Cross-sectional IgM and IgG profiles in SARS-CoV-2 infection

  1. Tugba Ozturk
  2. Christina Howell
  3. Karima Benameur
  4. Richard P Ramonell
  5. Kevin Cashman
  6. Shama Pirmohammed
  7. Leda Bassit
  8. John Roback
  9. Vince C Marconi
  10. Raymond F Schinazi
  11. Whitney Wharton
  12. F. Eun-Hyung Lee
  13. William T Hu

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Abstract

Background: Accurate serological assays can improve the early diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, but few studies have compared performance characteristics between assays in symptomatic and recovered patients. Methods: We recruited 32 patients who had 2019 coronavirus disease (COVID-19; 18 hospitalized and actively symptomatic, 14 recovered mild cases), and measured levels of IgM (against the full-length S1 or the highly homologous SARS-CoV E protein) and IgG (against S1 receptor binding domain [RBD]). We performed the same analysis in 103 pre-2020 healthy adult control (HC) participants and 13 participants who had negative molecular testing for SARS-CoV-2. Results: Anti-S1-RBD IgG levels were very elevated within days of symptom onset for hospitalized patients (median 2.04 optical density [OD], vs. 0.12 in HC). People who recovered from milder COVID-19 only reached similar IgG levels 28 days after symptom onset. IgM levels were elevated early in both groups (median 1.91 and 2.12 vs. 1.14 OD in HC for anti-S1 IgM, 2.23 and 2.26 vs 1.52 in HC for anti-E IgM), with downward trends in hospitalized cases having longer disease duration. The combination of the two IgM levels showed similar sensitivity for COVID-19 as IgG but greater specificity, and identified 4/10 people (vs. 3/10 by IgG) with prior symptoms and negative molecular testing to have had COVID-19. Conclusions: Disease severity and timing both influence levels of IgM and IgG against SARS-CoV-2, with IgG better for early detection of severe cases but IgM more suited for early detection of milder cases.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.10.20097535: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Standard Protocol Approvals, Registrations, and Patient Consents: This study was approved by Emory University Institutional Review Board.
    RandomizationReceiver-operating characteristic (ROC) curve analysis was first used to determine each serological test’s ability to distinguish between symptomatic COVID-19 cases and 78 randomly selected pre-2020 HC.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    For each antibody, linear regression was compared against other higher order models (second- or third-order polynomial, and exponential growth for anti-S1-SBD IgG in recovered cases) based on Akaike Information Criteria.
    anti-S1-SBD IgG
    suggested: None
    Software and Algorithms
    SentencesResources
    Statistical Analyses: All statistical analyses were performed using SPSS 26 (IBM SPSS, Armonk, NY) except for curve-fitting.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)
    Curve-fitting for relationships between antibody levels and time since symptom onset was performed in GraphPad Prism 8.4.2 (San Diego, CA).
    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.

    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.

  2. Version published to 10.1101/2020.05.10.20097535 on medRxiv