New-onset IgG autoantibodies in hospitalized patients with COVID-19

doi:10.1038/s41467-021-25509-3

Multicenter Study

. 2021 Sep 14;12(1):5417.

doi: 10.1038/s41467-021-25509-3.

New-onset IgG autoantibodies in hospitalized patients with COVID-19

Sarah Esther Chang^#^{1

2}, Allan Feng^#^{1

2}, Wenzhao Meng^#³, Sokratis A Apostolidis^#^{4

5}, Elisabeth Mack⁶, Maja Artandi^{7

8}, Linda Barman⁷, Kate Bennett⁹, Saborni Chakraborty¹⁰, Iris Chang^{2

11}, Peggie Cheung^{1

2}, Sharon Chinthrajah^{2

11}, Shaurya Dhingra^{1

2}, Evan Do^{2

11}, Amanda Finck¹², Andrew Gaano³, Reinhard Geßner¹³, Heather M Giannini¹⁴, Joyce Gonzalez³, Sarah Greib¹³, Margrit Gündisch¹³, Alex Ren Hsu^{1

2}, Alex Kuo^{1

2}, Monali Manohar^{2

10}, Rong Mao^{1

2}, Indira Neeli¹⁴, Andreas Neubauer⁶, Oluwatosin Oniyide¹⁵, Abigail E Powell^{16

17}, Rajan Puri⁷, Harald Renz^{13

18}, Jeffrey Schapiro¹⁹, Payton A Weidenbacher^{16

17}, Richard Wittman⁷, Neera Ahuja²⁰, Ho-Ryun Chung²¹, Prasanna Jagannathan^{2

10

22}, Judith A James²³, Peter S Kim^{14

16

24}, Nuala J Meyer^{5

15}, Kari C Nadeau^{2

11}, Marko Radic¹⁴, William H Robinson^{1

2

25}, Upinder Singh^{2

10

22}, Taia T Wang^{10

22

24}, E John Wherry^{5

12}, Chrysanthi Skevaki^{26

27}, Eline T Luning Prak^{28

29}, Paul J Utz^{30

31}

Affiliations

¹ Department of Medicine, Division of Immunology and Rheumatology, Stanford University School of Medicine, Stanford, CA, USA.
² Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, USA.
³ Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
⁴ Department of Medicine, Division of Rheumatology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
⁵ Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
⁶ Department of Hematology, Oncology, Immunology, Philipps University Marburg, Marburg, Germany.
⁷ Department of Medicine, Division of Primary Care and Population Health, Stanford University School of Medicine, Stanford, CA, USA.
⁸ Department of Medicine, Stanford CROWN Clinic, Stanford University School of Medicine, Stanford, CA, USA.
⁹ Molecular Pathology and Imaging Core, Department of Medicine, Gastroenterology Division, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
¹⁰ Department of Medicine, Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, CA, USA.
¹¹ Department of Medicine, Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, CA, USA.
¹² Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
¹³ Institute of Laboratory Medicine, Philipps University Marburg, Marburg, Germany.
¹⁴ Department of Microbiology, Immunology and Biochemistry, The University of Tennessee Health Science Center, Memphis, TN, USA.
¹⁵ Department of Medicine, Division of Pulmonary, Allergy, and Critical Care Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
¹⁶ Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA.
¹⁷ ChEM-H, Stanford University, Stanford, USA.
¹⁸ Member of the Universities of Giessen and Marburg Lung Center (UGMLC), and the German Center for Lung Research (DZL), Giessen, Germany.
¹⁹ TPMG Regional Reference Laboratory, Kaiser Permanente Northern California, Berkeley, CA, USA.
²⁰ Department of Medicine, Division of Hospital Medicine, Stanford University School of Medicine, Stanford, CA, USA.
²¹ Institute for Medical Bioinformatics and Biostatistics, Philipps University Marburg, Marburg, Germany.
²² Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA.
²³ Arthritis & Clinical Immunology Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA.
²⁴ Chan Zuckerberg Biohub, San Francisco, CA, USA.
²⁵ VA Palo Alto Health Care System, Palo Alto, CA, USA.
²⁶ Institute of Laboratory Medicine, Philipps University Marburg, Marburg, Germany. Chrysanthi.Skevaki@uk-gm.de.
²⁷ Member of the Universities of Giessen and Marburg Lung Center (UGMLC), and the German Center for Lung Research (DZL), Giessen, Germany. Chrysanthi.Skevaki@uk-gm.de.
²⁸ Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. luning@pennmedicine.upenn.edu.
²⁹ Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. luning@pennmedicine.upenn.edu.
³⁰ Department of Medicine, Division of Immunology and Rheumatology, Stanford University School of Medicine, Stanford, CA, USA. pjutz@stanford.edu.
³¹ Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, USA. pjutz@stanford.edu.

