Nature - The International Journal of Science / 22 February 2024

Table of contents :
Countries need to make good on pledged biodiversity funds.
Science can drive development in Africa — as it does in the US and Europe.
Generative AI is guzzling water and energy.
How to boost your research: take a sabbatical in policy.
GOING APE: ANIMALS SHOW TEASING ISN’T UNIQUE TO HUMANS.
WHO CAN KEEP COVID IN CHECK? IMMUNE MARKERS HOLD CLUES.
HOW SCIENTIFIC JOURNALS ARE FIGHTING BACK AGAINST A TORRENT OF QUESTIONABLE IMAGES.
CLIMATOLOGIST MICHAEL MANN WINS DEFAMATION CASE.
CHINA CONDUCTS FIRST NATIONWIDE REVIEW OF RETRACTIONS.
BIOLUMINESCENT HOUSEPLANT HITS US MARKET FOR FIRST TIME.
MEAT–RICE: GRAIN WITH ADDED MUSCLES BEEFS UP PROTEIN.
LARGEST POST-PANDEMIC SURVEY FINDS TRUST IN SCIENTISTS IS HIGH.
MIND-READING DEVICES ARE REVEALING THE BRAIN’S SECRETS.
SCIENTISTS TAKE ACTION OVER CLIMATE CHANGE.
Greener cities: a necessity or a luxury?
Rare isotopes formed in prelude to γ-ray burst.
A neural circuit that keeps flies on target.
Energetic laser pulses alter outcomes of X-ray studies.
Nanotraps boost light intensity for future optics.
Smoking’s lasting effect on the immune system.
How population size shapes fish evolution.
Natural killer cell therapies.
Heavy-element production in a compact object merger observed by JWST.
A lanthanide-rich kilonova in the aftermath of a long gamma-ray burst.
Avoiding fusion plasma tearing instability with deep reinforcement learning.
Signatures of a surface spin–orbital chiral metal.
Fractional quantum anomalous Hall effect in multilayer graphene.
Directive giant upconversion by supercritical bound states in the continuum.
A 3D nanoscale optical disk memory with petabit capacity.
Twisted-layer boron nitride ceramic with high deformability and strength.
Progressive unanchoring of Antarctic ice shelves since 1973.
Fertilizer management for global ammonia emission reduction.
Convergence of coronary artery disease genes onto endothelial cell programs.
Converting an allocentric goal into an egocentric steering signal.
Transforming a head direction signal into a goal-oriented steering command.
Smoking changes adaptive immunity with persistent effects.
The HIV capsid mimics karyopherin engagement of FG-nucleoporins.
HIV-1 capsids enter the FG phase of nuclear pores like a transport receptor.
Bile salt hydrolase acyltransferase activity expands bile acid diversity.
Bile salt hydrolase catalyses formation of amine-conjugated bile acids.
The nuclear factor ID3 endows macrophages with a potent anti-tumour activity.
Stress response silencing by an E3 ligase mutated in neurodegeneration.
An epigenetic barrier sets the timing of human neuronal maturation.
Translation selectively destroys non-functional transcription complexes.
Conformational ensembles of the human intrinsically disordered proteome.
HOW MY ACADEMIC SABBATICAL RESTARTED MY CAREER.
GENOME DIAGNOSTICS IN THE FAST LANE.
Corrections

Citation preview

https:/ www.foxitsoftware.cn/editmac/?MD=Mshanchu

The international journal of science / 22 February 2024

Countries need to make good on pledged biodiversity funds

These UN-mediated funds are just one source of biodiversity funding. In 2019, private and public sources contributed between $78 billion and $143 billion, according to a landmark 2021 review of biodiversity economics for the UK government (see go.nature.com/49fe686). But even this is a fraction of the up-to $967 billion needed annually to achieve the 2030 targets, according to a study of biodiversity financing (G. A. Karolyi & J. Tobin-de la Puente Financ. Manage. 52, 231–251; 2023). And that means the $219 million that countries have promised to the GBFF is, perhaps literally, a drop in the ocean. Other wealthy countries must contribute, too. More than two years ago, China established the Kunming Biodiversity Fund, worth $235 million. Yet this fund is still not operational. It needs to be allocated to projects as soon as possible. And the United States, too, should contribute an amount to the GBFF that reflects the size of its economy. In 2022, the US Agency for International Development contributed $383 million to biodiversity conservation programmes worldwide.

Nations’ promises to provide more than US$200 million to safeguard biodiversity need to be translated into money in the bank.

E

arlier this month, conservationists and biodiversity scientists received some rare, good news at the first meeting of a much-anticipated fund for projects aimed at preserving Earth’s biodiversity. The Global Biodiversity Framework Fund (GBFF) will provide grants for projects that protect biodiversity, especially in countries with a high variety of marine and terrestrial life, as measured by a global biodiversity index (see go.nature.com/3wekupz). So far, five nations — Canada, Germany, Japan, Spain and the United Kingdom — have pledged money to the tune of US$219 million. At the meeting on 8 and 9 February, the GBFF’s co-chairperson, Costa Rica’s former environment and energy minister Carlos Manuel Rodríguez, called the fund’s establishment “one of his proudest and most significant moments”, and he urged other countries to support the initiative, too. They should — and fast. Research suggesting that urgent action is needed to stem biodiversity loss is regularly published. The latest warnings come from the United Nations’ first report that looks at the state of the world’s migratory species — billions of birds, fish, insects, mammals and reptiles travel thousands of kilometres each year for food or to breed (see go.nature.com/4bxrmag). Published on 12 February by the UN Convention on the Conservation of Migratory Species of Wild Animals, the report reveals that 44% of migratory species are declining, and that 22% of them are threatened with extinction. There is no time to lose. The launch of a global public fund for biodiversity is rare. The GBFF’s parent fund, the Global Environment Facility in Washington DC, was established more than three decades ago with an initial endowment of $1 billion. Between 2022 and 2026, it plans to distribute $840 million between 45 projects related to biodiversity, climate, international waters and land degradation. But the GBFF has an extra purpose: to help countries to achieve targets for slowing down and, eventually, halting the decline in global biodiversity. These targets, agreed at a UN biodiversity meeting (COP15) in Montreal, Canada, in December 2022, are collectively known as the Kunming– Montreal Global Biodiversity Framework. One goal is to protect and restore 30% of the world’s land and seas by 2030.

China’s Kunming Biodiversity Fund is still not operational.”

Returns on investment The fact that the GBFF is committed to providing grants, not loans is important. But this might also be one of the reasons why current pledges are not being translated into funds that can be distributed. Climate funds, for example, are given mostly as loans and not grants. They support renewable energy projects, for instance, or factories that make electric batteries — meaning that international donors could expect to make money on what are essentially investments. By contrast, biodiversity funds that support projects to protect wetlands for migratory birds or manage agricultural lands in nature-friendly ways often do not provide returns — at least not in terms of cash. This is partly because current economic systems fail to see the value that a healthy planet provides through biodiversity and ecosystem services. To help increase the pot of money, the GBFF will accept funding from philanthropic foundations — an increasingly important source of environment and development grants. Getting such foundations to contribute to international public funds is not easy, and it’s good to see GBFF advocates working on persuading them. Foundations will need to give up some of their autonomy in deciding on which projects will receive a grant. But they should see the invitation to participate in the GBFF as a benefit, rather than a burden. The fund’s global nature means that more biodiversity projects can receive grants. This could help more parts of the planet and greater numbers of people than when projects are funded by a foundation on its own. Having foundations participate in international public funds can only be a good thing, especially at a time when they are in the spotlight for a perceived lack of accountability. Getting nearly 200 countries to reach an agreement on the make-up of any new institution, and then getting donors to fund it, is one of the hardest parts of multilateral policymaking. The architects of the GBFF should be congratulated on getting their fund off the ground and securing an early round of pledges. It’s now time to translate words into action.

Nature | Vol 626 | 22 February 2024 | 691

Editorials

Science can drive development in Africa — as it does in the US and Europe Efforts to establish Africa’s first continentwide science fund have struggled. More needs to be done to get it up and running.

“I

t is within the possibility of science and technology to make even the Sahara bloom into a vast field with verdant vegetation.” These words, which still hold true today, were spoken by Kwame Nkrumah, educator, political theorist and the first president of independent Ghana. Nkrumah made the remarks in a landmark speech some 60 years ago at the launch of the Organisation of African Unity (OAU). The OAU has since been succeeded by the African Union (AU), and the leaders of its 55 member states met last weekend for their annual summit in Addis Ababa. There have been some notable successes in advancing Nkrumah and his successors’ vision for scientific cooperation across Africa. One is the creation of the Africa Centres for Disease Control and Prevention, based in Addis Ababa, in 2016. The founding of the African Institute for Mathematical Sciences, which is now more than two decades old, is another. An African Medicines Agency that would harmonize approvals for new medicines is at an advanced stage of development. And the Great Green Wall project to tackle land degradation across borders, although beset by many problems, offers another example of states willingly setting aside their differences in an effort to achieve shared goals. But funding, especially for smaller-scale collaborations, remains a perennial sticking point. African countries share many challenges that science can help to overcome, among them food insecurity, climate adaptation and conflict, which are also priorities for the United Nations Sustainable Development Goals (SDGs). Studies show that researchers in low- and middleincome countries are more focused on the SDGs than are their counterparts in high-income nations. However, the AU does not have a continent-wide fund that would allow African researchers to work together on these shared challenges. Such a fund would not necessarily need to run to hundreds of millions of dollars. It could start small, providing grants for travel to meetings and conferences, for example, or for the development of larger research proposals, or fees to pay for specific training courses. But it is needed. A decade ago, the AU and the African Development Bank, based in Abidjan, Côte d’Ivoire, began work on 692 | Nature | Vol 626 | 22 February 2024

Africa’s researchers need their leaders to show that they are capable of forward thinking.”

establishing a research and innovation fund. Its scope has since been expanded to include education, and it is now called the African Education, Science, Technology and Innovation Fund. Countries were asked to contribute US$2 million each, which would be matched by the bank from its own sources. Progress has been slow. So far, only Botswana and Ghana have committed funding. The fund’s overall size has been set at $300 million, but who else will contribute and eligibility criteria for funding applications are yet to be worked out. The fund needs to become a priority, say the authors of a review of AU science policies over the past decade, commissioned by the AU and the UN science agency UNESCO, and published at the end of last year (see go.nature.com/3sixqis). Ultimately, that means more heads of government will need to authorize the required finance. The continent’s leaders do, of course, have more immediate concerns. Many countries are still gripped by an economic crisis that followed the COVID-19 pandemic and Russia’s 2022 invasion of Ukraine. There are also tensions between some of the union’s bigger and more influential member states. Ethiopia, which hosts the AU’s head offices, is at loggerheads with Egypt over the construction of a dam, and with Somalia over access to a seaport. Other nations have problems at home. In Sudan, some 13,000 people have been killed and 6 million displaced since April 2023, as a result of armed conflict. And this week, Senegal has seen huge protests after President Macky Sall controversially decided to delay elections. But there must be room on the agenda for forward-looking projects, too. These are also rocky times for other regional unions, not least the European Union, which works closely with — and provides considerable financial support to — the AU. It has lost one of its biggest members, the United Kingdom, as a result of Brexit, and is considering taking legal action against Hungary for violating democratic principles. Europe is also seeing a rise in popular support for parties that do not agree with many EU laws. Rifts in national and regional unions inevitably affect scientific cooperation between members. But in the history of such unions, including both the EU and the AU, leaders have generally recognized that there is more to be gained by permitting collaboration between people and institutions than by stopping it. By the 1960s, Nkrumah, who had spent a decade studying in the United States in the 1930s and 1940s, wanted for Africa what he saw the United States and European nations achieving. When he spoke at the OAU’s launch in 1963, he noted that “there is hardly any African state without a frontier problem with its adjacent neighbours”. He helped African countries to establish the OAU despite the divisions that existed between them. The continent’s researchers need their leaders to show that they are capable of the same forward-thinking now. Science is crucial to solving shared challenges such as hunger and environmental degradation. An Africa-wide fund, even a modest one, will also spark collaborations that could help to create or strengthen bonds of unity. And that alone would be no small win.

A personal take on science and society

World view By Kate Crawford

Generative AI is guzzling water and energy First-of-its-kind US bill would address the environmental costs of the technology, but there’s a long way to go.

CATH MUSCAT

L

ast month, OpenAI chief executive Sam Altman finally admitted what researchers have been saying for years — that the artificial intelligence (AI) industry is heading for an energy crisis. It’s an unusual admission. At the World Economic Forum’s annual meeting in Davos, Switzerland, Altman warned that the next wave of generative AI systems will consume vastly more power than expected, and that energy systems will struggle to cope. “There’s no way to get there without a breakthrough,” he said. I’m glad he said it. I’ve seen consistent downplaying and denial about the AI industry’s environmental costs since I started publishing about them in 2018. Altman’s admission has got researchers, regulators and industry titans talking about the environmental impact of generative AI. So what energy breakthrough is Altman banking on? Not the design and deployment of more sustainable AI systems — but nuclear fusion. He has skin in that game, too: in 2021, Altman started investing in fusion company Helion Energy in Everett, Washington. Most experts agree that nuclear fusion won’t contribute significantly to the crucial goal of decarbonizing by mid-century to combat the climate crisis. Helion’s most optimistic estimate is that by 2029 it will produce enough energy to power 40,000 average US households; one assessment suggests that ChatGPT, the chatbot created by OpenAI in San Francisco, California, is already consuming the energy of 33,000 homes. It’s estimated that a search driven by generative AI uses four to five times the energy of a conventional web search. Within years, large AI systems are likely to need as much energy as entire nations. And it’s not just energy. Generative AI systems need enormous amounts of fresh water to cool their processors and generate electricity. In West Des Moines, Iowa, a giant data-centre cluster serves OpenAI’s most advanced model, GPT-4. A lawsuit by local residents revealed that in July 2022, the month before OpenAI finished training the model, the cluster used about 6% of the district’s water. As Google and Microsoft prepared their Bard and Bing large language models, both had major spikes in water use — increases of 20% and 34%, respectively, in one year, according to the companies’ environmental reports. One preprint1 suggests that, globally, the demand for water for AI could be half that of the United Kingdom by 2027. In another2, Facebook AI researchers called the environmental effects of the industry’s pursuit of scale the “elephant in the room”. Rather than pipe-dream technologies, we need pragmatic

Within years, large AI systems are likely to need as much energy as entire nations.”

Kate Crawford is a professor at the University of Southern California Annenberg, a senior principal researcher at Microsoft Research in New York City and author of the 2021 book Atlas of AI. e-mail: kate@ katecrawford.net The author declares competing interests; see go.nature.com/3wobo1h

actions to limit AI’s ecological impacts now. There’s no reason this can’t be done. The industry could prioritize using less energy, build more efficient models and rethink how it designs and uses data centres. As the BigScience project in France demonstrated with its BLOOM model3, it is possible to build a model of a similar size to OpenAI’s GPT-3 with a much lower carbon footprint. But that’s not what’s happening in the industry at large. It remains very hard to get accurate and complete data on environmental impacts. The full planetary costs of generative AI are closely guarded corporate secrets. Figures rely on lab-based studies by researchers such as Emma Strubell4 and Sasha Luccioni3; limited company reports; and data released by local governments. At present, there’s little incentive for companies to change. But at last, legislators are taking notice. On 1 February, US Democrats led by Senator Ed Markey of Massachusetts introduced the Artificial Intelligence Environmental Impacts Act of 2024. The bill directs the National Institute for Standards and Technology to collaborate with academia, industry and civil society to establish standards for assessing AI’s environmental impact, and to create a voluntary reporting framework for AI developers and operators. Whether the legislation will pass remains uncertain. Voluntary measures rarely produce a lasting culture of accountability and consistent adoption, because they rely on goodwill. Given the urgency, more needs to be done. To truly address the environmental impacts of AI requires a multifaceted approach including the AI industry, researchers and legislators. In industry, sustainable practices should be imperative, and should include measuring and publicly reporting energy and water use; prioritizing the development of energy-efficient hardware, algorithms, and data centres; and using only renewable energy. Regular environmental audits by independent bodies would support transparency and adherence to standards. Researchers could optimize neural network architectures for sustainability and collaborate with social and environmental scientists to guide technical designs towards greater ecological sustainability. Finally, legislators should offer both carrots and sticks. At the outset, they could set benchmarks for energy and water use, incentivize the adoption of renewable energy and mandate comprehensive environmental reporting and impact assessments. The Artificial Intelligence Environmental Impacts Act is a start, but much more will be needed — and the clock is ticking. 1.

Li, P., Yang, J., Islam, M. A. & Ren, S. Preprint at https://arxiv.org/ abs/2304.03271 (2023). 2. Wu, C.-J. et al. Preprint at https://arxiv.org/abs/2111.00364 (2021). 3. Luccioni, A. S., Viguier, S. & Ligozat, A.-L. Preprint at https://arxiv.org/ abs/2211.02001 (2022). 4. Kaack, L. H. et al. Nature Clim. Chang. 12, 518–527 (2022).

Nature | Vol 626 | 22 February 2024 | 693 https://shop126307946.taobao.com/?spm=2013.1.1000126.3.c9895b91Isk2Oc

A personal take on science and society

World view

By Jordan Dworkin

How to boost your research: take a sabbatical in policy Academic researchers have a unique opportunity to serve the public good by spending time in government.

“F

or 20 years, I thought my job was, as a basic scientist, [to] publish papers and throw them over the wall for someone else to apply. I now realize that there’s no one on the other side of the wall, just a huge pile of papers that we’ve all thrown over.” These words from sociologist Duncan Watts at the University of Pennsylvania in Philadelphia will resonate with many researchers who are frustrated by the difficulties of translating good science into good governmental policy. But what can be done about it? One under-appreciated answer, for scientists who can take a sabbatical, is to spend time working in government. Tours of service, as these experiences are called in the United States, can boost agency capacity and expertise, bring fresh perspectives into policymaking and create lasting relationships between the government and external researchers. Spending a sabbatical in policy work can help scientists to identify urgent, understudied research areas, communicate their findings to decision makers and translate their knowledge into action. It can also enhance their reputation and visibility, both in academia and outside it. Governments around the world are facing unprecedented questions related to science and technology, from climate change to artificial intelligence, often while operating on insufficient budgets and struggling to find relevant experts. There is a pressing need for scientific expertise in crafting and implementing sound, robust, evidence-based policies. At the Federation of American Scientists (FAS) — a non-profit, non-partisan policy-research organization based in Washington DC — I have witnessed at first hand the impact of bringing technical expertise into government. Over the past few years, FAS’s Talent Hub has helped US federal agencies to bring on world-class fellows. Last year, the programme placed 71 researchers into one- to two-year tours of service. Agencies’ budgets and philanthropic funding can go only so far — as a result, fewer of these placements are offered than are needed. Last year, FAS and the Institute for Progress, a think tank in Washington DC, launched Sabbaticals in Service to address this gap. This pilot project taps into academia’s paid, flexible sabbaticals by helping to match academics who have sabbatical credits with federal agencies, where their expertise can have an impact on policy. It takes inspiration from successful university-level public-service programmes, such as Stanford Impact Labs at Stanford University in California. 694 | Nature | Vol 626 | 22 February 2024

Spending a sabbatical in policy work can help scientists to identify urgent, understudied research areas.”

Jordan Dworkin is the programme lead for metascience at the Federation of American Scientists in Washington DC. e-mail: jdworkin@ fas.org

To be sure, spending a sabbatical working in government instead of writing a book or completing a research fellowship will not appeal to everyone. But many of the concerns about this option are overblown. Fears that six months or a year are not enough time to make a difference don’t account for the long-term value of the relationships that such placements establish. Worries that policymakers will not be receptive to scientists’ perspectives overlook the impact of efforts to enhance evidence-based policymaking. And concerns about opportunity costs underestimate the insights and avenues for research that direct policy engagement can unlock. Many academics who have done tours of service trumpet these benefits. Ira Lit, an education researcher at Stanford, says that while doing a policy sabbatical through Stanford Impact Labs’ Scholars in Service programme, he learnt substantially more than he would have from a typical sabbatical — and in ways he couldn’t have anticipated. And after completing a fellowship at the US Department of Agriculture, veterinary epidemiologist Gay Miller at the University of Illinois Urbana–Champaign continued to collaborate with the agency to model the impacts of foreign livestock diseases. Many researchers who could excel in government roles simply don’t know about them. To start building awareness, scientists can seek out information on opportunities and supportive organizations — including FAS and other non-profit bodies such as the American Association for the Advancement of Science and the Horizon Institute for Public Service, both based in Washington DC, and Research4Impact in Baltimore, Maryland — and discuss policy roles with colleagues. Researchers can’t do this alone. More universities should support scientists pursuing tours of service by providing guidance, mentoring and networking opportunities, and recognizing policy sabbaticals in promotion, tenure and hiring processes. Government offices can smooth the way, too: they should start by publicly listing a point of contact for interested scientists, and those with experience in hiring and engaging with academics can share their knowledge with others. Ultimately, agencies could build sustainable programmes for short-term placements for researchers — excellent models include the US Congressional Budget Office’s Visiting Scholars programme, the US Patent and Trademark Office’s Croak Visiting Scholars programme and Jefferson Science Fellowships in the US Department of State and the US Agency for International Development. Together, we can reimagine the metaphorical wall between science and policy as something more inviting. Helping academics to navigate the other side effectively can advance evidence-based policy and impactful science.

Selections from the scientific literature

Research highlights CLUTCH CONTROL: DNA ENGAGES NEWLOOK NANOMOTOR

WHO CAN KEEP COVID IN CHECK? IMMUNE MARKERS HOLD CLUES

Microscopic motors can be carefully controlled thanks to the inclusion of programmable DNA strands. A basic motor consists of an engine and a revolving part called a rotor. In many motors, these two components can be selectively connected or disconnected from each other for control, safety and convenience. However, in nanometre-scale motors, the engine typically needs to be permanently linked to the rotor, preventing this capability. Mouhong Lin at the Institute for Basic Science Center for Nanomedicine in Seoul and his colleagues have developed a nanomotor that overcomes this limitation. In their device, the engine is a magnetic gold particle that is powered by remote magnetic fields, and the rotor is a porous, spherical gold cage that confines the particle. The engine and the rotor are coated with programmable DNA, which acts as a ‘clutch’, connecting or disconnecting the two components from each other when it recognizes particular molecular inputs. The researchers say that their nanomotor is straightforward to manufacture and could have medical applications.

A study of the intricacies of the body’s response to infection with SARS-CoV-2 has identified some key immune cells (such as T cells, example pictured) and proteins associated with clearing the virus. Helen Wagstaffe at Imperial College London and her colleagues gave doses of SARS-CoV-2 to 34 healthy young adults, none of whom showed evidence of previous infection or vaccination. Eighteen became infected, and developed mild symptoms. The team took daily blood samples and swabbed the volunteers’ noses and throats for virus throughout the course of infection. This allowed the researchers to track viral replication and the immune response in each participant. The authors found that a population of immune cells called CD8+ T cells, which target and destroy infected cells, probably contributes to the clearing of infectious virus, as does the presence in the nose of antibodies known as immunoglobulin A. A larger CD8+ T-cell response and an earlier antibody response correlated with a faster drop in viral load. Enhancing these immune responses with vaccines or therapies could help to better control viral spread.

L TO R: PHILIPP HOY; NIH/SPL; BOS FOUNDATION BPI

Nature Nanotechnol. https://doi. org/mgvw (2024)

UNDERWATER STUDY COMES UP AGAINST STONE AGE WALL Divers have helped to reveal the remnants of a kilometre-long wall that are submerged in the Baltic Sea off the coast of Rerik, Germany. The rocks (pictured) date back to the Stone Age. Jacob Geersen at the Leibniz Institute for Baltic Sea Research Warnemünde in Rostock, Germany, and his colleagues used camera images, sediment cores and data from reflected sound waves to characterize a string of boulders located at a depth of 21 meters around 10 kilometres from shore. They also used human divers and underwater autonomous vehicles to explore the site. The team counted around 1,673 rocks in a formation that stretches 971 metres. Most of the rocks weigh less than 100 kilograms and thus could be moved by small groups of people. Analysis suggests that the structure ran along the shoreline of a former lake or bog. It was probably built by huntergatherers more than 10,000 years ago, possibly as a tool to guide reindeer and other large game animals during hunts. The hunting structure, which was submerged around 8,500 years ago as the sea level rose, is one of the oldest known structures of its kind on Earth. Proc. Natl Acad. Sci. USA 121, e2312008121 (2024)

Sci. Immunol. 9, eadj9285 (2024)

GOING APE: ANIMALS SHOW TEASING ISN’T UNIQUE TO HUMANS Young apes get a kick out of teasing each other and joking around when they’re relaxed, just like humans do. Isabelle Laumer at the University of California, Los Angeles, and her colleagues recorded videos of five greatape species — orangutans (Pongo abelii, pictured), chimpanzees (Pan troglodytes), bonobos (Pan paniscus), western gorillas (Gorilla gorilla) and eastern gorillas (Gorilla beringei) — as they played at zoos in San Diego, California, and Leipzig, Germany. They noted the primates’ interactions, including how often they tried to provoke a response from one another rather than simply playing together. Like bored siblings in the back seat of a car, the apes would poke their targets repeatedly, dangle objects in their faces, pull their hair or stare at them until they responded. All five species seemed to tease each other in similar ways, and were most likely to play in this way when relaxed. The researchers say that this kind of play probably evolved at least 13 million years ago, before humans’ ancestors separated from those of these ape species. Proc. R. Soc. B 291, 20232345 (2024)

Nature | Vol 626 | 22 February 2024 | 695

The world this week

SHUTTERSTOCK

News in focus

Some journals require authors to submit raw images of the gels used to analyse proteins and DNA in an effort to detect manipulated images.

HOW SCIENTIFIC JOURNALS ARE FIGHTING BACK AGAINST A TORRENT OF QUESTIONABLE IMAGES Publishers are deploying AI-based tools to detect suspicious images, but generative AI threatens their efforts. By Nicola Jones

I

t seems that every month brings a fresh slew of high-profile allegations against researchers whose papers — some of them years old — contain signs of possible image manipulation. Scientist sleuths are using their own trained eyes, along with commercial software based on artificial intelligence (AI), to spot image duplication and other issues that might hint at sloppy record-keeping or worse. They are bringing these concerns to light in places such as PubPeer, an online forum in which many new

posts every day flag image concerns. Some of these efforts have led to action. Last month, for example, the Dana-Farber Cancer Institute (DFCI) in Boston, Massachusetts, said that it would ask journals to retract or correct a slew of papers authored by its staff members. The disclosure came after an observer raised concerns about images in the papers. The institute says it is investigating the concerns. That incident was just one of many. In the face of public scrutiny, academic journals are increasingly adopting tools, including commercial AI-based systems, to spot problematic

imagery before, rather than after, publication. Here, Nature reviews the problem and how publishers are attempting to tackle it.

What sorts of imagery problem are being spotted? Questionable image practices include the use of the same data across several graphs, the replication of photos and the splicing of images. Such issues can indicate an intent to mislead, but can also result from an innocent attempt to improve a figure’s aesthetics, for example. Nonetheless, even innocent mistakes can damage the integrity of science, experts say.

Nature | Vol 626 | 22 February 2024 | 697

News in focus How prevalent are these issues, and are they on the rise? The precise number of such incidents is unknown. A database maintained by the website Retraction Watch lists more than 51,000 documented retractions, corrections or expressions of concern. Of those, about 4% flag a concern about images. Elisabeth Bik, a scientific image sleuth and consultant in San Francisco, California, and her colleagues examined images in more than 20,000 papers that were published between 1995 and 2014 (E. M. Bik et al. mBio 7, e0080916; 2016). Overall, they found that nearly 4% of the papers contained problematic figures. Modern papers also contain more images than do those from decades ago, notes Bik. The high rate of reports of image issues might also be driven by “a rise in whistle-blowing because of the global community’s increased awareness of integrity issues”, says Renee Hoch, who works for the PLOS Publication Ethics team in San Francisco, California.

What happened at the Dana-Farber Cancer Institute? In January, biologist and investigator Sholto David, based in Pontypridd, UK, blogged about possible image manipulation in more than 50 biology papers published by scientists at the DFCI, which is affiliated with Harvard University in Cambridge, Massachusetts. Among the authors were DFCI president Laurie Glimcher and her deputy, William Hahn; a DFCI spokesperson said they are not speaking to reporters. David’s blog highlighted what seemed to be duplications or other image anomalies in papers spanning almost 20 years. The post was first reported by The Harvard Crimson. The DFCI, which had already been investigating some of these issues, is seeking retractions for several papers and corrections for many others. Barrett Rollins, the DFCI’s research-integrity officer, says that “moving as quickly as possible to correct the scientific record is important and a common practice of institutions with strong research integrity”. “It bears repeating that the presence of image duplications or discrepancies in a paper is not evidence of an author’s intent to deceive,” he adds.

What are journals doing to improve image integrity? To reduce the publication of mishandled images, some journals, including the Journal of Cell Science and PLoS ONE, either require or ask that authors submit raw images in addition to the cropped or processed images in their figures. Many publishers are also incorporating AI-based tools such as ImageTwin, ImaCheck and Proofig into consistent or spot prepublication checks. The Science family of journals announced in January it is now using 698 | Nature | Vol 626 | 22 February 2024

Proofig to screen all its submissions. Holden Thorp, editor in chief of the Science journal family, says Proofig has spotted things that led editors to decide against publishing papers. He says authors are usually grateful to have their errors identified.

What kinds of issue do these AI-based systems flag? All these systems can, for example, quickly detect duplicates of images in the same paper, even if those images have been rotated, stretched or cropped. Different systems have different merits. Proofig, for example, can spot splices created by chopping out or stitching together portions of images. ImageTwin, says Bik, has the advantage of allowing users to cross-check an image against a large data set of other papers. Some publishers, including Springer Nature, are developing their own AI image-integrity software. (Nature’s news team is editorially independent of its publisher, Springer Nature.) Many of the errors flagged by AI tools seem to be innocent. In a study of more than 1,300 papers submitted to 9 American Association for Cancer Research journals in 2021 and early 2022, Proofig flagged 15% as having possible image duplications that required follow-up with authors. Author responses indicated that 28% of the 207 duplications were intentional — driven, for example, by authors using the same image to illustrate multiple points. Sixty-three per cent were unintentional mistakes.

How well do these AI systems work? Users report that AI-based systems definitely make it faster and easier to spot some kinds of image problem. The Journal of Clinical Investigation trialled Proofig from 2021 to 2022 and

found that it tripled the proportion of manuscripts identified with potentially problematic images, from 1% to 3% (S. Jackson et al. J. Clin. Invest. 132, e162884; 2022). But such tools are less adept at spotting more complex manipulations, says Bik, or AI-generated fakery. The tools are “useful to detect mistakes and low-level integrity breaches, but that is but one small aspect of the bigger issue”, agrees Bernd Pulverer, chief editor of EMBO Reports. “The existing tools are at best showing the tip of an iceberg that may grow dramatically, and current approaches will soon be largely obsolete.”

Are pre-publication checks stemming image issues? A combination of expert teams, technological tools and increased vigilance seems to be working — for the time being. “We have applied systematic screening now for over a decade and for the first time see detection rates decline,” says Pulverer. But as image manipulation gets more sophisticated, catching it will become ever harder, he says. “In a couple of years, all of our current image-integrity screening will still be useful for filtering out mistakes, but certainly not for detecting fraud,” Pulverer says.

How can image manipulation best be tackled in the long run? Ultimately, stamping out image manipulation will involve changes to how science is done, says Bik, with more focus on rigour and repercussions for bad behaviour. “There are too many stories of bullying and highly demanding PIs spending too little time in their labs, and that just creates a culture where cheating is OK,” she says. “This needs to change.”

CLIMATOLOGIST MICHAEL MANN WINS DEFAMATION CASE Jury awards Mann more than US$1 million — raising hopes for scientists who are attacked politically. By Jeff Tollefson

C

limate scientist Michael Mann has prevailed in a lawsuit that accused two conservative commentators of defamation for challenging his research and comparing him to a convicted child molester. A jury awarded Mann, who is based at the University of Pennsylvania in Philadelphia, more than US$1 million in a

landmark case that legal observers see as a warning to those who attack scientists working in controversial fields, including climate science and public health. “It’s perfectly legitimate to criticize scientific findings, but this verdict is a strong signal that individual scientists shouldn’t be accused of serious misconduct without strong evidence,” says Michael Gerrard, a legal scholar at Columbia University’s Sabin Center for

JAMES ROSS/AUSTRALIAN ASSOCIATED PRESS/ALAMY

Mann is known for the famous ‘hockey-stick graph’ depicting climate warming.

Climate Change Law in New York City. The case stems from a 2012 blog post published by the Competitive Enterprise Institute (CEI), a libertarian think-tank in Washington DC. In it, policy analyst Rand Simberg compared Mann, then at Pennsylvania State University in State College, to Jerry Sandusky, a former football coach at the same university who was convicted of sexually assaulting children, saying that “instead of molesting children, he has molested and tortured data in the service of politicized science that could have dire economic consequences for the nation and planet”. Author Mark Steyn subsequently reproduced Simberg’s comparison as he accused Mann of fraud in a blog published by the conservative magazine National Review. In the same year, Mann sued both Simberg and Steyn, as well as the CEI and the National Review, for libel, without asking for damages. The case has been winding its way through the courts ever since. Mann tells Nature that he hopes the win “signals the beginning of the end of the open season on scientists by ideologically motivated bad actors. And maybe, just maybe, that facts and reason still matter even in today’s fraught political economy”.

Counting the cost After a three-week trial in the Washington DC Superior Court, the jury ordered both Simberg and Steyn to pay $1 in compensatory damages. In addition, Steyn was ordered to pay $1,000,000 in punitive damages, and Simberg was ordered to pay $1,000. The court had ruled earlier that neither the CEI nor the National Review could be held liable for the blog posts, because both Simberg and Steyn were independent contributors and not employees of the organizations. The jury’s

decision comes at a time of increasing political polarization that has left many scientists in the United States and beyond vulnerable to verbal abuse and harassment, both online and in person. Climate scientists have become accustomed to such attacks over more than a decade; a global survey published last year indicated that scientists are suffering both physically and emotionally as a result. Many biologists and public-health scientists have encountered similar attacks since the onset of the COVID-19 pandemic.

Mann hopes the win “signals the beginning of the end of the open season on scientists”. The verdict represents “a big victory for truth and scientists everywhere who dedicate their lives answering vital scientific questions impacting human health and the planet”, Mann’s attorney, Peter Fontaine, said in a prepared statement. Scientists who say that they, too, have faced harassment from science denialists are cautiously optimistic. “I have been subjected to similar classes of attacks, both on my science and on myself as a person,” says Kim Cobb, a palaeoclimatologist at Brown University in Providence, Rhode Island. “Mann is certainly out there on the front lines, and not by choice.” Mann achieved notoriety after reconstructing global temperature trends spanning a 1,000-year period in a pair of papers published in 1998 and 1999 (M. E. Mann et al. Nature 392, 779–787 (1998); M. E. Mann et al. Geophys. Res. Lett. 26, 759–762; 1999). That work included what came to be known as the ‘hockey-stick

graph’ — a plot depicting a gradual decline in temperatures over much of the past millennium, followed by a sharp spike in the twentieth century, after the Industrial Revolution boosted greenhouse-gas emissions in the atmosphere. The hockey-stick graph became a symbol of human interference in the climate system and was reproduced by many others, including the United Nations Intergovernmental Panel on Climate Change. “In a simple picture that a kindergartner can understand, you internalize just how unprecedented the current climate trends are in the context of natural variability,” says Cobb. Because of his work, Mann became a target of criticism from climate-science deniers. Some of his e-mails, as well as those of others discussing his work, were among a trove of thousands of documents that were released after being illegally obtained from the University of East Anglia in Norwich, UK, in 2009. Critics claimed that some of the e-mails showed an attempt to manipulate climate data to indicate global warming rather than cooling. The following year, Mann was targeted in an investigation by Virginia’s then attorneygeneral Ken Cuccinelli, a conservative who questioned whether Mann had used fraudulent data to obtain grants while at the University of Virginia in Charlottesville in 1999–2005. Demands for relevant documents and communications were eventually denied by the Virginia Supreme Court in a case that many saw as a win for academic freedom.

High burden of proof In the latest case, Mann went on the offensive. But he faced a high burden of proof owing to his own notoriety: as a public figure, Mann and his attorneys had to prove not only that the defendants published false statements, but also that they acted with malice. “It is not easy to prove defamation against a public figure,” says Lauren Kurtz, executive director of the Climate Science Legal Defense Fund, an organization in New York City that was formed in 2011 to advocate for Mann and other scientists who were being targeted and harassed by climate-change sceptics. Scientists whom Kurtz has worked with have expressed some hope for the future in response to yesterday’s verdict. But she warns that Mann’s case was unusually clear-cut: the defendants accused him of fraud, but multiple investigations run by institutions such as the US National Science Foundation, which provided him with funding, and Pennsylvania State University, his former employer, have cleared him of wrongdoing and upheld his research findings. “This case might give a few commentators a moment’s pause, but it is certainly not going to lead to a rush to the courthouse by other scientists,” Gerrard says.

Nature | Vol 626 | 22 February 2024 | 699

News in focus

CHINA CONDUCTS FIRST NATIONWIDE REVIEW OF RETRACTIONS

Universities must declare all their retractions and launch investigations into misconduct cases. By Smriti Mallapaty

C

hinese universities have been told to complete a nationwide audit of retracted research papers and a probe of research misconduct. By 15 February, universities had to submit to the government a comprehensive list of all academic articles retracted from English- and Chinese-language journals in the past three years. They had to clarify why the papers were retracted and investigate cases involving misconduct, according to a 20 November notice from the Ministry of Education’s Department of Science, Technology and Informatization. The government launched the review in response to Hindawi, a London-based subsidiary of the publisher Wiley, retracting a large number of papers by Chinese authors. These retractions, along with those from other publishers, “have adversely affected our country’s academic reputation and academic environment”, the notice states. A Nature analysis shows that last year, Hindawi issued more than 9,600 retractions, of which about 8,200 had a co-author in China. Nearly 14,000 retraction notices, of which some three-quarters involved a Chinese co-author, were issued by all publishers in 2023. This is “the first time we’ve seen such a national operation on retraction investigations”, says Xiaotian Chen, a library and information scientist at Bradley University in Peoria, Illinois, who has studied retractions and research misconduct in China. Previous investigations have largely been carried out on a case-by-case basis — but this time, all institutions have to conduct their investigations simultaneously, says Chen.

includes only English-language journals, more than 17,000 retraction notices for papers published by Chinese co-authors have been issued since 1 January 2021, which is the start of the period of review specified in the notice. The analysis, an update of one conducted in December, used the Retraction Watch database, augmented with retraction notices collated from the Dimensions research database, and involved assistance from Guillaume Cabanac, a computer scientist at the University of Toulouse in France. The ministry gave universities less than three months to complete their self-review — and this was cut shorter by the academic winter break, which typically starts in mid-January and concludes after the Chinese New Year, which fell this year on 10 February. “The timing is not good,” says Shu Fei, a bibliometrics scientist at Hangzhou Dianzi University in China. Shu expects that many universities have submitted only a preliminary report of their researchers’ retracted papers. Researchers with retracted papers will have to explain whether the retraction was owing to misconduct, such as image manipulation, or an honest mistake, such as authors identifying errors in their own work, says Chen. “In other

words, they may have to defend themselves.” Universities then must investigate and penalize misconduct. If a researcher fails to declare their retracted paper and it is later uncovered, they will be punished, according to the ministry’s notice. The cost of not reporting is high, says Chen. “This is a very serious measure.” It is not known what form punishment might take, but in 2021, China’s National Health Commission posted the results of its investigations into a batch of retracted papers. Punishments included salary cuts, withdrawal of bonuses, demotions and timed suspensions from applying for research grants and rewards. The notice states explicitly that the first corresponding author of a paper is responsible for submitting the response. This requirement will largely address the problem of researchers shirking responsibility for collaborative work, says Li Tang, a science- and innovation-policy researcher at Fudan University in Shanghai, China. The notice also emphasizes due process, says Tang. Researchers alleged to have committed misconduct have a right to appeal during the investigation.

What next The notice is a good approach for addressing misconduct, says Wang Fei, who studies research-integrity policy at Dalian University of Technology in China. Previous efforts by the Chinese government have stopped at issuing new research-integrity guidelines, she says, but these were poorly implemented. And when government bodies have launched self-investigations of published literature, they were narrower in scope and lacked clear objectives. This time, the target is clear — retractions — and the scope is broad, involving the entire university research community, she says.

The ministry’s notice set off a chain of alerts, cascading to individual university departments. Bulletins posted on university websites required researchers to submit their retractions by a range of dates, mostly in January — leaving time for universities to collate and present the data. Although the alerts included lists of retractions that the ministry or the universities were aware of, they also called for unlisted retractions to be added. According to Nature’s analysis, which 700 | Nature | Vol 626 | 22 February 2024

QILAI SHEN/BLOOMBERG/GETTY

Tight deadline

Chinese science has been “adversely affected” by retractions, a government notice says.

“Cultivating research integrity takes time, but China is on the right track,” says Tang. It is not clear what the ministry will do with the flurry of submissions. Wang says that, because the retraction notices are freely available, publicizing the collated lists and underlying reasons for retraction could be useful. She hopes that a similar review will be conducted every year “to put more pressure” on authors and universities to monitor research integrity. What happens next will reveal how seriously the ministry regards research misconduct, says

Shu. He suggests that, if the ministry does not take further action, the notice could be seen as just an attempt to respond to the reputational damage caused by the mass retractions. The ministry did not respond to Nature’s questions about the investigation. Chen says that, regardless of what the ministry does with the information, the reporting process itself will help to curb misconduct. But it might mainly affect researchers publishing in English-language journals. Retraction notices are rare in Chinese-language journals.

BIOLUMINESCENT HOUSEPLANT HITS US MARKET FOR FIRST TIME Engineered petunia emits a continuous green glow thanks to genes from a light-up mushroom. By Katherine Bourzac

C

onsumers in the United States can now pre-order a genetically engineered plant for their home or garden that glows continuously. At a base cost of US$29.00, residents of the 48 contiguous states can get a petunia (Petunia hybrida) with flowers that look white during the day; but, in the dark, the plant glows a faint green. Biotechnology firm Light Bio in Sun Valley, Idaho, will begin shipping a batch of 50,000 firefly petunias in April. Researchers contacted by Nature seem enamoured by them. This is a “groundbreaking event”  — to have made a plant that can bioluminesce brightly enough to be seen with the naked eye and can be sold to plant lovers, says Diego Orzáez, a plant biologist at the Institute of Plant Molecular and Cellular Biology in Valencia, Spain. “Being a European, I have envy that consumers in the United States can have their hands on these plants.”

too, and the plant will light up. Because this was “a cool thing”, Wood says, start-up companies then tried to make the plants for decorative purposes. But the plants glowed only faintly and needed special food to fuel their light-emitting chemical reaction. The firefly petunia glows brightly and doesn’t need special food thanks to a group of genes from the bioluminescent mushroom Neonothopanus nambi. The fungus feeds its light-emitting reaction with the molecule caffeic acid, which terrestrial plants also happen to make. By inserting the mushroom genes into the petunia, researchers made it possible for the plant to produce enzymes that can convert caffeic acid into the light-emitting molecule luciferin and then recycle it back into

“If you treat the plant really well, if it gets enough sunlight and it’s healthy, it will glow brighter.”

Growing and glowing Keith Wood, chief executive and co-founder of Light Bio, has been working on bioluminescent plants — which emit light through chemical reactions inside their cells — since the 1980s. In 1986, he and his colleagues reported1 making the first such plant, a type of tobacco (Nicotiana tabacum) into which they inserted the luciferase gene from fireflies (Photinus pyralis). At the time, the goal was to learn about the basics of gene expression, and the tool is still used by plant biologists. Researchers can engineer plants so that, when a particular gene of interest is activated, the luciferase gene is switched on

caffeic acid — enabling sustained bioluminescence2. Wood co-founded Light Bio with two of the researchers behind this work: Karen Sarkisyan, a synthetic biologist at the MRC Laboratory of Medical Sciences in London; and Ilia Yampolsky, a biomolecular chemist at the Pirogov Russian National Research Medical University in Moscow. Unlike fluorescence, which requires special light bulbs, the petunia’s bioluminescence happens without needing any particular type of light. That sets the plant apart from other glowing organisms on the market, the GloFish.

These aquarium pets, available in many species and colours — including electric green tetras — fluoresce under ultraviolet light. “If you treat the plant really well, if it gets enough sunlight and it’s healthy, it will glow brighter,” Sarkisyan says. But he wants to manage people’s expectations: it’s not bright enough to keep you awake at night. It’s a gentle green glow similar to the light of the full Moon.

Engineering in a different light The plant was approved by the US Department of Agriculture in September. Sarkisyan says that Light Bio chose petunias because they’re used widely as ornamental plants in the United States. It also chose them to minimize risk. This type of petunia is not native to North America, and is not considered an invasive species. So the chances of the modified genes spreading into native plants and disrupting ecosystems should be minimal. Scientists contacted by Nature didn’t see any safety risks. “I cannot imagine any reason why this should be a concern,” Orzáez says. “People’s reactions to genetically modified plants are complicated,” says Steven Burgess, a plant biologist at the University of Illinois Urbana–Champaign. Many concerns centre around who owns a technology and who benefits from it. A glowing houseplant is different from plants used by the agriculture industry, in which one company owns the seeds, he says. Burgess compares the glowing petunia with another timely product. The purple tomato (Solanum lycopersicum), for which seeds went on sale earlier this month in the United States, is the first genetically modified food product to be marketed directly to gardeners. Researchers inserted genes from a snapdragon plant (Antirrhinum majus) into the tomato3 to achieve its colour and high levels of anthocyanins, which are antioxidants. Asked whether Light Bio is worried about plant lovers sharing cuttings of the petunia with friends, Sarkisyan says that although the firm owns patents, it doesn’t plan to crack down aggressively on the behaviour. “The most positive way of dealing with it is to come up with new, better products,” he says. Orzáez is excited about the research potential of the technology behind the petunias. He is currently developing plants that use the mushroom luciferase system to communicate when they are stressed or infected by a virus. “Genetic engineering can be used for the good of humanity,” Orzáez says, acknowledging that many people are scared of it. “Having positive examples of genetic engineering, something people can touch and bring home” could help people to see such modifications in a different light, he says. 1. Ow, D. W. et al. Science 234, 856–859 (1986). 2. Mitiouchkina, T. et al. Nature Biotechnol. 38, 944–946 (2020). 3. Butelli, E. et al. Nature Biotechnol. 26, 1301–1308 (2008).

Nature | Vol 626 | 22 February 2024 | 701

News in focus

‘Geometry can be very simple, but totally deep’: meet top maths prizewinner Claire Voisin On 30 January, mathematician Claire Voisin was awarded the 2024 Crafoord Prize in Mathematics — one of the top awards in the field. Voisin, who is based at the Jussieu Institute of Mathematics in Paris, studies algebraic geometry, a field of research concerning geometric figures — called varieties — that are defined by algebraic equations. The prototypical example is the equation x2 + y2 = 1, which defines a circle. She has been described as the world’s foremost expert on the still-unsolved Hodge conjecture, an algebraic-geometry problem that concerns the nature of the varieties that are contained inside a larger variety. The conjecture is one of the Millennium Prize Problems — seven mathematical questions that each carry a US$1-million prize for the first person to solve them. Voisin has also worked on questions that arose from the speculative ideas from physics called string theory. She spoke to Nature about some of her best-known work.

Did you like mathematics as a child? I did. I learnt some from my father. He was an engineer, so he had a very practical style, and taught me very traditional mathematics. It was very different when I went to high school. In French schools at the time, there was the fashion of ‘modern mathematics’, which was an attempt to teach abstract mathematics, such as set

702 | Nature | Vol 626 | 22 February 2024

Claire Voisin enjoys the creativity of finding ways to solve mathematical problems.

“Physicists have extraordinary ideas. But they don’t work at the same scale of time as mathematicians.” theory. We had to do completely crazy things, like compute the development of numbers in base 2. Later, when I was in preparatory school, I did not think I wanted to be a mathematician. In fact, I was interested in philosophy, because I thought that mathematics was too mechanical. When you do mathematics in school, at no point are you supposed to produce really new ideas. Only much later on, I discovered that mathematics has this depth at the level of concepts.

People often talk about mathematical theories having depth. How do you define ‘deep’? I can give you an example: the Cartesian coordinates of the plane. You can explain to a child that you can associate two numbers, x and y, with each point on a plane — a flat surface — which means you have two functions defined on the plane. It’s very simple, but it’s totally deep — it’s close to having a philosophy of space. And this is due to the seventeenth-century mathematician René Descartes. My field of mathematics was revolutionized by the late French mathematician Alexander Grothendieck in the 1960s. And the starting point was a sort of revolution in the way of understanding geometry: what is a space? When you define what a space is, you give total priority to the study of functions.

CHRISTOPHE ARCHAMBAULT/AFP/GETTY

You are the first woman to be awarded a Crafoord Prize in Mathematics. Does this have a special significance for you? No. Since I do mathematics, I have always been the first woman to do this, or to do that. Sometimes I feel that the media, each time they speak about me, say, ‘the first woman who …’. Personally, I think it’s not good to put emphasis on that. For me, I am just a mathematician. I am happy if people appreciate the mathematics that I am doing. Bias certainly still exists. I certainly recognize that mathematics, as a world, is not encouraging to girls at school and to young women. Personally — maybe because of my personality, because I don’t care what people think about me — I didn’t suffer from this.

And is coming up with the definition of a deep concept a creative act? I consider it the most creative part of our work. I would contrast this to the technical developments of a theory — where you might still need some creativity, but it’s more like a Lego game, where you put together all the technical details. But the most creative part is to put down the right definition that gives you a new way to attack a problem. For me, it’s simply extraordinary. What are the prospects for solving the Hodge conjecture? I would say it’s a disaster! We have a lot of evidence to suggest that the Hodge conjecture is true. But I would say this evidence is based on arguments that everything happens as if the Hodge conjecture were true. The problem with the Hodge conjecture is that to prove it, you have to invent a way of constructing interesting varieties. And we have absolutely zero ideas on how to do that. So, at present, I see no hope. Some of your most celebrated work has been on the mirror-symmetry conjecture, which was inspired by string theory. Are you still working on physics-inspired problems? I worked on mirror symmetry maybe for three or four years. Then I left, because I didn’t feel I was doing my best. I was trying to understand what these people had in mind, but I gave up quickly. The problem is, physicists have extraordinary ideas — sort of like magic. But they don’t work at the same scale of time as us. We mathematicians need a lot of time to produce the right definitions and to prove theorems. And we are not happy if the statements are not proved rigorously. When you start doing that, and you come back three years later, and tell the physicists, “now I have proved your formula rigorously”, they already went in another direction. Some mathematicians have stayed in contact with physics and have done extraordinary things. But for me it was not good, because I like to work alone and to ask my own questions. Interview by Davide Castelvecchi This interview has been edited for length and clarity.

MEAT–RICE: GRAIN WITH ADDED MUSCLES BEEFS UP PROTEIN The laboratory-grown food uses rice as a scaffold for cultured meat. By Jude Coleman

R

ice has been used as a scaffold to grow beef muscle and fat cells, resulting in an edible, “nutty” rice–beef combo that can be prepared in the same way as normal rice. The study, published last week in Matter1 (S. Park et al. Matter https://doi.org/mgxj; 2024), uses manufacturing methods similar to those for other cultured meat products, in which animal cells are grown on a scaffold in a laboratory, bathed in a growth medium. Using rice as the scaffold has the benefit of adding nutrition to the rice, with the beef–rice having a slightly higher fat and protein content than standard rice. The team of South Korean researchers behind the project hopes that the beef–rice will find use as a supplement for food-insecure communities or to feed troops, and will reduce the environmental impact of rearing cattle for beef. “Finding alternative protein sources or making conventional livestock production more efficient is critical,” says Jon Oatley, an animal biotechnologist at Washington State University in Pullman. That need has spurred a variety of cultured meat projects in recent years, from salmon fillets to products similar to minced beef. As of last year, only the United States and Singapore had approved the sale of lab-grown meat.

The rice grains seeded with bovine cells.

Co-author Sohyeon Park, a chemical engineer now at Massachusetts General Hospital in Boston, says that the team tried to grow beef cells directly in the porous crevices of a grain of rice, but the cells didn’t take well to the grain. Instead, the researchers found that coating the rice in fish gelatin and the widely used food additive microbial transglutaminase improved cell attachment and growth. After glazing uncooked rice grains with the gelatin–additive mix, the team seeded the grains with bovine muscle and fat cells. Then, the cells sat in the growth medium for around a week. After the culturing period, Park washed and steamed the beef-infused rice as she would conventional rice. “It was definitely different from regular rice,” she says. “It was more nutty and harder.” The nutritional content is also different, but only marginally so. A 100-gram serving of the hybrid rice contains 0.01 grams more fat and 0.31 grams more protein, a 7% and 9% change, respectively. According to the study, it’s essentially the same as eating 100 grams of rice with one gram of beef brisket — less than half a teaspoon. That’s because the beef-cell content is low, and the cells probably form just a film over the rice, says John Yuen, a tissue engineer and molecular biologist at Tufts University in Medford, Massachusetts. He says the nutritional content could be boosted by increasing the number of bovine cells on the rice. It’s something the team is looking into, says Park. In particular, she hopes to improve the fat content of the rice, which could be tricky, because fat cells don’t grow as well as muscle cells. In addition to boosting the bovine content of the hybrid rice, researchers would need to keep the price low if the product were commercialized. The team estimates that 1 kilogram of the rice as it’s made now would cost US$2.23, comparable with normal rice ($2.20 per kilogram) and much less than beef ($14.88 per kilogram). And the study estimates that hybrid rice will have a lower emissions footprint than farmed beef. If production can be scaled up, Oatley says, the hybrid rice could be a cheaper, more efficient source of nutrition than large pieces of lab-grown meat, such as patties or steaks. Yuen also finds the concept exciting. “The idea seems really cool, that you can just have one rice and then take care of everything.”

Nature | Vol 626 | 22 February 2024 | 703

YONSEI UNIV.

Q&A

News in focus

LARGEST POST-PANDEMIC SURVEY FINDS TRUST IN SCIENTISTS IS HIGH

levels of trust in scientists, whereas those in Germany, Hong Kong and Japan had below-average trust levels.

Study of more than 70,000 people suggests that trust varies among countries and is linked to political views. By Carissa Wong

P

eople around the world have high levels of trust in scientists, and most want researchers to get more involved in policymaking, finds a global survey with more than 70,000 participants. But trust levels are influenced by political orientation and differ among nations, according to the study, which was described in a preprint posted online last month (V. Cologna et al. Preprint at OSF Preprints https://doi. org/mg2x; 2024). “The overall message is positive,” says James Liu, a psychologist at Massey University of New Zealand in Auckland. “Even in the wake of the COVID-19 pandemic, which could have been highly polarizing for people’s trust in scientists, trust levels are fairly high across a range of demographics.” “The researchers use a more robust measure of trust compared to previous studies that focus on just one or two dimensions,” says Nan Li, who studies how the public engages with science at the University of Wisconsin–Madison. “I really admired the authors’ ambitions of doing this type of study.”

Worldwide attitudes Social scientist Viktoria Cologna at Leibniz University Hannover, Germany, and her colleagues surveyed 71,417 people in 67 countries. In most places, the researchers recruited

participants online through marketing companies, with the exception of the Democratic Republic of the Congo, where they used in-person surveys. Respondents were asked to indicate how much they agreed with a dozen statements about the integrity, competency, benevolence and openness of scientists, on a scale of 1 to 5. A higher score indicated higher trust.

“Entering the public policy arena as a scientist can end up being a kind of blood sport.” Across all participants, the average trust score was moderately high, at 3.62. On a global scale, participants perceived scientists as having high competence, moderate integrity and benevolent intentions. The overall rating of openness to feedback was lower: 23% of participants think that scientists pay only somewhat or very little attention to other views. Three-quarters of people agreed that scientific methods are the best way to find out whether something is true. Participants from Egypt had the most trust in scientists, followed by India and Nigeria; in Albania, Kazakhstan and Bolivia, people had the least trust. Participants in countries including the United States, United Kingdom, Australia and China had above-average

ENGAGING WITH POLICY

Many survey respondents think that scientists should communicate with the public about their work and participate in policymaking. 5 — strongly agree

4

3

2

1 — strongly disagree Percentage of respondents*

Researchers should work closely with politicians to integrate scientific results into policymaking 27%

27%

26%

9%

11%

10%

11%

Researchers should be more involved in the policymaking process 26%

26%

27%

Researchers should communicate about science with the general public 29%

54%

14% 2% 2%

*Weighted by age, gender, level of education and country sample size. Percentages do not add up to 100 because of rounding.

704 | Nature | Vol 626 | 22 February 2024

The study also explored the links between participants’ trust in scientists and their political leanings. At the global level, a ‘left-leaning’ political orientation was linked to higher trust. The team saw this association at the country level in Canada, the United States, the United Kingdom, Norway and China. But of the 67 countries surveyed, in 41 — including New Zealand, Argentina and Mexico — the team found no significant association between political orientation and trust. And in some countries, including Georgia, Egypt, the Philippines, Nigeria and Greece, left-leaning views were linked to lower trust. “These contrasting findings may be explained by the fact that in some countries right-leaning parties may have cultivated reservations against scientists among their supporters, while in other countries left-leaning parties may have done so,” the researchers say in the preprint. For example, New Democracy, Greece’s right-wing ruling party, has since 2020 consistently cooperated with researchers in implementing a public-health agenda, which could explain why in that country a right-leaning political orientation is linked to higher trust in scientists. “It’s about the leadership of political parties and how they treat scientists,” says Liu. The concept of a right- or left-wing political orientation can also differ among people in different countries, making it hard to interpret the findings. More than half of the respondents think that researchers should be more involved in policymaking and should work closely with politicians to integrate scientific results into policymaking (see ‘Engaging with policy’). “These results are intuitive — if people trust scientists, they will want them to be involved,” says Liu. “But entering the public policy arena as a scientist can end up being a kind of blood sport,” he says. “We see that with, say, climate scientists being disregarded and doubted by some politicians.” Liu thinks that there needs to be more training for scientists who want to enter policymaking, and that many researchers need to improve their communication skills, “so we’re ready for that rough and tumble arena of public policy”. The study found that 80% of people think researchers should communicate about science with the general public. Although the study provides a general snapshot of trust in researchers, people’s trust levels will also vary depending on scientists’ fields, says Li. The team plans to make the global data set openly accessible online, to help other researchers study the topic.

SOURCE: V. COLOGNA ET AL. PREPRINT AT OSF PREPRINTS HTTPS://DOI. ORG/MG2X (2024)

Trust and politics

MIKE KAI CHEN/THE NEW YORK TIMES/REDUX/EYEVINE

Feature

Brain–computer interface technology is helping people with paralysis to speak — and providing lessons about brain anatomy.

MIND-READING DEVICES ARE REVEALING THE BRAIN’S SECRETS

Interfaces that decode neural activity can restore people’s abilities to move and speak. They’re also changing our understanding of brain function. By Miryam Naddaf

M

oving a prosthetic arm. Controlling a speaking avatar. Typing at speed. These are all things that people with paralysis have learnt to do using brain–computer interfaces (BCIs) — implanted devices that are powered by thought alone. These devices capture neural activity using dozens to hundreds of electrodes embedded in the brain. A decoder system analyses the signals and translates them into commands. Although the main impetus behind the work is to help restore functions to people with paralysis, the technology also gives researchers a unique way to explore how the human 706 | Nature | Vol 626 | 22 February 2024

brain is organized, and with greater resolution than most other methods. Scientists have used these opportunities to learn some basic lessons about the brain. Results are overturning assumptions about brain anatomy, for example, revealing that regions often have much fuzzier boundaries and job descriptions than was thought. Such studies are also helping researchers to work out how BCIs themselves affect the brain and, crucially, how to improve the devices. “BCIs in humans have given us a chance to record single-neuron activity for a lot of brain areas that nobody’s ever really been able to do in this way,” says Frank Willett, a neuroscientist at Stanford University in California who is

working on a BCI for speech. The devices also allow measurements over much longer time spans than classical tools do, says Edward Chang, a neurosurgeon at the University of California, San Francisco. “BCIs are really pushing the limits, being able to record over not just days, weeks, but months, years at a time,” he says. “So you can study things like learning, you can study things like plasticity, you can learn tasks that require much, much more time to understand.”

Recorded history The idea that the electrical activity of the human brain could be recorded first gained support 100 years ago. German psychiatrist

Hans Berger attached electrodes to the scalp of a 17-year-old boy whose surgery for a brain tumour had left a hole in his skull. When Berger recorded above this opening, he made the first observation of brain oscillations and gave the measurement a name: the EEG (electroencephalogram). Researchers immediately saw that recording from inside the brain could be even more valuable; Berger and others used surgery to place electrodes on the surface of the cortex to study the brain and diagnose epilepsy. Recording from implanted electrodes is still a standard method for pinpointing where epileptic seizures begin, so that the condition can be treated using surgery. Then, in the 1970s, researchers began to use signals recorded from further inside animal brains to control external machines, giving rise to the first implanted brain–machine interfaces. In 2004, Matt Nagle, who was paralysed after a spinal injury, became the first person to receive a long-term invasive BCI system that used multiple electrodes to record activity from individual neurons in his primary motor cortex1. Nagle was able to use his system to open and close a prosthetic hand, and to perform basic tasks with a robotic arm. Researchers have also used EEG readings — collected using non-invasive electrodes placed on a person’s scalp — to provide signals for BCIs. These have allowed paralysed people to control wheelchairs, robotic arms and gaming devices, but the signals are weaker and the data less reliable than with invasive devices. So far, about 50 people have had a BCI implanted, and advances in artificial intelligence, decoding tools and hardware have propelled the field forwards. Electrode arrays, for instance, are becoming more sophisticated. A technology called Neuropixels has not yet been incorporated into a BCI, but is in use for fundamental research. The array of silicon electrodes, each thinner than a human hair, has nearly 1,000 sensors and is capable of detecting electrical signals from a single neuron. Researchers began using Neuropixels arrays in animals seven years ago, and two papers published in the past three months demonstrate their use for questions that can be answered only in humans: how the brain produces and perceives vowel sounds in speech2,3. Commercial activity is also ramping up. In January, the California-based neurotechnology company Neuralink, founded by entrepreneur Elon Musk, implanted a BCI into a person for the first time. As with other BCIs, the implant can record from individual neurons, but unlike other devices, it has a wireless connection to a computer And although the main driver is clinical benefit, these windows into the brain have

revealed some surprising lessons about its function along the way.

Fuzzy boundaries Textbooks often describe brain regions as having discrete boundaries or compartments. But BCI recordings suggest that this is not always the case. Last year, Willett and his team were using a BCI implant for speech generation in a person with motor neuron disease (amyotrophic lateral sclerosis). They expected to find that neurons in a motor control area called the precentral gyrus would be grouped depending on which facial muscles they were tuned to — jaw, larynx, lips or tongue. Instead, neurons with different targets were jumbled up4. “The anatomy was very intermixed,” says Willett. They also found that Broca’s area, a brain region thought to have a role in speech production and articulation, contained little to no information about words, facial movements or units of sound called phonemes. “It seems surprising that it’s not really involved in speech production per se,” says Willett.

