Distinct Autoimmune Antibody Signatures Between Hospitalized Acute COVID-19 Patients, SARS-CoV-2 Convalescent Individuals, and Unexposed Pre-Pandemic Controls

  1. Nahid Bhadelia
  2. Anna C. Belkina
  3. Alex Olson
  4. Thomas Winters
  5. Patricia Urick
  6. Nina Lin
  7. Ian Rifkin
  8. Yachana Kataria
  9. Rachel R. Yuen
  10. Manish Sagar
  11. Jennifer E. Snyder-Cappione

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Abstract

Increasing evidence suggests that autoimmunity may play a role in the pathophysiology of SARS-CoV-2 infection during both the acute and ‘long COVID’ phases of disease. However, an assessment of autoimmune antibodies in convalescent SARS-CoV-2 patients has not yet been reported.

Methodology

We compared the levels of 18 different IgG autoantibodies (AABs) between four groups: (1) unexposed pre-pandemic subjects from the general population (n = 29); (2) individuals hospitalized with acute moderate-severe COVID-19 (n = 20); (3) convalescent SARS-COV-2-infected subjects with asymptomatic to mild viral symptoms during the acute phase with samples obtained between 1.8 and 7.3 months after infection (n = 9); and (4) unexposed pre-pandemic subjects with systemic lupus erythematous (SLE) (n = 6). Total IgG and IgA levels were also measured from subjects in groups 1-3 to assess non-specific pan-B cell activation.

Results

As expected, in multivariate analysis, AABs were detected at much higher odds in SLE subjects (5 of 6, 83%) compared to non-SLE pre-pandemic controls (11 of 29, 38%) [odds ratio (OR) 19.4,95% CI, 2.0 – 557.0, p = 0.03]. AAB detection (percentage of subjects with one or more autoantibodies) was higher in SARS-CoV-2 infected convalescent subjects (7 of 9, 78%) [OR 17.4, 95% CI, 2.0 – 287.4, p = 0.02] and subjects with acute COVID-19 (12 of 20, 60%) compared with non-SLE pre-pandemic controls, but was not statistically significant among the latter [OR 1.8,95% CI, 0.6 – 8.1, p = 0.23]. Within the convalescent subject group, AABs were detected in 5/5 with reported persistent symptoms and 2/4 without continued symptoms (p = 0.17). The multivariate computational algorithm Partial Least Squares Determinant Analysis (PLSDA) was used to determine if distinct AAB signatures distinguish subject groups 1-3. Of the 18 autoantibodies measured, anti-Beta 2-Glycoprotein, anti-Proteinase 3-ANCA, anti-Mi-2 and anti-PM/Scl-100 defined the convalescent group; anti-Proteinase 3-ANCA, anti-Mi-2, anti-Jo-1 and anti-RNP/SM defined acute COVID-19 subjects; and anti-Proteinase 3-ANCA, anti-Mi-2, anti-Jo-1, anti-Beta 2-Glycoprotein distinguished unexposed controls. The AABs defining SARS-COV-2 infected from pre-pandemic subjects are widely associated with myopathies, vasculitis, and antiphospholipid syndromes, conditions with some similarities to COVID-19. Compared to pre-pandemic non-SLE controls, subjects with acute COVID-19 had higher total IgG concentration (p-value=0.006) but convalescent subjects did not (p-value=0.08); no differences in total IgA levels were found between groups.

Conclusions

Our findings support existing studies suggesting induction of immune responses to self-epitopes during acute, severe COVID-19 with evidence of general B cell hyperactivation. Also, the preponderance of AAB positivity among convalescent individuals up to seven months after infection indicates potential initiation or proliferation, and then persistence of self-reactive immunity without severe initial disease. These results underscore the importance of further investigation of autoimmunity during SARS-CoV-2 infection and its role in the onset and persistence of post-acute sequelae of COVID-19.

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  1. ScreenIT

    SciScore for 10.1101/2021.01.21.21249176: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Antibodies
    SentencesResources
    Autoantibody and total IgG and IgA detection: A multiplex assay was used to examine 18 AABs across the four study subgroups with MILLIPLEX MAP Human Autoimmune Autoantibody Panel kit (Cat. No. HAIAB-10K).
    total IgG
    suggested: None
    Software and Algorithms
    SentencesResources
    Statistical analyses were performed using GraphPad version 9 or Matlab.
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)
    Matlab
    suggested: (MATLAB, RRID:SCR_001622)

    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:
    There are several limitations to our study including small sample size and the lack of longitudinal data from individual COVID-19 patients. The latter did not allow internal control to examine the dynamics of the autoimmune response over time or to control for preexistence of subclinical circulating AABs among participants before COVID-19 infection. Additionally, we had limited data on comorbidities for COVID-19 survivors and could not control for whether they had existing diagnosis of an autoimmune disorder. We also had limited data on concurrent medical comorbidities among subjects in our pre-pandemic sample. In our very small cohort of COVID-19 survivors, over half of the participants reported persistent or prolonged symptoms after acute infection lasting over one month. Bias may also have been introduced by who responded to recruitment for the anonymous blood collection study, if those suffering with persistence symptoms were more likely to agree to participate. We had only serum samples for two COVID-19 survivor participants but by performing matched analysis on other samples, we were able to demonstrate that this did not affect the measurement of AABs we observed. Despite these limitations, the discovery of AABs at such high levels in even a small, sampled group of convalescent subjects compared to pre-pandemic controls in this study is a significant finding worthy of further evaluation. Future investigations should attempt to correlate whether “long COVID” features wit...

    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.

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  2. Version published to 10.1101/2021.01.21.21249176v1 on medRxiv