^# Contributed equally.

PMID: 34521836
PMCID: PMC8440763
DOI: 10.1038/s41467-021-25509-3

Multicenter Study

New-onset IgG autoantibodies in hospitalized patients with COVID-19

Sarah Esther Chang et al. Nat Commun. 2021.

. 2021 Sep 14;12(1):5417.

doi: 10.1038/s41467-021-25509-3.

Authors

Affiliations

¹ Department of Medicine, Division of Immunology and Rheumatology, Stanford University School of Medicine, Stanford, CA, USA.
² Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, USA.
³ Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
⁴ Department of Medicine, Division of Rheumatology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
⁵ Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
⁶ Department of Hematology, Oncology, Immunology, Philipps University Marburg, Marburg, Germany.
⁷ Department of Medicine, Division of Primary Care and Population Health, Stanford University School of Medicine, Stanford, CA, USA.
⁸ Department of Medicine, Stanford CROWN Clinic, Stanford University School of Medicine, Stanford, CA, USA.
⁹ Molecular Pathology and Imaging Core, Department of Medicine, Gastroenterology Division, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
¹⁰ Department of Medicine, Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, CA, USA.
¹¹ Department of Medicine, Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, CA, USA.
¹² Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
¹³ Institute of Laboratory Medicine, Philipps University Marburg, Marburg, Germany.
¹⁴ Department of Microbiology, Immunology and Biochemistry, The University of Tennessee Health Science Center, Memphis, TN, USA.
¹⁵ Department of Medicine, Division of Pulmonary, Allergy, and Critical Care Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
¹⁶ Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA.
¹⁷ ChEM-H, Stanford University, Stanford, USA.
¹⁸ Member of the Universities of Giessen and Marburg Lung Center (UGMLC), and the German Center for Lung Research (DZL), Giessen, Germany.
¹⁹ TPMG Regional Reference Laboratory, Kaiser Permanente Northern California, Berkeley, CA, USA.
²⁰ Department of Medicine, Division of Hospital Medicine, Stanford University School of Medicine, Stanford, CA, USA.
²¹ Institute for Medical Bioinformatics and Biostatistics, Philipps University Marburg, Marburg, Germany.
²² Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA.
²³ Arthritis & Clinical Immunology Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA.
²⁴ Chan Zuckerberg Biohub, San Francisco, CA, USA.
²⁵ VA Palo Alto Health Care System, Palo Alto, CA, USA.
²⁶ Institute of Laboratory Medicine, Philipps University Marburg, Marburg, Germany. Chrysanthi.Skevaki@uk-gm.de.
²⁷ Member of the Universities of Giessen and Marburg Lung Center (UGMLC), and the German Center for Lung Research (DZL), Giessen, Germany. Chrysanthi.Skevaki@uk-gm.de.
²⁸ Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. luning@pennmedicine.upenn.edu.
²⁹ Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. luning@pennmedicine.upenn.edu.
³⁰ Department of Medicine, Division of Immunology and Rheumatology, Stanford University School of Medicine, Stanford, CA, USA. pjutz@stanford.edu.
³¹ Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, USA. pjutz@stanford.edu.

^# Contributed equally.

PMID: 34521836
PMCID: PMC8440763
DOI: 10.1038/s41467-021-25509-3

Abstract

COVID-19 is associated with a wide range of clinical manifestations, including autoimmune features and autoantibody production. Here we develop three protein arrays to measure IgG autoantibodies associated with connective tissue diseases, anti-cytokine antibodies, and anti-viral antibody responses in serum from 147 hospitalized COVID-19 patients. Autoantibodies are identified in approximately 50% of patients but in less than 15% of healthy controls. When present, autoantibodies largely target autoantigens associated with rare disorders such as myositis, systemic sclerosis and overlap syndromes. A subset of autoantibodies targeting traditional autoantigens or cytokines develop de novo following SARS-CoV-2 infection. Autoantibodies track with longitudinal development of IgG antibodies recognizing SARS-CoV-2 structural proteins and a subset of non-structural proteins, but not proteins from influenza, seasonal coronaviruses or other pathogenic viruses. We conclude that SARS-CoV-2 causes development of new-onset IgG autoantibodies in a significant proportion of hospitalized COVID-19 patients and are positively correlated with immune responses to SARS-CoV-2 proteins.

