Protective immune trajectories in early viral containment of non-pneumonic SARS-CoV-2 infection.

Adult Aged Aged, 80 and over Ambulatory Care COVID-19 / immunology Cytokines / blood Female Gene Expression Regulation Gene Regulatory Networks Humans Interferons / immunology Killer Cells, Natural / immunology Longitudinal Studies Male Middle Aged Monocytes / immunology Nasopharynx / immunology SARS-CoV-2 / physiology T-Lymphocytes / immunology

Journal

Nature communications

ISSN: 2041-1723

Titre abrégé: Nat Commun

Pays: England

ID NLM: 101528555

Informations de publication

Date de publication:
23 02 2022

Historique:

received: 21 10 2021

accepted: 28 01 2022

entrez: 24 2 2022

pubmed: 25 2 2022

medline: 5 3 2022

Statut: epublish

Résumé

The antiviral immune response to SARS-CoV-2 infection can limit viral spread and prevent development of pneumonic COVID-19. However, the protective immunological response associated with successful viral containment in the upper airways remains unclear. Here, we combine a multi-omics approach with longitudinal sampling to reveal temporally resolved protective immune signatures in non-pneumonic and ambulatory SARS-CoV-2 infected patients and associate specific immune trajectories with upper airway viral containment. We see a distinct systemic rather than local immune state associated with viral containment, characterized by interferon stimulated gene (ISG) upregulation across circulating immune cell subsets in non-pneumonic SARS-CoV2 infection. We report reduced cytotoxic potential of Natural Killer (NK) and T cells, and an immune-modulatory monocyte phenotype associated with protective immunity in COVID-19. Together, we show protective immune trajectories in SARS-CoV2 infection, which have important implications for patient prognosis and the development of immunomodulatory therapies.

Identifiants

DOI: 10.1038/s41467-022-28508-0 PMID: 35197461 PMC: PMC8866527

pubmed: 35197461

doi: 10.1038/s41467-022-28508-0

pii: 10.1038/s41467-022-28508-0

pmc: PMC8866527

doi:

Substances chimiques

Cytokines 0

Interferons 9008-11-1

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

Pagination

1018

Informations de copyright

Références

Zhu, N., et al. A novel coronavirus from patients with pneumonia in China, 2019. New Engl. J. Med. 382, 727–733 (2020).

Zhou, P. et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579, 270–273 (2020).

pubmed: 32015507 pmcid: 7095418 doi: 10.1038/s41586-020-2012-7

Ruan, Q., Yang, K., Wang, W., Jiang, L. & Song, J. Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China. Intensive Care Med. 46, 846–848 (2020).

Chen, G. et al. Clinical and immunological features of severe and moderate coronavirus disease 2019. J. Clin. Invest 130, 2620–2629 (2020).

pubmed: 32217835 pmcid: 7190990 doi: 10.1172/JCI137244

Laing, A. G. et al. A dynamic COVID-19 immune signature includes associations with poor prognosis. Nat. Med. 26, 1623–1635 (2020).

pubmed: 32807934 doi: 10.1038/s41591-020-1038-6

Mathew, D. et al. Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications. Science 369, eabc8511 (2020).

pubmed: 32669297 pmcid: 7402624 doi: 10.1126/science.abc8511

Torres Acosta, M. A. & Singer, B. D. Pathogenesis of COVID-19-induced ARDS: implications for an ageing population. Eur. Respir. J. 56, 2002049 (2020).

Vabret, N. et al. Immunology of COVID-19: current state of the science. Immunity 52, 910–941 (2020).

pubmed: 32505227 pmcid: 7200337 doi: 10.1016/j.immuni.2020.05.002

Mehta, P. et al. COVID-19: consider cytokine storm syndromes and immunosuppression. Lancet 395, 1033 (2020).

pubmed: 32192578 pmcid: 7270045 doi: 10.1016/S0140-6736(20)30628-0

Del Valle, D. M. et al. An inflammatory cytokine signature predicts COVID-19 severity and survival. Nat. Med. 26, 1636–1643 (2020).

pubmed: 32839624 pmcid: 7869028 doi: 10.1038/s41591-020-1051-9

Arunachalam, P. S. et al. Systems biological assessment of immunity to mild versus severe COVID-19 infection in humans. Science 369, 1210 (2020).

pubmed: 32788292 pmcid: 7665312 doi: 10.1126/science.abc6261

Hadjadj, J. et al. Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients. Science 369, 718–724 (2020).

pubmed: 32661059 pmcid: 7402632 doi: 10.1126/science.abc6027

Acharya, D., Liu, G. & Gack, M. U. Dysregulation of type I interferon responses in COVID-19. Nat. Rev. Immunol. 20, 397–398 (2020).

