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Tag: multidisciplinary

  • Structure of human phagocyte NADPH oxidase in the activated state

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  • Winterbourn, C. C., Kettle, A. J. & Hampton, M. B. Reactive oxygen species and neutrophil function. Annu. Rev. Biochem. 85, 765–792 (2016).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Lambeth, J. D. & Neish, A. S. Nox enzymes and new thinking on reactive oxygen: a double-edged sword revisited. Annu. Rev. Pathol. 9, 119–145 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Heyworth, P. G., Cross, A. R. & Curnutte, J. T. Chronic granulomatous disease. Curr. Opin. Immunol. 15, 578–584 (2003).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Diebold, B. A., Smith, S. M., Li, Y. & Lambeth, J. D. NOX2 as a target for drug development: indications, possible complications, and progress. Antioxid. Redox. Signal. 23, 375–405 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Magnani, F. et al. Crystal structures and atomic model of NADPH oxidase. Proc. Natl Acad. Sci. USA 114, 6764–6769 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Sun, J. Structures of mouse DUOX1–DUOXA1 provide mechanistic insights into enzyme activation and regulation. Nat. Struct. Mol. Biol. 27, 1086–1093 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Wu, J. X., Liu, R., Song, K. & Chen, L. Structures of human dual oxidase 1 complex in low-calcium and high-calcium states. Nat. Commun. 12, 155 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Liu, R. et al. Structure of human phagocyte NADPH oxidase in the resting state. eLife 11, e83743 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Noreng, S. et al. Structure of the core human NADPH oxidase NOX2. Nat. Commun. 13, 6079 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Warren, J. J., Ener, M. E., Vlcek, A., Winkler, J. R. & Gray, H. B. Electron hopping through proteins. Coord. Chem. Rev. 256, 2478–2487 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Winkler, J. R. & Gray, H. B. Long-range electron tunneling. J. Am. Chem. Soc. 136, 2930–2939 (2014).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Sumimoto, H. Structure, regulation and evolution of Nox-family NADPH oxidases that produce reactive oxygen species. FEBS J. 275, 3249–3277 (2008).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Lapouge, K., Smith, S. J., Groemping, Y. & Rittinger, K. Architecture of the p40-p47-p67phox complex in the resting state of the NADPH oxidase. J. Biol. Chem. 277, 10121–10128 (2002).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • van de Geer, A. et al. Inherited p40phox deficiency differs from classic chronic granulomatous disease. J. Clin. Invest. 128, 3957–3975 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Lapouge, K. et al. Structure of the TPR domain of p67phox in complex with Rac·GTP. Mol. Cell 6, 899–907 (2000).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Ogura, K. et al. NMR solution structure of the tandem Src homology 3 domains of p47phox complexed with a p22phox-derived proline-rich peptide. J. Biol. Chem. 281, 3660–3668 (2006).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Kami, K., Takeya, R., Sumimoto, H. & Kohda, D. Diverse recognition of non-PxxP peptide ligands by the SH3 domains from p67phox, Grb2 and Pex13p. EMBO J. 21, 4268–4276 (2002).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Wilson, M. I., Gill, D. J., Perisic, O., Quinn, M. T. & Williams, R. L. PB1 domain-mediated heterodimerization in NADPH oxidase and signaling complexes of atypical protein kinase C with Par6 and p62. Mol. Cell 12, 39–50 (2003).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Han, C. H., Freeman, J. L., Lee, T., Motalebi, S. A. & Lambeth, J. D. Regulation of the neutrophil respiratory burst oxidase. Identification of an activation domain in p67phox. J. Biol. Chem. 273, 16663–16668 (1998).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Dahan, I., Smith, S. M. & Pick, E. A Cys-Gly-Cys triad in the dehydrogenase region of Nox2 plays a key role in the interaction with p67phox. J. Leukoc. Biol. 98, 859–874 (2015).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Mizrahi, A., Berdichevsky, Y., Casey, P. J. & Pick, E. A prenylated p47phox-p67phox-Rac1 chimera is a quintessential NADPH oxidase activator: membrane association and functional capacity. J. Biol. Chem. 285, 25485–25499 (2010).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Nisimoto, Y., Motalebi, S., Han, C. H. & Lambeth, J. D. The p67phox activation domain regulates electron flow from NADPH to flavin in flavocytochrome b 558. J. Biol. Chem. 274, 22999–23005 (1999).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Roos, D. et al. Hematologically important mutations: the autosomal forms of chronic granulomatous disease (third update). Blood Cells Mol. Dis. 92, 102596 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Koker, M. Y. et al. Clinical, functional, and genetic characterization of chronic granulomatous disease in 89 Turkish patients. J. Allergy Clin. Immunol. 132, 1156–1163 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Stasia, M. J. et al. Molecular and functional characterization of a new X-linked chronic granulomatous disease variant (X91+) case with a double missense mutation in the cytosolic gp91phox C-terminal tail. Biochim. Biophys. Acta 1586, 316–330 (2002).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Rae, J. et al. X-linked chronic granulomatous disease: mutations in the CYBB gene encoding the gp91-phox component of respiratory-burst oxidase. Am. J. Hum. Genet. 62, 1320–1331 (1998).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Milburn, M. V. et al. Molecular switch for signal transduction: structural differences between active and inactive forms of protooncogenic ras proteins. Science 247, 939–945 (1990).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Boog, B. et al. Identification and functional characterization of two novel mutations in the alpha-helical loop (residues 484-503) of CYBB/gp91phox resulting in the rare X91+ variant of chronic granulomatous disease. Hum. Mutat. 33, 471–475 (2012).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Zhen, L., Yu, L. & Dinauer, M. C. Probing the role of the carboxyl terminus of the gp91phox subunit of neutrophil flavocytochrome b558 using site-directed mutagenesis. J. Biol. Chem. 273, 6575–6581 (1998).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Punjani, A. & Fleet, D. J. 3D variability analysis: resolving continuous flexibility and discrete heterogeneity from single particle cryo-EM. J. Struct. Biol. 213, 107702 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Wu, X. et al. Mechanistic insights on heme-to-heme transmembrane electron transfer within NADPH oxydases from atomistic simulations. Front. Chem. 9, 650651 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Hayward, S. & Lee, R. A. Improvements in the analysis of domain motions in proteins from conformational change: DynDom version 1.50. J. Mol. Graph. Model. 21, 181–183 (2002).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Veevers, R. & Hayward, S. Methodological improvements for the analysis of domain movements in large biomolecular complexes. Biophys. Physicobiol. 16, 328–336 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Deng, Z. et al. A productive NADP+ binding mode of ferredoxin–NADP+ reductase revealed by protein engineering and crystallographic studies. Nat. Struct. Biol. 6, 847–853 (1999).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Kean, K. M. et al. High-resolution studies of hydride transfer in the ferredoxin:NADP+ reductase superfamily. FEBS J. 284, 3302–3319 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Lans, I. et al. Theoretical study of the mechanism of the hydride transfer between ferredoxin-NADP+ reductase and NADP+: the role of Tyr303. J. Am. Chem. Soc. 134, 20544–20553 (2012).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Freeman, J. L. & Lambeth, J. D. NADPH oxidase activity is independent of p47phox in vitro. J. Biol. Chem. 271, 22578–22582 (1996).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Koshkin, V., Lotan, O. & Pick, E. The cytosolic component p47phox is not a sine qua non participant in the activation of NADPH oxidase but is required for optimal superoxide production. J. Biol. Chem. 271, 30326–30329 (1996).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Takemoto, D., Tanaka, A. & Scott, B. NADPH oxidases in fungi: diverse roles of reactive oxygen species in fungal cellular differentiation. Fungal Genet. Biol. 44, 1065–1076 (2007).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Kirchhofer, A. et al. Modulation of protein properties in living cells using nanobodies. Nat. Struct. Mol. Biol. 17, 133–138 (2010).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Guo, W., Wang, M. & Chen, L. A co-expression vector for baculovirus-mediated protein expression in mammalian cells. Biochem. Biophys. Res. Commun. 594, 69–73 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Pedelacq, J. D., Cabantous, S., Tran, T., Terwilliger, T. C. & Waldo, G. S. Engineering and characterization of a superfolder green fluorescent protein. Nat. Biotechnol. 24, 79–88 (2006).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Scheich, C., Kummel, D., Soumailakakis, D., Heinemann, U. & Bussow, K. Vectors for co-expression of an unrestricted number of proteins. Nucleic Acids Res. 35, e43 (2007).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Li, N. et al. Structure of a pancreatic ATP-sensitive potassium channel. Cell 168, 101–110 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Yamauchi, A. et al. Location of the epitope for 7D5, a monoclonal antibody raised against human flavocytochrome b558, to the extracellular peptide portion of primate gp91phox. Microbiol. Immunol. 45, 249–257 (2001).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Kim, J. et al. Structure and drug resistance of the Plasmodium falciparum transporter PfCRT. Nature 576, 315–320 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Pleiner, T., Bates, M. & Görlich, D. A toolbox of anti-mouse and anti-rabbit IgG secondary nanobodies. J. Cell Biol. 217, 1143–1154 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Guan, C. et al. Structural insights into the inhibition mechanism of human sterol O-acyltransferase 1 by a competitive inhibitor. Nat. Commun. 11, 2478 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Zhou, M., Diwu, Z., Panchuk-Voloshina, N. & Haugland, R. P. A stable nonfluorescent derivative of resorufin for the fluorometric determination of trace hydrogen peroxide: applications in detecting the activity of phagocyte NADPH oxidase and other oxidases. Anal. Biochem. 253, 162–168 (1997).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Jesaitis, A. J., Riesselman, M., Taylor, R. M. & Brumfield, S. in NADPH Oxidases (eds. Knaus, U. & Leto, T.) 39–59 (Humana Press, 2019).