PubMed Disclaimer

Conflict of interest statement

C.S. received consultancy fees and research funding from Hycor Biomedical and Thermo Fisher Scientific, research funding from Mead Johnson Nutrition (MJN), and consultancy fees from Bencard Allergie. E.J.W. has consulting agreements with and/or is on the scientific advisory board for Merck, Elstar, Janssen, Related Sciences, Synthekine, and Surface Oncology. E.J.W. is a founder of Surface Oncology and Arsenal Biosciences. E.J.W. has a patent licensing agreement on the PD-1 pathway with Roche/Genentech. E.M. received consultancy fees from Roche. E.T.L.P. receives research funding from Janssen Research and Development, consultancy fees, and research funding from Roche Diagnostics, is a paid consultant for Enpicom, and serves on the scientific advisory boards of the Antibody Society, the Immune Epitope Database, and the American Autoimmune Related Diseases Association. N.J.M. reports funding to her institution from Athersys Inc, Biomarck Inc, and Quantum Leap Healthcare Collaborative, outside of the funded work. S. Chinthrajah reports grants from NIAID, CoFAR, Aimmune, DBV Technologies, Astellas, Regeneron, FARE, and is an Advisory Board member for Alladapt, Genentech, Novartis, Sanofi, and received personal fees from Nutricia. All remaining authors declare no competing interests with the research reported in this paper.

Figures

**Fig. 1. High prevalence of autoantibodies in hospitalized COVID-19 patients.**
a Heatmap depicting serum IgG antibodies discovered using a 53-plex bead-based protein array containing the indicated autoantigens (x-axis). Autoantigens are grouped based on disease (scleroderma, myositis, and overlap syndromes such as mixed connective tissue disease (MCTD), SLE/Sjögren’s, gastrointestinal and endocrine disorders), DNA-associated antigens, and antigens associated with tissue inflammation or stress responses. COVID-19 patients (top panel), HC (n = 31, middle panel), and 8 prototype autoimmune disorders (bottom panel) are shown. Colors indicate autoantibodies whose MFI measurements are >5 SD (red) or <5 SD (black) above the average MFI for HC. MFIs <3000 were excluded. b Heatmap using a 41-plex array of cytokines, chemokines, growth factors, and receptors. The same samples in Panel A were also analyzed for anti-cytokine antibodies (ACA). Cytokines are grouped on the x-axis by category (interferons, interleukins, and other cytokines/growth factors/receptors). Prototype samples from patients with immunodeficiency disorders include three patients with Autoimmune Polyendocrine Syndrome Type 1 (APS-1), one patient with Pulmonary Alveolar Proteinosis (PAP), and three patients with Atypical Mycobacterial Infections (AMI). Colors indicate autoantibodies whose MFI measurements are >5 SD (red) or <5 SD (black) above the average MFI for HC. MFIs <3000 were excluded. Source data are provided as a Source Data file.

**Fig. 2. Serum autoantibodies in COVID-19 patients recognize antigens targeted in rare connective tissue diseases, and antigens associated with pathogenicity.**
Boxplots of twelve antigens corresponding to Fig. 1. a Antigens associated with autoimmune myositis and myocarditis (MDA5, troponin 1, MYH6 (alpha-myosin), PL-7, Jo-1, and Mi-2). b RNA-containing antigens associated with systemic sclerosis (RPP25 Th/To, Fibrillarin, and U11/U12). c Antigens that may be pathogenic (C1q and β2GP1) and associated with vasculitis (BPI). MFI data in a–c are represented as boxplots where the middle line is the median, the lower and upper hinges correspond to the first and third quartiles, the upper whisker extends from the hinge to the largest value, and the lower whisker extends from the hinge to the smallest value. Individual MFI values are displayed as dots. MFI is shown on the y-axis. Subjects are shown on the x-axis (n = 51 unpaired UPenn and Marburg COVID-19 patients and n = 31 HC). Source data are provided as a Source Data file.