Nicol, M. Q. et al. Lack of IFNgamma signaling attenuates spread of influenza A virus in vivo and leads to reduced pathogenesis. Virology 526, 155–164 (2019).

pubmed: 30390564 doi: 10.1016/j.virol.2018.10.017

Bastard, P. et al. Autoantibodies against type I IFNs in patients with life-threatening COVID-19. Science 370, eabd4585 (2020).

pubmed: 32972996 pmcid: 7857397 doi: 10.1126/science.abd4585

Zhang, Q. et al. Inborn errors of type I IFN immunity in patients with life-threatening COVID-19. Science 370, eabd4570 (2020).

pubmed: 32972995 pmcid: 7857407 doi: 10.1126/science.abd4570

Nicolai, L. et al. Vascular neutrophilic inflammation and immunothrombosis distinguish severe COVID-19 from influenza pneumonia. J. Thromb. Haemost. 19, 574–581 (2021).

Wilk, A. J. et al. A single-cell atlas of the peripheral immune response in patients with severe COVID-19. Nat. Med. 26, 1070–1076 (2020).

Liao, M. et al. Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19. Nat. Med. 26, 842–844 (2020).

Gao, Z. et al. A systematic review of asymptomatic infections with COVID-19. J. Microbiol. Immunol. Infect. 54, 12–16 (2020).

Garg, S. Hospitalization rates and characteristics of patients hospitalized with laboratory-confirmed coronavirus disease 2019—COVID-NET, 14 States, March 1–30, 2020. MMWR. Morb. Mortal. Weekly Rep. 69, 458–464 (2020).

Chua, R. L. et al. COVID-19 severity correlates with airway epithelium–immune cell interactions identified by single-cell analysis. Nat. Biotechnol. 38, 970–979 (2020).

pubmed: 32591762 doi: 10.1038/s41587-020-0602-4

Wu, Z. & McGoogan, J. M. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention. JAMA 323, 1239–1242 (2020).

pubmed: 32091533 doi: 10.1001/jama.2020.2648

Guan, W. J. et al. Clinical characteristics of Coronavirus Disease 2019 in China. New Engl. J. Med. 382, 1708–1720 (2020).

pubmed: 32109013 doi: 10.1056/NEJMoa2002032

Shu, T. et al. Plasma proteomics identify biomarkers and pathogenesis of COVID-19. Immunity 53, 1108–1122 (2020).

pubmed: 33128875 pmcid: 7574896 doi: 10.1016/j.immuni.2020.10.008

Kazer, S. W. et al. Integrated single-cell analysis of multicellular immune dynamics during hyperacute HIV-1 infection. Nat. Med. 26, 511–518 (2020).

pubmed: 32251406 pmcid: 7237067 doi: 10.1038/s41591-020-0799-2

Tran, T. N. & Bader, G. D. Tempora: cell trajectory inference using time-series single-cell RNA sequencing data. PLoS Comput. Biol. 16, e1008205 (2020).

pubmed: 32903255 pmcid: 7505465 doi: 10.1371/journal.pcbi.1008205

Busse, D. C. et al. Interferon-induced protein 44 and interferon-induced protein 44-like restrict replication of respiratory syncytial virus. J. Virol. 94, e00297–00220 (2020).

pubmed: 32611756 pmcid: 7459546 doi: 10.1128/JVI.00297-20

Yang, G., Xu, Y., Chen, X. & Hu, G. IFITM1 plays an essential role in the antiproliferative action of interferon-γ. Oncogene 26, 594–603 (2007).

pubmed: 16847454 doi: 10.1038/sj.onc.1209807

Verhelst, J., Parthoens, E., Schepens, B., Fiers, W. & Saelens, X. Interferon-inducible protein Mx1 inhibits influenza virus by interfering with functional viral ribonucleoprotein complex assembly. J. Virol. 86, 13445–13455 (2012).