  • Patel, A., Toso, D., Litvak, A. & Nogales, E. Efficient graphene oxide coating improves cryo-EM sample preparation and data collection from tilted grids. Preprint at bioRxiv https://doi.org/10.1101/2021.03.08.434344 (2021).

  • Zheng, S. Q. et al. MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Nat. Methods 14, 331–332 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Punjani, A., Rubinstein, J. L., Fleet, D. J. & Brubaker, M. A. cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination. Nat. Methods 14, 290–296 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Wang, N. et al. Structural basis of human monocarboxylate transporter 1 inhibition by anti-cancer drug candidates. Cell 184, 370–383 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Pettersen, E. F. et al. UCSF Chimera—a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D 66, 486–501 (2010).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Afonine, P. V. et al. Real-space refinement in PHENIX for cryo-EM and crystallography. Acta Crystallogr. D 74, 531–544 (2018).

    Article 
    CAS 

    Google Scholar     

  • Chen, S. et al. High-resolution noise substitution to measure overfitting and validate resolution in 3D structure determination by single particle electron cryomicroscopy. Ultramicroscopy 135, 24–35 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

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  • Smoking scars the immune system for years after quitting

    Smoking scars the immune system for years after quitting

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    Close up of a man as he smokes a cigarette in Sundbyberg, near Stockholm.

    The immune-system signature of cigarette smoking persists for many years after a person kicks the habit.Credit: Jonathan Nackstrand/AFP via Getty

    The impacts of cigarette smoking on the immune system lingers long after a smoker’s last puff, according to a study of the immune responses of 1,000 people1.

    The analysis, published in Nature on 14 February, is part of an effort to determine why immune responses vary so widely from person to person. In addition to cigarette smoking, the study found that having a higher-than-average body mass index and having previously been infected with a typically benign virus called cytomegalovirus also affect the immune response.

    “This highlights the importance of considering not only the immediate effects, but also the enduring consequences of lifestyle choices on immune function,” says Yang Luo, a computational immunologist at the University of Oxford, UK, who was not involved in the research.

    Shrugging off an illness

    The COVID-19 pandemic laid bare how divergent immune responses can be, with some people becoming seriously ill after a SARS-CoV-2 infection, whereas others had no symptoms. Previous studies have highlighted the importance of sex, genetics and age in explaining part of this diversity in immune responses, but the role of other factors has not been defined fully.

    Why is COVID life-threatening for some people? Genetics study offers clues

    Computational biologist Violaine Saint-André at the Pasteur Institute in Paris and her colleagues analysed blood samples and questionnaires collected by the Milieu Intérieur Consortium from 1,000 healthy people who live in Brittany, France. The researchers exposed the blood samples to molecules, microorganisms and viruses known to activate the immune system. They then measured the effect of each molecule or pathogen on the production of proteins called cytokines, which regulate the body’s inflammatory responses.

    The authors combined these results with information about 136 personal traits drawn from demographic, environmental and clinical data. They found that three traits stood out as having particularly strong associations with cytokine responses: cigarette smoking, body mass index and previous cytomegalovirus infection.

    The data on cigarette consumption were particularly striking: the effect of smoking on cytokine responses was as large as the effects of age, sex and genetics. And these effects lingered for years after participants had given up cigarettes. Saint-André and her team found that these factors correlated with patterns of chemical tags, called methyl groups, that were added to the cells’ DNA in certain regions. The addition of such methyl groups can alter gene activity.

    Nature plus nurture

    “It is a very important piece of work,” says Vinod Kumar, a geneticist at Radboud University Medical Center in Nijmegen, the Netherlands, not only because of the specific results about smoking, but also because of the overall effort to track sources of variability in immune responses. The study found that individual environmental factors, for example, can affect different cytokines to different degrees. “It makes me wonder how much detail we should consider when we are looking at targeted therapy or personalized medicine,” he says.

    But the study still needs to be repeated to ensure that the results are generalizable, says Saint-André. And, in future, it should include a more ethnically and racially diverse group of participants. The team has now expanded their study to include participants from Senegal and Hong Kong, she says. The researchers have also gone back to the original participants, and have collected fresh blood samples from 415 of them 10 years after the original samples were taken.

    It would be valuable to learn more about how smoking influences immune cell function, and, in turn, what the body’s responses to infection and vaccination are, says Luo. “That could offer valuable insights into the broader health consequences of smoking.”

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  • Great ‘Stone Age’ wall discovered in Baltic Sea

    Great ‘Stone Age’ wall discovered in Baltic Sea

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    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 date back to the Stone Age1.

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    doi: https://doi.org/10.1038/d41586-024-00409-w

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  • Why is Latin America on fire? It’s not just climate change, scientists say

    Why is Latin America on fire? It’s not just climate change, scientists say

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    In Chile, more than 130 people have died in this year’s wildfires — the deadliest in the nation’s history. In Colombia last month, wildfire smoke billowed just outside Bogotá, defying the city’s reputation for cold, wet weather. And in Argentina, a wildfire ravaged a forest that is listed as a World Heritage Site by the United Nations cultural organization UNESCO.

    These wildfires add to the destruction from record-setting fires in the Amazon in October 2023. This is not a normal pattern: in many parts of the region, wildfires are not part of the landscape’s natural history, except for blazes caused by “occasional lightning strikes”, says Francisco de la Barrera, an environmental scientist at University of Concepción in Chile.

    But scientists say that the flames have been fanned by a combination of a strong El Niño climate pattern, a profusion of non-native trees and climate change. Researchers warn that the same factors could put other cities on the continent at risk.

    “We are very worried, because each new fire is bigger, more threatening and with an ever-greater impact,” says de la Barrera.

    Climate change’s fiery legacy

    Catastrophic fires have multiple causes, but climate change is one of the key drivers, says climatologist Maisa Rojas Corradi, who is Chile’s environment minister. In the past decade, the country has had 16 megafires, which coincided with “the highest temperatures recorded for central Chile”, Rojas says. A megadrought that descended on the region in 2010 is one of the longest in a millennium, says Wenju Cai, a climatologist at Australia’s national science agency, CSIRO, in Melbourne.

    Climate change is also cutting cloud cover and shrinking glaciers in the Chilean Andes, says Cai. That means a decrease in reflected sunlight and, as a result, increasing temperatures.

    This year, the effects of climate change have been amplified by a strong El Niño climatic pattern, Cai says. Warm sea-surface temperatures off Chile’s coast have intensified inland temperatures and fuelled “warm easterly winds blowing across the Andes from Argentina toward Chile, fanning the fire,” he says.

    Where forest and city meet

    Humans have also provided ample fuel for local wildfires with well-intentioned tree planting. In the twentieth century, eucalyptus trees native to Australia were planted on the hills surrounding Bogotá, to stop heavy erosion, says Dolors Armenteras, a biologist at the National University of Colombia in Bogotá. Eucalyptus was chosen because it grows quickly and adapts well to a variety of conditions.

    Hawaii wildfires: did scientists expect Maui to burn?

    The planting had a “noble goal”, says Trent Penman, a bushfire scientist at the University of Melbourne in Australia, but large numbers of eucalyptus trees provide a bounty of flammable material in the form of bark sheddings. These ignite readily, producing numerous embers that can blow across roads, rivers and other fuel breaks, quickly spreading fire.

