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      Diverse Functional Autoantibodies in Patients with COVID-19

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          Abstract

          COVID-19 manifests with a wide spectrum of clinical phenotypes that are characterized by exaggerated and misdirected host immune responses 18 . While pathological innate immune activation is well documented in severe disease 1 , the impact of autoantibodies on disease progression is less defined. Here, we used a high-throughput autoantibody discovery technique called Rapid Extracellular Antigen Profiling (REAP) to screen a cohort of 194 SARS-CoV-2 infected COVID-19 patients and healthcare workers for autoantibodies against 2,770 extracellular and secreted proteins (the “exoproteome”). We found that COVID-19 patients exhibit dramatic increases in autoantibody reactivities compared to uninfected controls, with a high prevalence of autoantibodies against immunomodulatory proteins including cytokines, chemokines, complement components, and cell surface proteins. We established that these autoantibodies perturb immune function and impair virological control by inhibiting immunoreceptor signaling and by altering peripheral immune cell composition, and found that murine surrogates of these autoantibodies exacerbate disease severity in a mouse model of SARS-CoV-2 infection. Analysis of autoantibodies against tissue-associated antigens revealed associations with specific clinical characteristics and disease severity. In summary, these findings implicate a pathological role for exoproteome-directed autoantibodies in COVID-19 with diverse impacts on immune functionality and associations with clinical outcomes.

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          Most cited references41

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          Autoantibodies against type I IFNs in patients with life-threatening COVID-19

          The genetics underlying severe COVID-19 The immune system is complex and involves many genes, including those that encode cytokines known as interferons (IFNs). Individuals that lack specific IFNs can be more susceptible to infectious diseases. Furthermore, the autoantibody system dampens IFN response to prevent damage from pathogen-induced inflammation. Two studies now examine the likelihood that genetics affects the risk of severe coronavirus disease 2019 (COVID-19) through components of this system (see the Perspective by Beck and Aksentijevich). Q. Zhang et al. used a candidate gene approach and identified patients with severe COVID-19 who have mutations in genes involved in the regulation of type I and III IFN immunity. They found enrichment of these genes in patients and conclude that genetics may determine the clinical course of the infection. Bastard et al. identified individuals with high titers of neutralizing autoantibodies against type I IFN-α2 and IFN-ω in about 10% of patients with severe COVID-19 pneumonia. These autoantibodies were not found either in infected people who were asymptomatic or had milder phenotype or in healthy individuals. Together, these studies identify a means by which individuals at highest risk of life-threatening COVID-19 can be identified. Science, this issue p. eabd4570, p. eabd4585; see also p. 404
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            Longitudinal analyses reveal immunological misfiring in severe COVID-19

            Recent studies have provided insights into the pathogenesis of coronavirus disease 2019 (COVID-19) 1–4 . However, the longitudinal immunological correlates of disease outcome remain unclear. Here we serially analysed immune responses in 113 patients with moderate or severe COVID-19. Immune profiling revealed an overall increase in innate cell lineages, with a concomitant reduction in T cell number. An early elevation in cytokine levels was associated with worse disease outcomes. Following an early increase in cytokines, patients with moderate COVID-19 displayed a progressive reduction in type 1 (antiviral) and type 3 (antifungal) responses. By contrast, patients with severe COVID-19 maintained these elevated responses throughout the course of the disease. Moreover, severe COVID-19 was accompanied by an increase in multiple type 2 (anti-helminths) effectors, including interleukin-5 (IL-5), IL-13, immunoglobulin E and eosinophils. Unsupervised clustering analysis identified four immune signatures, representing growth factors (A), type-2/3 cytokines (B), mixed type-1/2/3 cytokines (C), and chemokines (D) that correlated with three distinct disease trajectories. The immune profiles of patients who recovered from moderate COVID-19 were enriched in tissue reparative growth factor signature A, whereas the profiles of those with who developed severe disease had elevated levels of all four signatures. Thus, we have identified a maladapted immune response profile associated with severe COVID-19 and poor clinical outcome, as well as early immune signatures that correlate with divergent disease trajectories.
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              Immunology of COVID-19: current state of the science

