraded by Ostrinia furnacalis Chi-h, a chitinase that specifically hydrolyses chitin, confirming that the product is chitin (Extended Data Fig. 2d). Using a scanning electron microscope, we observed that the synthesized chitin appeared as a fibrous material, and the amount of chitin fibre increased as the reaction time progressed (Extended Data Fig. 2g). Under a confocal laser scanning microscope, chitin was specifically detected by wheat germ agglutinin coupled to the fluorophore fluorescein isothiocyanate. It appeared as aggregated fibrillar material at high magnification by a scanning electron microscope, but as a ‘roundish’ soft material at lower magnification by a confocal laser scanning microscope (Extended Data Fig. 2h). The isomorphic type of the synthesized chitin was determined by X-ray diffraction and attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy (Extended Data Fig. 2i). As indicated in ATR-FTIR spectroscopy, the synthesized chitin showed the same adsorption spectrum as shrimp α-chitin, where the characteristic C=O stretching (amide I) band at 1,620–1,670 cm−1 appeared as a doublet, which was clearly distinguishable from a singlet that appeared in the crystalline β-chitin18,19,20. The ATR-FTIR spectra are in line with the X-ray diffraction results. The four sharp diffraction peaks of synthesized chitin observed at 9.3°, 12.7°, 19.3° and 26.4°, which corresponded to the 020, 021, 110 and 013 planes, respectively, are typical crystal patterns of α-chitin21,22. Therefore, the polymer synthesized by PsChs1 is α-chitin, which is consistent with data in the literature that have demonstrated that chitin formed in oomycete species is of the α-type[23].

Architecture of PsChs1

The different cryo-EM structures of PsChs1 were reconstructed by imposing a C2 symmetry and reached overall resolutions of 3.1 Å (UDP bound), 3.2 Å (NikZ bound), 3.3 Å (apo and UDP-GlcNAc bound) and 3.9 Å (UDP/(GlcNAc)3bound) (Extended Data Table 1 and Extended Data Figs. 3–7). The EM maps were of sufficient quality to allow de novo building of residues 40–860, with a disordered region of residues 743–758, in all five structures. The donor substrate-bound structure shows an additional N-terminal region from residues 23–39 (Extended Data Fig. 4e). All the structures include the N-terminal domain (NTD), the glycosyltransferase (GT) domain and all α-helices of the C-terminal transmembrane (TM) domain (Fig. 1a–c). The TM region comprises a cluster of six TM helices (TM1–6) that reside on top of three amphipathic interface helices (IF1–3) located at the boundary between the membrane and the cytosol (Fig. 1c and Extended Data Fig. 8a). Although TM topology algorithms predict IF3 to form a TM helix (Extended Data Fig. 8b), our structures revealed that it actually forms a bent helix parallel to the membrane, as suggested previously for Chs3 in yeast[24]. TM5 is an extraordinarily long helix (approximately 80 Å in length) that spans from the TM domain to the cytosolic region and projects into the opposite protomer like a sword (Fig. 1b).

Fig. 1: The apo PsChs1 structure.

a,b, The structure of the PsChs1 dimer is shown in surface (a) and ribbon (b) representations as viewed from the extracellular side of the membrane (top view), within the plane of the membrane (side view), or the cytoplasmic side (bottom view). The approximate position of the membrane is marked with grey shading, and the presumed chitin-translocating channel is marked with arrows. The TM helices, GT domain, IF helices, LG subdomain, MIT subdomain and SP subdomain of one protomer are coloured blue, violet, pink, green, purple and light grey, respectively. The other protomer is coloured yellow. The unresolved region (residues 743–758) is shown as dashed lines. c, Domain architecture and ribbon representation of a PsChs1 protomer in two orientations. d, Sliced-surface view of the presumed chitin-translocating channel. Pro454 and Trp539 are at the channel entrance and are highlighted in red and pink, respectively. e, The reaction chamber of PsChs1 (left) and the conserved motifs that constitute the reaction chamber (right) are shown. The uridine-binding tub, catalytic cave and entrance of the chitin-translocating channel are coloured grey, blue and red, respectively. Residues that are important for enzyme activity are underlined and represented as sticks.

The NTD comprises three subdomains: a microtubule interacting and trafficking (MIT) subdomain, a linkage (LG) subdomain and a swapping (SP) subdomain (Fig. 1c). The MIT subdomain is found in many types of proteins from a wide range of eukaryotes25,26. However, within the CHS family, the MIT subdomain has been identified only in those from some oomycetes[10]. The PsChs1 MIT subdomain comprises three helices that form an antiparallel helix bundle (Extended Data Fig. 8c). An overlay of the PsChs1 MIT structure with several other known MIT domains, including those from human, mouse, the oomycete Saprolegnia monoica and the fungus Saccharolobus solfataricus, reveals significant similarities in the overall topology (Extended Data Fig. 8e). Two conserved noncovalent interhelical interactions stabilize the structure of the MIT subdomain, including an alanine zipper connecting helices α1 and α2 (Extended Data Fig. 8d) and a canonical coiled coil connecting helices α2 and α3. The oomycete MIT subdomain has been previously proposed to be involved in CHS trafficking and targeting to the hyphal tip or in endocytic recycling[27].

The PsChs1 GT domain, which contains the catalytic machinery, adopts a classical GT-A fold consisting of an eight-stranded β-sheet surrounded by seven α-helices, which tether to the TM region via IF1 through IF3 (Fig. 1c). IF1 leans against TM4 and TM6. IF2 includes the Q(Q/R)XRW motif, which is also conserved among cellulose and hyaluronan synthases28,29. IF3 sits on top of IF1 and IF2 and interacts extensively with TM1 through TM4 to stabilize them at the cytosol–lipid interface (Extended Data Fig. 8a).

Reaction chamber

The reaction chamber in the GT domain includes a tub for substrate binding and a cave for synthetic reactions (Fig. 1d,e). The tub is formed by three previously reported motifs, TMYNE (residues 237–241), DGR (residues 291–293) and DVGT (residues 382–385)[10], along with a newly identified KASKL motif (residues 355–359) (Fig. 1e and Extended Data Fig. 1b). The KASKL motif forms a wall of the tub that separates the tub from the catalytic centre. Specifically, mutations of Asp291 and Asp382, the first two aspartic acids of the signature ‘D,D,D,Q(Q/R)XRW’ motif, which is conserved among processive β-glycosyltransferases, completely abolished enzyme activity (Extended Data Fig. 2j).

The cave next to the tub is flanked by Glu495 and the catalytic residue Asp496 of the conserved EDR motif (residues 495–497) on one side and by Leu359 of the KASKL motif on the other side (Fig. 1e). The reaction catalysed by CHS is supposed to occur through an SN2 displacement mechanism, with the deprotonated Asp496 acting as the general base that facilitates nucleophilic attack on the anomeric carbon[7]. Consistent with this hypothesis, mutating Asp496 to alanine abolished the catalytic activity and mutating it to asparagine strongly reduced the activity. In addition, mutating Glu495 or Leu359 to alanine resulted in approximately 95% and roughly 70% loss of activity, respectively, suggesting the importance of these residues in catalysis (Extended Data Fig. 2j).

The conserved SWG motif (residues 741–743) is located at a flexible cytoplasmic loop that connects IF3 and TM5. However, the EM map of this loop was weak. This motif might be associated with substrate entrance, as it is located close to the reaction chamber. This possibility is supported by the fact that the mutation of Trp742 to alanine abolished enzyme activity (Extended Data Fig. 2j).

Chitin-translocating channel

The presumed chitin-translocating channel is located in the TM region and connects the extracellular side of the membrane with the intracellular reaction chamber. The channel is approximately 8–12 Å in width and approximately 40 Å in length (Fig. 1d and Extended Data Fig. 9c). As GlcNAc is approximately 8 Å in width and approximately 5.5 Å in length (Extended Data Fig. 9b), this channel should be able to accommodate a chitin chain containing at least seven GlcNAc units. The entrance of this channel is formed by the VLPGA (residues 452–456), QHFEY (residues 429–433) and QRKRW (residues 535–539) motifs (Fig. 1e). The QHFEY and QRKRW motifs, which belong to IF1 and IF2, respectively, flank the two sides of the channel and interact with each other through a salt bridge formed between Glu432 and Arg536 and hydrophobic interactions between Tyr433 and Trp539. By contrast, the VLPGA motif is located on a flexible loop, which we have demonstrated to serve as a gate lock to control access to the chitin-translocating channel (see the following sections for more discussions). The mutation of residues Glu432, Tyr433, Val452, Pro454, Arg536 and Trp539 in those motifs to alanine greatly impaired enzyme activity (Extended Data Fig. 2j).

The dimeric PsChs1

In a side view, the PsChs1 dimer resembles a hexagonally shaped snowflake (Fig. 1a,b). The two PsChs1 protomers are related to each other by a twofold rotation around an axis perpendicular to the membrane. The PsChs1 dimer buries a large interaction interface, which is stabilized by both the NTD and the TM regions (Fig. 2a).

Fig. 2: Interface interactions within a PsChs1 dimer.

a, Overall view of the dimerization interface of PsChs1 depicted as a ribbon diagram. One protomer (PsChs1α) is shown in yellow and the other (PsChs1β) is shown in blue. The dimerization interface is shown in surface view. b–e, The four regions involved in interface interactions are highlighted: PsChs1α TM2 with PsChs1β TM5 (b), PsChs1α GT domain, MIT subdomain and SP subdomain with PsChs1β TM5 (c), PsChs1α SP subdomain with PsChs1β MIT subdomain (d) and PsChs1α SP subdomain with PsChs1β LG subdomain (e). The side chains of residues that are critical for dimerization are shown as sticks. The hydrogen bonds are labelled with black dashes.

In the TM region, TM2 of one protomer (PsChs1α) forms numerous hydrophobic interactions with TM5 of the other protomer (PsChs1β), and the loop between TM2 and TM3 of PsChs1α also interacts with TM5 of PsChs1β (Fig. 2b). The cytosolic part of PsChs1β TM5 in turn interacts with the SP, MIT and GT domains of PsChs1α (Fig. 2c). Two NTDs from the two PsChs1 protomers are wrapped by each other to form a symmetric and domain-swapped interface (Fig. 2a). Helix α1 of the SP subdomain is an amphipathic helix with its hydrophobic side facing the MIT subdomain of the opposite protomer, thereby forming hydrophobic interactions with helices α2 and α3 of the MIT subdomain (Fig. 2d). The β1 sheet of the PsChs1α SP subdomain lies parallel to the β1 sheet of the PsChs1β LG subdomain, forming hydrogen bonds and H–π and cation–π stacking interactions with each other (Fig. 2e). Truncation of the SP subdomain (ΔSP) impaired the formation of the PsChs1 dimer and resulted in protein aggregation, but had little effect on enzyme activity (Extended Data Fig. 2l).

The structures of dimeric PsChs1 in complex with the donor substrate or the nascent chitin oligomer suggest that each protomer functions independently but in parallel, that is, two chitin chains are produced concurrently by the two subunits of the CHS homodimer so that these chains assemble with their reducing ends pointing in the same direction. The plot of enzyme activity versus donor substrate concentration fits well to the Michaelis—Menten equation, which gives a Hill coefficient of 1, supporting the absence of cooperative effects between the two PsChs1 protomers (Extended Data Fig. 2e).

UDP-GlcNAc-bound PsChs1

To obtain the donor substrate UDP-GlcNAc-bound structure, we mutated the catalytically important residue Glu495 to alanine, which allows the enzyme to trap the substrate in its substrate-binding site (Fig. 3a, Extended Data Table 1 and Extended Data Fig. 4). The role of Glu495 is intriguing. In the apo structure, it adopts a conformation different from those in the UDP-bound and UDP/(GlcNAc)3-bound states, with its carboxyl group pointing to the substrate-binding site, which may facilitate the recognition and correct positioning of the donor substrate at the substrate-binding site (Fig. 3b,e). A few previously published substrate-bound GT-A glycosyltransferase structures have shown strong interactions of this glutamate with the sugar moiety of the donor substrate (Extended Data Fig. 8h), supporting that this residue could be important for the recognition and binding of the donor sugar to the substrate-binding site.