    De la Barrera says that non-native trees played a part in the fires in Chile. According to the country’s agriculture department, forest plantation areas in the Valparaiso region — the scene of January’s deadly fires — doubled in size to more than 41,000 hectares between 2006 and 2021. Eucalyptus accounts for almost 40% of the area covered by plantations in Chile.

    “In the last 20 to 30 years, the cities [have moved] much closer to plantations,” de la Barrera says, adding that populations on the rural–urban fringe of cities are at greater risk of fire in the future.

    A fire foretold

    “When I saw the fires in Bogotá, it was like seeing a Chronicle of a Death Foretold,” says Tania Marisol González, a conservation ecologist at Bogotá’s Pontifical Javeriana University. She’s referring to a novel by Colombian Nobel prizewinner Gabriel García Márquez in which no one in a small town can stop a murder, despite many opportunities to do so — a parallel to the inability to stop wildfires.

    ‘Apocalyptic’ fires are ravaging the world’s largest tropical wetland

    Latin America needs to take more preventive action, González says, including reducing fuel loads and building firebreaks. Armenteras says that fire risk could be reduced on the edges of Latin American cities by replacing invasive trees in these transitional zones with native species that are less susceptible to fire. But more research is needed before such a programme could begin.“We do not know enough about the flammability of the species in Latin America; we do not know which species can be used,” she says.

    Rojas, the Chilean environment minister, says that the government’s job is to make the country more resilient to fires. One possibility, she says, is to promote “biodiverse landscapes, with protected water sources and firebreak areas, especially in the urban–rural interface. This will reduce the risks to people and nature.”

    But a long road lies ahead: de la Barrera warns that the steps proposed by Rojas will require substantial legal and regulatory changes.

    Colombia’s environment ministry did not respond to Nature’s request for comment.

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  • Visuo-frontal interactions during social learning in freely moving macaques

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  • Emery, N. J. The eyes have it: the neuroethology, function and evolution of social gaze. Neurosci. Biobehav. Rev. 24, 581–604 (2000).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Nahm, F. K., Perret, A., Amaral, D. G. & Albright, T. D. How do monkeys look at faces? J. Cogn. Neurosci. 9, 611–623 (1997).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Emery, N. J., Lorincz, E. N., Perrett, D. I., Oram, M. W. & Baker, C. I. Gaze following and joint attention in rhesus monkeys (Macaca mulatta). J. Comp. Psychol. 111, 286–293 (1997).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Chang, S. W. C., Gariepy, J.-F. & Platt, M. L. Neuronal reference frames for social decisions in primate frontal cortex. Nat. Neurosci. 16, 243–250 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Aquino, T. G. et al. Value-related neuronal responses in the human amygdala during observational learning. J. Neurosci. 40, 4761–4772 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Falcone, R., Brunamonti, E., Ferraina, S. & Genovesio, A. Neural encoding of self and another agent’s goal in the primate prefrontal cortex: human-monkey interactions. Cereb. Cortex 26, 4613–4622 (2016).

    Article 
    PubMed 

    Google Scholar     

  • Haroush, K. & Williams, Z. M. Neuronal prediction of opponent’s behavior during cooperative social interchange in primates. Cell 160, 1233–1245 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Jamali, M. et al. Single-neuronal predictions of others’ beliefs in humans. Nature 591, 610–614 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Báez-Mendoza, R., Mastrobattista, E. P., Wang, A. J. & Williams, Z. M. Social agent identity cells in the prefrontal cortex of interacting groups of primates. Science 374, eabb4149 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Rose, M. C., Styr, B., Schmid, T. A., Elie, J. E. & Yartsev, M. M. Cortical representation of group social communication in bats. Science 374, eaba9584 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Padilla-Coreano, N. et al. Cortical ensembles orchestrate social competition through hypothalamic outputs. Nature 603, 667–671 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Kingsbury, L. et al. Correlated neural activity and encoding of behavior across brains of socially interacting animals. Cell 178, 429–446.e16 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Li, S. W. et al. Frontal neurons driving competitive behaviour and ecology of social groups. Nature 603, 661–666 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Visco-Comandini, F. et al. Do non-human primates cooperate? Evidences of motor coordination during a joint action task in macaque monkeys. Cortex. 70, 115–127 (2015).

    Article 
    PubMed 

    Google Scholar     

  • Mesterton-gibbons, M. & Dugatkin, L. A. Cooperation and the Prisoner’s Dilemma: towards testable models of mutualism versus reciprocity. Anim. Behav. 54, 551–557 (1997).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Silk, J. B. Nepotistic cooperation in non-human primate groups. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 364, 3243–3254 (2009).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Maestripieri, D. & Georgiev, A. V. What cortisol can tell us about the costs of sociality and reproduction among free-ranging rhesus macaque females on Cayo Santiago. Am. J. Primatol. 78, 92–105 (2016).

    Article 
    PubMed 

    Google Scholar     

  • Sliwa, J. & Freiwald, W. A. A dedicated network for social interaction processing in the primate brain. Science 356, 745–749 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Dal Monte, O. et al. Widespread implementations of interactive social gaze neurons in the primate prefrontal-amygdala networks. Neuron 110, 2183–2197.e7 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Mosher, C. P., Zimmerman, P. E. & Gothard, K. M. Neurons in the monkey amygdala detect eye contact during naturalistic social interactions. Curr. Biol. 24, 2459–2464 (2014).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Mathis, A. et al. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat. Neurosci. 21, 1281–1289 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Kobatake, E. & Tanaka, K. Neuronal selectivities to complex object features in the ventral visual pathway of the macaque cerebral cortex. J. Neurophysiol. 71, 856–867 (1994).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Schein, S. J. & Desimone, R. Spectral properties of V4 neurons in the macaque. J. Neurosci. 10, 3369–3389 (1990).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Pasupathy, A. & Connor, C. E. Responses to contour features in macaque area V4. J. Neurophysiol. 82, 2490–2502 (1999).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Wang, L. et al. Differential roles of delay-period neural activity in the monkey dorsolateral prefrontal cortex in visual-haptic crossmodal working memory. Proc. Natl Acad. Sci. USA 112, E214–9 (2015).

    CAS 
    PubMed 

    Google Scholar     

  • Viswanathan, P. & Nieder, A. Neuronal correlates of a visual ‘sense of number’ in primate parietal and prefrontal cortices. Proc. Natl Acad. Sci. USA 110, 11187–11192 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Gariépy, J.-F. et al. Social learning in humans and other animals. Front. Neurosci. 8, 58 (2014).

    PubMed 
    PubMed Central 

    Google Scholar     

  • Tanji, J. & Hoshi, E. Role of the lateral prefrontal cortex in executive behavioral control. Physiol. Rev. 88, 37–57 (2008).

    Article 
    PubMed 

    Google Scholar     

  • Dickey, A. S., Suminski, A., Amit, Y. & Hatsopoulos, N. G. Single-unit stability using chronically implanted multielectrode arrays. J. Neurophysiol. 102, 1331–1339 (2009).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Grabenhorst, F., Báez-Mendoza, R., Genest, W., Deco, G. & Schultz, W. Primate amygdala neurons simulate decision processes of social partners. Cell 177, 986–998.e15 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Suzuki, S. et al. Learning to simulate others’ decisions. Neuron 74, 1125–1137 (2012).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Rigotti, M. et al. The importance of mixed selectivity in complex cognitive tasks. Nature 497, 585–590 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Talluri, B.C., Kang, I., Lazere, A. et al. Activity in primate visual cortex is minimally driven by spontaneous movements. Nat. Neurosci. 26, 1953–1959 (2023).

  • Tremblay, S., Testard, C., DiTullio, R. W., Inchauspé, J. & Petrides, M. Neural cognitive signals during spontaneous movements in the macaque. Nat. Neurosci. https://doi.org/10.1038/s41593-022-01220-4 (2022).

  • Musall, S., Kaufman, M. T., Juavinett, A. L., Gluf, S. & Churchland, A. K. Single-trial neural dynamics are dominated by richly varied movements. Nat. Neurosci. 22, 1677–1686 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Shahidi, N. et al. Population coding of strategic variables during foraging in freely-moving macaques. Nat. Neurosci. (in the press).