**Fig. 3. Autoantibodies are triggered by SARS-CoV-2 infection.**
a Autoantibody (AutoAb, blue) and anti-cytokine antibody (ACA, yellow) counts are shown at Day 0 (n = 24 patients, left) and Day 7 (n = 21 patients, right). Counts were based on antibodies that were present at levels at or exceeding 5 SD above the average MFI for healthy control samples. b Examples of transient or fluctuating autoantibodies against MDA5, the tRNA synthetase PL-7, and the vasculitis antigen BPI are shown. c Examples of antigens (Scl-70, TPO, and C1q) that are likely to have been present at the time of infection and are unaffected by SARS-CoV-2 infection, are shown. d Examples of ACA that are inducible (e.g., IFN-ε), fluctuate (e.g., IFN-ω), or are present at baseline with little change over time (e.g., IFN-γ), are shown. Additional examples (IL-17, IL-22, and ACE-2) are also included. MFI is shown on the y-axis. Serum was collected at three time points (T1, T2, and T3) for two COVID-19 subjects, while serum was collected at two time points (T1 and T2) for all other COVID-19 subjects. COVID-19 subjects with notably high MFI at any time point are labeled. Source data are provided as a Source Data file.

**Fig. 4. Measurement of anti-viral IgG responses using a COVID-19 viral array.**
a Heatmap depicting IgG antibodies using a 28-plex bead-based protein array. Viral protein antigens are grouped based on sixteen proteins from SARS-CoV-2 (left panel), other coronaviruses (middle panel), and other viruses (right panel), labeled on the x-axis. Most recombinant viral proteins were engineered to include a 6X-His-tag, which was used to validate conjugation to beads using an anti-epitope monoclonal antibody (bottom of the panel). The same COVID-19 patients from Fig. 3 (see Supplementary Figures 9 and 10) were analyzed (top panel, n = 94 longitudinal COVID-19 samples, including paired samples from 44 subjects and 2 subjects who had 3 available time points each, subjects UP70 and UP71). HC (n = 16, middle panel). Two patient sample pairs (UP63 and UMR20) were excluded from analysis due to technical failure on the viral array assay. Colors correspond to the MFI values shown at right. b Heatmap depicting statistically significant anti-viral IgG responses. Colors indicate IgG antibodies whose MFI measurements are >5 SD (red) or <5 SD (black) above the average MFI for HC samples collected prior to the COVID-19 pandemic. Source data are provided as a Source Data file.

**Fig. 5. New-onset autoantibodies correlate with anti-SARS-CoV-2 IgG responses over time in recently infected patients who developed COVID-19.**
Twelve patients were identified who had low or absent anti-SARS-CoV-2 RBD or spike S1 protein responses at baseline and who went on to develop high MFI IgG SARS-CoV-2 antibodies at the next available time point. Autoantigen data (a) and ACA data (b) from Supplementary Figures 9 and 10 (using 5 SD and MFI > 3000 cutoffs) for these 12 patients and HC has been combined with anti-viral heatmap data from Fig. 4 (c). Multiple new autoantibodies are depicted with white boxes. Antigens are shown on the x-axis. Patients and HC are shown on the y-axis. Colors for viral IgG levels correspond to the MFI values shown at the far right. d Line graphs comparing MFI for IgG antibodies against four viral proteins at Early (D0) and Late (D7) time points for the same 12 patients in a–c. e Line graphs comparing MFI for IgG autoantibodies against eight autoantigens or cytokines at Early (D0) and Late (D7) time points for 4 patients in a–d. Source data are provided as a Source Data file.