Fig. 3: Interactions between PsChs1 and ligands.

a, Sliced-surface view (right) of the UDP-GlcNAc-binding site (right) and detailed interactions between UDP-GlcNAc and PsChs1 (left). The residues involved in interactions are labelled and shown as sticks. The hydrogen bonds are labelled as black dashed lines. The interaction between the manganese ion and the ligand is labelled with red dashed lines. The density of the ligand is shown as a grey mesh. b, Superimposition of the structures of apo-PsChs1 (light grey), UDP-GlcNAc-bound PsChs1 (yellow) and UDP/(GlcNAc)3-bound PsChs1 (pink) revealed conformational changes in the swinging loop and catalytic residues. c, Sliced-surface view of the two channels of PsChs1 in the UDP/(GlcNAc)3-bound state (middle). The density of UDP/(GlcNAc)3 (left) and detailed interactions with PsChs1 (right) are also shown. The hydrogen bonds are labelled as black dashed lines. d, Sliced-surface view (right) of the UDP-binding site and detailed interactions between UDP and PsChs1 (left). The hydrogen bonds are labelled as black dashed lines. e, Superimposition of the structures of UDP/(GlcNAc)3-bound PsChs1 (pink) and UDP-bound PsChs1 (cyan).

The uridine moiety of UDP-GlcNAc is fixed within the tub of the reaction chamber, which consists of a loop region from Thr237 to Glu241 in the GT domain of the enzyme, through a series of interactions with the residues in this region (Fig. 3a). The GlcNAc moiety appears to be solvent-exposed and oriented away from the catalytic site. Superposition of UDP-GlcNAc-bound PsChs1 with apo-PsChs1 showed no significant local or global structural differences (root mean square deviation of 0.54 Å for 693 Cα atoms). An N-terminal sequence (residues 23–39), unresolved in the PsChs1 apo structure, appeared as an α-helix in the UDP-GlcNAc complex (Extended Data Fig. 4e). This helix extends into the opposite protomer and interacts with the LG subdomain through hydrogen bonds (Extended Data Fig. 8g). Structural comparison between the apo and UDP-GlcNAc-bound states revealed that residues Pro38, Leu39 and Pro40 are in different locations, indicating that the N-terminal extension of the SP subdomain in the UDP-GlcNAc-bound state adopts a different conformation (Extended Data Fig. 8f).

UDP/(GlcNAc)3-bound PsChs1

The structure of dimeric PsChs1 bound with a pre-translocating (GlcNAc)3 and a UDP in each PsChs1 protomer was obtained (Fig. 3c, Extended Data Table 1 and Extended Data Fig. 5). This reflects the post-glycosyl transfer state during chitin biosynthesis. (GlcNAc)3 is trapped within the chitin-translocating channel through a series of interactions, including hydrogen bonds and hydrophobic interactions (Fig. 3c). The second GlcNAc moiety resides at the entrance of the channel, sandwiched between Pro454 and Trp539. The newly added GlcNAc moiety extends outside the channel, representing a pre-translocation state. As in the substrate-bound PsChs1, the uridine moiety of UDP is fixed in the uridine-binding tub. Unlike the apo structure, however, an approximately 7 Å retraction of the VLPGA loop (452–458) and an approximately 100° flip of the side chain of Pro454 are observed in the UDP/(GlcNAc)3-bound structure (Fig. 3b, left panel). As a result, the entrance of the chitin-translocating channel is opened. In this state, the side chain of the functionally important residue Glu495 is oriented away from the substrate, similar to that in the UDP-bound or UDP/(GlcNAc)3-bound states (Fig. 3b, right panel), representing a conformation in which the transfer of a sugar moiety from the donor substrate to the acceptor has just finished. Arg538 of the QRKRW motif is at the entrance of the reaction chamber, and its side chain is highly flexible and adopts different rotamers in apo and substrate-bound states (Fig. 3b, right panel). It forms charge interactions with the diphosphate group of either UDP-GlcNAc or UDP in all the substrate-bound, product-bound and UDP-bound structures. This suggests the role of Arg538 as a guide that directs the donor substrate to enter the reaction chamber and ensures the correct positioning of the substrate for chitin synthesis.

PsChs1 contains conserved active site residues similar to those previously characterized in GT2 enzymes (in particular, cellulose synthases), such as the RsBcsA monomer from Rhodobacter sphaeroides and PttCesA8 homotrimer from Populus tremula × tremuloides29,30. However, PsChs1 has a larger catalytic chamber and a wider translocating channel with smaller surrounding residues than RsBcsA, which allows PsChs1 to accommodate the bulky N-acetyl group of GlcNAc (Extended Data Fig. 9b,c). PsChs1 residues Thr385 and Leu412, which flank the substrate-binding tub and reside immediately below the entrance of the chitin-translocating channel, are replaced by bulky histidine residues in RsBcsA. Another small residue, Ser357, which belongs to the KASKL motif that borders the substrate-binding tub in PsChs1, is replaced by His224 in RsBcsA (Extended Data Fig. 9a,e). Mutation of these residues in PsChs1 to histidine decreases the activity of this enzyme (Extended Data Fig. 2j). Structural comparison shows that PsChs1 has an α-helix (residues 496–504) and a partially disordered loop (residues 736–759) located in positions similar to those of the ‘finger helix’ and ‘gating loop’ of RsBcsA (Extended Data Fig. 9a), which undergo large conformational changes during the translocation of nascent cellulose in RsBcsA[31]. However, our structures showed that neither binding to the donor substrate nor binding to the nascent chitin chain induces significant conformational changes in the finger helix of PsChs1. The corresponding gating loop in PsChs1 is only partially resolved, which makes it difficult to discern any conformational changes. Whether these two structural elements have comparable functions in CHSs requires further investigation.

Mn2+ preference for chitin synthesis

In the UDP/(GlcNAc)3-bound structure, the Mn2+ ion forms coordinate bonds with the β-phosphate group of the leaving group UDP with a bond length of 2.2–2.3 Å (Fig. 3c), which is consistent with the range of coordinate bonds between a first transition metal and a nitrogen-containing or oxygen-containing compound. The Mn2+ ion in the UDP-GlcNAc-bound structure only forms charge interaction with the phosphate groups, with a bond distance of more than 3.5 Å, which is much weaker than a coordinate bond. This may represent the different divalent ion-binding modes in different chitin biosynthesis states (post-catalysis and pre-catalysis), and confirms that the metal ion has an important role in assisting the transfer of the sugar moiety from the donor substrate to a receptor in CHSs and other glycosyltransferases[7].

Mn2+ ion is a transition metal with empty 3d electron orbitals and a strong Lewis acid for coordinate bond formation with a phosphate group. Mg2+ ion, however, is an alkaline earth metal, which has no empty d orbital, and a weak Lewis acid. Therefore, Mg2+ ion can only form charge interactions with a phosphate group, which are weaker than coordinate bonds. The ability of a Mg2+ ion in assisting catalysed chitin biosynthesis, therefore, is not as strong as that of a Mn2+ ion (Extended Data Fig. 2d).

A swinging loop directs chitin synthesis

The UDP-bound dimeric PsChs1 structure represents its nascent chitin-released state (Fig. 3d, Extended Data Table 1 and Extended Data Fig. 6). Compared with PsChs1 in complex with pre-translocating (GlcNAc)3 and UDP, the β-phosphate group in the UDP-bound structure is flipped by approximately 55° towards the centre of the reaction chamber, representing a post-synthesis state of the enzyme (Fig. 3c–e). The VLPGA loop (residues 452–456) in the UDP-bound structure moves 7 Å back to its location in the apo and UDP-GlcNAc-bound states, which is different from that in the UDP/(GlcNAc)3-bound state (Fig. 3b,e). This loop, specifically Pro454, not only functions as a gate to the channel but also stabilizes the second sugar of the nascent chitin oligomer. Mutating Pro454 to alanine abolished the enzyme activity (Extended Data Fig. 2j). The position of the VLPGA loop in different states of the enzyme suggests that this loop serves as a ‘gate lock’, which prevents the donor substrate from leaving before being linked to a growing chitin oligomer. In addition, it directs the head of the product polymer through the exit of the channel towards the extracellular side of the cell membrane.

The apo structure of PsChs1 is the first example of a membrane-integrated processive glycosyltransferase with an apparently continuous but empty transmembrane channel, providing a real off-state of the enzyme before chitin synthesis is initiated. Molecular dynamics simulations of the apo and UDP/(GlcNAc)3-bound states of PsChs1 suggested that the VLPGA motif also acts as a permeability barrier that prevents water flux across the membrane in the apo state (Extended Data Fig. 9f,g and Supplementary Videos 1 and 2).

This swinging loop appears to be highly conserved in CHSs because all chitin synthases contain a similar loop in the channel with the signature Pro454 residue (Extended Data Fig. 1b). It is interesting that RsBcsA contains an ‘FFCGS’ motif at the corresponding location (Extended Data Fig. 9d,e). Although its sequence shows some level of conservation with the VLPGA motif of CHSs, it is noticeable that the cysteine in this motif is different from the signature proline in the VLPGA motif, which can adopt specific conformations different from those of cysteine or other non-proline residues. The FFCGS motif in BcsA, therefore, may not function in the same mode as the VLPGA gate lock loop in CHSs[29].

Chitin biosynthesis inhibition by NikZ

The cryo-EM structure of PsChs1 in complex with NikZ, which competitively inhibits PsChs1 activity by a inhibition constant (Ki) value of 151.1 ± 4.8 μM, was resolved (Fig. 4, Extended Data Table 1 and Extended Data Fig. 7). As expected for a substrate analogue, NikZ binds to the uridine-binding tub through its uridine moiety in a manner identical to that of UDP-GlcNAc, thus blocking the binding of a donor substrate to this position (Figs. 3a and 4). Moreover, the hydroxypyridine moiety of NikZ occupies a large part of the reaction chamber as well as the entrance of the translocating channel, thereby further blocking the donor substrate from entering the reaction chamber for chitin biosynthesis (Fig. 4b). The hydroxypyridine ring is positioned at the entrance of the translocating channel and mimics a pre-translocating sugar unit to induce the opening of the gate lock-controlled channel. It hydrophobically interacts with the residues around the channel entrance, particularly Tyr433 from the QHFEY motif, Val452 and Pro454 from the VLPGA gate lock loop, and Trp539 from the QRKRW motif, as well as Leu412 of the β-strand near the gate lock loop. Mutation of each of these residues not only reduced the inhibitory activity of NikZ but also significantly impaired enzyme activity (Extended Data Fig. 2j,k).

Fig. 4: Inhibition of PsChs1 by NikZ.

a, Chemical structure and EM density of NikZ. b, Sliced-surface view (left) of the NikZ-binding site and detailed interactions between NikZ and PsChs1 (right). The hydrogen bonds are labelled as black dashed lines.

Discussion

PsChs1 exhibits a unique quaternary structure composed of two mirror-imaged protomers that are assembled into an N-terminally intertwined dimer, suggesting a parallel and directional mode of chitin biosynthesis. The formation of dimeric and oligomeric CHS complexes has been previously reported in vitro and in vivo24,32,33 and is in agreement with the structural data of PsChs1 in this study and the recently published CaChs2 from Candida albicans[34]. Both dimeric PsChs1 and CaChs2 produce α-chitin. Sequence alignment between PsChs1 and CaChs2 or other CHSs indicates that the dimeric interface residues are highly variable (Extended Data Fig. 1a) and that the N-terminal SP and MIT subdomains, which are important for PsChs1 dimerization, are present only in oomycete CHSs[10]. These observations suggest that the dimerization mechanism shown in our structures may be unique to CHSs from oomycetes and some fungi, and different oligomerization strategies may have evolved for CHSs from other taxa. Nonetheless, CHS oligomerization appears to be important for the formation of chitin fibrils because the well-ordered assembly of single catalytic units is related to proper alignment of nascent sugar chains before their coalescence and the formation of chitin nanofibrils[3].

We noticed that chitin biosynthesis was initiated in the absence of free GlcNAc (Extended Data Fig. 2d,f), which suggested that free GlcNAc is not required for biosynthesis and that the first molecule that binds to the active site in the absence of free GlcNAc is UDP-GlcNAc. The spatial restrictions in the substrate-binding pocket together with the nonreducing end-chain elongation mechanism of CHS[35] exclude the possibility that two UDP-GlcNAc molecules bind simultaneously to the active site to initiate the reaction. Although GlcNAc is not a positive effector for PsChs1 (Extended Data Fig. 2e), the addition of free GlcNAc instead of chito-oligosaccharides could speed up the synthesis (Extended Data Fig. 2f). These results together suggest that GlcNAc is an acceptor for the initial step of chitin biosynthesis. Similarly, a ‘self-priming’ mechanism has been proposed for yeast chitin synthase[36] and cellulose synthase37,38 and has very recently been suggested for a viral homologue of hyaluronan synthase[39].