  • Koren, V. Uncovering structured responses of neural populations recorded from macaque monkeys with linear support vector machines. STAR Protoc. 2, 100746 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Shahidi, N., Andrei, A. R., Hu, M. & Dragoi, V. High-order coordination of cortical spiking activity modulates perceptual accuracy. Nat. Neurosci. 22, 1148–1158 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Salinas, E. & Sejnowski, T. J. Correlated neuronal activity and the flow of neural information. Nat. Rev. Neurosci. 2, 539–550 (2001).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Bair, W., Zohary, E. & Newsome, W. T. Correlated firing in macaque visual area MT: time scales and relationship to behavior. J. Neurosci. 21, 1676–1697 (2001).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Miller, E. K. & Cohen, J. D. An integrative theory of prefrontal cortex function. Annu. Rev. Neurosci. 24, 167–202 (2001).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Hedrick, N. G. et al. Learning binds new inputs into functional synaptic clusters via spinogenesis. Nat. Neurosci. 25, 726–737 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Makino, H., Hwang, E. J., Hedrick, N. G. & Komiyama, T. Circuit mechanisms of sensorimotor learning. Neuron 92, 705–721 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Semedo, J. D., Zandvakili, A., Machens, C. K., Yu, B. M. & Kohn, A. Cortical areas interact through a communication subspace. Neuron 102, 249–259.e4 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Runyan, C. A., Piasini, E., Panzeri, S. & Harvey, C. D. Distinct timescales of population coding across cortex. Nature 548, 92–96 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Ong, W. S., Madlon-Kay, S. & Platt, M. L. Neuronal correlates of strategic cooperation in monkeys. Nat. Neurosci. https://doi.org/10.1038/s41593-020-00746-9 (2020).

  • Wallace, K. J. & Hofmann, H. A. Decision-making in a social world: integrating cognitive ecology and social neuroscience. Curr. Opin. Neurobiol. 68, 152–158 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Ghazanfar, A. A. & Santos, L. R. Primate brains in the wild: the sensory bases for social interactions. Nat. Rev. Neurosci. 5, 603–616 (2004).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Fernandez-Leon, J. A. et al. A wireless transmission neural interface system for unconstrained non-human primates. J. Neural Eng. 12, 56005 (2015).

    Article 

    Google Scholar     

  • Milton, R., Shahidi, N. & Dragoi, V. Dynamic states of population activity in prefrontal cortical networks of freely-moving macaque. Nat. Commun. 11, 1948 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Contestabile, A., Casarotto, G., Girard, B., Tzanoulinou, S. & Bellone, C. Deconstructing the contribution of sensory cues in social approach. Eur. J. Neurosci. 53, 3199–3211 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Froesel, M. et al. Socially meaningful visual context either enhances or inhibits vocalisation processing in the macaque brain. Nat. Commun. 13, 4886 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Fan, S., Dal Monte, O. & Chang, S. W. C. Levels of naturalism in social neuroscience research. iScience 24, 102702 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Miller, C. T. et al. Natural behavior is the language of the brain. Curr. Biol. 32, R482–R493 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Salvucci, D. D. & Goldberg, J. H. Identifying fixations and saccades in eye-tracking protocols. In Proc. 2000 Symposium on Eye Tracking Research & Applications 71–78 (Association for Computing Machinery, 2000).

  • Mahalanobis, P. C. On the generalised distance in statistics. Proc. Natl Acad. Sci. India 2, 45–49 (1936).


    Google Scholar     

  • Luo, T. Z. & Maunsell, J. H. R. Attentional changes in either criterion or sensitivity are associated with robust modulations in lateral prefrontal cortex. Neuron 97, 1382–1393.e7 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Green, D. M. & Swets, J. A. Signal Detection Theory and Psychophysics (Wiley, 1966).

  • Bishop, C. M. Pattern Recognition and Machine Learning (Springer, 2006).

  • Pojoga, S. A., Kharas, N. & Dragoi, V. Perceptually unidentifiable stimuli influence cortical processing and behavioral performance. Nat. Commun. 11, 6109 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Kruger, J. & Aiple, F. Multimicroelectrode investigation of monkey striate cortex: Spike train correlations in the infragranular layers. J. Neurophysiol. 60, 798–828 (1988).

    Article 
    CAS 
    PubMed 

    Google Scholar     

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  • Smoking changes adaptive immunity with persistent effects

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    Human samples

    Human samples came from the Milieu Intérieur cohort, which was approved by the Comité de Protection des Personnes–Ouest 6 on 13 June 2012, and by the French Agence Nationale de Sécurité du Médicament (ANSM) on 22 June 2012. The study is sponsored by Institut Pasteur (Pasteur ID-RCB Number: 2012-A00238-35) and was conducted as a single-centre interventional study without an investigational product. The original protocol was registered under ClinicalTrials.gov (study no. NCT01699893). The samples and data used in this study were formally established as the Milieu Intérieur biocollection (NCT03905993), with approvals by the Comité de Protection des Personnes–Sud Méditerranée and the Commission Nationale de l’Informatique et des Libertés (CNIL) on 11 April 2018. Donors gave written informed consent. The 1,000 donors of the Milieu Intérieur cohort were recruited by BioTrial to be composed of healthy individuals of the same genetic background (Western European) and to have 100 women and 100 men from each decade of life between 20 and 69 years of age. Donors were selected based on various inclusion and exclusion criteria that were previously described12. In brief, donors were required to have no history or evidence of severe, chronic or recurrent pathological conditions, neurological or psychiatric disorders, alcohol abuse, recent use of drugs, recent vaccine administration and recent use of immune modulatory agents. To avoid the influence of hormonal fluctuations in women, pregnant and peri-menopausal women were not included. To avoid genetic stratification in the study population, the recruitment of donors was restricted to individuals whose parents and grandparents were born in Metropolitan France. Additionally, we formally checked how the genetic background of the donors could affect cytokine levels by performing association tests between the first 20 genetic principal components out of the PCA on the individual genotypes and each of the induced cytokines in each stimulation. Although PC1 had significant association with IL-10 (Benjamini–Yekutieli adjusted P value < 0.05), we found that the first 20 principal components showed no significant associations with cytokine responses at the P value threshold (Benjamini–Yekutieli adjusted P value < 0.01) we use throughout this study. To illustrate the homogeneity of the genetic structure of the 1,000 individuals of the Milieu Intérieur cohort, a PCA was performed with EIGENSTRAT41 on 261,827 independent SNPs and 1,723 individuals, which include the 1,000 Milieu Intérieur donors together with 723 individuals from a selection of 36 populations originating from North Africa, the Near East, as well as western and northern Europe42 is shown, similarly to what was previously performed3. PC1 versus PC2, PC1 versus PC3 and PC2 versus PC3 are displayed as well as a bar plot of the variance explained by the first 20 components of the PCA (Extended Data Fig. 9b). Unless otherwise stated, all displayed results have been performed on the 955 individuals of the cohort who gave consent to share their data publicly, in order to ensure easy reproducibility of the results.

    TruCulture whole-blood stimulations

    TruCulture whole-blood stimulations were performed in a standardized way as previously described4,43. Briefly, tubes were prepared in batch with the indicated stimulus, resuspended in a volume of 2 ml buffered medium, and maintained at −20 °C until time of use. Stimuli used in this study were LPS derived from E. coli O111:B4 (Invivogen), E. coli O111:B4 (Invivogen), C. albicans (Invivogen), vaccine-grade poly I:C (Invivogen), live Bacillus Calmette-Guerin (Immucyst, Sanofi Pasteur), live H1N1 attenuated influenza A/PR8 (IAV) (Charles River), SEB (Bernhard Nocht Institute), CD3 + CD28 (R&D Systems and Beckman Coulter), and cytokines TNF (Miltenyi Biotech), IL-1β (Peprotech) and IFNγ (Boehringer Ingelheim). One millilitre of whole blood was distributed into each of the prewarmed TruCulture tubes, inserted into a dry block incubator, and maintained at 37 °C room air for 22 h. At the end of the incubation period, tubes were opened, and a valve was inserted in order to separate the sedimented cells from the supernatant and to stop the stimulation reaction. Liquid supernatants were aliquoted and immediately frozen at −80 °C until the time of use.