pubmed: 23015724 pmcid: 3503048 doi: 10.1128/JVI.01682-12

Sun, Y. et al. Regulation of XAF1 expression in human colon cancer cell by interferon beta: activation by the transcription regulator STAT1. Cancer Lett. 260, 62–71 (2008).

pubmed: 18035482 doi: 10.1016/j.canlet.2007.10.014

Schoggins, J. W. et al. A diverse range of gene products are effectors of the type I interferon antiviral response. Nature 472, 481–485 (2011).

pubmed: 21478870 pmcid: 3409588 doi: 10.1038/nature09907

Perng, Y. C. & Lenschow, D. J. ISG15 in antiviral immunity and beyond. Nat. Rev. Microbiol. 16, 423–439 (2018).

pubmed: 29769653 pmcid: 7097117 doi: 10.1038/s41579-018-0020-5

Park, A. & Iwasaki, A. Type I and type III interferons–induction, signaling, evasion, and application to combat COVID-19. Cell Host Microbe 27, 870–878 (2020).

Bizzotto, J. et al. SARS-CoV-2 infection boosts MX1 antiviral effector in COVID-19 patients. iScience 23, 101585 (2020).

pubmed: 32989429 pmcid: 7510433 doi: 10.1016/j.isci.2020.101585

Busse, D. C. et al. Interferon-induced protein 44 and interferon-induced protein 44-like restrict replication of respiratory syncytial virus. J. Virol. 94, e00297-20 (2020).

Shi, G. et al. Opposing activities of IFITM proteins in SARS-CoV-2 infection. EMBO J. 40, e106501 (2021).

pubmed: 33270927 doi: 10.15252/embj.2020106501

Pfaender, S. et al. LY6E impairs coronavirus fusion and confers immune control of viral disease. Nat. Microbiol. 5, 1330–1339 (2020).

pubmed: 32704094 pmcid: 7916999 doi: 10.1038/s41564-020-0769-y

Blanco-Melo, D. et al. Imbalanced host response to SARS-CoV-2 drives development of COVID-19. Cell 181, 1036–1045.e1039 (2020).

pubmed: 32416070 pmcid: 7227586 doi: 10.1016/j.cell.2020.04.026

Konno, Y. et al. SARS-CoV-2 ORF3b is a potent interferon antagonist whose activity Is increased by a naturally occurring elongation variant. Cell Rep. 32, 108185 (2020).

pubmed: 32941788 pmcid: 7473339 doi: 10.1016/j.celrep.2020.108185

Combes, A. J. et al. Global absence and targeting of protective immune states in severe COVID-19. Nature 591, 124–130 (2021).

pubmed: 33494096 pmcid: 8567458 doi: 10.1038/s41586-021-03234-7

Trapani, J. A. & Smyth, M. J. Functional significance of the perforin/granzyme cell death pathway. Nat. Rev. Immunol. 2, 735–747 (2002).

pubmed: 12360212 doi: 10.1038/nri911

Meckiff, B. J. et al. Imbalance of regulatory and cytotoxic SARS-CoV-2-reactive CD4(+) T cells in COVID-19. Cell 183, 1340–1353.e1316 (2020).

pubmed: 33096020 pmcid: 7534589 doi: 10.1016/j.cell.2020.10.001

Maucourant, C. et al. Natural killer cell immunotypes related to COVID-19 disease severity. Sci. Immunol. 5, eabd6832 (2020).

pubmed: 32826343 pmcid: 7665314 doi: 10.1126/sciimmunol.abd6832

Weng, N. P., Araki, Y. & Subedi, K. The molecular basis of the memory T cell response: differential gene expression and its epigenetic regulation. Nat. Rev. Immunol. 12, 306–315 (2012).

pubmed: 22421787 pmcid: 4686144 doi: 10.1038/nri3173

Wang, W. H., et al. The role of galectins in virus infection - a systemic literature review. J. Microbiol. Immunol. Infect. 53, 925–935 (2019).