“BCIs are really pushing the limits, being able to record over not just days, weeks, but months, years at a time.” Previous findings using other methods had hinted at this more nuanced picture (see, for example, ref. 5). In a 2020 paper about motion6, Willett and his colleagues recorded signals in two people with different levels of movement limitation, focusing on an area in the premotor cortex that is responsible for moving the hands. They discovered while using a BCI that the area contains neural codes for all four limbs together, not just for the hands, as previously presumed. This challenges the classical idea that body parts are represented in the brain’s cortex in a topographical map, a theory that has been embedded in medical education for nearly 90 years. “That’s something that you would only see if you’re able to record single-neuron activity from humans, which is so rare,” says Willett. Nick Ramsey, a cognitive neuroscientist at University Medical Center Utrecht in the Netherlands, made similar observations when his team implanted a BCI in a part of the motor cortex that corresponds to hand movement7. The motor cortex in one hemisphere of the brain typically controls movements on the opposite side of the body. But when the person attempted to move her right hand, electrodes implanted in the left hemisphere picked up signals for both the right hand and the left hand, a finding that was unexpected, says Ramsey. “We’re trying to

find out whether that’s important” for making movements, he says. Movement relies on a lot of coordination, and brain activity has to synchronize it all, explains Ramsey. Holding out an arm affects balance, for instance, and the brain has to manage those shifts across the body, which could explain the dispersed activity. “There’s a lot of potential in that kind of research that we haven’t thought of before,” he says. To some scientists, these fuzzy anatomical boundaries are not surprising. Our understanding of the brain is based on average measurements that paint a generalized image of how this complex organ is arranged, says Luca Tonin, an information engineer at the University of Padua in Italy. Individuals are bound to diverge from the average. “Our brains look different in the details,” says Juan Álvaro Gallego, a neuroscientist at Imperial College London. To others, findings from such a small number of people should be interpreted with caution. “We need to take everything that we’re learning with a grain of salt and put it in context,” says Chang. “Just because we can record from single neurons doesn’t mean that’s the most important data, or the whole truth.”

Flexible thinking BCI technology has also helped researchers to reveal neural patterns of how the brain thinks and imagines. Christian Herff, a computational neuroscientist at Maastricht University, the Netherlands, studies how the brain encodes imagined speech. His team developed a BCI implant capable of generating speech in real time when participants either whisper or imagine speaking without moving their lips or making a sound8. The brain signals picked up by the BCI device in both whispered and imagined speech were similar to those for spoken speech. They share areas and patterns of activity, but are not the same, explains Herff. That means, he says, that even if someone can’t speak, they could still imagine speech and work a BCI. “This drastically increases the people who could use such a speech BCI on a clinical basis,” says Herff. The fact that people with paralysis retain the programmes for speech or movement, even when their bodies can no longer respond, helps researchers to draw conclusions about how plastic the brain is — that is, to what extent it can reshape and remodel its neural pathways. It is known that injury, trauma and disease in the brain can alter the strength of connections between neurons and cause neural circuits to reconfigure or make new connections. For instance, work in rats with spinal cord injuries has shown that brain regions that once controlled now-paralysed limbs can begin to control parts of the body that are still functional9.

Nature | Vol 626 | 22 February 2024 | 707

Scientists have studied how brain–computer interfaces, such as this non-invasive cap, change brain activity.

But BCI studies have muddied this picture. Jennifer Collinger, a neural engineer at the University of Pittsburgh in Pennsylvania, and her colleagues used an intracortical BCI in a man in his 30s who has a spinal cord injury. He can still move his wrist and elbow, but his fingers are paralysed. Collinger’s team noticed that the original maps of the hand were preserved in his brain10. When the man attempted to move his fingers, the team saw activity in the motor area, even though his hand did not actually move. “We see the typical organization,” she says. “Whether they have changed at all before or after injury, slightly, we can’t really say.” That doesn’t mean the brain isn’t plastic, Collinger notes. But some brain areas might be more flexible in this regard than others. “For example, plasticity seems to be more limited in sensory cortex compared to motor cortex,” she adds. In conditions in which the brain is damaged, such as stroke, BCIs can be used alongside other therapeutic interventions to help train a new brain area to take over from a damaged region. In such situations, “people are performing tasks by modulating areas of the brain that originally were not evolved to do so”, says José del R. Millán, a neural engineer at the University of Texas at Austin, who studies how to deploy BCI-induced plasticity in rehabilitation. In a clinical trial, Millán and his colleagues trained 14 participants with chronic stroke — a long-term condition that begins 6 months or more after a stroke, marked by a slowdown in the recovery process — to use non-invasive BCIs for 6 weeks11. In one group, the BCI was connected to a device that applied electric currents to activate nerves in paralysed muscles, a therapeutic 708 | Nature | Vol 626 | 22 February 2024

technique known as functional electrical stimulation (FES). Whenever the BCI decoded the participants’ attempts to extend their hands, it stimulated the muscles that control wrist and finger extension. Participants in the control group had the same set-up, but received random electrical stimulation. Using EEG imaging, Millán’s team found that the participants using BCI-guided FES had increased connectivity between motor areas in the affected brain hemisphere compared with the control group. Over time, the BCI–FES participants became able to extend their hands, and their motor recovery lasted for 6–12 months after the end of the BCI-based rehabilitation therapy.

What BCIs do to the brain In Millán’s study, the BCI helped to drive learning in the brain. This feedback loop between human and machine is a key element of BCIs, which can allow direct control of brain activity. Participants can learn to adjust their mental focus to improve the decoder’s output in real time. Whereas most research focuses on optimizing BCI devices and improving their coding performance, “little attention has been paid to what actually happens in the brain when you use the thing”, says Silvia Marchesotti, a neuroengineer at the University of Geneva, Switzerland. Marchesotti studies how the brain changes when people use a BCI for language generation — looking not just in the regions where the BCI sits, but more widely. Her team found that, when 15 healthy participants were trained to control a non-invasive BCI over 5 days, activity across the brain increased in frequency bands known to be important for language and became more focused over time12.

One possible explanation could be that the brain becomes more efficient at controlling the device and requires fewer neural resources to do the tasks, says Marchesotti. Studying how the brain behaves during BCI use is an emerging field, and researchers hope it will both benefit the user and improve BCI systems. For example, recording activity across the brain allows scientists to detect whether extra electrodes are needed in other decoding sites to improve accuracy. Understanding more about brain organization could help to build better decoders and prevent them making errors. In a preprint posted last month13, Ramsey and his colleagues showed that a speech decoder can become confused between a user speaking a sentence and listening to it. They implanted BCIs in the ventral sensorimotor cortex — an area commonly targeted for speech decoding — in five people undergoing epilepsy surgery. They found that patterns of brain activity seen when participants spoke a set of sentences closely resembled those observed when they listened to a recording of the same sentences. This implies that a speech decoder might not be able to differentiate between heard and spoken words when trying to generate speech. The scope of current BCI research is still limited, with trials recruiting a very small number of participants and focusing mainly on brain regions involved in motor function. “You have at least tenfold as many researchers working on BCIs as you have patients using BCIs,” says Herff. Researchers value the rare chances to record directly from human neurons, but they are driven by the need to restore function and meet medical needs. “This is neurosurgery,” says Collinger. “It’s not to be taken lightly.” To Chang, the field naturally operates as a blend of discovery and clinical application. “It’s hard for me to even imagine what our research would be like if we were just doing basic discovery or only doing the BCI work alone,” he says. “It seems that both really are critical for moving the field forwards.” Miryam Naddaf is a correspondent for Nature in London. 1. Hochberg, L. R. et al. Nature 442, 164–171 (2006). 2. Leonard, M. K. et al. Nature https://doi.org/10.1038/ s41586-023-06839-2 (2023). 3. Khanna, A. R. et al. Nature https://doi.org/10.1038/ s41586-023-06982-w (2024). 4. Willett, F. R. et al. Nature 620, 1031–1036 (2023). 5. Tate, M. C. et al. Brain 137, 2773–2782 (2014). 6. Willett, F. R. et al. Cell 181, 396–409 (2020). 7. Vansteensel, M. J. et al. N. Engl. J. Med. 375, 2060–2066 (2016). 8. Angrick, M. et al. Commun. Biol. 4, 1055 (2021). 9. Ghosh, A. et al. Nature Neurosci. 13, 97–104 (2010). 10. Ting, J. E. et al. J. Neurophysiol. 126, 2104–2118 (2021). 11. Biasiucci, A. et al. Nature Commun. 9, 2421 (2018). 12. Bhadra, K., Giraud, A. L. & Marchesotti, S. Preprint at bioRxiv https://doi.org/10.1101/2023.09.11.557181 (2023). 13. Schippers, A., Vansteensel, M. J., Freudenburg, Z. V. & Ramsey, N. F. Preprint at medRxiv https://doi. org/10.1101/2024.01.21.23300437 (2024).

SILVIA MARCHESOTTI

Feature

ANA FERNANDEZ/SOPA IMAGES/LIGHTROCKET/GETTY

Feature

Members of the Scientist Rebellion group march in Brussels to protest against the lack of political action on the climate crisis.

SCIENTISTS TAKE ACTION OVER CLIMATE CHANGE

Fed up with a lack of political progress in solving the climate problem, some researchers are joining protests, getting arrested and becoming activists to slow global warming. By Daniel Grossman

C

limate scientist Peter Kalmus is freaked out. And he thinks everyone should be just as alarmed as he is over the state of the planet. When he was a graduate student in 2006, Kalmus was studying astrophysics and says he was “blissfully ignorant” about the dangers of climate change. But then he learnt how the greenhouse effect worked  —  how carbon dioxide pollution from the use of fossil fuels 710 | Nature | Vol 626 | 22 February 2024

is effectively trapping heat in the atmosphere and warming the planet at an accelerating pace. Over time, Kalmus was plagued by the increasing certainty that, “if we continue burning fossil fuels at this pace, that will render large parts of the planet uninhabitable”. By 2012, he had abandoned his budding career in astrophysics to pursue work at NASA’s Jet Propulsion Laboratory in Pasadena, California, on the impact of intensifying temperatures on humans and other species.

Kalmus became worried that the accumulation of evidence was not leading the world to necessary action. “Policymakers in general are not responding appropriately to the science that we’ve been giving them.” Hence the freak out. (Kalmus stresses that his views are his own, not NASA’s.) He decided he needed to do more to confront the problem. On 6 April 2022, Kalmus, two other scientists and an engineer blockaded a Los Angeles branch of JP Morgan Chase, an

investment banking firm that invests heavily in fossil-fuel extraction. “I’m willing to take a risk for this gorgeous planet and my son,” he said to a small crowd and in a video posted on Facebook, earning himself some 700,000 page views. He was arrested for trespassing. The protest was part of a global effort that day by members of the international environmentalist group Scientist Rebellion, which claims the event was “the largest civil disobedience campaign by scientists in history”. Researchers are noticing a rising tide of anger and action by climate scientists such as Kalmus, who are frustrated that ever-more dire forecasts and extreme events related to climate change aren’t provoking an effective response. They are “increasingly becoming aware that while science is necessary for moving towards policy-making, it is insufficient to get to policy-making on its own, and science cannot create political will”, said Dana Fisher, a sociologist at American University in Washington DC. Her book, Saving Ourselves: From Climate Shocks to Climate Action, which was published earlier this month, argues that this evergrowing group has become a ‘radical flank’ of concerned climate scientists who are doing things such as vandalizing art work, blocking entrances to buildings and interrupting traffic. These scientists are, she says, “getting blue in the face trying to use the normal channels through which we usually express how our science has relevance to the world”.

Eighty hours on a train Early last December, a train pulled slowly out of Boston’s South Station. In the dining car, earth scientist Rose Abramoff was starting an 80-hour cross-country train ride to the 2023 conference of the American Geophysical Union (AGU) in San Francisco, California. Out of concern for her carbon footprint, Abramoff no longer flies even if, as with this trip, the ground journey takes ten times as long and costs more. I joined her for the first leg of the trip. The lengthy journey gave her a lot of time to think about what happened a year before at the previous AGU annual meeting. At the very start of the conference, in a giant lecture hall, she and Kalmus leapt onto the stage and unfurled a banner for Scientist Rebellion. Kalmus yelled, “As scientists we have tremendous leverage, but we need to use it.” Abramoff pleaded, “Please. Please. Find a way to take action.” As they had anticipated, an official escorted them out of the hall. Their protest lasted all of 30 seconds. The AGU also confiscated their conference badges and officially expelled them from the rest of the meeting — a reaction that Abramoff says felt extreme. “Being asked to leave the session would have been a reasonable response,” Abramoff said during the train ride, sounding bitter. More than 2,000 researchers urged the AGU to reverse its sanctions on Abramoff and Kalmus.

That wasn’t the only consequence for Abramoff, who was then an associate scientist at Oak Ridge National Laboratory in Tennessee. Alerted of the event, Oak Ridge fired her. In her termination letter, she was accused of the “misuse of government resources” and of violating the “Code of Business Ethics and Conduct”. She says, in her defence, that her government work at the conference that week was finished by the time she took to the stage, and so the protest was done in her free time. (Kalmus did not lose his position, although Jet Propulsion Laboratory officials issued him a warning.)

MY JOB CAN’T JUST BE TO CALMLY DOCUMENT THE END OF THE WORLD.” A year later, in 2023, Abramoff, who now continues her research as an independent researcher in Maine, and Kalmus were again at the AGU conference (Kalmus joined remotely). But this time, the AGU ran four official sessions on climate activism and grief over climate change. In an e-mail to Nature, an AGU press officer said that removing Abramoff and Kalmus from the 2022 meeting was appropriate, citing the organization’s code of conduct. After the incident, the “AGU doubled down on making members aware of new opportunities”, such as activism. The AGU also stressed the need for civility, which rules out disrupting meeting sessions. Abramoff studied biology and dance for her undergraduate degree and then earned a PhD in ecology. Her political awakening occurred in 2019, while peer-reviewing several chapters of the latest report of the Intergovernmental Panel on Climate Change (IPCC). She had never before focused so intently on the effects that the climate crisis has had on the planet and its inhabitants. “In every single system is evidence of fundamental major breakdown that has implications for human health, for ecosystem services.” The document’s style, she says, betrayed no sense of existential urgency of the dangers at hand. “My job can’t just be to calmly document the end of the world.” While talking about that experience on the train, Abramoff welled up and wiped away a tear. It’s the third time in eight months that a climate scientist or climate negotiator has choked up during an interview with me, something I haven’t witnessed before in my 25 years of climate reporting. After working on the IPCC report, Abramoff decided that she needed to take more concrete

action. On 6 April 2022, she chained herself to the White House fences during a climate protest. She was arrested on the same day that Kalmus was arrested on the other side of the continent. There were news stories, with pictures of her dressed in a white lab coat. She draws on her background as a performer during protests. “The types of things that get media attention are a little theatrical and visually interesting.” Since her arrest two years ago, Abramoff has blockaded banks and the White House Correspondents’ Dinner, glued herself to a fence at a private jet terminal, occupied a state Capitol building and tried to shut down the construction of a natural-gas pipeline. Seven of her 14 actions have led to arrests.

Political awakening Although Abramoff’s activist rap sheet is an outlier among scientists, many researchers agree with her that the climate crisis needs an urgent response. A survey conducted last year of 9,220 researchers around the world, from a range of scientific and academic disciplines, found that more than 90% agree that “fundamental changes to social, political, and economic systems” are needed1. Fabian Dablander, one of three postdoctoral researchers at University of Amsterdam and Maastricht University in the Netherlands who led the research, says its the largest of only three global surveys that he is aware of regarding scientists’ attitudes on climate. The study, which has not yet been peer reviewed, surveyed researchers in 115 countries who had authored papers in 545 leading peer-reviewed journals between 2020 and 2022. Dablander cautions that the results are probably biased in favour of the concerned scientists, because they would be the most motivated to fill out the survey, which was sent to almost 250,000 authors. “I’m not sure how big this bias is exactly,” he says. Overall, 78% of the respondents had discussed climate change with someone other than a colleague; 29% had engaged in climate advocacy, 23% had joined legal protests and 10% — nearly 900 scientists — had engaged in civil disobedience. Political engagement varied by discipline and country. Scientists in Oceania were more likely to take civic actions (such as joining a climate protest). Europe and North America are virtually tied for second place. Scientists in Asia were least likely to engage in most of the civic actions included in the survey, Dablander found. A follow-up analysis of the survey data shows that scientists who were involved ‘a great deal’ in climate research were about 2.5 times more likely (37% of participants) to have joined protests, and at least 4 times more likely (18% of participants) to have engaged in civil disobedience than were non-climate researchers2.

Nature | Vol 626 | 22 February 2024 | 711

Feature arrested again, for the same offence. Some researchers worry that the more extreme forms of activism can have negative consequences. Jörg Geldmacher, a geochemist at the GEOMAR Helmholtz Centre for Ocean Research Kiel in Germany, says he doesn’t take part in more aggressive actions, such as vandalizing buildings, because they could be counterproductive. “If the masses are against it, because of these extreme activities, then I don’t know if that is very helpful for the movement,” he says. Instead, he is an active member of the German branch of Scientists for Future. Geldmacher joins legal demonstrations frequently, attends monthly meetings that send ideas to local politicians for conserving energy and often speaks at schools and to the general public about the climate crisis.

Earth scientist Rose Abramoff chained to a White House fence during a climate protest.

Another survey also found high levels of engagement among climate researchers. In a 2021 study of 1,100 climate scientists, 90% had participated in at least one form of public engagement on climate issues, including doing press interviews, briefing policymakers and being active on social media over the past year3. Viktoria Cologna, the lead author of the survey, says that long-held taboos against political participation by scientists on climate issues are waning. Cologna, a postdoctoral researcher at the University of Zurich in Switzerland, has previously been a member of Scientists for Future, the scientists’ wing of Fridays for the Future, which is a global student movement inspired by environmental activist Greta Thunberg. “I definitely see — also in my own circles, both within social science and natural science circles — that scientists are becoming more vocal; they are joining more protests,” she says. In the past, many scientists worried that they would lose credibility by taking political stances. But Cologna didn’t find that to be true in her study, which also surveyed 884 members of the public in the United States and Germany. She and her co-authors reported that 70% of Germans and 74% of Americans approve of scientists advocating for climate-related policies. The survey of researchers also uncovered hints that people who engage in advocacy do not lose the respect of their colleagues. It found that 73% of German climate scientists and 59% of US climate scientists agree that 712 | Nature | Vol 626 | 22 February 2024

IT’S EXTREMELY CALMING AND FORTIFYING.” people in their field should “actively advocate for specific climate-related policies”. A similar finding emerged from a 2020 survey about political engagement of 2,208 members of the US Union of Concerned Scientists (UCS). Less than 6% of respondents thought that scientists should ‘rarely’ or ‘never’ be politically active. Fernando Tormos-Aponte, a sociologist at the University of Pittsburgh in Pennsylvania who led the team that conducted the study, says that a cohort of scientists became politicized by policies widely seen as anti-scientific during the administration of former US president Donald Trump. These scientists continued their activism even when Trump left office. “The thing that persists is climate. There’s a sense of urgency around that, that’s almost unparallel to any other issue.” Greta Dargie, a geographer at the University of Leeds, UK, is one of many climate researchers who have ramped up their activism in the past few years. Last year she was arrested, for the first time in her life, for deliberately blocking traffic in London at an event organized by the British environmental activist group Just Stop Oil. Then, in the same week, she was

Halfway through the 2023 AGU gathering in San Francisco, I saw Abramoff again, this time in a crowd at the Chieftain Irish Pub. She had just come from the ‘climate grief circle’, an officially approved event that she and Kalmus had organized. A few dozen researchers sat in several intimate groups and discussed their feelings about confronting the deterioration of Earth’s systems each day and, for some, the fears they couldn’t share with their children. On the train, Abramoff had said that these circles serve both as group therapy and as motivation. “It’s extremely calming and fortifying,” she says. At the pub, a couple of dozen activists traded their stories and tips for organizing protests. Noah Liguori-Bills, a first-year atmospheric-science PhD student at North Carolina State University in Raleigh, received a short pep talk from Abramoff. Afterwards he said that this was his first scientific conference, and that he hadn’t expected to meet any radicals. But then he stumbled on an unsanctioned guerrilla-theatre performance on the pavement right outside the conference. It promoted one of the official activist events. The mixer at the pub is “definitely one of the most exciting things I’ve done here”, he says. “I’m really impressed with how committed everyone is.” Liguori-Bills says he expects to join a branch of Scientist Rebellion when he goes home. He says that it’s unlikely that he’ll face serious consequences, such as what happened to Abramoff. But he’s willing to take the risk. “I think it’s worth it. The whole world’s at stake.” Daniel Grossman is a freelance journalist in Watertown, Massachusetts. 1.

Dablander, F. et al. Preprint at PsyArXiv https://doi. org/10.31234/osf.io/73w4s (2023). 2. Dablander, F., Sachisthal, M. & Haslbeck, J. Preprint at PsyArXiv https://doi.org/10.31234/osf.io/5fqtr (2024). 3. Cologna, V., Knutti, R., Oreskes, N. & Siegrist, M. Environ. Res. Lett. 16, 024011 (2021).

WILLIAM DICKSON

Climate grief

Science in culture

GETTY

Books & arts

A vertical forest in Milan, Italy.

Greener cities: a necessity or a luxury? Do urban parks improve the environment and human health, or just boost gentrification? Two books debate these opposing views. By Timon McPhearson

I

n 2021, residents in Manhattan, New York City, chained themselves to trees in the local park. They were protesting the East Side Coastal Resiliency project — a roughly US$1.5-billion flood-protection effort that would raise the level of the park, install flood gates and reshape a 4-kilometre stretch of New York City’s shoreline. The protest was not just about saving trees, but about fundamental questions: whether the city should change, how it should change, whose vision for change matters and who should decide what changes will be implemented. Much of the work has gone ahead anyway since then, but the project continues to be contested by some.

Other cities around the world are grappling with similar conundrums. And these two books take radically different stances. In The Living City, Irish sociologist Des Fitzgerald takes aim at the concept of green urbanism, but misfires. The Living City: Why Cities Don’t Need to Be Green to Be Great Des Fitzgerald Basic Books (2023) Age of the City: Why our Future will be Won or Lost Together Ian Goldin and Tom Lee-Devlin Bloomsbury Continuum (2023)

By contrast, in Age of the City, UK-based globalization and development scholar Ian Goldin and journalist Tom Lee-Devlin make a convincing case for cities as key to global sustainability — as crucial nodes for flows of global resources, whether they be digital, cultural, economic or human. Cities are facing a polycrisis — from social challenges such as poverty, inequality and poor housing to ones arising from climate change and biodiversity loss, such as heatwaves, floods and sea-level rise. Solutions are needed. For example, Manhattan’s East Side project aims to avoid a repeat of 2012, when

Nature | Vol 626 | 22 February 2024 | 713

ERIK MCGREGOR/LIGHTROCKET/GETTY

Books & arts

Two activists chained to a tree, in protest of the destruction of the East River Park in Manhattan, New York City.

Hurricane Sandy caused tidal surges that flooded streets and power plants, leading to blackouts. Nature-based approaches are being increasingly deployed1, such as planting trees. Vegetation, wetlands and soils can absorb storm water, regulate air and water quality and cool streets and buildings. Those grounds can also act as recreational spaces, which have health and well-being benefits.

A garden city Yet, Fitzgerald derides such approaches in The Living City. Making his position clear — “I am against green cities” — he takes an impassioned stand against investing in nature as part of reimagining the city of the twenty-first century. The greening trend, he suggests, is being promoted by a group of elite architects and planners who want to realize a twentieth-century vision of the ‘garden city’. This outdated concept, Fitzgerald suggests, uses nature as a tool for improving the productivity of workers and creating amenities for wealthy people, including housing for middle-class professionals. Fitzgerald is right that greening can be part of larger gentrification forces that displace residents. Yet, he skirts solutions, such as the provision of affordable housing, inclusive decision-making and increased access to jobs and basic services. His portrayal of urban developers as facing a simple choice of whether ‘to green or not to green’ is a false dichotomy. Greening shouldn’t be done in isolation, but as part of a wholesale agenda of urban transformation, with equity and inclusion at its centre. Ultimately, Fitzgerald privileges his own 714 | Nature | Vol 626 | 22 February 2024

vision of cities. He loves the dirt, the grit and the seediness of the towns that he grew up in, such as Cork in Ireland, or has lived in such as Bristol, UK. He argues that cities don’t need to change, that they certainly don’t need to be made greener and that they are fine as they are: messy human constructs. Yes, people can be emotionally attached to trees, he writes, which could stem from sentimentality or an anxiety about a lack of control over urbanization and industrial progress. This attachment can also be used as a political

“People want to move to cities to better their lives, as well as to escape poverty, war or climate extremes.” tool. Fitzgerald isn’t swayed by experts, and summarily dismisses thousands of scientific papers from ecologists, planners, designers, economists, social scientists and medical professionals that have shown the benefits of urban nature to people’s lives and livelihoods2. Fitzgerald’s argument will resonate with some. But it misses the big picture. Gone must be the days of paving over nature, car-centric development and growth for growth’s sake. Decades of climate change are inevitable, given the levels of greenhouse gases already in the atmosphere. Existing cities will need to be retrofitted and redesigned to cope with the conditions of the future, and cities must adopt methods of climate-resilient development3. Resilience requires a diverse array of

approaches, including greening. Age of the City recognizes this urgent need for transformation. Goldin and Lee-Devlin set out the immense challenges that urban areas face, regarding food, water, climate, the economy, health and education. For example, cities consume 75% of global primary energy, release 70% of greenhouse-gas emissions and produce 10 billion tonnes of solid waste annually. Yet, the authors see cities as places that are also ripe with opportunity, where money, ideas, knowledge, goods and services mingle. Cities are engines of innovation and economic opportunity, with the potential to lift entire countries out of poverty, as they have in China, for example. People want to move to cities to better their lives, as well as to escape poverty, war or climate extremes — and nearly all of the global population growth this century will occur in cities. The authors argue that, because cities host more than half of the world’s people and most of its infrastructure and economic productivity, protecting and improving them is the greatest global challenge of the Anthropocene epoch. Working in, for and with cities is the best way to address global inequities and unsustainability. Goldin and Lee-Devlin are wary of gentrification, and its tendency to push people to the fringes of cities, through zoning laws, racist policies, lack of affordable housing, pay inequality and other forms of discrimination. How can cities become more equal, inclusive and fair? Through “fairer schooling, fairer housing and fairer public transportation”, alongside fairer wages and access to green spaces, which is crucial for physical and mental health.

Flipping how public transport is priced is one of the authors’ more compelling suggestions. In cities such as London and New York City, the cost of public transport is highest in outer zones. Yet, people living in more-affordable suburbs must endure long commutes and poor transport links, which make it hard for them to get to their jobs in the inner city. Travel from the outer rings should be less expensive, not the other way around, Goldin and Lee-Devlin propose.

A fairer city The authors document case studies of other mechanisms that work. For example, in Vienna, more than 60% of residents live in subsidized housing, compared with just 5% in New York City, with half of the units owned by the Austrian government and the other half by nonprofit cooperatives. Japan has balanced its regional development through policies such as decentralized production and investments in high-speed rail networks. These allow for consistent standards of living across cities, as well as ensuring that other regions benefit from Tokyo’s economic growth. Leipzig in Germany has devolved decision-making and redistributed tax revenue to increase support for education and transportation. However, both books focus on cities in high-income countries. Most examples are drawn from the United States and United Kingdom, despite three-quarters of urbanites living in low- or middle-income countries. Urban growth this century will be driven mainly by the expansion of cities in Africa, Asia and Latin America. China has more than 160 cities with more than one million residents each. Urban megaregions are emerging, such as the Pearl River Delta in China, which is home to 65 million people and has an economic output of $1.2 trillion. Two-thirds of cities with the worst air pollution are in India. Science has made clear that if we want human-positive cities, they also need to be nature-positive4. People need to reconnect with each other and with the biosphere, not because of some philosophical ideal of a garden city, as Fitzgerald worries, but for the practical reasons outlined by Goldin and Lee-Devlin. This can only be done by tinkering, experimenting and scaling up what works. Timon McPhearson is an urban ecologist and director of the Urban Systems Lab at the New School, New York City. e-mail: [email protected] 1.

McPhearson, T., Kabisch, N. & Frantzeskaki, N. (eds) Nature-Based Solutions for Cities (Edward Elgar Publishing, 2023). 2. Keeler, B. L. et al. Nature Sustain. 2, 29–38 (2019). 3. Dodman, D. et al. in Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Pörtner, H.-O. et al.) Ch. 6 (Cambridge Univ. Press, 2023). 4. Andersson, E. et al. AMBIO 43, 445–453 (2014).

Books in brief Machine Vision Jill Walker Rettberg Polity (2023) ‘Machine vision’ implies computing to many people, but not to digital culture scholar Jill Rettberg. Her book covers glass telescope lenses, photography, cinema and other modern imaging technologies, all of which pre-date computers, and fit her definition of machine vision — broadly as machines and algorithms used to register, analyse and represent visual information. Rettberg is aware that this extension of human vision can be seen as beneficial or disruptive, depending on perspective. Her own attitude? “I love our robot vacuum cleaner”.

Defending Animals Kendra Coulter MIT Press (2023) Opening her overview of animal protection, ethicist Kendra Coulter describes a violently mistreated German shepherd dog she adopted. Sunny looked right into her eyes while she was writing difficult sections, she notes. This is one of many moving details in a complex book that features cruelty investigators, forensic veterinarians, conservation leaders, animal lawyers and entrepreneurs. Effective protection, she argues, must work “hand in paw, and hand in hoof with the well-being of frontline workers” such as rangers.

Enchanted Forests Boria Sax Reaktion (2023) The New York Botanical Garden contains a forest advertised as ‘virgin’, although its trees offer no indisputable evidence that it has never been logged. Yet, other trees’ “scars”, “twists” and “breaks” tell fascinating stories about their histories, says author Boria Sax. His cultural history of forests from prehistory to today — inspired by his owning a forest in New York State — draws on writers and artists including Virgil, Dante, the Brothers Grimm, the Hudson River painters and Latin American folklorists.

Dinosaur Behavior Michael J. Benton Princeton Univ. Press (2023) When Tyrannosaurus rex was identified, estimates of its running speed varied wildly. Today, its stride length in fossil tracks and its estimated muscle dimensions suggest a speed of 27 kilometres per hour. Such progress is crucial, argues palaeontologist Michael Benton. Yet he also backs the long scientific tradition of sharing speculation about dinosaurs — a new species is named every ten days — with the wider public, using models, paintings and computer animations. His grippingly illustrated book brilliantly synthesizes science and art.

A Theory of Everyone Michael Muthukrishna Basic (2023) Economic psychologist Michael Muthukrishna initially trained in engineering. He grew up in Sri Lanka, Botswana and Papua New Guinea, and was exposed to ethnic hatred, violence and war. This unusual life path fuelled his search for a unified theory of human behaviour: the “theory of everyone”, deliberately borrowed from physics’ “theory of everything”. Understanding behaviour better, he argues, could make energy extraction more efficient, by reinvigorating innovation and cooperation. Andrew Robinson

Nature | Vol 626 | 22 February 2024 | 715

Readers respond

Correspondence Desert solar farms are a triple win As land degradation becomes more severe (see Nature 623, 666; 2023), desert photovoltaics are a triple-win, fostering not only clean-energy generation but also ecosystem recovery and local poverty reduction. Panels provide shade, cutting surface water evaporation by 20–30%. Water used for cleaning panels adds moisture to the soil and supports vegetation, while crops and grazing animals beneath the panels can enrich the soil and help to boost incomes (C. Song et al. Renew. Sust. Energ. Rev. 191, 114146; 2024). China has many solar projects in its northwestern deserts, including the Tala Shoal plant in Qinghai, which covers an area almost the size of Singapore and has a generating capacity of 22 gigawatts. Besides supplying energy, the project has halved local wind speeds, restored vegetation and boosted sheep herders’ incomes by 2 million yuan (US$280,000). China is looking at projects in the Gobi desert that could generate 450 gigawatts — 20 times the output of the Three Gorges Dam. As photovoltaic costs fall and energy-storage technologies improve, this model is ripe for expansion. International collaboration is key, as shown by China’s Belt and Road Initiative (see Nature 622, 669–670; 2023), to share technology and seed funding with countries in Central Asia, Africa, the Middle East and Latin America.

Stockholm AI declaration — others should sign The use of artificial intelligence (AI) in science has potential to do both harm and good. As a step towards preventing the harms, we have prepared the Stockholm Declaration on AI for Science. Signatories commit to using AI responsibly and ethically. We encourage researchers involved in developing AI to sign (see go.nature.com/486azp7). The declaration recognizes that AI’s ability to process vast data sets, identify patterns, generate hypotheses and control laboratory automation is revolutionizing science. If harnessed properly, AI could propel humanity towards a future in which groundbreaking research is fully automated. Signatories pledge to navigate this journey with diligence, ensuring transparency, fairness and respect for privacy, as well as upholding the highest ethical standards. They will prioritize equity, diversity and inclusion, and strive to avoid bias and discrimination. They will recognize the need for rigorous oversight, accountability and safeguards against misuse. Using AI in science as a power for good can help to meet the great challenges that the world faces, from climate change to food insecurity and disease. Ross D. King University of Cambridge, UK. [email protected]

Haimeng Liu Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China. [email protected]

Teresa Scassa University of Ottawa, Canada.

Jianguo Liu Michigan State University, East Lansing, Michigan, USA.

Hiroaki Kitano Okinawa Institute of Science and Technology, Japan.

Stefan Kramer Johannes Gutenberg University Mainz, Germany.

716 | Nature | Vol 626 | 22 February 2024

Serbia rewards top 10% of its scientists In January, Serbia increased the salaries of its top 10% of scientists employed in state-owned universities and institutes by 30% (see go.nature.com/3upemse). This initiative is the first time that Serbia has transparently acknowledged and recognized its best scientists in the public sector. This evaluation of excellence applies exclusively to individuals who are fully engaged in research, have a PhD and are certified as scientists by the Ministry of Science, Technological Development and Innovation. Of the nation’s 15,500 active academic staff with doctorates (including teaching staff), 5,171 are eligible candidates. Of these, the 10% rated as the best have been selected for the pay rises. First, scientists are classified by seniority: research associate, senior research associate and principal research fellow. They are then assessed for excellence in four research branches: physical and mathematical sciences, engineering, social sciences, and humanitarian sciences. Scientists’ publication records and citations are assessed within specified time frames. In each field and grade, the top scientists, determined either by publishing record or citations, receive the salary increase. Transparent overview of scientific results is provided by the newly established eScience portal (see enauka.gov.rs), which relies mostly on data from the scholarly database Web of Science. Dejan Brkić University of Niš, Niš, Serbia. [email protected]

Chinese databases are a boost for raredisease science Rare diseases are a worldwide challenge: there are around 7,000 types and it is estimated that 10% of the global population has one. China is home to the second-largest number of people diagnosed with rare diseases, behind the United States — approximately 20 million, from as many as 56 ethnic groups. Yet data on rare diseases in China are scant: whereas the first official document on such diseases in the United States dates back to 1983, it wasn’t until 2018 that China released its first national list of rare diseases. China is now expediting a data revolution, constructing databases to add to those that currently focus on Western populations. The National Rare Diseases Registry System of China (NRDRS), launched in 2020, integrates research and patient information. The Rare Disease Data Center, established online in 2022, focuses on using genetic big data for the development of artificial-intelligence tools for bioinformatics. And at the start of this year, the Shanghai Sixth People’s Hospital, affiliated with Shanghai Jiao Tong University, initiated a collaboration with University College London to establish the first Chinese–British joint proteomics database for people with Ewing’s sarcoma — a type of bone cancer — for which I am a co-investigator. These initiatives demonstrate China’s determination to advance rare-disease science, and I would encourage more scientists and clinicians to join in the construction of these databases. Jishizhan Chen University College London, London, UK. [email protected]

Expert insight into current research

News & views Astrophysics

Rare isotopes formed in prelude to γ-ray burst Daniel Kasen

The afterglow of a long burst of γ-rays suggests that the events leading to these explosions can be sizeable sources of some of the Universe’s rare isotopes — and that classifications of γ-ray bursts are too simplistic. See p.737 & p.742 In March 2023, an astrophysical blast of γ-rays named GRB 230307A was registered as the second-brightest known burst of its kind. Astronomers, however, became entranced by the faint, colourful glow of its aftermath, which turned from an ordinary blue to a deep red over several weeks. On pages 737 and 742, respectively, Levan et al.1 and Yang et al.2 conclude that this red colour was the radioactive glow of rare isotopes synthesized in the event that triggered the burst. The discovery is another step towards understanding the cosmic origins of the heaviest elements. But it also muddies the waters of a question that was thought to have been answered: what causes γ-ray bursts (GRBs)? Short GRBs, which have average durations of around 0.3 seconds, are generally thought to arise from mergers of compact objects, particularly the coalescence of two neutron stars in a binary system. By contrast, long GRBs, which have average durations of about 30 s, are considered to be derived from the collapse of a massive star (a collapsar). GRBs originating from either progenitor should evolve to a similar final configuration: a central black hole with an orbiting disk of debris. Accretion of disk material by the black hole can produce a jet that accelerates particles close to the speed of light, which, in turn, powers a GRB. The debris disks formed in compact-object mergers are small in radius (approximately tens of kilometres)3, and should accrete quickly, producing ‘engines’ that probably power up a GRB quickly4. Various observations have supported the idea that long and short GRBs have different progenitors. Astronomers have seen the bright lights from supernovae in the afterglows of long GRBs, indicating that massive stars had exploded5. By contrast, short GRBs are often

found in regions of space that are devoid of massive stars, and they have never been observed with an accompanying supernova4. Clear-cut evidence about the origins of short GRBs came in 2017, when a short burst was found to coincide with a source of gravitational waves (GW170817) — definitively identifying the progenitor of this GRB as the merger of a binary neutron star6. Just six years after the confirmation of the progenitor of GW170817, the dichotomy of long and short GRBs was called into question by GRB 230307A. With a duration of around 35 s, this burst is classified as a long GRB. But

it was found in a lonely part of space, with no young, massive stars in its vicinity, and around 40,000 parsecs (about 130,000 light years) away from the host galaxy (Fig. 1). That environment implies that GRB 230307A arose from a compact-object merger — from binary neutron stars that formed eons ago and drifted into the outskirts of the galaxy before merging. But even more intriguing is the GRB’s red afterglow. Amid the violent dynamics of a merger, matter is ejected from the neutron stars and transformed into heavy nuclei as a result of the copious free neutrons rapidly attaching to ‘seed’ nuclei (an isotope-formation pathway known as the r-process)7,8. The resulting mushroom cloud is radioactive and produces an observable glow known as a kilonova. The distinctive red colour of some kilonovae reflects their chemical make-up, and, in particular, tends to be indicative of the presence of nuclei from the lanthanide series of elements9,10. Just such a radioactive glow was observed for GW170817. Observations made by Levan et al. and Yang et al. showed that the colour, brightness and temporal evolution of the afterglow of GRB 230307A contained kilonova signatures that were remarkably similar to those of GW170817. Levan et al. therefore requested observations with the James Webb Space Telescope ( JWST), which provided unprecedented emission spectra of a kilonova long after the burst (29 and

Afterglow

Host galaxy

N

E

Figure 1 | The afterglow of the γ-ray burst GRB 230307A. GRB 20307A originated from a remote part of space, with no young, massive stars in its vicinity, and around 40,000 parsecs (about 130,000 light years) away from its host galaxy. This suggests that the burst arose from the merger of two neutron stars that formed eons ago and had drifted to the outskirts of the galaxy. Levan et al.1 and Yang et al.2 report analyses of observational data that support that model. A red hue seen in the afterglow indicates that lanthanide elements were produced in the event that formed the γ-ray burst. This JWST image combines images taken at three different wavelengths. Scale bar, 10 arcseconds. (Adapted from Fig. 2a of ref. 1.)

Nature | Vol 626 | 22 February 2024 | 717

News & views 61 days afterwards) and at long wavelengths (up to 5 micrometres). The emission spectra contain a line feature near 2 µm, similar to one seen in the GW170817 event. Building on a previously reported theoretical study11, the authors suggest (but do not prove) that this line arises from tellurium, an r-process element. Levan et al. and Yang et al. carried out modelling of the kilonova signatures, which suggested that a mass equivalent to one-twentieth to one-tenth of the mass of the Sun was ejected from the source of the GRB, and that this ejecta contained heavy elements (lanthanides) produced by the r-process. This corroborates what was indicated by studies of GW170817 — that kilonovas were a substantial, and possibly dominant, contributor to the production of r-process elements in the Universe. The idea that a long GRB such as GRB 230307A could be produced by a compact-object merger was suggested in 2021, when another long GRB (GRB 211211A) showed possible signatures of a kilonova12,13. So what is going on in these events? There are three possible explanations. First, it could be that GRB 230307A was derived from the collapse of a massive star, as expected for long GRBs, but that it happened to make a kilonova, rather than a brighter supernova. Some simulations suggest that a collapsar can produce and expel r-process elements14, but the yields would probably be about tenfold more than what was observed for GRB 230307A. A more compelling argument — which both Levan et al. and Yang et al. favour — is that GRB 230307A arose from a compact-object merger that somehow resulted in a long GRB. Although the small disks produced in such mergers should rapidly accrete onto the resulting black hole, simulations15 published in 2023 suggest that the power of a GRB engine might initially depend not only on the amount of mass that accretes on the black hole, but also on the magnetic field of the accreted debris. The mass feeding the black hole might dwindle quickly, but the magnetic field of the mass inflow might increase, and provide a relatively constant power to the engine over timescales that match the durations of long GRBs. If this theory is correct, then compact-object mergers could produce either long or short GRBs, depending on the magnetic-field geometry and whether the merger produces a black hole or a hypermassive neutron star. Finally, an overlooked scenario could be responsible. One possibility is a white dwarf merging with a black hole or a neutron star. White dwarfs have a much bigger radius than do neutron stars, and so their debris disks are large and the characteristic accretion timescales would be roughly consistent with the duration of long GRBs16. Material ejected from a disrupted white dwarf might produce a radioactive afterglow17, but this ejecta would probably lack the peculiar heavy elements that 718 | Nature | Vol 626 | 22 February 2024

give rise to a distinctive red hue. This scenario has not yet been investigated in detail, and further modelling of such white-dwarf mergers might resolve the contradiction. The puzzles posed by GRB 230307A will inspire continuing theoretical and observational studies. Fortunately, it might be only a matter of time before gravitational waves from an unusually long GRB are detected,

“The JWST observations provided unprecedented emission spectra of a kilonova.” which would definitively tell us whether or not the burst arose from a compact-object merger — and, if it did, what the masses of the component objects were. In the meantime, the misbehaviour of GRB 230307A is a reminder that the Universe is more interesting than the pedantic classifications of humans suggest. Daniel Kasen is in the Department of Astronomy, University of California, Berkeley,

Berkeley, California 94720, USA. e-mail: [email protected]

1. Levan, A. J. et al. Nature 626, 737–741 (2024). 2. Yang, Y.-H. et al. Nature 626, 742–745 (2024). 3. Fernández, R. & Metzger, B. D. Annu. Rev. Nucl. Part. Sci. 66, 23–45 (2016). 4. Berger, E. Annu. Rev. Astron. Astrophys. 52, 43–105 (2014). 5. Woosley, S. E. & Bloom, J. S. Annu. Rev. Astron. Astrophys. 44, 507–556 (2006). 6. Abbott, B. P. et al. Astrophys. J. 848, L13 (2017). 7. Lattimer, J. M. & Schramm, D. N. Astrophys. J. 192, L145 (1974). 8. Li, L.-X. & Paczyński, B. Astrophys. J. 507, L59 (1998). 9. Metzger, B. D. et al. Mon. Not. R. Astron. Soc. 406, 2650–2662 (2010). 10. Barnes, J. & Kasen, D. Astrophys. J. 775, 18 (2013). 11. Hotozeka, K., Tanaka, M., Kato, D. & Gaigalas, G. Mon. Not. R. Astron. Soc. 526, L155–L159 (2023). 12. Troja, E. et al. Nature 612, 228–231 (2022). 13. Rajinestad, J. C. et al. Nature 612, 223–227 (2022). 14. Siegel, D. M., Barnes, J. & Metzger, B. D. Nature 569, 241–244 (2019). 15. Gottlieb, O. et al. Astrophys. J. 958, L33 (2023). 16. Fryer, C. L., Woosley, S. E., Herant, M. & Davies, M. B. Astrophys. J. 520, 650–660 (1999). 17. Margalit, B. & Metzger, B. D. Mon. Not. R. Astron. Soc. 461, 1154–1176 (2016). The author declares no competing interests.

Neuroscience

A neural circuit that keeps flies on target Katherine Nagel

Studies reveal how neuronal populations in the fruit fly brain work together to compare the direction of a goal with the direction that the fly is facing, and convert this into a signal that steers the fly towards its target. See p.808 & p.819 Animals of all kinds show a remarkable ability to navigate, whether it is to the location of a remembered food source or back to the safety of a nest. To accomplish this kind of goal-directed movement, brains have evolved specialized navigation centres — the hippocampus in vertebrates and the central complex in insects — that allow each animal to build an internal map or compass of its environment. Although the way in which these maps are built by neural circuits has been studied for many years, neuroscientists are still trying to understand how the maps allow an animal to orient towards a goal. On pages 808 and 819, respectively, Mussells Pires et al.1 and Westeinde et al.2 reveal the detailed mechanisms by which the insect brain converts a map-like representation of direction into goal-oriented steering. The essence of a map is that it stays the same

as an animal moves through space — the map is tied to coordinates of the animal’s spatial environment (for example, north, south, east and west) rather than to the animal’s left or right. Turning such a map into a steering command requires some form of comparison. For example, if a map tells you that treasure is northeast and you are currently pointing north, you can compare these two directions and determine that your best course of action is to turn right by a few degrees. How might a neural circuit make this comparison? A possible answer first emerged from reconstructions of the brains of sweat bees (Megalopta genalis)3 and, later, fruit flies (Drosophila melanogaster)4. By painstakingly tracing and reconstructing neurons and their synaptic connections using electron microscopy images, researchers revealed surprisingly precise and selective connectivity

Fruit fly brain (posterior view)

Left steering centre

Right PFL3 neuron

PFL2 neuron

b

Left PFL3 neuron

Navigation centre

Heading direction

FC2 neuron

Right steering centre

Steering cues

Right steering centre

0.25

0 –180

Information about heading direction

between individual types of neuron. One circuit in the insect brain — the compass network — builds a map of the direction in which the animal is facing (its heading direction)5. These compass neurons communicate with another set of neurons, known as PFL3 neurons, that send projections to, and form connections with, an area of the brain involved in steering. There are two sets of PFL3 neurons: one on the left and one on the right side of the brain. Neurons in each hemisphere form an array across the navigation centre and communicate with the steering centres on the opposite sides, suggesting that they might translate the compass map into a steering command (Fig. 1a). Curiously, PFL3 neurons receive input from the compass network, but there is a characteristic ‘offset’ between the direction encoded by compass neurons and that encoded by PFL3 neurons. This means that PFL3 neurons should be preferentially responsive (tuned) to heading directions that are to the left or right of the way in which the animal is currently pointing. Guided by this connectivity pattern, these earlier studies3,4 proposed that PFL3 neurons might allow an insect to make a direction comparison, computing whether a left or right turn would bring its heading direction in line with a goal. The latest studies1,2 validate and extend the predictions of these models. Each group developed a different genetic tool to target PFL3 neurons in the brains of fruit flies. The researchers recorded the activity of these neurons as flies performed a navigational task called menotaxis, in which the fly adopts a straight course at an angle to a visual stimulus (the goal)6. Both groups found that PFL3

Left steering centre

0.50

Compass neuron

Figure 1 | A neural circuit in the fruit fly brain that enables goal-directed steering. a, In the navigation centre of the fruit fly (Drosophila melanogaster) brain, compass neurons encode information about the direction in which the fly is facing (heading direction). Mussells Pires et al.1 find that FC2 neurons encode information about the position of a visual stimulus (goal direction). These two sets of neurons input into PFL3 and PFL2 neurons, which connect to regions of the brain that control steering. b, Both teams measure PFL3 neuronal activity, and find that the right steering centre is most active when

Goal direction

0.75

Information about goal direction PFL3 neuron activity

a

–90 0 90 Angle of heading direction relative to goal direction (°)

180

the goal is to the right of the fly, and the left is most active when the goal is to the left. Westeinde et al.2 also find that PFL2 neurons are most active when the fly is facing in the opposite direction to its goal (not shown). Together these neurons integrate information about the heading direction and goal direction, and enable the fly to stay on course as it steers towards a target. PFL3 neuron activity is expressed as baseline-normalized fluorescence measurements that report neuronal activity following navigation behaviour. (Graph adapted from Fig. 5 in ref. 1.)

neurons are tuned to the heading direction, but that the neurons’ activity is modulated by the direction of the goal. When the goal was to the fly’s right, PFL3 neurons that connect to the right steering centre showed stronger responses, whereas when the goal was to the fly’s left, neurons connecting to the left steering centre showed stronger responses (Fig. 1b). Together, these experiments provide strong support for the model that PFL3 neurons compare map-like representations of heading direction and goal to drive targeted steering. In addition to finding support for the PFL3 steering model, Mussells Pires et al. identified a second group of neurons upstream of PFL3 neurons that can specify a goal direction.

“Internal maps of the environment are found in the brains of many animals, including humans.” Known as FC2 neurons, these neurons also form an array but they remain in the navigation centre rather than projecting out to the steering centres (Fig. 1a). Using a laser to artificially stimulate different parts of this array, Mussells Pires and colleagues found that flies adopted distinct orientations with respect to the visual stimulus. Unlike compass neurons, FC2 neurons do not change their firing when the fly turns, suggesting that they encode a map-like representation of the animal’s goal. These data are consistent with findings published last year for migratory monarch butterflies (Danaus plexippus): a population of

neurons in the navigation centre was thought to track the butterfly’s goal, not its heading direction, and the active population of neurons shifted only when the experimenter used electric shocks to force the butterfly to adopt a new goal7. They are also consistent with studies of another population of upstream local neurons in the fly navigation centre that produce orientations relative to wind direction when artificially stimulated8. Taken together, these studies suggest that insects might be able to learn and store multiple goal directions in different local neuron populations of the navigation centre. Understanding how distinct goals are learnt, remembered and prioritized during behaviour is a major focus for future research in the field. The study by Westeinde et al. revealed another aspect of goal-orientation circuitry: a set of anti-goal neurons called PFL2 neurons (Fig. 1a). These were known to send signals to both sides of the steering centre, but with a distinct offset, effectively tuning them to directions 180° away from the fly’s current heading direction4. By taking recordings from these neurons during menotaxis, Westeinde et al. found that the cells respond most strongly when the fly is pointing 180° in the opposite direction of its goal. Artificially activating these neurons caused the fly to slow down and increase its turning. The fly, therefore, is able to stay on target by combining three sets of steering neurons: right and left PFL3 neurons help the fly to stay on track when it makes small deviations from its goal, and PFL2 neurons turn the fly when it ventures too far off course. Both studies provide strong experimental

Nature | Vol 626 | 22 February 2024 | 719

News & views evidence that PFL3 and PFL2 neurons can generate goal-directed steering, as predicted by theoretical models — but they stop short of showing that these neurons are required for all goal-directed steering. Mussells Pires et al. investigate the effects of silencing PFL3 neurons in a task designed to assess memory of wind direction, which the authors show is dependent on the compass network. However, the effects of silencing PFL3 neurons are only modest. This might be because the genetic line used by the authors labels, and therefore silences, only a subset of neurons. Future studies will be needed to determine how PFL neurons as a population contribute to goal orientation during complex behaviours. Although the current studies focused on flies, internal maps of the environment are found in the brains of many animals, including humans. In vertebrates, navigational abilities are strongly linked to the hippocampus, which forms maps of both real and abstract environments. How these maps are translated into locomotor commands remains unclear. A study in Egyptian fruit bats (Rousettus aegyptiacus) found that a subset of neurons in the hippocampus is tuned to both the direction and distance (the vector) between the animal and the location of a hidden goal platform9. Another study found that place cells (neurons that fire when an animal is in a particular location in its environment) show directional tuning towards a goal when rats navigate a series of moving platforms10. Both of these coding schemes are reminiscent of the fly brain, in which the direction of a goal is represented by the pattern of activity across an array of neurons. Defining the precise neural architectures that allow insects to convert such maps of the environment into steering commands for the body might therefore help to reveal how human brains navigate both real and imaginary spaces. Katherine Nagel is in the Neuroscience Institute, New York University School of Medicine, New York City, New York 10016, USA. e-mail: [email protected]

Mussells Pires, P. et al. Nature 626, 808–818 (2024). Westeinde, E. A. et al. Nature 626, 819–826 (2024). Stone, T. et al. Curr. Biol. 27, 3069–3085 (2017). Hulse, B. K. et al. eLife 10, e66039 (2021). Seelig, J. D. & Jayaraman, V. Nature 521, 186–191 (2015). Giraldo, Y. M. et al. Curr. Biol. 28, 2845–2852 (2018). Beetz, M. J., Kraus, C. & el Jundi, B. Nature Commun. 14, 5859 (2023). 8. Matheson, A. M. M. et al. Nature Commun. 3, 4613 (2022). 9. Sarel, A., Finkelstein, A., Las, L. & Ulanovsky, N. Science 355, 176–180 (2017). 10. Ormond, J. & O’Keefe, J. Nature 607, 741–746 (2022).

1. 2. 3. 4. 5. 6. 7.

The author declares no competing interests. This article was published online on 7 February 2024.

720 | Nature | Vol 626 | 22 February 2024

Forum: Structural biology

Energetic laser pulses alter outcomes of X-ray studies Cutting-edge X-ray sources have enabled the structural dynamics of proteins to be tracked during biochemical processes, but the findings have been questioned. Two experts discuss the implications of a study that digs into this issue. See p.905 The paper in brief •

•

Ultrashort, intense X-ray pulses generated at facilities known as X-ray free-electron lasers (XFELs) have been used to probe light-induced structural changes in proteins. Light-responsive proteins typically absorb one optical or ultraviolet photon in natural settings, but could absorb more from the intense ‘pump’ lasers used to induce structural changes in these studies.

Richard Neutze Imperfect experiments can be informative Structural changes that occur in proteins during biochemical reactions can be measured using a technique called time-resolved X-ray diffraction (TR-XRD). In this method, reactions are initiated in protein crystals, and X-ray pulses are used to record X-ray diffraction data at selected times after initiation. TR-XRD has produced structural insights into the pathways of diverse biological processes2, including photosynthesis, sensory signalling, ion transport and photodissociation — the light-induced breakage of bonds between proteins and their ligand molecules. For light-sensitive proteins, a pump laser pulse is used to initiate the reaction of interest. All molecules probed in a crystal contribute to the measured X-ray diffraction pattern, yet typically only a subpopulation is activated by the pump laser. A quantity known as the crystallographic occupancy estimates the fraction of molecules in a crystal that are activated. Raising the pump-laser fluence — the energy delivered per unit area by the pump laser onto a crystal — can increase the crystallographic occupancy, but more than one photon can be absorbed by the protein at high laser fluences3,4. Barends et al. studied structural changes

•

• •

Such unnatural absorption of multiple pump photons might force proteins to behave in ways that are not biologically relevant. Questions have therefore been raised about how these studies should be interpreted. Barends et al.1 now show that the structure of a model protein changes in different ways depending on whether single or multiple photons are absorbed.

that occur in the carbon monoxide complex of the protein myoglobin (MbCO) after pump-laser-induced photodissociation of CO from the iron atom of a haem group (Fig. 1). This process was previously studied using TR-XRD at time resolutions of 7.5 nanoseconds (ref. 5) and 150 picoseconds (1 ps is 10–12 seconds; ref. 6) using relatively large protein crystals (dimensions in the range of about 0.1 to 0.3 millimetres) and X-ray pulses from a synchrotron facility, which is a less intense X-ray source than an XFEL. A 2015 study by some of the same researchers as Barends et al. used extremely short, intense XFEL pulses to record TR-XRD data from tens of thousands of much smaller MbCO crystals (average size 15 micrometres × 5 µm × 3 µm). This thereby achieved a time resolution of 250 femtoseconds (1 fs is 10–15 s) and revealed ultrafast conformational changes of the protein as photodissociation occurs7. But because those experiments used a high pump-laser fluence, Barends et al. have now repeated their study using a lower fluence that ensures single-photon excitation of MbCO. The authors used their TR-XRD data to determine difference Fourier maps, which show differences in electron density in MbCO before and after activation. Barends et al. found that lower pump-laser fluences yield lower crystallographic occupancies in maps produced 10 ps after protein activation. For this time delay, differences between structural

changes induced by single-photon and multiphoton excitation are difficult to see from the difference Fourier maps, but the authors argue that some effects can be determined from careful analysis and structural modelling. A caveat is that structural modelling incurs larger errors when the crystallographic occupancy is low. For sub-picosecond time delays, the authors’ analysis shows that the protein responds unexpectedly rapidly to the highest pump-laser fluence, and that CO swiftly populates a new location near the haem (Fig. 1). By contrast, it takes longer for the protein to respond and for this CO site to be populated after single-photon activation. Barends and colleagues’ recording of high-quality maps from very small crystals of MbCO after single-photon excitation is impressive. Their study will motivate other researchers to recover high-quality difference Fourier maps using single-photon excitation. However, this might not be possible for many biological systems. Crucially, the structural perturbations of MbCO induced by single photons are in keeping with findings from earlier work5–7, illustrating that useful insight does emerge from imperfect experiments. Historically, the field has chosen not to let the perfect be the enemy of the good. I contend that such pragmatism should continue, so that the diversity of biological systems studied by TR-XRD continues to grow2. Richard Neutze is in the Department of Chemistry and Molecular Biology, University of Gothenburg, 41390 Gothenburg, Sweden. e-mail: [email protected]

R. J. Dwayne Miller Protein ‘music’ must not be distorted Biological processes are driven by chemistry. Chemistry is dynamic, with all the interconnecting atoms in molecules jiggling around, vying for right of way to take part in reaction pathways. In proteins, chemical transformations occur at an active or binding site. These processes involve the coupling of reaction forces such that some 10–100 atoms at the active site direct the motions of the 1,000–10,000 (or more) atoms of the surrounding protein. An astronomical number of possible conformational pathways (sequences of molecular structures) can occur, but only one or a few motions at the active site — known as reaction modes — direct biological function. How can such a small number of reaction modes preferentially direct protein functions within the ocean of alternative conformational pathways? This apparent paradox is solved by considering spatially correlated motions — these arise when forces imprinted

E-helix Pump laser pulse CO molecule Fe

CO bond breakage Haem group flexing

Haem Histidine residue

Distance change

F-helix

F-helix displacement

Figure 1 | Structural dynamics of a model protein system. Barends et al.1 used a method called time-resolved X-ray diffraction (TR-XRD) to study crystals of the carbon monoxide complex of the protein myoglobin (MbCO; only part of the complex is shown). They used a ‘pump’ laser pulse to induce photodissociation (breakage of the bond between CO and the iron (Fe) atom of a haem group in the protein), and then used ultrashort, intense X-ray pulses to obtain X-ray structures of the protein over time. Three structural changes crucial to photodissociation are shown (right): flexing of the haem group; changes in the distance between the haem and a histidine amino-acid residue in the F α-helix of the protein; and displacement of the F-helix. The authors observed that the fluence of the pump laser (the energy delivered per unit area by the pump laser onto a crystal) alters the amplitude and timing of these motions, and also the motion of the CO away from the haem after photodissociation — suggesting that lower fluences are needed to observe structural changes that occur in natural settings.

in the protein structure cause atoms to move together, rather than randomly and independently. By directly observing atomic motions during the defining moments of a chemical reaction, the initial distillation of all possible pathways down to a few reaction modes can be seen8. Such observations allow relationships between protein structure and function to be determined, but only if the reaction is initiated correctly. The whole point of such studies is that we don’t know the length and timescales of protein responses to the chemical driving force. To help make sense of this issue, consider the fluid that forms by mixing cornflour and water. If you dip your finger slowly into this mixture, the fluid flows around it like a liquid. But if you rapidly poke the mixture, the material responds as an elastic solid. Similarly, the spatially varying barriers to motions in proteins mean that protein responses to forces depend on both the time and the length scales of the applied force. Given that protein structures are highly anisotropic (different in different directions), the response will also depend on the spatial location of the induced force. Until the work of Barends et al., femtosecond TR-XRD studies had used extremely high pump fluences to ensure that structural changes in proteins could be observed. Because the laser pulses are so short, this came at the cost of inducing multiphoton protein activation that produces initial atomic displacements different to those arising from single-photon activation, and at higher energies, with

the atoms being displaced farther from their equilibrium positions4. The driving forces for the observed structural changes were therefore both spatially different and significantly larger than those of the biological pathway of interest4. For the model MbCO system, there are three biologically crucial and spatially coupled motions (Fig. 1): the haem doming coordinate (which describes the flexing of the protein’s haem group); movement of a histidine amino-acid residue positioned close to the haem; and the displacement of α-helices resulting from the transfer of driving forces from the first two motions. Barends et al. observed that multiphoton activation resulted in much faster displacement of the histidine than did single-photon activation, and led to similarly faster motions of one of the helices. Moreover, the spatially correlated helical motions extended over different distances along the helix than did those induced by single-photon activation, and subsequently dissipated to different degrees. The impulsive nature of the force produced by multiphoton activation on the initial histidine motion, and the temporal evolution of the positioning of the CO molecule in the protein pocket that contains the haem, are evidence of a different reaction pathway to the one produced by single-photon activation. There are significant differences in the magnitude, temporal evolution and pathway of the structural dynamics throughout the protein. Given the importance of energetics and

Nature | Vol 626 | 22 February 2024 | 721

News & views spatially correlated motions for understanding the structural changes that underpin protein function, we need to get this right. Biologically relevant protein motions are like music, albeit playing at frequencies we can’t hear. We need to observe the motions that correspond to certain frequencies, and discern how those motions are damped to produce net displacements. Imagine listening to music in which violin strings are struck too strongly, causing the accidental playing of different chords together. You would not hear the music as written, but something else entirely. To understand how nature works, we need to listen to the molecular music as nature wrote it. Barends et al. have done just that.

R. J. Dwayne Miller is in the Departments of Chemistry and Physics, University of Toronto, Toronto M5S 3H6, Canada. e-mail: [email protected] 1. Barends, T. R. M. et al. Nature 626, 905–911 (2024). 2. Bränden, G. & Neutze, R. Science 373, eaba0954 (2021). 3. Miller, R. J. D., Paré-Labrosse, O., Sarracini, A. & Besaw, J. E. Nature Commun. 11, 1240 (2020). 4. Besaw, J. E. & Miller, R. J. D. Curr. Opin. Struct. Biol. 81, 102624 (2023). 5. Šrajer, V. et al. Science 274, 1726–1729 (1996). 6. Schotte, F. et al. Science 300, 1944–1947 (2003). 7. Barends, T. R. M. et al. Science 350, 445–450 (2015). 8. Ischenko, A. A., Weber, P. M. & Miller, R. J. D. Chem. Rev. 117, 11066–11124 (2017). The authors declare no competing interests. This article was published online on 14 February 2024.