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Update of

New-Onset IgG Autoantibodies in Hospitalized Patients with COVID-19.
Chang SE, Feng A, Meng W, Apostolidis SA, Mack E, Artandi M, Barman L, Bennett K, Chakraborty S, Chang I, Cheung P, Chinthrajah S, Dhingra S, Do E, Finck A, Gaano A, Geßner R, Giannini HM, Gonzalez J, Greib S, Gündisch M, Hsu AR, Kuo A, Manohar M, Mao R, Neeli I, Neubauer A, Oniyide O, Powell AE, Puri R, Renz H, Schapiro JM, Weidenbacher PA, Wittman R, Ahuja N, Chung HR, Jagannathan P, James J, Kim PS, Meyer NJ, Nadeau K, Radic M, Robinson WH, Singh U, Wang TT, Wherry EJ, Skevaki C, Prak ETL, Utz PJ. Chang SE, et al. medRxiv [Preprint]. 2021 Jan 29:2021.01.27.21250559. doi: 10.1101/2021.01.27.21250559. medRxiv. 2021. Update in: Nat Commun. 2021 Sep 14;12(1):5417. doi: 10.1038/s41467-021-25509-3 PMID: 33532787 Free PMC article. Updated. Preprint.

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References

1. Guan WJ, et al. Clinical characteristics of coronavirus disease 2019 in China. N. Engl. J. Med. 2020;382:1708–1720. doi: 10.1056/NEJMoa2002032. - DOI - PMC - PubMed
1. Chen N, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet. 2020;395:507–513. doi: 10.1016/S0140-6736(20)30211-7. - DOI - PMC - PubMed
1. Machhi J, et al. The natural history, pathobiology, and clinical manifestations of SARS-CoV-2 infections. J. Neuroimmune Pharm. 2020;15:359–386. doi: 10.1007/s11481-020-09944-5. - DOI - PMC - PubMed
1. Asfuroglu Kalkan E, Ates I. A case of subacute thyroiditis associated with Covid-19 infection. J. Endocrinol. Invest. 2020;43:1173–1174. doi: 10.1007/s40618-020-01316-3. - DOI - PMC - PubMed
1. Brancatella, A. et al. Subacute thyroiditis after Sars-COV-2 infection. J. Clin. Endocrinol Metab.10.1210/clinem/dgaa276 (2020). - PMC - PubMed

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[1] Guan WJ, et al. Clinical characteristics of coronavirus disease 2019 in China. N. Engl. J. Med. 2020;382:1708–1720. doi: 10.1056/NEJMoa2002032. - DOI - PMC - PubMed

[2] Guan WJ, et al. Clinical characteristics of coronavirus disease 2019 in China. N. Engl. J. Med. 2020;382:1708–1720. doi: 10.1056/NEJMoa2002032. - DOI - PMC - PubMed

[3] Chen N, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet. 2020;395:507–513. doi: 10.1016/S0140-6736(20)30211-7. - DOI - PMC - PubMed

[4] Chen N, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet. 2020;395:507–513. doi: 10.1016/S0140-6736(20)30211-7. - DOI - PMC - PubMed

[5] Machhi J, et al. The natural history, pathobiology, and clinical manifestations of SARS-CoV-2 infections. J. Neuroimmune Pharm. 2020;15:359–386. doi: 10.1007/s11481-020-09944-5. - DOI - PMC - PubMed

[6] Machhi J, et al. The natural history, pathobiology, and clinical manifestations of SARS-CoV-2 infections. J. Neuroimmune Pharm. 2020;15:359–386. doi: 10.1007/s11481-020-09944-5. - DOI - PMC - PubMed

[7] Asfuroglu Kalkan E, Ates I. A case of subacute thyroiditis associated with Covid-19 infection. J. Endocrinol. Invest. 2020;43:1173–1174. doi: 10.1007/s40618-020-01316-3. - DOI - PMC - PubMed

[8] Asfuroglu Kalkan E, Ates I. A case of subacute thyroiditis associated with Covid-19 infection. J. Endocrinol. Invest. 2020;43:1173–1174. doi: 10.1007/s40618-020-01316-3. - DOI - PMC - PubMed

[9] Brancatella, A. et al. Subacute thyroiditis after Sars-COV-2 infection. J. Clin. Endocrinol Metab.10.1210/clinem/dgaa276 (2020). - PMC - PubMed

[10] Brancatella, A. et al. Subacute thyroiditis after Sars-COV-2 infection. J. Clin. Endocrinol Metab.10.1210/clinem/dgaa276 (2020). - PMC - PubMed

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New-onset IgG autoantibodies in hospitalized patients with COVID-19

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New-onset IgG autoantibodies in hospitalized patients with COVID-19

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Abstract

Conflict of interest statement

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