    Luminex multi-analyte profiling

    Supernatants from TruCulture tubes were analysed by Rules Based Medicine using the Luminex xMAP technology. Samples were analysed according to the Clinical Laboratory Improvement Amendments (CLIA) guidelines. The lower limit of quantification (LLOQ) was determined as previously described43, and is the lowest concentration of an analyte in a sample that can be reliably detected and at which the total error meets CLIA requirements for laboratory accuracy. The 13 cytokines (CXCL5, CSF2, IFNγ, IL-1β, TNF, IL-2, IL-6, IL-8, IL-10, IL-12p70, IL-13, IL-17 and IL-23), which were measured in this study, were selected to best capture broad immune response variability. Among 109 analytes initially tested, these are the ones that captured the maximum variance following stimulation with the 4 stimuli (LPS, BCG, poly I:C and SEB) that showed the most distinct immune responses among 27 stimuli tested on a subset of 25 individuals of the Milieu Intérieur cohort.

    Principal components analysis

    The PCA in Extended Data Fig. 1 was created in R 4.2.1 using the FactoMineR 2.8 package. The data were log-transformed and by default scaled to unit and missing values were imputed by the mean of the variable.

    Cytokine induction visualization

    Cytokines were considered induced if the absolute value of their median concentration in the stimulated condition was 30%-fold of their concentration in the null condition. Standardized log mean differences were computed as follows (mean(concentration of the cytokine in the stimulated condition) − mean(concentration of the cytokine in the null condition))/s.d.((concentration of the cytokine in the stimulated condition) − (concentration of the cytokine in the null condition)) and the corresponding heat map was generated with heatmaply 1.0.0 and dendextend 1.13.12 with ‘complete’ clustering method and ‘euclidean’ distance in R version 4.2.1.

    Identification of CD3 + CD28 non-responders

    Levels of cytokines that we focused on are low to undetectable in the non-stimulated condition, and cytokine induction is generally homogenous within this healthy population of individuals, with no clear distinguishable groups of responders and non-responders, except for anti-CD3 + CD28 stimulation (Extended Data Fig. 2). For the anti-CD3 + CD28 stimulation, we identified through k-means clustering a group of 705 individuals that responded to the stimulation and a group of 295 individual did not. This lack of response of 295 individuals is explained by a FcγRIIA polymorphism (rs1801274) that was previously described as preventing response to this anti-CD3 + CD28 stimulation28 (Extended Data Fig. 9). All statistical analyses on anti-CD3 + CD28 stimulations in this study were thus performed on the responders only.

    eCRF criteria association tests with induced cytokines

    Variables were extracted from the eCRF filled by the donors with the help of a physician. To limit biases in associations, categorical variables had to have at least 5% of individuals in at least half of the categorical levels to be considered for association tests. Such categoricalvariables or numerical ones were tested for associations with the log-transformed induced cytokine levels in each stimulation through LRTs, using age, sex and the technical variable batchID (corresponding to two batches of TruCulture tubes produced at different periods of time) as covariates: the LRT compared the models lm(cytokine ~ variable + age + sex + batchID) with lm(cytokine ~ age + sex + batchID), followed by Benjamini–Yekutieli multiple testing correction applied to the whole heat maps, so taking into account the tests made for the 136 CRF variables with all the induced cytokines in a specific stimulation. For Extended Data Figs. 4 and 5, the models compared were lm(cytokine ~ age + sex + batchID) with lm(cytokine ~ sex + batchID) for age and lm(cytokine ~ sex + batchID) with lm(cytokine ~ age + batchID) for sex. P values of association tests were represented using ggplot2 3.2.1 in R 3.6.0. Adjusted P values on the box plots were computed with the wilcox.test function, correcting for multiple testing. Versions of the box plots and scatter plots made on the residuals after regression on age, sex and batchID are displayed on Extended Data Fig. 6d–f.

    Effect size plots

    Linear regression models were estimated in each stimulation using the log-transformed induced cytokine levels as outcome and age, sex, batchID, and the covariates of interest (for example, smoking status) as predictor variables. Interactions with the covariates of interest were considered when indicated. Exponential of the regression coefficient estimates, and their 95% confidence interval were plotted. When the covariate of interest is of categorical nature, each level of the variable is shown independently, considering the one specified as the reference. When the P value of the t-test testing if the coefficient estimate is different from zero is <0.01, it is plotted in black, otherwise it is plotted in grey. If the LRT comparing the regression with and without the variable of interest in the model with a Chi-square test is significant with a Benjamini–Yekutieli adjusted P value < 0.01, a red star is added above the effect size value and interval.

    Cell subset association tests

    Acquisition of flow cytometry data was detailed previously3. Association tests with log-transformed values of induced cytokines in each stimulation were performed as for the eCRF criteria association tests using log-transformed raw counts of cell subsets for each donor. P values of significance are indicated with asterisks as follows: *P < 0.05; **P < 0.01; ***P < 0.001.

    DNA methylation association tests

    CpG methylation profiles were generated using the Infinium MethylationEPIC BeadChip (Illumina) on genomic DNA treated with sodium bisulfite (Zymo Research) for 958 individuals of the Milieu Intérieur cohort as described19. Associations between the DNA methylation levels for the CpG sites located within 1 Mb of each cytokine gene transcription start site (TSS) and the levels of log-transformed induced cytokines in each stimulation, adjusting for age, sex, technical variable batchID and major immune cell population counts for each stimulation, were tested through LRT and identified CpG sites weakly associated with IL-17 in LPS (cg09582880), IL-2 in C. albicans (cg17850932 and cg25065535) and IL-8 in influenza (cg16468729) stimulations (FDR adjusted P value of LRT < 0.05) (Extended Data Fig. 10). These effects were mild compared with the identified associated genetic variants and the other associated variables identified in this study but are considered in the final global models (Fig. 5). CpG sites with DNA methylation levels that are directly affected by smoking have been selected as described19.

    Heat maps showing effects of covariates

    To test if the levels of some covariates, such as cell subsets, plasma proteins or DNA methylation probes, could modify the observed association of a variable, such as smoking status, with the log-transformed induced levels of cytokines in each stimulation, we compared with a LRT for each cytokine in each stimulation the model considering both the variable of interest and the covariate of interest (with interactions) plus the usual covariates age, sex and the technical covariate batchID, with a model containing all the covariates but not the variable of interest, followed by a Benjamini–Yekutieli multiple testing adjustment on the whole heat maps. For example, for Fig. 3a, the variable of interest was smoking status and the covariate of interest was each cell subset, so we compared lm(cytokine ~ smoking status × cell subset + age + sex + batchID) with lm(cytokine ~ cell subset + age + sex + batchID). When the LRT is significant, it means adding the variable of interest to the model improves the fit to the cytokine levels. For BMI-related variables, these do not improve the fit to both IL-2 and CXCL5 when T cell subsets are passed as covariates, showing that our approach is powered to identify cellular associations with effects on CXCL5 levels when present.

    pQTL analyses

    Protocols and quality-control filters for genome-wide SNP genotyping are detailed in ref. 3. In brief, the 1,000 Milieu Intérieur donors were genotyped on both the HumanOmniExpress-24 and the HumanExome-12 BeadChips (Illumina), which include 719,665 SNPs and 245,766 exonic SNPs, respectively. Average concordance rate between the two genotyping arrays was 99.99%. The final dataset included 732,341 high-quality polymorphic SNPs. After genotype imputation and quality-control filters, 11,395,554 SNPs were further filtered for minor allele frequencies > 5%, yielding a dataset composed of 1,000 donors and 5,699,237 SNPs for pQTL mapping. pQTL analyses were performed using the MatrixEQTL44 2.2 R package. SNPs were considered as cis-acting pQTLs if they were located within 1 Mb of the TSS of the gene, otherwise they were considered as trans-pQTLs. Protein expression data of the 1,000 individuals were log-transformed prior to pQTL analysis. Bonferonni correction for multiple testing (adjusted P value < 0.05) was applied to the results. We used detection thresholds of 10−3 for cis-pQTLs and 10−5 for trans-pQTLs and age, sex and the technical covariate batchID, as well as a main associated cell subset (monocytes for LPS, E. coli and C. albicans stimulations, CD4pos for SEB, CD8posEMRA for anti-CD3 + CD28, CD45pos for BCG, cDC3 for poly I:C, CD3pos for influenza, CD45pos for TNF, none for null, IL-1β and IFNγ) as covariates. SNPs associated with IFNγ in IFNγ stimulation, with IL-1β in IL-1β stimulation and with TNF in TNF stimulation were disregarded because each of these cytokines were respectively added to the TruCulture tubes and thus do not reflect endogenous secretion. To test the novelty of our pQTL results, we studied the SomaLogic plasma protein pQTL database20, for both cis– and trans-pQTLs listed in Table 1. This dataset allowed testing associations for CXCL5, IFNγ, IL-1β, IL-2, IL-6, IL-10 and IL-12a. Significant associations were identified between the variants rs352045 (cis), rs2393969 (trans), rs10822168 (trans) and the protein CXCL5 (respective FDR adjusted P = 3.02 × 10−10, P = 0.01 and P = 0.022), between rs35345753 (cis), rs62449491 (cis) and IL-6 (respective FDR adjusted P = 4.17 × 10−3 and P = 0.017) and between rs3775291 (trans) and IL-12A (FDR adjusted P = 0.049). To test associations for SNPs in linkage disequilibrium with the SNPs originally referenced in Table 1, we used a dataset of linkage disequilibrium from the ensemble database with similar ancestries as the Milieu Intérieur cohort (1000GENOMES:phase_3:CEU: Utah residents with Northern and Western European ancestry). To be inclusive, SNPs with a r2 > 0.2 were selected as associated alleles and underwent the same analysis as the one performed with the SNPs of reference. SNPs that came out as significant are those in linkage disequilibrium with the SNP referenced in Table 1 that is significantly associated with the corresponding protein. In addition, we also screened eQTL results. We compared our pQTL results with the eQTLs reported in our previous work based on nanostring transcriptomic data for common cytokines (CSF2, IFNγ, IL-1β, TNF, IL-2, IL-6, IL-8, IL-10, IL-12p70, IL-13, IL-17 and IL-23) and stimulations (E. coli, C. albicans, influenza, BCG, and SEB)4, which identified 2 main loci: the TLR1/6/10 locus and the CR1 locus. Association of variants referenced in Table 1 were found in the GTEx consortium database for rs1518110 and IL-10 (FDR adjusted P = 4.3 × 10−9), for rs352045 (cis) and CXCL5 (FDR adjusted P = 9.2 × 10−23) in whole blood and for rs143060887 (cis) and IL-12A (FDR adjusted P = 0.000076). Significant associations between rs352045 and CXCL5 and between rs1518110 and IL-10 were also found in the eQTLgen catalogue.

