Predominance of Distinct Autoantibodies in Response to SARS-CoV-2 Infection

  1. Yunxian Liu
  2. Joseph E. Ebinger
  3. Rowann Mostafa
  4. Petra Budde
  5. Jana Gajewski
  6. Brian Walker
  7. Sandy Joung
  8. Manuel Bräutigam
  9. Franziska Hesping
  10. Elena Schäfer
  11. Ann-Sophie Schubert
  12. Hans-Dieter Zucht
  13. Gil Y. Melmed
  14. Kimia Sobhani
  15. Jonathan Braun
  16. Dermot P.B. McGovern
  17. Jennifer E. Van Eyk
  18. Susan Cheng
  19. Justyna Fert-Bober

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Abstract

Background

Improved knowledge regarding the prevalence and clinical significance of the broad spectrum of autoantibodies triggered by SARS-CoV2 infection can clarify the underlying pathobiology, enhance approaches to evaluating heterogeneity of COVID-19 clinical manifestations, and potentially guide options for targeting immunosuppressive therapy as the need for more effective interventions continues to evolve. In this study, we sought to determine the prevalence of autoimmune antibodies in diverse cohort of SARS-CoV-2 positive healthcare workers and measure the extent to which factors associated with triggered autoimmunity are activated even following mild and asymptomatic infection.

Methods

Antigen microarrays were used to profile reactivity of IgG autoantibodies against 91 proteins and cytokines based on autoantibody profiling studies in autoimmune diseases.

Results

In this discovery screening study, we found that 90% of the IgG positive individuals demonstrated reactivity to at least one autoantibody. When compared to results of the same assays conducted on samples from pre-COVID-19 controls, our primary cohort of individuals with SARS-CoV-2 IgG antibody positivity had significantly elevated IgG against twelve additional proteins including CHD3, CTLA4, HARS, IFNA4, INS, MIF, MX1, RNF41, S100A9, SRP19, TROVE2, and VEGFA. These findings confirmed that all severity levels of SARS-CoV-2 infection, even asymptomatic infections, trigger a robust and diverse autoimmune response; our results also highlight the utility of multiparametric autoantibody detection in this setting.

Interpretation

Taken together, our findings underscore the serological diversity underlying the clinical heterogeneity of COVID-19 infection and its sequelae, including the long-Covid phenotypes.

Funding

This work was supported in part by Cedars-Sinai Medical Center (JEE; SC), the Erika J Glazer Family Foundation (JEE; JEVE; SC), CSMC Precision Health Grant (JFB), the F. Widjaja Family Foundation (JGB, GYM, DM), the Helmsley Charitable Trust (JGB, GYM, DM), and NIH grants K23-HL153888 (JEE) and DK062413 (DPBM).

RESEARCH IN CONTEXT

Evidence before this study

Currently, several studies have shown the possible involvement of autoimmunity in patients affected by coronavirus disease 2019 (COVID-19). In contrast to cytokine storms, which tend to cause systemic, short-duration problems, autoantibodies (AABs) are thought to result in targeted, longer-term damage and development of autoimmune diseases.

Added value of this study

According to our knowledge, we evaluated the largest number of protein antigens to characterize the prevalence and heterogeneity of the AABs signature in SARS-CoV-2 convalescent individuals. We examined autoimmune reactivity to SARS-CoV-2 in the absence of extreme clinical disease to acknowledge the existence of AABs even among those who had mild-to-moderate or no symptoms during their illness, as a hallmark of ongoing long-COVID syndrome. Through our analysis we suggest that VEGFA, MIF, IFNA4, SPP1 and APOH could be used as hallmark for SARS-CoV-2 infection and activation of the autoimmune system.

Implications of all the available evidence

Our study comprehensively characterized the heterogeneity of the AABs signature in SARS-CoV-2 convalescent individuals. The results established a list of diagnostic signatures and potential therapeutic targets for long-Covid-19 patients although follow-up long-term studies are required. We believe that our findings will serve as a valuable resource, to drive further exploration of long-COVID syndrome pathogenesis.

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  1. ScreenIT

    SciScore for 10.1101/2021.09.14.21263603: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    EthicsConsent: All study participants provided written informed consent and all study protocols were approved by the Cedars-Sinai Medical Center institutional
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Coupling efficiency was confirmed by incubation of 625 beads from each coupled region with a phycoerythrin-conjugated anti-6×□HisTag antibody (Abcam, Cambridge, UK) at a concentration of 10 μg/mL for 45 min shaking at 900 rpm and room temperature.
    anti-6×□HisTag
    suggested: None
    The bound AABs were detected by addition of R-phycoerythrin-labelled goat anti-human IgG detection antibody (Ab) (5□µg/ml, Dianova, Hamburg, Germany) for 1 hr at RT, after several PBS washes.
    anti-human IgG
    suggested: None
    Recombinant DNA
    SentencesResources
    Other antigens were produced in-house using E.coli SCS1 carrying plasmid pSE111, which contains an N-terminally located hexa-histidine-tag.
    pSE111
    suggested: None
    Software and Algorithms
    SentencesResources
    For all HCW participants, EDTA plasma specimens were transported within 1 hour of phlebotomy to the Cedars-Sinai Department of Pathology and Laboratory Medicine and underwent serology testing using the Abbott Diagnostics SARS-CoV-2 IgG chemiluminescent microparticle immunoassay (Abbott Diagnostics, Abbott Park, Illinois) against the nucleocapsid (N) antigen of the SARS-CoV-2 virus (12, 13).
    Abbott
    suggested: (Abbott, RRID:SCR_010477)
    Statistical analyses: Data processing and analysis were performed using R v3.5.1 and KNIME 2.12 (https://www.knime.org/) (21, 22).
    KNIME
    suggested: (Knime, RRID:SCR_006164)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

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  2. Version published to 10.1101/2021.09.14.21263603v1 on medRxiv