Narumi, K. et al. Proinflammatory proteins S100A8/S100A9 activate NK cells via interaction with RAGE. J. Immunol. 194, 5539–5548 (2015).

pubmed: 25911757 doi: 10.4049/jimmunol.1402301

Klein, E., Di Renzo, L. & Yefenof, E. Contribution of CR3, CD11b/CD18 to cytolysis by human NK cells. Mol. Immunol. 27, 1343–1347 (1990).

pubmed: 1980339 doi: 10.1016/0161-5890(90)90041-W

Reyes, R., Cardenes, B., Machado-Pineda, Y. & Cabanas, C. Tetraspanin CD9: a key regulator of cell adhesion in the immune system. Front. Immunol. 9, 863 (2018).

pubmed: 29760699 pmcid: 5936783 doi: 10.3389/fimmu.2018.00863

Huntington, N. D., Cursons, J. & Rautela, J. The cancer-natural killer cell immunity cycle. Nat. Rev. Cancer 20, 437–454 (2020).

pubmed: 32581320 doi: 10.1038/s41568-020-0272-z

Peixoto, A. N. et al. CD8 single-cell gene coexpression reveals three different effector types present at distinct phases of the immune response. J. Exp. Med. 204, 1193–1205 (2007).

pubmed: 17485515 pmcid: 2118592 doi: 10.1084/jem.20062349

Huang, C. et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395, 497–506 (2020).

pubmed: 31986264 pmcid: 7159299 doi: 10.1016/S0140-6736(20)30183-5

Nicolai, L. et al. Immunothrombotic dysregulation in COVID-19 pneumonia is associated with respiratory failure and coagulopathy. Circulation 142, 1176–1189 (2020).

pubmed: 32755393 pmcid: 7497892 doi: 10.1161/CIRCULATIONAHA.120.048488

Saini, R. V. et al. Granulysin delivered by cytotoxic cells damages endoplasmic reticulum and activates caspase-7 in target cells. J. Immunol. 186, 3497–3504 (2011).

pubmed: 21296981 doi: 10.4049/jimmunol.1003409

Silvin, A. et al. Elevated calprotectin and abnormal myeloid cell subsets discriminate severe from mild COVID-19. Cell 182, 1401–1418 (2020).

pubmed: 32810439 pmcid: 7405878 doi: 10.1016/j.cell.2020.08.002

Chalifour, A. et al. Direct bacterial protein PAMP recognition by human NK cells involves TLRs and triggers α-defensin production. Blood 104, 1778–1783 (2004).

pubmed: 15166032 doi: 10.1182/blood-2003-08-2820

Brook, M. et al. Neutrophil-derived alpha defensins control inflammation by inhibiting macrophage mRNA translation. Proc. Natl Acad. Sci. USA 113, 4350 (2016).

pubmed: 27044108 pmcid: 4843457 doi: 10.1073/pnas.1601831113

Martinvalet, D., Zhu, P. & Lieberman, J. Granzyme A induces caspase-independent mitochondrial damage, a required first step for apoptosis. Immunity 22, 355–370 (2005).

pubmed: 15780992 doi: 10.1016/j.immuni.2005.02.004

Wherry, E. J. & Kurachi, M. Molecular and cellular insights into T cell exhaustion. Nat. Rev. Immunol. 15, 486–499 (2015).

pubmed: 26205583 pmcid: 4889009 doi: 10.1038/nri3862

Groom, J. R. & Luster, A. D. CXCR3 in T cell function. Exp. Cell Res. 317, 620–631 (2011).

pubmed: 21376175 pmcid: 3065205 doi: 10.1016/j.yexcr.2010.12.017

De Rosa, S. C., Herzenberg, L. A., Herzenberg, L. A. & Roederer, M. 11-color, 13-parameter flow cytometry: identification of human naive T cells by phenotype, function, and T-cell receptor diversity. Nat. Med. 7, 245–248 (2001).

pubmed: 11175858 doi: 10.1038/84701

Kumar, B. V., Connors, T. J. & Farber, D. L. Human T cell development, localization, and function throughout Life. Immunity 48, 202–213 (2018).

pubmed: 29466753 pmcid: 5826622 doi: 10.1016/j.immuni.2018.01.007

Koizumi, S.-i et al. JunB regulates homeostasis and suppressive functions of effector regulatory T cells. Nat. Commun. 9, 5344 (2018).