Our structural data and previously published cellulose synthase structures have shown that each promoter in a homodimeric or oligomeric enzyme synthesizes and translocates a single sugar chain independently and in parallel. Recent structural insights into cellulose biosynthesis explained that the 180° alternating arrangement of the sugar units can be achieved by a simple rotation of the terminal sugar unit around the glycosidic bond, eliminating the need for a dual substrate-binding site29,31.

It is clear that chitin polymer elongation is achieved by repeated steps of adding a sugar moiety from the donor substrate to the acceptor substrate through an SN2 displacement reaction. The elongated chitin polymer is supposed to be discharged from the head end through the translocating channel to the extracellular side of the transmembrane domain. Our PsChs1 structures revealed that the entrance to this channel is blocked by the swinging loop, specifically residue Pro454, in the apo and substrate-bound states, but it is open in the (GlcNAc)3-bound state. Thus, the swinging (VLPGA) loop may function as a gate lock that facilitates the directed transport of nascent chitin across the cell membrane (Fig. 3b,e). With the gate lock loop being closed, the space around the catalytic centre and the channel entrance allows only a single unit GlcNAc acceptor to be positioned. A large conformational change is needed in the VLPGA loop to open the gate lock and to allow access of the nascent disaccharide to the channel when a GlcNAc unit joins the acceptor GlcNAc (exogenously added or derived from UDP-GlcNAc) to form a GlcNAc dimer. The gate lock is supposed to be kept open during chain elongation until biosynthesis is completed and the chitin chain is discharged.

Putting together our five PsChs1 structures and biochemical data, we propose a model of chitin biosynthesis, as shown in Fig. 5. Furthermore, the synthesized chitin chains might be aligned in parallel along the a axis (perpendicular to the pyranose ring) through the hydrophobic surface of their pyranose rings to form chitin sheets, which may be self-assembled in an antiparallel manner along the b axis (parallel to the pyranose ring) to finally form α-chitin (Extended Data Fig. 9h), the more stable chitin allomorph[40]. The dimeric PsChs1 is consistent with the formation of chitin sheets by the parallel arrangement of single chains.

Fig. 5: A model of chitin biosynthesis.

a, In the apo enzyme, the entrance of the chitin-translocating channel is blocked by the gate lock loop. b, Chitin synthesis starts when the donor substrate UDP-GlcNAc enters the reaction chamber and resides in the uridine-binding tub. The white arrows indicate the moving direction of the GlcNAc moiety. c,d, A GlcNAc unit produced from UDP-GlcNAc hydrolysis (self-priming mechanism) or an exogenously added GlcNAc is proposed to act as an acceptor to initiate chitin biosynthesis. This process should include the following steps: Glu495 binds and stabilizes the donor substrate, and the catalytic residue Asp496 interacts with the acceptor GlcNAc and assists the nucleophilic attack on a donor substrate; a divalent metal ion binds to the diphosphate group of the donor substrate and helps in the release of the UDP moiety to complete the formation of a disaccharide. It is likely that disaccharide formation induces conformational changes in the gate lock loop and the flipping of Pro454, thereby opening the entrance and allowing access of the nascent sugar chain to the translocating channel. When the addition of a sugar unit is completed, the catalytic Glu495 and the metal ion-bound diphosphate of the leaving UDP rotate away from the reaction centre (d). The white arrows in c and d indicate the moving directions of the products. e, After many rounds of reaction (dashed arrow), the enzyme adopts a post-synthesis state and a chitooligosaccharide product is discharged: the translocating channel is closed by the gate lock, the leaving group UDP sitting at the substrate-binding site needs to be replaced by a new donor substrate, and the positions of Glu495 and the catalytic residue Asp496 are restored to their pre-synthesis states for a new cycle of chitin biosynthesis to be initiated.

Methods

Protein expression and purification

The codon-optimized cDNA of PsChs1 was synthesized (GenScript) and ligated into the pcDNA3.1 vector in C-terminal fusion with tandem twin Strep tag and Flag tag. For PsChs1 overexpression, HEK293F cells (Invitrogen) were cultured in SMM 293T-II medium (Sino Biological Inc.) at 37 °C in the presence of 5% CO_2. When the cell density reached 1.5–2.0 × 10^6 cells per ml, a mixture of recombinant plasmids and polyethylenimine 40000 (Polysciences) at a ratio of 1:3 was added to the cell culture to transiently overexpress PsChs1.

Cells were harvested by centrifugation at 1,000 g after transfection for 48 h and resuspended in lysis buffer (25 mM Tris-HCl pH 8.0 and 150 mM NaCl) supplemented with protease inhibitor cocktail (Topscience) including 2.6 μg ml–1 aprotinin, 1.4 μg ml–1 pepstatin, 10 μg ml–1 leupeptin hemisulfate and 1 mM PMSF. Cells were lysed through a chilled high-pressure homogenizer (ATS) and centrifuged at 16,000 g for 30 min at 4 °C. The supernatant was ultracentrifuged at 150,000 g for 1 h at 4 °C to collect the membranes. The membranes were solubilized in lysis buffer supplemented with 1% digitonin (Biosynth) and the protease inhibitor cocktail. After incubation at 4 °C for 2 h, the solution was ultracentrifuged at 150,000 g for 30 min at 4 °C, and the supernatant was loaded onto a preequilibrated Strep-TactinXT 4Flow cartridge (IBA Lifesciences). The column was washed with buffer W containing 25 mM Tris-HCl pH 8.0, 150 mM NaCl, 0.05% digitonin and the protease inhibitor cocktail. The target proteins were eluted with buffer W supplemented with 50 mM biotin. The eluate was then loaded onto anti-Flag M2 affinity resin (Sigma). The target protein was eluted with buffer W supplemented with 200 mg ml–1 Flag peptide. The sample was concentrated using a 100-kDa cut-off Centricon (Millipore) and subjected to size-exclusion chromatography on a Superdex 200 Increase 10/300 GL column (GE Healthcare) equilibrated with buffer W. The peak fractions were analysed by SDS—PAGE. The expression and purification procedures for mutated or truncated recombinant proteins were the same as those conducted with the native PsChs1. The expression of mutated or truncated recombinant proteins was confirmed by western blot analysis using an anti-Flag M2 antibody (1:1,000 dilution; Sigma). For gel source data, see Supplementary Fig. 1.

Activity assays

The chitin synthase activity assay was performed according to published procedures with slight modifications[41]. The microtitre plates were coated with wheat germ agglutinin (50 μg ml–1; Sigma) and blocked with bovine serum albumin blocking buffer. The plates were stored at −20 °C for further assays. The plates were emptied by centrifugation before usage. Next, 50 μl of the reaction mixture (1 mM UDP-GlcNAc, 50 mM Tris-HCl buffer pH 7.5 and 150 mM NaCl) was added to each well followed by 50 μl of enzyme suspension to a final volume of 100 μl. After incubation at 30 °C for 1 h, the plates were emptied and washed three times. Then, 100 μl of wheat germ agglutinin–horseradish peroxidase (0.5 μg ml–1; Sigma) was added, and the mixture was incubated for 30 min at 30 °C. The plate was emptied through centrifugation and washed three times. Of peroxidase substrate reagent, 100 μl was added, and absorbance at 652 nm was immediately determined for 5 min. The GlcNAc content and chitin synthase activity after each treatment were calculated by using a standard curve, which was prepared following the same procedure as previously described[42]. The specific enzyme activity was expressed as nmol GlcNAc per µg protein per hour. Each test was repeated three times.

For measuring enzyme kinetics, the reaction mixtures containing 1 µg of PsChs1, 4.5 µM to 10 mM UDP-GlcNAc, 50 mM Tris-HCl buffer (pH 7.5) and 150 mM NaCl were incubated in a final volume of 100 µl in the absence or presence of 1 mM GlcNAc. The data were analysed by GraphPad Prism software.

Characterization of synthesized chitin

To visualize synthesized chitin in vitro through a scanning electron microscope, 10 µl of the reaction sample was applied onto a silicon support (5 × 5 mm) and dried at room temperature to fix the sample. The silicon supports containing the samples were washed with distilled water five times to remove salt and sputter-coated with gold-palladium. Samples were observed using a Regulus 8100 microscope (Hitachi) at 10 kV.

For confocal laser scanning microscopy, reaction samples were incubated with wheat germ agglutinin coupled to fluorescein isothiocyanate (0.02 mg ml–1; Genetex) for 15 min. After washing five times with distilled water, the samples were imaged for fluorescein isothiocyanate fluorescence (absorption at 490 nm; emission at 520 nm) using an LSM 880 laser scanning confocal microscope and appropriate filter sets (Zeiss).

ATR-FTIR spectra and X-ray diffraction were used to determine the crystal isomorph of synthesized chitin. Chitin derived from shrimp and chitin derived from Satsuma tubeworm were used as reference samples for α-chitin and β-chitin, respectively. FTIR spectra were obtained using a Nicolet iS5 FTIR Spectrometer (Thermo Fisher Scientific) with a diamond ATR unit over the frequency range of 4,000 to 600 cm−1 in absorbance mode. Spectroscopy was recorded at a resolution of 4 cm−1 and 128 scans. The data were analysed with Omnic software. X-ray diffraction data were obtained at 40 kV, 30 mA and 2θ with a scan angle from 5° to 40° using a Rigaku D max 2000 system (Rigaku) at the Institute of Chemistry, Chinese Academy of Sciences. The data were analysed with Jade software.

EM sample preparation and data collection

All cryo-EM grids were prepared by loading 3 µl of protein at a concentration of approximately 5 mg ml–1 onto glow-discharged holey carbon grids (Au R1.2/1.3, 300-mesh; Quantifoil or Beijing EBO Technology Limited). Grids were blotted for 6–8 s at 4 °C and 100% humidity and plunged into liquid ethane cooled by liquid nitrogen using Vitrobot Mark IV (FEI). The ligand-bound PsChs1 complexes were prepared by incubating the protein with 0.5 mM UDP-GlcNAc or 5 mM NikZ before sample preparation. Grids were screened and checked using a 200 kV Tecnai G2 F20 TWIN TMP microscope (FEI) in the State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Science.

Data collection for apo-bound, UDP-GlcNAc-bound, UDP/(GlcNAc)3-bound and NikZ-bound PsChs1 was performed using a 300 kV Titan Krios microscope (FEI) equipped with a Gatan K3 direct detector by SerialEM in the Center for Biological Imaging, Institute of Biophysics, Chinese Academy of Science. Images were recorded in super-resolution mode by beam-image shift data collection methods[43] at a magnification of ×22,500, resulting in a physical pixel size of 1.07 Å. The exposure time for each stack of 32 frames was 3.43 s, corresponding to a total dose of approximately 60 e− Å−2 and a defocus ranging from −1.2 to −2.0 μm. Data collection for UDP-bound PsChs1 was performed using a 300 kV Titan Krios microscope equipped with a Gatan K2 direct detector at a magnification of ×130,000 by SerialEM in the Cryo-Electron Microscopy Research Center, Southern University of Science and Technology. Images were automatically acquired using the same conditions as those used for apo-PsChs1.

Cryo-EM data processing

For the PsChs1 apo structure, the output movie stacks were subjected to beam-induced motion correction and dose-weighting using MotionCor2 (ref. [44]). Contrast transfer function parameters on each summed image were estimated with the Gctf program[45]. We selected a subset of the particles using the Laplacian-of-Gaussian method, processed with reference-free 2D classification, and the five 2D classes were selected as references for automatic particle picking of the complete dataset of 3,341 images. This resulted in a total of 2,379,125 particles. After one round of reference-free 2D classification, 927,432 particles were selected for an additional two rounds of 3D classification. Particles were classified into five classes using the initial model obtained from the 3D initial model in 3D classification with C2 symmetry. A class with more complete N-terminal regions was selected with 161,907 particles for 3D auto-refinement, contrast transfer function refinement and Bayesian polishing, which resulted in a 3.3 Å density map. All the processing steps were conducted in RELION 3.08 (ref. [46]) as shown in Extended Data Fig. 3.