    Computation of variance explained

    For each stimulation, all the variables associated with at least one induced cytokine were considered to compute the percentage of each induced cytokine variance explained by each associated variable (q value < 0.05) with the R package relaimpo 2.2.3 and plotted with the R package ggplot2 3.2.1. The R2 contribution averaged over orderings among regressors was computed using the lmg type in the calc.relimp function of the relaimpo R package. For this analysis log-transformed induced cytokine data and log-transformed raw counts of cell subsets were used, as well as data for cis– and trans-associated SNPs and methylation probes. For each stimulation, all associated cis-pQTLs (rs352045, rs143060887, rs62449491 and rs1518110 for LPS; rs352045 for anti-CD3 + CD28, rs352045, rs62449491 and rs113845942 for poly I:C; rs352045 and rs35345753 for influenza), and trans-pQTLs (rs3764613 for LPS; rs4833095 for E. coli; rs11936050 for SEB; rs1801274 for anti-CD3 + CD28; rs4833095, rs72636686 and rs10013453 for BCG; rs10779330 and rs11117956 for C. albicans; rs3775291 and rs10822168 for poly I:C), as well as methylation probes (cg09582880 for LPS; cg25065535 for C. albicans, cg17850932 for poly I:C and cg16468729 for influenza) and a main associated cell subset (monocytes for LPS, E. coli and C. albicans stimulations, CD4pos for SEB, CD8posEMRA for anti-CD3 + CD28, CD45pos for BCG, cDC3 for poly I:C, CD3pos for influenza) were considered in the models.

    Reporting summary

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

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  • New type of magnetism splits from convention

    New type of magnetism splits from convention

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    Nature, Published online: 14 February 2024; doi:10.1038/d41586-024-00190-w

    Magnetic materials with zero net magnetization fall into two classes: conventional antiferromagnets and altermagnets. Physicists have identified a property in altermagnets that widens the divide between the two groups.

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  • Author Correction: CEACAM1 regulates TIM-3-mediated tolerance and exhaustion

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  • These authors contributed equally:Chen Zhu, Yasuyuki Kondo

  • Division of Gastroenterology, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis Street, Boston, 02115, Massachusetts, USA

    Yu-Hwa Huang, Yasuyuki Kondo, Amit Gandhi, Espen Melum, Michal Pyzik & Richard S. Blumberg

  • Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women’s Hospital, Harvard Institutes of Medicine, 77 Avenue Louis Pasteur, Boston, 02115, Massachusetts, USA

    Chen Zhu, Ana C. Anderson, Thomas Pertel & Vijay K. Kuchroo

  • Rosenstiel Basic Medical Sciences Research Center, Brandeis University, 415 South Street, Waltham, 02454, Massachusetts, USA

    Andrew Russell & Gregory A. Petsko

  • Whitehead Institute, Massachusetts Institute of Technology, Cambridge, 02142, Massachusetts, USA

    Stephanie K. Dougan & Hidde L. Ploegh

  • Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel 24105, Germany

    Britt-Sabina Petersen & Andre Franke

  • Division of Cancer Medicine, Norwegian PSC Research Center, Surgery and Transplantation, Oslo University Hospital, Oslo 0424, Norway

    Espen Melum

  • Department of Immunology, University of Toronto, Toronto, Ontario M5S1A8, Canada

    Kiera L. Clayton & Mario A. Ostrowski

  • Department of Pathology, Cell Signalling Section, University of Cambridge, Cambridge CB2 1QP, UK

    Monika Raab & Christopher E. Rudd

  • State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China

    Qiang Chen

  • Goodman Cancer Research Centre, McGill University, Montreal H3G 1Y6, Canada

    Nicole Beauchemin

  • Beckman Institute, City of Hope, Duarte, 91010, California, USA

    Paul J. Yazaki

  • Keenan Research Centre of St. Michael’s Hospital, Toronto, Ontario M5S1A8, Canada

    Mario A. Ostrowski

  • GI Pathology, Miraca Life Sciences, Newton, 02464, Massachusetts, USA

    Jonathan N. Glickman

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  • Evidence of superconducting Fermi arcs

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    ARPES measurements

    ARPES measurements were carried out on the 12 and 13ARPES endstations26 at BESSY II synchrotron (Helmholtz-Zentrum Berlin), as well as in the Leibniz-Institut für Festkörper und Werkstoffforschung Dresden (IFW) laboratory using the 5.9 eV laser light source. Samples were cleaved in situ at a pressure lower than 1 × 10−10 mbar and measured at the temperatures of 15 K and 1.5 K at BESSY II and 3-30 K in the IFW laboratory. The experimental data were obtained using the synchrotron light in the photon energy range from 15 to 50 eV with horizontal polarization and laser light with horizontal and circular polarizations. Angular resolution was set to 0.2–0.5° and energy resolution to 2–20 meV. The findings from the experiments were consistent and reproducible across multiple samples.

    The simultaneous presence of bulk non-superconducting and surface superconducting states hinders the detection of true coherence peaks with ARPES. Our experiments at the synchrotron, with energy resolution of the order of 5 meV, turned out to be insufficient to detect even the shifts of the leading edges of the corresponding arc peaks having FWHM of the order of 10 meV and peak-to-background ratio of approximately 5. This is because the arc states are always on top of the bulk continuum. Only by measuring with energy resolution of the order of 1–2 meV did we manage to observe sufficiently sharp peaks (Fig. 3c,d and Extended Data Fig. 4) and their sensitivity to temperature. The sharpest features need to be found on the surface.

    A superconducting gap on the arcs is most likely anisotropic. We included error bars in Fig. 4e to show the influence of a small shift of the beam spot and thus slightly different emission angle. Taking into account the very high localization in momentum space, this could lead to probing a different part of the arc and thus different kF, where the superconducting gap is slightly different.

    Bulk band structure and Fermi arc position

    In Extended Data Fig. 1, we show ARPES Fermi surface maps obtained using the photon energies from 15 eV to 43 eV. Relatively strong variation of the pattern suggests a reasonable kz-sensitivity of our experiment. We found the optimal value of the inner potential to be equal to 10.5 eV. This agrees with the previous study of Jiang et al.17.

    In Extended Data Fig. 2, we present further evidence that our assignment of the surface and bulk features is correct. Extended Data Fig. 2a shows EDCs taken across the Fermi arc for different photon energies (from synchrotron and laser sources), alongside the theoretical EDC for the fully integrated kz. The peak corresponding to the Fermi arc remains clearly visible without any noticeable dispersion for different values of kz, whereas the peaks located further below the Fermi level disperse. Such absence of the dispersion is peculiar to the surface states.