pubmed: 30559442 pmcid: 6297218 doi: 10.1038/s41467-018-07735-4

Karin, M., Liu, Z. & Zandi, E. AP-1 function and regulation. Curr. Opin. Cell Biol. 9, 240–246 (1997).

pubmed: 9069263 doi: 10.1016/S0955-0674(97)80068-3

Rincon, M. & Flavell, R. A. T-cell subsets: transcriptional control in the Th1/Th2 decision. Curr. Biol. 7, R729–R732 (1997).

pubmed: 9382795 doi: 10.1016/S0960-9822(06)00368-X

Ho, I. C., Tai, T. S. & Pai, S. Y. GATA3 and the T-cell lineage: essential functions before and after T-helper-2-cell differentiation. Nat. Rev. Immunol. 9, 125–135 (2009).

pubmed: 19151747 pmcid: 2998182 doi: 10.1038/nri2476

Berger, A. Th1 and Th2 responses: what are they? BMJ 321, 424–424 (2000).

pubmed: 10938051 pmcid: 27457 doi: 10.1136/bmj.321.7258.424

Juelke, K. et al. CD62L expression identifies a unique subset of polyfunctional CD56dim NK cells. Blood 116, 1299–1307 (2010).

pubmed: 20505160 doi: 10.1182/blood-2009-11-253286

Schulte-Schrepping, J. et al. Severe COVID-19 is marked by a dysregulated myeloid cell compartment. Cell 182, 1419–1440 (2020).

pubmed: 32810438 pmcid: 7405822 doi: 10.1016/j.cell.2020.08.001

Narasimhan, P. B., Marcovecchio, P., Hamers, A. A. J. & Hedrick, C. C. Nonclassical monocytes in health and disease. Annu. Rev. Immunol. 37, 439–456 (2019).

pubmed: 31026415 doi: 10.1146/annurev-immunol-042617-053119

Chen, L. et al. CD83-stimulated monocytes suppress T-cell immune responses through production of prostaglandin E2. Proc. Natl Acad. Sci. USA 108, 18778 (2011).

pubmed: 22065790 pmcid: 3219128 doi: 10.1073/pnas.1018994108

Segovia, M. et al. Targeting TMEM176B enhances antitumor immunity and augments the efficacy of immune checkpoint blockers by unleashing inflammasome activation. Cancer Cell 35, 767–781 (2019).

pubmed: 31085177 pmcid: 6521897 doi: 10.1016/j.ccell.2019.04.003

Hardbower, D. M. et al. EGFR regulates macrophage activation and function in bacterial infection. J. Clin. Investig. 126, 3296–3312 (2016).

pubmed: 27482886 pmcid: 5004944 doi: 10.1172/JCI83585

Zaiss, D. M. et al. Amphiregulin enhances regulatory T cell-suppressive function via the epidermal growth factor receptor. Immunity 38, 275–284 (2013).

pubmed: 23333074 pmcid: 3582723 doi: 10.1016/j.immuni.2012.09.023

Minutti, C. M. et al. Epidermal growth factor receptor expression licenses type-2 helper T cells to function in a T cell receptor-independent fashion. Immunity 47, 710–722 (2017).

pubmed: 29045902 pmcid: 5654729 doi: 10.1016/j.immuni.2017.09.013

Shirasawa, S. et al. Dermatitis due to epiregulin deficiency and a critical role of epiregulin in immune-related responses of keratinocyte and macrophage. Proc. Natl Acad. Sci. USA 101, 13921–13926 (2004).

pubmed: 15365177 pmcid: 518854 doi: 10.1073/pnas.0404217101

Hoffmann-Vold, A.-M. et al. High level of chemokine CCL18 is associated with pulmonary function deterioration, lung fibrosis progression, and reduced survival in systemic sclerosis. Chest 150, 299–306 (2016).

pubmed: 26997242 doi: 10.1016/j.chest.2016.03.004

Pritsch, M. et al. Prevalence and risk factors of infection in the representative COVID-19 cohort munich. Int. J. Environ. Res. Public Health 18, 3572 (2021).