For the UDP-GlcNAc-bound PsChs1 complex, a total of 1,855,418 particles were selected in automatic particle picking from a total of 3,878 images. A total of 1,811,918 particles were used in two rounds of 3D classification with C1 and C2 symmetry, using the native density as the initial model. The class with more complete N-terminal regions was selected with 350,132 particles for 3D auto-refinement, contrast transfer function refinement and Bayesian polishing, which resulted in a 3.3 Å density map. The processing steps for UDP/(GlcNAc)3-bound, UDP-bound and NikZ-bound PsChs1 complexes were the same as those for the UDP-GlcNAc-bound PsChs1 complex. All the processing steps are shown in Extended Data Figs. 4–7.

Model building and refinement

The de novo model building of PsChs1 was performed based on the BcsA subunit of the cellulose synthase structure (Protein Data Bank code 4HG6), which was initially docked into half of a 3.3 Å resolution cryo-EM map of apo-PsChs1 using UCSF Chimera[47]. The model of one protomer was rebuilt manually based on the cryo-EM density with COOT[48], and the other half was symmetrically docked using this rebuilt model. The dimeric structure was real-space refined using Phenix[49] in C2 symmetry.

For PsChs1 complexes, the models were rebuilt using the apo structure as an initial model and refined following the same procedure. The ligands were modelled into the cryo-EM density map. MolProbity[50] was used to evaluate the geometries of the structures, and the statistical information is listed in Extended Data Table 1. Figures were prepared with UCSF ChimeraX[51].

Molecular dynamics simulation

The molecular dynamics software package GROMACS v2019.3 was used with the Gromos53a5 force field to compare the structural properties obtained from computational simulation with the structural properties determined from experiments[52]. The experimentally determined structures of apo-PsChs1 (closed conformation) and product-bound PsChs1 (open conformation, with the removal of UDP and (GlcNAc)3 in the channel) were used for the comparative validation study. The simulation cell consists of a phosphatidylethanolamine-phosphatidylglycerol (POPE) bilayer with 598 lipids and 48,555 explicit simple point charge (SPC) solvent molecules and was simulated in NVT (number of particles, system volume and temperature) and NPT (number of particles, system pressure and temperature) ensembles (pressure at 1 atm; temperature at 300 K) using the Parinello–Rahman barostat and Berendsen thermostat (300 K)53,54. Electrostatic interactions for long-range electrostatics were calculated using Particle Mesh-Ewald. All bond lengths and the geometry of covalent bonds containing water molecules were constrained using the LINCS[55] and SETTLE[56] algorithms, respectively. A molecular dynamics simulation of 50 ns was carried out to evaluate the water flux through the channel.

Reporting summary

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

Data availability

The atomic coordinates and EM map for the apo, UDP-GlcNAc-bound, UDP/(GlcNAc)3-bound, UDP-bound and NikZ-bound PsChs1 have been deposited in the Protein Data Bank (www.rcsb.org) with accession codes 7WJM, 7WJN, 7X05, 7X06 and 7WJO, respectively, and in the Electron Microscopy Data Bank (www.ebi.ac.uk/pdbe/emdb/) with the accession codes EMD-32545, EMD-32546, EMD-32917, EMD-32918 and EMD-32547, respectively.

Change history

- 07 October 2022

In the version of this article initially published, the third affiliation listed for Qing Yang was incorrect, and has been amended to read School of Bioengineering, Dalian University of Technology, Dalian, China, in the HTML and PDF versions of the article.

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Acknowledgements

We thank Z. Rao and H. Li for their valuable comments during the preparation of this manuscript; P. Xia for his help on EM sample screening; Y. Huo and T. Jiang for their help on apo-PsChs1 data collection; X. Li, B. Zhu, D. Fan, T. Niu, X. Huang and all staff at the Center for Biological Imaging, Institute of Biophysics, Chinese Academy of Sciences, and S. Xu, X. Ma and the staff at the Cryo-EM Center of Southern University of Science and Technology for their support of single-particle cryo-EM data collection; X. Gao, M. Zhao, Y. Zhu, X. Song, W. Liu and D. Zheng, and the Large-scale Instruments and Equipments Sharing Platform of Beijing University of Technology for their support; and L. Niu, M. Zhang and F. Yang for mass spectrometer analysis. This work was supported by the National Natural Science Foundation of China (31830076, 32161133010 and 31901916), the Shenzhen Science and Technology Program (KQTD20180411143628272), and the Special Funds for Science Technology Innovation and Industrial Development of Shenzhen Dapeng New District (PT202101-02).

Author information

Author notes

1. These authors contributed equally: Wei Chen, Peng Cao, Yuansheng Liu

Authors and Affiliations

1. State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China

Wei Chen & Qing Yang

2. Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China

Wei Chen, Ailing Yu, Lei Chen & Qing Yang

3. Faculty of Environment and Life, Beijing University of Technology, Beijing, China

Peng Cao

4. School of Bioengineering, Dalian University of Technology, Dalian, China

Yuansheng Liu, Dong Wang & Qing Yang

5. Tianjin Key Laboratory for Modern Drug Delivery and High-Efficiency, Collaborative Innovation Center of Chemical Science and Engineering, School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, China

Rajamanikandan Sundarraj & Zhiguang Yuchi

6. Center for Multi-disciplinary Research, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China

Yong Gong

7. Department of Chemistry and Biology, School of Science and Technology, University of Siegen, Siegen, Germany

Hans Merzendorfer

Contributions

Q.Y. conceived and designed this project. W.C., Y.L. and D.W. prepared the samples and screened the cryo grids. W.C. and A.Y. performed the data acquisition. W.C. and L.C. performed the product characterization. R.S. and Z.Y. performed the molecular dynamics simulation. P.C. and Y.G. performed the image processing and structure determination. Q.Y., W.C. and H.M. wrote the manuscript.

Corresponding authors

Correspondence to Yong Gong or Qing Yang.

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Competing interests

The authors declare no competing interests.

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Nature thanks Jochen Zimmer 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 Sequence conservation among CHSs from oomycetes, fungi, and arthropods.

a, The structure of PsChs1 is colored according to sequence conservation among CHSs from different species. Residues are colored red, white and blue to indicate identical, similar and variable residues, respectively. The following sequences were used: PsChs1 (XP_009524159) (this study), PeChs1 (RMX65634) from Peronospora effusa, SmChs1 (ADE62520) from Saprolegnia monoica, AaChs1 (RHZ26641) from Aphanomyces astaci, ScChs1 (NP_014207), ScChs2 (NP_009594), and ScChs3 (NP_009579) from Saccharomyces cerevisiae (XP_011318411), CaChs2 (KHC63241) from Candida albicans, CiChs2 (KMP06892) from Coccidioides immitis, SbChs1 from Sporothrix brasiliensis (XP_040620288), TcChs1 from Tribolium castaneum (AAQ55059), PxChs1 (BAF47974) from Plutella xylostella, DmChsA (AAG22215) from Drosophila melanogaster, SfChs2 (AAS12599) from Spodoptera frugiperda, and TuChs1 (AFG28412) from Tetranychus urticae. b, Multiple sequence alignment of the catalytic domain of CHSs from different species was generated using Clustal X. Amino acids are highlighted by white (on red background), red and gray letters based on sequence identity, similarity and variability, respectively. Black boxes refer to conserved motifs, including motif 1 (TMYNE), motif 2 (DGR), motif 3 (KASKL), motif 4 (DVGT), motif 5 (QHFEY), motif 6 (VLPG), motif 7 (EDR), motif 8 (QRKRW), and motif 9 (SWG).

Extended Data Fig. 2 Identification of recombinant PsChs1.

a, Size exclusion chromatography profile and SDS–PAGE analysis of purified PsChs1 (inset, right). b, The two protein bands (SF-1 and SF-2) in SDS–PAGE were verified by peptide mass fingerprint. The histogram presents the distributions of protein scores reported in Mascot Server (http://www.matrixscience.com/), which uses a two-sided expectation value test without any adjustments for data analysis. Protein scores > 93 are significant (p < 0.05). c, The molar mass of recombinant PsChs1 was determined by analytical ultracentrifugation. The theoretical molar mass of the PsChs1 monomer is 107 kDa, and the molar mass of the main peak is somewhere between that of the dimer and trimer. Considering that these peaks were obtained in the presence of detergent, PsChs1 may exist as a dimer in solution. d, The graph shows that PsChs1 activity is affected by metal ions, and the product is degraded by chitinase. The activity is increased by the addition of GlcNAc. Chi: chitinase. e, The Michaelis–Menten curve (left) and Hill plot (right) of the reaction catalyzed by PsChs1 in the presence (▲) or absence (○) of free GlcNAc. Error bars, mean ± SD. f, Enzymatic activity of PsChs1 in the presence of different chitooligosaccharides at a concentration of 1 mM. g, Scanning electron microscopy images of in vitro synthesized chitin on a time scale of hours. h. Scanning electron microscopy and laser confocal microscopy analysis of synthesized chitin aggregates. i, Fourier transform infrared spectra (left panel) and X-ray diffraction profiles (right panel) of α-chitin, β-chitin, and the synthesized chitin. j, Enzyme activities of wild-type PsChs1 and various mutants. k, Relative inhibition rate of NikZ for different PsChs1 mutants. N.D.: not determined, the enzyme activity of the mutant was too low to measure the inhibitory activity of NikZ. l, Truncation of SP affects dimerization but not enzyme activity. Data in d, e, f, g, l are presented as the mean ± s.d. from three independent experiments (n = 3). Data in g, h are repeated independently three times with similar results. The uncropped gel images for a, j, l are available as supplementary information Fig. 1.

Extended Data Fig. 3 Cryo-electron microscopy (Cryo-EM) analysis of apo PsChs1.

a, Flowchart of cryo-EM data processing for apo-PsChs1. b, Gold-standard Fourier shell correlation (FSC) curves of apo-PsChs1, including FSC between two independently refined half-maps generated by RELION (blue) and model-to-map FSC generated by Phenix (red). c, Angular distribution of the particles contributing to the final reconstruction of PsChs1. d, The map of PsChs1 is colored according to local resolution estimation. e, Sample maps of TM helices, IF helices, N-terminal subdomains, and eight conserved motifs of apo-PsChs1.

Extended Data Fig. 4 Cryo-EM data analysis of UDP-GlcNAc-bound PsChs1.

a, Flowchart of cryo-EM data processing. b, FSC curves of UDP-GlcNAc-bound PsChs1, including FSC between two independently refined half-maps generated by RELION (blue) and model-to-map FSC generated by Phenix (red). c, Angular distribution of the particles contributing to the final reconstruction of PsChs1. Each column represents one view, and the size of the column is proportional to the number of particles in that view. d, The map of UDP-GlcNAc-bound PsChs1 is colored according to estimated local resolution. e, Sample maps of TM helices, N-terminal subdomains, and IF helices of UDP-GlcNAc-bound PsChs1. f, EM density of UDP-GlcNAc/Mn2+.

Extended Data Fig. 5 Cryo-EM data analysis of UDP/(GlcNAc)[3]-bound PsChs1.

a, Flowchart of cryo-EM data processing. b, FSC curves of UDP/(GlcNAc)3-bound PsChs1, including FSC between two independently refined half-maps generated by RELION (blue) and model-to-map FSC generated by Phenix (red). c, Angular distribution of the particles contributing to the final reconstruction of PsChs1. d, The map of UDP/(GlcNAc)3-bound PsChs1 is colored according to estimated local resolution. e, Sample maps of TM helices, N-terminal subdomains, and IF helices of UDP/(GlcNAc)3-bound PsChs1.

Extended Data Fig. 6 Cryo-EM data analysis of UDP-bound PsChs1.

a, Flowchart of cryo-EM data processing. b, FSC curves of UDP-bound PsChs1, including FSC between two independently refined half-maps generated by RELION (blue) and model-to-map FSC generated by Phenix (red). c, Angular distribution of the particles contributing to the final reconstruction of PsChs1. d, The map of UDP-bound PsChs1 is colored according to estimated local resolution. e, Sample maps of TM helices, N-terminal subdomains, and IF helices of UDP-bound PsChs1. f, EM density of UDP/Mg2+.

Extended Data Fig. 7 Cryo-EM data analysis of NikZ-bound PsChs1.

a, Flowchart of cryo-EM data processing. b, FSC curves of NikZ-bound PsChs1, including FSC between two independently refined half-maps generated by RELION (blue) and model-to-map FSC generated by Phenix (red). c, Angular distribution of the particles contributing to the final reconstruction of PsChs1. d, The map of NikZ-bound PsChs1 is colored according to estimated local resolution. e, Sample maps of TM helices, N-terminal subdomains, and IF helices of NikZ-bound PsChs1. f, EM density of NikZ.

