The majority of SARS-CoV-2-specific antibodies in COVID-19 patients with obesity are autoimmune and not neutralizing

  1. Daniela Frasca
  2. Lisa Reidy
  3. Maria Romero
  4. Alain Diaz
  5. Carolyn Cray
  6. Kristin Kahl
  7. Bonnie B. Blomberg

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Abstract

Background/objectives

Obesity decreases the secretion of SARS-CoV-2-specific IgG antibodies in the blood of COVID-19 patients. How obesity impacts the quality of the antibodies secreted, however, is not understood. Therefore, the objective of this study is to evaluate the presence of neutralizing versus autoimmune antibodies in COVID-19 patients with obesity.

Subjects/methods

Thirty serum samples from individuals who tested positive for SARS-CoV-2 infection by RT-PCR were collected from inpatient and outpatient settings. Of these, 15 were lean (BMI < 25) and 15 were obese (BMI ≥ 30). Control serum samples were from 30 uninfected individuals, age-, gender-, and BMI-matched, recruited before the current pandemic. Neutralizing and autoimmune antibodies were measured by ELISA. IgG autoimmune antibodies were specific for malondialdehyde (MDA), a marker of oxidative stress and lipid peroxidation, and for adipocyte-derived protein antigens (AD), markers of virus-induced cell death in the obese adipose tissue.

Results

SARS-CoV-2 infection induces neutralizing antibodies in all lean but only in few obese COVID-19 patients. SARS-CoV-2 infection also induces anti-MDA and anti-AD autoimmune antibodies more in lean than in obese patients as compared to uninfected controls. Serum levels of these autoimmune antibodies, however, are always higher in obese versus lean COVID-19 patients. Moreover, because the autoimmune antibodies found in serum samples of COVID-19 patients have been correlated with serum levels of C-reactive protein (CRP), a general marker of inflammation, we also evaluated the association of anti-MDA and anti-AD antibodies with serum CRP and found a positive association between CRP and autoimmune antibodies.

Conclusions

Our results highlight the importance of evaluating the quality of the antibody response in COVID-19 patients with obesity, particularly the presence of autoimmune antibodies, and identify biomarkers of self-tolerance breakdown. This is crucial to protect this vulnerable population at higher risk of responding poorly to infection with SARS-CoV-2 than lean controls.

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  1. Version published to 10.1038/s41366-021-01016-9
  2. ScreenIT

    SciScore for 10.1101/2021.05.05.21256686: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: The research was approved and reviewed by the Institutional Review Board (IRB, protocol #20200504, PI Dr. Reidy) at the University of Miami, which reviews all human research conducted under the auspices of the University of Miami.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    ELISA to measure Spike-specific IgG antibodies: Serum IgG antibodies against SARS-CoV-2 Spike protein were measured by an ELISA developed and standardized in our laboratory.
    Spike-specific IgG
    suggested: None
    SARS-CoV-2 Spike protein
    suggested: None
    ELISA to measure autoimmune antibodies: To measure MDA-specific IgG antibodies, ELISA plates were coated with MDA-modified Bovine Serum Albumin protein (MDA-BSA, MyBioSource MBS390120), at the concentration of 10 µg/mL.
    MDA-specific IgG
    suggested: None
    To measure AD-specific IgG antibodies, we isolated the adipocytes from the subcutaneous adipose tissue of patients undergoing weight reduction surgeries (bilateral breast reduction), as previously described (28).
    AD-specific IgG
    suggested: None
    Software and Algorithms
    SentencesResources
    GraphPad Prism version 8.4.3 software was used to construct all graphs.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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

    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.

  3. Version published to 10.1101/2021.05.05.21256686v1 on medRxiv