Photonics

Nanotraps boost light intensity for future optics Kirill Koshelev

A method for configuring light-trapping devices promises better optical nanodevices by amplifying light and enhancing the emission efficiency of luminescent nanomaterials — without the need for complex technology upgrades. See p.765 Intense light beams are crucial for myriad applications, ranging from medicine to electronics, but they are challenging to produce with everyday light sources. They can, however, be generated by lasers. Lasers work by trapping light in a cavity, called an optical resonator, in which reflected light waves interfere constructively to amplify the light’s intensity through a phenomenon known as optical resonance. But light can be emitted, scattered or absorbed by the resonator material, limiting the extent to which its intensity can be enhanced — especially in devices that operate on a nanometre scale, such as ultraprecise sensors. On page 765, Schiattarella et al.1 report a smart way of balancing the possibilities for light to escape a nanoresonator, and therefore increase light intensity by a factor of up to 36,000. In the past two decades, advances in nanoscale materials have enabled researchers to engineer visible and infrared light resonators that are no thicker than a human hair2. However, decreasing the size of a resonator inevitably leads to an increase in light emission. One way around this involves a special optical resonance called a bound state in the continuum, also known as a dark mode. This mode amplifies light intensity with very small losses3. Dark modes are produced by carefully tuning the properties of a resonator to induce 722 | Nature | Vol 626 | 22 February 2024

destructive interference between two or more ‘bright’ waves, which are formed through constructive interference. Confining light with dark modes might limit unwanted emission, but it doesn’t overcome the challenges posed by absorption and by fabrication defects that lead to light being scattered. Optimal light intensity is usually achieved by satisfying the critical coupling condition4, in which the escape rates of light through emission, scattering and absorption

“This approach could benefit biosensing by enhancing sensitivity to small sample volumes.” are perfectly matched. But Schiattarella and colleagues showed that they could enhance light confinement beyond the range of conventional critical coupling by tailoring the exchange of energy between a dark mode and a bright mode. In doing so, they achieved ‘supercritical’ coupling. The authors investigated a resonator consisting of a 130-nm-thick slab of silicon nitride that was patterned with a square lattice of circular holes and placed on a silicon dioxide substrate of 0.1–1 millimetres in length (Fig. 1). They first

calculated how the optical resonances of the slab could be optimized by adjusting various structural parameters of the slab, including its crystal-lattice spacing and thickness, as well as the diameter of the holes. They then used this information to create a dark mode and a bright mode with similar frequencies and waveforms. By illuminating the centre of the slab with light that has the same frequency as the dark mode, the authors showed that they could induce conventional critical coupling. This offered moderate intensity enhancement that was limited by imperfections in the surface of the silicon nitride slab. They then showed that illuminating the edge of the slab had the effect of inducing a specific energy-exchange rate between the dark and bright waves, which modified the critical coupling. The authors’ calculations suggested that incorporating this exchange into the usual loss-balancing equation could lead to the fulfilment of a supercritical coupling condition that would substantially improve the enhancement of the light intensity. Schiattarella et al. used a process known as upconversion to demonstrate that their resonator could achieve the predicted supercritical coupling. Upconversion involves two or more photons combining and being absorbed to generate one higher-energy photon. It occurs, for example, when nanoparticles fabricated from the lanthanide series of elements absorb infrared light and convert it into visible light. These nanoparticles upconvert with increased efficiency when they are integrated with nanoresonators5. The authors covered their silicon nitride slabs uniformly with two layers of upconverting nanoparticles: one layer contained a compound that emits green light when excited by infrared light, whereas the particles in the other layer emitted red light. Using a laser producing extremely short pulses of light, they measured the change in luminescence as a result of upconversion, and found that it was substantially more enhanced at the edge of the resonator than it was at the centre. This is consistent with the authors’ model predictions, which suggest that emission from the edge of the nanoparticle–resonator system should be up to 36,000 times higher than that from a thick bulk layer of these nanoparticles. As well as being brighter, the luminescence from the edge was also more precisely focused than that from the centre — emerging from the side of the device as a beam that remained collimated (that is, its rays were parallel) for several millimetres. Compared with emission from the bulk, the directionality of this beam further enhanced the emission — by a factor of more than 100 million, in the authors’ estimation. Schiattarella et al. also showed that the direction of emission could be gradually swapped by changing the direction in which the incoming laser light was polarized (the

Laser light (sample centre)

Laser light (sample edge)

From the archive Tutankhamun’s magnificent coffin is revealed, and Charles Darwin gets a letter that he has to share.

100 years ago Polarization switch

Directive emission Luminescent nanomaterials Silicon nitride

150 years ago

Silicon dioxide

Figure 1 | An optical nanoresonator for enhancing light’s intensity. Schiattarella et al.1 designed a device called an optical nanoresonator, which boosts the intensity of light by trapping light waves. The nanoresonator comprises a 130-nm-thick slab of silicon nitride, patterned with circular holes, on a silicon dioxide substrate. By optimizing the device, the authors induced an exchange of energy between trapped light waves that led to a huge increase in the intensity of the light it emitted. It also increased the efficiency of a process called upconversion, in which luminescent nanoparticles on the nanoresonator’s surface emit photons with higher energies than those of a laser that excites them. Emission from the nanoparticles at the edge was more intense and more focused than that from those in the centre of the device, and the direction of the emission at the edge could be switched by changing the laser’s polarization (the plane in which the electric and magnetic fields of its light waves oscillate). (Adapted from Fig. 1 of ref. 1.)

plane in which the light waves’ electric and magnetic fields oscillate). Schiattarella and colleagues’ main innovation is a smart photonic engineering method that significantly enhances light intensity in nanostructured optical devices through optical optimization alone, without requiring advances in fabrication technology or material quality. This approach will certainly enable more efficient upconversion processes, but it could also benefit biosensing by enhancing sensitivity to small sample volumes and improve quantum communications by helping quantum bits (qubits) to retain information. A key limitation of the study is that strong emission occurs only at the sample’s edge. Many nanodevices need light to be emitted perpendicular to the surface of a device, as is the case for conventional optical components, such as lenses. Another issue is that Schiattarella et al. achieved supercritical coupling by precisely adjusting several of the slab’s structural parameters. Simplifying this approach would make the photonic design process much easier. Advances in the physics of optical resonance have already improved the efficiency of nanoscale optical devices, and their performance is now nearing that of conventional optical devices, such as lasers. Schiattarella

In the Times of February 13, an account is given of the raising of the lid of the sarcophagus of Tutankhamen, which took place on the previous day in the tomb at Luxor ... [T]here appeared an anthropoid coffin ... of colossal size ... with gilt lion heads superbly modelled at the head ... The face was of gold ... with eyes of crystal ... The face was evidently a portrait. From Nature 23 February 1924

and colleagues’ work in improving the resonant properties of optical nanostructures is expected to give rise to even smaller and more efficient nanodevices. This progress could eventually lead to the lenses in spectacles and cameras being replaced with ultrathin optical components that boast superior performance. Kirill Koshelev is in the Nonlinear Physics Centre, Research School of Physics, Australian National University, Canberra, 2601, Australia. e-mail: [email protected]

1. Schiattarella, C. et al. Nature 626, 765–771 (2024). 2. Chen, H. T., Taylor, A. J. & Yu, N. Rep. Prog. Phys. 79, 076401 (2016). 3. Hsu, C. W., Zhen, B., Stone, A. D., Joannopoulos, J. D. & Soljačić, M. Nature Rev. Mater. 1, 16048 (2016). 4. Seok, T. J. et al. Nano Lett. 11, 2606–2610 (2011). 5. Das, A., Bae, K. & Park, W. Nanophotonics 9, 1359–1371 (2020).

The accompanying letter, just received from Fritz Müller, in Southern Brazil, is so interesting that it appears to me well worth publishing in NATURE. His discovery of the two sexually mature forms of Termites, and of their habits ... now published in Germany ... justly compares, as far as function is concerned, the winged males and females of the one form, and the wingless males and females of the second form, with those plants which produce flowers of two forms, serving different ends ... The facts ... given by Fritz Müller with respect to the stingless bees of Brazil will surprise and interest entomologists. Charles Darwin “For some years I have been engaged in studying ... our Termites ... The most interesting fact in the natural history of these curious insects is the existence of two forms of sexual individuals, in some (if not in all) of the species ... I have lately turned my attention to ... stingless honeybees (Melipona and Trigona) ... Wasps and hive-bees have no doubt independently acquired their social habits, as well as the habit of constructing combs of hexagonal cells, and so, I think, has Melipona. The genera Apis and Melipona may even have separated from a common progenitor, before wax was used in the construction of the cells ... [I]n hive-bees ... wax is secreted on the ventral side: in Melipona ... on the dorsal side of the abdomen; now it is not probable, that the secretion of wax, when established, should have migrated from the ventral to the dorsal side, or vice versâ.” From Nature 19 February 1874

The author declares no competing interests.

Nature | Vol 626 | 22 February 2024 | 723

News & views Medical research

Smoking’s lasting effect on the immune system Yang Luo & Simon Stent

It emerges from a study of human cells that smoking can influence certain immune responses to the same extent as can age or genetics. Smoking can alter the immune system in ways that persist long after quitting the habit. See p.827 When our bodies encounter pathogens such as bacteria and viruses, immune cells release molecules called cytokines to coordinate the body’s defence mechanisms. These cytokines send signals to other immune cells to mount an appropriate response against the invading pathogens. The secretion of cytokines can vary among individuals and is influenced by both environmental and inherited factors. On page 827, Saint-André et al.1 report their examination of data from the Milieu Intérieur project2, a research initiative designed to study the variability in the immune system among 1,000 healthy individuals. The authors systematically examined 136 variables that might contribute to differences in cytokine secretion. These factors related to socio-demographics, diet and lifestyle. The authors discovered that three factors in particular — smoking, a dormant (latent) infection by a type of virus called cytomegalovirus, and a measure of body weight called body mass index (BMI) — were the main contributors to variability in cytokine response, and had comparable effects to those of age, sex and genetics. To measure the effect of immune responses quantitatively, the authors analysed the production of 13 disease-relevant cytokine proteins. These were assessed in blood samples tested in vitro by exposure to 12 different immune stimulations, such as proteins associated with microbial and viral infections (Fig. 1). These stimulations elicited reactions from both lines of immune defence — the faster, more general, innate defence, and the slower, more targeted and adaptive, memory-based defence — serving as indicators of the body’s immune activities. Among the environmental factors studied, the authors report that smoking-related variables showed the most statistically significant associations across immune stimulations. Smoking was found to exert a transient effect on immediate, non-specific, innate immune responses. Surprisingly, its enduring influence on specialized adaptive immune 724 | Nature | Vol 626 | 22 February 2024

responses was found to persist well beyond smoking cessation. To investigate how smoking leaves this lasting effect on the adaptive immune system, the authors specifically tested its link with epigenetic alterations — molecular changes that help our cells to ‘remember’ their specific roles and functions. The study reveals that the association between smoking and cytokines in the adaptive branch of the immune system is shaped by a specific epigenetic process called DNA methylation, which modifies DNA sequences in the nucleus. This process functions in a way that is similar to issuing cellular instructions, directing cells to either activate or deactivate the expression of particular genes. Smoking was shown to decrease the level of DNA methylation at specific sites that are related to the regulation a

Immune-cell stimulant such as viral protein

b

Immune cells of innate and adaptive defences

of genes associated with signalling processes and metabolism in the body, causing altered levels of cytokines in response to immune challenges. The authors did not identify any specific cellular mediators for the increased inflammatory response to stimulation of innate defences in smokers compared with non-smokers. Instead, the authors found a clear link between active smoking and an upregulation of the bacterially induced inflammatory cytokine CXCL5. This particular cytokine has a role in orchestrating the immune response and is associated with an increase in the level of the protein CEACAM6 in the bloodstream of active smokers. CEACAM6 is involved in inflammatory processes and immune regulation, and it has been proposed to represent a clinical biomarker of disease for multiple cancers3. The higher-than-usual level of CEACAM6 in smokers suggests a mechanistic involvement in the pro-inflammatory cascade, triggered by bacterial stimuli, that is associated with smoking. Saint-André and colleagues’ study not only provides a scientific basis for further promoting non-smoking and a healthy lifestyle, but also highlights two key aspects for future studies. First, it indicates a way to search for more-realistic disease-prevention measures, including the possibility of identifying new molecular signatures of interactions between environmental factors and diseases, such as those observed in smokers compared with non-smokers. Second, it emphasizes the dynamic and context-specific nature of gene and protein activities, underscoring the need E coli to stimulate innate defences

Immune cells in blood sample of current smoker 13 cytokines assessed

Cytokines coordinate defence response

Abnormal rise in CXCL5

c

SEB to stimulate adaptive defences

Immune cells in blood sample of current or ex-smoker 13 cytokines assessed IL-2 IL-13

Abnormal rise in two cytokines

Figure 1 | Identifying factors that influence the production of cytokine molecules. a, When human immune cells encounter signs of problems they release various cytokines that orchestrate a defence response. Some immune cells function in the innate branch of the immune system, which provides a swift response to broad signs of infection. The other, adaptive, branch of the immune system enables highly specific targeting and generates a ‘memory’ of past infections. b, Saint-André et al.1 examined cytokine production in response to immune-cell stimulants in blood samples from healthy people for whom 136 characteristics, such as smoking status, were known. The authors assessed whether any characteristics were associated with abnormalities in cytokine production. Current smokers had an impaired innate defence response to the bacterium Escherichia coli that was associated with a higher-than-normal level of the cytokine CXCL5. c, Current or ex-smokers had an abnormal adaptive response to a bacterial protein called Staphylococcus aureus enterotoxin B superantigen (SEB) that was associated with higher-than-normal levels of the cytokine proteins IL-2 and IL-13.

to understand disease-associated genes and proteins in their proper context. Although the authors’ findings demonstrate the short-term and long-term effects of smoking on cytokine responses in healthy individuals, replication of the study in a clinical setting and with more genetically diverse populations would further aid understanding and modelling of these effects. This work also highlights the importance of considering other environmental factors that can have both short-term and long-term effects on the immune system. Although some aspects of our immune responses are influenced by inherent factors that cannot be changed — such as age and genetics — other variables, such as smoking, BMI and viral infections, also have a key role in shaping human immune responses. Taking a step back to consider the bigger picture, epidemiological studies have shown that environmental factors such as smoking and pollution are contributing to a global increase in the prevalence of cancer and cardiovascular and respiratory diseases4. However, there is still a lack of detailed understanding about the specific underlying cellular and molecular processes that are influenced by these environmental factors. Saint-André and colleagues have shown that environmental exposures can affect immune responses associated with cancer through various mechanisms. These mechanisms include ‘upstream’ changes, such as DNA methylation, and ‘downstream’ effects on protein abundance. Epigenetic modifications and protein levels, such as those of CEACAM6, are therefore crucial for understanding how environmental exposures result in measurable immune responses. It will be essential to determine how environmental stressors affect epigenetic modifications, gene activity and protein function to better identify and mitigate the effects of environmental exposures on the immune system, and to understand the development of environmentally driven diseases. Yang Luo is at the Kennedy Institute of Rheumatology, University of Oxford, Oxford OX3 7FY, UK. Simon Stent is a research scientist based in Oxford, UK. e-mail: [email protected]

1. Saint-André, V. et al. Nature 626, 827–835 (2024). 2. Thomas, S. et al. Clin. Immunol. 157, 277–293 (2015). 3. Burgos, M. et al. Ther. Adv. Med. Oncol. 14, 17588359211072621 (2022). 4. Prüss-Ustün, A. et al. BMJ 364, l265 (2019). The authors declare no competing interests. This article was published online on 14 February 2024.

Animal behaviour

How population size shapes fish evolution Bernt-Erik Sæther

A long-term fish experiment reveals how a mechanism called density dependence, in which the population growth rate slows as the number of individuals rises, affects population dynamics on time scales relevant for ecology and evolution. As populations grow, a decrease in their growth rate, occurring as a phenomenon referred to as density dependence, affects the dynamics of most species1. Writing in The American Naturalist, Travis et al.2 provide evidence from guppy fish (Poecilia reticulata) on the island of Trinidad that sheds light on the wide-ranging consequences of this type of scenario. The authors demonstrate that variation in population fluctuations can lead to the evolution of large differences in life-history strategies in different populations affecting the pattern of survival of juveniles or adults, the timing of sexual maturity and the numbers of offspring produced. This research delivers a key finding because most populations in the natural world are affected by this general feedback mechanism — the changes in population size from one point in time to the next depend on the number of individuals present in the population3. For nearly 100 years4 it has been known from theoretical analyses that this type of internal feedback loop should have strong effects on the expected patterns of fluctuations in population size5. Density dependence is also known to result in natural selection of certain traits, (for example, the number of eggs produced per season by birds such as the great tit Parus major)6, resulting in evolutionary consequences7. Yet, despite its general importance, experimental evidence from natural populations on how density dependence affects dynamic processes, on both ecological and evolutionary time scales, remains rare. Travis and colleagues’ study of Trinidadian guppies fills a large gap in this lack of knowledge by experimentally demonstrating how patterns in the fluctuations in population size affect evolution through density-dependent selection, which affects variation in crucial characteristics of the life history of these fish. The critical age-class7 is a key concept in studies of evolution in density-regulated populations8. This a function that describes the age of individuals in a population at which

the strongest regulation of population density occurs. A general prediction from theoretical analyses is that in density-regulated populations, evolution tends to maximize the expected value of the function that determines how the change in the number of individuals is affected by population size7,8. For example, the key variable affecting the density-dependent regulation of the size of a population might be either the total number or the total biomass of the individuals present9. Testing such effects of density dependence on life evolution in density-regulated populations requires that two conditions are fulfilled8. First, the stage of the life cycle that is most strongly affected by fluctuations in population size must be identified. Second, differences in ‘fitness’ of individuals in terms of the production of offspring (also described as recruits) by individuals must be closely associated with characteristics (phenotypes) that are present at this key stage of the life cycle. An exceptional feature of studying guppies is that they provide a unique opportunity to examine the validity of these key assumptions experimentally. On Trinidad, guppies (Fig. 1) live in streams where they experience either high or low levels of predation from other species of fish. The composition of these predator communities was previously thought to be the primary selection pressure generating genetic differences underlying the life-history strategies of guppies, which relate to variation in the timing of sexual maturity corresponding to the age and size of the fish10. Nearly 15 years ago, the authors moved individual guppies from a high-predation location to generate four new experimental populations subject to two levels (high or low) of resource availability. Because the new populations were initially established using only a few individuals, monthly censuses provided precise estimates of the strength of density dependence. These included how the change in the number of individuals related to the population size; how fluctuations in

Nature | Vol 626 | 22 February 2024 | 725

NICK UPTON/NATURE PICTURE LIBRARY

News & views

Figure 1 | Trinidadian guppy fish (Poecilia reticulata).

the total biomass of the population (measured by weighing the fish) affected seasonal variation in the probability of an individual’s survival from one month to the next; and the number of individuals produced per female (the recruitment rate). This experimental set-up produced a remarkably clear set of results. First, in all four populations, a model for the data that included density dependence fitted the data better than models that did not (‘random walk’ models). This provides evidence that the regulation of the population growth rate was influenced by the total biomass of fish in each population in three of the four populations. However, the authors did not find a ‘steady state’ consistent with regulation in the fourth population. Such long-term experimental studies of population dynamics are rare in ecology11. Second, the density dependence acting on the change in population size occurred through a consistent decline in recruitment rates associated with a rise in the total biomass. By contrast, adult survival was unaffected by an increase in biomass. Thus, these data indicate that juveniles are the critical age-class7,8 for regulating the size of these populations, because juvenile survival influences the individual fitness of adults in terms of the number of new recruits produced. Consequently, increased biomass decreased the high reproductive rate of adults and the juvenile survival rates, whereas adult survival rates were independent of biomass. 726 | Nature | Vol 626 | 22 February 2024

This indicates strong density-dependent selection for delayed sexual maturation when population biomass is high7,8. Surprisingly, the presence of a competing species, the killifish (Rivulus hartii) had only minor effects on survival and recruitment rates for guppies, indicating that the major selection pressure for delayed sexual maturation was related to a reduction in food availability when fish biomass was high. The authors’ experiments have generated many questions, one of which is how the adults’ ability to produce new recruits at different resource levels is related to their own phenotypes or the phenotypic characteristics of the juveniles. Analyses of how the average fitness of individuals depends on traits such as body mass might provide evidence for a phenotype that maximizes the average fitness in the population12. However, the characteristics of an optimal phenotype might differ between populations: the authors found inter-population variation for the strength of density dependence, the degree of environmental fluctuations and resource availability. Another central question is whether density-dependent selection that influences the age of maturity10 will cause correlated changes to traits at later life stages, which affect the degree of reproductive success or survival at older ages. A clear message arising from this study is that understanding the capacity for natural populations to adaptively evolve in

response to new environments must include density-dependent selection to provide realistic conclusions. This study also provides evidence for the suggestion 13 that density-dependent processes can be a key selective agent in determining differences in life-history strategies that arise within a species or between different species. Bernt-Erik Sæther is at the Gjærevoll Centre for Foresight Analyses of Biodiversity, Norwegian University of Science and Technology, N-7055 Trondheim, Norway. e-mail: [email protected] 1. Brook, B. W. & Bradshaw, C. J. A. Ecology 87, 1445–1451 (2006). 2. Travis, J., Bassar, R. D., Coulson, T., Lopez-Sepulcre, A. & Reznick, D. Am. Nat. 202, 4 (2023). 3. Turchin, P. in Population Dynamics (eds Cappucino, N. & Price, P. W. ) 19–40 (Academic, 1995). 4. Nicholson, A. J. J. Anim. Ecol. 2, 132–178 (1933). 5. May, R. M. Nature 261, 459–467 (1976). 6. Sæther, B.-E., Visser, M. E., Grøtan, V. & Engen, S. Proc. R. Soc. B 283, 20152411 (2016). 7. Charlesworth, B. Evolution in Age — Structured Populations 2nd edn (Cambridge Univ. Press, 1994). 8. Engen, S. & Sæther, B.-E. Oikos 125, 1577–1585 (2016). 9. Engen, S., Wright, J., Araya-Ajoy, Y. G. & Sæther, B.-E. Evolution 74, 1923–1941 (2020). 10. Reznick, D. N. & Travis, J. Annu. Rev. Ecol. Evol. Syst. 50, 335–354 (2019). 11. Hixon, M. A., Pacala, S. W. & Sandin, S. A. Ecology 83, 1490–1508 (2002). 12. Sæther, B.-E., Engen, S., Gustafsson, L., Grøtan, V. & Vriend, S. J. G. Am. Nat. 197, 93–110 (2021). 13. Ricklefs, R. E. Condor 102, 9–22 (2000). The author declares no competing interests. This article was published online on 6 February 2024.

Review

Natural killer cell therapies https://doi.org/10.1038/s41586-023-06945-1 Received: 11 September 2023

Eric Vivier1,2,3,4 ✉, Lucas Rebuffet2, Emilie Narni-Mancinelli2, Stéphanie Cornen1, Rob Y. Igarashi5 & Valeria R. Fantin5

Accepted: 6 December 2023 Published online: 21 February 2024 Check for updates

Natural killer (NK) cells are lymphocytes of the innate immune system. A key feature of NK cells is their ability to recognize a wide range of cells in distress, particularly tumour cells and cells infected with viruses. They combine both direct effector functions against their cellular targets and participate in the generation, shaping and maintenance of a multicellular immune response. As our understanding has deepened, several therapeutic strategies focused on NK cells have been conceived and are currently in various stages of development, from preclinical investigations to clinical trials. Here we explore in detail the complexity of NK cell biology in humans and highlight the role of these cells in cancer immunity. We also analyse the harnessing of NK cell immunity through immune checkpoint inhibitors, NK cell engagers, and infusions of preactivated or genetically modified, autologous or allogeneic NK cell products.

2023 heralds the fiftieth anniversary of the pioneering publications that set the stage for the discovery of NK cells1–3. Further characterized as a unique cellular entity distinct from other known immune cells and also officially named in 19754,5, NK cells are now known to belong to the group of innate lymphoid cells (ILCs)6—a family of cells that has been recognized as such since 20087. ILCs are lymphocytes of the innate immune system that do not express the type of diversified antigen receptors found on T cells and B cells8. Among the five major ILC subsets, type 1, 2 and 3 ILCs (ILC1, ILC2 and ILC3 cells, respectively) are mostly tissue-resident cells and mirror CD4+ T helper type 1 (TH1), TH2 and TH17 cells, respectively, in terms of cytokine production, whereas NK cells that are present both in the blood and tissues can be considered to be innate counterparts of CD8+ cytotoxic T cells9. Over the past five decades, the importance and potential of NK cells has been extensively explored. What began as academic intrigue has evolved into a promising area of immunotherapy, particularly in the fight against cancer.

What are NK cells? NK cells are effector ILCs that arise from bone marrow progenitor cells6,10–14. The total number of NK cells in humans has been estimated to be 2 × 1010 cells (95% confidence interval: 0.5 × 1010–6 × 1010), making up around 1% of total immune cell types in the body and 2% of total lymphocytes15. At steady state in healthy individuals, NK cells are present mainly in the liver, bone marrow and blood where they constitute around 10% of the total number of peripheral lymphocytes. Their functions are tightly regulated by a repertoire of inhibitory and activating receptors, enabling them to recognize and to directly or indirectly eliminate stressed cells while sparing normal cells. The vast majority of mature NK cells are cytolytic, and all NK cells can produce a number of cytokines, including interferon-γ (IFNγ), growth factors such as FMS-like tyrosine kinase 3 ligand (FLT-3L) and granulocyte– macrophage colony-stimulating factor (GM-CSF), and chemokines, including XCL1 and CCL56,10–14.

Human NK cells are usually classified on the basis of the expression of the two surface molecules CD56 (encoded by NCAM) and CD16a (encoded by FCGR3A)12. CD56brightCD16− NK cells exhibit lower cytotoxicity but produce cytokines, growth factors and chemokines. By contrast, CD56dimCD16+ NK cells are highly cytotoxic due to their expression of granzymes (GZMA, GZMB) and perforin (PRF1), and can also produce cytokines, growth factors and chemokines16. More recently, single-cell RNA-sequencing analyses have provided insights into the diversity of NK cells17,18. Unsupervised classification algorithms based on gene expression enabled the identification of three major NK cell populations (Fig. 1) and also revealed the presence of several subpopulations within them. Type 1 NK (NK1) cells, which are the most abundant in blood, correspond to CD56dimCD16+ NK cells and, besides the strong expression of CD16 (FCGR3A) and cytotoxicity effector molecules (GZMA, GZMB, PRF1), they selectively express additional genes such as SPON2, the biological function of which in NK cells remains to be elucidated. The NK2 cell population corresponds to CD56brightCD16− NK cells. These cells exhibit a characteristic transcriptional signature, including granzyme K (GZMK), a characteristic chemokine profile (XCL1, XCL2), cell surface markers (CD44, SELL, KLRC1) and strong expression of the transcription factor TCF1 (encoded by TCF7). Finally, a third major population, tentatively referred to as NK3 cells, comprises mainly CD16dim adaptive NKG2Chigh (encoded by KLRC2) NK cells, including CD57+ cells19–21. This subset presents memory-like properties with enhanced functional responses, in a manner resembling memory T cells, after recognition of various viral, bacterial, cytokine or hapten stimuli22–26. They exhibit a characteristic cytotoxic signature (GZMH), surface markers (KLRC2, CD3E) and a specific cytokine signature (IL32 and CCL5)27,28. The abundance of each of the various NK cell subsets depends on their anatomical localization and pathophysiological conditions. NK1 cells are present at higher concentrations in the bone marrow, spleen and blood, whereas NK2 cells are more prevalent in the lungs, tonsils, lymph nodes and intestines. NK2 cells show greater tissue imprinting compared with NK1 cells and share several common markers with CD8+ tissue-resident memory T cells, notably, increased expression

Innate Pharma Research Laboratories, Innate Pharma, Marseille, France. 2Aix Marseille Université, CNRS, INSERM, Centre d’Immunologie de Marseille-Luminy, Marseille, France. 3APHM, Hôpital de la Timone, Marseille-Immunopôle, Marseille, France. 4Paris-Saclay Cancer Cluster, Le Kremlin-Bicêtre, France. 5Sanofi Oncology Research, Boston, MA, USA. ✉e-mail: [email protected]

1

Nature | Vol 626 | 22 February 2024 | 727

Review NK1 CD16

CD56

CCL3, CCL4

HOPX, GTF3C1

SPON2, LGALS1, FCER1G, MYOM2, CLIC3, LAIR2, FGFBP2, CX3CR1, KLRB1, NKG7

CD161 NKp46 KIR

GZMA, GZMB, PRF1

TCF7, MYC, SCML1, EEF1G, ID2 GPR183, TPT1, LTB, AREG, KLRC1, CD44, SELL, IL7R, CCR7, CXCR3, FCER1G

NK2

NK3

CD16

CD57 CD16

CD56

XCL1, XCL2 CD161

NKp46 GZMK

CD56

PRDM1

CCL5, IL-32 NKG2C

VIM, CRIP1, LGALS1, KLRC2, CD3E, S100A6, S100A4, KLRC3, CD52, FGFBP2

CD161 NKp46 GZMH

KIR

Fig. 1 | Prominent subsets of circulating NK cells in humans. In the peripheral blood of healthy individuals, three dominant NK cell subsets—NK1, NK2 and NK3—are discernible through single-cell transcriptomic analysis. This figure

delineates the distinct transcription factors, secreted molecules and cell surface receptors that are characteristic of each subset.

of CD103 (encoded by ITGAE) and CD49a (in the lungs and intestines) and CXCR6 (in the bone marrow, spleen and lymph node)9,14. Recently, a human pan-cancer atlas of NK cells described tumour-associated NK cells characterized by reduced cytotoxicity and high expression of stress-associated proteins (DNAJB1 and HSP gene family), inhibitory MHC-class-I-specific receptors (members of the KIR family) and several transcription factors that control the fitness of NK cells such as NR4A1, EGR3 and KLF629. Another level of complexity in the dissection of NK cell biology resides in the strong similarities with ILC1 cells30. For example, NK cells and ILC1 cells that express the activating NK cell receptor NKp46 (encoded by NCR1, also known as CD335) are dependent upon T-bet (encoded by TBX21) and secrete IFNγ 30. Moreover, NK cells can transition to ILC1-like cells in the presence of environmental cues such as transforming growth factor-β (TGFβ), and these cells have been shown to promote tumour development in some preclinical models31,32. Yet, cytotoxic ILC1 cells or ILC1-like cells have also been described to have a role in immunosurveillance of malignancies33. More studies are needed to understand the biology of NK cells as compared to ILC1 cells, as distinctive mechanisms involved in the control of NK cells and ILC1 function have already been described34. To effectively harness the antitumour properties of NK cells, it is therefore essential to distinguish the phenotype and effector functions of closely related types of ILCs, that is, circulating NK cells, tissue infiltrating NK cells, ILC1 cells and tumour-associated NK cells. In mice, bona fide NK cells and ILC1 cells can be distinguished by the mutually exclusive cell surface expression of CD49b (encoded by Itga2) and CD49a (encoded by Itga1) respectively30. Syndecan-4 is also a useful marker that enables discrimination between mouse ILC1 and NK cells35. In humans, it has been proposed that CD200R, CXCR3, CXCR6, DNAM-1 and TRAIL could distinguish ILC1 cells from NK cells36,37, but the phenotype of ILC1 cells varies according to the tissue in which these cells reside, making the identification of pan-ILC1 markers challenging38. At the transcriptomic level, a gene signature with eight cross-species and cross-organ NK-specific markers, EOMES, GZMA, IRF8, JAK1, NKG7, PLEK, PRF1 and ZEB2, has been proposed35. Importantly, these eight markers also discriminate human NK cells from the other ILC subtypes and CD4 T cells. By contrast, only three cross-species and cross-organ ILC1-specific markers, IL7R, LTB and RGS1, have been identified, but these three markers were also expressed by other ILC populations and CD4+ T cells.

molecules in mice and CD94–NKG2A heterodimers in both species. These MHC-I receptors belong to the large family of inhibitory receptors that mediate their function through intracytoplasmic immune receptor tyrosine-based inhibitory motifs40,41. Moreover, NK cells recognize self-molecules that are induced or upregulated on the cell surface of stressed cells42,43. The prototypical example of this type of stress-induced self-recognition is the activation of NK cells triggered by cell surface receptors such as NKG2D (encoded by KLRK1), NKp46 and NKp30 (encoded by NCR3, also known as CD337), and which recognize MICA/B and ULBPs44, ecto-calreticulin45 and B7-H646, respectively, on the surface of stressed cells. The ligands for NKG2D are induced in response to the DNA-damage-response pathway, the integrated stress response, cellular hyperproliferation, activated p53 and heat-shock-induced stress43,47. Ligands for other activating receptors are also thought to be dependent on these stress-induced signalling pathways, but some of these ligands remain undefined and the precise mechanisms regulating the expression of the identified ligands are unclear. The NK cell activating receptors associate with adaptors that carry immunoreceptor tyrosine-based activation motifs40,48. With inputs of both activating and inhibitory signals, NK cells can recognize and contribute to the elimination of a variety of tumours of all histotypes49,50. NK cells can also recognize their cellular targets in the presence of antibodies and trigger antibody-dependent cell cytotoxicity (ADCC), owing to the low-affinity IgG Fc region receptor CD16a, which is

Recognizing cells in distress An essential feature of NK cells is their ability to distinguish between normal cells and cells in distress, and to eliminate the latter directly or indirectly6,10–14,39 (Table 1). NK cells recognize their targets by expressing multiple cell surface receptors6,10–14,39. NK cells sense the level of expression of MHC class I (MHC-I) molecules through a variety of MHC-I-specific inhibitory receptors. These include killer cell immunoglobulin-like receptors (KIRs) in humans, lectin-like Ly49 728 | Nature | Vol 626 | 22 February 2024

Table 1 | The ten hallmarks of tumour immunity of NK cells compared with T cells NK cells

T cells

Detection of stressed cells

Yes

Yes

Multiple ligands: tumour-antigen-agnostic activity against a vast array of tumour cells

Yes

No (TCR mediated)

Combat tumour cells with low mutation load

Yes

No

Natural recognition of cancer cells

No antigen-specific priming required

Yes

No

No need for MHC-I expression; activity increased in absence of MHC-I expression

Yes

No

Direct killing of tumour cells

Yes

Yes

Production of cytokines and chemokines that shape T cell responses

Yes

Yes

Activity against primary tumours and metastasis

Yes

Yes

Efficacy in haematological malignancies

Yes

Yes

Excellent safety profile of cell infusions

Yes

No (graft-versus-host disease)

Elimination of cancer cells

Clinical studies have demonstrated

mainly expressed on the surface of NK1 and NK3 cells. NK-cell-mediated ADCC has been postulated to contribute to the efficacy of therapeutic antibodies such as the anti-CD20 antibody rituximab51. After recognition, NK cells can eliminate their cellular targets through two main mechanisms: direct cytotoxicity and production of cytokines. Once activated, NK cells form an immunological synapse with the target cell, enabling the cytotoxic granules released by the NK cell to be directed towards the target cell and not to surrounding bystander cells52. Direct cytotoxicity of NK cells is also mediated by the interaction between FAS ligand expressed on NK cells and FAS expressed on the target cells. Indeed, engagement of FAS ligand by FAS leads to target cell death by apoptosis53. After the target cell is marked for death, the NK cell detaches and can move on to find another potential target. This ability to engage multiple targets sequentially is referred to as ‘serial killing’, and improves NK cell immunity. Moreover, the production of pro-inflammatory cytokines such as IFNγ and TNF by NK cells might also exert anti-proliferating, anti-angiogenic and pro-apoptotic effects on cancer cells, which could contribute to their antitumour activity54.

Why harness NK cells for cancer treatment? Immunotherapy has undoubtedly revolutionized clinical oncology over the past decade55,56. In particular, immune checkpoint inhibitors and CAR-T cells have shown impressive responses across multiple malignancies55,56. However, only a subset of patients benefit from these treatments and unmet medical needs remain high. New immunotherapies, in particular, approaches with the potential to circumvent the ability of tumours to evade T cell-directed strategies, are warranted. Given the critical role that innate immune responses have in immunity, harnessing these responses opens new possibilities for tumour control57 but remains a challenge58. In that regard, NK cells have a unique set of antitumour properties that differ from those of T cells (Table 1). NK cells do not require antigen-specific priming and recognize a wide range of cells in distress, regardless of their embryological origin or the type of cell-stress trigger49,50. They also exhibit the notable property of controlling the development of metastases by putting metastatic cells into a dormant state through the production of IFNγ or by killing circulating tumour cells before they invade the metastatic niche59–61. Importantly, NK cells are not only killer cells that help to suppress tumours, but they are also involved in the generation, shaping and maintenance of adaptive immune responses by promoting, for example, the antigen-presenting function of dendritic cells, such as type 1 conventional dendritic cells at the tumour bed, through the secretion of FLT-3L, XCL1 and CCL5, and by acting directly on T cells through IFNγ62–64. In contrast to T cells, infusion of NK cells has been shown to be safe in patients with allogeneic grafts, as NK cells do not mediate graft-versus-host disease49,50,65,66. Finally, a very important distinguishing factor between T and NK cells lies in the increase in NK cell function when tumour cells downregulate MHC-I expression on the cell surface. Loss of MHC-I expression is a common T cell immune evasion mechanism67. By contrast, as NK cells express inhibitory MHC-I receptors, MHC-I loss contributes to the recognition and efficient elimination of tumour cells by NK cells68–71. Thus, several features of NK cell biology make their use an interesting and complementary to other modalities used in oncology, including monoclonal-antibody-based therapies, cell-based therapies or a combination of both (Fig. 2).

Unleashing NK cells NK cell activity can be restrained by the engagement of checkpoint inhibitors expressed on their surface. Several inhibitory receptors such as NKG2A, lymphocyte activation gene 3 (LAG3), T cell immunoglobulin and mucin domain-containing 3 (TIM-3) and T cell immunoreceptor with Ig and immunoreceptor tyrosine-based inhibitory motif

domains (TIGIT), which mediate inhibition on both NK and T cells, have been described. A number of monoclonal antibodies blocking these immune checkpoints are currently undergoing clinical evaluation. In addition to the anticipated indirect effect on T cell function therapies, both preclinical and clinical data point to their potential impact on unleashing the NK cell compartment (Fig. 2 and Table 2). Here we present studies assessing the therapeutic impact of these four inhibitory checkpoint inhibitors on NK cell functionality. However, more research is warranted to understand the effect of targeting these inhibitory cell surface receptors on NK cells and their potential contribution to therapeutic response.

NKG2A The CD94–NKG2A heterodimer receptor is expressed by approximately half of NK cells, as well as by a subset of CD8+ T cells. Blocking NKG2A can unleash both T cell- and NK-cell-mediated antitumour immune responses72,73. One such antibody is monalizumab, a humanized IgG4 antibody that specifically blocks the interaction between human NKG2A and its cognate non-classical MHC-I molecule HLA-E72. Current development efforts for monalizumab are focused on evaluating its efficacy in various combination strategies for lung cancer.

LAG3 LAG3 is expressed on various immune cells, including NK cells, activated and exhausted T cells, B cells, regulatory T cells and dendritic cells74–77. LAG3 is a protein that shares similarities with the CD4 molecule and has the ability to interact with MHC-II molecules78,79. Moreover, LAG3 can also bind to galectin-3, fibrinogen-like protein 1 and liver sinusoidal endothelial cell lectin80–84. The exact mechanism of action of LAG3 is not fully understood at this time. Nevertheless, it is a promising immunotherapeutic target with more than 20 LAG3-targeting therapeutics in clinical trials. Within the class, relatlimab has been approved in combination with the anti-PD-1 antibody nivolumab for the treatment of unresectable or metastatic melanoma. Preclinical studies have shown that blocking LAG3 boosts NK cell tumour immunity85.

TIM-3 TIM-3 is an inhibitory receptor that is expressed by various immune cells, including T cells, NK cells, regulatory T cells, dendritic cells and macrophages. In healthy adults, NK1 cells and a subset of NK2 cells express TIM-386. Blockade of TIM-3 in combination with anti-PD1 suppress disease progression in MHC-I-deficient tumour-bearing mice87. In patients with metastatic melanoma, NK cells are functionally impaired compared with those from healthy individuals. This impairment was reversed by blocking TIM-388. Furthermore, TIM-3 expression is correlated with the disease stage, and blocking TIM-3 reverses the dysfunction of NK cells in patients with lung adenocarcinoma89. In multiple myeloma, both peripheral blood and bone marrow NK cells from patients express higher levels of TIM-3 compared with the control individuals90. Blockade of TIM-3 restored NK-cell-mediated killing of multiple myeloma tumour cells in vitro and significantly inhibited tumour growth in mouse models of multiple myeloma.

TIGIT TIGIT is an inhibitory receptor expressed by T cells and NK cells. It binds to its ligands, poliovirus receptor (encoded by PVR, also known as CD155), nectin-2 and nectin-391, triggering inhibitory signalling. TIGIT competes with DNAM-1 (encoded by CD226) for binding to PVR and nectin-292. TIGIT signalling reduces NK cytotoxicity and cytokine release. TIGIT-positive NK cells have been detected in various human cancers and tumour-infiltrating lymphocytes isolated from patients Nature | Vol 626 | 22 February 2024 | 729

Review NK cell

Monoclonal-antibody-based therapies

NK cell

Unleashing NK cells

Activating NK cells receptors

Stimulating NK cells

Inhibitory NK cell receptors

Tumour antigen

Immune checkpoint inhibitors

NK cell engagers

Patient with cancer

Patient with cancer

Tumour cell

Ex vivo conditioned NK cell

Cell-based therapies

Genetically manipulated NK cell

Tumour cell

CAR NK cell

CAR NK cell products

Patient with cancer

Tumour cell

Tumour cell

Tumour cell

Fig. 2 | NK cell therapies. Two primary therapeutic strategies are being explored to enhance the antitumour efficacy of NK cells: monoclonal-antibody-based therapies (top) and cell-based therapies (bottom). The monoclonal-antibodybased NK therapies encompass the activation of NK cell antitumour immunity using immune checkpoint inhibitors (red antibodies) such as anti-LAG3, antiNKG2A, anti-TIM-3 and anti-TIGIT monoclonal antibodies, and the augmentation of NK cell antitumour response through monoclonal-antibody-derived tools

that stimulate their activating receptors, such as NK cell engagers (light green). The cell-based NK therapies use various sources of NK cell products that are injected into the patients, such as ex vivo conditioned NK cells, genetically manipulated NK cells and CAR NK cells. Activating and inhibitory NK cell receptors (purple) and their cognate ligands expressed on tumour cells are shown (dark green).

with colorectal cancer. Blockade of TIGIT restored NK cell dysfunction and promoted NK-cell-mediated tumour immunity in different mouse models of cancer93.

Various strategies have been used to enhance NK-cell-mediated antitumour potential95. These include glycoengineering the Fc region of cytotoxic antibodies to improve their binding to CD16a96,97, as well as amino acid substitutions98. Another approach involves the development of CD16-engaging Fv fragments, which directly target CD16a on NK cells along with the tumour antigen. This approach bypasses the CD16 Phe/Val158 polymorphism, minimizes off-target interactions with complement or other Fc receptors, and inhibits the displacement of the Fc portion of the therapeutic monoclonal antibodies by the high concentration of circulating antibodies. AFM13, a bispecific NKCE targeting CD30 and CD16, has been evaluated as a monotherapy in a registrational phase 2 trial for CD30-positive relapsed or refractory lymphomas99. However, despite initial promising data as a monotherapy, the development of this drug is now focused on combinations, mainly with allogeneic NK cells. Other bispecific antibodies targeting EGFR (AFM24) and CD123 (AFM28) are currently undergoing phase 1/2 and phase 1 trials, respectively (Table 2). Moreover, several antibodies targeting CD16 and tumour antigens such as CD19, CD20, CD33, CD133 and EPCAM have demonstrated efficacy in preclinical studies100–103. Trispecific natural killer engagers have been developed by incorporating an IL-15 cytokine element linking the two antibody domains104.

Boosting NK cells Immune cell engagers are bioengineered molecules designed to steer immune cells toward tumours. These engagers primarily consist of multi-armed antibodies that act as bridges between tumour cells and effector cells, facilitating the establishment of an immune synapse. Cell engagers achieve this by targeting tumour antigens and activating receptors expressed on immune effectors. NK cell engagers (NKCEs) have emerged as promising immunotherapies to redirect NK cells and activate their antitumour activity (Fig. 2).

CD16-based NKCEs NK cells exhibit effector activity through ADCC by recognizing and killing cells opsonized with IgG1 and IgG3 antibodies through CD16a. A common polymorphism in CD16, either phenylalanine (Phe158, low affinity) or valine (Val158, high affinity), affects its affinity for the Fc portion of antibodies and can influence NK-cell-mediated ADCC94. 730 | Nature | Vol 626 | 22 February 2024

Table 2 | Clinical landscape of NK-cell-targeting approaches in cancer therapy Phase 1

Phase 2

Phase 3 Approved

Monoclonal-antibody-based therapies

ligand (such as MICA, ULBP1 or ULBP2) with a single-chain variable fragment targeting a tumour antigen. These molecules directed against antigens such as BCMA, CEA, CD19, CD24, CD138 or VEGFR2 have demonstrated antitumour activity in both in vitro and in vivo preclinical models113,114.

Target molecules Checkpoint inhibitors

TIGIT NKG2A TIM3

LAG3

NKCEs NKp46 CD20 BCMA CD123 CD16

CD123 EGFR

CD30*

NKG2D CD33 BCMA EGFR HER2 c-MET Cell-based therapies NK cells

Allogeneic NK cells (iPS cell-, UCB-, placenta-, PBMC-derived; >10) Autologous NK cells (×2)

Allogeneic NK cells (UCB-derived and PBMC-derived ×2) Autologous NK cells (×2)

CAR NK cells

BCMA HER2 CD123 NKG2D ligands

PD-L1 CD19

The landscape shows the highest level of current clinical development for each target, broken down by approaches targeting NK cells (checkpoint inhibitors, NKCEs, allogeneic or autologous NK cell infusions and CAR NK cells). The analysis is based on active industrysponsored clinical trials published at https://clinicaltrials.gov/ as of October 2023, from drug candidates in phase 1 clinical trials to approved molecules. The asterisk indicates that AFM13, the bispecific NKCE targeting CD30 and CD16, was tested in combination with allogeneic NK cells derived from umbilical cord blood (UCB) in patients with CD30-positive lymphomas. iPS cell, induced pluripotent stem cell; PBMC, peripheral blood mononuclear cell.

IL-15 provides activation, proliferation and survival signals to NK cells105. GTB-3550 (anti-CD16, anti-CD33 and IL-15) showed a clinical benefit but a second-generation camelid nanobody CD33-targeting trispecific natural killer engager (GTB-3650) has been developed with improved tissue penetration106. Furthermore, GTB-7550, targeting CD19 (anti-CD16, anti-CD19 and IL-15), has shown enhanced NK cell proliferation and function against multiple B cell malignancies in vitro and is currently undergoing preclinical testing107. Note that the expression of CD16 is downregulated in the tumour microenvironment (TME) through its shedding108, which may limit the benefit of these molecules.

NKG2D-based NKCEs NKG2D is a cell surface activating receptor expressed on all NK cells in humans and mice, as well as on the cell surface of all CD8+ T cells in humans. The chronic binding of NKG2D to its cognate ligands on the cell surface can lead to desensitization of NK cells, impairing their effector functions109. An NK cell engager targeting NKG2D and the multiple-myeloma-associated antigen CS1 has demonstrated efficacy in a preclinical mouse model of multiple myeloma110. Another bispecific NKCE has been developed to target both NKG2D and HER2, promoting cytotoxicity of NK cells in vitro111. Furthermore, trispecific NK cell engager therapies targeting HER2 (DF1001), BCMA (DF3001), CD33 (DF2001) and EGFR (DF9001) on tumour cells are currently being evaluated in phase 1/2 or phase 1 clinical trials for several haematologic and solid tumours (Table 2). Early signs of clinical activity without dose-limiting toxicities have been reported for DF1001112. Moreover, several molecules have been designed fusing an NKG2D

Natural cytotoxicity receptor-based NKCEs NKCEs have been developed to target activating receptors on NK cells with a preferential expression on NK cells, such as the natural cytotoxicity receptors NKp46 and NKp30. In contrast to CD16a and NKG2D, which are expressed by myeloid cells and T cells, respectively, and of which the cell surface expression is downregulated in cancer conditions115, NKp46 exhibits stable expression in various cancers. Although it is expressed by ILC1 cells, subpopulations of ILC3 cells and discrete T cell subsets, NKp46 is the most preferentially expressed activating cell surface receptor for NK cells116,117. The trispecific antibody-based NK cell engager therapeutic (ANKET) platform consists of an antigen-binding antibody fragment that engages NKp46 on NK cells, another antibody fragment that binds to a tumour antigen and an Fc fragment that binds to Fcγ receptors, such as CD16a on NK cells115. In vitro studies have shown that NKp46-ANKET induces potent NK cell activation and promotes NK-cell-mediated lysis of tumour cells. Across a variety of haematological and solid tumour models, NKp46-ANKET has been shown to effectively increase the recruitment of NK cells to the tumour site and control tumour growth115. An NKp46-ANKET targeting CD123 (NKp46-ANKETCD123 also known as SAR′579/IPH6101) is currently being evaluated in a phase 1/2 clinical trial for the treatment of relapsed/ refractory AML118 (Table 2). Moreover, NKp46-ANKET targeting of CD19 or CD20 has shown promising preclinical results in promoting tumour cell killing in models of paediatric B cell precursor acute lymphoblastic leukaemia119. A tetraspecific NKp46-ANKET has also been developed, incorporating a variant of interleukin-2 (IL-2v) that stimulates the IL-2 receptor complex without binding to IL-2Rα (CD25) to prevent regulatory T cell activation and endothelial cell binding120. This tetraspecific NKp46-ANKET, targeting CD20, has demonstrated preferential NK cell activation and proliferation compared with T cells in vitro and massive NK cell infiltration at the tumour bed in preclinical models. Additional multifunctional NK cell engagers targeting NKp46 and alternative tumour antigens have been generated. For example, CYT-303 targets NKp46 and glypican 3, which is overexpressed in hepatocellular carcinomas, and has shown antitumour activity121. Another NK cell engager, CYT-338, targets NKp46 and CD38 and have demonstrated efficacy against multiple myeloma in vitro122. NKp30 is a type I transmembrane activating receptor that is expressed on NK cells, subsets of ILC2 cells, ILC3 cells, and CD8+ αβ and γδ T cells in humans123,124. NKp30 binds to the surface molecule B7-H6 and the nuclear factor HLA-B-associated transcript 3 (BAT3) expressed in diverse tumour cell lines125. However, in tumour contexts, the soluble form of its ligand, B7-H6, has been associated with the downregulation of NKp30 on NK cells126. This downregulation could limit the potential of NKp30 as a target for bispecific antibodies. NKp30-NKCE targeting EGFR or BCMA has shown preclinical efficacy. NKCEs have entered the clinic quite recently and, with the exception of AFM13, all molecules are still in phase 1 (Table 2). In contrast to T cell engagers, which can be associated with adverse events such as neurotoxicity and cytokine release syndrome (an acute systemic inflammatory syndrome characterized by fever and multiple-organ dysfunction), NK cell engagers aim to provide a safer alternative, which ongoing clinical trials seem to have confirmed so far. While T cell engagers have demonstrated single-agent activity in several haematologic malignancies, both NK cell and T cell engagers face a common challenge in effectively controlling solid tumours. Of the ten NKCEs in the clinic for which the tumour targeting antigens have been disclosed, three are directed against solid tumours (Table 2). Nature | Vol 626 | 22 February 2024 | 731

Review NK cells as drug products In addition to monoclonal-antibody-based approaches, the use of NK cells as drug products constitutes a rapidly evolving sector of oncological biotherapy. This paradigm encompasses a spectrum of clinical strategies, including both autologous and allogeneic applications, leveraging cellular progenitors from diverse biological origins such as placental tissues, umbilical cord blood, peripheral blood and induced pluripotent stem cell-derived lineages. These approaches further diverge into distinct modalities, spanning from in vitro pre-activation techniques to cutting-edge genomic editing interventions49,50,127,128. Several cancer conditions and oncological treatments, notably chemotherapy, are known to attenuate both the abundance and the operative capacity of patient’s endogenous NK cells. This depletion underscores the therapeutic rationale for adoptive NK cell transfer, a strategy to enhance the efficacy and resilience of NK cells within the TME. Allogeneic NK cell infusions have emerged as particularly advantageous49,50,127,128. Their attributes include immediate availability, robust antitumoural potency against a wide spectrum of neoplastic targets and absence of graft-versus-host disease. Many ongoing clinical trials are investigating the therapeutic efficacy of allogeneic NK cells across a variety of malignancies, underscoring both the potential and the burgeoning research interest in this domain. The favourable safety profile of allogeneic NK cell infusions has catalysed the development of ready-to-use off-the-shelf therapeutic products. However, allogeneic NK cell infusions necessitate preconditioning regimens, currently comprising agents such as fludarabine and cyclophosphamide to mitigate rejection risks49,50,127,128. Conversely, autologous NK cell therapy, which harnesses a patient’s own cellular resources, obviates the need for such conditioning treatments. Thus, autologous NK cells could have a role in clinical situations in which patients are not eligible for an allogeneic product (for example, in the context of consolidation treatment or minimal residual disease), because the latter would require harsh conditioning. Nonetheless, despite advancements in feeder-cell-free expansion protocols and the attainment of significant proliferative yields—typically 1,000- to 2,000-fold within a 2 to 3 week timeframe—the production of autologous NK cells remains time intensive129. This temporal demand represents a logistical challenge in the clinical deployment of autologous NK-cell-based interventions.

Enhancing NK cell performance through ex vivo conditioning A crucial element of current adoptive NK cell therapy is administering large doses of robustly stimulated NK cells. Ex vivo cytokine stimulation is a widely used method for activating NK cells and facilitating their large-scale expansion. This enables their formulation and cryo-preservation, rendering them ready for infusion as a therapeutic product. Although IL-15 and IL-21 enhance NK cell proliferation and cytotoxicity, their effects vary based on dose, stimulation sequence and duration. Specifically, IL-15 enhances NK cell longevity and antitumour activity by boosting the metabolic fitness of NK cells130. IL-21 increases the NK cell ability to identify and kill tumour cells by raising activating receptor expression and promoting the production of key anti-tumour mediators such as IFNγ and TNF. Notably, sustained conditioning of NK cells with TGFβ during ex vivo expansion can render them more resilient in the TME131. Among the methods to stimulate the ex vivo expansion of NK cells, feeder cell methods using membrane-bound IL-15 (mbIL-15) and another leveraging soluble IL-12, IL-15 and IL-18 are notable. The latter produces NK cells in a cytokine-induced memory-like (CIML) state, reminiscent of viral reactivation in T cell memory132. Indeed, memory capacities of NK cells have been documented22 and their reactions against tumours could possibly be substantially and sustainably amplified133. Early clinical trials using these cytokine-activated NK cells appear promising both in terms of safety and efficacy134,135. Expansions 732 | Nature | Vol 626 | 22 February 2024

using feeder cells with mbIL-21 (FC21) have also been successful, yielding a 1,000-fold increase in potent NK cells within 2 weeks. Such cells display enhanced cytotoxicity against diverse tumours136,137. The adoption of a cell-free method using particles derived from plasma membranes bearing mbIL-21 (PM21) has further expanded the potential of NK cell therapies to better enable manufacturability and safety. A key feature of these NK cell methods is that they allow for cryo-preservation to enable clinical delivery. Importantly, FC21-NK or PM21-NK cells, which result from these methods, align well with NKCEs due to their co-expression of NKp46 and CD16138. Adoption of these methodologies awaits results from clinical trials.

Enhancing NK cell function through genetic engineering Despite their remarkable abilities, the effectiveness of NK cells in controlling tumour growth can be limited by immunosuppressive factors in the TME139–141. Advances in molecular engineering and gene editing will potentially help to overcome some of these limitations. A primary concern is TGFβ, which is overproduced in many cancers and dampens NK cell function. Introducing dominant-negative TGFβ receptors make NK cells resistant to these effects142. Hypoxia in the TME can hamper NK cell function139–141. Cells engineered to express a high-affinity CD16a receptor or IL-2 exhibit tolerance to hypoxic conditions comparable to those encountered in the TME143. Moreover, strategies to optimize the metabolism of NK cells have been shown to enhance their function. For example, deleting or reducing the cytokine-inducible SH2-containing protein (CISH) in NK cells enhances their metabolic fitness and anti-tumour response, offering another route for immunotherapy enhancement144,145. It has also been shown that MYC expression acts as a metabolic rheostat that regulates NK cell growth and effector responses146. Thus, ectopic MYC expression in NK cells could help promote cell survival and self-renewal and improve NK cell anti-tumour activity and persistence of NK cells in the TME. The ability to target NK cells to tumours can be further enhanced by engineering them to express CARs that can recognize specific antigens on tumour cells, and trigger NK cell activation and cytotoxicity independent of the native NK cell receptors147. Several clinical trials are underway to address the efficacy of CAR NK cells against human tumours148. Promising results were reported from a first-in-human phase 1/2 clinical trial of a CAR NK designed to target CD19 for the treatment of B cell lymphoma149. In this case, the NK cells were also engineered to produce IL-15 to stimulate proliferation and persistence.

Driving durable responses through combination strategies As alluded to earlier, NK cell therapies have achieved some success in haematologic malignancies, but their efficacy against solid tumours remains largely unexplored. As we deepen our understanding of tumour cell evasion mechanisms and develop therapies, the potential for combination strategies to magnify NK cell function becomes clear. These combinations aim to harness both innate and adaptive immunity for optimal anti-tumour activity. Current investigational practices combine NK cell therapies with cytotoxic antibodies or checkpoint inhibitors49,50,127,128. For example, allogeneic NK cells paired with the anti-PD-1 antibody pembrolizumab have demonstrated promising results in advanced biliary tract cancer, surpassing previous monotherapy outcomes150. An emerging method involves combining NK cell therapy with NKCEs, leading to both direct and NKCE-mediated tumour attacks. For example, the AFM-13 and CIML-NK cell combination is undergoing clinical evaluation and has shown promisingearly signs of activity134. Oncolytic viruses, which target and eliminate cancer cells while stimulating immunity, can further potentiate NK cell therapy151. Their combination might amplify NK cell tumour infiltration and activity.

Lastly, the combination of NK cell therapies and targeted therapies such as MEK and CDK4/6 inhibitors152, BH3 mimetics153, and radiotherapy154, represent promising oppotunities for therapeutic exploration. Depending on the treatment, they can trigger immunogenic cell deaths, making tumours more recognizable to the immune system. Moreover, targeted therapies may reduce the presence of immunosuppressive cells, fostering NK cell function. Together, these multifaceted approaches could herald a new era of enhanced tumour control.

Conclusions and future directions Beyond the potential of four immune checkpoint inhibitors to activate both T cells and NK cells (Table 2), considerable advancements are evident in NK cell therapies. As it stands, the most advanced of these therapies are in phase 2 clinical trials, featuring at least one NK cell engager and seven NK cell products, of which two are CAR NK cells (Table 2). Over 40 additional clinical trials are in phase 1, with numerous assets under exploration at the preclinical stage, signal a burgeoning interest in harnessing NK cells for cancer therapy and a shift towards clinical validation. NK cell therapy holds promise to improve oncology treatments. Achieving this vision demands progress in understanding NK cell biology, technology enhancements, efficient manufacturing methods and clear regulatory guidelines. With a plethora of immuno-oncology options available, the challenge lies in determining the best combinations partners for NK cell therapy tailored to specific patient needs and therapeutic scenarios. The broad tumour-recognition ability of NK cells increases demand for scalable, cost-effective manufacturing solutions. Maintaining cell viability and potency post-cryopreservation is crucial. This capability enables centralized, off-the-shelf production and wider distribution to clinics. NK cell therapies that do not cause infusion-related complications may enable potential re-dosing or the possibility to use adoptive NK cells either as an induction therapy to lead to complete response followed by a bridging therapy with well-tolerated treatments, or as maintenance therapy. To optimize pharmacokinetic persistence and anti-tumour effects, the need for conditioning regimens is a frequent topic of debate. The resolution of this debate will probably require clinical studies to explicitly test the requirement of conditioning. The use of a conditioning chemotherapeutic regimen would be optimal if it could also have synergistic antitumour effects. A key element to assess such questions will be correlative studies to track adoptive versus endogenous NK cells to enable assessment of whether the use of conditioning makes a difference in the pharmacokinetics of the adoptive NK cells. Such studies will be key for back-translational knowledge on how to design future cell therapeutics.

Augmented NK cells To amplify the therapeutic index of NK-cell-based treatments, there is a need to promote NK cell trafficking to the tumour sites, their metabolic profile and their effector capacity (both direct cytotoxicity and soluble factor production) (Box 1). This can be realized through specific drugs or advanced NK cell products. With regard to next-generation NK cell products, techniques such as gene editing (for example, targeting CISH155 or CD38156) or pretreatment with agents like nicotinamide157 can be used. As CD38 is an ectoenzyme that consumes NAD+, inhibition of CD38 or supplementation of nicotinamide leads to NAD+ accumulation and boosts NK cell metabolic fitness and effector functions156,157. Moreover, growing evidence highlights the importance of epigenetic regulation in immune cell differentiation and function. Exhausted CD8+ T cells display unique epigenetic modifications that set them apart from functional memory CD8+ T cells. Along this line, the histone-methyltransferase enhancer of zeste homologue 2 (encoded by EZH2), is known to influence NK cell differentiation and function158,

Box 1

Ten challenges to optimize NK cell efficacy Addressing NK cells to the tumour: • Enhance NK cell homing to the tumour site • Overcome the physical barrier • Optimize NK cell recognition of tumour cells Enhancing NK cell cytotoxicity and viability: • Determine the requirements for NK cell persistence at the tumour bed • Define NK cell fitness in patients with cancer • Investigate NK cell metabolic adaptations in the TME • Improve activation signalling Treatment optimization and standardization: • Explore synergies of treatment combinations • Develop real-time monitoring techniques • Standardize protocols

prompting exploration of how harnessing NK cell epigenetic regulation might contribute to the efficacy of NK cell therapies.

NK cell persistence The persistence of NK cells in the host is an important consideration in immunotherapy. The long-term persistence of CAR-T cells, spanning months to years, was initially postulated to contribute to sustained responses159. However, it remains to be explored whether the peak of cell expansion can also be a determinant of efficacy. Thus, how long NK cells should reside in the host to be effective, and how the duration could be extended to enhance the clinical efficacy remain to be clarified (Box 1). Robust pharmacokinetic studies during ongoing clinical studies and associated back-translational studies will be very important to better understand the immunological persistence and how it can be further improved.

Building on NK cell hallmarks The ability of NK cells to identify and target stressed cells offers expansive therapeutic possibilities. Indeed, their ability to detect surface molecules on cells infected by intracellular pathogens or those in other stress conditions could be used to deploy NK cell therapies beyond the realm of cancer treatment. Whether these therapies are antibody based, solely cell based or a mixture of the two, they are poised to be explored in therapeutic areas such as infection, inflammation, ageing and metabolic disorders. Along this line, the development of autologous, non-genetically-modified NK cell products in Alzheimer’s disease and of allogeneic NK cell infusions alone or in combination with rituximab in lupus nephritis have been launched. Moreover, as follicular T cells promote autoimmunity and express high levels of PD-1, CAR NK cells expressing the extracellular domain of PDL-1 have been generated and showed efficacy in preclinical settings160. Finally, it is noteworthy that the phenotypic similarities between NK and ILC1 cells and their potential functional differences have prompted investigations into whether some of the NK cell therapies, either monoclonal-antibody based or cell based, can involve ILC1. Regardless of this important clarification, the progress in the application of NK cells underscores their versatility and importance for the advancement of modern medicine. Nature | Vol 626 | 22 February 2024 | 733

Review 1.