Extended Data Fig. 8 Architectures of PsChs1.

a, The assemblies of six TM helices (TM1–6) and three IF helices (IF1–3). Note that IF3 is indeed not a membrane spanning helix but forms a bent helix parallel to the cytosolic side of the membrane. b, TM topology of PsChs1 according to various topology and topography prediction methods. IF3 is frequently predicted to be a TM helix. c, Ribbon diagram of the PsChs1 MIT subdomain (residues 94–162). d, The “alanine zipper” connecting PsChs1 MIT helices α1 and α2. The five alanine side chains within this motif are specifically shown. e, Structural alignments of the PsChs1 MIT subdomain with other known MIT domains downloaded from the PDB database. The compared structures included the following: human VPS4B-MIT (1WR0), human VPS4A-MIT (1YXR), Saprolegnia monoica CHS1 MIT1 (SmChs1-MIT1; 2MPK), mouse katanin p60-vMIT (2RPA), and Saccharolobus solfataricus VPS4-like-MIT (2V6Y). f, Structural comparison between apo PsChs1 and UDP-GlcNAc-bound PsChs1 showed that the extended N-terminal region in UDP-GlcNAc-bound PsChs1 represents a different conformation. g, The interaction interface formed between the extra helix of one protomer (blue) and the neighboring protomer (gray) is depicted. h, Active sites of several metal-dependent inverting GT-A fold glycosyltransferases. The hydrogen bonds between the D/E in the (D/E)DX motif and donor sugar are shown.

Extended Data Fig. 9 Comparison of PsChs1 and Rhodobacter sphaeroides bacterial cellulose synthase A (RsBcsA) and molecular dynamics simulation.

a, The structures of PsChs1 and RsBcsA are superimposed by ChimeraX and depicted in two perspectives. The catalytic domains are magnified (right). Functionally important residues are labeled and shown as sticks. b, Dimensions of (GlcNAc)4 and (Glc)4. The structures of the (GlcNAc)4 and (Glc)4 oligosaccharides were downloaded from the PDB database (5Y2C and 4HG6). c, Length and diameter of the presumed chitin (left, green mesh) and cellulose (right, orange mesh) channels of PsChs1 and RsBcsA, calculated by PyMOL Caver plugin. The calculated channel of RsBcsA fits perfectly with the dimension of the cellulose chain included in the RsBcsA crystal structure. d, Structural comparison of the VLPGA loop with BcsA’s FFCGSA loop. e, Structure-weighted sequence alignment of the catalytic domains of RsBcsA and PsChs1. Conserved motifs are labeled and indicated by black boxes. f, Backbone RMSD of apo PsChs1 (closed conformation) and product-bound PsChs1 (open conformation) during the MD simulation of 50 ns. g, From a snapshot of 50 ns, apo PsChs1 shows little permeability for water (red ball), while product-bound PsChs1 (with the removal of UDP/(GlcNAc)3 in the simulations) shows water flow through the channel. h, The chitin sheets are self-assembled in an anti-parallel manner along the b-axis to finally form α-chitin.

Extended Data Table 1 Cryo-EM data collection, refinement and validation statistics

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Chen, W., Cao, P., Liu, Y. et al. Structural basis for directional chitin biosynthesis. Nature 610, 402–408 (2022). https://doi.org/10.1038/s41586-022-05244-5

- Received: 02 March 2022

- Accepted: 17 August 2022

- Published: 21 September 2022

- Issue Date: 13 October 2022

- DOI: https://doi.org/10.1038/s41586-022-05244-5

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- Author Correction: Late Quaternary dynamics of Arctic biota from ancient environmental genomics

- Publisher Correction: Brain charts for the human lifespan

- Publisher Correction: A RORγt+ cell instructs gut microbiota-specific Treg cell differentiation

Author Correction Published: 22 September 2022

Author Correction: Recycling and metabolic flexibility dictate life in the lower oceanic crust

Jiangtao Li,

Paraskevi Mara,

Florence Schubotz,

Jason B. Sylvan,

Gaëtan Burgaud,

Frieder Klein,

David Beaudoin,

Shu Ying Wee,

Henry J. B. Dick,

Sarah Lott,

Rebecca Cox,

Lara A. E. Meyer,

Maxence Quémener,

Donna K. Blackman &

…

Virginia P. Edgcomb

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Nature volume 610, page E3 (2022)

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The Original Article was published on 11 March 2020

Correction to: Nature https://doi.org/10.1038/s41586-020-2075-5 Published online 11 March 2020

It has been brought to the authors’ attention that the pipeline steps for metatranscriptome contamination removal could miss mapping any sample reads to control reads that didn’t assemble into the control co-assembly. Therefore, an additional analysis of the manually checked transcripts behind the themes discussed in the paper was performed using Magic-BLAST, a mapping tool that allows detection and removal of reads in sample data sets that are mapped to by control reads. Transcripts discussed in the paper flagged by Magic-BLAST were removed from the dataset if they recruited one or more reads from a control sample at 98% local identity over 50% or more of the alignment. The new determinations did not compromise any of the pathways discussed in our paper, but removal of reads (~19%) changed the calculated FPKM values reported throughout Supplementary Table 4 of the original paper. Although we advise against using FPKM values to compare expression levels of selected genes between samples for amplified data sets, this amendment provides updated FPKM values for Supplementary Table 4, and highlights a few instances where all transcripts for individual genes within categories were removed. At the same time, we updated taxonomic annotations as of spring 2020, as databases are constantly improving, and where possible, we now provide synonyms and abbreviations for the reported functions in an effort to make the table more useful to the reader. Additional useful detail is also provided about the analysis of metatranscriptome data within the selected categories of metabolism discussed in the paper, and about the clean room facility used for the cell counts.

As Supplementary Information files to this amendment please find (1), a brief Supplementary Methods describing the clean room cell counting facility used for cell counts, providing detection limits of the instrument used for the ATP assays, and explaining details of the additional analysis of the mRNA data, and (2), a Supplementary Discussion file showing minor text updates to the mRNA section of our original Supplementary Discussion reflecting the revised Supplementary Table 4 (also included in this amendment). Fasta sequences for contigs reported in Supplementary Table 4 within different categories of metabolism are provided as a Supplementary Data file. A note has been added to the dataset names for our originally deposited transcriptome data sets under BioProject PRJNA497074 to make it clear that they are raw data, and contaminating sequences have not been removed. Sample data set names now include “…control sample sequences not removed”.

Author information

Author notes

1. These authors contributed equally: Jiangtao Li, Paraskevi Mara, Virginia P. Edgcomb

Authors and Affiliations

1. State Key Laboratory of Marine Geology, Tongji University, Shanghai, China

Jiangtao Li

2. Department of Geology and Geophysics, Woods Hole Oceanographic Institution, Woods Hole, MA, USA

Jiangtao Li, Paraskevi Mara, David Beaudoin, Henry J. B. Dick, Sarah Lott, Rebecca Cox & Virginia P. Edgcomb

3. MARUM, University of Bremen, Bremen, Germany

Florence Schubotz & Lara A. E. Meyer

4. Department of Geosciences, University of Bremen, Bremen, Germany

Florence Schubotz & Lara A. E. Meyer

5. Department of Oceanography, Texas A&M University, College Station, TX, USA

Jason B. Sylvan & Shu Ying Wee

6. Université de Brest, EA 3882, Laboratoire Universitaire de Biodiversité et Ecologie Microbienne, ESIAB, Technopôle Brest-Iroise, Plouzané, France

Gaëtan Burgaud & Maxence Quémener

7. Department of Marine Chemistry and Geochemistry, Woods Hole Oceanographic Institution, Woods Hole, MA, USA

Frieder Klein

8. Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA

Donna K. Blackman

Corresponding author

Correspondence to Virginia P. Edgcomb.

Additional information

Supplementary information is available in the online version of this Amendment.

Supplementary information

Supplementary Methods

Supplementary Discussion

Supplementary Data

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Li, J., Mara, P., Schubotz, F. et al. Author Correction: Recycling and metabolic flexibility dictate life in the lower oceanic crust. Nature 610, E3 (2022). https://doi.org/10.1038/s41586-022-05321-9

- Published: 22 September 2022

- Issue Date: 13 October 2022

- DOI: https://doi.org/10.1038/s41586-022-05321-9

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Author Correction Published: 23 September 2022

Author Correction: Genomic basis of geographical adaptation to soil nitrogen in rice

Yongqiang Liu,

Hongru Wang,

Zhimin Jiang,

Wei Wang,

Ruineng Xu,

Qihui Wang,

Zhihua Zhang,

Aifu Li,

Yan Liang,

Shujun Ou,

Xiujie Liu,

Shouyun Cao,

Hongning Tong,

Yonghong Wang,

Feng Zhou,

Hong Liao,

Bin Hu

…

Chengcai Chu

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Nature volume 610, page E4 (2022)

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The Original Article was published on 06 January 2021

Correction to: Nature https://doi.org/10.1038/s41586-020-03091-w Published online 6 January 2021

In the version of this article initially published online, the P values reported in Supplementary Table 2 were from an earlier analysis and are now updated. In the last sentence of the “Rice cultivation conditions” section in Methods now reading “Each seedling was grown … urea was used as the nitrogen source with net nitrogen 0.5 kg 100 m−2 for low nitrogen and 1.5 kg 100 m−2 for moderate nitrogen”, the units “kg 100 m−2” replace the original “kg m−2”. The changes have been made in the online version of the article

Author information

Author notes

1. These authors contributed equally: Yongqiang Liu, Hongru Wang

Authors and Affiliations

1. State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, The Innovative Academy for Seed Design, Chinese Academy of Sciences, Beijing, China

Yongqiang Liu, Hongru Wang, Zhimin Jiang, Wei Wang, Zhihua Zhang, Aifu Li, Yan Liang, Shujun Ou, Xiujie Liu, Shouyun Cao, Yonghong Wang, Bin Hu & Chengcai Chu

2. College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing, China

Yongqiang Liu, Aifu Li, Xiujie Liu & Chengcai Chu

3. State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, China

Ruineng Xu & Hong Liao

4. Root Biology Center, Haixia Institute of Science and Technology, Fujian Agriculture and Forestry University, Fuzhou, China

Ruineng Xu & Hong Liao

5. Sino-France Institute of Earth Systems Science, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, China

Qihui Wang & Feng Zhou

6. National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China

Hongning Tong

Corresponding authors

Correspondence to Bin Hu or Chengcai Chu.

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Liu, Y., Wang, H., Jiang, Z. et al. Author Correction: Genomic basis of geographical adaptation to soil nitrogen in rice. Nature 610, E4 (2022). https://doi.org/10.1038/s41586-022-05362-0

- Published: 23 September 2022

- Issue Date: 13 October 2022

- DOI: https://doi.org/10.1038/s41586-022-05362-0

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Author Correction Open Access Published: 26 September 2022

Author Correction: Late Quaternary dynamics of Arctic biota from ancient environmental genomics

Yucheng Wang,

Mikkel Winther Pedersen,

Inger Greve Alsos,

Bianca De Sanctis,

Fernando Racimo,

Ana Prohaska,

Eric Coissac,

Hannah Lois Owens,

Marie Kristine Føreid Merkel,

Antonio Fernandez-Guerra,

Alexandra Rouillard,

Youri Lammers,

Adriana Alberti,

France Denoeud,

Daniel Money,

Anthony H. Ruter,

Hugh McColl,

Nicolaj Krog Larsen,

Anna A. Cherezova,

Mary E. Edwards,

Grigory B. Fedorov,

James Haile,

Ludovic Orlando,

Lasse Vinner,

Thorfinn Sand Korneliussen,

David W. Beilman,

Anders A. Bjørk,

Jialu Cao,

Christoph Dockter,

Julie Esdale,

Galina Gusarova,

Kristian K. Kjeldsen,

Jan Mangerud,

Jeffrey T. Rasic,

Birgitte Skadhauge,

John Inge Svendsen,

Alexei Tikhonov,

Patrick Wincker,

Yingchun Xing,

Yubin Zhang,

Duane G. Froese,

Carsten Rahbek,

David Nogues Bravo,

Philip B. Holden,

Neil R. Edwards,

Richard Durbin,

David J. Meltzer,

Kurt H. Kjær,

Per Möller &

…

Eske Willerslev

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Nature volume 610, page E5 (2022)

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The Original Article was published on 20 October 2021

Correction to: Nature https://doi.org/10.1038/s41586-021-04016-x Published online 20 October 2021

In the version of this article initially published, there were mismatches between sample ID and the data in the second tag “damage” for Supplementary Data 6: Animal distribution & DNA damage. This occurred through reformatting in manuscript preparation and does not affect the rest of the paper. The corrected table has been uploaded to the HTML version of the article.