              The coronavirus disease 2019 (COVID-19) pandemic, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has affected millions of people worldwide, igniting an unprecedented effort from the scientific community to understand the biological underpinning of COVID19 pathophysiology. In this review, we summarize the current state of knowledge of innate and adaptive immune responses elicited by SARS-CoV-2 infection and the immunological pathways that likely contribute to disease severity and death. We also discuss the rationale and clinical outcome of current therapeutic strategies as well as prospective clinical trials to prevent or treat SARS-CoV-2 infection.
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                Author and article information

                Journal
                medRxiv
                MEDRXIV
                medRxiv
                Cold Spring Harbor Laboratory
                01 February 2021
                : 2020.12.10.20247205
                Affiliations
                [1 ]Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
                [2 ]Department of Pharmacology, Yale School of Medicine, New Haven, CT, USA
                [3 ]Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
                [4 ]Department of Medicine, Section of Pulmonary and Critical Care Medicine, Yale School of Medicine, New Haven, CT, USA
                [5 ]Department of Internal Medicine (Infectious Diseases), Yale School of Medicine, New Haven, CT, USA
                [6 ]Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA
                [7 ]Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA
                [8 ]Howard Hughes Medical Institute, Chevy Chase, MD, USA
                Author notes

                Consortia (Yale IMPACT Team)

                Abeer Obaid, Adam Moore, Arnau Casanovas-Massana, Alice Lu-Culligan, Allison Nelson, Anderson Brito, Angela Nunez, Anjelica Martin, Annie Watkins, Bertie Geng, Camila D. Odio, Cate Muencker, Chaney Kalinich, Christina Harden, Codruta Todeasa, Cole Jensen, Daniel Kim, David McDonald, Denise Shepard, Edward Courchaine, Elizabeth B. White, Eric Song, Erin Silva, Eriko Kudo, Giuseppe DeIuliis, Harold Rahming, Hong-Jai Park, Irene Matos, Jessica Nouws, Jordan Valdez, Joseph Fauver, Joseph Lim, Kadi-Ann Rose, Kelly Anastasio, Kristina Brower, Laura Glick, Lokesh Sharma, Lorenzo Sewanan, Lynda Knaggs, Maksym Minasyan, Maria Batsu, Mary Petrone, Maxine Kuang, Maura Nakahata, Melissa Campbell, Melissa Linehan, Michael H. Askenase, Michael Simonov, Mikhail Smolgovsky, Nicole Sonnert, Nida Naushad, Pavithra Vijayakumar, Rick Martinello, Rupak Datta, Ryan Handoko, Santos Bermejo, Sarah Prophet, Sean Bickerton, Sofia Velazquez, Tara Alpert, Tyler Rice, William Khoury-Hanold, Xiaohua Peng, Yexin Yang, Yiyun Cao & Yvette Strong

                Author Contributions

                E.Y.W., T.M., J.K., Y.D., J.D.H., A.I., and A.M.R. designed experiments. E.Y.W. and Y.D. performed the REAP assay. E.Y.W., Y.D., J.D.H., F.L., E.S.P., and S.F. performed biochemical and functional validations. T.M., B.I., and E.S. performed mouse experiments. C.L., P.W., J.K., J.S., T.M., and J.E.O. defined parameters for flow cytometry experiments, collected and processed patient PBMC samples. A.L.W., C.B.F.V., I.M.O., C.C.K., M.E.P., and A.E.W. performed the virus RNA concentration assays. N.D.G. supervised the virus RNA concentration assays. B.I. and J.K. collected epidemiological and clinical data. C.D.C., S.F., A.I.K., M.C., and J.F. assisted in designing, recruiting, and following in-patient and HCW cohorts. W.L.S. supervised clinical data management. E.Y.W., T.M., J.K., N.S.Z., Y.D., J.D.H., A.I., and A.M.R. analyzed data. E.Y.W., T.M., J.K., A.I., and A.M.R. wrote the paper. A.M.R. and A.I. supervised the research. Authors from the Yale IMPACT Research Team contributed to collection and storage of patient samples, as well as the collection of the patients’ epidemiological and clinical data.

                [*]

                These authors contributed equally to this work

                [¶]

                A list of authors and their affiliations appears at the end of the paper

                [# ]Correspondence: Aaron.Ring@ 123456yale.edu (A.M.R.); Akiko.Iwasaki@ 123456yale.edu (A.I.)
                Article
                10.1101/2020.12.10.20247205
                7743105
                33330894
                1d2e1fea-e4e1-41f5-bb95-45146a3f3718

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.

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