2.

3. 4.

5.

6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16.

17. 18.

19.

20. 21.

22.

23. 24. 25. 26. 27.

28. 29. 30. 31.

32. 33.

34.

35. 36. 37.

Herberman, R. B., Nunn, M. E., Lavrin, D. H. & Asofsky, R. Effect of antibody to theta antigen on cell-mediated immunity induced in syngeneic mice by murine sarcoma virus. J. Natl Cancer Inst. 51, 1509–1512 (1973). Oldham, R. K. & Herberman, R. B. Evaluation of cell-mediated cytotoxic reactivity against tumor associated antigens with 125I-iododeoxyuridine labeled target cells. J. Immunol. 111, 862–871 (1973). Takasugi, M., Mickey, M. R. & Terasaki, P. I. Reactivity of lymphocytes from normal persons on cultured tumor cells. Cancer Res. 33, 2898–2902 (1973). Kiessling, R., Klein, E. & Wigzell, H. “Natural” killer cells in the mouse. I. Cytotoxic cells with specificity for mouse Moloney leukemia cells. Specificity and distribution according to genotype. Eur. J. Immunol. 5, 112–117 (1975). Kiessling, R., Klein, E., Pross, H. & Wigzell, H. “Natural” killer cells in the mouse. II. Cytotoxic cells with specificity for mouse Moloney leukemia cells. Characteristics of the killer cell. Eur. J. Immunol. 5, 117–121 (1975). Vivier, E. et al. Innate or adaptive immunity? The example of natural killer cells. Science 331, 44–49 (2011). Spits, H. et al. Innate lymphoid cells—a proposal for uniform nomenclature. Nat. Rev. Immunol. 13, 145–149 (2013). Vivier, E. et al. Innate lymphoid cells: 10 years on. Cell 174, 1054–1066 (2018). Sojka, D. K. et al. Tissue-resident natural killer (NK) cells are cell lineages distinct from thymic and conventional splenic NK cells. eLife 3, e01659 (2014). Moretta, L. et al. Human natural killer cells: their origin, receptors and function. Eur. J. Immunol. 32, 1205–1211 (2002). Moretta, A., Bottino, C., Mingari, M. C., Biassoni, R. & Moretta, L. What is a natural killer cell? Nat. Immunol. 3, 6–8 (2002). Vivier, E., Tomasello, E., Baratin, M., Walzer, T. & Ugolini, S. Functions of natural killer cells. Nat. Immunol. 9, 503–510 (2008). Morvan, M. G. & Lanier, L. L. NK cells and cancer: you can teach innate cells new tricks. Nat. Rev. Cancer 16, 7–19 (2016). Freud, A. G., Mundy-Bosse, B. L., Yu, J. & Caligiuri, M. A. The broad spectrum of human natural killer cell diversity. Immunity 47, 820–833 (2017). Sender, R. et al. The total mass, number, and distribution of immune cells in the human body. Proc. Natl Acad. Sci. USA 120, e2308511120 (2023). Lanier, L. L., Le, A. M., Civin, C. I., Loken, M. R. & Phillips, J. H. The relationship of CD16 (Leu-11) and Leu-19 (NKH-1) antigen expression on human peripheral blood NK cells and cytotoxic T lymphocytes. J. Immunol. 136, 4480–4486 (1986). Crinier, A. et al. High-dimensional single-cell analysis identifies organ-specific signatures and conserved NK cell subsets in humans and mice. Immunity 49, 971–986 (2018). Vivier, E. et al. High-dimensional single-cell analysis of natural killer cell heterogeneity in human blood. Preprint at Research Square https://doi.org/10.21203/rs.3.rs-3870228/v1 (2024). Hendricks, D. W. et al. Cutting edge: NKG2ChiCD57+ NK cells respond specifically to acute infection with cytomegalovirus and not Epstein-Barr virus. J. Immunol. 192, 4492–4496 (2014). Lopez-Verges, S. et al. CD57 defines a functionally distinct population of mature NK cells in the human CD56dimCD16+ NK-cell subset. Blood 116, 3865–3874 (2010). Lopez-Verges, S. et al. Expansion of a unique CD57+NKG2Chi natural killer cell subset during acute human cytomegalovirus infection. Proc. Natl Acad. Sci. USA 108, 14725–14732 (2011). Sun, J. C., Beilke, J. N. & Lanier, L. L. Adaptive immune features of natural killer cells. Nature 457, 557–561 (2009). This study, along with ref. 23, describes NK cells with memory-like functions. Cooper, M. A. et al. Cytokine-induced memory-like natural killer cells. Proc. Natl Acad. Sci. USA 106, 1915–1919 (2009). Cooper, M. A. & Yokoyama, W. M. Memory-like responses of natural killer cells. Immunol. Rev. 235, 297–305 (2010). Lee, J. et al. Epigenetic modification and antibody-dependent expansion of memory-like NK cells in human cytomegalovirus-infected individuals. Immunity 42, 431–442 (2015). Schlums, H. et al. Cytomegalovirus infection drives adaptive epigenetic diversification of NK cells with altered signaling and effector function. Immunity 42, 443–456 (2015). Rückert, T., Lareau, C. A., Mashreghi, M. F., Ludwig, L. S. & Romagnani, C. Clonal expansion and epigenetic inheritance of long-lasting NK cell memory. Nat. Immunol. 23, 1551–1563 (2022). Brownlie, D. et al. Expansions of adaptive-like NK cells with a tissue-resident phenotype in human lung and blood. Proc. Natl Acad. Sci. USA 118, e2016580118 (2021). Tang, F. et al. A pan-cancer single-cell panorama of human natural killer cells. Cell 186, 4235–4251 (2023). Spits, H., Bernink, J. H. & Lanier, L. NK cells and type 1 innate lymphoid cells: partners in host defense. Nat. Immunol. 17, 758–764 (2016). Cortez, V. S. et al. SMAD4 impedes the conversion of NK cells into ILC1-like cells by curtailing non-canonical TGF-β signaling. Nat. Immunol. 18, 995–1003 (2017). This study, along with ref. 32, describes the TGFβ-induced transition of NK cells into ILC1-like cells with a decrease antitumour functions. Gao, Y. et al. Tumor immunoevasion by the conversion of effector NK cells into type 1 innate lymphoid cells. Nat. Immunol. 18, 1004–1015 (2017). Kansler, E. R. et al. Cytotoxic innate lymphoid cells sense cancer cell-expressed interleukin-15 to suppress human and murine malignancies. Nat. Immunol. 23, 904–915 (2022). Scarno, G. et al. Divergent roles for STAT4 in shaping differentiation of cytotoxic ILC1 and NK cells during gut inflammation. Proc. Natl Acad. Sci. USA 120, e2306761120 (2023). Lopes, N. et al. Tissue-specific transcriptional profiles and heterogeneity of natural killer cells and group 1 innate lymphoid cells. Cell Rep. Med. 3, 100812 (2022). Nagasawa, M. et al. KLRG1 and NKp46 discriminate subpopulations of human CD117+CRTH2− ILCs biased toward ILC2 or ILC3. J. Exp. Med. 216, 1762–1776 (2019). Crinier, A. et al. Multidimensional molecular controls defining NK/ILC1 identity in cancers. Semin. Immunol. 52, 101424 (2021).

734 | Nature | Vol 626 | 22 February 2024

38. Yudanin, N. A. et al. Spatial and temporal mapping of human innate lymphoid cells reveals elements of tissue specificity. Immunity 50, 505–519 (2019). 39. Björkström, N. K., Strunz, B. & Ljunggren, H. G. Natural killer cells in antiviral immunity. Nat. Rev. Immunol. 22, 112–123 (2022). 40. Vivier, E., Nunes, J. A. & Vely, F. Natural killer cell signaling pathways. Science 306, 1517–1519 (2004). 41. Long, E. O. Negative signaling by inhibitory receptors: the NK cell paradigm. Immunol. Rev. 224, 70–84 (2008). 42. Gasser, S., Orsulic, S., Brown, E. J. & Raulet, D. H. The DNA damage pathway regulates innate immune system ligands of the NKG2D receptor. Nature 436, 1186–1190 (2005). This study, along with refs. 43–46, describes stress-induced ligands recognized by NK-cell-activating receptors. 43. Raulet, D. H. & Guerra, N. Oncogenic stress sensed by the immune system: role of natural killer cell receptors. Nat. Rev. Immunol. 9, 568–580 (2009). 44. Bauer, S. et al. Activation of NK cells and T cells by NKG2D, a receptor for stress-inducible MICA. Science 285, 727–729 (1999). 45. Sen Santara, S. et al. The NK cell receptor NKp46 recognizes ecto-calreticulin on ER-stressed cells. Nature 616, 348–356 (2023). 46. Brandt, C. S. et al. The B7 family member B7-H6 is a tumor cell ligand for the activating natural killer cell receptor NKp30 in humans. J. Exp. Med. 206, 1495–1503 (2009). 47. Wolf, N. K., Kissiov, D. U. & Raulet, D. H. Roles of natural killer cells in immunity to cancer, and applications to immunotherapy. Nat. Rev. Immunol. 23, 90–105 (2023). 48. Lanier, L. L. Up on the tightrope: natural killer cell activation and inhibition. Nat. Immunol. 9, 495–502 (2008). 49. Myers, J. A. & Miller, J. S. Exploring the NK cell platform for cancer immunotherapy. Nat. Rev. Clin. Oncol. 18, 85–100 (2021). 50. Laskowski, T. J., Biederstadt, A. & Rezvani, K. Natural killer cells in antitumour adoptive cell immunotherapy. Nat. Rev. Cancer 22, 557–575 (2022). 51. Hatjiharissi, E. et al. Increased natural killer cell expression of CD16, augmented binding and ADCC activity to rituximab among individuals expressing the FcγRIIIa-158 V/V and V/F polymorphism. Blood 110, 2561–2564 (2007). 52. Friedman, D. et al. Natural killer cell immune synapse formation and cytotoxicity are controlled by tension of the target interface. J. Cell Sci. 134, jcs258570 (2021). 53. Lettau, M., Paulsen, M., Schmidt, H. & Janssen, O. Insights into the molecular regulation of FasL (CD178) biology. Eur. J. Cell Biol. 90, 456–466 (2011). 54. Braumuller, H. et al. T-helper-1-cell cytokines drive cancer into senescence. Nature 494, 361–365 (2013). 55. Sharma, P. et al. Immune checkpoint therapy-current perspectives and future directions. Cell 186, 1652–1669 (2023). 56. Baker, D. J., Arany, Z., Baur, J. A., Epstein, J. A. & June, C. H. CAR T therapy beyond cancer: the evolution of a living drug. Nature 619, 707–715 (2023). 57. Demaria, O. et al. Harnessing innate immunity in cancer therapy. Nature 574, 45–56 (2019). 58. Cao, L. L. & Kagan, J. C. Targeting innate immune pathways for cancer immunotherapy. Immunity 56, 2206–2217 (2023). 59. Lopez-Soto, A., Gonzalez, S., Smyth, M. J. & Galluzzi, L. Control of metastasis by NK cells. Cancer Cell 32, 135–154 (2017). This report, along with refs. 60 and 61, describes anti-metastatic functions of NK cells. 60. Liu, X. et al. Immune checkpoint HLA-E:CD94-NKG2A mediates evasion of circulating tumor cells from NK cell surveillance. Cancer Cell 41, 272–287 (2023). 61. Correia, A. L. et al. Hepatic stellate cells suppress NK cell-sustained breast cancer dormancy. Nature 594, 566–571 (2021). 62. Bottcher, J. P. et al. NK cells stimulate recruitment of cDC1 into the tumor microenvironment promoting cancer immune control. Cell 172, 1022–1037 (2018). This study, along with ref. 63, describes the role of cytokines and chemokines secreted by NK cells in eliciting a multicellular antitumour immune response. 63. Barry, K. C. et al. A natural killer-dendritic cell axis defines checkpoint therapy-responsive tumor microenvironments. Nat. Med. 24, 1178–1191 (2018). 64. Kirchhammer, N. et al. NK cells with tissue-resident traits shape response to immunotherapy by inducing adaptive antitumor immunity. Sci. Transl. Med. 14, eabm9043 (2022). 65. Olson, J. A. et al. NK cells mediate reduction of GVHD by inhibiting activated, alloreactive T cells while retaining GVT effects. Blood 115, 4293–4301 (2010). 66. Simonetta, F., Alvarez, M. & Negrin, R. S. Natural killer cells in graft-versus-host-disease after allogeneic hematopoietic cell transplantation. Front. Immunol. 8, 465 (2017). 67. Dhatchinamoorthy, K., Colbert, J. D. & Rock, K. L. Cancer immune evasion through loss of MHC class I antigen presentation. Front. Immunol. 12, 636568 (2021). 68. Kärre, K., Ljunggren, H. G., Piontek, G. & Kiessling, R. Selective rejection of H-2-deficient lymphoma variants suggests alternative immune defence strategy. Nature 319, 675–678 (1986). 69. Moretta, A. et al. Major histocompatibility complex class I-specific receptors on human natural killer and T lymphocytes. Immunol. Rev. 155, 105–117 (1997). 70. Yokoyama, W. M. Natural killer cell immune responses. Immunol. Res. 32, 317–325 (2005). 71. Parham, P. MHC class I molecules and KIRs in human history, health and survival. Nat. Rev. Immunol. 5, 201–214 (2005). 72. André, P. et al. Anti-NKG2A mAb is a checkpoint inhibitor that promotes anti-tumor immunity by unleashing both T and NK cells. Cell 175, 1731–1743 (2018). This study, along with ref. 74, describes the role of monalizumab in promoting antitumour immunity. 73. van Montfoort, N. et al. NKG2A blockade potentiates CD8 T cell immunity induced by cancer vaccines. Cell 175, 1744–1755 (2018). 74. Andreae, S., Buisson, S. & Triebel, F. MHC class II signal transduction in human dendritic cells induced by a natural ligand, the LAG-3 protein (CD223). Blood 102, 2130–2137 (2003). 75. Baixeras, E. et al. Characterization of the lymphocyte activation gene 3-encoded protein. A new ligand for human leukocyte antigen class II antigens. J. Exp. Med. 176, 327–337 (1992).

76.

77. 78. 79.

80. 81.

82. 83. 84. 85. 86. 87.

88. 89.

90. 91. 92. 93. 94.

95.

96.

97. 98. 99.

100.

101.

102.

103. 104.

105.

106. 107. 108. 109.

110. 111.

Kisielow, M., Kisielow, J., Capoferri-Sollami, G. & Karjalainen, K. Expression of lymphocyte activation gene 3 (LAG-3) on B cells is induced by T cells. Eur. J. Immunol. 35, 2081–2088 (2005). Merino, A. et al. Chronic stimulation drives human NK cell dysfunction and epigenetic reprograming. J. Clin. Invest. 129, 3770–3785 (2019). Huard, B. et al. Characterization of the major histocompatibility complex class II binding site on LAG-3 protein. Proc. Natl Acad. Sci. USA 94, 5744–5749 (1997). Workman, C. J., Rice, D. S., Dugger, K. J., Kurschner, C. & Vignali, D. A. Phenotypic analysis of the murine CD4-related glycoprotein, CD223 (LAG-3). Eur. J. Immunol. 32, 2255–2263 (2002). Hu, S., Liu, X., Li, T., Li, Z. & Hu, F. LAG3 (CD223) and autoimmunity: emerging evidence. J. Autoimmun. 112, 102504 (2020). Kouo, T. et al. Galectin-3 shapes antitumor immune responses by suppressing CD8+ T cells via LAG-3 and inhibiting expansion of plasmacytoid dendritic cells. Cancer Immunol. Res. 3, 412–423 (2015). Liu, E. et al. Cord blood NK cells engineered to express IL-15 and a CD19-targeted CAR show long-term persistence and potent antitumor activity. Leukemia 32, 520–531 (2018). Sordo-Bahamonde, C. et al. LAG-3 blockade with relatlimab (BMS-986016) restores anti-leukemic responses in chronic lymphocytic leukemia. Cancers 13, 2112 (2021). Wang, J. et al. Fibrinogen-like protein 1 is a major immune inhibitory ligand of LAG-3. Cell 176, 334–347 (2019). Khan, M., Arooj, S. & Wang, H. NK cell-based immune checkpoint inhibition. Front. Immunol. 11, 167 (2020). Ndhlovu, L. C. et al. Tim-3 marks human natural killer cell maturation and suppresses cell-mediated cytotoxicity. Blood 119, 3734–3743 (2012). Seo, H. et al. IL21 therapy combined with PD-1 and Tim-3 blockade provides enhanced NK cell antitumor activity against MHC class I-deficient tumors. Cancer Immunol. Res. 6, 685–695 (2018). da Silva, I. P. et al. Reversal of NK-cell exhaustion in advanced melanoma by Tim-3 blockade. Cancer Immunol. Res. 2, 410–422 (2014). Xu, L. et al. Increased Tim-3 expression in peripheral NK cells predicts a poorer prognosis and Tim-3 blockade improves NK cell-mediated cytotoxicity in human lung adenocarcinoma. Int. Immunopharmacol. 29, 635–641 (2015). Jiang, W. et al. Tim-3 blockade elicits potent anti-multiple myeloma immunity of natural killer cells. Front. Oncol .12, 739976 (2022). Sanchez-Correa, B. et al. DNAM-1 and the TIGIT/PVRIG/TACTILE axis: novel immune checkpoints for natural killer cell-based cancer immunotherapy. Cancers 11, 877 (2019). Alteber, Z. et al. Therapeutic targeting of checkpoint receptors within the DNAM1 axis. Cancer Discov. 11, 1040–1051 (2021). Zhang, Q. et al. Blockade of the checkpoint receptor TIGIT prevents NK cell exhaustion and elicits potent anti-tumor immunity. Nat. Immunol. 19, 723–732 (2018). Temming, A. R. et al. Functional attributes of antibodies, effector cells, and target cells affecting nk cell-mediated antibody-dependent cellular cytotoxicity. J. Immunol. 203, 3126–3135 (2019). van der Horst, H. J., Nijhof, I. S., Mutis, T. & Chamuleau, M. E. D. Fc-engineered antibodies with enhanced Fc-effector function for the treatment of B-cell malignancies. Cancers 12, 3041 (2020). Shields, R. L. et al. Lack of fucose on human IgG1 N-linked oligosaccharide improves binding to human FcγRIII and antibody-dependent cellular toxicity. J. Biol. Chem. 277, 26733–26740 (2002). Suzuki, E. et al. A nonfucosylated anti-HER2 antibody augments antibody-dependent cellular cytotoxicity in breast cancer patients. Clin. Cancer Res. 13, 1875–1882 (2007). Wang, X., Mathieu, M. & Brezski, R. J. IgG Fc engineering to modulate antibody effector functions. Protein Cell 9, 63–73 (2018). Rothe, A. et al. A phase 1 study of the bispecific anti-CD30/CD16A antibody construct AFM13 in patients with relapsed or refractory Hodgkin lymphoma. Blood 125, 4024–4031 (2015). Wiernik, A. et al. Targeting natural killer cells to acute myeloid leukemia in vitro with a CD16 × 33 bispecific killer cell engager and ADAM17 inhibition. Clin. Cancer Res. 19, 3844–3855 (2013). Vallera, D. A. et al. Heterodimeric bispecific single-chain variable-fragment antibodies against EpCAM and CD16 induce effective antibody-dependent cellular cytotoxicity against human carcinoma cells. Cancer Biother. Radiopharm. 28, 274–282 (2013). Gleason, M. K. et al. Bispecific and trispecific killer cell engagers directly activate human NK cells through CD16 signaling and induce cytotoxicity and cytokine production. Mol. Cancer Ther. 11, 2674–2684 (2012). Gleason, M. K. et al. CD16xCD33 bispecific killer cell engager (BiKE) activates NK cells against primary MDS and MDSC CD33+ targets. Blood 123, 3016–3026 (2014). Vallera, D. A. et al. IL15 trispecific killer engagers (TriKE) make natural killer cells specific to CD33+ targets while also inducing persistence, in vivo expansion, and enhanced function. Clin. Cancer Res. 22, 3440–3450 (2016). Schmohl, J. U. et al. Engineering of anti-CD133 trispecific molecule capable of inducing NK expansion and driving antibody-dependent cell-mediated cytotoxicity. Cancer Res. Treat. 49, 1140–1152 (2017). Felices, M. et al. Potent cytolytic activity and specific IL15 delivery in a second-generation trispecific killer engager. Cancer Immunol. Res. 8, 1139–1149 (2020). Miller, J. et al. P618: second-generation CD19 targeting tri-specific killer engager drives robust nk cell function against B cell malignancies. HemaSphere 6, 517–518 (2022). Romee, R. et al. NK cell CD16 surface expression and function is regulated by a disintegrin and metalloprotease-17 (ADAM17). Blood 121, 3599–3608 (2013). Coudert, J. D., Scarpellino, L., Gros, F., Vivier, E. & Held, W. Sustained NKG2D engagement induces cross-tolerance of multiple distinct NK cell activation pathways. Blood 111, 3571–3578 (2008). Chan, W. K. et al. A CS1-NKG2D bispecific antibody collectively activates cytolytic immune cells against multiple myeloma. Cancer Immunol. Res. 6, 776–787 (2018). Raynaud, A. et al. Anti-NKG2D single domain-based antibodies for the modulation of anti-tumor immune response. Oncoimmunology 10, 1854529 (2020).

112. Safran, H. et al. Phase 1/2 study of DF1001, a novel tri-specific, NK cell engager therapy targeting HER2, in patients with advanced solid tumors: phase 1 DF1001 monotherapy dose-escalation results. J. Clin. Oncol. 41, 2508–2508 (2023). 113. von Strandmann, E. P. et al. A novel bispecific protein (ULBP2-BB4) targeting the NKG2D receptor on natural killer (NK) cells and CD138 activates NK cells and has potent antitumor activity against human multiple myeloma in vitro and in vivo. Blood 107, 1955–1962 (2006). 114. Wang, Y. et al. BCMA-targeting bispecific antibody that simultaneously stimulates NKG2D-enhanced efficacy against multiple myeloma. J. Immunother. 43, 175–188 (2020). 115. Gauthier, L. et al. Multifunctional natural killer cell engagers targeting NKp46 trigger protective tumor immunity. Cell 177, 1701–1713 (2019). 116. Pessino, A. et al. Molecular cloning of NKp46: a novel member of the immunoglobulin superfamily involved in triggering of natural cytotoxicity. J. Exp. Med. 188, 953–960 (1998). 117. Walzer, T. et al. Identification, activation, and selective in vivo ablation of mouse NK cells via NKp46. Proc. Natl Acad. Sci. USA 104, 3384–3389 (2007). 118. Gauthier, L. et al. Control of acute myeloid leukemia by a trifunctional NKp46-CD16a-NK cell engager targeting CD123. Nat. Biotechnol. 41, 1296–1306 (2023). 119. Colomar-Carando, N. et al. Exploiting natural killer cell engagers to control pediatric B-cell precursor acute lymphoblastic leukemia. Cancer Immunol. Res. 10, 291–302 (2022). 120. Demaria, O. et al. Antitumor immunity induced by antibody-based natural killer cell engager therapeutics armed with not-alpha IL-2 variant. Cell Rep. Med. 3, 100783 (2022). This study describes the first tetraspecific NK cell engagers armed with a variant of IL-2. 121. Arulanandam, A. et al. 756P glypican-3 (GPC3) and NKp46 directed FLEX-NK engager antibody (CYT-303) recruits natural killer (NK) cells to tumors in a preclinical hepatocellular carcinoma (HCC) mouse model. Ann. Oncol. 33, S889 (2022). 122. Lin, L. et al. P842: novel multifunctional tetravalent CD38 NKp46 FLEX-NK engagers actively target and kill multiple myeloma cells. HemaSphere 6, 736–737 (2022). 123. Pende, D. et al. Identification and molecular characterization of NKp30, a novel triggering receptor involved in natural cytotoxicity mediated by human natural killer cells. J. Exp. Med. 190, 1505–1516 (1999). 124. Pinheiro, P. F., Justino, G. C. & Marques, M. M. NKp30—a prospective target for new cancer immunotherapy strategies. Br. J. Pharmacol. 177, 4563–4580 (2020). 125. Medjouel Khlifi, H., Guia, S., Vivier, E. & Narni-Mancinelli, E. Role of the ITAM-bearing receptors expressed by natural killer cells in cancer. Front. Immunol. 13, 898745 (2022). 126. Pesce, S. et al. B7-H6-mediated downregulation of NKp30 in NK cells contributes to ovarian carcinoma immune escape. Oncoimmunology 4, e1001224 (2015). 127. Berrien-Elliott, M. M., Jacobs, M. T. & Fehniger, T. A. Allogeneic natural killer cell therapy. Blood 141, 856–868 (2023). 128. Piccinelli, S., Romee, R. & Shapiro, R. M. The natural killer cell immunotherapy platform: an overview of the landscape of clinical trials in liquid and solid tumors. Semin. Hematol. 60, 42–51 (2023). 129. Nahi, H. et al. Autologous NK cells as consolidation therapy following stem cell transplantation in multiple myeloma. Cell Rep. Med. 3, 100508 (2022). 130. Li, L. et al. Loss of metabolic fitness drives tumor resistance after CAR-NK cell therapy and can be overcome by cytokine engineering. Sci. Adv. 9, eadd6997 (2023). 131. Foltz, J. A., Moseman, J. E., Thakkar, A., Chakravarti, N. & Lee, D. A. TGFβ imprinting during activation promotes natural killer cell cytokine hypersecretion. Cancers 10, 423 (2018). 132. Terrén, I. et al. Cytokine-induced memory-like NK cells: from the basics to clinical applications. Front. Immunol. 13, 884648 (2022). 133. Fehniger, T. A. & Cooper, M. A. Harnessing NK cell memory for cancer immunotherapy. Trends Immunol. 37, 877–888 (2016). 134. Kerbauy, L. N. et al. Combining AFM13, a bispecific CD30/CD16 antibody, with cytokineactivated blood and cord blood-derived NK cells facilitates CAR-like responses against CD30+ malignancies. Clin. Cancer Res. 27, 3744–3756 (2021). This study describes the advantages of combining NK cell infusions and NK cell engagers. 135. Shapiro, R. M. et al. Expansion, persistence, and efficacy of donor memory-like NK cells infused for posttransplant relapse. J. Clin. Invest. 132, e154334 (2022). 136. Oyer, J. L. et al. Natural killer cells stimulated with PM21 particles expand and biodistribute in vivo: Clinical implications for cancer treatment. Cytotherapy 18, 653–663 (2016). 137. Denman, C. J. et al. Membrane-bound IL-21 promotes sustained ex vivo proliferation of human natural killer cells. PLoS ONE 7, e30264 (2012). 138. Oyer, J. L. et al. Cryopreserved PM21-particle-expanded natural killer cells maintain cytotoxicity and effector functions in vitro and in vivo. Front. Immunol. 13, 861681 (2022). 139. Coyle, K. M., Hawke, L. G. & Ormiston, M. L. Addressing natural killer cell dysfunction and plasticity in cell-based cancer therapeutics. Cancers 15, 1743 (2023). 140. Balzasch, B. M. & Cerwenka, A. Microenvironmental signals shaping NK-cell reactivity in cancer. Eur. J. Immunol. 53, e2250103 (2023). 141. Tong, L. et al. NK cells and solid tumors: therapeutic potential and persisting obstacles. Mol. Cancer 21, 206 (2022). 142. Burga, R. A. et al. Engineering the TGFβ receptor to enhance the therapeutic potential of natural killer cells as an immunotherapy for neuroblastoma. Clin. Cancer Res. 25, 4400–4412 (2019). 143. Solocinski, K. et al. Overcoming hypoxia-induced functional suppression of NK cells. J. Immunother. Cancer 8, e000246 (2020). 144. Zhu, H. et al. Metabolic reprograming via deletion of CISH in human iPSC-derived NK cells promotes in vivo persistence and enhances anti-tumor activity. Cell Stem Cell 27, 224–237 (2020). 145. Daher, M. et al. Targeting a cytokine checkpoint enhances the fitness of armored cord blood CAR-NK cells. Blood 137, 624–636 (2021). 146. Loftus, R. M. et al. Amino acid-dependent cMyc expression is essential for NK cell metabolic and functional responses in mice. Nat. Commun. 9, 2341 (2018). 147. Guedan, S., Calderon, H., Posey, A. D. Jr & Maus, M. V. Engineering and design of chimeric antigen receptors. Mol. Ther. Methods Clin. Dev. 12, 145–156 (2019).

Nature | Vol 626 | 22 February 2024 | 735

Review 148. Biederstädt, A. & Rezvani, K. Engineering the next generation of CAR-NK immunotherapies. Int. J. Hematol. 114, 554–571 (2021). 149. Liu, E. et al. Use of CAR-transduced natural killer cells in CD19-positive lymphoid tumors. N. Engl. J. Med. 382, 545–553 (2020). This study describes the clinical efficacy of CAR NK cells against B cell malignancies. 150. Leem, G. et al. Safety and efficacy of allogeneic natural killer cells in combination with pembrolizumab in patients with chemotherapy-refractory biliary tract cancer: a multicenter open-label phase 1/2a trial. Cancers 14, 4229 (2022). 151. Marotel, M., Hasim, M. S., Hagerman, A. & Ardolino, M. The two-faces of NK cells in oncolytic virotherapy. Cytokine Growth Factor Rev. 56, 59–68 (2020). 152. Ruscetti, M. et al. NK cell-mediated cytotoxicity contributes to tumor control by a cytostatic drug combination. Science 362, 1416–1422 (2018). 153. Pan, R., Ryan, J., Pan, D., Wucherpfennig, K. W. & Letai, A. Augmenting NK cell-based immunotherapy by targeting mitochondrial apoptosis. Cell 185, 1521–1538 (2022). 154. Walle, T. et al. Radiotherapy orchestrates natural killer cell dependent antitumor immune responses through CXCL8. Sci. Adv. 8, eabh4050 (2022). 155. Delconte, R. B. et al. CIS is a potent checkpoint in NK cell-mediated tumor immunity. Nat. Immunol. 17, 816–824 (2016). 156. Cichocki, F. et al. Quadruple gene-engineered natural killer cells enable multi-antigen targeting for durable antitumor activity against multiple myeloma. Nat. Commun. 13, 7341 (2022). 157. Cichocki, F. et al. Nicotinamide enhances natural killer cell function and yields remissions in patients with non-Hodgkin lymphoma. Sci. Transl. Med. 15, eade3341 (2023). 158. Lordo, M. R., Stiff, A. R., Oakes, C. C. & Mundy-Bosse, B. L. Effects of epigenetic therapy on natural killer cell function and development in hematologic malignancy. J. Leukocyte Biol. 113, 518–524 (2023). 159. Young, R. M., Engel, N. W., Uslu, U., Wellhausen, N. & June, C. H. Next-generation CAR T-cell therapies. Cancer Discov. 12, 1625–1633 (2022). 160. Reighard, S. D. et al. Therapeutic targeting of follicular T cells with chimeric antigen receptor-expressing natural killer cells. Cell Rep. Med. 1, 100003 (2020).

736 | Nature | Vol 626 | 22 February 2024

Acknowledgements We thank the colleagues at Innate-Pharma and CIML for help and advice, and M.-A. Rarivoson and K. Lam for help in the preparation of the manuscript. The E.V. laboratory at CIML and Assistance-Publique des Hôpitaux de Marseille was supported by funding from the European Research Council (ERC) under the EU Horizon 2020 research and innovation program (TILC, grant agreement no. 694502), the Agence Nationale de la Recherche including the PIONEER Project (ANR-17-RHUS-0007), MSDAvenir, Innate Pharma, and institutional grants awarded to the CIML (INSERM, CNRS and Aix-Marseille University) and Marseille Immunopole. Author contributions All of the authors participated in writing the manuscript. Competing interests E.V. and S.C. are employees of Innate Pharma. R.Y.I. and V.R.F. are employees of SANOFI. The other authors declare no competing interests. Additional information Correspondence and requests for materials should be addressed to Eric Vivier. Peer review information Nature thanks Hergen Spits and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Reprints and permissions information is available at http://www.nature.com/reprints. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. © Springer Nature Limited 2024

Article

Heavy-element production in a compact object merger observed by JWST https://doi.org/10.1038/s41586-023-06759-1 Received: 3 July 2023 Accepted: 17 October 2023 Published online: 25 October 2023 Open access Check for updates

Andrew J. Levan1,2 ✉, Benjamin P. Gompertz3,4, Om Sharan Salafia5,6, Mattia Bulla7,8,9, Eric Burns10, Kenta Hotokezaka11,12, Luca Izzo13,14, Gavin P. Lamb15,16, Daniele B. Malesani1,17,18, Samantha R. Oates3,4, Maria Edvige Ravasio1,5, Alicia Rouco Escorial19, Benjamin Schneider20, Nikhil Sarin21,22, Steve Schulze22, Nial R. Tanvir16, Kendall Ackley2, Gemma Anderson23, Gabriel B. Brammer17,18, Lise Christensen17,18, Vikram S. Dhillon24,25, Phil A. Evans16, Michael Fausnaugh20,26, Wen-fai Fong27,28, Andrew S. Fruchter29, Chris Fryer30,31,32,33, Johan P. U. Fynbo17,18, Nicola Gaspari1, Kasper E. Heintz17,18, Jens Hjorth13, Jamie A. Kennea34, Mark R. Kennedy35,36, Tanmoy Laskar1,37, Giorgos Leloudas38, Ilya Mandel39,40, Antonio Martin-Carrillo41, Brian D. Metzger42,43, Matt Nicholl44, Anya Nugent27,28, Jesse T. Palmerio45, Giovanna Pugliese46, Jillian Rastinejad27,28, Lauren Rhodes47, Andrea Rossi48, Andrea Saccardi45, Stephen J. Smartt44,47, Heloise F. Stevance47,49, Aaron Tohuvavohu50, Alexander van der Horst33, Susanna D. Vergani45, Darach Watson17,18, Thomas Barclay51, Kornpob Bhirombhakdi29, Elmé Breedt52, Alice A. Breeveld53, Alexander J. Brown24, Sergio Campana5, Ashley A. Chrimes1, Paolo D’Avanzo5, Valerio D’Elia54,55, Massimiliano De Pasquale56, Martin J. Dyer24, Duncan K. Galloway39,40, James A. Garbutt24, Matthew J. Green57, Dieter H. Hartmann58, Páll Jakobsson59, Paul Kerry24, Chryssa Kouveliotou33, Danial Langeroodi13, Emeric Le Floc’h60, James K. Leung40,61,62, Stuart P. Littlefair24, James Munday2,63, Paul O’Brien16, Steven G. Parsons24, Ingrid Pelisoli2, David I. Sahman24, Ruben Salvaterra64, Boris Sbarufatti5, Danny Steeghs2,40, Gianpiero Tagliaferri5, Christina C. Thöne65, Antonio de Ugarte Postigo66 & David Alexander Kann67

The mergers of binary compact objects such as neutron stars and black holes are of central interest to several areas of astrophysics, including as the progenitors of gamma-ray bursts (GRBs)1, sources of high-frequency gravitational waves (GWs)2 and likely production sites for heavy-element nucleosynthesis by means of rapid neutron capture (the r-process)3. Here we present observations of the exceptionally bright GRB 230307A. We show that GRB 230307A belongs to the class of long-duration GRBs associated with compact object mergers4–6 and contains a kilonova similar to AT2017gfo, associated with the GW merger GW170817 (refs. 7–12). We obtained James Webb Space Telescope ( JWST) mid-infrared imaging and spectroscopy 29 and 61 days after the burst. The spectroscopy shows an emission line at 2.15 microns, which we interpret as tellurium (atomic mass A = 130) and a very red source, emitting most of its light in the mid-infrared owing to the production of lanthanides. These observations demonstrate that nucleosynthesis in GRBs can create r-process elements across a broad atomic mass range and play a central role in heavy-element nucleosynthesis across the Universe.

GRB 230307A was detected by the Fermi Gamma-ray Burst Monitor (GBM) and GECAM at 15:44:06 UT on 7 March 2023 (refs. 13,14). Its measured duration of T90 ≈ 35 s and exceptionally high prompt fluence of (2.951 ± 0.004) × 10−3 erg cm−2 in the 10–1,000-keV band make it the second brightest GRB ever detected and ostensibly a ‘long-soft’ GRB (Fig. 1). The burst was also detected by several other high-energy instruments (Methods), enabling source triangulation by the InterPlanetary Network (IPN). The Neil Gehrels Swift Observatory (Swift) tiled the IPN localization15, which revealed one candidate X-ray afterglow16. We obtained optical observations of the field with the ULTRACAM instrument, mounted

on the 3.5-m New Technology Telescope (NTT). These observations revealed a new source coincident with the Swift X-ray source and we identified it as the optical afterglow of GRB 230307A (ref. 17). Given the very bright prompt emission, the afterglow is unusually weak (Fig. 1). We obtained extensive follow-up observations in the optical and near-infrared with the Gemini South telescope and the Very Large Telescope (VLT); in the X-ray with the Swift X-ray Telescope (XRT) and the Chandra X-ray Observatory; and in the radio with the Australia Telescope Compact Array (ATCA) and MeerKAT. Multi Unit Spectroscopic Explorer (MUSE) integral field spectrograph observations provided the redshift of a bright spiral galaxy at z = 0.0646 ± 0.0001

A list of affiliations appears at the end of the paper.

Nature | Vol 626 | 22 February 2024 | 737

Article a

b

2,000

B1 = 8–25 keV

15.0

B2 = 25–100 keV

1,750

B3 = 100–900 keV

−2

1,500

12.5

750 500 250

101 Ratio

10.0

−6

7.5

5.0

−8

0 B3/B2 B3/B1

100 10–1

−4

0

20 40 60 Time since trigger (s)

−10

80

Fraction (%)

1,000

log FX/fluenceγ (s−1)

Counts

1,250

230307A 080503, 170817A, 191019A, 211211A Long GRBs Short GRBs without EE Short GRBs with EE 2

3

4

5

2.5

6

7

0

log tobs (s)

Fig. 1 | The high-energy properties of GRB 230307A. a, The light curve of the GRB at 64-ms time resolution with the Fermi/GBM. The shaded region indicates the region in which saturation may be an issue. The burst begins very hard, with the count rate dominated by photons in the hardest (100–900-keV) band, but rapidly softens, with the count rate in the hard band being progressively overtaken by softer bands (such as 8–25 keV and 25–100 keV) beyond about 20 s. This strong hard-to-soft evolution is reminiscent of GRB 211211A (ref. 20) and is caused by the motion of two spectral breaks through the gamma-ray regime (see Methods). b, The X-ray light curves of GRBs from the Swift X-ray

telescope. These have been divided by the prompt fluence of the burst, which broadly scales with the X-ray light curve luminosity, resulting in a modest spread of afterglows. The greyscale background represents the ensemble of long GRBs. GRB 230307A is an extreme outlier of the >1,000 Swift GRBs, with an extremely faint afterglow for the brightness of its prompt emission. Other merger GRBs from long bursts, and those suggested to be short with extended emission (EE), occupy a similar region of the parameter space. This suggests that the prompt to afterglow fluence could be a valuable tool in distinguishing long GRBs from mergers and those from supernovae.

offset 30.2 arcsec (38.9 kiloparsec in projection) from the burst position (Fig. 2; also ref. 18). Our ground-based campaign spans 1.4 to 41 days after the burst (Extended Data Tables 1 and 2). At 11 days, infrared observations demonstrated a transition from an early blue spectral slope to a much redder one, consistent with the appearance of a kilonova3,19. On the basis of this detection, we requested JWST observations, which were initiated on 5 April 2023. At the first epoch (+28.4 days after the GRB), we took six-colour observations with the Near Infrared Camera (NIRCam) (Fig. 2), as well as a spectrum with the Near Infrared Spectrograph (NIRSpec) covering 0.5–5.5 microns (Fig. 3). The NIRCam observations reveal an extremely red source with F150W(AB) = 28.11 ± 0.12 mag and F444W(AB) = 24.62 ± 0.01 mag. A faint galaxy is detected in these data, with NIRSpec providing a redshift of z = 3.87, offset approximately 0.3 arcsec from the burst position. The probability of chance alignment for this galaxy and the z = 0.065 spiral are comparable. However, the properties of the burst are inconsistent with an origin at z = 3.87; the implied isotropic equivalent energy release would exceed all known GRBs by an order of magnitude or more, the luminosity and colour evolution of the counterpart would be unlike any observed GRB afterglow or supernova (Supplementary Information). A second epoch of JWST observations was obtained approximately 61 days after the burst. These observations showed that the source had faded by 2.4 mag in F444W, demonstrating a rapid decay expected in a low-redshift kilonova scenario and effectively ruling out alternatives (Supplementary Information). We therefore conclude that GRB 230307A originated from the galaxy at z = 0.065. Some properties of GRB 230307A are remarkably similar to those of the bright GRB 211211A, which was also accompanied by a kilonova4–6. In particular, the prompt emission consists of a hard pulse lasting for approximately 19 s, followed by much softer emission. The prompt emission spectrum is well modelled by a double broken power law with two spectral breaks moving through the gamma-ray band (Methods), suggesting a synchrotron origin of the emission20. The X-ray afterglow

is exceptionally faint, much fainter than most bursts when scaled by the prompt GRB fluence (see Fig. 1 and Supplementary Information). The development of the optical and infrared counterpart is also similar to GRB 211211A, with an early blue colour and a subsequent transition to red on a timescale of a few days. In Fig. 4, we plot the evolution of the counterpart compared with the kilonova AT2017gfo (refs. 7–12,21,22), identified in association with the GW-detected binary neutron star merger, GW170817 (ref. 2). AT2017gfo is the most rapidly evolving thermal transient ever observed, much more rapid than supernovae or even fast blue optical transients23. The counterpart of GRB 230307A seems to show near-identical decline rates to AT2017gfo both at early times in the optical and infrared as well as later in the mid-infrared (ref. 24). These similarities are confirmed by a joint fit of afterglow and kilonova models to our multiwavelength data (Supplementary Information). The JWST observations provide a detailed view of kilonova evolution. On timescales of roughly 30 days, it is apparent that the kilonova emits almost all of its light in the mid-infrared, beyond the limits of sensitive ground-based observations. This is consistent with some previous model predictions25. Notably, despite its powerful and long-lived prompt emission that strongly contrasts GW170817/GRB 170817A, the GRB 230307A kilonova is remarkably similar to AT2017gfo. This was also the case for GRB 211211A (refs. 4–6,26) and suggests that the kilonova signal is relatively insensitive to the GRB. Our NIRSpec spectrum shows a broad emission feature with a central wavelength of 2.15 microns, visible in both epochs of JWST spectroscopy (Fig. 3). At longer wavelengths, the spectrum shows a slowly rising continuum up to 4.5 microns, followed by either an extra feature or a change of spectral slope. The colours of the counterpart at this time can be explained by kilonova models (Supplementary Information). A similar emission-like feature is also visible in the later epochs of X-shooter observations of AT2017gfo (ref. 9), measured at 2.1 microns in ref. 27. Furthermore, the late-time mid-infrared emission and colours are consistent with those observed with AT2017gfo with Spitzer24. These similarities further strengthen both the kilonova interpretation

738 | Nature | Vol 626 | 22 February 2024

a

GRB 230307A JWST/NIRCam

Afterglow/kilonova

Host galaxy z = 0.065

N E

b F070W

c

d

F115W

F150W

e

f

F277W

F356W

g

F444W

Fig. 2 | JWST images of GRB 230307A at 28.5 days post burst. a, The wide-field image combining the F115W, F150W and F444W images. The putative host is the bright face-on spiral galaxy, whereas the afterglow appears at a 30-arcsec offset, within the white box. The scale bar at the lower left represents 10″. b–g, Cut-outs of the NIRCam data around the GRB afterglow location. The source

is faint and barely detected in the bluer bands but very bright and well detected in the red bands. In the red bands, a faint galaxy is present northeast of the transient position. This galaxy has a redshift of z = 3.87 but we consider it to be a background object unrelated to the GRB (see Supplementary Information).

and the redshift measurement of GRB 230307A (Fig. 3). We interpret this feature as arising from the forbidden [Te III] transition between the ground level and the first fine-structure level of tellurium, with an experimentally determined wavelength of 2.1019 microns (ref. 28). The presence of tellurium is plausible, as it lies at the second peak in the r-process abundance pattern, which occurs at atomic masses around

A ≈ 130 (ref. 29). Therefore, it should be abundantly produced in kilonovae, as seen in hydrodynamical simulations of binary neutron star mergers with nucleosynthetic compositions similar to those favoured for AT2017gfo (ref. 30). Furthermore, the typical ionization state of Te in kilonova ejecta is expected to be Te III at this epoch because of the efficient radioactive ionization31. Tellurium has recently been suggested

29 days 61 days AT2017gfo, 10 days

Fλ (10–20 erg s–1 cm–2 Å–1)

1.5

29-day model

Photometry, 61 days

Photometry, 29 days

AT2017gfo, 43 days

[Te III]

[W III]

[Se III]

1.0

0.5

0

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

Observed wavelength (microns) Fig. 3 | JWST/NIRSpec spectroscopy of the counterpart of GRB 230307A. The top portion shows the 2D spectrum rectified to a common wavelength scale. The transient is well detected beyond 2 microns but not shortward, indicative of an extremely red source. Emission lines from the nearby galaxy at z = 3.87 can also be seen offset from the afterglow trace. The lower panel shows the 1D extraction of the spectrum in comparison with the latest (10-day) AT2017gfo epoch and a kilonova model. A clear emission feature can be seen at about

2.15 microns at both 29 and 61 days. This feature is consistent with the expected location of [Te III], whereas redder features are compatible with lines from [Se III] and [W III]. This line is also clearly visible in the scaled late-time spectrum of AT2017gfo (refs. 27,32), whereas the red colours are also comparable with those of AT2017gfo as measured with Spitzer (ref. 24; shown scaled to the 29-day NIRSpec spectrum). Error bars on photometry refer to the 1σ error bar on the y axis and the filter width on the x axis.

Nature | Vol 626 | 22 February 2024 | 739

Article 21

AT2017gfo @ z = 0.065 (K) AT2017gfo @ z = 0.065 (i) AT2017gfo @ z = 0.065 (4.5) 230307A K 230307A i 230307A F444W

22

AB magnitude

23 24 25 26 27 28 29

0

10

20

30 40 50 Time since burst (days)

60

70

80

Fig. 4 | A comparison of the counterpart of GRB 230307A with AT2017gfo associated with GW170817. AT2017gfo has been scaled to the same distance as GRB 230307A. Beyond about 2 days, the kilonova dominates the counterpart. The decay rates in both the optical and infrared are very similar to those in AT2017gfo. These are too rapid for any plausible afterglow model. There is also good agreement in the late-time slope between the measurements made at 4.4 microns with the JWST and at 4.5 microns for AT2017gfo with Spitzer24. Error bars refer to the 1σ uncertainty.

as the origin of the same feature in the spectrum of AT2017gfo (ref. 32). A previous study27 also identified this tellurium transition and noted that the observed feature is most likely two blended emission lines. Tellurium can also be produced by means of the slower capture of neutrons in the s-process. Indeed, this line is also seen in planetary nebulae33. The detection of [Te III] 2.1 µm provides an extra r-process element, building on the earlier detection of strontium34. Notably, although strontium is a light r-process element associated with the so-called first peak, tellurium is a heavier second-peak element, requiring different nucleosynthetic pathways. The mass of Te III estimated from the observed line flux is about 10−3 M⊙ (Supplementary Information). Although weaker, we also note that the spectral feature visible at 4.5 microns is approximately consistent with the expected location of the first-peak element selenium and the near-third-peak element tungsten35. In future events, further elemental lines can be used to resolve this difference35, with very different appearances redward of the NIRSpec cut-off (5.5 microns). For nearby kilonovae, observations should also be plausible by the JWST with the Mid-Infrared Instrument (MIRI) out to 15 microns. Detailed spectral fitting at late epochs is challenging because of the breakdown of the assumptions about local thermodynamic equilibrium, which are used to predict kilonova spectra at earlier ages, as well as fundamental uncertainties in the atomic physics of r-process elements. However, these observations provide a calibration sample for informing future models. The red continuum emission indicates large opacity in the mid-infrared at low temperatures, for example, about 10 cm2 g−1 at around 700 K, which may suggest that lanthanides (atomic numbers 58–71) are abundant in the ejecta. The host galaxy is a low-mass system (about 2.5 × 109 M⊙) dominated by an old population. The large offset is consistent with the largest offsets seen in short GRBs36,37 and could be attained by a binary with a velocity of a few hundred km s−1 and a merger time >108 years. Alternatively, the faint optical/infrared detection of the source at the second JWST observation could be because of an underlying globular cluster host, which could create compact object systems at enhanced rates through dynamical interactions38. 740 | Nature | Vol 626 | 22 February 2024

It is notable that GRB 230307A is an extremely bright GRB, with only the exceptional GRB 221009A being brighter39. The detection of kilonovae in two of the ten most fluent Fermi/GBM GRBs implies that mergers may contribute substantially to the bright GRB population (see Supplementary Information). Indeed, several further long GRBs, including GRB 060614 (refs. 40,41), GRB 111005A (ref. 42) and GRB 191019A (ref. 43), have been suggested to arise from mergers. If a substantial number of long GRBs are associated with compact object mergers, they provide an essential complement to GW detections. First, joint GW–GRB detections, including long GRBs, can push the effective horizons of GW detectors to greater distances and provide much smaller localizations4,44. Second, long GRBs can be detected without GW detectors, providing a valuable route for enhancing kilonova detections. Third, JWST can detect kilonova emission at redshifts substantially beyond the horizons of the current generation of GW detectors, enabling the study of kilonovae across a greater volume of the Universe. The duration of the prompt gamma-ray emission in these mergers remains challenging to explain. In particular, the natural timescales for emission in compact object mergers are much shorter than the measured duration of GRB 230307A. Previously suggested models that may also explain GRB 230307A include magnetars45, black hole–neutron star mergers46,47 or even neutron star–white dwarf systems6. It has also been suggested that collapsars may power the r-process48, in which case one may interpret GRB 230307A as an unusual collapsar. However, such a progenitor is not plausible, as there is no star formation at the location of GRB 230307A. The duration problem might become immaterial if the jet timescale does not directly track the accretion timescale in the post-merger system. Such a behaviour has recently been proposed on the basis of insights from general-relativistic magnetohydrodynamical simulations49,50, which suggest that the duration of the jet can extend up to several times the disk viscous timescale, creating long GRBs from short-lived mergers.

Online content Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi.org/10.1038/s41586-023-06759-1.

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.

13. 14.

Tanvir, N. R. et al. A ‘kilonova’ associated with the short-duration γ-ray burst GRB 130603B. Nature 500, 547–549 (2013). Abbott, B. P., Abbott, R. & Abbott, T. D. et al. GW170817: observation of gravitational waves from a binary neutron star inspiral. Phys. Rev. Lett. 119, 161101 (2017). Metzger, B. D. Kilonovae. Living Rev. Relativ. 23, 1 (2020). Rastinejad, J. C. et al. A kilonova following a long-duration gamma-ray burst at 350 Mpc. Nature 612, 223–227 (2022). Troja, E. et al. A nearby long gamma-ray burst from a merger of compact objects. Nature 612, 228–231 (2022). Yang, J. et al. A long-duration gamma-ray burst with a peculiar origin. Nature 612, 232–235 (2022). Arcavi, I. et al. Optical emission from a kilonova following a gravitational-wave-detected neutron-star merger. Nature 551, 64–66 (2017). Coulter, D. A. et al. Swope Supernova Survey 2017a (SSS17a), the optical counterpart to a gravitational wave source. Science 358, 1556–1558 (2017). Pian, E. et al. Spectroscopic identification of r-process nucleosynthesis in a double neutron-star merger. Nature 551, 67–70 (2017). Smartt, S. J. et al. A kilonova as the electromagnetic counterpart to a gravitational-wave source. Nature 551, 75–79 (2017). Tanvir, N. R. et al. The emergence of a lanthanide-rich kilonova following the merger of two neutron stars. Astrophys. J. Lett. 848, L27 (2017). Soares-Santos, M. et al. The electromagnetic counterpart of the binary neutron star merger LIGO/Virgo GW170817. I. Discovery of the optical counterpart using the dark energy camera. Astrophys. J. Lett. 848, L16 (2017). Fermi GBM Team. GRB 230307A: Fermi GBM final real-time localization. GRB Coordinates Network, Circular Service, No. 33405 (2023). Xiong, S., Wang, C. & Huang, Y. & Gecam Team. GRB 230307A: GECAM detection of an extremely bright burst. GRB Coordinates Network, Circular Service, No. 33406 (2023).

15. 16. 17. 18.

19. 20. 21. 22.

23. 24.

25. 26. 27.

28. 29. 30.

31. 32. 33. 34. 35.

36. 37. 38. 39. 40. 41. 42.

43. 44.

45.

46. 47.

48. 49. 50.

Kozyrev, A. S. et al. Further improved IPN localization for GRB 230307A. GRB Coordinates Network, Circular Service, No. 33461 (2023). Burrows, D. N. et al. GRB 230307A: Swift-XRT observations. GRB Coordinates Network, Circular Service, No. 33429 (2023). Levan, A. J., Gompertz, B., Ackley, K., Kennedy, M. & Dhillon, V. GRB 230307A: possible optical afterglow. GRB Coordinates Network, Circular Service, No. 33439 (2023). Gillanders, J., O’Connor, B., Dichiara, S. & Troja, E. GRB 230307A: continued Gemini-South observations confirm rapid optical fading. GRB Coordinates Network, Circular Service, No. 33485 (2023). Li, L.-X. & Paczyński, B. Transient events from neutron star mergers. Astrophys. J. Lett. 507, L59–L62 (1998). Gompertz, B. P. et al. The case for a minute-long merger-driven gamma-ray burst from fast-cooling synchrotron emission. Nat. Astron. 7, 67–79 (2023). Valenti, S. et al. The discovery of the electromagnetic counterpart of GW170817: kilonova AT 2017gfo/DLT17ck. Astrophys. J. Lett. 848, L24 (2017). Villar, V. A. et al. The combined ultraviolet, optical, and near-infrared light curves of the kilonova associated with the binary neutron star merger GW170817: unified data set, analytic models, and physical implications. Astrophys. J. Lett. 851, L21 (2017). Drout, M. R. et al. Rapidly evolving and luminous transients from Pan-STARRS1. Astrophys. J. 794, 23 (2014). Kasliwal, M. M. et al. Spitzer mid-infrared detections of neutron star merger GW170817 suggests synthesis of the heaviest elements. Mon. Not. R. Astron. Soc. 510, L7–L12 (2022). Wollaeger, R. T. et al. Impact of ejecta morphology and composition on the electromagnetic signatures of neutron star mergers. Mon. Not. R. Astron. Soc. 478, 3298–3334 (2018). Mei, A. et al. Gigaelectronvolt emission from a compact binary merger. Nature 612, 236–239 (2022). Gillanders, J. H., Sim, S. A., Smartt, S. J., Goriely, S. & Bauswein, A. Modelling the spectra of the kilonova AT2017gfo – II: beyond the photospheric epochs. Preprint at https://arxiv. org/abs/2306.15055 (2023). Joshi, Y. N., Tauheed, A. & Davison, I. G. The analysis of the 5s25p2, 5s5p3, 5s25p5d, and 5s25p6s configurations of Te III. Can. J. Phys 70, 740–744 (1992). Hotokezaka, K., Beniamini, P. & Piran, T. Neutron star mergers as sites of r-process nucleosynthesis and short gamma-ray bursts. Int. J. Mod. Phys. D 27, 1842005 (2018). Gillanders, J. H., Smartt, S. J., Sim, S. A., Bauswein, A. & Goriely, S. Modelling the spectra of the kilonova AT2017gfo – I. The photospheric epochs. Mon. Not. R. Astron. Soc. 515, 631–651 (2022). Pognan, Q., Jerkstrand, A. & Grumer, J. On the validity of steady-state for nebular phase kilonovae. Mon. Not. R. Astron. Soc. 510, 3806–3837 (2022). Hotokezaka, K., Tanaka, M., Kato, D. & Gaigalas, G. Tellurium emission line in kilonova AT 2017gfo. Mon. Not. R. Astron. Soc. 526, L155–L159 (2023). Madonna, S. et al. Neutron-capture elements in planetary nebulae: first detections of near-infrared [Te III] and [Br V] emission lines. Astrophys. J. Lett. 861, L8 (2018). Watson, D. et al. Identification of strontium in the merger of two neutron stars. Nature 574, 497–500 (2019). Hotokezaka, K., Tanaka, M., Kato, D. & Gaigalas, G. Tungsten versus selenium as a potential source of kilonova nebular emission observed by Spitzer. Mon. Not. R. Astron. Soc. 515, L89–L93 (2022). Fong, W.-f et al. Short GRB host galaxies. I. Photometric and spectroscopic catalogs, host associations, and galactocentric offsets. Astrophys. J. 940, 56 (2022). O’Connor, B. et al. A deep survey of short GRB host galaxies over z ~ 0–2: implications for offsets, redshifts, and environments. Mon. Not. R. Astron. Soc. 515, 4890–4928 (2022). Grindlay, J., Portegies Zwart, S. & McMillan, S. Short gamma-ray bursts from binary neutron star mergers in globular clusters. Nat. Phys. 2, 116–119 (2006). Burns, E. et al. GRB 221009A: the BOAT. Astrophys. J. Lett. 946, L31 (2023). Gehrels, N. et al. A new γ-ray burst classification scheme from GRB 060614. Nature 444, 1044–1046 (2006). Gal-Yam, A. et al. A novel explosive process is required for the γ-ray burst GRB 060614. Nature 444, 1053–1055 (2006). MichałowskI, M. J. et al. The second-closest gamma-ray burst: sub-luminous GRB 111005A with no supernova in a super-solar metallicity environment. Astron. Astrophys. 616, A169 (2018). Levan, A. J. et al. A long-duration gamma-ray burst of dynamical origin from the nucleus of an ancient galaxy. Nat. Astron. 7, 976–985 (2023). Sarin, N., Lasky, P. D. & Nathan, R. S. Missed opportunities: GRB 211211A and the case for continual gravitational-wave coverage with a single observatory. Mon. Not. R. Astron. Soc. 518, 5483–5489 (2023). Metzger, B. D., Quataert, E. & Thompson, T. A. Short-duration gamma-ray bursts with extended emission from protomagnetar spin-down. Mon. Not. R. Astron. Soc. 385, 1455–1460 (2008). Rosswog, S. Fallback accretion in the aftermath of a compact binary merger. Mon. Not. R. Astron. Soc. 376, L48–L51 (2007). Desai, D., Metzger, B. D. & Foucart, F. Imprints of r-process heating on fall-back accretion: distinguishing black hole–neutron star from double neutron star mergers. Mon. Not. R. Astron. Soc. 485, 4404–4412 (2019). Siegel, D. M., Barnes, J. & Metzger, B. D. Collapsars as a major source of r-process elements. Nature 569, 241–244 (2019). Gottlieb, O. et al. Large-scale evolution of seconds-long relativistic jets from black hole– neutron star mergers. Astrophys. J. Lett. 954, L21 (2023). Gottlieb, O., Metzger, B. D., Quataert, E., Issa, D. & Foucart, F. A Unified picture of short and long gamma-ray bursts from compact binary mergers. Preprint at https://arxiv.org/ abs/2309.00038 (2023).

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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. © The Author(s) 2023 Department of Astrophysics, Institute for Mathematics, Astrophysics and Particle Physics (IMAPP), Radboud University, Nijmegen, The Netherlands. 2Department of Physics, University of Warwick, Coventry, UK. 3Institute for Gravitational Wave Astronomy, University of Birmingham, Birmingham, UK. 4School of Physics and Astronomy, University of Birmingham, Birmingham, UK. 5INAF - Osservatorio Astronomico di Brera, Merate, Italy. 6INFN - Sezione di Milano Bicocca, Milano, Italy. 7Department of Physics and Earth Science, University of Ferrara, 1

Ferrara, Italy. 8INFN - Sezione di Ferrara, Ferrara, Italy. 9INAF - Osservatorio Astronomico d’Abruzzo, Teramo, Italy. 10Department of Physics & Astronomy, Louisiana State University, Baton Rouge, LA, USA. 11Research Center for the Early Universe, Graduate School of Science, The University of Tokyo, Bunkyo, Japan. 12Kavli IPMU (WPI), UTIAS, The University of Tokyo, Kashiwa, Chiba, Japan. 13DARK, Niels Bohr Institute, University of Copenhagen, Copenhagen N, Denmark. 14INAF - Osservatorio Astronomico di Capodimonte, Naples, Italy. 15Astrophysics Research Institute, Liverpool John Moores University, Liverpool, UK. 16School of Physics and Astronomy, University of Leicester, Leicester, UK. 17Cosmic Dawn Center (DAWN), Copenhagen, Denmark. 18Niels Bohr Institute, University of Copenhagen, Copenhagen N, Denmark. 19European Space Agency (ESA), European Space Astronomy Centre (ESAC), Madrid, Spain. 20Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA, USA. 21Nordita, Stockholm University and KTH Royal Institute of Technology, Stockholm, Sweden. 22The Oskar Klein Centre, Department of Physics, Stockholm University, AlbaNova University Center, Stockholm, Sweden. 23International Centre for Radio Astronomy Research, Curtin University, Perth, Western Australia, Australia. 24 Department of Physics and Astronomy, University of Sheffield, Sheffield, UK. 25Instituto de Astrofísica de Canarias, La Laguna, Tenerife, Spain. 26Department of Physics & Astronomy, Texas Tech University, Lubbock, TX, USA. 27Center for Interdisciplinary Exploration and Research in Astrophysics, Northwestern University, Evanston, IL, USA. 28Department of Physics and Astronomy, Northwestern University, Evanston, IL, USA. 29Space Telescope Science Institute, Baltimore, MD, USA. 30Center for Theoretical Astrophysics, Los Alamos National Laboratory, Los Alamos, NM, USA. 31Department of Astronomy, The University of Arizona, Tucson, AZ, USA. 32Department of Physics and Astronomy, The University of New Mexico, Albuquerque, NM, USA. 33Department of Physics, The George Washington University, Washington, DC, USA. 34Department of Astronomy and Astrophysics, The Pennsylvania State University, University Park, PA, USA. 35School of Physics, University College Cork, Cork, Ireland. 36Jodrell Bank Centre for Astrophysics, Department of Physics and Astronomy, The University of Manchester, Manchester, UK. 37Department of Physics & Astronomy, University of Utah, Salt Lake City, UT, USA. 38DTU Space, National Space Institute, Technical University of Denmark, Lyngby, Denmark. 39School of Physics and Astronomy, Monash University, Clayton, Victoria, Australia. 40ARC Centre of Excellence for Gravitational Wave Discovery (OzGrav), Monash University, Clayton, Victoria, Australia. 41School of Physics and Centre for Space Research, University College Dublin, Dublin, Ireland. 42Columbia Astrophysics Laboratory, Department of Physics, Columbia University, New York, NY, USA. 43Center for Computational Astrophysics, Flatiron Institute, New York, NY, USA. 44Astrophysics Research Centre, School of Mathematics and Physics, Queen’s University Belfast, Belfast, UK. 45GEPI, Observatoire de Paris, Université PSL, CNRS, Meudon, France. 46Anton Pannekoek Institute for Astronomy, University of Amsterdam, Amsterdam, The Netherlands. 47Department of Physics, University of Oxford, Oxford, UK. 48INAF - Osservatorio di Astrofisica e Scienza dello Spazio, Bologna, Italy. 49Department of Physics, The University of Auckland, Auckland, New Zealand. Department of Astronomy & Astrophysics, University of Toronto, Toronto, Ontario, Canada.

