Autoantibodies in COVID‐19 correlate with antiviral humoral responses and distinct immune signatures

  1. Patrick Taeschler
  2. Carlo Cervia
  3. Yves Zurbuchen
  4. Sara Hasler
  5. Christian Pou
  6. Ziyang Tan
  7. Sarah Adamo
  8. Miro E. Raeber
  9. Esther Bächli
  10. Alain Rudiger
  11. Melina Stüssi‐Helbling
  12. Lars C. Huber
  13. Petter Brodin
  14. Jakob Nilsson
  15. Elsbeth Probst‐Müller
  16. Onur Boyman

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Abstract

Background

Several autoimmune features occur during coronavirus disease 2019 (COVID‐19), with possible implications for disease course, immunity, and autoimmune pathology. In this study, we longitudinally screened for clinically relevant systemic autoantibodies to assess their prevalence, temporal trajectory, and association with immunity, comorbidities, and severity of COVID‐19.

Methods

We performed highly sensitive indirect immunofluorescence assays to detect antinuclear antibodies (ANA) and antineutrophil cytoplasmic antibodies (ANCA), along with serum proteomics and virome‐wide serological profiling in a multicentric cohort of 175 COVID‐19 patients followed up to 1 year after infection, eleven vaccinated individuals, and 41 unexposed controls.

Results

Compared with healthy controls, similar prevalence and patterns of ANA were present in patients during acute COVID‐19 and recovery. However, the paired analysis revealed a subgroup of patients with transient presence of certain ANA patterns during acute COVID‐19. Furthermore, patients with severe COVID‐19 exhibited a high prevalence of ANCA during acute disease. These autoantibodies were quantitatively associated with higher SARS‐CoV‐2‐specific antibody titers in COVID‐19 patients and in vaccinated individuals, thus linking autoantibody production to increased antigen‐specific humoral responses. Notably, the qualitative breadth of antibodies cross‐reactive with other coronaviruses was comparable in ANA‐positive and ANA‐negative individuals during acute COVID‐19. In autoantibody‐positive patients, multiparametric characterization demonstrated an inflammatory signature during acute COVID‐19 and alterations of the B‐cell compartment after recovery.

Conclusion

Highly sensitive indirect immunofluorescence assays revealed transient autoantibody production during acute SARS‐CoV‐2 infection, while the presence of autoantibodies in COVID‐19 patients correlated with increased antiviral humoral immune responses and inflammatory immune signatures.

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  1. Version published to 10.1111/all.15302
  2. Version published to 10.1101/2022.01.08.22268901v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2022.01.08.22268901: (What is this?)

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

    Table 1: Rigor

    EthicsConsent: Human subjects and patient characteristics: Following written informed consent, adult individuals were recruited for medical history and blood sampling between April 2020 and May 2021.
    IRB: The study was approved by the Cantonal Ethics Committee of Zurich (BASEC #2016-01440).
    Field Sample Permit: Follow-up visits for medical history and blood collection were conducted approximately six months and one year after symptom onset.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    BlindingFor paired analyses of ANA patterns, 129 pairs of IIF pictures at 1:320 dilution were blinded for patient characteristics and sampling timepoint, and examined pairwise by the same observer.
    Power Analysisnot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Antibodies against myeloperoxidase and proteinase 3 were measured on PhadiaTM 250 (ThermoFisher Scientific) or on Bioflash® (Werfen) according to manufacturer’s instructions.
    Antibodies against myeloperoxidase and proteinase 3
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    Autoantibody detection: ANA were measured by IIF on HEp-2 cells (Euroimmun) with a cut-off dilution of 1:320.
    HEp-2
    suggested: None
    Software and Algorithms
    SentencesResources
    Reads were mapped to the epitope library with Bowtie2, and counts were obtained using SAMtools.
    Bowtie2
    suggested: (Bowtie 2, RRID:SCR_016368)
    SAMtools
    suggested: (SAMTOOLS, RRID:SCR_002105)
    Pairwise protein alignment for 56- amino acid (AA) long peptides with SARS-CoV-2 spike (Uniprot Entry P0DTC2) and nucleoprotein (P0DTC9) was generated using Biostrings (2.60.2), with BLOSUM62 substitution matrix and gap opening and extension penalty of -11 and -1, respectively.
    Biostrings
    suggested: (Biostrings, RRID:SCR_016949)
    Data visualization was performed using ggplot2 (version 3.3.5), ggfortify (0.4.12), ggVennDiagram (1.1.4), UpSetR (1.4.0), and corrplot (0.90)
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)

    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: We detected the following sentences addressing limitations in the study:
    Limitations of this study include the use of highly sensitive IIF assays that yielded a high prevalence of positive results in healthy subjects and COVID-19 patients. Most of the measured ANA titers were at or just above the threshold level, which usually would be considered of irrelevant clinical significance. Moreover, we did not assess the specificity of autoantibodies, but recent studies have shown reactivity to a wide spectrum of autoantigens (11, 16). Altogether, our study shows autoantibodies in COVID-19 appear to be transient and correlate with increased anti-viral humoral immune responses and a distinct immune signature. As questions arise regarding long-term consequences of COVID-19, including the risk of immune dysregulation and autoimmune disease, understanding the mechanisms involved in balancing self-tolerance and protective immune responses become crucial to recognize and manage patients at risk for developing autoimmune diseases.

    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

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  4. Version published to 10.1101/2022.01.08.22268901v1 on medRxiv