Radon, K. et al. Protocol of a population-based prospective COVID-19 cohort study Munich, Germany (KoCo19). BMC Public Health 20, 1036 (2020).

pubmed: 32605549 pmcid: 7324773 doi: 10.1186/s12889-020-09164-9

Tam, O. H. et al. Pseudogene-derived small interfering RNAs regulate gene expression in mouse oocytes. Nature 453, 534–538 (2008).

pubmed: 18404147 pmcid: 2981145 doi: 10.1038/nature06904

Cheetham, S. W., Faulkner, G. J. & Dinger, M. E. Overcoming challenges and dogmas to understand the functions of pseudogenes. Nat. Rev. Genet. 21, 191–201 (2020).

pubmed: 31848477 doi: 10.1038/s41576-019-0196-1

Ziegler, C. G. K., et al. Impaired local intrinsic immunity to SARS-CoV-2 infection in severe COVID-19. bioRxiv https://doi.org/10.1101/2021.02.20.431155 (2021).

Ordovas-Montanes, J. et al. Allergic inflammatory memory in human respiratory epithelial progenitor cells. Nature 560, 649–654 (2018).

pubmed: 30135581 pmcid: 6133715 doi: 10.1038/s41586-018-0449-8

Giovannini-Chami, L. et al. Distinct epithelial gene expression phenotypes in childhood respiratory allergy. Eur. Respir. J. 39, 1197–1205 (2012).

pubmed: 22005912 doi: 10.1183/09031936.00070511

Cheemarla, N. R. et al. Dynamic innate immune response determines susceptibility to SARS-CoV-2 infection and early replication kinetics. J. Exp. Med. 218, e20210583 (2021).

pubmed: 34128960 pmcid: 8210587 doi: 10.1084/jem.20210583

Thomas, E. & Saito, T. Special issue “IFN-independent ISG expression and its role in antiviral cell-intrinsic innateimmunity”. Viruses 11, 981 (2019).

Lee, J. S. & Shin, E.-C. The type I interferon response in COVID-19: implications for treatment. Nat. Rev. Immunol. 20, 585–586 (2020).

pubmed: 32788708 pmcid: 8824445 doi: 10.1038/s41577-020-00429-3

Banerjee, A. et al. Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study. Lancet 395, 1715–1725 (2020).

Chen, Z. & John Wherry, E. T cell responses in patients with COVID-19. Nat. Rev. Immunol. 20, 529–536 (2020).

pubmed: 32728222 pmcid: 7389156 doi: 10.1038/s41577-020-0402-6

Gil-Etayo, F. J. et al. T-helper cell subset response is a determining factor in COVID-19 progression. Front. Cell. Infect. Microbiol. 11, 79 (2021).

doi: 10.3389/fcimb.2021.624483

Maucourant, C. et al. Natural killer cell immunotypes related to COVID-19 disease severity. Sci. Immunol. 5, eabd6832 (2020).

Su, Y. et al. Multi-omics resolves a sharp disease-state shift between mild and moderate COVID-19. Cell 183, 1479–1495.e1420 (2020).

pubmed: 33171100 pmcid: 7598382 doi: 10.1016/j.cell.2020.10.037

Giamarellos-Bourboulis, E. J. et al. Complex immune dysregulation in COVID-19 patients with severe respiratory failure. Cell Host Microbe 27, 992–1000.e3 (2020).

Krämer, B. et al. Early IFN-a signatures and persistent dysfunction are distinguishing features of NK cells in severe COVID-19. Immunity 54, 2650–2669.e14 (2021).

Smith, N. et al. Distinct systemic and mucosal immune responses during acute SARS-CoV-2 infection. Nat. Immunol. 22, 1428–1439 (2021).

Sposito, B. et al. The interferon landscape along the respiratory tract impacts the severity of COVID-19. Cell 184, 4953–4968.e16 (2021).

Loske, J. et al. Pre-activated antiviral innate immunity in the upper airways controls early SARS-CoV-2 infection in children. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-01037-9 (2021).