50

51 NASA Goddard Space Flight Center, Greenbelt, MD, USA. 52Institute of Astronomy, University of Cambridge, Cambridge, UK. 53Mullard Space Science Laboratory, University College London, Holmbury St. Mary, UK. 54Agenzia Spaziale Italiana (ASI) Space Science Data Center

(SSDC), Rome, Italy. 55INAF - Osservatorio Astronomico di Roma, Rome, Italy. 56Department of Mathematics, Physics, Informatics and Earth Sciences, University of Messina, Polo Papardo, Messina, Italy. 57School of Physics and Astronomy, Tel Aviv University, Tel Aviv, Israel. 58 Department of Physics and Astronomy, Clemson University, Clemson, SC, USA. 59Centre for Astrophysics and Cosmology, Science Institute, University of Iceland, Reykjavik, Iceland. 60 CEA, IRFU, DAp, AIM, Université Paris-Saclay, Université Paris Cité, Sorbonne Paris Cité, CNRS, Gif-sur-Yvette, France. 61Sydney Institute for Astronomy, School of Physics, The University of Sydney, Sydney, New South Wales, Australia. 62CSIRO Space and Astronomy,

Epping, New South Wales, Australia. 63Isaac Newton Group of Telescopes, Santa Cruz de La Palma, Spain. 64INAF IASF-Milano, Milano, Italy. 65Astronomical Institute of the Czech Academy of Sciences, Ondřejov, Czechia. 66Artemis, Observatoire de la Côte d’Azur, Université Côte d’Azur, Nice, France. 67Hessian Research Cluster ELEMENTS, Giersch Science

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Center (GSC), Goethe University Frankfurt, Campus Riedberg, Frankfurt am Main, Germany. ✉e-mail: [email protected]

Nature | Vol 626 | 22 February 2024 | 741

Article Methods Observations Below we outline the observational data that were used in this paper. Magnitudes are given in the AB system unless stated otherwise. We use cosmology resulting from the Planck observations51. All uncertainties are given at the 1σ level unless explicitly stated. Gamma-ray observations. GRB 230307A was first detected by Fermi/ GBM and GECAM at 15:44:06 UT on 7 March 2023 (refs. 13,14). It had a duration of T90 ≈ 35 s and an exceptionally bright prompt fluence of (2.951 ± 0.004) × 10−3 erg cm−2 (ref. 52). The burst fell outside the coded field of view of the Swift Burst Alert Telescope (BAT) and so did not receive a sub-degree localization despite a strong detection. However, detections by Swift, GECAM14, STIX on the Solar Orbiter53, AGILE54, ASTROSAT55, GRBalpha56, VZLUSAT57, Konus-WIND58 and ASO-HXI59 enabled an enhanced position by means of the IPN to increasingly precise localizations of 1.948 deg2 (ref. 60), 30 arcmin2 (ref. 61) and, ultimately, to 8 arcmin2 (ref. 15). This was sufficiently small to enable tiling with Swift and ground-based telescopes. Fermi/GBM data analysis. In Fig. 1, we plot the light curve of GRB 230307A as seen by the Fermi/GBM in several bands, built by selecting time-tagged event data, binned with a time resolution of 64 ms. The highlighted time interval of 3–7 s after trigger is affected by data loss owing to the bandwidth limit for time-tagged event data62. For the spectral analysis, we made use of the CSPEC data, which have 1,024-ms time resolution. Data files were obtained from the online archive at https://heasarc.gsfc.nasa.gov/W3Browse/fermi/fermigbrst. html. Following the suggestion reported by the Fermi Collaboration62, we analysed the data detected by NaI 10 and BGO 1, which had a source viewing angle less than 60°, and excluded the time intervals affected by pulse pile-up issues (from 2.5 s to 7.5 s). The data extraction was performed with the public software GTBURST, whereas data were analysed with XSPEC. The background, whose time intervals have been selected before and after the source, was modelled with a polynomial function whose order is automatically found by GTBURST and manually checked. In the fitting procedure, we used inter-calibration factors among the detectors, scaled to the only NaI analysed and free to vary within 30%. We used the PG-statistic, valid for Poisson data with a Gaussian background. The best-fit parameters and their uncertainties were estimated through a Markov chain Monte Carlo approach. We selected the time intervals before and after the excluded period of 2.5–7.5 s owing to instrumental effects. In particular, we extracted two time intervals from 0 to 2.5 s (1.25 s each) and 14 time intervals from 7.5 s to 40.5 s (bin width of 2 s except the last two with integration of 5 s to increase the signal-to-noise ratio), for a total of 16 time intervals. We fitted the corresponding spectra with the two smoothly broken power law function63,64, which has been shown to successfully model the synchrotron-like spectral shape of bright long GRBs, including the merger-driven GRB 211211A (ref. 20). From our spectral analysis, we found that all spectra up to about 20 s are well modelled by the two smoothly broken power law function, namely, they are described by the presence of two spectral breaks inside the GBM band (8 keV–40 MeV). In particular, in the time intervals between 7.5 s and 19.5 s, the low-energy break Ebreak is coherently +4.3 decreasing from 304.3+5.2 −2.6 keV to 52.1−5.1 keV, and the typical νFν peak energy Epeak is also becoming softer, moving from approximately 1 MeV to 450 keV. The spectral indices of the two power laws below and above the low-energy break are distributed around the values of −0.82 and −1.72, which are similar to the predictions for synchrotron emission in marginally fast-cooling regime (that is, −2/3 and −3/2). This is consistent with what has been found in GRB 211211A (ref. 20). We notice, however, that—in all spectra—the high-energy power law above Epeak is characterized by a much softer index (with a mean value of −4.10 ± 0.24) with respect to the value of roughly −2.5 typically found in Fermi GRBs.

This suggests that the spectral data might require a cut-off at high energy, although further investigations are needed to support this. From 19.5 s until 40.5 s (the last time interval analysed), all the break energies are found to be below 20 keV, close to the GBM low-energy threshold. In the same time intervals, the peak energy Epeak decreases +5.4 from 682.4+3.2 −6.1 keVto 123.1−4.9 keV, and the index of the power law below the peak energy is fully consistent (mean value of −1.45 ± 0.06) with the synchrotron predicted value of −1.5. Optical observations. NTT: afterglow discovery. Following the refinement of the IPN error box to an area of 30 arcmin2 (ref. 61), we obtained observations of the field of GRB 230307A with the ULTRACAM instrument65, mounted on the 3.5-m NTT at La Silla, Chile. The instrument obtains images in three simultaneous bands and is optimized for short-exposure, low-dead-time observations65. We obtained ten 20-s exposures in two pointings in each of the Super SDSS u, g and r bands (for which the Super SDSS bands match the wavelength range of the traditional SDSS filters but with a higher throughput66). The observations began at 01:53:21 UT on 9 March 2023, approximately 34 h after the GRB. The images were reduced through the HIPERCAM pipeline66 using bias and flat frames taken on the same night. Visual inspection of the images compared with those obtained with the Legacy Survey67 revealed a new source coincident with an X-ray source identified through Swift/ XRT observations16, and we identified it as the likely optical afterglow of GRB 230307A (ref. 17). The best available optical position of this source (ultimately measured from our JWST observations, see below) is RA( J2000) = 04 h 03 min 26.02 s, dec.( J2000) = −75° 22′ 42.76″, with an uncertainty of 0.05 arcsec in each axis (Supplementary Fig. 1). This identification was subsequently confirmed through observations from several other observatories, including refs. 18,68–72. We acquired two further epochs of observations with ULTRACAM on the following nights with ten 20-s exposures in the Super SDSS u, g and i bands. Aperture photometry of the source is reported in Extended Data Table 1 and is reported relative to the Legacy Survey for the g, r and i bands and to SkyMapper for the u band. TESS. The prompt and afterglow emission of GRB 230307A was detected by the Transiting Exoplanet Survey Satellite (TESS), which observed the field continuously from 3 days before the Fermi trigger to 3 days after at a cadence of 200 s (ref. 73). A reference image was subtracted from the observations to obtain GRB-only flux over this period. The measured flux in the broad TESS filter (600–1,000 nm) is corrected for Galactic extinction and converted to the Ic band assuming a power-law spectrum with F ∝ ν−0.8. We then bin the light curve logarithmically, taking the mean flux of the observations in each bin and converting to AB magnitudes. A systematic error of 0.1 mag was added in quadrature to the measured statistical errors to account for the uncertainties in the data processing. These data are presented in Extended Data Table 1. Swift/UVOT. The Swift Ultraviolet/Optical Telescope (UVOT74) began observing the field of GRB 230307A about 84.6 ks after the Fermi/GBM trigger13. The source counts were extracted using a source region of 5 arcsec radius. Background counts were extracted using a circular region of 20 arcsec radius located in a source-free part of the sky. The count rates were obtained from the image lists using the Swift tool UVOTSOURCE. A faint catalogued unrelated source also falls within the 5 arcsec radius; this will affect the photometry, particularly at late times. We therefore requested a deep template image in white to estimate the level of contamination. We extracted the count rate in the template image using the same 5 arcsec radius aperture. This was subtracted from the source count rates to obtain the afterglow count rates. The afterglow count rates were converted to magnitudes using the UVOT photometric zero points75,76. Gemini. We obtained three epochs of K-band observations using the FLAMINGOS-2 instrument on the Gemini South telescope. These observations were reduced through the DRAGONS pipeline to produce dark

and sky-subtracted and flat-fielded images77. At the location of the optical counterpart to GRB 230307A, we identify a relatively bright K-band source in the first and second epochs, with only an upper limit in epoch 3. We report our photometry, performed relative to secondary standards in the VISTA Hemisphere Survey78, in Extended Data Table 1. VLT imaging. We carried out observations of the GRB 230307A field with the 8.2-m VLT located in Cerro Paranal, Chile. The observations were obtained with the FORS2 camera (mounted on the Unit Telescope 1, UT1, Antu) in B, R, I and z bands at several epochs and with the HAWK-I instrument (mounted on the Unit Telescope 4, UT4, Yepun) in the K band at one epoch. All images were reduced using the standard European Southern Observatory (ESO) Reflex pipeline79. The source was detected in the FORS2 z-band image at about 6.4 days after the Fermi/GBM detection. A single r′-band observation of the GRB 230307A was also executed with the 2.6-m VLT Survey Telescope (VST) after 2.37 days from the GRB discovery. In later observations, the source was not detected (see  Supplementary Information) and the upper-limit values at the 3σ level are reported in Extended Data Table 1. VLT spectroscopy. To attempt to measure the redshift of GRB 230307A and the nearby candidate host galaxies, we obtained spectroscopy with the VLT using both the X-shooter and MUSE instruments, mounted, respectively, on the Unit Telescope 3 (UT3, Melipal) and on the UT4 (Yepun). X-shooter spectroscopy, covering the wavelength range 3,000– 22,000 Å, was undertaken on 15 March 2023. Observations were taken at a fixed position angle, with the slit centred on a nearby bright star. X-shooter data have been reduced with standard esorex recipes. Given that only two of the four nod exposures were covering the GRB position, resulting in a total exposure time of 2,400 s on-source, we reduced each single exposure using the stare mode data reduction. Then, we stacked the two 2D frames covering the GRB position using dedicated post-processing tools developed in a Python framework80. We further obtained observations with the MUSE integral field unit on 23 March 2023. The MUSE observations cover several galaxies in the field, as well as the GRB position, and cover the wavelength range 4,750–9,350 Å. MUSE data were reduced using standard esorex recipes embedded in a single Python script that performs the entire data-reduction procedure. Later, the resulting datacube was corrected for sky emission residuals using ZAP (ref. 81). The MUSE observations reveal the redshifts for a large number of galaxies in the field, including a prominent spiral G1 at z = 0.0646 (see also ref. 18) and a group of galaxies, G2, G3 and G4, at z = 0.263; details are provided in Extended Data Table 3. X-ray afterglow. Swift began tiled observations of the IPN localization region with its XRT82 at 12:56:42 on 8 March 2023 (ref. 83) (https://www. swift.ac.uk/xrt_products/TILED_GRB00110/). XRT made the first reported detection of the afterglow (initially identified as ‘Source 2’) with a count rate of 0.019 ± 0.004 cts−1 (ref. 16) and later confirmed it to be fading with a temporal power-law index of 1.1+0.6 −0.5 (ref. 84). XRT data were downloaded from the UK Swift Science Data Centre (UKSSDC85,86). We further obtained observations with the Chandra X-ray observatory (programme ID 402458; PI: Fong/Gompertz). A total of 50.26 ks (49.67 ks of effective exposure) of data were obtained in three visits between 31 March 2023 and 2 April 2023. The source was placed at the default aim point on the S3 chip of the ACIS detector. At the location of the optical and X-ray afterglow of GRB 230307A, we detect a total of 12 counts, with an expected background of approximately 1, corresponding to a detection of the afterglow at >5σ based on the photon statistics of ref. 87. To obtain fluxes, we performed a joint spectral fit of the Chandra and Swift/XRT data. The best-fitting spectrum, adopting uniform priors on all parameters, is a power law with a photon 21 −2 index of Γ = 2.50+0.30 −0.29 when fitting with a Galactic NH = 1.26 × 10  cm

(ref. 88) and zero intrinsic absorption (neither XRT nor Chandra spectra have sufficient signal to noise to constrain any intrinsic absorption component). The resultant flux in the 0.3–10-keV band is +0.89 FX(1.7 days) = 4.91−0.79 × 10−13 erg cm−2 s−1 during the XRT observation +0.87 and FX(24.8 days) = 1.19−0.62 × 10−14 erg cm−2 s−1 during the Chandra observation. Owing to the low count number, the Chandra flux posterior support extends to considerably below the reported median, with the 5th percentile being as low as F X,5th = 3 × 10−15 erg cm−2 s−1. If a uniform-in-the-logarithm prior on the flux were adopted, this would extend to even lower values. Chandra and XRT fluxes are converted to 1 keV flux densities using the best-fit spectrum (Extended Data Table 2). ATCA. Following the identification of the optical afterglow89, we requested Target of Opportunity (ToO) observations of GRB 230307A (proposal identification CX529) with the ATCA to search for a radio counterpart. These data were processed using MIRIAD90, which is the native reduction software package for ATCA data using standard techniques. Flux and bandpass calibration were performed using PKS 1934638, with phase calibration using interleaved observations of 0454-810. The first observation took place on 12 March 2023 at 4.46 days post-burst, which was conducted using the 4-cm dual receiver with frequencies centred at 5.5 GHz and 9 GHz, each with a 2 GHz bandwidth. The array was in the 750C configuration (https://www.narrabri.atnf. csiro.au/operations/array_configurations/configurations.html) with a maximum baseline of 6 km. A radio source was detected at the position of the optical afterglow at 9 GHz with a flux density of 92 ± 22 µJy but went undetected at 5.5 GHz (3σ upper limit of 84 µJy). Two further follow-up observations were also obtained, swapping between the 4-cm and 15-mm dual receivers (the latter with central frequencies of 16.7 GHz and 21.2 GHz, each with a 2 GHz bandwidth). During our second epoch at 10.66 days, we detected the radio counterpart again, having become detectable at 5.5 GHz with marginal fading at 9 GHz. By the third epoch, the radio afterglow had faded below detectability. We did not detect the radio transient at 16.7 GHz or 21.2 GHz in either epoch. All ATCA flux densities are listed in Extended Table 2. MeerKAT. We were awarded time to observe the position of GRB 230307A with the MeerKAT radio telescope through a successful Director’s Discretionary Time proposal (PI: Rhodes, DDT-20230313-LR-01). The MeerKAT radio telescope is a 64-dish interferometer based in the Karoo Desert, Northern Cape, South Africa91. Each dish is 12 m in diameter and the longest baseline is about 8 km, allowing for an angular resolution of roughly 7 arcsec and a field of view of 1 deg2. The observations we were awarded were made at both L and S bands. GRB 230307A was observed over three separate epochs between seven and 41 days post-burst. The first two observations were made at both L and S4 bands (the highest frequency of the five S-band sub-bands), centred at 1.28 GHz and 3.06 GHz with bandwidths of 0.856 GHz and 0.875 GHz, respectively. Each observation spent two hours at L band and 20 min at S4 band. The final observation was made only at S4 band with 1 h on target. Please see the paper by Max Planck Institute for Radio Astronomy (MPIfR) for further details on the new MeerKAT S-band receiver. Each observation was processed using OXKAT, a series of semiautomated Python scripts designed specifically to deal with MeerKAT imaging data92. The scripts average the data and perform flagging on the calibrators, from which delay, bandpass and gain corrections are calculated and then applied to the target. The sources J0408-6545 and J0252-7104 were used at the flux and complex gain calibrators, respectively. Flagging and imaging of the target field are performed. We also perform a single round of phase-only self-calibration. We do not detect a radio counterpart in any epoch in either band. The root mean square noise in the field was measured using an empty region of the sky and used to calculate 3σ upper limits, which are given in Extended Data Table 2.

Article JWST observations. We obtained two epochs of observations of the location of GRB 230307A with the JWST. The first on 5 April 2023, with observations beginning at 00:16 UT (MJD = 60039.01), 28.4 days after the burst (under programme GO 4434; PI: Levan), and the second on 8 May 2023, 61.5 days after the burst (programme 4445; PI: Levan). The observations were at a post-peak epoch because the source was not in the JWST field of regard at the time of the burst and only entered it on 2 April 2023. At the first epoch, we obtained observations in the F070W, F115W, F150W, F277W, F356W and F444W filters of NIRCam93, as well as a prism spectrum with NIRSpec94. In the second epoch, we obtained NIRCam observations in F115W, F150W, F277W and F444W and a further NIRSpec prism observation. However, in the second epoch, the prism observation is contaminated by light from the diffraction spike of a nearby star and is of limited use, in particular at the blue end of the spectrum. We therefore use only light redward of 1.8 microns. However, even here, we should be cautious in interpreting the overall spectral shape. The feature at 2.15 microns is visible in both the 29-day and 61-day spectra. We reprocessed and redrizzled the NIRCam data products to remove 1/f striping and aid point-spread-function recovery, with the final images having plate scales of 0.02 arcsec per pixel (blue channel) and 0.04 arcsec per pixel (red channel). In the NIRCam imaging, we detect a source at the location of the optical counterpart of GRB 230307A. This source is weakly detected in all three bluer filters (F070W, F115W and F150W), but is at high signal-to-noise ratio in the redder channels (see Fig. 2). The source is compact and unresolved. We also identify a second source offset (H1) approximately 0.3 arcsec from the burst location. This source is also weakly or non-detected in the bluer bands, and is brightest in the F277W filter. Because of the proximity of the nearby star and a contribution from diffraction spikes close to the afterglow position, we model point spread functions for the appropriate bands using WebbPSF (ref. 95) and then scale and subtract these from the star position. Photometry is measured in small (0.05 arcsec (blue) and 0.1 arcsec (red)) apertures and then corrected using tabulated encircled energy corrections. As well as the direct photometry of the NIRCam images, we also report a K-band point based on folding the NIRSpec spectrum (see below), through a Two Micron All-Sky Survey (2MASS) Ks filter. This both provides a better broadband spectral energy distribution and a direct comparison with ground-based K-band observations. Details of photometric measurements are shown in Extended Data Table 1 For NIRSpec, we use the available archive-processed level 3 2D spectrum (Fig. 3). In this spectrum, we clearly identify the trace of the optical counterpart, which seems effectively undetected until 2 microns and then rises rapidly. We also identify two likely emission lines that are offset from the burst position. These are consistent with the identification with Hα and [O III] (4959/5007) at a redshift of z = 3.87. Both of these lines lie within the F277W filter in NIRCam and support the identification of the nearby source as the origin of these lines. We extract the spectrum in two small (two-pixel) apertures. One of these is centred on the transient position, whereas the other is centred on the location of the emission lines. Because the offset between these two locations is only about 0.3 arcsec, there is naturally some contamination of each spectrum with light from both sources, but this is minimized by the use of small extraction apertures. The counterpart spectra are shown in Fig. 3. The counterpart is very red, with a sharp break at 2 microns and an apparent emission feature at 2.15 microns. The spectrum then continues to rise to a possible second feature (or a change in the associated spectral slope) at around 4.5 microns.

Data availability JWST data are directly available from the MAST archive at archive.stsci. edu. ESO data can be obtained from archive.eso.org and Gemini data

from archive.gemini.edu. Core reduced optical and infrared products can also be downloaded directly from the Electronic Research Data Archive at the University of Copenhagen (ERDA) at https://sid.erda.dk/ sharelink/b35FULIcV5. This research has made use of Fermi data, which are publicly available and can be obtained through the High Energy Astrophysics Science Archive Research Center (HEASARC) website at https://heasarc.gsfc.nasa.gov/W3Browse/fermi/fermigbrst.html. Swift data can be obtained from http://www.swift.ac.uk/xrt_curves and Chandra observations from https://cda.harvard.edu/chaser/.

Code availability Much analysis for this paper has been undertaken with publicly available codes and the details required to reproduce the analysis are contained in the manuscript.

51. Planck Collaboration. Planck 2018 results. VI. Cosmological parameters. Astron. Astrophys. 641, A6 (2020). 52. Dalessi, S., Roberts, O. J. & Meegan, C. & Fermi GBM Team. GRB 230307A: Fermi GBM observation of a very bright burst. GRB Coordinates Network, Circular Service, No. 33411 (2023). 53. Xiao, H. & Krucker, S. Solar Orbiter STIX observation of GRB 230307A. GRB Coordinates Network, Circular Service, No. 33410 (2023). 54. Casentini, C. et al. GRB 230307A: AGILE/MCAL detection. GRB Coordinates Network, Circular Service, No. 33412 (2023). 55. Navaneeth, P. K. et al. GRB 230307A: AstroSat CZTI detection. GRB Coordinates Network, Circular Service, No. 33415 (2023). 56. Dafcikova, M. et al. GRB 230307A: detection by GRBAlpha. GRB Coordinates Network, Circular Service, No. 33418 (2023). 57. Ripa, J. et al. GRB 230307A: VZLUSAT-2 detection. GRB Coordinates Network, Circular Service, No. 33424 (2023). 58. Svinkin, D. et al. Konus-Wind detection of GRB 230307A. GRB Coordinates Network, Circular Service, No. 33427 (2023). 59. Li, Z., Su, Y. & HXI Team. GRB 230307A: ASO-S/HXI detection. GRB Coordinates Network, Circular Service, No. 33438 (2023). 60. Kozyrev, A. S. et al. IPN triangulation of GRB 230307A (long/very bright). GRB Coordinates Network, Circular Service, No. 33413 (2023). 61. Kozyrev, A. S. et al. Improved IPN localization for GRB 230307A. GRB Coordinates Network, Circular Service, No. 33425 (2023). 62. Dalessi, S. & Fermi GBM Team. GRB 230307A: bad time intervals for Fermi GBM data. GRB Coordinates Network, Circular Service, No. 33551 (2023). 63. Ravasio, M. E. et al. Consistency with synchrotron emission in the bright GRB 160625B observed by Fermi. Astron. Astrophys. 613, A16 (2018). 64. Ravasio, M. E., Ghirlanda, G., Nava, L. & Ghisellini, G. Evidence of two spectral breaks in the prompt emission of gamma-ray bursts. Astron. Astrophys. 625, A60 (2019). 65. Dhillon, V. S. et al. ULTRACAM: an ultrafast, triple-beam CCD camera for high-speed astrophysics. Mon. Not. R. Astron. Soc. 378, 825–840 (2007). 66. Dhillon, V. S. et al. HiPERCAM: a quintuple-beam, high-speed optical imager on the 10.4-m Gran Telescopio Canarias. Mon. Not. R. Astron. Soc. 507, 350–366 (2021). 67. Dey, A. et al. Overview of the DESI Legacy Imaging Surveys. Astron. J. 157, 168 (2019). 68. Lipunov, V. et al. GRB 230204B: MASTER detection of an object near NTT optical afterglow candidate. GRB Coordinates Network, Circular Service, No. 33441 (2023). 69. O’Connor, B. et al. GRB 230307A: Gemini-South confirmation of the optical afterglow. GRB Coordinates Network, Circular Service, No. 33447 (2023). 70. Im, M. et al. GRB 230307A: GECKO optical observations and detections of the afterglow. GRB Coordinates Network, Circular Service, No. 33449 (2023). 71. Bom, C. R. et al. GRB 230307A: SOAR/Goodman detection of the possible host galaxy. GRB Coordinates Network, Circular Service, No. 33459 (2023). 72. Breeveld, A. A., Salvaggio, C. & Swift/UVOT Team. GRB 230307A: Swift/UVOT detection. GRB Coordinates Network, Circular Service, No. 33471 (2023). 73. Fausnaugh, M. M. et al. Observations of GRB 230307A by TESS. Res. Notes AAS 7, 56 (2023). 74. Roming, P. W. A. et al. The Swift Ultra-Violet/Optical Telescope. Space Sci. Rev. 120, 95–142 (2005). 75. Poole, T. S. et al. Photometric calibration of the Swift ultraviolet/optical telescope. Mon. Not. R. Astron. Soc. 383, 627–645 (2008). 76. Breeveld, A. A. et al. An updated ultraviolet calibration for the Swift/UVOT. AIP Conf. Proc. 1358, 373–376 (2011). 77. Labrie, K., Anderson, K., Cárdenes, R., Simpson, C. & Turner, J. E. H. in Proc. Astronomical Data Analysis Software and Systems XXVIII Vol. 523 (eds Teuben, P. J., Pound, M. W., Thomas, B. A. & Warner, E. M.) 321 (Astronomical Society of the Pacific, 2019). 78. McMahon, R. G. et al. First scientific results from the VISTA Hemisphere Survey (VHS). Messenger 154, 35–37 (2013). 79. Freudling, W. et al. Automated data reduction workflows for astronomy. The ESO Reflex environment. Astron. Astrophys. 559, A96 (2013). 80. Selsing, J. et al. The X-shooter GRB afterglow legacy sample (XS-GRB). Astron. Astrophys. 623, A92 (2019). 81. Soto, K. T., Lilly, S. J., Bacon, R., Richard, J. & Conseil, S. ZAP – enhanced PCA sky subtraction for integral field spectroscopy. Mon. Not. R. Astron. Soc. 458, 3210–3220 (2016). 82. David, N. et al. The Swift X-ray telescope. Space Sci. Rev. 120, 165–195 (2005).

83. Evans, P. A. & Swift Team. GRB 230307A: tiled Swift observations. GRB Coordinates Network, Circular Service, No. 33419 (2023). 84. Burrows, D. N. et al. GRB 230307A: Swift-XRT afterglow detection. GRB Coordinates Network, Circular Service, No. 33465 (2023). 85. Evans, P. A. et al. An online repository of Swift/XRT light curves of γ-ray bursts. Astron. Astrophys. 469, 379–385 (2007). 86. Evans, P. A. et al. Methods and results of an automatic analysis of a complete sample of Swift-XRT observations of GRBs. Mon. Not. R. Astron. Soc. 397, 1177–1201 (2009). 87. Kraft, R. P., Burrows, D. N. & Nousek, J. A. Determination of confidence limits for experiments with low numbers of counts. Astrophys. J. 374, 344 (1991). 88. Willingale, R., Starling, R. L. C., Beardmore, A. P., Tanvir, N. R. & O’Brien, P. T. Calibration of X-ray absorption in our Galaxy. Mon. Not. R. Astron. Soc. 431, 394–404 (2013). 89. Levan, A. J. et al. The first JWST spectrum of a GRB afterglow: no bright supernova in observations of the brightest GRB of all time, GRB 221009A. Astrophys. J. Lett. 946, L28 (2023). 90. Sault, R. J., Teuben, P. J. & Wright, M. C. H. in Astronomical Data Analysis Software and Systems IV, ASP Conference Series Vol. 77 (eds Shaw, R. A., Payne, H. E. & Hayes, J. J. E.) 433 (Astronomical Society of the Pacific, 1995). 91. Jonas, J. & MeerKAT Team. in Proc. MeerKAT Science: On the Pathway to the SKA 1 (SISSA, 2016). 92. Heywood, I. oxkat: semi-automated imaging of MeerKAT observations. Astrophysics Source Code Library, record ascl:2009.003 (2020). 93. Beichman, C. A. et al. in Proc. Space Telescopes and Instrumentation 2012: Optical, Infrared, and Millimeter Wave, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series Vol. 8442 (eds Clampin, M. C., Fazio, G. G., MacEwen, H. A. & Oschmann, J. M.) 84422N (SPIE, 2012). 94. Jakobsen, P. et al. The Near-Infrared Spectrograph (NIRSpec) on the James Webb Space Telescope. I. Overview of the instrument and its capabilities. Astron. Astrophys. 661, A80 (2022). 95. Perrin, M. D. et al. in Proc. Space Telescopes and Instrumentation 2014: Optical, Infrared, and Millimeter Wave, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series Vol. 9143 (eds Oschmann, J. M., Clampin, M., Fazio, G. G. & MacEwen, H. A.) 91433X (SPIE, 2014). Acknowledgements We dedicate this paper to David Alexander Kann, who passed on 10 March 2023. He was the first to realize the exceptional brightness of GRB 230307A, and the final messages he sent were about its follow-up. We hope it would satisfy his curiosity to know the final conclusions. This work is based on observations made with the NASA/ESA/CSA James Webb Space Telescope. The data were obtained from the Mikulski Archive for Space Telescopes (MAST) at the Space Telescope Science Institute (STScI), which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-03127 for the JWST. These observations are associated with programme nos. 4434 and 4445. This paper is partly based on observations collected at the European Southern Observatory under ESO programme 110.24CF (PI: Tanvir) and on observations obtained at the international Gemini Observatory (programme ID GS-2023A-DD-105), a programme of NOIRLab, which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation (NSF) on behalf of the Gemini Observatory partnership: the NSF (USA), National Research Council (Canada), Agencia Nacional de Investigación y Desarrollo (Chile), Ministerio de Ciencia, Tecnología e Innovación (Argentina), Ministério da Ciência, Tecnologia, Inovações e Comunicações (Brazil) and Korea Astronomy and Space Science Institute (Republic of Korea). Processed using the Gemini IRAF package and DRAGONS (Data Reduction for Astronomy from Gemini Observatory North and South). A.J.L., D.B.M. and N.R.T. were supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 725246). M.B. acknowledges the Department of Physics and Earth Science of the University of Ferrara for the financial support through the FIRD 2022 grant. K.H. is supported by JST FOREST Program (JPMJFR2136) and the JSPS Grant-in-Aid for Scientific Research (20H05639, 20H00158, 23H01169, 20K14513). G.P.L. is supported by a Royal Society Dorothy Hodgkin Fellowship (grant nos. DHF-R1-221175 and DHF-ERE-221005). M.E.R. acknowledges support from the research programme Athena with project number 184.034.002, which is financed by

the Dutch Research Council (NWO). N.S. is supported by a Nordita fellowship. Nordita is supported in part by NordForsk. S.S. acknowledges support from the G.R.E.A.T. research environment, funded by Vetenskapsrådet, the Swedish Research Council, project number 2016-06012. V.S.D. and ULTRACAM are funded by STFC grant ST/V000853/1. G.L. was supported by a research grant (19054) from VILLUM FONDEN. The Cosmic Dawn Center (DAWN) is funded by the Danish National Research Foundation under grant no. 140. J.P.U.F. is supported by the Independent Research Fund Denmark (DFF-4090-00079) and thanks the Carlsberg Foundation for support. D.W. is co-funded by the European Union (ERC, HEAVYMETAL, 101071865). D.K.G. acknowledges support from the Australian Research Council Centre of Excellence for Gravitational Wave Discovery (OzGrav), through project number CE170100004. N.G. acknowledges support from the NWO under project number 680.92.18.02. K.E.H. acknowledges support from the Carlsberg Foundation Reintegration Fellowship Grant CF21-0103. J.H. and D.L. were supported by a VILLUM FONDEN Investigator grant (project number 16599). B.D.M. is supported in part by the NSF (grant AST-2002577). M.N. is supported by the ERC under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 948381) and by UK Space Agency grant no. ST/Y000692/1. S.J.S. acknowledges funding from STFC grants ST/X006506/1 and ST/T000198/1. H.F.S. is supported by the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship, a Schmidt Futures programme. A.A.B. acknowledges funding from the UK Space Agency. P.O.B. acknowledges funding from STFC grant ST/W000857/1. D.S. acknowledges funding from STFC grants ST/ T000406/1, ST/T003103/1 and ST/X001121/1. A.S. acknowledges support from DIM-ACAV+ and CNES. S.C., P.D.A., B.Sb. and G.T. acknowledge funding from the Italian Space Agency, contract ASI/INAF no. I/004/11/4. Author contributions A.J.L. led the project, including the location of the optical afterglow and kilonova and the JWST observations. B.P.G. first identified the source as a likely compact object merger, was co-PI of the Chandra observations and contributed to analysis and writing. O.S.S. contributed to afterglow and kilonova modelling and led the writing of these sections. M.B. was involved in kilonova modelling, E.Bu. contributed to interpretation, placing the burst in context and high-energy properties. K.H. was involved in kilonova spectral modelling and identified the 2.15-µm feature. L.I. contributed to the X-shooter data analysis, reduced the MUSE data and led the host analysis. G.P.L. contributed to afterglow and kilonova modelling. D.B.M. organized the VLT observations and contributed to the data analysis. M.E.R. analysed the Fermi data. A.R.E. analysed the Chandra observations. B.Sc. reduced and analysed VLT observations. N.S. contributed to afterglow and kilonova modelling. S.S. was responsible for placing the burst afterglow in context and demonstrating its faintness. N.R.T. contributed to observations and interpretation. K.A. was involved in the ULTRACAM observations. G.A. led the ATCA observations. G.B.B. reduced the JWST NIRCam data. L.C. processed and analysed the MUSE observations. V.S.D. is the ULTRACAM PI. J.P.U.F. studied the high-z possibilities. W.-f.F. was the PI on the Chandra observations. C.F. contributed to the theoretical interpretation. N.G. was involved in host analysis. J.T.P. contributed to the JWST spectrum visualization. K.E.H., G.P., A.R., S.D.V., S.C., P.D.A., D.H.H., M.D.P., C.C.T., A.d.U.P. and D.A.K. contributed to ESO observations and discussion. D.W. contributed to spectral and progenitor modelling. M.J.D., P.K., S.P.L., J.M., S.G.P., I.P. and D.I.S. contributed to the ULTRACAM observations. A.S. reduced X-shooter observations. G.L. investigated potential similarities with other transients. A.T., P.A.E., B.Sb. and J.A.K. contributed to the Swift observations. M.F. extracted and flux-calibrated the TESS light curve. S.J.S. analysed the JWST spectral lines. H.F.S. performed the BPASS-hoki-ppxf fits to the integrated MUSE flux and contributed the associated figure and text. All authors contributed to manuscript preparation through contributions to concept development, discussion and text. Competing interests The authors declare no competing interests. Additional information Supplementary information The online version contains supplementary material available at https://doi.org/10.1038/s41586-023-06759-1. Correspondence and requests for materials should be addressed to Andrew J. Levan. Peer review information Nature thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available. Reprints and permissions information is available at http://www.nature.com/reprints.

Article Extended Data Table 1 | Optical and infrared observations of the optical counterpart of GRB 230307A

Errors are given at the 1σ level and limits are given at the 3σ level.

Extended Data Table 2 | X-ray and radio observations of the afterglow of GRB 230307A

Errors are given at the 1σ level and upper limits are given at the 3σ level.

Article Extended Data Table 3 | Properties of possible host galaxies for GRB 230307A

*Formally, because the galaxy is undetected in the r-band, Pchance is unbounded. This probability is based on the magnitudes measured at other wavelengths.

Article

A lanthanide-rich kilonova in the aftermath of a long gamma-ray burst https://doi.org/10.1038/s41586-023-06979-5 Received: 31 July 2023 Accepted: 14 December 2023 Published online: 21 February 2024 Check for updates

Yu-Han Yang1 ✉, Eleonora Troja1,2 ✉, Brendan O’Connor3,4,5, Chris L. Fryer6,7,8,9,10, Myungshin Im11, Joe Durbak4,5, Gregory S. H. Paek11, Roberto Ricci12,13, Clécio R. Bom14,15, James H. Gillanders1, Alberto J. Castro-Tirado16,17, Zong-Kai Peng18,19, Simone Dichiara20, Geoffrey Ryan21, Hendrik van Eerten22, Zi-Gao Dai23, Seo-Won Chang11, Hyeonho Choi11, Kishalay De24, Youdong Hu16, Charles D. Kilpatrick25, Alexander Kutyrev4,5, Mankeun Jeong11, Chung-Uk Lee26, Martin Makler14,27, Felipe Navarete28 & Ignacio Pérez-García16

Observationally, kilonovae are astrophysical transients powered by the radioactive decay of nuclei heavier than iron, thought to be synthesized in the merger of two compact objects1–4. Over the first few days, the kilonova evolution is dominated by a large number of radioactive isotopes contributing to the heating rate2,5. On timescales of weeks to months, its behaviour is predicted to differ depending on the ejecta composition and the merger remnant6–8. Previous work has shown that the kilonova associated with gamma-ray burst 230307A is similar to kilonova AT2017gfo (ref. 9), and mid-infrared spectra revealed an emission line at 2.15 micrometres that was attributed to tellurium. Here we report a multi-wavelength analysis, including publicly available James Webb Space Telescope data9 and our own Hubble Space Telescope data, for the same gamma-ray burst. We model its evolution up to two months after the burst and show that, at these late times, the recession of the photospheric radius and the rapidly decaying bolometric luminosity (Lbol ∝ t−2.7±0.4, where t is time) support the recombination of lanthanide-rich ejecta as they cool.

An extremely bright burst, dubbed gamma-ray burst (GRB) 230307A, triggered the Gamma-ray Burst Monitor aboard NASA’s Fermi mission at 15:44:06.67 UTC on 7 March 2023 (hereafter T0). Observationally, GRB 230307A stands out from the general population of long GRBs for three properties: a record-setting gamma-ray fluence10 (about 3 × 10−3 erg cm−2; 10–1,000 keV), a weak X-ray counterpart (Fig. 1f) and a strong blue-to-red colour evolution. Early observations at optical and near-infrared (NIR) wavelengths identify a weak counterpart, whose brightness (H ≈ 20.2 AB mag at T0 + 1.2 d) matches the extrapolation of the X-ray spectrum. The spectral energy distribution (SED) of the GRB counterpart thus does not show evidence for absorption by gas and dust along the sightline (Methods). After T0 + 4 d, the X-ray and optical emission decay quickly, with temporal power-law indices αX = 1.71 ± 0.10 and αO = 2.64+0.16 −0.26, respectively. Instead, the NIR emission persists for several days after the explosion (K-band magnitude K ≈ 22 AB mag at T0 + 7 d) and then rapidly

declines (Extended Data Fig. 1). Late-time (about T0 + 29 d) observations with the Hubble Space Telescope (HST) and the James Webb Space Telescope ( JWST)9 show that the peak of the NIR emission shifts from about 22,000 Å at T0 + 7 d to ≳44,000 Å at T0 + 29 d. At this time (about T0 + 29 d), the continuum is adequately described by the superposition of a power-law spectrum with spectral index βOX ≈ 0.6 and a blackbody spectrum with temperature T ≈ 638 K (observer frame; Methods). The key ingredient to interpret these observations is the GRB distance scale. Unfortunately, in the case of GRB 230307A, no direct redshift measurement is available. Our analysis of the photometric dataset provides evidence for a redshift z ≲ 3.3 (at the 95% confidence level (CL); Methods). This leaves a range of possible distance scales that is still too broad. An alternative route to estimate the GRB’s distance is to identify its host galaxy using probabilistic arguments11. In the case of GRB 230307A, this methodology leads to several possible host galaxies: (1) a distant (z ≳ 3.9) star-forming galaxy (G* in Fig. 1e); (2) a

Department of Physics, University of Rome “Tor Vergata”, Rome, Italy. 2INAF - Istituto Nazionale di Astrofisica, Rome, Italy. 3Department of Physics, The George Washington University, Washington DC, USA. 4Department of Astronomy, University of Maryland, College Park, MD, USA. 5Astrophysics Science Division, NASA Goddard Space Flight Center, Greenbelt, MD, USA. 6 Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, USA. 7Center for Theoretical Astrophysics, Los Alamos National Laboratory, Los Alamos, NM, USA. 8The University of Arizona, Tucson, AZ, USA. 9Department of Physics and Astronomy, The University of New Mexico, Albuquerque, NM, USA. 10The George Washington 1

University, Washington DC, USA. 11SNU Astronomy Research Center, Astronomy Program, Department of Physics and Astronomy, Seoul National University, Seoul, Republic of Korea. 12Istituto Nazionale di Ricerca Metrologica, Turin, Italy. 13INAF - Istituto di Radioastronomia, Bologna, Italy. 14Centro Brasileiro de Pesquisas Físicas, Rio de Janeiro, Brazil. 15Centro Federal de Educação Tecnológica Celso Suckow da Fonseca, Rodovia Mário Covas, Itaguaí, Brazil. 16Instituto de Astrofísica de Andalucía (IAA-CSIC), Granada, Spain. 17Unidad Asociada al CSIC Departamento de Ingeniería de Sistemas y Automática, Escuela de Ingenierías Industriales, Universidad de Málaga, Málaga, Spain. 18Institute for Frontier in Astronomy and Astrophysics, Beijing Normal University, Beijing, China. 19Department of Astronomy, Beijing Normal University, Beijing, China. 20Department of Astronomy and Astrophysics, The Pennsylvania State University, University Park, PA, USA. 21Perimeter Institute for Theoretical Physics, Waterloo, Ontario, Canada. 22Physics Department, University of Bath, Claverton Down, UK. 23Department of Astronomy, School of Physical Sciences, University of Science and Technology of China, Hefei, China. 24Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA, USA. Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA) and Department of Physics and Astronomy, Northwestern University, Evanston, IL, USA. 26Korea Astronomy and Space Science Institute, Daejeon, Republic of Korea. 27International Center for Advanced Studies and Instituto de Ciencias Físicas, ECyT-UNSAM and CONICET, Buenos Aires, Argentina. 28 SOAR Telescope/NSF’s NOIRLab, La Serena, Chile. ✉e-mail: [email protected]; [email protected] 25

742 | Nature | Vol 626 | 22 February 2024

a F150W F277W F444W

f

GRB 230307A

221009A

log(FX,11h/ γ )

–5

G1

–6

060614

130427A

–7 5 arcsec

b

c

d

e G*

–8

10–1 HST/F105W T0 + 29 d

JWST/F277W T0 + 29 d

JWST/F444W T0 + 29 d

JWST/F277W T0 + 61 d

211211A

Short GRBs Long GRBs

230307A 100

101

102

Projected physical offset (kpc)

Fig. 1 | The environment of GRB 230307A. a, False-colour image combining three filters from JWST (F150W, F277W and F444W). The bright galaxy labelled by G1 is the most likely host galaxy at an offset of 40 kpc. b–e, Zoom-in on the transient location, corresponding to the white box in a. The field is shown in filters HST/F105W (b), JWST/F277W (c) and JWST/F444W (d) at T0 + 29 d. The same field is shown in filter JWST/F277W at T0 + 61 d (e). The transient has a very red colour in the near-simultaneous HST and JWST observations. The high-redshift galaxy G* is marked in the magenta circle in e. f, Ratio of 0.3–10 keV

X-ray flux at 11 h (F X,11h) to the 15–150 keV gamma-ray fluence (φ γ) versus the projected physical offset from the GRB host galaxy. The purple and grey data points represent short and long GRBs, respectively. The purple solid line and dashed lines indicate the best-fit model and the 95% CL for short GRBs, respectively. The bright long GRBs 221009A and 130427A are shown in black circles. Hybrid long GRBs 060614 and 211211A are shown in blue circles. GRB 230307A is marked by a red star, lying at the bottom of the distribution. Error bars are 1σ.

local origin in the Magellanic Clouds; (3) a nearby (z ≈ 0.0647, corresponding to 291 Mpc; ref. 12) face-on spiral galaxy (G1 in Fig. 1a). Each of these three possibilities leads to extreme properties for GRB 230307A (Methods). Further insights can be gleaned from the SED of the GRB counterpart, modelled with a power law plus a blackbody component (Methods). The results show that a thermal component exists in all spectra acquired >T0 +1 d (Fig. 2a–f). Until about T0 + 10 d, this component shows a trend of decreasing temperature and increasing radius, which is consistent with an expanding fireball. By assuming homologous expansion and imposing that the velocity v ≈ (1 + z)Rph/t, where Rph is the photosphere radius and t is time, cannot exceed the speed of light, we obtain z  140). For our fiducial afterglow model (Fig. 3), we find strong evidence in favour of two kilonova components over the single kilonova component (∆BIC = 19). The former component is produced by

fast moving ejecta (v ≈ 0.2c, where c is the speed of light) with mass M ≈ 0.03 M⊙ and opacity κ ≲ 3 cm2 g−1 (3σ CL). This component mostly contributes to the optical and NIR emission over the first few days, then quickly fades away. The latter component is produced by slightly more massive (M ≈ 0.05 M⊙), slower (v ≈ 0.03c) ejecta with a significantly higher opacity (κ ≳ 13 cm2 g−1, 3σ CL). This component becomes visible after about T0 + 10 d and dominates the late-time emission. Its inclusion is mostly driven by the mid-infrared detections and their steep spectral profile (βIR ≈ 3.2), and relies on the assumption that the contribution of emission lines remains subdominant. After estimating the contribution of the underlying non-thermal continuum, we derive the kilonova properties (Fig. 2). At approximately T0 + 7 d, the effective temperature of the thermal component drops below 2,000 K (Fig. 2h), and the photospheric layer exhibits a tendency to recede into the inner regions (Fig. 2i). The velocity distribution as a function of mass can affect the evolution of the photosphere, but would produce a more gradual transition. A similar trend is instead observed in some type II supernovae during their hydrogen recombination phase22. In the case of a kilonova, the drop in effective temperature changes the ionization states of lanthanides and actinides, transitioning from singly ionized to neutral states, at a critical temperature of around 2,500 K (ref. 23). With a lower number of free electrons, the number of infrared bound–bound lines decreases considerably24–26. This causes a drop in the optical depth (Extended Data Fig. 6), accelerating the recession of the photosphere. The outer layers instead enter into an optically thin phase. This complex evolution is not accounted for by simple constant-opacity kilonova models and may explain why two kilonova components provide a better description of the dataset. The kilonova bolometric luminosity is seen to rapidly decrease as Lbol ∝ t−2.7±0.4 (Fig. 2g), ranging from about 6 × 1039 erg s−1 at 29 d to about Nature | Vol 626 | 22 February 2024 | 743

Article t = 1.2 d

10–4

HJYZRu Swift-UVOT Swift-XRT

10–5

g 1042

10–6 1041

10–7 Lbol (erg s–1)

Flux density (Jy)

a

b

10–4

Flux density (Jy)

10–8 10–5 10–6

1038

10–8 z′Ir Swift-XRT

t = 2.4 d

10–5

Flux density (Jy)

GRB 230307A GRB 211211A GRB 170817A

104

10–8 Kzr XMM-MOS1 XMM-MOS2 XMM-pn

t = 7.4 d

10–5 10–6 10–7

Temperature (K)

10–7

t –0.54 103

10–8 10–9 JWST HST XMM-pn

t = 28.9 d

10–6 10–7

i 1016

10–8 10–9 10–10

f

t –2.7 ± 0.4

h

10–6

JWST HST XMM-pn

t = 61.4 d

10–7 10–8

Rph (cm)

Flux density (Jy) Flux density (Jy) Flux density (Jy)

e

1040

1039

10–7

c

d

IR Swift-UVOT Swift-XRT

t = 1.8 d

t –0.95

1015

10–9 10–10

v=

10–11 1013

1014

1015

1016

1017

1018

Frequency (Hz)

2c

0.

v=

1c

0.

v=

02

0.

100

c

101 Time (day)

Fig. 2 | SED of the GRB counterpart. a–f, The observed spectra at different epochs (1.2 d (a), 1.8 d (b), 2.4 d (c), 7.4 d (d), 29 d (e) and 61 d (f) after the GRB) are fit with an empirical power law plus blackbody model (black solid thick lines). The grey shaded areas show the unabsorbed blackbody components and the grey thin solid lines show the power-law components. Different symbols represent different telescopes (JWST, HST) or detectors (Swift-UVOT, Swift-XRT, XMM-MOS1, XMM-MOS2, XMM-pn), and the filters of optical data points correspond from left to right according to the label (for example, HJYZRu).

g–i, The bolometric luminosity, effective temperature and photospheric radius of the thermal emission. GRB 211211A (black circles)15 and GRB 170817A (grey circles)13,14 are shown for comparison. The early bolometric luminosity and temperature (about 1–7 d) conform to simple power-law decay with slopes of −0.95 and −0.54, respectively 29. The bolometric luminosity at late times decays with a steeper slope of −2.7 ± 0.4. The dotted lines in i indicate R ≈ vt/(1 + z). Error bars and upper limits are the 1σ CL and the 3σ CL, respectively.

7 × 1038 erg s−1 at 61 d. A rapid decay of the luminosity was identified in the late-time observations of the kilonova AT2017gfo (refs. 13,14), and interpreted as a possible signature of short-lived heavy isotopes dominating the heating rate and thus the observed emission. However, in the case of AT2017gfo, the weak signal and limited coverage of the Spitzer data were not sufficient to characterize the spectral shape, and only placed lower limits on the true bolometric luminosity. In the case of GRB 230307A, the sensitivity and multi-colour coverage of the JWST and HST observations allow for better sampling. The data show that the ejecta is only partially optically thin and its late-time NIR luminosity is still dominated by photospheric emission of the inner layers. The evolution of the photosphere is consistent with adiabatic expansion and does not require a drop in the heating rate to explain the change in luminosity. Although this implies that no specific element can be identified based on temporal evolution, the fast decay of the luminosity can still inform us on the properties of this kilonova.

Predictions of the late-time evolution of a kilonova span a wide range of behaviours, depending on nuclear inputs and ejecta properties (for example, total mass, total energy, velocity distribution and ejecta composition). A common expectation is that, if translead nuclei such as 254Cf are produced in the explosion, their decay products would deposit energy into the ejecta and cause the kilonova luminosity to flatten over time27,28. A hot central engine (for example, magnetar, pulsar or fall-back accretion) powering the lightcurve can also alter the late-time emission8. By comparing the bolometric lightcurve with different models (Fig. 4), we find that the efficient energy deposition of a long-lasting magnetar8 or actinide fission fragments27,28 is not consistent with the red colour and rapid decay of the bolometric lightcurve. A radioactive-powered kilonova containing r-process elements beyond the first peak (atomic mass number A ≳ 85) shows a better agreement with the data. This is because lighter elements have shorter lifetimes

744 | Nature | Vol 626 | 22 February 2024

5.5 GHz 9.0 GHz 16.7 GHz 21.2 GHz F444W F356W F277W K H F150W F140W J F115W z′ F105W Y z Z I F070W R r White u 1 keV

107

Flux density (Jy)

104

101

10–2

10–5

10–8

100

102

101 Time (day)

merger ejecta, and confirms kilonovae are a cosmic site of heavy r-process elements.

Online content Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi.org/10.1038/s41586-023-06979-5. 1. 2. 3. 4. 5.

6. 7.

Fig. 3 | The multi-wavelength counterpart of GRB 230307A. The best-fit model to the multi-wavelength lightcurves. Except for the X-ray and the u/white bands, the nth lightcurve, when viewed from the bottom upwards, is vertically shifted by a factor of 10n . The corresponding observed energy, filter and frequency are shown on the right side. Owing to the minor difference in effective wavelengths of some filters, we merged these observations into a single lightcurve. These lightcurves were fit by the emission contributed by a GRB afterglow (solid lines) and two-component kilonova (hatched and shaded areas). Error bars and upper limits are 1σ CL and 3σ CL, respectively.

8. 9. 10. 11.

12.

13.

and cannot provide sufficient radioactive power at these late epochs, resulting in a dimmer and cooler kilonova. The bolometric lightcurve, coupled with the observed evolution of the photospheric radius and the inferred high opacity, points to lanthanide production in the 1042

14. 15. 16. 17. 18.

1041

19.

Lbol (erg)

20.

1040

1039

1038

21. 22.

85 ≤ A ≤ 209 72 ≤ A ≤ 209 72 ≤ A ≤ 85

23. 24.

254Cf

Magnetar-fed

100

25.

101 Time (day)

Fig. 4 | Comparison of the bolometric lightcurve with different models. The black lines are calculated using the model from ref. 6 with the solar r-process abundance pattern of different atomic mass ranges of 85 ≤ A ≤ 209 (black solid line), 72 ≤ A ≤ 209 (black dashed line) and 72 ≤ A ≤ 85 (black dotted line). The ejecta parameters are Mej = 0.07 M ⊙, vej = 0.1c, vej,max = 0.4c and β v = 1.5. The opacity is κ1 = 0.6 cm2 g −1 and κ 2 = 20 cm2 g −1 with the velocity threshold 0.15c. The purple solid line illustrates the effects of efficient energy deposition by the spontaneous fission of 254Cf, calculated for Mej = 0.05M ⊙ and with effective heating rates from ref. 27. The blue line with dashed extrapolation illustrates the generic evolution of a magnetar-fed kilonova. We show the same model used to describe AT2017gfo (ref. 8) with Mej = 0.001 M ⊙, vej,max/2  = 0.45c, characteristic spindown luminosity L0 = 2 × 1044 erg s−1, gravitational-wave-dominated spindown timescale tgw = 495 s and magnetar lifetime tcut = 23 d.

26.

27. 28. 29.

Eichler, D., Livio, M., Piran, T. & Schramm, D. N. Nucleosynthesis, neutrino bursts and γ-rays from coalescing neutron stars. Nature 340, 126–128 (1989). Li, L.-X. & Paczyński, B. Transient events from neutron star mergers. Astrophys. J. 507, L59–L62 (1998). Freiburghaus, C., Rosswog, S. & Thielemann, F. K. R-process in neutron star mergers. Astrophys. J. 525, L121–L124 (1999). Korobkin, O., Rosswog, S., Arcones, A. & Winteler, C. On the astrophysical robustness of the neutron star merger r-process. Mon. Not. R. Astron. Soc. 426, 1940–1949 (2012). Barnes, J., Kasen, D., Wu, M.-R. & Martínez-Pinedo, G. Radioactivity and thermalization in the ejecta of compact object mergers and their impact on kilonova light curves. Astrophys. J. 829, 110 (2016). Hotokezaka, K. & Nakar, E. Radioactive heating rate of r-process elements and macronova light curve. Astrophys. J. 891, 152 (2020). Zhu, J.-P. et al. Long-duration gamma-ray burst and associated kilonova emission from fast-spinning black hole–neutron star mergers. Astrophys. J. 936, L10 (2022). Wollaeger, R. T. et al. Impact of pulsar and fallback sources on multifrequency kilonova models. Astrophys. J. 880, 22 (2019). Levan, A. et al. Heavy element production in a compact object merger observed by JWST. Nature https://doi.org/10.1038/s41586-023-06759-1 (2023). Sun, H. et al. Magnetar emergence in a peculiar gamma-ray burst from a compact star merger. Preprint at https://arxiv.org/abs/2307.05689 (2023). Bloom, J. S., Kulkarni, S. R. & Djorgovski, S. G. The observed offset distribution of gamma-ray bursts from their host galaxies: a robust clue to the nature of the progenitors. Astron. J. 123, 1111–1148 (2002). Freedman, W. L. et al. The Carnegie-Chicago Hubble Program. VIII. An independent determination of the Hubble constant based on the tip of the red giant branch. Astrophys. J. 882, 34 (2019). Waxman, E., Ofek, E. O. & Kushnir, D. Late-time kilonova light curves and implications to GW170817. Astrophys. J. 878, 93 (2019). Kasliwal, M. M. et al. Spitzer mid-infrared detections of neutron star merger GW170817 suggests synthesis of the heaviest elements. Mon. Not. R. Astron. Soc. 510, L7–L12 (2022). Troja, E. et al. A nearby long gamma-ray burst from a merger of compact objects. Nature 612, 228–231 (2022). Gehrels, N. et al. A new γ-ray burst classification scheme from GRB060614. Nature 444, 1044–1046 (2006). Yang, B. et al. A possible macronova in the late afterglow of the long-short burst GRB 060614. Nat. Commun. 6, 7323 (2015). Jin, Z.-P. et al. The light curve of the macronova associated with the long-short burst GRB 060614. Astrophys. J. 811, L22 (2015). Rastinejad, J. C. et al. A kilonova following a long-duration gamma-ray burst at 350 Mpc. Nature 612, 223–227 (2022). Yang, J. et al. A long-duration gamma-ray burst with a peculiar origin. Nature 612, 232–235 (2022). Ryan, G., van Eerten, H., Piro, L. & Troja, E. Gamma-ray burst afterglows in the multimessenger era: numerical models and closure relations. Astrophys. J. 896, 166 (2020). Valenti, S. et al. The diversity of type II supernova versus the similarity in their progenitors. Mon. Not. R. Astron. Soc. 459, 3939–3962 (2016). Barnes, J. et al. Kilonovae across the nuclear physics landscape: the impact of nuclear physics uncertainties on r-process-powered emission. Astrophys. J. 918, 44 (2021). Frey, L. H. et al. The Los Alamos Supernova Light-curve Project: computational methods. Astrophys. J. 204, 16 (2013). Fontes, C. J., Fryer, C. L., Hungerford, A. L., Wollaeger, R. T. & Korobkin, O. A line-binned treatment of opacities for the spectra and light curves from neutron star mergers. Mon. Not. R. Astron. Soc. 493, 4143–4171 (2020). Fontes, C. J., Fryer, C. L., Wollaeger, R. T., Mumpower, M. R. & Sprouse, T. M. Actinide opacities for modelling the spectra and light curves of kilonovae. Mon. Not. R. Astron. Soc. 519, 2862–2878 (2023). Zhu, Y. et al. Californium-254 and kilonova light curves. Astrophys. J. 863, L23 (2018). Holmbeck, E. M. et al. Superheavy elements in kilonovae. Astrophys. J. 951, L13 (2023). Waxman, E., Ofek, E. O., Kushnir, D. & Gal-Yam, A. Constraints on the ejecta of the GW170817 neutron star merger from its electromagnetic emission. Mon. Not. R. Astron. Soc. 481, 3423–3441 (2018).

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. © The Author(s), under exclusive licence to Springer Nature Limited 2024

Nature | Vol 626 | 22 February 2024 | 745

Article Methods Spectral energy distribution We used XSPEC30 to jointly fit the near-infrared, optical and X-ray SEDs at 1.2 d, 1.8 d, 2.4 d, 7.4 d, 28.9 d and 61.4 d (Fig. 2 and Extended Data Table 1). The observed optical data were converted to spectral files using the ftflx2xsp and uvot2pha tools within HEASOFT v6.31. Necessary data were extrapolated based on observations at nearby times and fit results of empirical lightcurve modelling (Extended Data Fig. 1 and Supplementary Information). The Galactic contribution was modelled using the model phabs for X-ray photoelectric absorption with a fixed  hydrogen column density NH = 1.26 × 1021 cm−2 (ref. 31), and the model redden for optical dust reddening32 with fixed parameters E(B − V ) = 0.0758 mag. Each SED was fit using a power-law model and a blackbody plus power-law model. To constrain the presence of absorption systems at the GRB site, we include two additional components (zphabs and zdust), and varied the GRB distance scale up to a redshift z shibirets versus empty split>shibirets flies (P = 0.15). All p-values are reported without correction for multiple comparisons.

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

Data availability Source data for scatter plots and tuning curves, minimally processed data (such as mean fluorescence time series) and immunohistochemistry stacks are available at https://doi.org/10.5281/zenodo.10145317. Raw data can be made available upon request. Source data are provided with this paper.

Code availability Code for processing and analysing data is available at https://doi. org/10.5281/zenodo.10232698. 51.

52. 53.

54.

55. 56. 57. 58. 59.

60. 61. 62. 63. 64. 65. 66. 67.

Wolff, T. & Rubin, G. M. Neuroarchitecture of the Drosophila central complex: a catalog of nodulus and asymmetrical body neurons and a revision of the protocerebral bridge catalog. J. Comp. Neurol. 526, 2585–2611 (2018). Otsuna, H., Ito, M. & Kawase, T. Color depth MIP mask search: a new tool to expedite split-GAL4 creation. Preprint at bioRxiv https://doi.org/10.1101/318006 (2018). Clements, J. et al. NeuronBridge: an intuitive web application for neuronal morphology search across large data sets. Preprint at bioRxiv https://doi.org/10.1101/2022.07.20.500311 (2022). Nern, A., Pfeiffer, B. D. & Rubin, G. M. Optimized tools for multicolor stochastic labeling reveal diverse stereotyped cell arrangements in the fly visual system. Proc. Natl Acad. Sci. USA 112, E2967–E2976 (2015). Moore, R. J. D. et al. FicTrac: a visual method for tracking spherical motion and generating fictive animal paths. J. Neurosci. Methods 225, 106–119 (2014). Handler, A. et al. Distinct dopamine receptor pathways underlie the temporal sensitivity of associative learning. Cell 178, 60–75.e19 (2019). Okubo, T. S., Patella, P., D’Alessandro, I. & Wilson, R. I. A neural network for wind-guided compass navigation. Neuron 107, 924–940.e18 (2020). Currier, T. A. & Nagel, K. I. Multisensory control of orientation in tethered flying Drosophila. Curr. Biol. 28, 3533–3546.e6 (2018). Kaushik, P. K., Renz, M. & Olsson, S. B. Characterizing long-range search behavior in Diptera using complex 3D virtual environments. Proc. Natl Acad. Sci. USA 117, 12201–12207 (2020). Pologruto, T. A., Sabatini, B. L. & Svoboda, K. ScanImage: flexible software for operating laser scanning microscopes. Biomed. Eng. Online 2, 13 (2003). Wilson, R. I. Role of GABAergic inhibition in shaping odor-evoked spatiotemporal patterns in the Drosophila antennal lobe. J. Neurosci. 25, 9069–9079 (2005). Ramer, U. An iterative procedure for the polygonal approximation of plane curves. Comput. Graph. Image Process. 1, 244–256 (1972). Douglas, D. H. & Peucker, T. K. Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartographica 10, 112–122 (1973). Giovannucci, A. et al. CaImAn an open source tool for scalable calcium imaging data analysis. eLife 8, e38173 (2019). Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020). Jammalamadaka, S. R. & Sengupta, A. Topics in Circular Statistics (World Scientific, 2001). Turner-Evans, D. B. et al. The neuroanatomical ultrastructure and function of a biological ring attractor. Neuron 108, 145–163.e10 (2020).