    In Extended Data Fig. 3, we show an analogue of Fig. 1e–g, but here we compare experimental data with the results of band structure calculations carried out using the linear muffin-tin orbital (LMTO) method in the atomic sphere approximation as implemented in PY LMTO computer code27. As is seen from the figure, the agreement is at the same level as earlier, underpinning the previous conclusion as regards the good agreement between experimental and theoretical 3D band structure.

    In Extended Data Fig. 4b, we present the sharpest EDCs from among the various samples and cleaves. Most have FWHM below 3 meV and a peak-to-background ratio of over 30.

    Band structure calculations

    We performed density functional theory calculations using the full-potential nonorthogonal local-orbital scheme of ref. 28 within the general gradient approximation29 and extracted a Wannier function model. This allows determination of bulk projected spectral densities (without surface states) and the spectral densities of semi-infinite slabs via Green’s function techniques30. To model surface superconductivity of the semi-infinite slab, the Wannier model is extended into the BdG formalism with a zero-gap function except for a constant Wannier orbital diagonal singlet gap function matrix at the first three PtBi2 layers. A modification of the Green’s function method is used to accommodate this surface-specific term.

    Surface superconductivity calculations

    To model a system which has a non-zero gap function only at the surface—in the first 30aB which is 3(PtBi2) layers—we modified the standard Green’s function technique for semi-infinite slabs. The system is built by a semi-infinite chain of identical blocks consisting of 3(PtBi2) layers, repeating indefinitely away from the surface. Each block has a Hamiltonian Hk for each pseudo momentum k in the plane perpendicular to the surface and a hopping matrix Vk, which couples neighbouring blocks. The blocks’ minimum size is determined by the condition that H and V describe all possible hoppings. To add superconductivity, the BdG formalism is used by extending the matrices in the following way:

    $$\begin{array}{rcl}{H}_{k,{\rm{BdG}}} & = & \left(\begin{array}{cc}{H}_{k} & {\varDelta }_{k}\\ {\varDelta }_{k}^{+} & -{H}_{-k}^{* }\end{array}\right),\\ {V}_{k,{\rm{BdG}}} & = & \left(\begin{array}{cc}{V}_{k} & 0\\ 0 & -{V}_{-k}^{* }\end{array}\right),\end{array}$$

    where we choose \({\varDelta }_{k}={\delta }_{i{i}^{{\prime} }}\left(\begin{array}{cc}0 & {V}_{0}\\ -{V}_{0} & 0\end{array}\right)\) with i being a spinless Wannier function index and the 2 × 2 matrix to act in a single Wannier function’s spin subspace. This choice also leads to \(\varDelta \left[{V}_{k,{\rm{BdG}}}\right]=0\), since V is an off-diagonal part of the full Hamiltonian. To model surface-only superconductivity, we let V0 = 0 for all (infinite) blocks, except the first one, which gets a finite V0 = 2 meV.

    The standard Green’s function solution for this problem consists of determining the propagator X which encompasses all diagrams that describe paths that start at a certain block, propagate anywhere towards the infinite side of that block and return to that block. X also describes the Green’s function G00 of the first block and the self-energy to be added to the Hamiltonian to obtain G00 (a self-consistency condition) \({G}_{00}=X={\left({\omega }^{+}-H-\Sigma \right)}^{-1}\), Σ = VXV+ (in practice, however, self-consistency is obtained by an accelerated algorithm). From this recursion, relations can calculate all other Green’s-function blocks. These can be derived by subdividing propagation diagrams into irreducible parts using known components, in particular X.

    If the first block differs from all the others (as is the case due to Δk) one needs to modify the method in the following way. Let the first block have Hamiltonian h and hoppings to the second block v (while all other blocks are described by H and V). Then the irreducible subdivision of the propagation diagrams for G00 results in \(g={\left({\omega }^{+}-h\right)}^{-1}\).

    $$\begin{array}{l}{G}_{00}=g+gvX{v}^{+}g+(\,gvX{v}^{+})g\\ \,=\,\frac{1}{{\omega }^{+}-h-vX{v}^{+}}\end{array}$$

    which contains the surface Hamiltonian and a modified self-energy depending on the X of the unmodified semi-infinite slab. From this we can derive the second block’s Green’s function

    $${G}_{11}=X+X{v}^{+}{G}_{00}vX$$

    and all others

    $${G}_{n+1,n+1}=X+X{V}^{+}{G}_{nn}VX,\quad n > 0$$

    which can be used to obtain the spectral density up to a certain penetration depth. Note that in our BdG case \(H={H}_{k,{\rm{BdG}}}\left[{V}_{0}=0\right]\), \(V={V}_{k,{\rm{BdG}}}\left[{V}_{0}=0\right]\) and \(h={H}_{k,{\rm{BdG}}}\left[{V}_{0}\ne 0\right]\), v = V. The BdG spectral density is particle–hole symmetric and to obtain results that resemble ARPES data, one needs to use the particle–particle block Gee (the upper left quarter of the G matrix) only.

    Extended Data Fig. 5b shows the resulting spectra of this method along the path denoted in Extended Data Fig. 5a. Note that a gap is opened at the surface band pockets close to the Fermi energy, while the rest of the spectrum stays gapless (if we let V0 ≠ 0 for all blocks, we get a completely gapped spectrum). Extended Data Fig. 5c shows a zoomed-in region around the surface state. Note that the bulk bands are gapless (dark blue vertical features) while the surface state shows a gap and corresponding band back-bending. The particle–hole symmetry becomes apparent, although with a larger spectral weight for the occupied part because we use Gee only.

    Further discussion

    One approach to demonstrate the existence of topologically protected states with a topological insulator is to perform spin-resolved ARPES. In this technique, the spin-locking effect determines the spin structure in the vicinity of the surface Dirac node. However, the situation is quite different for Weyl semimetals. Here, there is no specific spin structure or configuration associated with the Weyl nodes, which can occur at generic points in the Brillouin zone. As inversion is broken and spin-orbital coupling present, each band at a generic k-point naturally possesses a spin direction, but this spin texture is smooth. Consequently, spin-resolved ARPES measurements cannot directly reveal Weyl points.

    We would like to exclude the interpretation of our data based on density-wave order, which could, in principle, result in the similar features in the spectra. Charge density-waves require a redistribution of the spectral weight in the momentum space, characterized by the particular k-vector (vectors). We have always observed almost the same Fermi surface maps and underlying dispersions, independent of temperature. In line with these observations are the results of the STM studies which never detected any kind of a reconstruction. We have never observed any replica of the arcs or of the deeper lying surface states, such as a strong feature at (−0.2, −0.2) in Fig. 3f,g. It is also not clear which k-vector would be suitable for characterizing the density-wave order. If the arcs are simply superimposed in momentum, they all are of electron-like topology, so the opening of the hybridization gaps seems very unlikely. Finally, the fundamental difference between the density-wave gaps and superconducting gaps is that the latter are always pinned to the Fermi level. This is the only energy interval where we observe the changes in the spectra of PtBi2 with temperature.

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  • Signatures of a surface spin–orbital chiral metal

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    The samples of Sr2RuO4 were grown using the floating-zone technique, following a previously published procedure43. Single crystals were postcleaved in an ultrahigh vacuum at a base pressure of 1 × 10−10 mbar and a temperature of 20 K (and 77 K). The temperature was kept constant throughout the measurements. The experiment was performed at the NFFA–APE Low Energy beamline laboratory at the Elettra synchrotron radiation facility and designed with an APPLE-II aperiodic source for polarized extreme UV radiation and a vectorial twin-VLEED spin-polarization detector downstream of a DA30 Scienta ARPES analyser44. The photon energy used for our measurements was 40 eV, which was found to maximize the spectral intensity, as shown previously45. The energy and momentum resolutions were better than 12 meV and 0.018 Å−1, respectively. Importantly, as already mentioned, to eliminate the geometrical contribution to the circular polarization, the crystals were aligned as in Fig. 1c,d. For completeness, seminal works on ARPES and dichroism that might aid the understanding of our measurements can be found in refs. 39,41,46,47,48.

    In the following sections, we report additional measurements that help to corroborate the message and conclusions given in the main text.