Lopez, J. et al. Early nasal type I IFN immunity against SARS-CoV-2 is compromised in patients with autoantibodies against type I IFNs. J. Exp. Med. 218, e20211211 (2021).

pubmed: 34357402 pmcid: 8352718 doi: 10.1084/jem.20211211

Lee, S. et al. Clinical course and molecular viral shedding among asymptomatic and symptomatic patients with SARS-CoV-2 infection in a community treatment center in the Republic of Korea. JAMA Intern. Med. 180, 1447–1452 (2020).

pubmed: 32780793 doi: 10.1001/jamainternmed.2020.3862

Le Borgne, P. et al. SARS-CoV-2 viral load in nasopharyngeal swabs in the emergency department does not predict COVID-19 severity and mortality. Acad. Emerg. Med. 28, 306–313 (2021).

pubmed: 33481307 pmcid: 8014851 doi: 10.1111/acem.14217

Huang, C. et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395, 497–506 (2020).

pubmed: 31986264 pmcid: 7159299 doi: 10.1016/S0140-6736(20)30183-5

Zhou, F. et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 395, 1054–1062 (2020).

pubmed: 32171076 pmcid: 7270627 doi: 10.1016/S0140-6736(20)30566-3

Wang, D. et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA 323, 1061–1069 (2020).

pubmed: 32031570 pmcid: 7042881 doi: 10.1001/jama.2020.1585

Wu, Z. & McGoogan, J. M. Characteristics of and important lessons from the Coronavirus Disease 2019 (COVID-19) outbreak in China: summary of a report of 72314 cases From the Chinese Center for Disease Control and Prevention. JAMA 323, 1239–1242 (2020).

pubmed: 32091533 doi: 10.1001/jama.2020.2648

pubmed: 32125452 doi: 10.1007/s00134-020-05991-x

McIntosh, K. COVID-19: Clinical features: Table 2 Laboratory features associated with severe COVID-19. In (ed Post, T. W.) (Waltham, MA, 2022).

Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

pubmed: 29608179 pmcid: 6700744 doi: 10.1038/nbt.4096

Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).

pubmed: 31870423 pmcid: 6927181 doi: 10.1186/s13059-019-1874-1

Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 1–15 (2019).

doi: 10.1186/s13059-019-1874-1

Franzén, O., Gan, L.-M. & Björkegren, J. L. M. PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data. Database 2019, baz046 (2019).

Ashburner, M. et al. Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).

pubmed: 10802651 pmcid: 3037419 doi: 10.1038/75556

Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

pubmed: 22455463 pmcid: 3339379 doi: 10.1089/omi.2011.0118

Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010).

Patzelt, J. & Langer, H. F. Platelets in angiogenesis. Curr. Vasc. Pharmacol. 10, 570–577 (2012).

pubmed: 22338572 doi: 10.2174/157016112801784648

Liao, Y., Smyth, G. K. & Shi, W. The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads. Nucleic Acids Res. 47, e47 (2019).

pubmed: 30783653 pmcid: 6486549 doi: 10.1093/nar/gkz114

Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

pubmed: 23104886 doi: 10.1093/bioinformatics/bts635

Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

pubmed: 25516281 pmcid: 4302049 doi: 10.1186/s13059-014-0550-8

Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2009).

pubmed: 19910308 pmcid: 2796818 doi: 10.1093/bioinformatics/btp616

Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

pubmed: 25605792 pmcid: 4402510 doi: 10.1093/nar/gkv007

Van Gassen, S. et al. FlowSOM: using self-organizing maps for visualization and interpretation of cytometry data. Cytometry A 87, 636–645 (2015).

pubmed: 25573116 doi: 10.1002/cyto.a.22625

Hughes, C. S. et al. Ultrasensitive proteome analysis using paramagnetic bead technology. Mol. Syst. Biol. 10, 757 (2014).

pubmed: 25358341 doi: 10.15252/msb.20145625

Bruderer, R. et al. Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues. Mol. Cell. Proteom. 14, 1400–1410 (2015).

doi: 10.1074/mcp.M114.044305

Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

pubmed: 25605792 pmcid: 4402510 doi: 10.1093/nar/gkv007

Jensen, L. J. et al. STRING 8—a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res. 37, D412–D416 (2009).

pubmed: 18940858 doi: 10.1093/nar/gkn760

Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

pubmed: 16199517 pmcid: 1239896 doi: 10.1073/pnas.0506580102

Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2019).

pubmed: 30395289 doi: 10.1093/nar/gky1106