Acknowledgements The authors thank members of the Maimon laboratory for helpful discussions; J. Green, C. Lyu and I. Ishida for feedback on the manuscript; Y. Wiesenfeld and Q. Zhao for help with preliminary experiments on the wind-induced angular memory task; T. Mohren and I. Ishida for developing the closed-loop airflow system and providing technical support; H. Akhlaghpour for developing the laser-based closed-loop temperature control system used in pin-tethered experiments; S. Sethi and C. Lyu for technical advice on patch-clamp experiments; A. Janke for help in interpreting immunohistochemistry images; J. Weisman for the design and fabrication of various 3D printed parts; T. Nöbauer for help in

setting up two-photon light paths; J. Petrillo for the fabrication of fly plates for imaging and electrophysiology experiments; and G. Jaindl for software advice for simultaneous imaging and stimulation experiments. Stocks obtained from the Bloomington Drosophila Stock Center (NIH P40OD018537) were used for this study. Research reported in this publication was supported by a Brain Initiative grant from the National Institute of Neurological Disorders and Stroke (R01NS104934) to G.M. L.F.A. was supported by the Simons, Gatsby and Kavli Foundations and by NSF NeuroNex Award DBI-1707398. G.M. is a Howard Hughes Medical Institute Investigator. Author contributions P.M.P. and G.M. conceived the project. P.M.P. performed all experiments and analyses containing imaging or electrophysiology. P.M.P. conceived of the the wind-induced angular memory task and developed it with input from L.Z. and G.M. L.Z. performed the windinduced angular memory task experiments. P.M.P. and L.Z. analysed the wind task data. P.M.P.

and V.P. performed immunohistochemistry experiments. P.M.P., L.Z., L.F.A. and G.M. jointly interpreted the data. L.F.A. developed and implemented the models. P.M.P., L.F.A. and G.M. wrote the paper. Competing interests The authors declare no competing interests. Additional information Supplementary information The online version contains supplementary material available at https://doi.org/10.1038/s41586-023-07006-3. Correspondence and requests for materials should be addressed to Gaby Maimon. Peer review information Nature thanks Stanley Heinze and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Reprints and permissions information is available at http://www.nature.com/reprints.

Article

Extended Data Fig. 1 | See next page for caption.

Extended Data Fig. 1 | FC2 and PFL3 split-Gal4 lines characterization. a, Whole-brain GFP expression driven by the split-Gal4 line VT065306-AD ∩  VT029306-DBD (green), which labels FC2 neurons, and anti-Bruchpilot neuropil stain (magenta). b, Each panel shows a maximum z-projection at a different depth of the anterior-posterior axis. Top: The number of GFP positive somas, roughly 70 to 100, is comparable to the 88 FC2 neurons identified in the hemibrain12. Middle: fan-shaped body. Bottom: crepine. Each FC2 neuron projects unilaterally to the crepine, a symmetric structure that flanks the central complex and is situated dorsal to the lateral accessory lobes. c, Multicolor flip-out of a single FC2 neuron (left) and several FC2 neurons (right) labeled by VT065306-AD ∩ VT029306-DBD. The innervation pattern in the fan-shaped body is consistent with the FC2B or FC2C subtypes. While the GFP expression in this line suggests that it is selective for crepine projecting neurons with FC2-like anatomy, it is possible that there are some non-FC2 central complex neurons labeled by the line as well. d, Whole-brain GFP expression in the 57C10-AD ∩ VT037220-DBD split-Gal4 line (used for LAL imaging and silencing experiments), which labels PFL3 neurons. e, Top: protocerebral bridge.

The white asterisk highlights a glomerulus lacking clear PFL3 signal, indicating that the line does not target all 24 PFL3 cells. The yellow asterisk shows a glomerulus innervated by a non-PFL3 neuron (likely a PEG neuron), since PFL3 neurons do not innervate the outer two glomeruli in the bridge. Middle: fan-shaped body. Bottom: lateral accessory lobes. White arrows highlight PFL3 expression in the left and right LAL. Yellow arrows mark non-PFL3 expression, which we excluded from our regions of interest for imaging analysis. f-g, Same as panels d-e but for VT000355-AD ∩ VT037220-DBD split-Gal4 line (used for patch-clamp and LAL-stimulation experiments). This line also stochastically labels PEG neurons. This was not a concern for either our patch-clamp (see Extended Data Fig. 6) or our LAL-stimulation experiments, since PEG neurons do not innervate the LAL. h-i, Same as panels d-e but for 27E08-AD ∩ VT037220DBD split-Gal4 line (used for silencing experiments). Whereas this line drives significant GFP expression outside the central complex, including in the mushroom body (panel i, bottom), TNT expression driven by this same line appeared to be sparse outside the central complex (see Extended Data Fig. 11j).

Article

Extended Data Fig. 2 | See next page for caption.

Extended Data Fig. 2 | Using the fly’s virtual 2D trajectory to analyze menotaxis behaviour; and following a virtual rotation, flies slow down and turn so as to return to their previous heading. a, Example virtual 2D trajectory of a fly performing menotaxis (during a PFL3 patch-clamp recording). Red dot marks the start of the trajectory. b, Ramer-Douglas-Peucker algorithm reduces the number of x,y coordinates in the trajectory using the parameter ξ, the maximum allowed distance between the simplified and original trajectories. Black dots show the simplified coordinates. c, The fly’s displacement between each x,y point of the simplified trajectory, L, is computed. Segments of the fly’s trajectory where L > 200 mm were considered “menotaxis bouts” and thus further analyzed (colored portions of the trajectory). d, An example menotaxis bout from the trajectory in panel c. The fly’s goal angle is defined as the fly’s mean heading direction during the bout, excluding timepoints when the fly is standing still. e, All menotaxis bouts from flies used in this paper. First column: PFL3 patch-clamp dataset (related to Fig. 3). Middle column: EPG and FC2 imaging dataset (related to Fig. 1). Third column: PFL3 LAL imaging dataset (related to Fig. 5d). f, Goal angles for each menotaxis bout for each fly for

datasets shown in panel e. g, To assess whether a fly was actively maintaining its heading direction, we virtually rotated the fly by discontinuously jumping the bar ±90° from its position immediately before the jump. The bar remained static at its new position for 2 s and then the fly regained closed-loop control. h, The fly’s heading relative to its goal angle for ±90° rotation trials from our PFL3 patch-clamp dataset. Only trials where the circular standard deviation of the fly’s heading direction during the 60 s prior to the bar jump was less than 45° (excluding timepoints when the fly was standing still) were analyzed here (55-74% of all trials were analyzed depending on the dataset). For this analysis, we defined the fly’s goal angle as its mean heading in the 60 s before the bar jump, excluding timepoints in which the fly was standing still. i, Mean heading relative to the fly’s goal angle during the 30 to 60 s after the bar jump for trials from each dataset shown in panels e-f. Each dot is the mean for an individual fly. Horizontal lines show mean ± s.e.m. across flies. j, Mean forward walking velocity around the time of bar jumps for trials shown in panel h. Shaded area marks the 2 s when the bar remained static. Mean ± s.e.m. across flies is shown.

Article

Extended Data Fig. 3 | See next page for caption.

Extended Data Fig. 3 | Relationship between FC2 activity and fly behaviour. a, Correlation between EPG phase or FC2 phase and fly heading. Each dot represents one fly. Mean ± s.e.m. across flies is indicated. b, Cross-correlation between phase velocity and behavioural turning velocity. FC2 data are in purple and EPG data are in grey. A positive lag means that a change in heading precedes a change in the neuronal signal. Mean ± s.e.m. across flies is shown. c, Individual ±90° rotation trials for 113 trials from 9 flies in which we imaged EPG neurons. In contrast to Fig. 1, here we did not require for a trial to occur within a menotaxis bout (see Methods) or require that the fly return within 45° from its heading before the bar jump. Thick lines show the mean across flies. d, Same as panel c but for 140 trials from 15 flies in which we imaged FC2 neurons. Note that, on average, the FC2 phase slowly drifts away from its initial position. This small drift may be due to trials where the fly’s goal angle genuinely drifted to the fly’s new heading angle after the bar jump, which seems plausible given that on many trials analyzed here the fly did not turn so as to reorient themselves along their previous heading. e, Mean phase value during final 1 s of the open-loop period in panels c and d. Each dot is the mean for one fly. Horizontal lines show the mean ± s.e.m. across flies. V-test for EPG flies: µ = 90°, p = 2.49 × 10 −5. V-test for FC2 flies: µ = 0°, p = 7.69 × 10 −8. f, Example trace showing an abrupt change in the position of the FC2 bump in the fan-shaped body. g, Left: Each thin line shows an algorithmically-detected rapid change in the FC2 phase position, zeroed to the onset of the change in phase. Right: bar position, zeroed to the onset of the change in phase, during these moments. Thick lines show the mean across 141 transients from 15 flies. That the FC2 phase has the capacity to move by more than 90° within less than 2 s (the magnitude and duration of our bar jumps) suggests that the stability of the FC2 phase during virtual rotations was not due to the FC2 phase simply reflecting a low-pass filtered estimate of the fly’s heading. h, Left: example FC2 ∆F/F0 signal and behavioural traces from a fly that occasionally deviated from its goal angle. The teal arrow marks a moment when the FC2 phase did not remain stable, but the fly nonetheless returned to its putative goal direction. One interpretation of the moment marked in teal is that inputs other than the longer-term menotaxis goal input

to the FC2 system briefly dominated, which led the FC2 phase to drift. However, once the fly re-entered a menotaxis behavioural state and wished to progress forward, the FC2 phase locked back in to the menotaxis angle, communicating it to the PFL3 population to guide steering. In this view, the fan-shaped body may encode multiple potential goals, with the actual goal chosen from this set in a state-dependent manner and the FC2 calcium signal might be best viewed as a conduit between these long-term navigational goals and the centralcomplex’s pre-motor output. The red arrow marks an occasion when the FC2 phase remained stable throughout a brief deviation in heading direction. Right: expanded view of time period marked by teal box and red box. i, Example FC2 ∆F/F0 signal and behavioural traces from a fly that was rotating in time and not stabilizing a consistent heading direction. This trace highlights that the FC2 phase can be well-estimated during moments where our algorithm would not detect that the fly is performing menotaxis. j, FC2 activity across the fan-shaped body from a single timeframe. k, Schematic of how we computed the population vector average (PVA) strength from FC2 activity. Each fan-shaped body column region-of-interest (ROI) is treated as a vector (thin arrows). The angle of each vector is determined by the position of the column in the fan-shaped body and the length of the vector is determined using the ∆F/F0 value. The PVA strength is the length of the resulting mean vector (thick arrow). l, Difference between the mean ∆F/F0 two seconds before and during the bar jump for EPG neurons in the bridge, and FC2 neurons in the fan-shaped body. Each dot is the mean across trials for an individual fly. Mean ± s.e.m. across flies shown (5 EPG and 7 FC2 flies). m, Same as panel l but for the difference in max-min ∆F/F0. n, Same as panel l but for the difference in PVA strength. o, Trajectory of a fly color-coded by the vector strength of the fly’s mean heading direction, R (not to be confused with the FC2 PVA strength), calculated with a 60 s window (see Methods). p, FC2 activity as a function of R, computed using either a 30, 60 or 120 s time window. Mean ± s.e.m. across flies shown (n = 15). q, FC2 activity as a function of the fly’s forward walking velocity (left) and turning velocity (right). Mean ± s.e.m. across flies shown (n = 15).

Article

Extended Data Fig. 4 | See next page for caption.

Extended Data Fig. 4 | FC2 neurons in one column of the fan-shaped body inhibit FC2 neurons in distant columns; an approximately one-to-one mapping exists between the FC2 phase and the goal angle within, but not across, flies; and flies modulate their forward walking velocity as function of their heading relative to an FC2-defined goal heading. a, Schematic of scan paths for the entire imaging region (black) alongside the stimulation (red) regions of interest (ROI). b, Trial structure for columnar stimulation. Top: 16 fan-shaped body column ROIs (regions delineated by the dotted lines) and the stimulation ROI (red square). Note that the stimulation ROI can overlap with several column ROIs. Middle: average z-projection of the raw fluorescence signal during stimulation in position A (stim. A; blue), the inter-trial period and stimulation at position B (stim. B; orange). c, Left: mean column ROI ∆F/F0 aligned to the onset of stimulation (pink background) from flies expressing CsChrimson in FC2 neurons for ROIs that overlap with the stimulation ROI (purple) or ROIs that do not overlap with the stimulation ROI (black). Right: same as left, but for control flies that do not express CsChrimson. Mean ± s.e.m. across flies is shown. d, Change in non-stimulated ROI ∆F/F0 as a function of the ROI’s wrapped distance from the stimulation site for CsChrimson expressing flies. Each grey dot is the mean for an individual fly. Black dots and thick line show mean ± s.e.m. across flies (n = 16). The increase in activity of column ROIs with a distance of 2 or 3 could reflect lateral excitation or alternatively, could simply be due to neurites of stimulated neurons within the stimulation ROI extending into non-stimulated ROIs. e, Distribution of the estimated angular difference—assuming the fan-shaped body left/right extent maps to 360° of azimuthal space—between stimulation location A and B for all flies (see Methods for how stimulation location angle is computed). f, Distribution of the angular

difference between the mean FC2 phase position during stimulation A and B for all flies. g, Heading as a function of the FC2 phase position in the fan-shaped body for flies expressing CsChrimson in FC2 neurons. Each dot is a trial, colorcoded by the simulation location. In this plot, a phase value of zero signifies that the FC2 bump is in the middle of the fan-shaped body. Note that the same phase position can be reliably associated with a similar heading direction within a fly, but not necessarily across flies (e.g., compare fly 7 to fly 9). The fact that individual flies show a variable offset between the stimulated fan-shaped body location and the stabilized behavioural heading angle is expected if the FC2/PFL3 system signals angles in the same allocentric reference frame set by the EPG heading bump. This is because the EPG bump in the ellipsoid body shows a variable fly-to-fly offset between the fly’s heading in the world and the bump-position in the brain2. h, Left: same data as in panel g, but all trials for all flies are shown in the same plot. Note that there is no clear relationship between phase position and bar position across flies. Right: same as left but for control flies that do not express CsChrimson in FC2 neurons. i, Heading relative to predicted goal angle, inferred using the stimulation location (see Methods), for flies expressing CsChrimson in FC2 neurons (left) and no CsChrimson controls (right). Trials are parsed by the fly’s initial distance to the predicted goal angle (different colors). Mean ± s.e.m. across trials is shown. j, Absolute distance to the predicted goal angle over time (bottom) binned by the fly’s forward walking behaviour 1 s before the stimulation onset (top). Mean ± s.e.m. across trials is shown. k, Left: forward walking velocity as a function of flies’ heading relative to their predicted goal angle. Stimulation A and B trials are combined together. Mean ± s.e.m. across flies is shown. Right: same as left but for control flies.

Article

Extended Data Fig. 5 | See next page for caption.

Extended Data Fig. 5 | PFL3 neurons receive inputs from heading-sensitive neurons in the protocerebral bridge and FC2 neurons represent a columnarneuron class with a large number of synaptic inputs to PFL3 neurons. All data in this figure were extracted from the hemibrain connectome, neuPrint v1.213. a, PFL3 neurons receive inputs from two sets of heading-sensitive neurons in the protocerebral bridge: EPG neurons (14% of all PFL3 bridge inputs) and ∆7 neurons (77% of all PFL3 bridge inputs). b, A single EPG neuron innervates one wedge of the ellipsoid body and projects to one glomerulus in the bridge (top). c, If one assumes that the ellipsoid body circle represents 360° of azimuthal space around the fly, consistent with physiological observations2,34, then each bridge glomerulus can be assigned an angle based on the wedge in the ellipsoid body from which the EPG cells that innervate that glomerulus originate (bottom). The angles thus assigned to the bridge yield 45° azimuthal spacing between bridge glomeruli, except the inner two inner glomeruli, which are separated by only 22.5° (see ref. 4). d, A single ∆7 neuron receives dendritic inputs (thin neurites in image) from EPG neurons across multiple glomeruli in the protocerebral bridge and expresses axonal terminals in 2-3 bridge glomeruli, typically spaced eight glomeruli apart4,12,31. Two axon terminals are visible in the example ∆7 cell shown. e, Based on the anatomy of ∆7 neurons, one can index the glomeruli of the bridge with angles that repeat every 8 glomeruli, creating a 45° spacing between adjacent glomeruli4. Given that individual ∆7 axons are offset from the peak density of their dendritic arbors by ~180°, the angular assignments to their axon terminals in specific bridge glomeruli could be expected to be ~180° offset from the EPG assignments to those glomeruli. However, because ∆7 cells are glutamatergic67 and appear to act in sign-inverting/inhibitory fashion on most of their downstream targets, their influence is expected to be roughly aligned with that of EPG cells, with a slight offset. Therefore, the resulting ∆7 angles have a + 11.25° and –11.25° offset relative to EPG angles for the right and left bridge respectively. f, Three different ∆7 neurons. Each ∆7 cell is assigned an angle (grey arrows) based on the glomeruli in which it has its outputs using the mapping shown in e. Note that ∆7 L4R6 (middle) has outputs that are nine glomeruli apart instead of the usual eight. In this case, the cell is assigned the same angle as ∆7 L3R6 (top), since its dendritic arborization pattern across the bridge is more similar than that of ∆7 L4R5 (bottom). Likewise, ∆7 L6R4 can be assigned the same angle as ∆7 L6R3 and ∆7 L7R3 can be assigned the same angle as L7R2. g, ∆7 to PFL3 connectivity matrix. Each row represents a different ∆7 cell (42 total). Each column represents a postsynaptic PFL3 neuron (24 total, each labeled by the glomerulus or glomeruli it innervates). The heatmap depicts the total number of synapses between each ∆7-PFL3 pair. The arrows at the bottom of the heatmap are the angles assigned to each PFL3 neuron based on the angle of the ∆7 class from which it receives the most of its inputs. We used these angles as the value for Hpref in our full PFL3 neuron model. These angles are the same as

one would obtain from assigning each PFL3 neuron its angle based on which bridge glomerulus it innervates and the mapping shown in e, except for the two PFL3 neurons that innervate two glomeruli (PFL3 L3/L4 and PFL3 R3/R4). Within the L4 and R4 glomeruli, these PFL3 cells receive inputs from ∆7 L4R6 and ∆7 L6R4 respectively, and are therefore assigned angles corresponding to the more inner glomeruli that they innervate. h, The top 50 cell classes with synaptic inputs to PFL3 neurons in the fan-shaped body. These cell classes constitute 94% of all PFL3 inputs in the fan-shaped body. Each bar shows the total number of synapses between a presynaptic cell type and PFL3 neurons. FC2 neurons (purple) are a population of columnar neurons composed of three subtypes: FC2A, FC2B and FC2C. Together they constitute a third of columnar-cell synapses onto PFL3 cells in the fan-shaped body. Other columnar cell classes, such hDeltaA, hDeltaI, and hDeltaM cells could also provide goal information to PFL3 neurons during menotaxis or other goal-directed behaviours. Unlike columnar neurons, tangential cells have neurites that cut across all the columns of the fan-shaped body. These cells are likely to serve a role in modulating and impacting columnar goal information to the PFL3 cells, but their anatomy makes it less likely that they communicate column-specific information independent of their interaction with columnar neurons. i, Skeletons of FC2A, FC2B and FC2C populations. j, FC2 to PFL3 connectivity matrix. Each column represents an individual PFL3 neuron, sorted by its column in the fan-shaped body (C1 to C12) and whether it innervates the left (L) or right (R) LAL. C1 is on the very left of the fan-shaped body and C12 on the very right. Each row represents an individual FC2 neuron. k, Pairwise Pearson correlation matrix between individual PFL3 neurons based on their FC2 neuron inputs. The synaptic connections from all FC2 neurons to a given PFL3 neuron are treated as a vector and the correlation between each vector is computed. This analysis highlights that left and right PFL3 neurons innervating the same column receive highly similar inputs. PFL3 neurons can be viewed as forming nine functional columns instead of twelve12. In this view, the four PFL3 neurons innervating the anatomical columns C3 and C4 (in the 12-column numbering scheme) would form a single functional column. The same would be true for C6 and C7, and C9 and C10. The cell groupings of the 9 and 12-column schemes are illustrated by the dendrograms in the margins. One justification for the 9-column scheme is that the PFL3 neurons which would be combined to form a single functional column, and innervate the same side of the LAL, share the same angles (see Fig. 4b). However, given that PFL3 neurons innervating C6 and C7, for example, receive different FC2 inputs, physiological evidence demonstrating that these FC2 inputs are in fact functionally identical would be required, we believe, to justify merging two anatomical columns into a single functional column and employing a 9-column fan-shaped body functional scheme instead of the 12-column scheme used herein.

Article

Extended Data Fig. 6 | PFL3 neurons can be distinguished from PEG neurons based on their electrophysiological properties and individual PFL3 neurons are tuned to heading, with different cells showing different preferred heading angles. a, Biocytin fill of a PFL3 neuron (left) and a PEG neuron (right) recorded in the split-Gal4 line VT000355-AD ∩ VT037220-DBD. PEG and PFL3 neurons can be differentiated based on their innervation patterns. Specifically, PFL3 neurons innervate the fan-shaped body (FB) and lateral accessory lobe (LAL) whereas PEG neurons innervate the ellipsoid body (EB) and the gall (GA). Each image is a maximum z-projection from a subset of slices. One of eight PFL3 cells and one of three PEG cells in which such a fill was visualized is shown here; in most recordings we used the electrophysiological properties of the neuron recorded to identify it as a PFL3 or PEG cell (Methods). b, Sample Vm from the PFL3 and PEG neuron depicted in the anatomy panels directly above.

At depolarized membrane potentials, the spikes of PFL3 neurons were relatively small (left) whereas those from the PEG neurons were relatively large (right). Black dots indicate detected spikes. c, At hyperpolarized membrane potentials, PFL3 neurons display rhythmic oscillations (left), whereas the membrane potential of PEG neurons tends to be more flat (right). d-e, Vm (spikes removed) (left) and spike rate (right) tuning curves to heading direction for all PFL3 cells. Dashed line in the Vm curves represents a sinusoidal fit to data, which was used for estimating the cell’s preferred-heading direction (see Methods). Shaded area represents 90° gap at the back of the arena where the bar is not visible. Cells are sorted and numbered based on their estimated preferred-heading direction. We use this numbering scheme throughout the manuscript to refer to specific cells.

Extended Data Fig. 7 | Goal-dependent scaling of PFL3 activity is more prominent in the spike rate than in the somatic membrane potential. a, After determining a cell’s preferred heading angle from the overall tuning curve (Extended Data Fig. 6d), we plotted a set of tuning curves with a shifted x-axis for each cell, so as to always have this preferred angle at zero. Here we show such preferred-phase nulled tuning curves binned by the fly’s goal angle relative to the cell’s preferred direction. Each row represents a different cell. Each column (and color) represents a different bin of goal angles relative to cell’s preferred direction, with the middle angle of that bin represented by the purple arrow. Because single flies typically adopted only a few goal directions throughout a recording session, this led to the many missing tuning curves. Likewise, some tuning curves are missing data in some portions of the x-axis because for each goal direction, a fly does not typically experience the full range of heading directions, even with our bar jumps aiming to minimize this

issue. For each cell, there is between 40 ms to 14 min of data contributing to each heading/goal bin. The horizontal, dotted, grey lines indicate a spike rate of 0 Hz. Error bars show s.e.m. b, Mean spike rate across all cells. Thin lines: individual cells. Thick line: mean across cells. Top row is the same as Fig. 3f. c, Same as in panel a but plotting membrane potential (spikes removed) (Methods). For each row (i.e., cell), the grey dotted line represents the row’s minimum membrane potential. The cell # identifiers shown on the right are identical to those used in Extended Data Fig. 6 and these numbers apply also to panel a. d, Mean membrane potential (spikes removed) across all cells. These plots were generated by averaging the raw membrane potential, which was corrected for the same 13 mV liquid-liquid junction potential across all recordings, but not shifted by the minimum membrane potential for each cell prior to averaging. Thin lines: individual cells. Thick line: mean across cells.

Article

Extended Data Fig. 8 | The goal-dependent scaling of PFL3 spike-rate tuning curves is not a simple consequence of the fly’s instantaneous walking dynamics. a, Heatmap showing mean PFL3 spiking activity as a function of heading (x-axis) and forward walking velocity (y-axis). We combined our six recordings from right PFL3 neurons with our 15 recordings from left PFL3 neurons by flipping the heading-relative-to-the-cell’s-preferred-heading prior to averaging. b, Heatmap showing mean PFL3 spiking activity as a function of heading (x-axis) and turning velocity (y-axis). In this panel, we flipped the flies’ turning velocity for the right PFL3 neuron recordings so that we could combine their data with the left PFL3 recordings. c, Given that PFL3 spiking activity varies with the flies’ locomotor behaviour and because flies that perform menotaxis show different walking statistics depending on their angular orientation relative to the goal11—flies walk forward faster when aligned with their goal, for example—one possibility is that the goal-dependent modulation observed in PFL3 activity is not due to a genuine goal input. To the address this

possibility, we replotted the population-averaged, PFL3 spike-rate tuning curves to heading, parsed by the flies’ goal angle—as in Fig. 3f—but in this case, we only included timepoints when the fly was nominally standing still. Our criteria for the fly standing still was that the filtered forward walking velocity was below 0.5 mm/s and the fly’s turning velocity was between −5 °/s and 5 °/s. For right PFL3 neurons, the goal-heading-relative-to-the-cells’-preferredheading values were flipped prior to averaging. Thin lines: individual cells; thick line: mean across cell. That a qualitatively similar scaling in the amplitude of PFL3 tuning curves is observed when flies are standing still, or nearly still, suggests that PFL3 goal-direction modulation is not a simple consequence of the fly’s walking dynamics, but is more likely generated by FC2 inputs, or some similar goal-input signals, which maintain a baseline activity level in standing flies (Extended Data Fig. 3q). d, Mean forward walking velocity, analyzed as described in panel c. e, Mean turning velocity, analyzed as described in panel c.

Extended Data Fig. 9 | See next page for caption.

Article Extended Data Fig. 9 | Model for how heading and goal information is integrated in individual PFL3 neurons and predicting PFL3 output using FC2 activity as the goal signal. a, Schematic for how PFL3 neurons integrate heading and goal information. Two inputs contribute to the membrane potential of a PFL3 cell. One input comes from the protocerebral bridge and yields a membrane potential signal, VPB , in the PFL3 cell that is proportional to a cosine function of the difference between the fly’s heading angle, H, and the PFL3 cells’ preferred heading angle, Hpref. The other input comes from the fan-shaped body and results in the membrane potential signal, VFB , in the PFL3 cell that is a cosine function of the difference between the fly’s goal angle, G, and the cell’s preferred goal angle, Gpref. The membrane potential measured at the soma, Vm, is dominated by VPB because the fan-shaped body is electrotonically farther from the soma than the protocerebral bridge (consistent with the more modest goal-dependent changes in Vm, compared to spike rate, that we showed in Extended Data Fig. 7). The spike rate of the neuron is given by a nonlinear function of a sum of the cosine functions describing VPB and VFB (with VFB scaled by a weighting factor d, reflecting the relative strengths of these two inputs at the spike initiation zone). b, Spike-rate vs Vm (spikes removed) curves from our whole-cell recordings. Data from different goal angles relative to the cell’s preferred heading are shown in different colors. We assume the relationship between the PFL3 Vm and spike rate would have been constant—i.e., not vary with goal direction—if we were measuring Vm at the spike initiation zone. The fact that this relationship depends on the fly’s goal angle in our somatic measurements, is, we believe, due to the somatic membrane potential predominantly reflecting heading input from the bridge and thus missing the goal-related Vm changes from the fan-shaped body. In the model, we assume that the spike-initiation zone has access to both the heading- and goal-related Vm signals. Each dot shows the mean across cells. Right PFL3 neurons were included by flipping the sign of the goal-to-preferred heading angle (Methods). c, The same curves as in panel b, but shifted along the horizontal axis in order to

maximally align them. The black curve is a softplus function fit to the data points (see Methods for details). d, The shifts from panel c, plotted as a function of the goal angle of the corresponding spike-rate curve. The fact that these shifts have a roughly cosine shape as a function of the goal angle is consistent with: (1) the existence of a cosine-shaped goal input in the fan-shaped body (as our model assumes) and (2) our hypothesis that the voltage consequences of the goal in the fan-shaped body are not fully evident in the soma, thus requiring the Vm shift in the plot in panel b, to align all the curves to a common spike-rate vs. Vm underlying function (as our model assumes). e, Overlay of model predictions from Fig. 4f (lines) and calcium imaging results from Fig. 5d (open circles) for right and left LAL signals and for the R–L turning signal. f, The model error—i.e., the angular difference between the zero heading (the heading angle where the turning signal is zero and the slope is negative) and G (the goal angle)—as a function of G. g, An example virtual rotation trial from our FC2 imaging dataset alongside a computer-generated (i.e., synthetic) EPG/∆7 heading signal and the fly’s behaviour. The synthetic EPG/∆7 heading signal was generated using the term for the heading input in our PFL3 model, with the fly’s heading, H, taken to be the inverse of the bar angle. The rightmost column shows the predicted Right-Left (R-L) PFL3 activity from the model, when using the measured FC2 calcium signal (normalized) and the synthetic heading signal as model inputs (see Methods for details). h, Turning velocity as a function of predicted R-L asymmetry during the 2 s open-loop period of the bar jump. Each grey dot is a trial from our FC2 imaging dataset. Bar-jump trials used in Fig. 1 are shown in black. The example bar-jump trial in panel g is shown in red. i, Turning velocity as a function of measured R-L asymmetry (z-scored) during the 2 s open-loop period of the bar jump. Each grey dot is a trial from our PFL3 LAL imaging dataset. Trials selected using the same behavioural criteria as in Fig. 1 are shown in black. j, Predicted R-L asymmetry as a function of flies’ angular distance to goal angle (black) and turning velocity (grey) for FC2 imaging dataset. Mean ± s.e.m. across flies is shown (n = 10).

Extended Data Fig. 10 | Transient asymmetries in PFL3 GCaMP activity track the flies’ heading-relative-to-goal with a lag and the turning behaviour induced by unilaterally stimulating subsets of PFL3 neurons in the lateral accessory lobes is probabilistic. a, Instead of plotting the flies’ turning velocity (that is, the derivative of the flies' heading) during transient increases (top) or decreases (bottom) in PFL3 Right-Left (R-L) activity (black), as we did in Fig. 5c, we plotted the flies’ mean heading-relative-to-goal (teal) during these moments. Mean ± s.e.m. across transients is shown (from 10 flies). Inset shows that the maximum deviation in the R-L PFL3 GCaMP signal occurs ~200 ms after the peak in flies’ heading relative to goal deviation. This delay is in agreement with previous measurements of the lag between the fly’s turning velocity and the change in the EPG bump position in the bridge, measured with GCaMP34 (Extended Data Fig. 3b). This latency is consistent with the transients in LAL activity reflecting a change in the PFL3 heading inputs from the bridge. b, Further analyzing the PFL3 CsChrimson data from Fig. 5, we show the mean ipsilateral turning velocity as a function of the ipsilateral LAL asymmetry (z-scored) during the 2 s stimulation period. The ipsilateral LAL asymmetry is taken as the right–left ∆F/F0 signal, with the sign of the values flipped for left LAL stimulation trials. Each dot is a trial and all trials from PFL3 CsChrimson

flies are shown. In a minority (8%) of all trials, the average turning velocity was in the contralateral (i.e., the unpredicted) direction, despite measuring an LAL asymmetry consistent with the simulation side (trials below the dotted line). An important caveat, when interpreting this result, is that the driver line does not label all PFL3 neurons (see Extended Data Fig. 1g). The measured asymmetry, therefore, does not necessarily reflect the true population-level activity. As such, it is formally possible that during these anomalous trials, the true left/ right asymmetry in PFL3 activity was in agreement with the fly’s turning direction. c, Same stimulation trials shown in panel b but here we plotted the mean ipsilateral turning velocity as a function of flies’ mean forward walking velocity 1 s before the onset of the stimulation. Note that “incorrect” trials are not always preceded by moments when the fly is not walking forward (i.e., the points below the dotted line are not all clustered around zero on the x-axis). d, Same stimulation trials shown in panel b but here we plotted the mean ipsilateral turning velocity as a function of flies’ mean ipsilateral turning velocity 1 s before the onset of the stimulation. Note that “incorrect” trials are not always preceded by a contralateral turn (i.e., the points below the dotted line are not all to the left of zero on the x-axis).

Article

Extended Data Fig. 11 | See next page for caption.

Extended Data Fig. 11 | Additional analyses of data relevant for the windinduced angular memory task. a, Probability distribution of heading relative to wind direction from control flies (empty split > shibirets) in which in the airflow was set to a zero flow rate during the period in which wind would normally be on (left) and during the test period (right). b, First column: absolute angular difference between heading and wind direction over time for flies expressing TNT (red) and TNTinactive (black) in cells targeted by PFL3 line 1 (57C10-AD ∩ VT037220-DBD). Data shown are from the first experimental replicate. Mean ± s.e.m. across flies is shown. Only the second and third trial of each wind block were analyzed. Second column: same as the first column but for the second experimental replicate from PFL3 line 1. Third through sixth column: PFL3 line 3 (27E08-AD ∩ VT037220-DBD) > TNT vs. PFL3 line 3 >  TNTinactive, PFL3 line 1 > shibirets vs. empty split > shibirets flies, EPG > shibirets flies and empty split-Gal4 > shibirets flies in which the airflow was turned off. c, Mean absolute distance between heading and wind angles during the test period as a function of the trial number within a block for each group. Mean ± s.e.m. across flies. d, Performance index (PI) during the wind period (top) and during the test period (bottom) (See Methods for definition of PI). Each dot shows the mean for a fly across all wind directions. Mean ± s.e.m. across flies is indicated. e, Wind-direction error (error) during the wind period (top) and during the test period (bottom). Each dot shows the mean for a fly across all wind directions. Mean ± s.e.m. across flies is indicated. f, Number of wind directions that each fly correctly oriented along. Each dot represents one fly. Mean ± s.e.m. across flies is indicated. In panels e and f, columns one through four, and columns eight through ten, are re-plotted from Fig. 6. g, PFL3 line

1 > TNT flies (rep. 2) had a greater wind-direction error during the wind period than control flies (p = 3.15 × 10 −3, compare columns two and three of the top row of panel e). To test whether their poorer performance during wind-on period could explain the poorer performance during the test period, we plot the winddirection error during the wind period (top) and during the test period (bottom) as in panel e, but after selecting for flies whose mean error during the wind period was between 12° and 45°. That the selected PFL3 line 1 > TNT flies still show a greater wind-direction error during the test period than their respective control flies (p = 6.47 × 10 −4) argues that the poorer performance of these flies was not simply due to a lower motivational drive or a reduced ability to orient upwind when the wind was on. Mean ± s.e.m. across flies is indicated. h, Top: model simulation of the effect of silencing increasing number of PFL3 cells on the average absolute wind-direction error (orange dots). Error bars at x = 0 shows the s.e.m. range for PFL3 line 1 > TNTinactive control flies (rep. 1: black, n = 22; rep. 2: grey, n = 50). The two red horizontal lines and shaded areas show the mean ± s.e.m. of the absolute wind-direction error for the two PFL3 line 1 > TNT replicates we tested (rep. 1: solid line, n = 25; rep. 2: dotted line, n = 57). Bottom: same as top but for the number of correct goal directions. i, First column: whole-brain anti-TNT stain (green) and anti-Bruchpilot neuropil stain (magenta) from a PFL3 line 1 > TNT fly. Second and third columns show antiTNT labeling in the left and right lateral accessory lobes (LAL). We estimated the number of PFL3 neurons that were targeted by manually counting the number of LAL-projecting neurites (see Methods). j, Same as in panel i but for PFL3 line 3.

Article

Extended Data Fig. 12 | Schematic models for how the fly’s brain performs egocentric-to-allocentric and allocentric-to-egocentric coordinate transformations. a, The PFNd/PFNv circuit converts the fly’s egocentric traveling direction, as signaled in sensory inputs to the central complex, into an allocentric-traveling direction signal in h∆B cells (adapted from ref. 4). Two arrays of PFNd cells and two arrays of PFNv cells express sinusoidal activity patterns whose phase and amplitude represent four vectors with a specific angle and length (brown and orange vectors). To calculate the allocentric traveling direction, the four neurally represented vectors all initially signal the angle in which the fly is translating in relation to its body axis, with the amplitude of each activity pattern representing a projection of that egocentric traveling vector onto a different (basis) direction. All four vectors are then rotated together based on the fly’s heading relative to external cues, (e.g., the sun), which implements the egocentric-to-allocentric transformation. Finally, the circuit finds the max position of the summed, output vector, which represents the fly’s traveling angle in reference to external cues. When the fly is traveling forward, the two forward-facing PFNd vectors are long and the two backward-facing PFNv vectors are short, yielding an output traveling vector in h∆B cells (pink vector) that points in the direction of the fly’s heading, as encoded by the EPG bump (left schematic). When the fly is traveling backward, the two, forward-facing, PFNd vectors are short and the two, backward-facing,

PFNv vectors are long, yielding an output traveling vector in h∆B cells that points in a direction 180° opposite of the fly’s heading, as encoded by the EPG bump (right schematic). b, Left: The PFL3 circuit that converts an allocentric goal angle into an egocentric steering signal can be considered, computationally, to be taking the difference between two dot products. The left and right PFL3 neurons form two non-orthogonal axes (blue and red dotted lines). Each axis represents the fly’s heading angle rotated either clockwise and counterclockwise by the same angle. The fly’s allocentric goal angle, signaled by the position of the FC2 bump in the fan-shaped body, is represented by the purple vector. The projection of the goal vector onto the blue PFL3 axis (which can be considered as the output of a dot product between the goal vector and a unit vector pointing along the blue axis) reflects the sum of the left PFL3 activity in the LAL (and vice versa for the right PFL3 axis). When the fly is aligned with its goal, the difference between the red and blue dot products is zero. Right: When the fly changes its heading, the axes rotate and the difference between the two dot products now tells the fly to turn left. Neuronally, the left and right PFL3 axes represent vectors generated by projecting their heading inputs in the bridge onto the fan-shaped body. The amount by which the left and right PFL3 axes are offset from one another is determined by the anatomical shift in the PFL3 projection pattern from the bridge to the fan-shaped body.

Last updated by author(s): 11/28/2023

Reporting Summary Nature Portfolio wishes to improve the reproducibility of the work that we publish. This form provides structure for consistency and transparency in reporting. For further information on Nature Portfolio policies, see our Editorial Policies and the Editorial Policy Checklist.

nature portfolio | reporting summary

Corresponding author(s): Gaby Maimon

Statistics For all statistical analyses, confirm that the following items are present in the figure legend, table legend, main text, or Methods section. n/a Confirmed The exact sample size (n) for each experimental group/condition, given as a discrete number and unit of measurement A statement on whether measurements were taken from distinct samples or whether the same sample was measured repeatedly The statistical test(s) used AND whether they are one- or two-sided Only common tests should be described solely by name; describe more complex techniques in the Methods section.

A description of all covariates tested A description of any assumptions or corrections, such as tests of normality and adjustment for multiple comparisons A full description of the statistical parameters including central tendency (e.g. means) or other basic estimates (e.g. regression coefficient) AND variation (e.g. standard deviation) or associated estimates of uncertainty (e.g. confidence intervals) For null hypothesis testing, the test statistic (e.g. F, t, r) with confidence intervals, effect sizes, degrees of freedom and P value noted Give P values as exact values whenever suitable.

For Bayesian analysis, information on the choice of priors and Markov chain Monte Carlo settings For hierarchical and complex designs, identification of the appropriate level for tests and full reporting of outcomes Estimates of effect sizes (e.g. Cohen's d, Pearson's r), indicating how they were calculated Our web collection on statistics for biologists contains articles on many of the points above.

Software and code Policy information about availability of computer code Data collection

Two-photon images were collected using ScanImage 2018b. Ball positions were measured using a customized version of FicTrac (https:// rjdmoore.net/fictrac/). All time series data (except images and thermal camera temperature measurements) were recorded as voltages using the pClamp software suite (Clampex 11.1.0.23 for electrophysiology experiments and Axoscope 10.7.03 for imaging experiments). Images were registered using CaImAn 1.8.5 (https://github.com/flatironinstitute/CaImAn).

Data analysis

Electron microscopy connectome data was analyzed using neuPrint's Python interface (hemibrain v1.2.1, Python 3.8). Immunohistochemistry images were analyzed using Fiji (Image J). Data were analyzed using custom code written in Python 3.6. Code is available from the corresponding author upon request.

For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Portfolio guidelines for submitting code & software for further information.

March 2021

1

Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: - Accession codes, unique identifiers, or web links for publicly available datasets - A description of any restrictions on data availability - For clinical datasets or third party data, please ensure that the statement adheres to our policy Data will be made available on an online repository upon publication.

Human research participants Policy information about studies involving human research participants and Sex and Gender in Research. Reporting on sex and gender

There were no human research participants in this study.

Population characteristics

There were no human research participants in this study.

Recruitment

There were no human research participants in this study.

Ethics oversight

There were no human research participants in this study.

nature portfolio | reporting summary

Data

Note that full information on the approval of the study protocol must also be provided in the manuscript.

Field-specific reporting Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection. Life sciences

Behavioural & social sciences

Ecological, evolutionary & environmental sciences

For a reference copy of the document with all sections, see nature.com/documents/nr-reporting-summary-flat.pdf

Life sciences study design All studies must disclose on these points even when the disclosure is negative. In general, we determined sample sizes (number of flies) based on sample sizes used in previous, similar, studies (see e.g., Lyu et al. 2021, Lu et al. 2021, Kim et al. 2019 and Green et al. 2019). In the case of PFL3 line 1 TNT experiments (Fig. 6), after collecting an initial dataset, we used a bootstrap power analysis to determine that a sample size approximately twice the size was needed to achieve 80% statistical power given the distribution of the data; this informed the higher sample size used in our second experimental replicate.

Data exclusions

For menotaxis experiments during two-photon imaging, we excluded fies that walked less than 10% of the time since they would contribute very little data to most analyses (see Methods). We also excluded recordings where there was significant brain movement (due to an unglued proboscis, for example), which was determined by manually inspecting two-photon time-series images. Otherwise, we did not exclude flies unless they appeared unhealthy at the time of the experiment or if a technical issue arose during a recording (e.g., saline leaking from the holder or an LED arena crash). Data exclusion for specific analyses are described in the Methods.

Replication

For PFL3 line 1 TNT experiment we performed two experimental replicates, which are both included in the paper. All other experiments discussed in the paper were conducted once at the conditions shown and no experimental replicates were excluded. For some experiments we performed preliminary experiments under slightly different conditions (e.g., for FC2 stimulation experiments we performed the same experiments using GCaMP7 instead of sytGCaMP7f as the calcium indicator) and found similar results. For other datasets (imaging or electrophysiology), data were collected over several months due to the nature of the experiments and therefore we did not attempt to replicate our results.

Randomization

Organisms were not allocated to control and experimental groups by the experimenter in this work, rather the flies' genotype determines their group. Thus, randomization of individuals into treatments groups is not relevant.

Blinding

The experimenters were not blind to the flies' genotype. Blinding was not possible for physiology experiments since different genotypes either expressed different patterns of fluorescence that were easily distinguished and, in the case of stimulation experiments, showed obvious changes in GCaMP activity upon stimulation. For purely behavioural experiments, data collection and analysis were done computationally, and thus the experimenter was not blind to the flies' genotype.

March 2021

Sample size

2

We require information from authors about some types of materials, experimental systems and methods used in many studies. Here, indicate whether each material, system or method listed is relevant to your study. If you are not sure if a list item applies to your research, read the appropriate section before selecting a response.

Materials & experimental systems

Methods

n/a Involved in the study

n/a Involved in the study

Antibodies

ChIP-seq

Eukaryotic cell lines

Flow cytometry

Palaeontology and archaeology

MRI-based neuroimaging

Animals and other organisms Clinical data

nature portfolio | reporting summary

Reporting for specific materials, systems and methods

Dual use research of concern

Antibodies Antibodies used

-chicken anti-GFP (Rockland, #600-901-215) -rabbit anti-dsRed (Takara #632496) -mouse anti-nc82 (DSHB, #AB_2314866) -rabbit anti-HA Tag (Cell Signaling #3724S) -rat anti-FLAG Tag (Novus #NBP1-06712) -goat anti-chicken AF 488 (Invitrogen #A11039) -goat anti-rabbit AF 594 (Invitrogen #A11037) -donkey anti-rabbit AF 594 (Jackson Immuno Research #711-585-152) -donkey anti-rat AF 647 (Jackson Immuno Research #712-605-153) -goat anti-mouse AF 488 (Invitrogen #A11029) -goat anti-mouse AF 633 (Invitrogen #A21052) -DyLight 550 mouse anti-V5 Tag (AbD Serotec MCA1360D550GA) -streptavidin AF 568 (Invitrogen #S11226) -rabbit anti-TNT (Cedarlane, #65873(SS))

Validation

The antibodies used in this study are routinely used for Drosophila immunohistochemistry.

Animals and other research organisms Policy information about studies involving animals; ARRIVE guidelines recommended for reporting animal research, and Sex and Gender in Research Laboratory animals

We used Drosophila melanogaster females, aged 1-4 days old. All fly genotypes used in this study are listed in the Methods.

Wild animals

No wild animals were used in this study.

Reporting on sex

All experiments were conducted with female flies since they are slightly larger, making them easier to use for physiology experiments.

Field-collected samples

No field-collected sample were used in this study.

Ethics oversight

No ethical oversight was required since all experiments were conducted on Drosophila.

Note that full information on the approval of the study protocol must also be provided in the manuscript.

March 2021

3

Article

Transforming a head direction signal into a goal-oriented steering command https://doi.org/10.1038/s41586-024-07039-2 Received: 10 November 2022

Elena A. Westeinde1, Emily Kellogg1, Paul M. Dawson1, Jenny Lu1, Lydia Hamburg2,3, Benjamin Midler2,3, Shaul Druckmann2,3 & Rachel I. Wilson1 ✉

Accepted: 5 January 2024 Published online: 7 February 2024 Open access Check for updates

To navigate, we must continuously estimate the direction we are headed in, and we must correct deviations from our goal1. Direction estimation is accomplished by ring attractor networks in the head direction system2,3. However, we do not fully understand how the sense of direction is used to guide action. Drosophila connectome analyses4,5 reveal three cell populations (PFL3R, PFL3L and PFL2) that connect the head direction system to the locomotor system. Here we use imaging, electrophysiology and chemogenetic stimulation during navigation to show how these populations function. Each population receives a shifted copy of the head direction vector, such that their three reference frames are shifted approximately 120° relative to each other. Each cell type then compares its own head direction vector with a common goal vector; specifically, it evaluates the congruence of these vectors via a nonlinear transformation. The output of all three cell populations is then combined to generate locomotor commands. PFL3R cells are recruited when the fly is oriented to the left of its goal, and their activity drives rightward turning; the reverse is true for PFL3L. Meanwhile, PFL2 cells increase steering speed, and are recruited when the fly is oriented far from its goal. PFL2 cells adaptively increase the strength of steering as directional error increases, effectively managing the tradeoff between speed and accuracy. Together, our results show how a map of space in the brain can be combined with an internal goal to generate action commands, via a transformation from world-centric coordinates to body-centric coordinates.

Accurate navigation requires us to fix a goal direction and then maintain our orientation towards that goal in the face of perturbations. This is also a basic problem in mechanical engineering: how can we keep the angle of some device directed at a target6? One solution to this problem is to use a resolver servomechanism to measure the discrepancy or error between the current angle and the goal angle. This produces a rotational velocity command that varies sinusoidally with error (Fig. 1a). Specifically, the mechanism drives leftward rotation when the device is positioned to the right of the goal, and vice versa. The stable fixed point of this system is the angle where the rotational velocity command crosses zero with negative slope (Fig. 1a). Sixty years ago, Mittelstaedt suggested that a similar process might be implemented in the brain’s navigation centres to control an organism’s heading and thus its path through the environment7. Since then, Webb and colleagues have proposed neural network implementations of this idea8–11, which have been extended by other investigators4,5,12–14. All these models exploit the notion that an angle or vector can be represented as a sinusoidal spatial pattern of activity across a neural population15,16 (Extended Data Fig. 1). These sinusoids can then be combined to produce a directional control signal9. Data from locusts17, zebrafish18 and Drosophila19 show that head direction is in fact encoded as a sinusoidal spatial pattern of activity (Fig. 1b). The Drosophila brain contains a cell type (PFL3) that is anatomically positioned to receive shifted copies of this head direction

representation while also making direct lateralized connections onto descending neurons involved in steering4,5 (Fig. 1c). This ‘copy-and-shift’ architecture9,20 is reminiscent of the design of a resolver servomotor (Extended Data Fig. 1). PFL3 cells also receive anatomical input from the fan-shaped body, a brain region where goals might be stored (Fig. 1d). Notably, almost all the inputs to PFL3 cells are shared by another cell type, PFL2 (refs. 5,21). Individual PFL2 cells make bilateral connections onto descending neurons (Fig. 1c), implying that they do not guide steering. Their function is enigmatic, but proposals suggest they increase forward walking speed5,13. In short, both PFL2 and PFL3 cells are anatomically positioned to integrate head direction information with stored goal information for navigation control. These cells stand out because they form a link between an allocentric map of space and an egocentric system of motor control. Encouragingly, recordings from analogous cells in other insects have confirmed that they receive topographic input from the head direction system17,22,23. However, there have been no functional studies of these cells in Drosophila, and recent models have made conflicting predictions about their roles in motor control4,5,11–13.

Comparing model predictions with behaviour To begin, we describe an updated computational model that differs from previous models in several key ways (Methods). In this model,

Department of Neurobiology, Harvard Medical School, Boston, MA, USA. 2Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA. 3Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA. ✉e-mail: [email protected]

1

Nature | Vol 626 | 22 February 2024 | 819

Article Current angle ( )

a

b

c

Head direction cells

Rotational velocity (d /dt)

Goal angle ( g) R Rotational velocity 0 d /dt L

= 0° PFL3R cells

=

g

=

g

+ 90°

=

+

g

+ 90°

=

g

+ 180°

e

ΣPFL3R

ΣPFL3L

ΣPFL3R – ΣPFL3L

+ 0

Goal input

Nonlinearity

Nonlinearity

Resulting activity in PFL3R/L

ΣPFL2

Resulting activity in PFL2

g

0° +180° –180° Directional error ( – g) PFL3L

PFL3R

115

+90°

–90°

i

h

Goal vectors 0°

Jump

=

g

L R Descending neurons

Head direction input to PFL2

Goal input

f

PFL2 cells

PFL3L cells

R L Descending neurons

= 90°

Neural space Head direction input to PFL3R/L

Head direction cells

Stable fixed point

–180° 0° +180° Directional error ( – g)

d

Head direction cells

Rotational speed |d /dt| (° s–1)

PFL2 Turn left

Turn right

Turn faster

80 Inferred goal direction

180°

0° –180° Directional error ( –

+180° g)

Fig. 1 | Comparing model predictions with behaviour. a, A rotational servomechanism works to keep the angle θ of some device close to a goal value θg. The output is a rotational velocity command that depends on the system’s error (θ − θg). Rotational velocity is close to zero around the goal (θ = θg) and the anti-goal (θ = θg + 180°). Whereas the goal is a stable fixed point, the anti-goal is an unstable fixed point. b, In the Drosophila brain, head direction is represented in ∆7 cells as a sinusoid over two spatial cycles. c, PFL3L, PFL3R and PFL2 populations extract spatially shifted copies of the head direction representation. These three populations are aligned in the fan-shaped body, where they share inputs from putative goal cells (Extended Data Fig. 1c). d, Model: each PFL population adds its head direction input with a shared input from goal cells. This is passed through a nonlinearity and then integrated over space. e, Model: activity of each PFL population versus directional error. f, Data: path of a fly in a

virtual environment with a visual head direction cue (a bright bar). Dots indicate 90° and 180° jumps of the environment; here the fly is correcting for all these jumps with rapid turns. g, Mean head direction θ in 10 min epochs with periodic jumps. Radial length denotes the consistency of head direction over time ρ, which ranges here from 0 to 0.8 in n = 56 epochs from 56 flies; 0° is towards the cue. h, Data: mean rotational speed versus directional error, the s.e.m. across flies (n = 46 flies). i, Model: PFL populations have shifted head direction inputs that tile the space of compass directions. Each population detects overlap between its shifted head direction vectors and a shared goal vector. The PFL3L population drives left turning, whereas the PFL3R population drives right turning and PFL2 drives increased rotational speed. Scale bar (f), 30 mm.

direction is represented as a sinusoid24 whose phase rotates as direction changes, relative to a flexible and arbitrary offset19. We divide PFL3 cells into two populations (PFL3R and PFL3L) that converge onto right or left descending neurons, respectively (Fig. 1c). Each population extracts a copy of the head direction representation, with phase shifts of ±67.5°, relative to the original head direction representation. Meanwhile, PFL2 cells extract a head direction representation with a phase shift of 180° (Fig. 1c). These three PFL populations are aligned within the fan-shaped body, where they share inputs from orderly arrays of cells5 which could represent the goal angle, θg. We model the goal representation as a spatial

sinusoid whose phase represents θg (Fig. 1d). The firing rate of each model PFL cell is the sum of its head direction input and its goal input, passed through a nonlinearity (Fig. 1d). These sinusoids should be understood as representations of vectors (Extended Data Fig. 1): the two PFL3 populations extract shifted copies of the head direction vector, and the goal vector is added to each copy. The resulting vector with the larger magnitude dictates the direction the fly should rotate to reach its goal. This model predicts that PFL3R should be most active when the fly is facing to the left of its goal—in other words, when there is a negative directional error (θ − θg) (Fig. 1e), with the reverse situation holding for PFL3L.

820 | Nature | Vol 626 | 22 February 2024

If we neglect the contribution of PFL2 cells, then we would predict that the system’s rotational velocity commands should just resemble the right–left difference in PFL3 activity (ΣPFL3R − ΣPFL3L), which varies sinusoidally as a function of directional error (Fig. 1e). In other words, the system would behave like a classical resolver servomechanism (Fig. 1a). In this sort of mechanism, rotational velocity is nearly zero around the goal and also opposite the goal; engineers call this ‘false nulling’ because it can allow the servomechanism to settle at an angle opposite the goal (Extended Data Fig. 1). To seek this phenomenon in fly behaviour, we placed flies in a virtual-reality environment with a single prominent visual head direction cue; this environment rotated in closed loop with the fly’s rotational velocity on a spherical treadmill (Fig. 1f). The fly’s head was rigidly coupled to its body, so that heading and head direction are identical. In this type of environment, flies often follow straight paths towards a goal (Fig. 1f), with different flies adopting different goal directions (Fig. 1g); this behaviour requires an intact head direction system25–27. During these epochs of straight walking, we could infer the fly’s goal direction from its behavioural orientation. Every minute, we jumped the virtual environment by 90° or 180°; this often caused the fly to turn back towards its goal, implying that these jumps are perceived by the brain as head direction changes4,25. In agreement with our model predictions, we found that the fly’s rotational speed was generally low when it was oriented towards its goal (Fig. 1h). However, contrary to predictions, the fly’s rotational speed was high—not low—around its anti-goal, and 180° jumps evoked rotational speeds that were no lower than those evoked by 90° jumps (Extended Data Fig. 3). A model that considers only PFL3 cells cannot explain these behavioural results (Fig. 1e), suggesting an additional mechanism is recruited around the anti-goal to increase rotational speed. PFL2 cells are good candidates for this mechanism, because their population amplitude should be highest when the fly is oriented towards its anti-goal (Fig. 1e). If PFL2 cells promote high rotational speeds around the anti-goal, this would mitigate the false-nulling problem: in essence, the anti-goal is already an unstable fixed point of the system, and a mechanism that specifically increased rotational speed around the anti-goal would further destabilize that unstable fixed point, ensuring that the system could not settle there. To summarize, we can think of these three cell populations (PFL2, PFL3R, PFL3L) as dividing the range of compass angles into three different sectors (Fig. 1i), reflecting the different shifts in their head direction inputs. Each population detects the congruence between its shifted head direction vector and a goal vector. Congruence detection is implemented by a nonlinear transformation that produces maximal output in response to maximal congruence. These outputs are then combined to generate steering commands with the appropriate direction and speed, so that small deviations from the goal are corrected with slower turns, whereas large deviations from the goal are corrected with faster turns.

Dynamics around the anti-goal To test the predictions of this model, we constructed split-Gal4 lines to target PFL2 and PFL3 cells. We were able to generate a selective PFL2 line, as well as a line targeting PFL2 and PFL3 together. We validated these lines by using genetic mosaic analysis to identify single-cell clones and then comparing these clones to morphologies from connectome data (Extended Data Fig. 2). We will focus initially on our results for PFL2 cells, as this line was the more specific line. First, to directly activate PFL2 cells, we used a chemogenetic approach: we expressed ATP-gated ion channels (P2X2 receptors) in these cells, and we activated them specifically using iontophoresis of ATP into the protocerebral bridge, where their dendrites are located (Fig. 2a and Extended Data Fig. 4). We made a whole-cell recording from a PFL2 cell in every experiment to confirm the effects of ATP (Fig. 2a,b). At the same time, we monitored the fly’s behaviour on a spherical treadmill, again in a virtual-reality environment with a visual

cue. We found that stimulating PFL2 cells generally produced turning, although the direction of the turn was often unpredictable (Fig. 2a,b). Moreover, if the fly was walking forward at the time of the stimulus, it consistently reversed direction and stepped backward (Fig. 2a,b). This response may be related to the fact that bidirectional excitation in some steering-related descending neurons is correlated with slowing or backward walking4. In short, PFL2 cells drive an increase in rotational movement, accompanied by a decrease in forward velocity. Next, we used our selective PFL2 line to drive expression of GCaMP7b, and we imaged the activity of these cells with a two-photon microscope. We saw that activity in PFL2 dendrites generally formed a sinusoidal spatial pattern across the horizontal axis of the fan-shaped body (Fig. 2c). We fit a sinusoid to this pattern and extracted its phase and amplitude; we call this the ‘bump phase’ and ‘bump amplitude’. We found that the bump phase generally moved left as the fly rotated to the right (Fig. 2c,d), as expected from the anatomical inputs to PFL2 cells from the head direction system. Notably, we found that bump amplitude was minimal when the fly was oriented towards its goal and maximal around the anti-goal (Fig. 2e). Moreover, we found that high bump amplitude correlated with high rotational speed (Fig. 2f) and low forward velocity (Fig. 2g). Taken together with our chemogenetic simulation results, these data argue that PFL2 cells are recruited when the fly is facing its anti-goal, driving an increase in rotational speed, accompanied by a decrease in forward velocity. Thus, these cells provide a solution to the ‘false nulling’ problem that characterizes a classical servomechanism: they function to further destabilize the unstable fixed point in the steering control system, so that it cannot come to rest at the anti-goal.

Dynamics around the goal Next, we imaged GCaMP7b expressed under the control of the mixed split-Gal4 line that targets both PFL2 and PFL3 cells (Extended Data Fig. 2). Here, rather than imaging the dendritic arbours, we focused on the lateral accessory lobes, where PFL2 and PFL3 axons terminate, in order to separate PFL3L from PFL3R. PFL2 and PFL3 axon terminals are intermingled in the lateral accessory lobes, but we found that calcium signals in the mixed line were quite different from the signals we observed in PFL2 cells. In the PFL2-specific line, calcium signals in the lateral accessory lobes were generally maximal around the anti-goal (Fig. 3a), as we would expect from our imaging data from their dendritic arbours (Fig. 2e). However, in the mixed line, we saw the opposite: calcium signals in the lateral accessory lobes were generally maximal around the goal (Fig. 3b); this is what the model predicts for the PFL3 populations, and it implies that the signals in the mixed line are dominated by PFL3 rather than PFL2. This could be due to stronger Gal4 expression in PFL3 versus PFL2, or other differences between these cell types. Regardless, this result implies that we can treat the right and left lateral accessory lobe signals as a readout of the summed activity of each PFL3 population (ΣPFL3R and ΣPFL3L). In agreement with model predictions, we found that ΣPFL3R is highest when the fly is just to the left of its goal, and vice versa for ΣPFL3L (Fig. 3c).The right–left difference between these signals is a roughly sinusoidal function of the fly’s orientation relative to its goal, supporting the predictions of the model (Fig. 3d). Moreover, we found that the right–left difference was predictive of the fly’s rotational velocity, again consistent with the model (Fig. 3e) and consistent with the idea that these cells drive rotational velocity changes. This differs from what we see in our PFL2-specific line, where axonal projections are symmetrically active regardless of head direction, as we would predict based on PFL2 anatomy (Extended Data Fig. 5). In summary, our data argue that PFL3 cells drive directional steering manoeuvres that correct small deviations from the fly’s intended path. PFL3R cells are most active when the fly is oriented just to the left of its goal, and the reverse is true for PFL3L. Finally, right–left differences in PFL3 activity are predictive of rotational velocity, consistent Nature | Vol 626 | 22 February 2024 | 821

b

Forward velocity (mm s–1)

–40 –80

0

Forward velocity (mm s–1)

–100

4 0 –4

20 0 1 0 –1 Control genotype

0 –50 40 20 0 1 0 –1

0° –180° 4 2 0 180° 0°

Forward velocity (mm s–1)

–180° 8 4 0

PFL2 bump amplitude

180°

4 2 0 –2

e 180°

0°

–180° –180°

0°

180°

Change in directional error ( –

f

1.6

1.4

1.2 –180°

g)

0° Directional error ( –

180° g)

g 1.4

1.3

PFL2 bump amplitude

L

Change in PFL2 bump phase

R

PFL2 bump amplitude

Bump phase

40

d

z-scored ΔF/F

c

Head Bump direction amplitude

PFL2 cells express P2X2

0 –50

100 Forward Rotational Vm (mV) velocity speed (mm s–1) (° s–1)

Rotational PFL2 velocity (° s–1) Vm (mV)

a

Rotational Vm speed (mV) (° s–1)

Article

1.2

–200

–100 0 100 Rotational velocity (° s–1)

200

1.0

–4

0 4 8 Forward velocity (mm s–1)

Fig. 2 | Dynamics around the anti-goal. a, Example experiment. ATP (red shading) depolarizes PFL2 cells expressing P2X2 (top), evoking an increase in the absolute value of rotational velocity, that is, rotational speed (middle). It also evokes a decrease in forward velocity (bottom). This fly turns right in response to the first two pulses but left in response to the last two pulses. b, Summary data for flies where PFL2 cells expressed P2X2 and genetic controls (mean ± s.e.m. across flies, n = 12 P2X2+ flies and 11 control flies). Results are shown for two ATP pulse durations (100 ms and 500 ms). See also Extended Data Fig. 4. c, PFL2 activity (∆F/F) across the horizontal axis of the fan-shaped body over time. During this epoch, the fly is walking relatively straight. The fly’s mean head direction is taken as its goal (θg). After the environment is jumped by

180° (blue arrowhead), the fly makes a compensatory turn to reorient towards θg. We fit a sinusoid to ∆F/F at each time point to extract bump phase and amplitude. d, Change in PFL2 bump phase versus change in directional error. The phase of PFL2 activity moves right when the fly turns left. Each symbol denotes one time point (Pearson’s r = −0.63, P = 9 × 10 −13), with the line of unity in grey. Shown here are data for one example fly; Extended Data Fig. 9 shows two other examples and shows the effect of z-scoring ∆F/F. e, PFL2 bump amplitude versus directional error (mean ± s.e.m. across flies, n = 33 flies). f, PFL2 bump amplitude versus the fly’s rotational velocity (mean ± s.e.m. across flies, n = 33 flies). g, PFL2 bump amplitude versus the fly’s forward velocity (mean ± s.e.m. across flies, n = 33 flies). Scale bars, 5 s (b), 10 s (a,c).

with the direct excitatory projections of these cells to steering-related descending neurons.