Author information

Author notes

1. These authors contributed equally: Yucheng Wang, Mikkel Winther Pedersen, Inger Greve Alsos

Authors and Affiliations

1. Department of Zoology, University of Cambridge, Cambridge, UK

Yucheng Wang, Bianca De Sanctis, Ana Prohaska, Daniel Money & Eske Willerslev

2. Lundbeck Foundation GeoGenetics Centre, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark

Yucheng Wang, Mikkel Winther Pedersen, Fernando Racimo, Antonio Fernandez-Guerra, Alexandra Rouillard, Anthony H. Ruter, Hugh McColl, Nicolaj Krog Larsen, James Haile, Lasse Vinner, Thorfinn Sand Korneliussen, Jialu Cao, David J. Meltzer, Kurt H. Kjær & Eske Willerslev

3. The Arctic University Museum of Norway, UiT— The Arctic University of Norway, Tromsø, Norway

Inger Greve Alsos, Eric Coissac, Marie Kristine Føreid Merkel, Youri Lammers & Galina Gusarova

4. Department of Genetics, University of Cambridge, Cambridge, UK

Bianca De Sanctis & Richard Durbin

5. Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS, LECA, Grenoble, France

Eric Coissac

6. Center for Macroecology, Evolution and Climate, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark

Hannah Lois Owens, Carsten Rahbek & David Nogues Bravo

7. Department of Geosciences, UiT—The Arctic University of Norway, Tromsø, Norway

Alexandra Rouillard

8. Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), Gif-sur-Yvette, France

Adriana Alberti

9. Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Université Evry, Université Paris-Saclay, Evry, France

Adriana Alberti, France Denoeud & Patrick Wincker

10. Institute of Earth Sciences, St Petersburg State University, St Petersburg, Russia

Anna A. Cherezova & Grigory B. Fedorov

11. Arctic and Antarctic Research Institute, St Petersburg, Russia

Anna A. Cherezova & Grigory B. Fedorov

12. School of Geography and Environmental Science, University of Southampton, Southampton, UK

Mary E. Edwards

13. Alaska Quaternary Center, University of Alaska Fairbanks, Fairbanks, AK, USA

Mary E. Edwards

14. Centre d’Anthropobiologie et de Génomique de Toulouse, Université Paul Sabatier, Faculté de Médecine Purpan, Toulouse, France

Ludovic Orlando

15. National Research University, Higher School of Economics, Moscow, Russia

Thorfinn Sand Korneliussen

16. Department of Geography and Environment, University of Hawaii, Honolulu, HI, USA

David W. Beilman

17. Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark

Anders A. Bjørk

18. Carlsberg Research Laboratory, Copenhagen, Denmark

Christoph Dockter & Birgitte Skadhauge

19. Center for Environmental Management of Military Lands, Colorado State University, Fort Collins, CO, USA

Julie Esdale

20. Faculty of Biology, St Petersburg State University, St Petersburg, Russia

Galina Gusarova

21. Department of Glaciology and Climate, Geological Survey of Denmark and Greenland, Copenhagen, Denmark

Kristian K. Kjeldsen

22. Department of Earth Science, University of Bergen, Bergen, Norway

Jan Mangerud & John Inge Svendsen

23. Bjerknes Centre for Climate Research, Bergen, Norway

Jan Mangerud & John Inge Svendsen

24. US National Park Service, Gates of the Arctic National Park and Preserve, Fairbanks, AK, USA

Jeffrey T. Rasic

25. Zoological Institute, , Russian Academy of Sciences, St Petersburg, Russia

Alexei Tikhonov

26. Resource and Environmental Research Center, Chinese Academy of Fishery Sciences, Beijing, China

Yingchun Xing

27. College of Plant Science, Jilin University, Changchun, China

Yubin Zhang

28. Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton, Alberta, Canada

Duane G. Froese

29. Center for Global Mountain Biodiversity, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark

Carsten Rahbek

30. School of Environment, Earth and Ecosystem Sciences, The Open University, Milton Keynes, UK

Philip B. Holden & Neil R. Edwards

31. Department of Anthropology, Southern Methodist University, Dallas, TX, USA

David J. Meltzer

32. Department of Geology, Quaternary Sciences, Lund University, Lund, Sweden

Per Möller

33. Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge, UK

Eske Willerslev

34. MARUM, University of Bremen, Bremen, Germany

Eske Willerslev

Corresponding author

Correspondence to Eske Willerslev.

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Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Wang, Y., Pedersen, M.W., Alsos, I.G. et al. Author Correction: Late Quaternary dynamics of Arctic biota from ancient environmental genomics. Nature 610, E5 (2022). https://doi.org/10.1038/s41586-022-05359-9

- Published: 26 September 2022

- Issue Date: 13 October 2022

- DOI: https://doi.org/10.1038/s41586-022-05359-9

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Publisher Correction Open Access Published: 23 September 2022

Publisher Correction: Brain charts for the human lifespan

R. A. I. Bethlehem

J. Seidlitz

S. R. White,

J. W. Vogel,

K. M. Anderson,

C. Adamson,

S. Adler,

G. S. Alexopoulos,

E. Anagnostou,

A. Areces-Gonzalez,

D. E. Astle,

B. Auyeung,

M. Ayub,

J. Bae,

G. Ball,

S. Baron-Cohen,

R. Beare,

S. A. Bedford,

V. Benegal,

F. Beyer,

J. Blangero,

M. Blesa Cábez,

J. P. Boardman,

M. Borzage,

J. F. Bosch-Bayard,

N. Bourke,

V. D. Calhoun,

M. M. Chakravarty,

C. Chen,

C. Chertavian,

G. Chetelat,

Y. S. Chong,

J. H. Cole,

A. Corvin,

M. Costantino,

E. Courchesne,

F. Crivello,

V. L. Cropley,

J. Crosbie,

N. Crossley,

M. Delarue,

R. Delorme,

S. Desrivieres,

G. A. Devenyi,

M. A. Di Biase,

R. Dolan,

K. A. Donald,

G. Donohoe,

K. Dunlop,

A. D. Edwards,

J. T. Elison,

C. T. Ellis,

J. A. Elman,

L. Eyler,

D. A. Fair,

E. Feczko,

P. C. Fletcher,

P. Fonagy,

C. E. Franz,

L. Galan-Garcia,

A. Gholipour,

J. Giedd,

J. H. Gilmore,

D. C. Glahn,

I. M. Goodyer,

P. E. Grant,

N. A. Groenewold,

F. M. Gunning,

R. E. Gur,

R. C. Gur,

C. F. Hammill,

O. Hansson,

T. Hedden,

A. Heinz,

R. N. Henson,

K. Heuer,

J. Hoare,

B. Holla,

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R. Holt,

H. Huang,

K. Im,

J. Ipser,

C. R. Jack Jr,

A. P. Jackowski,

T. Jia,

K. A. Johnson,

P. B. Jones,

D. T. Jones,

R. S. Kahn,

H. Karlsson,

L. Karlsson,

R. Kawashima,

E. A. Kelley,

S. Kern,

K. W. Kim,

M. G. Kitzbichler,

W. S. Kremen,

F. Lalonde,

B. Landeau,

S. Lee,

J. Lerch,

J. D. Lewis,

J. Li,

W. Liao,

C. Liston,

M. V. Lombardo,

J. Lv,

C. Lynch,

T. T. Mallard,

M. Marcelis,

R. D. Markello,

S. R. Mathias,

B. Mazoyer,

P. McGuire,

M. J. Meaney,

A. Mechelli,

N. Medic,

B. Misic,

S. E. Morgan,

D. Mothersill,

J. Nigg,

M. Q. W. Ong,

C. Ortinau,

R. Ossenkoppele,

M. Ouyang,

L. Palaniyappan,

L. Paly,

P. M. Pan,

C. Pantelis,

M. M. Park,

T. Paus,

Z. Pausova,

D. Paz-Linares,

A. Pichet Binette,

K. Pierce,

X. Qian,

J. Qiu,

A. Qiu,

A. Raznahan,

T. Rittman,

A. Rodrigue,

C. K. Rollins,

R. Romero-Garcia,

L. Ronan,

M. D. Rosenberg,

D. H. Rowitch,

G. A. Salum,

T. D. Satterthwaite,

H. L. Schaare,

R. J. Schachar,

A. P. Schultz,

G. Schumann,

M. Schöll,

D. Sharp,

R. T. Shinohara,

I. Skoog,

C. D. Smyser,

R. A. Sperling,

D. J. Stein,

A. Stolicyn,

J. Suckling,

G. Sullivan,

Y. Taki,

B. Thyreau,

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N. Traut,

K. A. Tsvetanov,

N. B. Turk-Browne,

J. J. Tuulari,

C. Tzourio,

É. Vachon-Presseau,

M. J. Valdes-Sosa,

P. A. Valdes-Sosa,

S. L. Valk,

T. van Amelsvoort,

S. N. Vandekar,

L. Vasung,

L. W. Victoria,

S. Villeneuve,

A. Villringer,

P. E. Vértes,

K. Wagstyl,

Y. S. Wang,

S. K. Warfield,

V. Warrier,

E. Westman,

M. L. Westwater,

H. C. Whalley,

A. V. Witte,

N. Yang,

B. Yeo,

H. Yun,

A. Zalesky,

H. J. Zar,

A. Zettergren,

J. H. Zhou,

H. Ziauddeen,

A. Zugman,

X. N. Zuo,

3R-BRAIN,

AIBL,

Alzheimer’s Disease Neuroimaging Initiative,

Alzheimer’s Disease Repository Without Borders Investigators,

CALM Team,

Cam-CAN,

CCNP,

COBRE,

cVEDA,

ENIGMA Developmental Brain Age Working Group,

Developing Human Connectome Project,

FinnBrain,

Harvard Aging Brain Study,

IMAGEN,

KNE96,

The Mayo Clinic Study of Aging,

NSPN,

POND,

The PREVENT-AD Research Group,

VETSA,

E. T. Bullmore &

…

A. F. Alexander-Bloch

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Nature volume 610, page E6 (2022)

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The Original Article was published on 06 April 2022

Correction to: Nature https://doi.org/10.1038/s41586-022-04554-y Published online 6 April 2022

In the version of this article initially published, there were errors in the affiliations for K. Im (missing affiliation, Division of Newborn Medicine and Neuroradiology, Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA), J. Lerch (missing affiliation, Mouse Imaging Centre, Toronto, Ontario, Canada), S. Villeneuve and X. N. Zuo (incorrect affiliation numbers listed), H. Yun (missing affiliation, Division of Newborn Medicine and Neuroradiology, Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA), and H. J. Zar (extra affiliation shown). In addition, the affiliation numbers for all authors listed in the consortium membership section were incorrect by 1–3 digits. The errors have been corrected in the HTML and PDF versions of the article.