    Sample alignment and experimental geometry

    When using circularly polarized light, the disentanglement between geometrical and intrinsic matrix elements is crucial but problematic. A solution is to have the incoming radiation exactly within one of the mirror planes of the system studied and to measure in the direction orthogonal to that plane, as we show in Fig. 1c. In such a configuration, the differences in the CP-spin-ARPES signal can be attributed to intrinsic differences in LS, and the geometrical contributions are well defined. In this regard, it is of paramount importance to align the sample carefully. In the present case, the symmetric character of the material’s Fermi surface45,49,50 allows us to carefully align the sample with the incoming beam of photons lying in a mirror plane. The alignment of the sample was carried out by monitoring the experimental Fermi surface and by making sure that the analyser slit direction was perpendicular to the mirror plane. As shown in Extended Data Figs. 1 and 2, we estimated our alignment to be better than 0.9° from the ideal configuration, a value within the uncertainty considering our angular azimuthal precision (about 1°). Furthermore, different samples gave us the same results, corroborating the robustness of the measurement outputs within this azimuth uncertainty.

    In the NFFA–APE Low Energy beamline laboratory, our sample was placed in the manipulator in normal emission conditions, with the synchrotron light impinging on the sample surface at an angle of 45°. This means that standard linear polarizations, such as linear vertical and linear horizontal (Extended Data Fig. 1), would act differently on the matrix elements’ selection rules. In particular, linear vertical light would be fully within the sample plane, whereas linear horizontal light would have one component within the plane and one out of plane (with 50% intensity each). Now, when using circularly polarized light, to distinguish between real and geometrical matrix element effects, the incoming light needed to be aligned within the experimental error, within one of the mirror planes of the sample.

    To estimate the azimuthal value we fitted the k-loci of the Fermi surface contours (red markers in Extended Data Fig. 2a,b) and we then aligned the horizontal and vertical axes (see ‘Details of the fitting’). In our configuration, there is negligible misalignment between the states at positive and negative values of k (Extended Data Fig. 2c,d). In Extended Data Fig. 2, we show that by extracting momentum distribution curves (coloured horizontal lines in Extended Data Fig. 2c), the peak positions are symmetric within the resolution of the instrument (12 meV for energy and 0.018 Å−1). We can therefore confidently perform the measurements shown in the main text.

    Details of the fitting

    The k-loci of the Fermi surfaces shown in Extended Data Fig. 2a,b and the positions of the peaks in Extended Data Fig. 1d have been extracted by fitting the ARPES data. The fitting procedure used is standard and consists of fitting both energy distribution curves (EDCs) and momentum distribution curves by using Lorentzian curves convoluted by a Gaussian contribution that accounts for the experimental resolutions. Then, as part of the fit results, we extracted the k positions of the peaks, which are shown as red markers in Extended Data Fig. 2 and the values in Extended Data Fig. 2d.

    Spin-ARPES data

    To obtain the values reported, the spin data shown have also been normalized to include the action of the Sherman function of the instrument. In particular, the data for spin-up and spin-down channels have been normalized to their background, so they matched in both cases. In the present study, the background normalization was done on the high-energy tails of the EDCs far from the region where the spin polarization was observed. After normalization, to extract the spin intensity, we used the following relations:

    $${I}^{{\rm{TRUE}}}({\bf{k}},\uparrow )=\frac{{I}^{{\rm{TOT}}}({\bf{k}})}{2}\times (1+P),$$

    $${I}^{{\rm{TRUE}}}({\bf{k}},\downarrow )=\frac{{I}^{{\rm{TOT}}}({\bf{k}})}{2}\times (1-P),$$

    where P is the polarization of the system, ITRUE is the intensity value (for either spin-up or -down species) obtained after inclusion of the Sherman (see below) function of the spin detector, and  and ITOT = Ibg.norm(k, ↑) + Ibg.norm(k, ↓) is simply the sum of the intensity for EDCs with spin-up and spin-down after normalization to the background. For the polarization P, the Sherman function from the instrument was included and defined as η = 0.3 (ref. 44). The Sherman function was calibrated from measurements on a single gold crystal. Therefore, P is described by:

    $$P({\bf{k}})=\frac{1}{\eta }\times \frac{{I}^{{\rm{bg.norm}}}({\bf{k}},\uparrow )-{I}^{{\rm{bg.norm}}}({\bf{k}},\downarrow )}{{I}^{{\rm{bg.norm}}}({\bf{k}},\uparrow )+{I}^{{\rm{bg.norm}}}({\bf{k}},\downarrow )}.$$

    This procedure was done for all light polarizations. We also characterized the spin channels by using different polarization-vector directions, as shown in Extended Data Fig. 3.

    Dichroism and spin-dichroism amplitudes

    A way to visualize the breaking of the time-reversal symmetry is to analyse the dichroic signal shown in Fig. 2c but resolved in the two different spin channels, up and down, which gives rise to different amplitude values when measured at ±k (expected for time-reversal symmetry breaking but not expected otherwise). We show this here at selected momentum values. The amplitude values have been extracted from the data shown in Fig. 3a and Extended Data Fig. 3, after including the Sherman function normalization.

    To corroborate the claim in the main text, that is, the observation of a signal compatible with the existence of chiral currents, Extended Data Fig. 4 shows the relative amplitudes of the dichroic versus spin-dichroic signal. First, let us consider the spin-integrated dichroism shown in Extended Data Fig. 4a. Here, the orange and green curves represent positive and negative k values, respectively, and their behaviour is overall symmetric with respect to zero. However, a small asymmetry can still be noticed, estimated to be as large as 10%, which is close to a previously reported value39 of 8%. As we will clarify from a theoretical point of view, a small degree of asymmetry in the spin-integrated dichroism can still be expected, although the amplitudes of the dichroism selected in their spin channels are supposed to be larger. To demonstrate this difference, we have shown how the dichroism curves, resolved in their spin channels, up (red) and down (blue), appear at negative k (Extended Data Fig. 4d–f) and at positive k (Extended Data Fig. 4g–i). By also considering their residuals, we can compare them to the amplitude of the spin-integrated signal. We reported this comparison in Extended Data Fig. 5. The spin-down channel shows an amplitude as high as 30% and the spin-up one is as high as 20%. These values are three times and two times bigger, respectively, than the residual extracted for the spin-integrated signal. Such a large difference corroborates the validity of our methodology and the claims of our work. Note that summing the positive and negative momentum is also counteracting any possible effects caused by small sample misalignment.

    Data and temperature

    For completeness, we also performed C+(+k, ↑) and C−(−k, ↓) on the sample after cleaving it, also at high temperature (70 K), which is above the magnetic transition of Sr2RuO4. We report the results in Extended Data Fig. 6. In particular, in Extended Data Fig. 6a–c, the top panels with blue lines show the difference between C+(+k, ↑) and C−(−k, ↓), normalized by their sum, at three values of k and at low temperature, but the bottom line is the same for the data collected at 70 K. If in the low-temperature configuration we observe a varying finite signal, at high temperature we did not see such a variation. It is important to mention that even with our resolution, we do not see any finite signal, although there might be some differences that could be observed above the magnetic transition, because it is likely that not all magnetic excitations are turned off immediately, although a reduction should be still observed. Furthermore, the high-temperature data are more noisy. Even if we cleaved the samples at high temperature, and the ARPES shown in Extended Data Fig. 6d,e confirms their presence, they are much weaker than at low temperature and are broadened thermally. Such a thermal broadening is not surprising to see in ARPES. Nevertheless, even with reduced intensity, the surface states are still clearly visible.

    Calibrating the VLEED

    Within the uncertainty of the instrument (1° integration region), the VLEED has been calibrated by acquiring spin EDCs at various angles, both positive and negative, for the sample. This is done for both spin species and with the used light polarizations. In the present case, for consistency, we did this with circularly polarized light (both left- and right-handed). Afterwards, by summing both circular polarizations and both spin species, we can reconstruct the ARPES spectra (Extended Data Fig. 7). This procedure was done by using only the spin detector to directly access the probed states and be sure that, when selecting the angular values on the deflectors, we effectively probe the selected state.

    Uncertainties and additional calibration

    To evaluate the uncertainty we used a controlled and known sample with no asymmetries in the dichroic signal, as in our previous work39. We used a kagome lattice because at the Γ point there is a well defined energy gap, opened by the action of spin–orbit coupling. Furthermore, at this point the bands are spin-degenerate; the system is also not magnetic. This allowed us to check the asymmetry, not only in the circular dichroism signal, but also in the spin-resolved circular dichroism. We estimated the uncertainty to be approximately 10% on the residual of the dichroism. Note that this is also consistent with that obtained by standard ARPES in our set-up: at the centre of the Brillouin zone, the difference between circular right- and circular left-polarized spectra (each spectrum was normalized by its own maximum intensity beforehand) is indeed 10%.

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