θp (Fig. 4d). These results support the conclusion that head direction tuning in PFL2 and PFL3 cells arises largely from ∆7 cells, which is important because ∆7 cells reformat the head direction signal as a spatial sinusoid5,24. In the model, each PFL2 or PFL3 cell adds its head direction input with goal input, and the result is passed through a nonlinearity. From the perspective of a single PFL2 or PFL3 cell, goal input is simply a fixed bias. This bias pushes the cell’s total input up or down the nonlinearity, thereby changing the amplitude of the head direction tuning curve (Fig. 4e). In the model PFL2 population, the goal input that each cell receives increases as the cell’s preferred head direction θp moves away from the goal direction θg (Fig. 1d), and so cells with θp near the anti-goal have the largest-amplitude head direction tuning curves; indeed, our electrophysiological data confirm this prediction (Fig. 4f). Conversely, in the model PFL3 population, goal input is largest for cells whose preferred head direction θp is shifted just counterclockwise or clockwise from θg (for PFL3R or PFL3L, respectively; Fig. 1d). These should be the cells with the largest-amplitude head direction tuning curves, and again our data confirm this prediction (Fig. 4g); an independent study of PFL3 cells reached a similar conclusion30. Interestingly, we only find these effects at the level of spike rate; we do not see these trends at the level of the cell’s membrane potential (Fig. 4f,g and Extended Data Fig. 6); this finding implies that the nonlinearity in the model is implemented

Mechanisms underlying network dynamics Next, to understand the inputs to PFL2 and PFL3 cells, we performed genetically targeted in vivo patch-clamp recordings. In line with model predictions, we found that individual PFL2 and PFL3 cells are often strongly tuned to head direction (Fig. 4a), with different cells having different preferred directions (θp). Connectome data indicate that PFL2 and PFL3 cells receive some direct synaptic input from primary head direction cells (EPG cells) but that they receive most of their head direction input (about 80%) from secondary head direction cells, called ∆7 cells5. Because ∆7 cells are glutamatergic, and because glutamate is largely an inhibitory neurotransmitter in the Drosophila brain28,29, we would expect that the majority of the head direction input to PFL2 and PFL3 cells would arrive in the form of synaptic inhibition (Fig. 4b). Indeed, we found that PFL2 and PFL3 cells are bombarded by inhibitory postsynaptic potentials (IPSPs) whose frequency depends on head direction (θ). Jumping the virtual environment around the fly often evoked an abrupt change in IPSP frequency (Fig. 4c), with IPSP frequency increasing if the jump brought θ away from θp and IPSP frequency decreasing if the jump brought θ towards 822 | Nature | Vol 626 | 22 February 2024

a

b

ΣPFL2

2.0

1.5

Data 0° +180° Directional error ( – g)

–180°

Data 0° +180° Directional error ( – g)

Model –180°

e

–0.4 –180°

Data

0° +180° Directional error ( – g)

ΣPFL3R – ΣPFL3L activity (z-scored ΔF/F)

0.4

0

ΣPFL3L

1.2 1.0

0.8 –180°

Data 0° +180° Directional error ( – g)

Model –180°

0° +180° Directional error ( – g)

ΣPFL3R – ΣPFL3L

ΣPFL3R – ΣPFL3L activity (z-scored ΔF/F)

d

ΣPFL3R

Model

0° 0° +180° +180° –180° Directional error ( – g) Directional error ( – g)

Model –180°

0° +180° Directional error ( – g)

0.2

0

ΣPFL3R – ΣPFL3L

–180°

PFL3 activity (z-scored ΔF/F)

2.5

1.5

c

ΣPFL3 2.5

ΣPFL3 activity (z-scored ΔF/F)

ΣPFL2 activity (z-scored ΔF/F)

3.5

Data –0.2 0 –200 +200 Rotational velocity (° s–1)

Model Left

0 Right Rotational velocity

Fig. 3 | Dynamics around the goal. a, ΣPFL2 activity (∆F/F) versus directional error (mean ± s.e.m. across flies, n = 33 flies). Shown here is the summed activity of the right and left PFL2 axons, where they terminate near DNa03 dendrites in the lateral accessory lobe. Model prediction is shown for comparison. b, ΣPFL3 activity (∆F/F) versus directional error (mean ± s.e.m. across flies, n = 23 flies). As in a, the activity is summed across the right and left lateral accessory lobe, where PFL3 cells terminate onto DNa03 and DNa02. Here we used a mixed split-Gal4 line that targets PFL2 and PFL3 cells together; because our results are opposite for what we observe for PFL2 cells alone, and because our results match the predictions of our PFL3 model (shown below),

we treat this as measurement of PFL3 activity (Extended Data Fig. 5). c, ΣPFL3R and ΣPFL3L activity in the right and left lateral accessory lobe, respectively, plotted versus directional error (mean ± s.e.m. across flies, n = 23 flies). Signals are imaged from our mixed split-Gal4 line but are likely dominated by PFL3, as noted above. Model predictions are shown for comparison. d, Right–left difference in PFL3 activity versus directional error (mean ± s.e.m. across flies, n = 23 flies) and model prediction. e, Right–left difference in PFL3 activity versus the fly’s rotational velocity (mean ± s.e.m. across flies, n = 23 flies) and model prediction.

by the voltage-gated conductances that transform membrane potential to spiking.

to correct deviations from the goal. Conversely, if S is too high, the system overshoots the goal and tends to oscillate. With the direct pathway alone, S must be tuned within a narrow range of values to avoid these outcomes, but with the indirect pathway, there is a wider range of acceptable values (Fig. 5d) because the indirect pathway has high gain around the anti-goal but low gain around the goal (Fig. 5c). In short, the indirect pathway in general and PFL2 cells in particular function to manage the tradeoff between speed and accuracy, favouring speed for large errors, but accuracy for small errors. This model illustrates how variations in S can produce variations in the vigour of goal-directed steering. In fact, in our data, we noticed variations in the vigour of goal-directed steering: we observed vigorous corrective steering after some jumps of the virtual environment, but no corrective steering after other jumps. Jumps that triggered corrective steering during epochs of high head direction consistency (high ρ) produced larger changes in PFL2 and PFL3 membrane potential, as compared to uncorrected jumps that occurred during epochs of low head direction consistency (low ρ, Fig. 5e–g). This observation suggests that the brain regulates the scale of the synaptic inputs to PFL2 and PFL3 cells as a way to modulate the strength of goal-directed steering. Importantly, jump-evoked changes in membrane potential preceded steering (Fig. 5h and Extended Data Fig. 7), supporting the idea that PFL2 and PFL3 cells are causal for steering. We also quantified head direction consistency (ρ) over long time epochs (Fig. 5i). During epochs of high ρ, our imaging data revealed that the amplitude of the PFL2 bump depended strongly on head direction, and indeed our model predicts this as a consequence of high S (Fig. 5j,k). Conversely, during epochs of low ρ, the amplitude of the PFL2 bump depended only weakly on head direction, and again our model predicts this as a consequence of low S (Fig. 5j,k). These findings further support the idea that the brain can modulate the strength of goal-directed steering by scaling the inputs to PFL2 and PFL3 cells.

Modulating the scale of network activity Our data indicate that PFL2 cells specifically boost steering gain around the anti-goal. But why would it be useful for this boost to be restricted to head directions around the anti-goal? Why not steer with high gain at all times? To develop an intuition for this issue, we modelled the relationship between PFL2 and PFL3 activity and steering. PFL3 cells synapse directly onto descending neurons (DNa02; Fig. 5a), and the right–left difference in DNa02 activity is linearly proportional to the fly’s subsequent rotational velocity4. Meanwhile, PFL3 cells also synapse onto DNa03, which is one of the strongest inputs to DNa02 in the brain4,5,31,32; we call this the ‘indirect pathway’ (Fig. 5a). Each DNa03 cell also receives input from every PFL2 cell. In the model, DNa03 sums PFL3 and PFL2 input and then passes this sum through a nonlinear activation function (Fig. 5b). Note that each PFL2 axon projects bilaterally, but it can still influence steering in our model, because it creates an excitatory drive that pushes DNa03 output towards the steeper part of its nonlinear activation function, amplifying the right–left asymmetry that DNa03 inherits from PFL3. DNa02 then sums PFL3 and DNa03 input (from the direct and indirect pathway, respectively), and this sum is again passed through the same nonlinearity. We add a small random component to the steering signal, to account for noise and other factors influencing steering, and we feed the resulting steering commands back into the head direction system, thereby closing the loop for feedback control. A free parameter in this model is the scalar value (S) that controls the overall magnitude of the synaptic input to PFL2 and PFL3 cells (Fig. 5a), and thus the strength of the downstream steering commands evoked by any given head direction (Fig. 5c). If S is too low, feedback is slow

Nature | Vol 626 | 22 February 2024 | 823

Article –55

Primary head direction cells (EPG)

–65

–64 20

Secondary head direction cells (Δ7)

10

10 0

b

PFL3

–60

p

PFL2

p

0 180° –180° 0° 180° –180° 0° Head direction ( ) Head direction ( )

c

d

Vm (mV) –90 –60

–80

f

Preferred head direction versus goal ( 0°

+72°

p

–

g

g)

ΔFiring rate (spikes per second)

2 0 –2 –4 180° towards 0° 180° away Change from preferred head direction

Preferred head direction versus goal (

+144°

–144° ΔFiring rate

PFL2 model

ΔVm

Input Vm (Head direction input + goal input)

–72°

4

–72°

0°

p

+72°

–

g)

+144°

PFL3 model

ΔVm

Output firing rate

ΔFiring rate

–144°

PFL2 data

ΔFiring rate (spikes per second)

Vm (mV)

180° jump

p

0° –180° +180° Head direction relative to preferred direction ( – p)

PFL3

180° jump

–70

e

Increasing inhibition

PFL2

–60

Change in IPSP frequency (Hz)

–56

Firing rate

Vm (mV)

a

10

0

PFL3 data 20

0

Fig. 4 | Navigation dynamics at cellular resolution. a, Head direction tuning in an example PFL2 cell and an example PFL3 cell. Preferred direction is θp. b, Each PFL2 and PFL3 cell is predicted to receive synaptic inhibition that varies sinusoidally with head direction. c, Whole-cell recordings from PFL2 and PFL3 cells showing changes in IPSP frequency when we impose a rotational jump on the virtual environment, emulating a change in θ. d, Change in IPSP frequency versus change in θ (relative to θp, mean ± s.e.m. across cells, n = 12 PFL3 and 10 PFL2 cells in 22 flies, Pearson’s r = 0.53). The effect of θ is significant (P = 8 × 10 −3, two-way ANOVA, with θ and fly identity as the two factors). This analysis uses time points when the fly was standing still, because this makes individual IPSPs more clearly detectable. e, Model: a nonlinearity transforms Vm into firing rate

Discussion Whereas the brain’s maps of space are allocentric (referenced to objects in the world), motor commands are egocentric. This poses a coordinate transformation problem. Here we describe a network that solves this problem. This network creates two opponent copies of the allocentric head direction representation, with equal and opposite shifts (θ ± shift). Each copy is then separately compared with an allocentric goal representation, to measure congruence with the goal. The difference between the two opponent congruence values becomes an egocentric motor command. Elements of this scheme have been predicted in algorithmic models7 and network models4,5,8–14. Our data demonstrate that these theoretical predictions are largely correct, and we show that the two opponent copies are represented by the PFL3R and PFL3L populations; this conclusion is supported by an independent companion paper30. At the same time, our results highlight the unexpected role of PFL2 cells. These cells provide a solution to a classic problem—namely, the 824 | Nature | Vol 626 | 22 February 2024

p)

0°

Head direction relative to cell’s preference ( –

+180°

–180°

0°

+180°

0°

–180°

–180°

+180°

0°

+180°

+180° –180°

0

0°

0°

–180°

Head direction relative to cell’s preference ( –

+180°

0°

+180°

–180°

0°

+180°

0°

–180°

+180°

0°

+180° –180°

0

4

–180°

ΔVm (mV)

8 4

–180°

Goal input

ΔVm (mV)

8

p)

for each model cell. Each cell receives head direction input that is cosine tuned to (θ − θp). The goal cell input to each cell represents a bias that does not change with head direction. This bias moves the cell’s input along the nonlinear function, changing the amplitude of the firing rate tuning curve. f, PFL2 cells are divided into bins based on (θp − θg). For each cell, we subtract the minimum y-axis value in the tuning curve, then we compute the mean of cells in the bin, for both firing rate and Vm. Model output (top) is compared with data (bottom, n = 11 cells, mean ± s.e.m. across cells). g, Same but for PFL3 neurons (n = 15 cells, mean ± s.e.m. across cells). Here we combine results from PFL3R and PFL3L (after reversing the left–right order of the five bins for the PFL3L cells, so that the model outputs are identical for R and L). Scale bar, 2 s.

fundamental tradeoff between speed and accuracy. High feedback gain allows a system to converge quickly towards its goal, and so it makes sense that gain should be high when error is large, that is, when there is a large discrepancy between the system’s current state and its goal. However, high gain can cause overshooting of the goal, especially when error is already small. We show that PFL2 cells effectively adjust the system’s gain, depending on the magnitude of the system’s current error. Specifically, PFL2 cells selectively increase the gain of steering commands around the anti-goal, where error is maximal. This allows gain to be lower around the goal, which should minimize overshooting. In this manner, PFL2 cells dynamically adjust feedback gain to match the needs of the system, a concept known as adaptive control33. Notably, the adaptive control exerted by PFL2 cells occurs only in the ‘indirect’ pathway, where PFL2 signals converge with PFL3 signals (Fig. 5a); the function of the ‘direct’ pathway is less clear, but it may help to initiate steering manoeuvres with minimal delay. It is likely that there are multiple sites of adaptive gain control in this network. In particular, our data suggest that the inputs to

Steering commands

–180° 180° S = 0.4

S = 0.6

0° –180° 180°

S = 0.8

Indirect pathway

0° –180° 180°

0

Corrected, high 20

S = 1.0

PFL2

0° Time

180° jump ΔVm (mV)

10 0

180° jump

180° jump

8 ΔVm (mV)

10 0

4

High

Data 4 z-scored ΔF/F

z-scored ΔF/F

j High

8

80 180° jump 60

0

40

2 0

–8

–20

i

40 2

4

–4

–10

3 2 1 0 –1

High

0 –1 Neural space

Model

Low

S = 0.8

Model

S = 0.2 Activity

Activity

Data

3 2 1

Neural space

Low

Low

k

0.5 Time (s)

4 3

1.0

Data

2 1 0 Model

0 0

Neural space

60

0

–8 PFL3

90° jump

4

–4

20

8

8

–20

g

–180°

Time

h

Uncorrected, low

180° jump

–10

ΔVm (mV)

Start

–180° 180°

0

f End

Uncorrected 90° jump

Corrected 180° jump

0°

Direct pathway

Left –180° 0° 180° Directional error ( – g)

ΔVm (mV)

Uncorrected 180° jump

0°

Max. – min. PFL2 bump amplitude

Rotational velocity

Output firing rate Uncorrected –90° jump

–180° 180°

0

Right

Synaptic input

e

0°

Left

Random input

b

180°

S=0

Directional error

DNa02R

Indirect pathway

S = 0.2

Right

DNa02L

Direct pathway

Rotational speed (° s–1)

DNa03R

0

d

Absolute ΔVm (mV)

DNa03L

PFL3R

PFL2 bump amplitude

PFL2

S 1.0 0.8 0.6 0.4 0.2 0

Max.

– ΣPFL3R – ΣPFL3L

Indirect pathway

PFL3L Direct pathway

c

Head direction input + goal input S S

Rotational velocity

a

Neural space

0.2 0.4 0.6 Consistency of head direction ( )

Fig. 5 | Modulating the scale of network activity. a, Direct and indirect pathways. S adjusts the magnitude of total input to PFL2 and PFL3 cells. b, Nonlinear activation function. c, Top, model PFL2 bump amplitude and (ΣPFL3R − ΣPFL3L) versus directional error. Bottom, rotational velocity produced by the direct or indirect pathway alone. With both pathways, results are similar to the indirect pathway alone. d, Model: directional error over time. As S increases, the network brings head direction towards the goal (red line). If the indirect pathway is omitted, high S produces overshooting. e, Data: example path during four jumps of the virtual environment, separated by 60 s. The fly corrects for the first jump, but not the rest. The probability of correction typically did not change over time. f, Change in PFL2 membrane potential (∆Vm) before and after each 180° jump, comparing corrected jumps with high ρ (n = 31 of 276 jumps) or uncorrected jumps with low ρ (n = 27 of 276 jumps). Variance in ∆Vm is higher for corrected versus uncorrected jumps (P = 0.01363, Brown–Forsythe test). See also Extended Data Fig. 7. g, Same but for PFL3 (n = 60 of 348 corrected, 17 of 348 uncorrected, P = 0.02776).

h, Absolute ∆Vm and rotational speed during corrected jumps. Mean ± s.e.m., n = 157 of 701 (90°) and n = 91 of 624 (180°), pooling data from PFL2 and PFL3 cells. i, Path of two flies in a virtual environment over 10 min, one with high consistency of head direction (high ρ) and the other with low ρ. j, Spatial profile of PFL2 activity, divided into four bins based on head direction, relative to the directions associated with the highest and lowest PFL2 bump amplitude (darkest and lightest traces, respectively). Data (top) are from the two paths in i. Model results (bottom) are generated by setting S = 0.8 or S = 0.2, producing high or low ρ, respectively, as shown in d. k, Data (top): for each 10 min trial we computed ρ and also analysed the spatial profile of PFL2 activity as in j, taking the difference between the maximum and minimum bump amplitudes. Across trials (symbols), bump amplitude modulation is correlated with ρ (Pearson’s r = 0.37096, P = 1.7 × 10 −4, n = 33 flies). Model (bottom): same analysis on model output. Here we obtained a range of model outcomes by varying S and using different random seeds. Scale bars, 5 cm (e,i), 1 s (f). In k, Max., maximum; min., minimum.

PFL2 and PFL3 cells change in scale over time (which we model as changes in the parameter S); this may provide a way to modulate the organism’s commitment to remembered or internalized goals. For example, S might increase when the organism needs to be moving vigorously towards a high-value remembered goal; conversely, S might decrease when the organism needs to be more open to exploration of the local environment. Mechanistically, this modulation could be

implemented by inhibitory tangential cell inputs to the fan-shaped body that are well-positioned to shunt the inputs to PFL2 and PFL3 dendrites, and it could explain why, in other insect species, these cells sometimes show unusually weak responses to head direction changes22. Alternatively, the strength of goal-directed steering could be altered by modulating the amplitude of goal cell output (Extended Data Fig. 8). Nature | Vol 626 | 22 February 2024 | 825

Article In the future, it will be interesting to investigate how and where goals are written into memory. The companion paper to this study identifies one goal cell population30, but there are dozens of candidate goal cell types in the fan-shaped body with the appropriate anatomy to represent a goal as a spatial sinusoid5,9,12,13. In principle, multiple goals could be stored as spatial patterns of persistent activity or synaptic weights. This network also suggests a solution to the problem of representational drift34–36. As the phase of the head direction representation drifts over time during spatial learning37–39, the same process that first initialized the goal representation could continually update that representation, to keep it aligned with the coordinate frame of the head direction system. As a result, motor commands would be protected from drift, which might explain why representational drift is less obvious in cells more strongly correlated with motor performance40. In summary, our results reveal how the sense of direction can be used to generate locomotor commands with adaptive gain that manages the tradeoff between speed and accuracy. Our conclusions generate testable predictions for how goals could be stored in memory, retrieved on demand, modulated by context and protected from drift. Because the basic problems of navigation are fundamental problems of geometry and information retrieval, the solutions we describe here may have general relevance for other systems.

15. 16. 17. 18.

19. 20. 21. 22. 23. 24. 25.

26. 27.

28. 29.

Online content Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi.org/10.1038/s41586-024-07039-2.

30.

31. 32. 33. 34.

1. 2. 3. 4. 5.

6. 7. 8. 9. 10. 11.

12.

13. 14.

Schöne, H. Spatial Orientation: The Spatial Control of Behavior in Animals and Man (Princeton Univ. Press, 2014). Knierim, J. J. & Zhang, K. Attractor dynamics of spatially correlated neural activity in the limbic system. Annu. Rev. Neurosci. 35, 267–285 (2012). Hulse, B. K. & Jayaraman, V. Mechanisms underlying the neural computation of head direction. Annu. Rev. Neurosci. 43, 31–54 (2020). Rayshubskiy, A. et al. Neural circuit mechanisms for steering control in walking Drosophila. Preprint at bioRxiv https://doi.org/10.1101/2020.04.04.024703 (2020). Hulse, B. K. et al. A connectome of the Drosophila central complex reveals network motifs suitable for flexible navigation and context-dependent action selection. eLife 10, e66039 (2021). Bennett, S. A History of Control Engineering, 1800–1930 Ch. 4 (Peregrinus, 1979). Mittelstaedt, H. Control systems of orientation in insects. Annu. Rev. Entomol. 7, 177–198 (1962). Haferlach, T., Wessnitzer, J. & Mangan, M. Evolving a neural model of insect path integration. Adapt. Behav. 15, 273–287 (2007). Stone, T. et al. An anatomically constrained model for path integration in the bee brain. Curr. Biol. 27, 3069–3085.e11 (2017). Le Moël, F., Stone, T., Lihoreau, M., Wystrach, A. & Webb, B. The central complex as a potential substrate for vector based navigation. Front. Psychol. 10, 690 (2019). Goulard, R., Buehlmann, C., Niven, J. E., Graham, P. & Webb, B. A unified mechanism for innate and learned visual landmark guidance in the insect central complex. PLoS Comput. Biol. 17, e1009383 (2021). Dan, C., Kappagantula, R., Hulse, B. K., Jayaraman, V. & Hermundstad, A. M. Flexible control of behavioral variability mediated by an internal representation of head direction. Preprint at bioRxiv https://doi.org/10.1101/2021.08.18.456004 (2022). Matheson, A. M. M. et al. A neural circuit for wind-guided olfactory navigation. Nat. Commun. 13, 4613 (2022). Sun, X., Yue, S. & Mangan, M. A decentralised neural model explaining optimal integration of navigational strategies in insects. eLife 9, e54026 (2020).

826 | Nature | Vol 626 | 22 February 2024

35. 36. 37. 38. 39. 40.

Touretzky, D. S., Redish, A. D. & Wan, H. S. Neural representation of space using sinusoidal arrays. Neural Comput. 5, 869–884 (1993). Wittmann, T. & Schwegler, H. Path integration — a network model. Biol. Cybern. 73, 569–575 (1995). Heinze, S. & Homberg, U. Maplike representation of celestial E-vector orientations in the brain of an insect. Science 315, 995–997 (2007). Petrucco, L. et al. A hindbrain ring attractor network that integrates heading direction in the larval zebrafish. In Computational and Systems Neuroscience Meeting T35 (Cosyne, 2022). Seelig, J. D. & Jayaraman, V. Neural dynamics for landmark orientation and angular path integration. Nature 521, 186–191 (2015). Sun, X., Yue, S. & Mangan, M. How the insect central complex could coordinate multimodal navigation. eLife 10, e73077 (2021). Lin, C.-Y. et al. A comprehensive wiring diagram of the protocerebral bridge for visual information processing in the Drosophila brain. Cell Rep. 3, 1739–1753 (2013). Bockhorst, T. & Homberg, U. Amplitude and dynamics of polarization-plane signaling in the central complex of the locust brain. J. Neurophysiol. 113, 3291–3311 (2015). el Jundi, B. et al. Neural coding underlying the cue preference for celestial orientation. Proc. Natl Acad. Sci. USA 112, 11395–11400 (2015). Turner-Evans, D. B. et al. The neuroanatomical ultrastructure and function of a biological ring attractor. Neuron 108, 145–163 e10 (2020). Green, J., Vijayan, V., Mussells Pires, P., Adachi, A. & Maimon, G. A neural heading estimate is compared with an internal goal to guide oriented navigation. Nat. Neurosci. 22, 1460–1468 (2019). Giraldo, Y. M. et al. Sun navigation requires compass neurons in Drosophila. Curr. Biol. 28, 2845–2852.e4 (2018). Warren, T. L., Weir, P. T. & Dickinson, M. H. Flying Drosophila melanogaster maintain arbitrary but stable headings relative to the angle of polarized light. J. Exp. Biol. 221, jeb177550 (2018). Liu, W. W. & Wilson, R. I. Glutamate is an inhibitory neurotransmitter in the Drosophila olfactory system. Proc. Natl Acad. Sci. USA 110, 10294–10299 (2013). McCarthy, E. V. et al. Synchronized bilateral synaptic inputs to Drosophila melanogaster neuropeptidergic rest/arousal neurons. J. Neurosci. 31, 8181–8193 (2011). Mussells Pires, P., Zhang, L., Parache,V., Abbott, L. F. & Maimon, G. Converting an allocentric goal into an egocentric steering signal. Nature https://doi.org/10.1038/s41586023-07006-3 (2024). Schretter, C. E. et al. Cell types and neuronal circuitry underlying female aggression in Drosophila. eLife 9, e58942 (2020). Li, F. et al. The connectome of the adult Drosophila mushroom body provides insights into function. eLife 9, e62576 (2020). Cowan, N. J. et al. Feedback control as a framework for understanding tradeoffs in biology. Integr. Comp. Biol. 54, 223–237 (2014). Driscoll, L. N., Pettit, N. L., Minderer, M., Chettih, S. N. & Harvey, C. D. Dynamic reorganization of neuronal activity patterns in parietal cortex. Cell 170, 986–999.e16 (2017). Schoonover, C. E., Ohashi, S. N., Axel, R. & Fink, A. J. P. Representational drift in primary olfactory cortex. Nature 594, 541–546 (2021). Marks, T. D. & Goard, M. J. Stimulus-dependent representational drift in primary visual cortex. Nat. Commun. 12, 5169 (2021). Fisher, Y. E., Lu, J., D’Alessandro, I. & Wilson, R. I. Sensorimotor experience remaps visual input to a heading-direction network. Nature 576, 121–125 (2019). Kim, S. S., Hermundstad, A. M., Romani, S., Abbott, L. F. & Jayaraman, V. Generation of stable heading representations in diverse visual scenes. Nature 576, 126–131 (2019). Fisher, Y. E., Marquis, M., D’Alessandro, I. & Wilson, R. I. Dopamine promotes head direction plasticity during orienting movements. Nature 612, 316–322 (2022). Rule, M. E. et al. Stable task information from an unstable neural population. eLife 9 e51121 (2020).

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. © The Author(s) 2024

Methods Flies Unless otherwise specified, flies were raised on cornmeal-molasses food (Archon Scientific) in an incubator on a 12 h:12 h light:dark cycle at 25 °C at 50–70% relative humidity. Experimenters were not blinded to fly genotype. For iontophoresis stimulus experiments (Fig. 2a,b) flies were grouped for analysis based on genotype. Sample sizes were chosen based on conventions in our field for standard sample sizes; these sample sizes are conventionally determined on the basis of the expected magnitude of animal-to-animal variability, given published results and pilot data. All experiments used flies with at least one wild-type copy of the white (w) gene. Genotypes used in each figure are as follows. Fig. 1: PFL2 and PFL3 calcium imaging, w/+;P{VT033284-p65AD}attP40/ 20XUAS-IVS-cyRFP{VK00037}; P{y[+t7.7];w[+mC]=VT044709-GAL4. DBD}attP2/PBac{y[+t7.7] w[+mC]=20XUAS-IVS-jGCaMP7b}VK00005. PFL2 calcium imaging, w/+;P{VT033284-p65AD}attP40/20XUASIVS-cyRFP{VK00037}; P{y[+t7.7];P{VT007338-Gal4DBD}attP2/ PBac{y[+t7.7] w[+mC]=20XUAS-IVS-jGCaMP7b}VK00005. Fig. 2: PFL2 cells expressing P2X2, w/+;P{VT033284-p65AD}attP40/ P{w[+mC]=UAS-Rnor\P2rx2.L}4/;P{VT007338-Gal4DBD} attP2/20XUAS-mCD8::GFP {attP2}. Empty split control, w/+;P{y[+t7.7] w[+mC]=p65.AD.Uw}attP40/P{w[+mC]=UAS-Rnor\ P2rx2.L}4;P{y[+t7.7] w[+mC]=GAL4.DBD.Uw}attP2/20XUAS-mCD8::GFP {attP2}. PFL2 calcium imaging, w/+;P{VT033284-p65AD}attP40/20XUAS-IVS-cyRFP {VK00037}; P{y[+t7.7];P{VT007338-Gal4DBD}attP2/PBac{y[+t7.7] w[+mC]=20XUAS-IVS-jGCaMP7b}VK00005. Fig. 3: PFL2 calcium imaging, w/+;P{VT033284-p65AD}attP40/20XUAS-IVS-cyRFP {VK00037}; P{y[+t7.7];P{VT007338-Gal4DBD}attP2/PBac{y[+t7.7] w[+mC]=20XUAS-IVS-jGCaMP7b}VK00005. PFL2 and PFL3 calcium imaging, w/+;P{VT033284-p65AD}attP40/20XUAS-IVS-cyRFP{VK00037}; P{y[+t7.7];w[+mC]=VT044709-GAL4.DBD}attP2/PBac{y[+t7.7] w[+mC]=20XUAS-IVS-jGCaMP7b}VK00005. Fig. 4: w/+;P{VT033284-p65AD}attP40/P{20XUAS-IVS-mCD8::GFP} attP40;P{y[+t7.7] w[+mC]=VT044709-GAL4.DBD}attP2/+. Fig. 5: PFL2 calcium imaging, w/+;P{VT033284-p65AD}attP40/20XUAS-IVS-cyRFP {VK00037}; P{y[+t7.7];P{VT007338-Gal4DBD}attP2/PBac{y[+t7.7] w[+mC]=20XUAS-IVS-jGCaMP7b}VK00005. PFL2 and PFL3 recordings, w/+;P{VT033284-p65AD}attP40/P{20XUAS-IVS-mCD8::GFP} attP40;P{y[+t7.7] w[+mC]=VT044709-GAL4.DBD}attP2/+. Extended Data Fig. 2: MCFO, w[1118] P{y[+t7.7] w[+mC]=R57C10-FLPG5}su(Hw)attP8; PBac{y[+mDint2] w[+mC]=10xUAS(FRT.stop)myr::smGdP-HA} VK00005 P{y[+t7.7] w[+mC]=10xUAS(FRT.stop)myr::smGdP-V5-THS10xUAS(FRT.stop)myr::smGdP-FLAG}su(Hw)attP1. PFL2 and PFL3 line: w/+;P{VT033284-p65AD}attP40/P{20XUAS-IVS-mCD8::GFP} attP40;P{y[+t7.7] w[+mC]=VT044709-GAL4.DBD}attP2/+. PFL2 line: w/+;P{VT033284-p65AD}attP40/P{20XUAS-IVS-mCD8::GFP}attP40; P{y[+t7.7];P{VT007338-Gal4DBD}attP2/+. Extended Data Fig. 3: PFL2 calcium imaging,

w/+;P{VT033284-p65AD}attP40/20XUAS-IVS-cyRFP {VK00037}; P{y[+t7.7];P{VT007338-Gal4DBD}attP2/PBac{y[+t7.7] w[+mC]=20XUAS-IVS-jGCaMP7b}VK00005. PFL2 and PFL3 calcium imaging, w/+;P{VT033284-p65AD}attP40/20XUAS-IVS-cyRFP{VK00037}; P{y[+t7.7];w[+mC]=VT044709-GAL4.DBD}attP2/PBac{y[+t7.7] w[+mC]=20XUAS-IVS-jGCaMP7b}VK00005. Extended Data Fig. 4: PFL2 cells expressing P2X2, w/+;P{VT033284-p65AD}attP40/ P{w[+mC]=UAS-Rnor\P2rx2.L}4/;P{VT007338-Gal4DBD} attP2/20XUAS-mCD8::GFP {attP2}. Empty split control, w/+;P{y[+t7.7] w[+mC]=p65.AD.Uw}attP40/P{w[+mC]=UAS-Rnor\ P2rx2.L}4;P{y[+t7.7] w[+mC]=GAL4.DBD.Uw}attP2/20XUAS-mCD8::GFP {attP2}. Extended Data Fig. 5: PFL2 calcium imaging, w/+;P{VT033284-p65AD}attP40/20XUAS-IVS-cyRFP {VK00037}; P{y[+t7.7];P{VT007338-Gal4DBD}attP2/PBac{y[+t7.7] w[+mC]=20XUAS-IVS-jGCaMP7b}VK00005. PFL2 and PFL3 calcium imaging, w/+;P{VT033284-p65AD}attP40/20XUAS-IVS-cyRFP{VK00037}; P{y[+t7.7];w[+mC]=VT044709-GAL4.DBD}attP2/PBac{y[+t7.7] w[+mC]=20XUAS-IVS-jGCaMP7b}VK00005. Extended Data Fig. 6: w/+;P{VT033284-p65AD}attP40/P{20XUAS-IVS-mCD8::GFP} attP40;P{y[+t7.7] w[+mC]=VT044709-GAL4.DBD}attP2/+. Extended Data Fig. 7: w/+;P{VT033284-p65AD}attP40/P{20XUAS-IVS-mCD8::GFP} attP40;P{y[+t7.7] w[+mC]=VT044709-GAL4.DBD}attP2/+. Extended Data Fig. 8–10: PFL2 calcium imaging, w/+;P{VT033284-p65AD}attP40/20XUAS-IVS-cyRFP {VK00037}; P{y[+t7.7];P{VT007338-Gal4DBD}attP2/PBac{y[+t7.7] w[+mC]=20XUAS-IVS-jGCaMP7b}VK00005.

Origins of transgenic stocks The following stocks were obtained from the Bloomington Drosophila Stock Center (BDSC) and previously published as follows: P{y[+t7.7]w[+mC]=VT044709-GAL4.DBD}attP2 (BDSC_75555)41, P{y[+t7.7] w[+mC]=p65.AD.Uw}attP40; P{y[+t7.7] w[+mC]=GAL4. DBD.Uw}attP2 (BDSC_79603), P{w[+mC]=UAS-Rnor\P2rx2.L}4/ CyO (BDSC_91223)42, w[1118] P{y[+t7.7] w[+mC]=R57C10-FLPG5} su(Hw)attP8; PBac{y[+mDint2] w[+mC]=10xUAS(FRT.stop) myr::smGdP-HA}VK00005 P{y[+t7.7] w[+mC]=10xUAS(FRT.stop) myr::smGdP-V5-THS-10xUAS(FRT.stop)myr::smGdP-FLAG}su(Hw)attP1 (BDSC_64088)43. The following stocks were obtained from WellGenetics: w[1118];P{VT007338-p65ADZp}attP40/CyO;+ (SWG9178/A), w[1118];P{VT033284-p65AD}attP40/CyO;+ (A/SWG8077). Using these lines, we constructed a split-Gal4 line whose expression in the lateral accessory lobe (LAL) is specific to PFL2 and PFL3 cells (+;P{VT033284-p65AD}attP40;P{y[+t7.7] w[+mC]=VT044709-GAL4. DBD}attP2). We validated the expression of this line using immunohistochemical anti-GFP staining and also using Multi-Color-Flip-Out (MCFO)43 to visualize single-cell morphologies. This line has significant non-specific expression throughout the brain but is specific for PFL2 and PFL3 in the LAL. We also constructed a split-Gal4 line to target PFL2 neurons, +;P{VT033284-p65AD}attP40; P{y[+t7.7];P{VT007338-Gal4DBD} attP2. We validated the expression of this line using immunohistochemical anti-GFP staining and also using MCFO to visualize single-cell morphologies. This line exhibits expression in various peripheral neurons but is selective for PFL2 neurons within the central complex—specifically, the protocerebral bridge, fan-shaped body and LAL.

Article Fly preparation and dissection Flies used for all experiments were isolated the day before the experiment by single-housing on molasses food. For calcium imaging experiments we used female flies 20–72 h posteclosion. For electrophysiology experiments, including the iontophoresis experiments, we used female flies 16–30 h posteclosion. No circadian restriction was imposed for the time of experiments. Manual dissections in preparation for experiments were as follows. Flies were briefly cold-anaesthetized and inserted using fine forceps (Fine Science Tools) into a custom platform machined from black Delrin (Autotiv or Protolabs). The platform was shaped like an inverted pyramid to minimize occlusion of the fly’s eyes. The head was pitched slightly forward, so the posterior surface was more accessible to the microscope objective. The wings were removed, then the fly head and thorax were secured to the holder using UV-curable glue (Loctite AA 3972) with a brief pulse of ultraviolet light (LED-200, Electro-Lite Co.). To prevent large brain movements, the proboscis was glued in place using a small amount of the same UV-curable glue. Using fine forceps in extracellular Drosophila saline, a window was opened in the head cuticle, and tracheoles and fat were removed to expose the brain. To further reduce brain movement, muscle 16 was stretched by gently tugging the oesophagus, or else it was removed by clipping the muscle anteriorly. For electrophysiology and iontophoresis experiments only, the perineural sheath was minimally removed with fine forceps over the brain region of interest. For all experiments, saline was continuously superfused over the brain. Drosophila extracellular saline composition was: 103 mM NaCl, 3 mM KCl, 5 mM TES, 8 mM trehalose, 10 mM glucose, 26 mM NaHCO3, 1 mM NaH2PO4, 1.5 mM CaCl2 and 4 mM MgCl2 (osmolarity 270–275 mOsm). Saline was oxygenated by bubbling with carbogen (95% O2, 5% CO2) and reached a final pH of about 7.3. Two-photon calcium imaging We used a two-photon microscope equipped with a galvo-galvo-resonant scanhead (Thorlabs Bergamo II GGR) and ×25, 1.10 numerical aperture (NA) objective (Nikon CFI APO LWD; Thorlabs, WDN25X-APO-MP). For volumetric imaging, we used a fast piezoelectric objective scanner (Thorlabs PFM450E). To excite GCaMP we used a wavelength-tunable femtosecond laser with dispersion compensation (Mai Tai DeepSee, Spectra Physics) set to 920 nm. GCaMP fluorescence signals were collected using GaAsP PMTs (PMT2100, Thorlabs) through a 405–488 nm band-pass filter (Thorlabs). All image acquisition and microscope control was conducted in MATLAB 2021a (MathWorks Inc), using ScanImage 2021 Premium with vDAQ hardware (Vidrio Technologies LLC) and custom MATLAB scripts for further experimental control. The region for imaging the fan-shaped body and protocerebral bridge was 150 × 250 pixels, whereas the region for imaging the LAL was 150 × 400 pixels. We acquired 10–12 slices in the z axis for each volume (4 µm per slice), resulting in 6–8 Hz volumetric scanning rate. For experiments using the selective PFL2 split-Gal4 line, we imaged in the protocerebral bridge, fan-shaped body, or LAL for different trials. For experiments imaging the mixed PFL2 and PFL3 split-Gal4 line, we only imaged in the LAL. Patch-clamp recordings Patch pipettes were pulled from filamented borosilicate capillary glass (outer diameter: 1.5 mm, inner diameter 0.86 mm; BF150-867.5HP, Sutter Instrument Company), using a horizontal pipette puller (P-97, Sutter Instrument Company) to a resistance range of 9–13 MΩ. Pipettes were filled with an internal solution44 consisting of 140 mM KOH, 140 mM aspartic acid, 1 mM KCl, 10 mM HEPES, 1 mM EGTA, 4 mM MgATP, 0.5 mM Na3GTP and 15 mM neurobiotin citrate, filtered twice through a 0.22 µm PVDF filter (Millipore). All electrophysiology experiments used a semicustom upright microscope consisting of a motorized base (Thorlabs Cerna), with conventional collection and epifluorescence attachment (Olympus BX51),

but no substage optics in order to better fit the virtual-reality system. The microscope was equipped with a ×40 water immersion objective (LUMPlanFLN 40×W, Olympus) and CCD Monochrome Camera (Retiga ELECTRO; 01-ELECTRO-M-14-C Teledyne). For GFP excitation and detection, we used a 100 W Hg arc lamp (Olympus U-LH100HG) and an eGFP long-pass filter cube (Olympus F-EGFP LP). The fly was illuminated from below using a fibre optic coupled LED (M740F2, Thorlabs) coupled to a ferrule-terminated patch cable (200 µM core, 0.22 NA, Thorlabs) attached to a fibre optic cannula (200 µM core, 0.22, Thorlabs). The cannula was glued to the ventral side of the holder and positioned approximately 135° from the front of the fly to be unobtrusive to the fly’s visual field. Throughout the experiment, saline bubbled with 95% O2 and 5% CO2 was superfused over the fly using a gravity fed pump at a rate of 2 ml min−1. Whole-cell current-clamp recordings were performed using an Axopatch 200B amplifier with a CV-203BU headstage (Molecular Devices). Data from the amplifier were low-pass filtered using a 4-pole Bessel low-pass filter with a 5 kHz corner frequency, then acquired on a data acquisition card at 20 kHz (NiDAQ PCIe-6363, National Instruments). The liquid junction potential was corrected by subtracting 13 mV from recorded voltages45. Membrane potential data was then resampled to a rate of 1 kHz for ease of use and compatibility with behavioural data. To estimate baseline membrane voltage (Fig. 5e–g), we removed spikes from voltage traces by median filtering using a 50 ms window and lightly smoothed using the smoothdata function in MATLAB (loess method, 20 ms window). For all electrophysiology experiments in the mixed PFL2 and PFL3 line, we recorded from only one cell per fly. During recordings the cell was filled using internal solution containing neurobiotin citrate, so that we could visualize the cell morphology in order to determine its identity, using the protocol described in the ‘Immunohistochemistry’ section.

Spherical treadmill and locomotion measurement Experiments used an air-cushioned spherical treadmill and machine-vision system to track the intended movement of the animal. The treadmill consisted of a 9-mm-diameter ball machined from foam (FR-4615, General Plastics), sitting in a custom-designed concave hemispherical holder three-dimensionally printed from clear acrylic (Autotiv). The ball was floated with medical-grade breathing air (Med-Tech) through a tapered hole at the base of the holder using a flow meter (Cole Parmer). For machine-vision tracking, the ball was painted with a high-contrast black pattern using a black acrylic pen and illuminated with an IR LED (880 nm for two-photon experiments; M880L3, Thorlabs, or 780 nm for electrophysiology experiments; M780L3, Thorlabs). Ball movement was captured online at 60 Hz using a CMOS camera (CM3-U3-13Y3M-CS for two-photon imaging, or CM3-U3-13Y3C-CS for electrophysiology, Teledyne FLIR) fitted with a macro zoom lens InfiniStix (68 mm ×0.66 for two-photon, InfiniStix 94 mm ×0.5 for electrophysiology). The camera faced the ball from behind the fly (at 180°). Machine vision software (FicTrac v.2.1) was used to track the position of the ball43 in real time. We used a custom Python script to output the forward axis ball displacement, yaw axis ball displacement, forward ball displacement and gain-modified yaw ball displacement to an analogue output device (Phidget Analog 4-Output 1002_0B) and recorded these signals along with other experimental timeseries data on a data acquisition card (NiDAQ PCIe-6363) card at 20 kHz. The gain-modified yaw ball displacement voltage signal was also used to update the azimuthal position of the visual cues displayed by the visual panorama. Visual panorama and visual stimuli To display visual stimuli, we used a circular panorama built from modular square (8 × 8 pixel) LED panels46. The circular arena was twelve panels in circumference and two panels tall. To accommodate the ball-tracking camera view and the light source the upper panel 180° behind the fly was removed. In all experiments, the modular panels contained blue LEDs

with peak blue (470 nm) emission; blue LEDs were chosen to reduce overlap with the GCaMP emission spectrum. For calcium imaging experiments, four layers of gel filters were added in front of the LED arena (Rosco, R381) to further reduce overlap in spectra. For electrophysiology experiments, only two layers of gel filters were used. On top of the gel filters in both cases we added a final diffuser layer to prevent reflections (SXF-0600, Snow White Light Diffuser, Decorative Films). The visual cue was a bright (positive contrast) 2-pixel-wide (7.5°) vertical bar. The bar’s height was the full two-panel height of the area (except for −165 to +165° behind the fly with a single visual display panel, where the bar was half this height). The bar intensity was set at a luminance value of 4 with a background luminance of 0 (maximum value 15). The azimuthal position of the cue was controlled during closed-loop experiments by the yaw motion of the ball (see section ‘Spherical treadmill and locomotion measurement’). For all experiments, a yaw gain of 0.7 was used, meaning that the visual cue displacement was 0.7 times the ball’s yaw displacement. For calcium imaging and electrophysiology experiments the cue was instantaneously jumped every 60 s by ±90° or 180°. Immediately following each jump, the cue would continue to move in closed loop with the fly’s movements. We recorded the position of the cue during experiments using analogue output signals from the visual panels along with other experimental timeseries data on a data acquisition card at 20 kHz (PCIe-6363, National Instruments). We converted analogue signals from the visual panels into cue position in pixels during offline analysis. Cue positions were then converted into head direction as follows: 0° when the fly was directly facing the cue, 90° when the fly’s head direction was 90° clockwise to the cue, −90° when the fly was 90° counterclockwise and 180° when the fly was facing directly away from the cue. These signals were lightly smoothed and values above 180° or below −180° were set to ±180°.

Experimental trial structure Before data collection in each experiment, the fly walked for a minimum of 15 min in closed loop with the visual cue. For calcium imaging experiments, data were collected in 10 min trials. In each trial, the fly was in closed loop with the cue, and every 60 s the cue jumped to a new location relative to its current one, alternating between +90°, 180° and −90°, in that order. Between trials during calcium imaging experiments, there was 30 s of darkness. Electrophysiology experiments followed a similar protocol, though occasionally 20 min trials were collected rather than 10 min trials. Additionally, during the intertrial period, flies viewed the cue in closed loop. As these experiments were heavily dependent on spontaneously performed behaviour, trials were run until the fly stopped walking or, in the case of electrophysiology experiments, the cell recording quality significantly decreased. Iontophoresis stimuli Pipettes for iontophoresis were pulled from aluminosilicate capillary glass (outer diameter 1.5 mm, inner diameter 1.0 mm, Sutter Instrument Company) to a resistance of approximately 75 MΩ using a horizontal pipette puller (P-97, Sutter Instrument Company). Pipettes were filled with a solution47 consisting of 10 mM ATP disodium in extracellular saline with 1 mM AlexFluor 555 hydrazine (Thermo Fisher Scientific) for visualization. This solution was stored in aliquots at −20 °C, thawed fresh daily and kept on ice during the experiment. The tip of the iontophoresis pipette was positioned to be approximately in the medial region of the protocerebral bridge every trial. During experimental trials, we simultaneously recorded from a PFL2 neuron. During control trials, we recorded from unidentified neurons with somata in the same approximate region as PFL2 somata (medial area dorsal to the protocerebral bridge). Pulses of ATP were delivered using a dual current generator iontophoresis system (Model 260, World Precision Instruments). Holding current was set to 10 nA to prevent solution leakage, and a current of −200 nA was used for ejection. Visual confirmation of ATP ejection following current pulses was obtained before and after

each trial. For the duration of the 10 min trial period, flies viewed a visual cue that moved in closed loop with their rotational movements, as described above. Throughout the trial, pulses were delivered every 30 s with lengths of 100, 200, 300 and 500 ms, repeating in that order.

Immunohistochemistry Brains were dissected from female flies 1–3 days posteclosion in Drosophila external saline and fixed in 4% paraformaldehyde (Electron Microscopy Sciences, catalogue no. 15714) in phosphate-buffered saline (PBS, Thermo Fisher Scientific, 46-013-CM) for 15 min at room temperature. Brains were washed with PBS before adding a blocking solution containing 5% normal goat serum (Sigma-Aldrich, catalogue no. G9023) in PBS with 0.44% Triton-X (Sigma-Aldrich, catalogue no. T8787) for 20 min. Brains were then incubated in primary antibody with blocking solution for roughly 24 h at room temperature, washed in PBS and incubated in secondary antibody with blocking solution for roughly 24 h at room temperature. Primary and secondary antibodies were protocol-specific (see below). Brains were then rinsed with PBS and mounted in antifade mounting medium (Vectashield, Vector Laboratories, catalogue no. H-1000) for imaging. For MCFO protocols, a tertiary incubation step for about 24 h at room temperature and wash with PBS was performed before mounting. Mounted brains were imaged on a Leica SPE confocal microscope using a ×40, 1.15 NA oil-immersion objective. Image stacks comprised 50 to 200 z-slices at a depth of 1 µm per slice. Image resolution was 1,024 × 1,024 pixels. For visualizing Gal4 expression patterns, the primary antibody solution contained chicken anti-GFP (1:1,000, Abcam, catalogue no. ab13970) and mouse anti-Bruchpilot (1:30, Developmental Studies Hybridoma Bank, nc82). The secondary antibody solution contained Alexa Fluor 488 goat anti-chicken (1:250, Invitrogen, catalogue no. A11039) and Alexa Fluor 633 goat anti-mouse (1:250, Invitrogen, catalogue no. A21050). For visualizing cell fills after whole-cell patch-clamp recordings, 1:1,000 streptavidin::Alexa Fluor 568 (Invitrogen, catalogue no. S11226) was added to the primary and secondary solutions. For MCFO48, the primary antibody solution contained mouse anti-Bruchpilot (1:30, Developmental Studies Hybridoma Bank, nc82), rat anti-Flag (1:200, Novus Biologicals, catalogue no. NBP1-06712B) and rabbit anti-HA (1:300, Cell Signaling Technology, catalogue no. 3724S). The secondary antibody solution contained Alexa Fluor 488 goat anti-rabbit (1:250, Invitrogen, catalogue no. A11039), ATTO 647 goat anti-rat (1:400, Rockland, catalogue no. 612-156-120) and Alexa Fluor 405 goat anti-mouse (1:500, Invitrogen, catalogue no. A31553). The tertiary antibody solution contained DyLight 550 mouse anti-V5 (1:500, Bio-Rad, catalogue no. MCA1360D550GA). Processing calcium imaging data Analysis was performed in either MATLAB 2019 or MATLAB R2021a. The calcium imaging dataset comprised 23 flies expressing GCaMP under the control of the PFL3 + 2 split-Gal4 line and 33 flies expressing GCaMP under the control of the PFL2 split-Gal4 line. Rigid motion correction in the x, y and z axes was performed for each trial using the NoRMCorre algorithm49. Each region of interest (ROI) was defined across the z-stack. For each ROI ∆F/F was calculated with the baseline fluorescence (F) defined as the mean of the bottom 10% of fluorescence values in a given trial (600 s in length). From this measurement a modified z-score was calculated using the median absolute deviation (MAD) normalized difference from the median, which we refer to as the z-scored ∆F/F (Extended Data Fig. 9): yi =

xi − X , where X = median of X , MAD = median ( xi − X ) MAD

(1)

For protocerebral bridge imaging, ten ROIs were defined, one for each of the ten glomeruli occupied by PFL2 dendrites and defined to be approximately the same width and without overlap, constrained

Article by estimated anatomical boundaries. For fan-shaped-body imaging, nine ROIs were defined for PFL2 neurites corresponding to the nine columns spanning the horizontal axis of the fan-shaped body. ROIs were approximately the same width without overlap. For LAL imaging, two ROIs were defined, one for the left LAL and one for the right. In any given 10 min epoch, we imaged either the protocerebral bridge or the fan-shaped body, or the LAL, that is, one brain region only. Signals in the protocerebral bridge and fan-shaped body had a similar sinusoidal profile, similar bump amplitude and a similar relationship to fly behaviour, so we used both protocerebral-bridge-imaging epochs and fan-shaped-body-imaging epochs to obtain our measurements of bump amplitude, and we pooled these bump amplitude measurements without regard to whether they came from the protocerebral bridge or fan-shaped body, see Figs. 2e–g, 3a and 5k, and Extended Data Figs. 3 and 5. The single fly examples shown in Fig. 5j, and Extended Data Figs. 8 and 9 are from trials where we imaged the protocerebral bridge.

Processing locomotion and visual arena data The position of the spherical treadmill was computed online using machine vision software (Fictrac v.2.1) and output as a voltage signal for acquisition. For post hoc analysis, the voltage signal was converted into radians and unwrapped. Signals were then low-pass filtered using a second-order Butterworth filter with 0.003 corner frequency and downsampled to half the ball-tracking update rate.Velocity was calculated using the MATLAB gradient function. Artefactually large velocity values (greater than 20 rad s−1) were set to 20 rad s−1, and timeseries were then smoothed using the smooth function in MATLAB (using the loess method with an 33 ms window) and resampled to 60 Hz, the ball-tracking update rate. Forward and sideways velocities were then converted to millimetres per second while yaw (rotational) velocity was converted to degrees per second. During calcium imaging, we acquired a signal from our imaging software indicating the end of each volumetric stack on the same acquisition card as online ball tracking signals. These imaging time points were then resampled to the ball-kinematic data update rate of 60 Hz, allowing us to align the acquired volumes. Electrophysiology data were collected on the same acquisition card as online ball tracking signals, so alignment was not required; however, ball-tracking data were resampled to 1 kHz to match the sampling rate of the electrophysiology data. Computing inferred goal direction and consistency of head direction across trials Head direction (θ) and consistency of head direction (ρ) were calculated for every datapoint over each entire trial using a 30 s window centred on each datapoint index. Here we excluded datapoints where the fly’s cumulative speed (forward + sideways + rotational) was less than 0.67 rad s−1. At values below this threshold, the fly is essentially standing still, so including these time points might result in an overestimation of the fly’s internal drive to maintain its head direction. We also excluded time points within 5 s after a cue jump; this was to avoid underestimating the fly’s internal drive to maintain its head direction, as these points represent a forced deviation from the angle the flies were attempting to maintain. If no datapoints within the 30 s window satisfied these requirements, then the window was excluded from further analyses. Head directions were treated as unit vectors and used to compute the goal direction θg and the consistency of head direction ρ: θg = atan2(Σsin(θw), Σcos(θw)) 2

 Σcos(θw)   Σsin(θw)  ρt =    + N Nw w    

(2) 2

(3)

In equation (2), θg represents the goal direction associated with time point t, θw is a vector consisting of all head directions within the 15 s

before and after time point t at which the fly was moving, and the atan2 function is the two-argument arctangent. As each head direction is treated as a unit vector we can simply convert each value of θw into Cartesian coordinates, calculate the sum of these values along each axis and take the arctangent to convert them back to polar coordinates to find the average angle the fly travelled at during that window. In equation (3), ρt represents the ρ value associated with time point t, and Nw is the number of data points over which ρ is calculated. Again, we first convert each θ value into Cartesian coordinates and find the average distance travelled along each axis before calculating ρ, so that ρ ranges between 0 and 1. Note that ρ = 1 would indicate that the fly maintained the same head direction for the entire window, while ρ = 0 would indicate that the fly uniformly sampled all possible head directions during the window. Figure 1g shows mean ρ and θ values from each trial, with radial length proportional to ρ.

Path segmentation based on walking straightness We observed that flies often walked in a straight line for an extended segment and then switched to a different apparent goal direction (θg) to initiate a new segment (Extended Data Fig. 10). To infer the fly’s goal direction, we automatically divided each path into segments. We reasoned that a switch in θg, would coincide with a dip in head direction consistency. Therefore, we looked for moments when ρ crossed a threshold value, and we broke the path into segments at those moments of threshold-crossing. The only exception was if ρ fell below threshold only very briefly (less than 0.5 s); here we did not count these as segment breaks, but lumped those time points together as part of a continuous segment with the preceding and following time points. We found that a threshold of ρ = 0.88 matched our commonsense notion of when a new segment should start, but varying the threshold value over a wide range (0.70–0.98) did not dramatically change the outcome of our segmentation process nor the resulting relationships between neural activity and behaviour. We then calculated the average θ and ρ for each of these segments and used the mean θ value as the inferred goal head direction. For all analyses, segments were discarded if ρ was equal to 1, as this indicated the panels had not been initiated correctly and that the cue had remained in a single location for the duration of the trial. Segments were also discarded if the fly was inactive (that is, if the fly’s cumulative velocity was not above a threshold of 0.67 rad s−1 for at least 2 s). For population analyses shown in Figs. 1h, 2e–g and  3, all remaining segments were used regardless of ρ. For the head direction tuning analysis shown in Fig. 4f,g we used a threshold of ρ = 0.7, and we only used data from segments where ρ ≥ 0.7. We lowered the threshold on ρ for this analysis because we needed to include a larger number of time points in the analysis, to improve the resolution for binning the activity of cells into groups defined by θp − θg. Classifying jumps as ‘corrected, high ρ’ versus ‘uncorrected, low ρ’ To analyse cue jumps (Figs. 1f and  5e–h and Extended Data Figs. 3 and 7), we classified jumps as ‘corrected, high ρ’ or ‘uncorrected, low ρ’. Here we rejected jumps where the fly was essentially immobile in the epoch before the jump (meaning its cumulative speed did not exceed 0.67 rad s−1 for at least 1 s in the 15 s before the jump). For each jump, we measured the original mean head direction (θ) during the 15 s before the jump, and we judged jumps as ‘corrected’ if θ returned to within 30° of its original value for ±90° jumps, or within 60° for 180° jumps, in the 10 s after the jump. We classified a jump trial as ‘high ρ’ if the average ρ was equal to or greater than 0.88 as calculated over time points within the 15 s before the jump, where the fly’s cumulative speed was over 0.67 rad s−1 and ‘low ρ’ otherwise. In principle, it is possible that the jumps we categorized as uncorrected might have happened (by chance) to produce a smaller absolute change in the distance between a fly’s head direction and a cell’s preferred head direction |∆(θ − θp)|, as compared to the jumps in the corrected category. If this sampling artefact existed, it could produce

an overall smaller absolute change in membrane potential for uncorrected jumps, leading us to misinterpret this result. However, we found no difference in the variance of ∆(θ − θp) or the mean value of |∆(θ − θp)| for uncorrected versus corrected jumps (Extended Data Fig. 7d).

Computing average response to iontophoresis stimulation For the plots shown in Fig. 2a,b and Extended Data Fig. 4, data from the ±10 s period around each ATP pulse were averaged within individual flies to get the fly-averaged response to the 100 ms, 200 ms, 300 ms and 500 ms pulses for the membrane potential, forward velocity, sideways velocity and rotational velocity (each condition had at least four repetitions per fly). We then calculated the grand mean and s.e.m. across all flies using these per-fly averages. Computing activity bump parameters To track the amplitude and phase of PFL2 activity for analyses in Figs. 2c–g and 5j,k and Extended Data Figs. 3, 5 and 8, a sinusoid was fit independently to each time point of the z-scored ∆F/F activity across fan-shaped body and protocerebral bridge imaging trials: PFL2 activity = a × sin(x − u) + c

(4)

Here, PFL2 activity is a vector of z-scored ∆F/F values at a single time point such that it has ten bins if from a protocerebral bridge trial or nine if from an fan-shaped body trial, corresponding to the number of ROIs specified for each region. Here, u sets the phase of the sinusoid, c is the vertical offset term, a represents the bump amplitude, and the position in brain space where the peak of the sinusoid is located defines the bump phase. A bump phase of +180° represents the rightmost position in the protocerebral bridge and fan-shaped body while a phase of −180° represents the leftmost position.

Computing change in bump phase versus change in head direction We calculated the relative changes in PFL2 bump phase and head direction in 1.5 s bins as shown in Fig. 2d. In each time window, we took the difference between start and end points for θ or bump phase. Positive differences represent a clockwise shift while a negative difference represents a counterclockwise shift. The relationship between changes in θ and changes in bump phase was strongest when a 200 ms lag was implemented, such that changes in bump phase lagged 200 ms behind changes in θ. The line of best fit for the relationship between the two variables was found with the polyfit and polyval functions. We then used the corrcoef function to find the correlation coefficients and P value of the relationship. We excluded indices where the adjusted r2 value of the sinusoidal fit for bump parameters was below 0.1 or the fly was not moving. Computing population activity as a function of behaviour To determine the relationship between neural activity and various behavioural parameters (Figs. 2e–g and 3 and Extended Data Fig. 5) we binned conditioned data. Within each segment described above, indices with cumulative velocity less than 0.67 rad s−1 were removed, and head directions were recalculated to be relative to the inferred goal head directions, meaning that a negative value indicated that the fly was facing counterclockwise to its goal head direction, and a positive value indicated that the fly was facing clockwise to its goal head direction. The z-scored ∆F/F was then averaged within bins of 10° s−1 for rotational velocity, 1 mm s−1 for forward velocity, or 10° for head direction. For Fig. 3d,e and Extended Data Fig. 5, the sum or difference between right and left LAL activity was calculated per segment following binning. The mean and s.e.m. was then calculated across flies. Computing preferred head direction To show preferred cell head direction in Fig. 4a, we divided the estimated baseline membrane voltage (see section ‘Patch-clamping’) into

20° bins, based on the fly’s head direction. We considered the preferred head direction to be the value with the maximum binned membrane potential. The amplitude of the preferred head direction was calculated by taking the difference between the maximum and minimum binned membrane potential values.

Analysis of IPSPs To detect IPSPs for analyses in Fig. 4d, we focused only on jump trials where the fly was essentially immobile, to avoid any confounds associated with the membrane potential fluctuations in these cells that are associated with movement transitions. Action potentials were first removed from the voltage trace by median filtering the membrane potential with a 25 ms window, then lightly smoothing (smoothdata function in MATLAB, window size 20 ms, using the loess method). We then calculated the derivative of the membrane potential (gradient function in MATLAB) and found local minima corresponding to periods of rapid decreases in membrane potential (findpeaks function in MATLAB, peak distance of 20 ms, threshold determined for each cell). We also generated a detrended version of the membrane potential by subtracting the median filtered membrane potential (500 ms window) and found local minima (findpeaks function in MATLAB, peak distance of 20 ms, threshold determined for each cell). We categorized IPSPs as indices where a negative peak was detected from the derivative of the membrane potential trace within 30 ms before a negative peak in the baseline corrected trace. Computing change in IPSP parameters as a function of the change in head direction To examine changes in IPSP parameters as a function of change in head direction, ±5 s windows around cue jumps in which the fly did not move for the entire 10 s period were used (Fig. 4c). All jumps fitting this category were analysed for 20 of 27 neurons in this dataset; the remaining 7 neurons were not included, as there were no cue jumps around which the fly was stopped for the entire 10 s window around the jump. Detected IPSP frequency was calculated for the 5 s before or after the cue jump. The change in frequency before jump versus after jump was then compared to change in head direction relative to the cell’s preferred head direction produced by the cue jump. This was determined by first finding the absolute angular difference between the head direction before the jump and the cell’s preferred head direction (see section ‘Computing preferred head direction’) and doing the same for the new head direction following the cue jump. Then the precue jump value was subtracted from the postcue jump value. This means that a negative value indicated that the head direction was closer to the cell’s preferred head direction following the jump while a positive value indicated that the distance between the head direction and the cell’s preferred head direction increased following the jump. The change in IPSP frequency was then plotted against the change in the distance from the cell’s preferred head direction for each jump. MATLAB’s polyfit and polyval functions were used to find the line of best fit for the relationship between the two variables, while the corrcoef function was used to find the correlation coefficients of the relationship. Additionally, we used an unbalanced two-factor ANOVA to determine the significance of the relationship between change in frequency and change in head direction compared to that with cell identity. Exploring interactions between goal head direction and single-cell head direction tuning curves To explore how single-cell dynamics lead to the population level relationships between neural activity and behaviour, we first segmented electrophysiology data into groups of continuous data points based on their associated goal head directions and ρ values (see section ‘Trial segmentation based on walking straightness and inferred goal direction’). For each trial, the cell’s preferred heading was determined (see section ‘Computing preferred head direction’) and the difference between the

Article preferred heading and goal was found (θg − θp). Segments were assigned to 72˚ wide bins based on the θg − θp value and for each segment, the head direction tuning curve was recalculated for both firing rate and membrane potential, using the data points within the bin. For Fig. 4f,g, the minimum value of each tuning curve was calculated and subtracted from that tuning curve. Following the subtraction, the mean and s.e.m. values were calculated across all tuning curves within each θg − θp bin. For Extended Data Fig. 6, the only difference is that the minimum value of the tuning curves was not subtracted.