Author information

Author notes

1. These authors contributed equally: R. A. I. Bethlehem, J. Seidlitz, S. R. White

2. These authors jointly supervised this work: E. T. Bullmore, A. F. Alexander-Bloch

Authors and Affiliations

1. Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK

R. A. I. Bethlehem, B. Auyeung, S. Baron-Cohen, S. A. Bedford, R. Holt & M. V. Lombardo

2. Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK

R. A. I. Bethlehem & M. G. Kitzbichler

3. Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA

J. Seidlitz, J. W. Vogel, R. E. Gur, R. C. Gur, T. D. Satterthwaite & A. F. Alexander-Bloch

4. Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA

J. Seidlitz & A. F. Alexander-Bloch

5. Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA

J. Seidlitz, C. Chertavian, R. E. Gur, R. C. Gur & A. F. Alexander-Bloch

6. Department of Psychiatry, University of Cambridge, Cambridge, UK

S. R. White, I. M. Goodyer, R. N. Henson, P. B. Jones, M. G. Kitzbichler, N. Medic, S. E. Morgan, R. Romero-Garcia, L. Ronan, J. Suckling, P. E. Vértes, V. Warrier, M. L. Westwater, H. Ziauddeen & E. T. Bullmore

7. MRC Biostatistics Unit, University of Cambridge, Cambridge, UK

S. R. White

8. Lifespan Informatics & Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA

J. W. Vogel & T. D. Satterthwaite

9. Department of Psychology, Yale University, New Haven, CT, USA

K. M. Anderson, C. T. Ellis & N. B. Turk-Browne

10. Developmental Imaging, Murdoch Children’s Research Institute, Melbourne, Victoria, Australia

C. Adamson, G. Ball & R. Beare

11. Department of Medicine, Monash University, Melbourne, Victoria, Australia

C. Adamson & R. Beare

12. UCL Great Ormond Street Institute for Child Health, London, UK

S. Adler

13. Weill Cornell Institute of Geriatric Psychiatry, Department of Psychiatry, Weill Cornell Medicine, New York, USA

G. S. Alexopoulos

14. Department of Pediatrics University of Toronto, Toronto, Canada

E. Anagnostou

15. Holland Bloorview Kids Rehabilitation Hospital, Toronto, Canada

E. Anagnostou

16. The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China

A. Areces-Gonzalez & D. Paz-Linares

17. University of Pinar del Río “Hermanos Saiz Montes de Oca”, Pinar del Río, Cuba

A. Areces-Gonzalez

18. MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK

D. E. Astle & R. N. Henson

19. Department of Psychology, School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, UK

B. Auyeung

20. Queen’s University, Department of Psychiatry, Centre for Neuroscience Studies, Kingston, Ontario, Canada

M. Ayub

21. University College London, Mental Health Neuroscience Research Department, Division of Psychiatry, London, UK

M. Ayub

22. Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea

J. Bae

23. Department of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia

G. Ball

24. Cambridge Lifetime Asperger Syndrome Service (CLASS), Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK

S. Baron-Cohen

25. Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India

V. Benegal

26. Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

F. Beyer, A. Villringer & A. V. Witte

27. Department of Human Genetics, South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Edinburg, TX, USA

J. Blangero

28. MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, UK

M. Blesa Cábez, J. P. Boardman & G. Sullivan

29. Fetal and Neonatal Institute, Division of Neonatology, Children’s Hospital Los Angeles, Department of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

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30. McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Montreal, Quebec, Canada

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31. McGill University, Montreal, Quebec, Canada

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32. Department of Brain Sciences, Imperial College London, London, UK

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33. Care Research and Technology Centre, Dementia Research Institute, London, UK

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34. Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA

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35. Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada

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36. Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

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37. Normandie Univ, UNICAEN, INSERM, U1237, PhIND “Physiopathology and Imaging of Neurological Disorders”, Institut Blood and Brain @ Caen-Normandie, Cyceron, Caen, France

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38. Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore, Singapore

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39. Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

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40. Centre for Medical Image Computing (CMIC), University College London, London, UK

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41. Dementia Research Centre (DRC), University College London, London, UK

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42. Department of Psychiatry, Trinity College, Dublin, Ireland

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43. Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Quebec, Canada

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44. Undergraduate program in Neuroscience, McGill University, Montreal, Quebec, Canada

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45. Department of Neuroscience, University of California, San Diego, San Diego, CA, USA

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46. Autism Center of Excellence, University of California, San Diego, San Diego, CA, USA

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47. Institute of Neurodegenerative Disorders, CNRS UMR5293, CEA, University of Bordeaux, Bordeaux, France

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48. Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Victoria, Australia

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49. The Hospital for Sick Children, Toronto, Ontario, Canada

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50. Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile

N. Crossley

51. Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

N. Crossley & P. McGuire

52. Instituto Milenio Intelligent Healthcare Engineering, Santiago, Chile

N. Crossley

53. Child and Adolescent Psychiatry Department, Robert Debré University Hospital, AP-HP, Paris, France

R. Delorme

54. Human Genetics and Cognitive Functions, Institut Pasteur, Paris, France

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55. Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

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56. Cerebral Imaging Centre, McGill Department of Psychiatry, Douglas Mental Health University Institute, Montreal, QC, Canada

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57. Department of Psychiatry, McGill University, Montreal, QC, Canada

G. A. Devenyi

58. Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

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59. Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK

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60. Wellcome Centre for Human Neuroimaging, London, UK

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61. Division of Developmental Paediatrics, Department of Paediatrics and Child Health, Red Cross War Memorial Children’s Hospital, Cape Town, South Africa

K. A. Donald

62. Neuroscience Institute, University of Cape Town, Cape Town, South Africa

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63. Center for Neuroimaging, Cognition & Genomics (NICOG), School of Psychology, National University of Ireland Galway, Galway, Ireland

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64. Weil Family Brain and Mind Research Institute, Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA

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65. Centre for the Developing Brain, King’s College London, London, UK

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66. Evelina London Children’s Hospital, London, UK

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67. MRC Centre for Neurodevelopmental Disorders, London, UK

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68. Institute of Child Development, Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA

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69. Haskins Laboratories, New Haven, CT, USA

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70. Department of Psychiatry, Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA

J. A. Elman, C. E. Franz & W. S. Kremen

71. Desert-Pacific Mental Illness Research Education and Clinical Center, VA San Diego Healthcare, San Diego, CA, USA

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72. Department of Psychiatry, University of California San Diego, Los Angeles, CA, USA

L. Eyler

73. Department of Psychiatry, University of Cambridge, and Wellcome Trust MRC Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK

P. C. Fletcher

74. Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK

P. C. Fletcher, P. B. Jones, J. Suckling & H. Ziauddeen

75. Department of Clinical, Educational and Health Psychology, University College London, London, UK

P. Fonagy

76. Anna Freud National Centre for Children and Families, London, UK

P. Fonagy

77. Cuban Center for Neuroscience, La Habana, Cuba

L. Galan-Garcia & M. J. Valdes-Sosa

78. Computational Radiology Laboratory, Boston Children’s Hospital, Boston, MA, USA

A. Gholipour & S. K. Warfield

79. Department of Child and Adolescent Psychiatry, University of California, San Diego, San Diego, CA, USA

J. Giedd

80. Department of Psychiatry, University of California San Diego, San Diego, CA, USA

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81. Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA

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82. Department of Psychiatry, Boston Children’s Hospital and Harvard Medical School, Boston, MA, USA

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83. Harvard Medical School, Boston, MA, USA

D. C. Glahn, K. A. Johnson, A. P. Schultz & R. A. Sperling

84. Division of Newborn Medicine and Neuroradiology, Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA

P. E. Grant, K. Im & H. Yun

85. Department of Paediatrics and Child Health, Red Cross War Memorial Children’s Hospital, SA-MRC Unit on Child & Adolescent Health, University of Cape Town, Cape Town, South Africa

N. A. Groenewold & H. J. Zar

86. Weill Cornell Institute of Geriatric Psychiatry, Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA

F. M. Gunning & L. W. Victoria

87. Mouse Imaging Centre, Toronto, Ontario, Canada

C. F. Hammill & J. Lerch

88. Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden

O. Hansson

89. Memory Clinic, Skåne University Hospital, Malmö, Sweden

O. Hansson

90. Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

T. Hedden

91. Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

T. Hedden

92. Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Psychiatry and Psychotherapy, Charité Campus Mitte, Berlin, Germany

A. Heinz

93. Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

K. Heuer

94. Université de Paris, Paris, France

K. Heuer & R. Toro

95. Department of Psychiatry, University of Cape Town, Cape Town, South Africa

J. Hoare

96. Department of Integrative Medicine, NIMHANS, Bengaluru, India

B. Holla

97. Accelerator Program for Discovery in Brain disorders using Stem cells (ADBS), Department of Psychiatry, NIMHANS, Bengaluru, India

B. Holla

98. Departments of Psychology and Psychiatry, Yale University, New Haven, CT, USA

A. J. Holmes

99. Radiology Research, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

H. Huang & M. Ouyang

100. The Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

H. Huang

101. Department of Psychiatry and Mental Health, Clinical Neuroscience Institute, University of Cape Town, Cape Town, South Africa

J. Ipser

102. Department of Radiology, Mayo Clinic, Rochester, MN, USA

C. R. Jack Jr & D. T. Jones

103. Department of Psychiatry, Universidade Federal de São Paulo, São Paulo, Brazil

A. P. Jackowski

104. National Institute of Developmental Psychiatry, Beijing, China

A. P. Jackowski

105. Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China

T. Jia

106. Key Laboratory of Computational Neuroscience and BrainInspired Intelligence (Fudan University), Ministry of Education, Shanghai, China

T. Jia

107. Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology and Neuroscience, SGDP Centre, King’s College London, London, UK

T. Jia

108. Harvard Aging Brain Study, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA

K. A. Johnson, A. P. Schultz & R. A. Sperling

109. Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women’s Hospital, Boston, MA, USA

K. A. Johnson & R. A. Sperling

110. Department of Radiology, Massachusetts General Hospital, Boston, MA, USA

K. A. Johnson

111. Department of Neurology, Mayo Clinic, Rochester, MN, USA

D. T. Jones

112. Department of Psychiatry, Icahn School of Medicine, Mount Sinai, NY, USA

R. S. Kahn

113. Department of Clinical Medicine, Department of Psychiatry and Turku Brain and Mind Center, FinnBrain Birth Cohort Study, University of Turku and Turku University Hospital, Turku, Finland

H. Karlsson, L. Karlsson & J. J. Tuulari

114. Centre for Population Health Research, Turku University Hospital and University of Turku, Turku, Finland

H. Karlsson & L. Karlsson

115. Institute of Development, Aging and Cancer, Tohoku University, Seiryocho, Aobaku, Sendai, Japan

R. Kawashima, Y. Taki & B. Thyreau

116. Queen’s University, Departments of Psychology and Psychiatry, Centre for Neuroscience Studies, Kingston, Ontario, Canada

E. A. Kelley

117. Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden

S. Kern, I. Skoog & A. Zettergren

118. Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry, Cognition and Old Age Psychiatry Clinic, Gothenburg, Sweden

S. Kern & I. Skoog

119. Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, South Korea

K. W. Kim

120. Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea

K. W. Kim

121. Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea

K. W. Kim

122. Institute of Human Behavioral Medicine, SNU-MRC, Seoul, South Korea

K. W. Kim

123. Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD, USA

F. Lalonde & A. Raznahan

124. Department of Brain & Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, South Korea

S. Lee

125. Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada

J. Lerch

126. Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK

J. Lerch

127. Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada

J. D. Lewis

128. The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China

J. Li, W. Liao & P. A. Valdes-Sosa

129. Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA

C. Liston

130. Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy

M. V. Lombardo

131. School of Biomedical Engineering and Brain and Mind Centre, The University of Sydney, Sydney, New South Wales, Australia

J. Lv

132. Department of Psychology, University of Texas, Austin, TX, USA

T. T. Mallard

133. Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, Maastricht, The Netherlands

M. Marcelis

134. Institute for Mental Health Care Eindhoven (GGzE), Eindhoven, The Netherlands

M. Marcelis

135. McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada

R. D. Markello, B. Misic, L. Vasung & S. Villeneuve

136. Ludmer Centre for Neuroinformatics and Mental Health, Douglas Mental Health University Institute, Montreal, Quebec, Canada

B. Mazoyer & M. J. Meaney

137. Singapore Institute for Clinical Sciences, Singapore, Singapore

M. J. Meaney

138. Bordeaux University Hospital, Bordeaux, France

A. Mechelli

139. Department of Computer Science and Technology, University of Cambridge, Cambridge, UK

S. E. Morgan

140. The Alan Turing Institute, London, UK

S. E. Morgan & P. E. Vértes

141. Department of Psychology, School of Business, National College of Ireland, Dublin, Ireland

D. Mothersill

142. School of Psychology and Center for Neuroimaging and Cognitive Genomics, National University of Ireland Galway, Galway, Ireland

D. Mothersill

143. Department of Psychiatry, Trinity College Dublin, Dublin, Ireland

D. Mothersill

144. Department of Psychiatry, School of Medicine, Oregon Health and Science University, Portland, OR, USA

J. Nigg

145. Center for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

M. Q. W. Ong, X. Qian & J. H. Zhou

146. Department of Pediatrics, Washington University in St Louis, St Louis, MO, USA

C. Ortinau

147. Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands

R. Ossenkoppele

148. Lund University, Clinical Memory Research Unit, Lund, Sweden

R. Ossenkoppele

149. Robarts Research Institute and The Brain and Mind Institute, University of Western Ontario, London, Ontario, Canada

L. Palaniyappan

150. Department of Psychiatry, Federal University of Sao Poalo (UNIFESP), Sao Poalo, Brazil

P. M. Pan

151. National Institute of Developmental Psychiatry for Children and Adolescents (INPD), Sao Poalo, Brazil

P. M. Pan & A. Zugman

152. Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, Victoria, Australia

C. Pantelis

153. Melbourne School of Engineering, The University of Melbourne, Parkville, Victoria, Australia

C. Pantelis

154. Florey Institute of Neuroscience and Mental Health, Parkville, Victoria, Australia

C. Pantelis

155. Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada

M. M. Park

156. Department of Psychiatry, Faculty of Medicine and Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, Quebec, Canada

T. Paus

157. Departments of Psychiatry and Psychology, University of Toronto, Toronto, Ontario, Canada

T. Paus

158. Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto, Ontario, Canada

Z. Pausova

159. Cuban Neuroscience Center, Havana, Cuba

D. Paz-Linares

160. Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Quebec, Canada

A. Pichet Binette & S. Villeneuve

161. Douglas Mental Health University Institute, Montreal, Quebec, Canada

A. Pichet Binette & S. Villeneuve

162. School of Psychology, Southwest University, Chongqing, China

J. Qiu

163. Department of Biomedical Engineering, The N.1 Institute for Health, National University of Singapore, Singapore, Singapore

A. Qiu & A. Pichet Binette

164. Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK

T. Rittman, K. A. Tsvetanov & A. Pichet Binette

165. Department of Neurology, Harvard Medical School, Boston, MA, USA

C. K. Rollins

166. Department of Neurology, Boston Children’s Hospital, Boston, MA, USA

C. K. Rollins

167. Instituto de Biomedicina de Sevilla (IBiS) HUVR/CSIC/Universidad de Sevilla, Dpto. de Fisiología Médica y Biofísica, Seville, Spain

R. Romero-Garcia

168. Department of Psychology and Neuroscience Institute, University of Chicago, Chicago, IL, USA

M. D. Rosenberg

169. Department of Paediatrics and Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK

D. H. Rowitch

170. Department of Psychiatry, Universidade Federal do Rio Grande do Sul (UFRGS), Hospital de Clinicas de Porto Alegre, Porto Alegre, Brazil

G. A. Salum

171. National Institute of Developmental Psychiatry (INPD), São Paulo, Brazil

G. A. Salum

172. Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

H. L. Schaare

173. Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Juelich, Juelich, Germany

H. L. Schaare

174. Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA

A. P. Schultz

175. Centre for Population Neuroscience and Stratified Medicine (PONS), Institute for Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China

G. Schumann

176. PONS-Centre, Charite Mental Health, Dept of Psychiatry and Psychotherapy, Charite Campus Mitte, Berlin, Germany

G. Schumann

177. Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden

M. Schöll

178. Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden

M. Schöll

179. Dementia Research Centre, Queen’s Square Institute of Neurology, University College London, London, UK

M. Schöll

180. Care Research and Technology Centre, UK Dementia Research Institute, London, UK

D. Sharp

181. Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

R. T. Shinohara

182. Departments of Neurology, Pediatrics, and Radiology, Washington University School of Medicine, St Louis, MO, USA

C. D. Smyser

183. SA MRC Unit on Risk and Resilience in Mental Disorders, Dept of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa

D. J. Stein

184. Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK

A. Stolicyn & H. C. Whalley

185. Department of Neuroscience, Institut Pasteur, Paris, France

R. Toro & N. Traut

186. Center for Research and Interdisciplinarity (CRI), Université Paris Descartes, Paris, France

N. Traut

187. Department of Psychology, University of Cambridge, Cambridge, UK

K. A. Tsvetanov

188. Wu Tsai Institute, Yale University, New Haven, CT, USA

N. B. Turk-Browne

189. Department of Clinical Medicine, University of Turku, Turku, Finland

J. J. Tuulari

190. Turku Collegium for Science, Medicine and Technology, University of Turku, Turku, Finland

J. J. Tuulari

191. Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, U1219, CHU Bordeaux, Bordeaux, France

C. Tzourio

192. Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Quebec, Canada

É. Vachon-Presseau

193. Alan Edwards Centre for Research on Pain (AECRP), McGill University, Montreal, Quebec, Canada

P. A. Valdes-Sosa

194. Institute for Neuroscience and Medicine 7, Forschungszentrum Jülich, Jülich, Germany

S. L. Valk

195. Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

S. L. Valk

196. Department of Psychiatry and Neurosychology, Maastricht University, Maastricht, The Netherlands

T. van Amelsvoort

197. Department of Biostatistics, Vanderbilt University, Nashville, TN, USA

S. N. Vandekar

198. Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA

S. N. Vandekar

199. Clinic for Cognitive Neurology, University of Leipzig Medical Center, Leipzig, Germany

A. Villringer & A. V. Witte

200. State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China

Y. S. Wang, N. Yang & X. N. Zuo

201. Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China

Y. S. Wang, N. Yang & X. N. Zuo

202. National Basic Science Data Center, Beijing, China

Y. S. Wang, N. Yang & X. N. Zuo

203. Research Center for Lifespan Development of Brain and Mind, Institute of Psychology, Chinese Academy of Sciences, Beijing, China

Y. S. Wang, N. Yang & X. N. Zuo

204. Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden

E. Westman

205. Faculty of Medicine, CRC 1052 ‘Obesity Mechanisms’, University of Leipzig, Leipzig, Germany

A. V. Witte

206. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore

B. Yeo & J. H. Zhou

207. Centre for Sleep and Cognition and Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

B. Yeo

208. N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore

B. Yeo

209. Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore

B. Yeo

210. Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia

A. Zalesky

211. Center for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

J. H. Zhou

212. Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK

H. Ziauddeen

213. National Institute of Mental Health (NIMH), National Institutes of Health (NIH), Bethesda, MD, USA

A. Zugman

214. Department of Psychiatry, Escola Paulista de Medicina, São Paulo, Brazil

A. Zugman

215. Key Laboratory of Brain and Education, School of Education Science, Nanning Normal University, Nanning, China

X. N. Zuo

216. Memory Disorders Clinic, Austin Health, Melbourne, Victoria, Australia

C. Rowe

217. University Hospitals and University of Geneva, Geneva, Switzerland

G. B. Frisoni

218. IRCCS Fatebenefratelli, The National Centre for Alzheimer’s and Mental Diseases, Brescia, Italy

G. B. Frisoni

Consortia

3R-BRAIN

- X. N. Zuo

AIBL

- C. Rowe

Alzheimer’s Disease Neuroimaging Initiative

- C. R. Jack Jr

Alzheimer’s Disease Repository Without Borders Investigators

- G. B. Frisoni

CALM Team

- D. E. Astle

Cam-CAN

- R. N. Henson

CCNP

- Y. S. Wang

- , N. Yang

- & X. N. Zuo

COBRE

- V. D. Calhoun

cVEDA

- B. Holla

ENIGMA Developmental Brain Age Working Group

- J. H. Cole

- , N. Bourke

- , H. C. Whalley

- , D. C. Glahn

- , J. Seidlitz

- , R. A. I. Bethlehem

- & A. F. Alexander-Bloch

Developing Human Connectome Project

- A. D. Edwards

FinnBrain

- H. Karlsson

- , L. Karlsson

- , J. D. Lewis

- & J. J. Tuulari

Harvard Aging Brain Study

- K. A. Johnson

- , K. A. Johnson

- , K. A. Johnson

- & K. A. Johnson

IMAGEN

- S. Desrivieres

- , A. Heinz

- , T. Jia

- & G. Schumann

KNE96

- J. Bae

- , K. W. Kim

- & S. Lee

The Mayo Clinic Study of Aging

- C. R. Jack Jr

- & D. T. Jones

NSPN

- E. T. Bullmore

- , R. Dolan

- , P. Fonagy

- , I. M. Goodyer

- & P. B. Jones

POND

- E. Anagnostou

- , M. Ayub

- , J. Crosbie

- , C. F. Hammill

- , E. A. Kelley

- , J. Lerch

- & R. J. Schachar

The PREVENT-AD Research Group

- A. Pichet Binette

- & S. Villeneuve

VETSA

- J. A. Elman

- , C. E. Franz

- & W. S. Kremen

Corresponding authors

Correspondence to R. A. I. Bethlehem or J. Seidlitz.

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Bethlehem, R.A.I., Seidlitz, J., White, S.R. et al. Publisher Correction: Brain charts for the human lifespan. Nature 610, E6 (2022). https://doi.org/10.1038/s41586-022-05300-0

- Published: 23 September 2022

- Issue Date: 13 October 2022

- DOI: https://doi.org/10.1038/s41586-022-05300-0

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James M. Gardner,

Marlon Stoeckius &

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Dan R. Littman

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Nature volume 610, page E7 (2022)

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The Original Article was published on 07 September 2022

Correction to: Nature https://doi.org/10.1038/s41586-022-05089-y Published online 7 September 2022

In the version of this article initially published online, the Author contributions section was truncated. It has been amended to read “R.K., T.A.N., K.R.M. and D.R.L. designed the study and analysed the data. R.K. and T.A.N. performed mouse genetic experiments with assistance from A.G. M.P., M.X. and J.T. performed early experiments to launch the study. K.R.M. and R.K. performed intravital multiphoton microscopy. R.K., S.H. and M.S. performed CITE-seq studies. A.T.S. and C.A.L. performed scRNA-seq. R.K., L.K., Y.H. and J.M.G. did bioinformatics analyses. J.W., M.S.A. and J.M.G. provided biological samples, genomics data and experimental support. H.P., T.M.L., I.A., J.M.B. and P.M.A. contributed mouse strains. H.P. contributed phenotypic analysis. R.K. and D.R.L. wrote the manuscript, with input from the other authors. D.R.L. supervised the research” in the HTML and PDF versions of the article.

Author information

Author notes

1. Maria Pokrovskii

Present address: Calico Life Sciences, LLC, South San Francisco, CA, USA

2. Mo Xu

Present address: National Institute for Biological Sciences, Beijing, China

3. Jhimmy Talbot

Present address: Fred Hutchinson Cancer Center, Seattle, WA, USA

4. Juliet M. Bartleson

Present address: Federation Bio, South San Francisco, CA, USA

5. Marlon Stoeckius

Present address: 10X Genomics, Stockholm, Sweden

Authors and Affiliations

1. Molecular Pathogenesis Program, The Kimmel Center for Biology and Medicine of the Skirball Institute, New York University School of Medicine, New York, NY, USA

Ranit Kedmi, Tariq A. Najar, Kailin R. Mesa, Allyssa Grayson, Lina Kroehling, Maria Pokrovskii, Mo Xu, Jhimmy Talbot & Dan R. Littman

2. Howard Hughes Medical Institute, New York, NY, USA

Allyssa Grayson & Dan R. Littman

3. Center for Genomics and Systems Biology, New York University, New York, NY, USA

Yuhan Hao

4. New York Genome Center, New York, NY, USA

Yuhan Hao

5. Technology Innovation Lab, New York Genome Center, New York, NY, USA

Stephanie Hao & Marlon Stoeckius

6. Diabetes Center, University of California, San Francisco, San Francisco, CA, USA

Jiaxi Wang, Joe Germino, Mark S. Anderson & James M. Gardner

7. Department of Pathology, Stanford University, Stanford, CA, USA

Caleb A. Lareau & Ansuman T. Satpathy

8. Parker Institute for Cancer Immunotherapy, Stanford University, Stanford, CA, USA

Caleb A. Lareau & Ansuman T. Satpathy

9. Gladstone–UCSF Institute of Genomic Immunology, San Francisco, CA, USA

Caleb A. Lareau & Ansuman T. Satpathy

10. Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Terri M. Laufer

11. Department of Medicine, C. Michael Crescenz Veterans Administration Medical Center, Philadelphia, PA, USA

Terri M. Laufer

12. Department of Pathology, New York University School of Medicine, New York, NY, USA

Iannis Aifantis

13. Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA

Juliet M. Bartleson & Paul M. Allen

14. CIRI, Centre International de Recherche en Infectiologie, Université de Lyon, INSERM U1111, Université Claude Bernard Lyon 1, CNRS UMR5308, ENS de Lyon, Lyon, France

Helena Paidassi

15. Department of Surgery, University of California, San Francisco, San Francisco, CA, USA

James M. Gardner

Corresponding author

Correspondence to Dan R. Littman.

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Kedmi, R., Najar, T.A., Mesa, K.R. et al. Publisher Correction: A RORγt+ cell instructs gut microbiota-specific Treg cell differentiation. Nature 610, E7 (2022). https://doi.org/10.1038/s41586-022-05358-w

- Published: 23 September 2022

- Issue Date: 13 October 2022

- DOI: https://doi.org/10.1038/s41586-022-05358-w

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