Determining the temporal relationship between neural activity and behaviour The figures shown in Fig. 5h were created using the same method as previous jump analyses (see section ‘Classifying jumps as “corrected, high ρ“ versus ”uncorrected, low ρ”’) but pooling data across the PFL2 and PFL3 corrected jumps. For Extended Data Fig. 7e, jumps were categorized as corrected as done previously, except jumps were deemed corrected if within 4 s following the cue jump, the cue was returned to within 40˚ for ±90˚ jumps, or within 75˚ for 180˚ jumps. This was done to select for jumps where the fly initiated a behavioural response quite rapidly following the cue jump, as behavioural response times varied across and within flies. For each corrected jump, the mean membrane potential was calculated from data in the 4 s preceding the cue jump and subtracted from the membrane potential in the 4 s following the cue jump, in order to focus on the change in membrane potential. Pearson’s linear correlation coefficient was then found between the absolute change in membrane potential from the 4 s following the jump and the lagged copies of the rotational speed over the same time window using MATLAB’s corr function. The mean and s.e.m. for each lag (stepped by 0.01 s with a maximum and minimum lag of ±1 s) across all individual correlations was then calculated. Examining single-cell responses around cue jumps For Fig. 5e–g and Extended Data Fig. 7a–d, jumps were categorized as either corrected or uncorrected as described previously (see section ‘Classifying jumps as “corrected, high ρ” versus “uncorrected, low ρ”’). For each jump, the difference between the mean membrane potentials calculated over the 1 s before and following each jump was found, and the distribution of these values is shown for both categories in Fig. 5f,g. A two sample Brown–Forsythe test was used to determine whether the variance of membrane potential changes was significantly different between the two categories. Examining the relationship between PFL2 activity and consistency of head direction For Fig. 5j,k and Extended Data Fig. 8, we binned data from each ROI (protocerebral bridge glomerulus) individually across the entire non-segmented trial to obtain the average response of each glomerulus across different values of (θ − θg). Here we inferred θg from neural activity rather than behaviour, because we wanted to include epochs with low ρ, and it is difficult to infer θg from the fly’s behaviour when ρ is low. To infer θg from neural activity, we grouped PFL2 bump amplitude data points by θ in 5˚ bins, and we calculated the difference in bump amplitude between pairs of bins 180˚ apart. Our model predicts that the absolute bump amplitude difference should be largest between the bins representing the goal and anti-goal, and so we searched for the pair of opposing bins with the largest difference in bump amplitude, and we took θg as the value of θ corresponding to the bin with the smaller bump amplitude. For Fig. 5k, we plotted the largest bump amplitude difference against the trial’s average ρ value, as calculated over the entire trial. For the individual brain space plots shown in Fig. 5i,j and Extended Data Fig. 8, we used this θg value to calculate the directional error (θ − θg) and we binned the z-scored ∆F/F data points from each individual ROI into 90˚ bins based on their associated directional error value. We then plotted the z-scored ∆F/F within each directional error

bin against neural space (ROI identity), with the rightmost glomeruli represented by an angular position of +180˚ and the leftmost by −180˚. Note that this analysis assumes that θg does not change very much over the course of a trial. If θg did change dramatically, this would result in a lower ρ value for the trial and possibly also a reduced bump amplitude range value, despite the fly potentially being in a state of high goal fixation strength for the entire trial. Flies that switched between periods of very strong and weak goal fixation would be expected to result in a similar potential mismatch between ρ and bump amplitude range. Therefore, the limitations of the analysis in Fig. 5k should, if anything, reduce our ability to detect a relationship between PFL2 activity and behaviour.

Neurotransmitter predictions There are 12 complete PFL2 cells, 13 complete PFL3 cells and one nearly complete DNa03 cell in the hemibrain connectome, with over 100 presynapses associated with each of these cells. Although the axon terminal of DNa03 is not present in the hemibrain dataset, DNa03 makes many output synapses in the brain, so there are still many EM images of the presynaptic sites within this cell. A recent algorithm50,51 automatically infers transmitter identification from electron micrographs in the hemibrain dataset, and it predicts that, of these, 12 of 12 PFL2 neurons are cholinergic, 13 of 13 PFL3 neurons are cholinergic and 1 of 1 DNa03 neuron is cholinergic. This algorithm predicts transmitters on a per-synapse basis, with an error rate that varies with cell and transmitter type. For PFL2 and PFL3 neurons, 74% of high-confidence presynapses (confidence score greater than or equal to 0.5) are predicted as cholinergic; the second most commonly predicted transmitter is glutamate (11%). For DNa03, 85.2 % of high-confidence presynapses are predicted as cholinergic; the second most commonly predicted neurotransmitter is glutamate (5.6%). This algorithm used 3,094 hemibrain neurons in its ground-truth data to train the model and included ground truth neurons identified as cholinergic using light microscopy pipelines and antibody staining or RNA sequencing. Among this ground-truth population, 73% of presynapses are correctly predicted as cholinergic. All synapse predictions are available from ref. 51. Connectome analyses Cell connectivity data was obtained from the hemibrain connectome at https://neuprint.janelia.org/ and analysis of this data was performed using the neuprintr natverse 1.1 software package52 available at https:// natverse.org/. Network model Our model shares features with several other recent models of central complex steering control4,5,11–13. These studies, in turn, built upon the existing idea that vectors should be represented as sinusoidal spatial patterns of neural activity, so that vector addition can be implemented via the addition of sinusoids9,15,16,53,54. Webb and colleagues extended this idea to an explicit notion of how rotational velocity commands might be generated via vector addition, by using right–left shifted basis vectors9. While our model incorporates these previous insights, it also takes advantage of new information from the automatic assignment of neurotransmitters50, as well as our neurophysiological experiments. For these reasons, it differs from previous models in a few important ways, as noted below. Most notably, our model shows how this network can adaptively control steering gain based on the magnitude of directional error (via PFL2 cells). Previous studies did not mention PFL2 cells, or else proposed that they have a non-steering-related role (as putative positive regulators of forward speed5,13). In contrast, our model gives these cells a strong influence over steering, and it shows how they can prevent oscillations in the steering system by boosting steering only when error is high, while throttling down steering when error is low. In broad terms, the aim of the model is to understand how steering signals arise from the head direction system. We take the steering

signal as the right–left difference in the activity of DNa02 descending neurons, because these neurons have been shown to predict and influence steering4:

dθ /dt ∝ DNa02R − DNa02L + #

(5)

where θ is head direction and ε is a random term that accounts for neural noise and the influence of unmodeled circuits (that is, the influence of other brain regions that affect steering and other descending pathways4,55,41). Here, (dθ /dt > 0) denotes rightward (clockwise) steering. DNa02 receives direct input from central complex output neurons (PFL3 cells), as well as indirect input from PFL2 and PFL3 cells via DNa03. We model the activity of each DNa02 cell by taking the weighted sum of its synaptic inputs and passing this through a nonlinearity:

DNa02R = f (ΣWDNa02R,PFL3R j × PFL3Rj + ΣWDNa02R,DNa03R × DNa03R)

(6)

DNa02L = f (ΣWDNa02L,PFL3L j × PFL3L j + ΣWDNa02L,DNa03L × DNa03L) whereW denotes an array of synaptic weights and f represents a nonlinear activation function (see below). We define PFL3R cells as the members of the PFL3 cell class that project their axons to the right hemisphere; PFL3L cells are the members of the PFL3 cell class that project their axons to the left hemisphere. This differs from some previous work where PFL3 cells were divided according to dendritic location rather than their axonal projection5. We model the activity of each DNa03 cell by taking the weighted sum of its synaptic inputs and passing this sum through the same type of nonlinearity. Here the relevant inputs to each DNa03 cell are from PFL3 cells and PFL2 cells. Each PFL2 axon projects bilaterally to both right and left brain hemispheres, and we model these connections as right–left symmetric, because we do not find any systematic asymmetry in connectome data; thus we use the same weights for PFL2 connections onto DNa03R and DNa03L:

DNa03R = f (ΣWDNa03R,PFL3R j × PFL3Rj + ΣWDNa03,PFL2 j × PFL2 j )

(7)

DNa03L = f (ΣWDNa03L,PFL3L j × PFL3L j + ΣWDNa03,PFL2 j × PFL2 j ) We then combine equations (5)–(7) to obtain an expression that predicts steering as a function of PFL2 and PFL3 activity. Here we assume that DNa03 output is anatomically symmetric in the right and left hemispheres. For compactness, we notate weight arrays using the abbreviations D2 (DNa02), D3 (DNa03) P2 (PFL2) and P3 (PFL3):

dθ /dt ∝ DNa02R − DNa02L + # = f (ΣWD2R,P3R j × P3Rj + ΣWD2,D3 × D3R) − f (ΣWD2L,P3Lj × P3Lj + ΣWD2,D3 × D3L) + # = f (ΣWD2R,P3R j × P3Rj + ΣWD2,D3 × f (ΣWD3R,P3R j × P3Rj

(8)

+ΣWD3,PFL2j × PFL2j )) − f (ΣWD2L,P3Lj × P3Lj + ΣWD2,D3 × f (ΣWD3L,P3Lj × P3Lj +ΣWD3,PFL2j × PFL2j )) + # If the activation function f is linear, the PFL2 terms will cancel out and PFL2 cells will have no effect on steering; therefore, we require f

to be nonlinear, at least for DNa03 cells. Below we will see that f must also be nonlinear for PFL3 cells. For consistency, we give f the same form for all cells in the model (see below). If f is an expansive nonlinearity and if PFL2 cells are excitatory (as inferred from neurotransmitter predictions, see above), then PFL2 cells will increase the gain of steering commands, because they push DNa03 cells up into the steeper part of the nonlinearity. We specify the weight array W for each connection type based on data from the hemibrain 1.2.1 (ref. 5) connectome, following the heuristic that functional weights are roughly proportional to the number of synaptic contacts per unitary connection42,45. Connectome data imply that PFL3 → DNa03 connections are approximately equal in strength to PFL3 → DNa02 connections, on average; all these weights are set to 1 in our model. Meanwhile, connectome data imply that PFL2 → DNa03 connections are approximately 4-fold stronger than PFL3 → DNa02 and PFL3 → DNa03 connections, on average; therefore, we set PFL2 → DNa03 weights equal to 4. Finally, connectome data imply that DNa03 → DNa02 connections are approximately 12-fold stronger than PFL3 → DNa02 and PFL3 → DNa03 connections; therefore, we set DNa03 → DNa02 connections to 12. We verified that our conclusions were not altered if we chose somewhat different scaling factors for these connections. Within each weight array W , we set all entries to the same value; in other words, all connections of the same type were given the same weight. All weights were positive, because all the presynaptic cells are cholinergic and thus excitatory (see section ‘Neurotransmitter predictions’). Some previous studies assumed that PFL3 cells are inhibitory5,11, which produces different model behaviour, because it aligns the system’s stable fixed point with the point of maximum PFL2 activity (not the minimum of PFL2 activity), resulting in more oscillatory steering around the goal. Our model contains 1,000 PFL2 units, 1,000 PFL3R units, 1,000 PFL3L units and 1,000 goal cell units. We chose to use a large number of units for these cell types, so that model output resembles a quasi-continuous function over neural space, because this makes it easier to see how spatial patterns of ensemble neural activity might resemble a sinusoidal function. In reality, however, there are only 12 PFL2 cells, 12 PFL3R cells and 12 PFL3L cells in the brain, according to the hemibrain 1.2.1 (ref. 5) connectome, so activity in the brain is actually more discretized than in our model. We verified that discretizing neural activity to match these numbers does not alter our conclusions. In our model, the activity of each PFL cell depends on both head direction and goal direction. ∆7 cells provide most of the head direction input to PFL2 and PFL3 cells5. Available data indicate that there are two complete linearized topographic maps of head direction in ∆7 cells, positioned side-by-side and formatted as two cycles of a sinusoidal function over neural space5,24,47,56. The spatial phase of the ∆7 activity pattern should have an arbitrary offset (θ0) relative to the fly’s head direction, with different values of θ0 in different individuals and at different times in the same individual, because this is true of EPG cells, which provide head direction input to ∆7 (ref. 19). We define the offset θ0 as the angular position of the EPG bump at a head direction of 0°. For simplicity, we lump the contributions of EPG output and ∆7 cells, and we treat their lumped contributions as a sinusoidal function over neural space. Specifically, we model their lumped output as cos(θ − θ0 − h), where h is a vector with 1,000 entries that uniformly tile the full 360° of angular space, representing the preferred head directions of 1,000 units. As the fly rotates rightward (clockwise), the sinusoidal pattern of neural activity moves leftward across the protocerebral bridge47,56. We define PFL3 cells as R or L depending on whether they project to right or left descending neurons, respectively. The head direction maps in PFL3 cells are shifted ±67.5° relative to the map in ∆7 cells, according to hemibrain connectome data41 (not ±90° as reported previously5,12,13). Therefore, we model the head direction input to PFL3R cells as cos(θ − θ0 − h + 67.5°), and we model the head direction input to PFL3L cells as cos(θ − θ0 − h − 67.5°). Meanwhile, PFL2 cells sample one

Article full head direction map from the middle section of the protocerebral bridge. Therefore, their head direction map is offset by 180°, relative to the map in ∆7 cells. Thus, we model the head direction input to PFL2 cells as cos(θ − θ0 − h + 180°). We model the neural representation of the goal direction (θg) as another sinusoidal pattern over neural space, which is reasonable, because the goal direction can be thought of as just a special head direction, and head direction is represented as a sinusoid. Because PFL2, PFL3R and PFL3L cells receive almost identical inputs in the fan-shaped body, we assume the goal input is the same in the PFL2, PFL3R and PFL3L populations. The output of goal cells is modelled as A × cos(θg − θ0 − h); note that if there is a shift in the offset of the head direction system (θ0), the goal representation will shift accordingly. As the goal direction rotates rightward (clockwise), the peak of activity in goal cells will move leftward across the fan-shaped body. We use A = 1 in our model implementations, so that the amplitude of the goal signal is equal to the amplitude of the head direction signal, but some of our results can be potentially explained by a mechanism that modulates A (Extended Data Fig. 8). To obtain PFL activity levels, we sum head direction inputs and goal inputs. We then rescale this sum according to a scaling factor S. Finally, we pass the result through a nonlinear activation function f : PFL2 = f (S × (cos(θ − θ0 − h + 180∘) + A × cos(θg − θ0 − h))) PFL3R = f (S × (cos(θ − θ0 − h + 67 . 5∘) + A × cos(θg − θ0 − h)))

(9)

PFL3L = f (S × (cos(θ − θ0 − h − 67 . 5∘) + A × cos(θg − θ0 − h)))

Note that the activation function f must be nonlinear or else the goal input will have no influence on the right–left difference in PFL3 activity (ΣPFL3R − ΣPFL3L). We use S = 1 by default, except in Figs. 5c,d and 5j,k, where we investigate the effect of lowering S. For simplicity, we use the same nonlinear activation function f for all units in this model (meaning all PFL2, PFL3, DNa03 and DNa02 cells). Specifically, we use an exponential linear unit or ELU. We chose an ELU because it is biologically highly plausible (as a ‘soft’ expansive nonlinearity57) and it is a good fit to our data. The input to the ELU is an array M that represents the weighted sum of the inputs to each cell, over its lifetime, for all values of head direction (θ), goal direction (θg), scaling parameter (S) and cell index (j). We rescale M so that min(M) = −1 and max(M) = 1. Then, we apply the function

ELU(M ) = M for M ≥ 0 ELU(M ) = e M − 1 for M < 0

are unchanged if we substitute different nonlinear activation functions (sigmoid or ReLU rather than ELU); other published models have assumed a multiplicative12 or divisive nonlinearity13. To model steering behaviour over time (Fig. 5d), we closed the loop on the brain’s feedback control system for steering: we took the fly’s predicted rotational velocity (dθ /dt) at each time point, and we fed it back into the head direction representation at the next time point, in order to compute updated PFL2 and PFL3 activity. The simulation was updated at a frequency of 10 Hz, and Fig. 5d shows 10 s of simulated time. We arbitrarily took 0° as the goal direction, so directional error is equal to θ. We drew the random steering component ε (equation (5)) from a Gaussian distribution, then we low-pass filtered ε(t) at 2 Hz, before rescaling ε(t) to enforce a standard deviation of 10°. This was done for different values of S, using the same frozen noise sample ε(t) in each case. In Fig. 5k, we used many independent random samples of ε(t), each simulation run included 100 s of simulated time, and we swept through many values of S, computing PFL2 bump amplitude and the consistency of head direction (p) for each run, with p = (one-circular variance(θ)). Model code was written and implemented in Python v.3.9.5.

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

Data availability The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.

Code availability Code for data analysis and model implementation is available at https:// github.com/wilson-lab/WesteindeWilson_AnalysisCode. 41. 42. 43. 44.

45.

(10)

before finally rescaling the resulting array ELU(M ) so that it ranges from 0 to 1. These rescaling procedures are motivated by the idea that a neuron’s inputs are adjusted (over development and/or evolution) to fit into some standard dynamic range dictated by the biophysical properties of a typical neuron; rescaling in this way is useful because it ensures that every cell type has a similar overall level of activity, and every cell has an activation function with the same shape. Note that from the perspective of a single PFL cell, the goal input is a fixed value that does not change as head direction changes, and when this goal signal becomes more positive (again, from the perspective of a single PFL cell), it pushes the cell’s activity up to a steeper part of the nonlinear function f , effectively amplifying the cell’s head direction tuning. This aspect of the model captures our experimental observation that head direction tuning is stronger in some cells than in other cells, in a way that depends systematically on the distance between the cell’s preferred head direction (θp) and the goal direction (θg). Notably, this observation emerges only at the level of spike rate, not membrane potential (Fig. 4f,g), and this implies that the nonlinearity f is largely due to the voltage-gated conductances that transform total synaptic input into spike rate. We verified that the basic conclusions of our model

46. 47. 48.

49. 50.

51. 52. 53. 54. 55. 56. 57.

Aymanns, F., Chen, C.-L. & Ramdya, P. Descending neuron population dynamics during odor-evoked and spontaneous limb-dependent behaviors. eLife 11, e81527 (2022). Tobin, W. F., Wilson, R. I. & Lee, W. A. Wiring variations that enable and constrain neural computation in a sensory microcircuit. eLife 6, e24838 (2017). Moore, R. J. et al. FicTrac: a visual method for tracking spherical motion and generating fictive animal paths. J. Neurosci. Methods 225, 106–119 (2014). Wilson, R. I. & Laurent, G. Role of GABAergic inhibition in shaping odor-evoked spatiotemporal patterns in the Drosophila antennal lobe. J. Neurosci. 25, 9069–9079 (2005). Liu, T. X., Davoudian, P. A., Lizbinski, K. M. & Jeanne, J. M. Connectomic features underlying diverse synaptic connection strengths and subcellular computation. Curr. Biol. 32, 559–569.e5 (2022). Reiser, M. B. & Dickinson, M. H. A modular display system for insect behavioral neuroscience. J. Neurosci. Methods 167, 127–139 (2008). Turner-Evans, D. et al. Angular velocity integration in a fly heading circuit. eLife 6, e23496 (2017). Nern, A., Pfeiffer, B. D. & Rubin, G. M. Optimized tools for multicolor stochastic labeling reveal diverse stereotyped cell arrangements in the fly visual system. Proc. Natl Acad. Sci. USA 112, E2967–76 (2015). Pnevmatikakis, E. A. & Giovannucci, A. NoRMCorre: An online algorithm for piecewise rigid motion correction of calcium imaging data. J. Neurosci. Methods 291, 83–94 (2017). Eckstein, N. et al. Neurotransmitter classification from electron microscopy images at synaptic sites in Drosophila melanogaster. Preprint at bioRxiv https://doi.org/10.1101/2020.06.12.148775 (2023). Funke, J. Neurotransmitter prediction from EM. GitHub https://github.com/funkelab/synister (2024). Bates, A. S. et al. The natverse, a versatile toolbox for combining and analysing neuroanatomical data. eLife 9, e53350 (2020). Lyu, C., Abbott, L. F. & Maimon, G. Building an allocentric travelling direction signal via vector computation. Nature 601, 92–97 (2021). Lu, J. et al. Transforming representations of movement from body- to world-centric space. Nature 601, 98–104 (2021). Chen, C.-L. et al. Imaging neural activity in the ventral nerve cord of behaving adult Drosophila. Nat. Commun. 9, 4390 (2018). Green, J. et al. A neural circuit architecture for angular integration in Drosophila. Nature 546, 101–106 (2017). Anderson, J. S., Lampl, I., Gillespie, D. C. & Ferster, D. The contribution of noise to contrast invariance of orientation tuning in cat visual cortex. Science 290, 1968–1972 (2000).

Acknowledgements We thank N. Eckstein, A. S. Bates, A. Champion, G. S. X. E. Jefferis and J. Funke for early access to neurotransmitter prediction results for the hemibrain connectome.

M. Dickinson and the Research Instrumentation Core at Harvard Medical School provided hardware and software assistance. We are grateful to M. Dickinson and A. Rayshubskiy for helpful conversations and to S. Holtz, M. Collie and N. Pettit for comments on the manuscript. This work was supported by NIH grant U19NS104655 (to R.I.W. and S.D.). R.I.W. is an HHMI Investigator. Author contributions E.A.W. performed all the experiments and analysed the data. E.K., P.M.D. and J.L. contributed to the identification and characterization of split-Gal4 lines. L.H., B.M., S.D., E.A.W. and R.I.W. contributed to conceptualization and modelling.

Competing interests The authors declare no competing interests. Additional information Supplementary information The online version contains supplementary material available at https://doi.org/10.1038/s41586-024-07039-2. Correspondence and requests for materials should be addressed to Rachel I. Wilson. Peer review information Nature thanks Stanley Heinze and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Reprints and permissions information is available at http://www.nature.com/reprints.

Article

a

yc

b

θc

xg

Vectors b

xc

c

a+b=c a

Head direction Head direction Head direction input to PFL3R input to PFL3L input to PFL2

c yg

θg

Continuous sinusoids

f

∑PFL3L

∑PFL2

0

S 1.0 0.8 0.6 0.4 0.2 0.0

PFL2 silenced

0 Right 0 Left -180°

180° -180° 0° 0° Directional error (θ - θg)

Full network

S=0.0

S=0.4 0°

+180°

S=0.6 S=0.8 Left Right Spatial phase of goal input to PFL cells

Extended Data Fig. 1 | See next page for caption.

S=1.0 Time

180°

PFL2 silenced

S=0.2

-180° Head direction (θ) at stable fixed point

∑PFL3R

Goal input

Time

Directional error

e

-180° 0° +180° Head direction (θ)

=

Full network

g Left Right Neural space

+

=

Max

∑PFL3R - PFL2 bump ∑PFL3L amplitude

Right 0 Left

Rotational velocity output (dθ/dt)

Rotational velocity

Goal input to PFL cells

+

= Neural activity over space

Right Rotational velocity 0 (dθ/dt) Left 0° -180° +180° Directional error (θc - θg)

d

+

180° 0° -180°

Extended Data Fig. 1 | Model predictions. a, A resolver measures the current angle of some object (θ c, e.g., the angular position of a shaft) and resolves that angle into its Cartesian components, xc and yc. The goal angle θ g is similarly resolved into its Cartesian components, xg and yg. These components are crossmultiplied, and their difference is used to generate a rotational velocity command dθ/dt ∝ xcyg - xgyc. We treat positive velocity values as clockwise (CW) rotations. In this example, the current angle is rotated CW relative to the goal, meaning a positive directional error. This drives a CCW rotation. But because the error at this point (●) is almost 180°, rotational speed will be small. Mittelstaedt suggested that a similar process might be implemented in the brain’s navigation centres to control an organism’s heading, and thus its path through the environment; this is known as “Mittelstaedt ‘s bicomponent model” of steering control7. Arrowhead denotes the system’s stable fixed point. b, Any vector can be represented as a sinusoidal function whose amplitude represents the magnitude of the vector, and whose phase represents the angle of the vector. Although it is convenient to represent these sinusoids as continuous functions, they can also be discretized into spatial activity

patterns over neural space15,16. Adding these sinusoids is equivalent to performing vector addition. c, Model elements shown in Fig. 1d, here schematized as spatial activity patterns over neural space. The horizontal axis of this space represents the horizontal axis of the fan-shaped body. d, Model: goal input to PFL2&3 cells (left). When this spatial pattern is shifted leftward, this produces a clockwise shift in the model’s rotational velocity as a function of head direction (right). Arrowheads denote the system’s stable fixed point. e, Model: shifts in the spatial phase of goal input to PFL cells produce equal shifts in the head direction corresponding to the system’s stable fixed point. This is true for all values of S > 0. f, Model: The effect of silencing PFL2 cells on rotational velocity is similar to the effect of removing the indirect pathway (compare with Fig. 5c). In both cases, the rotational velocity function becomes equally steep around the goal and the anti-goal. g, Model: The effect of silencing PFL2 cells on steering dynamics is similar to the effect of removing the indirect pathway (compare with Fig. 5d). In both cases, the system oscillates when S is high.

Article

a

mixed PFL2/3 split-Gal4 line: VT033284AD;VT044709DBD GFP

MCFO

50 μm

b

50 μm

PFL2 cell fill FB

LAL

PB

FB

LAL

neurobiotin

GFP + neurobiotin

GFP + neuropil

PB

PFL3 cell fill

c

50 μm

50 μm

PFL2 split-Gal4 line: VT033284AD;VT007338DBD

GFP

MCFO

MCFO

50 μm

Extended Data Fig. 2 | See next page for caption.

50 μm

MCFO

50 μm

50 μm

Extended Data Fig. 2 | Example images for mixed and PFL2 split-Gal4 lines. a, Left panel shows a max z-projection of the mixed PFL2/3 split-Gal4 line expressing GFP. Right panel shows a max z-projection from an MCFO clone with 3 PFL3 neurons (PFL3 identity confirmed by counting axons) along with unidentified neurons outside of the central complex. Across 7 brains, we counted 7 PFL3 neurons and 4 PFL2 neurons, no expression from other cell types was observed in the region of the lateral accessory lobes where PFL2 and PFL3 axon terminals are found. b. Example cell fills obtained from electrophysiology experiments. The left 3 columns show an example PFL2 cell fill while the right 3 columns show an example PFL3 cell fill. It was common for the axonal arbors in the lateral accessory lobe (LAL) to exhibit bright fluorescence, while the

dendritic arbors in the protocerebral bridge and fan-shaped body exhibited relatively dim fluorescence. The identity (PFL2, PFL3, or other) of every recorded cell was confirmed by comparing the morphology of the filled cell to the known morphology of PFL2 and PFL3 neurons; of the 30 cells recorded, 12 were verified in this manner as PFL2 cells, 15 were verified as PFL3 cells, and 3 were found to represent other cell types. c. Left panel shows a max z-projection of the PFL2 split-Gal4 line expressing GFP. The right three panels each show a max z-projection for three brains, each containing one PFL2 neuron. The left and rightmost of these panels also show an unidentified neuron ventral to the central complex. Across 13 brains we counted 15 PFL2 neurons, and 0 other cell types in the central complex.

Article 90° jumps

N = 34 N = 48

180° jumps ∑PFL3R - ∑PFL3L | z-scored ∆F/F |

1.8 N = 49 N = 71 1.6

b

PFL2

1.4 1.2 1.0 0.8

Forward velocity (mm/s)

3

Rotational speed (°/s)

-10 -5

70

0 5 Time (s)

10

15

-10

-5

0 5 Time (s)

2 1 0

-10 -5

0 5 Time (s)

10

15

-10

-5

0 5 Time (s)

10

60 50 40 30 20 10

-10 -5

c

0 5 Time (s)

10

15

-10

1.4 N = 40 1.2 N = 46

0 5 Time (s)

10

PFL3

90° jumps

N = 28 N = 41

180° jumps

1.0 0.8 0.6 0.4 -10 -5

0 5 Time (s)

10

15

-10 -5

0 5 Time (s)

10

15

-10 -5

0 5 Time (s)

10

15

-10 -5

0 5 Time (s)

10

15

-10 -5

0 5 Time (s)

10

15

-10 -5

0 5 Time (s)

10

15

6 5 4 3 2 1 0 80 70 60 50 40 30 20

90° jumps, corrected, high !"#N = 20 jumps) -90° jumps, corrected, high !"#N = 20 jumps) Rotational velocity (°/s)

1 ∑PFL3R - ∑PFL3L z-scored ∆F/F

-5

Corrected, high ! Uncorrected, low !

10 Forward velocity (mm/s)

Corrected, high ! Uncorrected, low !

Rotational speed (°/s)

PFL2 bump amplitude

a

0

-1 -10

-5

0 5 Time (s)

10

15

30 20 10 0 -10 -20 -30 -10

-5

0 5 Time (s)

Extended Data Fig. 3 | Jumps of the virtual environment during imaging experiments. a,. PFL2 bump amplitude, forward velocity, and rotational speed and behavior for 180˚ jumps and ±90˚ jumps of the virtual environment (mean ± s.e.m across jumps). b, Absolute difference in right-left PFL3 signals (|ΣPFL3R - ΣPFL3L| ), forward velocity, and rotational speed and behavior for 180˚ jumps

10

15

and ±90˚ jumps of the virtual environment (mean ± s.e.m across jumps). c, Difference in right-left PFL3 signals (ΣPFL3R - ΣPFL3L) and rotational speed, comparing +90˚ and −90˚ jumps. Clockwise (positive) jumps of the virtual environment produce higher activity in PFL3R, and rightward (positive) turns, as expected (mean ± s.e.m across jumps).

Extended Data Fig. 4 | Additional data on PFL2 chemogenetic stimulation. a, Expanded summary data for flies where PFL2 cells expressed P2X2 showing PFL2 voltage responses to 100-ms, 200-ms, 300-ms, and 500-ms pulses of ATP, as well as simultaneously recorded locomotor activity (mean ± s.e.m across jumps). Here we show forward velocity, absolute rotational velocity

(i.e., rotational speed), and absolute sideways velocity (sideways speed). b, Same but for genetic controls where PFL2 cells did not express P2X2 (mean ± s.e.m across jumps). c, Response of one example PFL2 cell to all four pulse durations over a 120-s period.

Article 0.2

b

PFL2-specific line

0.2

∑R - ∑L activity (z-scored ∆F/F)

∑R - ∑L activity (z-scored ∆F/F)

a

0

∑ R + ∑L activity (z-scored ∆F/F)

2.2

∑R - ∑L activity (z-scored ∆F/F)

0.2

-100 0 100 Rotational velocity (°/s)

200

PFL2&3 mixed line

0

-0.2

0° +180° Directional error (θ - θg)

-200

PFL2-specific line

3.0

2.0 1.8 1.6 -200

-200

PFL2&3 mixed line

0

-0.4 -180°

c

0° +180° Directional error (θ - θg)

∑R + ∑L activity (z-scored ∆F/F)

∑R - ∑L activity (z-scored ∆F/F)

0.4

0

-0.2

-0.2 -180°

PFL2-specific line

0 Rotational velocity (°/s)

+200

Extended Data Fig. 5 | Comparing the PFL2-specific line with the PFL2&3 mixed line. a. Right minus left activity in the lateral accessory lobe (LAL) versus directional error, for both the PFL2-specific line and the PFL2&3 mixed line (mean ± s.e.m. n = 33 flies and n = 23 flies). b. Right minus left activity in the LAL versus rotational velocity, for both the PFL2-specific line and the PFL2&3 mixed line (mean ± s.e.m. n = 33 flies and n = 23 flies). c. Left panel: summed

0 Rotational velocity (°/s)

+200

PFL2&3 mixed line

2.5

2.0 -200

0 Rotational velocity (°/s)

+200

right and left activity in the LAL versus rotational velocity, for both the PFL2-specific line and the PFL2&3 mixed line (mean ± s.e.m. n = 33 and n = 23 flies). The dissimilarity in results for these two lines is further evidence that PFL2 cells make a minimal contribution to the mixed line. Therefore, we treat the results from the mixed line as a readout of PFL3 activity.

PFL3

20

20

15

15

15

15

15

10

10

10

10

10

5

5

5

5

-55

-55

-55

-55

-60

-60

-60

-60

-60

-65

-65

-65

-65

-65

-60

-65

-65

-65

-65

-65

-70

-70

-70

-70

-70 -180°

-60

+180°

-60

0°

-60

-180°

-60

0°

-55

+180°

-55

-180°

-55

+180°

-55

Distance from preferred head direction

+180°

-55

-55

+180°

0

0°

0

-180°

0

0 -50

0°

0

0 -50

+180°

0

0

-180°

10

+180°

10

0°

10

-180°

10

0°

10

-180°

5

+180°

20

0°

20

-180°

20

0°

20

+180°

20

-180°

30

-50

0°

144°

30

0

-180°

72°

30

-50

Distance from preferred head direction

Distance from preferred head direction

Extended Data Fig. 6 | Changes in single cell head direction tuning curves depending on the distance between the cell’s preferred head direction and the goal head direction. a, Tuning curves for PFL2 and PFL3 cells across different distances between the cell’s preferred head direction and the goal head direction (mean ± s.e.m across jumps). The vertical offset of the cell’s

0°

-180°

+180°

0°

-180°

+180°

0°

-180°

+180°

0°

-180°

0°

+180°

-180°

+180°

0°

-180°

+180°

0°

-180°

+180°

0°

-180°

+180°

0°

-180°

∆Vm (mV)

∆Firing rate (spikes/s)

b

0°

30

0

0°

-72°

30

-50

+180°

-144°

144°

0°

20

72°

+180°

20

0°

20

-180°

∆Vm (mV)

PFL2 -72°

+180°

Preferred head direction relative to goal -144° (θp - θg)

∆Firing rate (spikes/s)

a

Distance from preferred head direction

head direction tuning curve changes depending on this distance but the amplitude of the tuning curves only change at the level of firing rate. b, The same as (a) but for model generated output. These are the same results as in Fig. 4f, g; the only difference is that we have not subtracted the minimum value of each tuning curve.

Article Corrected, high ! Uncorrected, low !

PFL2

180° jumps

b

90° jumps

N = 65 N = 34

6 N = 31 N = 27 4

|∆Vm|

2 0 1

2 -2

-1

0 Time (s)

1

2

90° jumps

N = 90 N = 21

2

4 2 0 -2

-1

0 Time (s)

1

2 -2

-1

0 Time (s)

1

2

60 40 20 -2

PFL2

0 Time (s)

1

PFL3

2 -2

-1

0 Time (s)

d

1

PFL2

2

-1

0 Time (s)

1

2 -2

-1

0 Time (s)

1

2

-1

0 Time (s)

1

2 -2

-1

0 Time (s)

1

2

-1

0 Time (s)

1

2 -2

-1

0 Time (s)

1

2

6 4 2 0 -2 60 40 20 0 -2

e

PFL3

150 Distance increased

12

100

10

50 ∆(θ - θp)

8 6

0 -50

2

-100

0

-150

Distance decreased

4

Correlation (R)

c

-1

-2 Forward velocity (mm/s)

0 Time (s)

Rotational speed (°/s)

Forward velocity (mm/s) Rotational speed (°/s)

-1

6

|∆Vm (mV)|

PFL3

180° jumps

6 N = 60 N = 17 4

0

-2

0

Corrected, high ! Uncorrected, low !

0.2

PFL2

0.1

0

-0.1

PFL3

0.2 Correlation (R)

|∆Vm|

a

0.1 0 -0.1

ed ct re or nc ! U w d lo te c re or ! C gh hi ed ct re or nc ! U w d lo te c re or ! C igh h

ps um ° j ps 90 jum 0° 18 ps um ° j ps 90 jum 0°

18

Extended Data Fig. 7 | Jumps of the virtual environment during electrophysiology experiments. a, PFL2 recordings: absolute change in membrane potential, forward velocity, and rotational speed, for 180˚ jumps and ±90˚ jumps of the virtual environment (mean ± s.e.m across jumps, N is the number of jumps). b, Same but for PFL3. c, As expected, 180˚ jumps produce significantly larger changes in membrane potential, as compared to 90˚ jumps (PFL2 p = 0.041, PFL3 p = 0.0064, 2-sample, 2-tailed t-tests). d, Change in the difference between the fly’s head direction (θ ) and the cell’s preferred direction (θ p) resulting from each cue jump in the corrected and uncorrected categories. There was no significant difference in the variance of these values between the two categories (PFL2 p = 0.68793, PFL3 p = 0.99764, Brown-Forsythe test).

-1

-0.5

0 0.5 Lag (s)

1

There was also no difference between the corrected and uncorrected categories in the mean absolute ∆(θ -θ p) (PFL2 p = 0.59721, PFL3 p = 0.99723, 2-sample, 2-tailed t-tests). Thus, we might expect the two types of jumps to produce similar changes in membrane potential. The fact that we see a larger membrane potential response following a corrected jump suggests that the state of the network is different before a corrected jump, and this contributes to the behavioral response. e, Correlation between membrane potential and rotational speed, as a function of lag time (PFL2: n = 54, PFL3 n = 85, mean ± s.e.m across jumps). The maximum correlation is seen when we compare membrane potential with rotational speed 150 − 200 ms later. This is what we would expect if PFL2&3 cells are exerting a causal influence on behavior.

b

High !

Data

4 3 2 1 0 -1

Low !

5 cm

Extended Data Fig. 8 | Modeling changes in behavioral state and PFL2 dynamics. a, Path of three flies in a virtual environment over 10 min, one with high consistency of head direction (high ρ) and two with low ρ. The third path is shown in an inset with high magnification. The first two paths are reproduced from Fig. 5i. b, Same as Fig. 5j, but now including data and model for the third (purple) path. Here, to model, the third path, we adjust A rather than S. Whereas S scales the total synaptic input to PFL2 and PFL3 cells, A specifically scales the amplitude of the goal signal: PFL2 = f (S·(cos(θ -θ 0 -h + 180°) + A·cos(θ g-θ 0 -h))) PFL3R = f (S·(cos(θ -θ 0 -h + 67.5°) + A·cos(θ g-θ 0 -h))) PFL3L= f (S·(cos(θ -θ 0 -h-

Low !

Neural space

Low !

Model S=0.2 A=1

Activity

S=0.8 A=1

Activity

5 mm

Model

Data

4 3 2 1 0 -1 Neural space

Neural space

Neural space

High !

Low !

Data

4 3 2 1 0 -1

z-scored ∆F/F

Low !

Model S=0.8 A=0.25

Activity

Low !

z-scored ∆F/F

High !

z-scored ∆F/F

a

Neural space

Neural space

67.5°) + A·cos(θ g-θ 0 -h))) where f is a nonlinear function, θ is head direction, θ 0 is the angular position of the EPG bump at a head direction of 0°, θ g is the goal angle, and h is a vector with entries that tile the full 360° of angular space (equation (9), Methods). Reductions in S decrease the overall scale of PFL2&3 activity, without changing the dependence of bump amplitude on head direction. By contrast, reductions in A cause bump amplitude to become more invariant to head direction. The examples shown here suggest that changes in behavioral state may arise in some cases from changes in S, and in other cases from changes in A.

Article

a

0.06 0.05

R ∆F/F

∆F/F

0.04 0.03 0.02 0.01

R

0 2

L

1.5 0.5

z-scored ∆F/F

Head dIrection

z-scored ∆F/F

L

180° 0°

0

-0.5

-180°

-1

Time (s)

-180°

b

0° Neural space

180°

0.04

∆F/F

∆F/F

L

0.03 0.02 0.01 0

R

5 4

L

3

z-scored ∆F/F

z-scored ∆F/F

180°

0.05

R

Head dIrection

0° Neural space

180° 0° -180°

2 1 0 -1

Time (s)

-2 -180°

Extended Data Fig. 9 | ∆F/F compared to z-scored ∆F/F. a, Left: ∆F/F binned into 1-s increments across an entire 10-min trial for a fly that exhibited low goal fixation, before and after z-scoring, compared with head direction. Right: ∆F/F is divided into 4 bins based on the fly’s head direction relative to the head direction associated with the lowest PFL2 bump amplitude. This is done

separately for raw ∆F/F (top) and z-scored ∆F/F (bottom). Here, z-scoring reduces but does not eliminate the difference in bump amplitude across bins. b, The same as (a) but for a fly that exhibited high goal fixation during the 10-min trial. Here, there is a strong variation in bump amplitude that persists across bins, even after z-scoring. These data are shown in Fig. 5j.

10 cm

Head direction Head direction consistency (!)

180° 0°

-180° 1

1 min 0

Extended Data Fig. 10 | Path segmentation. To obtain an accurate estimate of the fly’s current goal direction (θg), and thus an accurate estimate of directional error (θ - θg), we needed to identify moments when θg might switch. We reasoned that a switch in θg, would coincide with a dip in head direction consistency. Therefore, we looked for moments when p crossed a threshold value, and we broke the path into segments at those moments of threshold-crossing. This allowed us to segment a path into straight segments and to identify points

where goal direction seemed to have switched. We used a threshold value of p = 0.88 because this produced results that corresponded to our visual impression of when the fly’s goal direction seemed to have changed, but we also confirmed that our conclusions are similar for a range of threshold values. Here we show an example path recorded over 10 min. Time points belonging to different segments are alternately shaded gray and black. Dotted line shows the threshold used to define these segments.

Last updated by author(s): 2023/11/13

Reporting Summary Nature Portfolio wishes to improve the reproducibility of the work that we publish. This form provides structure for consistency and transparency in reporting. For further information on Nature Portfolio policies, see our Editorial Policies and the Editorial Policy Checklist.

nature portfolio | reporting summary

Corresponding author(s): Rachel Wilson

Statistics For all statistical analyses, confirm that the following items are present in the figure legend, table legend, main text, or Methods section. n/a Confirmed The exact sample size (n) for each experimental group/condition, given as a discrete number and unit of measurement A statement on whether measurements were taken from distinct samples or whether the same sample was measured repeatedly The statistical test(s) used AND whether they are one- or two-sided Only common tests should be described solely by name; describe more complex techniques in the Methods section.

A description of all covariates tested A description of any assumptions or corrections, such as tests of normality and adjustment for multiple comparisons A full description of the statistical parameters including central tendency (e.g. means) or other basic estimates (e.g. regression coefficient) AND variation (e.g. standard deviation) or associated estimates of uncertainty (e.g. confidence intervals) For null hypothesis testing, the test statistic (e.g. F, t, r) with confidence intervals, effect sizes, degrees of freedom and P value noted Give P values as exact values whenever suitable.

For Bayesian analysis, information on the choice of priors and Markov chain Monte Carlo settings For hierarchical and complex designs, identification of the appropriate level for tests and full reporting of outcomes Estimates of effect sizes (e.g. Cohen's d, Pearson's r), indicating how they were calculated Our web collection on statistics for biologists contains articles on many of the points above.

Software and code Policy information about availability of computer code Data collection

MATLAB 2019b, R2021a, ScanImage 2021, FicTrac v2.1 (https://github.com/rjdmoore/fictrac), neuprint (https://neuprint.janelia.org/), NeuprintR 1.1 (https://github.com/natverse/neuprintr) and natverse 1.1 (https://github.com/natverse/natverse)

Data analysis

Motion correction of calcium imaging data was performed using NoRMCorre (https://github.com/flatironinstitute/NoRMCorre). Ball tracking was performed using FicTrac v2.1. Computational modeling and analysis of calcium imaging, behavior, and electrophysiology data was performed using custom code written in MATLAB (2019b, R2021a) and Python 3.9.5. Code will be deposited in a public repository (e.g., github or zenodo) at the time of publication.

For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Portfolio guidelines for submitting code & software for further information.

March 2021

1

Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: - Accession codes, unique identifiers, or web links for publicly available datasets - A description of any restrictions on data availability - For clinical datasets or third party data, please ensure that the statement adheres to our policy The hemibrain v1.2.1 connectome data is available via a publicly accessible website, https://neuprint.janelia.org (also accessible via https://doi.org/10.25378/ janelia.11676099.v2). The datasets generated during the current study are available from the corresponding author on reasonable request. NOTE TO EDITOR: the datasets we collected are extremely large and have complex and unique metadata structures associated with them, making deposition of the data in a public repository impractical but are available from the corresponding author on reasonable request.

Human research participants

nature portfolio | reporting summary

Data

Policy information about studies involving human research participants and Sex and Gender in Research. Reporting on sex and gender

n/a

Population characteristics

n/a

Recruitment

n/a

Ethics oversight

n/a

Note that full information on the approval of the study protocol must also be provided in the manuscript.

Field-specific reporting Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection. Life sciences

Behavioural & social sciences

Ecological, evolutionary & environmental sciences

For a reference copy of the document with all sections, see nature.com/documents/nr-reporting-summary-flat.pdf

Life sciences study design All studies must disclose on these points even when the disclosure is negative. All sample sizes were chosen based on conventions in our field for standard sample sizes. These sample sizes are conventionally determined on the basis of the expected magnitude of animal-to-animal variability, given published results and pilot data. Statistical analyses were not performed until data collection was completed. No formal power calculations were performed due to the expected variability and exploratory nature of the dataset.

Data exclusions

We did not exclude any flies from the calcium imaging, iontophoresis, or electrophysiology datasets. Trial segments were excluded from analyses shown in Fig 1h, 2e-g, 3, 4d, and Extended Data Fig 5 if the fly only sampled a single heading during the entire segment, as this indicated that the visual arena did not initialize properly at the beginning of the segment (a technical problem that occurred rarely but in a few trials) . Trial segments were also excluded if the fly's total velocity was not above a set threshold for at least 2 seconds as this provided an insufficient time window to measure the fly's likely goal. In Fig 4f,g, and Extended Data Fig 6a data was excluded as described in the methods as required by the definition of the analysis to focus on segments with high associated values of rho. Rho threshold values were set empirically but we confirmed that small changes in this threshold did not change our conclusions, as described in the methods.

Replication

For all experiments, results were replicated in different individual flies across each dataset, the number of replicates performed are described in the figure legends. We did not omit any replicates on the basis of the experimental result. A few trials were excluded due to factors that prevented us from analyzing the data; all these cases of data exclusion are noted explicitly above and in the Online Methods

Randomization

For PFL2 activation experiments (Fig. 2, Extended data Fig. 5) flies were grouped for analysis based on genotype. Beyond these cases, flies were not assigned to treatment groups. For all other experiments allocation of data into different categories is described in the associated methods sections.

Blinding

The experimenter was not blind to genotype in this study. This is because the different genotypes in the study were used to target a genetically encoded fluorescent indicator to different cell types, and so the genotype of the flies was obvious during the course of the experiments, based on the observed pattern of fluorescence.

March 2021

Sample size

2

We require information from authors about some types of materials, experimental systems and methods used in many studies. Here, indicate whether each material, system or method listed is relevant to your study. If you are not sure if a list item applies to your research, read the appropriate section before selecting a response.

Materials & experimental systems

Methods

n/a Involved in the study

n/a Involved in the study

Antibodies

ChIP-seq

Eukaryotic cell lines

Flow cytometry

Palaeontology and archaeology

MRI-based neuroimaging

Animals and other organisms Clinical data

nature portfolio | reporting summary

Reporting for specific materials, systems and methods

Dual use research of concern

Antibodies Antibodies used

chicken anti-GFP (1:1,000, Abcam, # ab13970), mouse anti-Bruchpilot (1:30, Developmental Studies Hybridoma Bank, nc82), Alexa Fluor 488 goat anti-chicken (1:250, Invitrogen, #A11039), Alexa Fluor 633 goat anti-mouse (1:250, Invitrogen, #A21050), streptavidin::Alexa Fluor 568 (1:1000, Invitrogen, #S11226) , rat anti-Flag (1:200, Novus Biologicals, #NBP1-06712B), rabbit anti-HA (1:300, Cell Signal Technologies, #NBP106712B), Alexa Fluor 488 goat anti-rabbit (1:250, Invitrogen, #A11039), ATTO 647 goat antirat (1:400, Rockland, #612-156-120), Alexa Fluor 405 goat anti-mouse (1:500, Invitrogen, #A31553), DyLight 550 mouse anti-V5 (1:500, Bio-Rad, #MCA1360D550GA)

Validation

The anti-GFP antibody (Abcam) is the standard antibody used in the field for labeling Green Fluorescent Protein (GFP) in Drosophila, note that this protein is not endogenously expressed in the Drosophila genome. Manufacturer's datasheet confirm that this anti-GFP antibody has been validated using western blot and immunohistochemistry to have specificity for Green Fluorescent Protein. Manufacturer also confirms the use of this antibody for immunolabeling of GFP in Drosophila across 3182 peer-reviewed manuscripts (e.g. Sykes et al. 2005 PMID: 16122730). The antibruchpilot antibody (nc82, DSHB) is a standard in the field as a background stain that labels presynaptic active zones to provide neuropil labeling for analysis of anatomy. This antibody was originally validated for use in Drosophila to label presynaptic active zones using immunohistochemistry and to be specific to Bruchpilot protein (Wagh et al. 2006). The secondary antibody we used to label GFP expressing cells (Alexa Fluor 488 goat anti-chicken) was verified by us to target only those cells which express live GFP fluorescence. The secondary antibody used for background (neuropil) staining (Alexa Fluor 488 goat anti-chicken, Alexa Fluor goat anti-mouse 633) was verified by us to reproduce the known patterns of neuropil borders (nC82 immunoreactivity) in published atlases (VirtualFlyBrain.org). The streptavidin::Alexa Fluor 568 for visualizing cell fills was verified by us to only label a single cell in a given brain, the one filled with neurobiotin citrate during the experiment. Antibodies used for MCFO immunostaining (rat anti-FLAG, rabbit anti-HA, DyLight 550 mouse anti-V5, AlexaFluor 488 goat antirabbit, ATTO 647 goat anti-rat) are validated in Drosophila melanogaster for this application in Nern et al., 2015. These antibodies have also each been validated prior to Nern et al: rat anti-FLAG: Manufacturer notes confirms that rat anti-FLAG (Cat#: NBP1-06712B) has also been validated as FLAG-Tag specific in Drosophila (PMID: 26573957). Rabbit anti-HA: Manufacturer confirmed rabbit anti-HA antibody has Epitope tag specificity using western blot and immunohistochemical analysis comparing untransfected with HA-tag transfected COS cells (https:// www.cellsignal.com/products/primary-antibodies/ha-tag-c29f4-rabbit-mab/3724#validation-data). DyLight 550-conjugated mouse anti-V5: Manufacturer notes confirm that the DyLight 550-conjugated-Mouse anti V5-Tag, clone SV5-Pk1 recognizes the sequence, IPNPLLGLD, present on the P/V proteins of the paramyxovirus, SV5 (Dunn et al.1999) and can be used to detect recombinant proteins labeled with this V5-tag (Randall et al.1993 and Zhao et al. 2005).

Animals and other research organisms Policy information about studies involving animals; ARRIVE guidelines recommended for reporting animal research, and Sex and Gender in Research Laboratory animals

We used female Drosophila melanogaster flies for all experiments. Newly eclosed flies were collected ~16-24 hrs (electrophysiology) or 1-4 days (imaging) before the experiment.

The following stocks were obtained from the Bloomington Drosophila Stock Center (BDSC) and published previously: P{y[+t7.7]w[+mC]=VT044709-GAL4.DBD}attP2 (BDSC_75555), P{y[+t7.7]w[+mC]=p65.AD.Uw}attP40; P{y[+t7.7] w[+mC]=GAL4.DBD.Uw}attP2 (BDSC_79603), P{w[+mC]=UAS-Rnor\P2rx2.L}4/CyO (BDSC_91223), w[1118]; PBac{y[+mDint2] w[+mC]=20XUAS-IVS-jGCaMP7b}VK00005. w+;20XUAS-cyRFP {VK00037};+ was obtained in house and P{20XUAS-IVSmCD8::GFP}attP40 was a gift from Gerry Rubin and has been published previously

March 2021

The following stocks were obtained from Well Genetics: w[1118];P{VT007338-p65ADZp}attP40/CyO;+ (A/SWG9178), w[1118];P{VT033284-p65AD}attP40/CyO;+ (A/SWG8077).

We constructed a split-Gal4 line to target PFL2 neurons, w+ ;P{VT033284-p65AD}attP40; P{y[+t7.7];P{VT007338-Gal4DBD}attP2. We

3

Wild animals

No wild animals were used in this study.

Reporting on sex

All animals used in this study were female, due to the experimental difficulty presented by the use of male flies (which are smaller).

Field-collected samples

No field samples were collected for this study.

Ethics oversight

No ethical approval was required because experiments were performed on Drosophila melanogaster.

Note that full information on the approval of the study protocol must also be provided in the manuscript.

nature portfolio | reporting summary

validated the expression of this line using immunohistochemical anti-GFP staining, and also using Multi-Color-Flip-Out to visualize single-cell morphologies. We also constructed a split-Gal4 line that targets PFL2 & PFL3 neurons in the lateral accessory lobes, w+;P{VT033284-p65AD}attP40;P{y[+t7.7] w[+mC]=VT044709-GAL4.DBD}attP2. We validated the expression of this line using immunohistochemical anti-GFP staining, and also using Multi-Color-Flip-Out. (MCFO) to visualize single-cell morphologies.

March 2021

4

Article

Smoking changes adaptive immunity with persistent effects https://doi.org/10.1038/s41586-023-06968-8 Received: 1 November 2022 Accepted: 13 December 2023 Published online: 14 February 2024 Open access Check for updates

Violaine Saint-André1,2 ✉, Bruno Charbit3, Anne Biton2, Vincent Rouilly4, Céline Possémé1, Anthony Bertrand1,5, Maxime Rotival6, Jacob Bergstedt6,7,8, Etienne Patin6, Matthew L. Albert9, Lluis Quintana-Murci6,10, Darragh Duffy1,3 ✉ & The Milieu Intérieur Consortium*

Individuals differ widely in their immune responses, with age, sex and genetic factors having major roles in this inherent variability1–6. However, the variables that drive such differences in cytokine secretion—a crucial component of the host response to immune challenges—remain poorly defined. Here we investigated 136 variables and identified smoking, cytomegalovirus latent infection and body mass index as major contributors to variability in cytokine response, with effects of comparable magnitudes with age, sex and genetics. We find that smoking influences both innate and adaptive immune responses. Notably, its effect on innate responses is quickly lost after smoking cessation and is specifically associated with plasma levels of CEACAM6, whereas its effect on adaptive responses persists long after individuals quit smoking and is associated with epigenetic memory. This is supported by the association of the past smoking effect on cytokine responses with DNA methylation at specific signal transactivators and regulators of metabolism. Our findings identify three novel variables associated with cytokine secretion variability and reveal roles for smoking in the shortand long-term regulation of immune responses. These results have potential clinical implications for the risk of developing infections, cancers or autoimmune diseases.

High levels of variability exist among individuals and populations in relation to responses to immune challenges2,7. This has been highlighted by the COVID-19 pandemic through the diverse clinical outcomes observed after infection with SARS-CoV-26,8. Variables such as age, sex and genetics have a major effect on the way individuals respond to infection2–6,9,10. However, such immune variability is generally not considered in the design of treatments or vaccines, and there is a need to better identify the variables associated with immune response variation11. The Milieu Intérieur project was developed to assess the factors that contribute to variable ‘healthy’ immune responses12. The cohort is equilibrated in terms of age and sex and comprises individuals of a homogenous genetic background, to facilitate identification of novel immune determinants, in addition to age, sex and genetic variants. The Milieu Intérieur project has already advanced our understanding of the variables that regulate immune homeostasis. In particular, by quantifying the effects of age, sex, genetics and cellular composition on the transcript levels of immune-related genes4, and the effects of age, sex, cytomegalovirus (CMV) latent infection and smoking on blood leukocyte composition3. To identify new environmental factors associated with variability in response to immune stimulation, we focused on cytokine protein secretion as an immune response phenotype. The concentrations of 13 disease- and medically-relevant cytokines (CXCL5, CSF2, IFNγ, IL-1β,

TNF, IL-2, IL-6, IL-8, IL-10, IL-12p70, IL-13, IL-17 and IL-23) were measured with Luminex technology, after 22 h of standardized whole-blood stimulation with 11 immune agonists for the 1,000 Milieu Intérieur donors (Supplementary Table 1), as well as in a non-stimulated control (null condition). The stimulations are classified into 4 categories: microbial (Bacillus Calmette-Guérin (BCG), Escherichia coli (E. coli), lipopolysaccharide (LPS) and Candida albicans (C. albicans)) and viral (influenza and polyinosinic–polycytidylic acid (poly I:C)) agents, which are predominantly recognized by receptors on innate immune cells; T cell activators (Staphylococcus aureus enterotoxin B superantigen (SEB) and anti-CD3 and anti-CD28 antibodies (anti-CD3 + CD28)), which induce adaptive immune responses; and cytokines (TNF, IL-1β and IFNγ).

Smoking, CMV and BMI associations Principal component analysis (PCA) (Extended Data Fig. 1) and heat maps (Extended Data Fig. 2) of the 13 cytokines in the 12 immune stimulations highlight the specific cytokines that were induced in each independent condition. Hierarchical clustering of the standardized log mean differences of the cytokine levels (Fig. 1a) clearly distinguishes groups that broadly correspond to stimulation type. Immune responses induced by innate (E. coli and LPS) and adaptive (SEB and anti-CD3 + CD28) stimulations cluster separately, and show greater

Translational Immunology Unit, Department of Immunology, Institut Pasteur, Université Paris Cité, Paris, France. 2Institut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, Paris, France. 3Cytometry and Biomarkers UTechS, Center for Translational Research, Institut Pasteur, Université Paris Cité, Paris, France. 4DATACTIX, Paris, France. 5Frontiers of Innovation in Research and Education PhD Program, LPI Doctoral School, Université Paris Cité, Paris, France. 6Institut Pasteur, Université Paris Cité, CNRS UMR2000, Human Evolutionary Genetics Unit, Paris,

1

France. 7Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden. 8Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. Octant Biosciences, San Francisco, CA, USA. 10Chair Human Genomics and Evolution, Collège de France, Paris, France. *A list of authors and their affiliations appears at the end of the paper. ✉e-mail: [email protected]; [email protected]

9

Nature | Vol 626 | 22 February 2024 | 827

Article a

b

IL-1β IL-6 IL-23 IL-17 IL-8 CXCL5 TNF IL-10 IL-13 CSF2 IL-2 IFNγ IL-12p70

BCG Poly I:C Influenza IFNγ TNF IL-1β

SEB CD3 + CD28 E. coli LPS C. albicans

2 1 0 –1

Standardized log mean difference

SEB CD3 + CD28 E. coli LPS C. albicans BCG Poly I:C Influenza IFNγ TNF IL-1β

Cytokines

Systolic measure Heart rate Abdominal circumference BMI Cooked meals CMV serology status Beer Total no. of cigarettes Years since last smoke No. of years smoking Smoking status

T cells Microbial Viral Cytokine Stimulations

c

SEB ****** * ** ** ** ** ** *** *** *** ***

0 10 20

*** ** *** ****** ** *** **

*** ***

No. of years smoking

**

**

Years since last smoke

***

* **

Smoking status

**

* **

CXCL5 CSF2 IFNγ IL-1β IL-2 IL-6 IL-8 IL-10 IL-13 IL-17 TNF

02468

No. of years smoking ***

**

Years since last smoke **

*

0 5.0 10.0

LPS Total no. of cigarettes *** No. of years smoking ***

***

Years since last smoke ***

**

Smoking status ***

**

*

CXCL5 CSF2 IFNγ IL-1β IL-2 IL-6 IL-8 IL-10 IL-13 IL-17 IL-23 TNF

CXCL5 CSF2 IFNγ IL-1β IL-2 IL-6 IL-8 IL-10 IL-17 IL-23 TNF

*

0 1.0 2.0

BCG

* ***

CMV status Total no. of cigarettes

E. coli

Smoking status ***

0 2 4 6

CD3 + CD28

***

CXCL5 CSF2 IFNγ IL-1β IL-2 IL-6 IL-8 IL-10 IL-13 IL-17 IL-23 TNF

CMV status BMI Abdominal circumference Total no. of cigarettes No. of years smoking Years since last smoke Smoking status Cooked meals

012345

Poly I:C Systolic measure

latent infection is associated with CSF2, IFNγ and TNF upon adaptive immune stimulations, in line with our previous work showing strong associations between CMV seropositivity and increased numbers of T cell effector memory subsets3. We also observed that BMI-related variables are associated with CXCL5 after BCG stimulation, and with IL-2 after SEB stimulation, which is consistent with the dysregulation of CXCL5 and IL-2 in obesity16,17. As potential interactions may exist between our tested variables and age, we performed the same analysis considering age and smoking interactions in the models. The results are very similar to the ones obtained without considering interactions, and some smoking-related variables are associated with even higher significance in SEB, anti-CD3 + CD28, E. coli and LPS stimulation conditions (Extended Data Fig. 3). Notably, by including these interactions, smoking-related variables are significantly associated with IL-2 responses after BCG stimulation. This IL-2 response may reflect a long-lived antigen-specific T cell response to BCG vaccination, which all of the cohort received at birth owing to mandatory BCG vaccination in France prior to 2007, further strengthening the associations of smoking with T cell immunity. Individual effects of age and sex have also been tested, and corresponding LRT results and effects sizes are shown on Extended Data Figs. 4 and 5. In addition, as human leukocyte antigen (HLA) is a well-known determinant of immune response variability, which is mostly relevant for antigen-specific responses, we tested associations between previously identified HLA types3 and induced cytokine responses following the same procedure that we used for the other donor variables. We detected only one significant association, between the major histocompatibility complex class II, DQ beta 1 HLA.DBQ1.1P and IL-6 in the non-stimulated control condition. However no associations were observed with induced cytokine responses after stimulation.

** **

Heart rate

Smoking status

***

Beer

**

The smoking effect is persistent

TNF

IL-8

IL-12p70

IL-6

IFNγ

IL-1β

CXCL5 CSF2 IFNγ IL-1β IL-2 IL-6 IL-8 IL-10 IL-17 IL-23 TNF

Abdominal circumference **

CXCL5

BMI **

Fig. 1 | Variables associated with cytokine levels in diverse immune stimulations. a, Standardized log mean differences of 13 cytokines in 12 immune stimulations. b, Significant associations (Benjamini–Yekutieli adjusted P value of likelihood ratio test (LRT)