Kinetics of antibody responses dictate COVID-19 outcome

  1. Carolina Lucas
  2. Jon Klein
  3. Maria Sundaram
  4. Feimei Liu
  5. Patrick Wong
  6. Julio Silva
  7. Tianyang Mao
  8. Ji Eun Oh
  9. Maria Tokuyama
  10. Peiwen Lu
  11. Arvind Venkataraman
  12. Annsea Park
  13. Benjamin Israelow
  14. Anne L. Wyllie
  15. Chantal B. F. Vogels
  16. M. Catherine Muenker
  17. Arnau Casanovas-Massana
  18. Wade L. Schulz
  19. Joseph Zell
  20. Melissa Campbell
  21. John B. Fournier
  22. Yale IMPACT Research Team
  23. Nathan D. Grubaugh
  24. Shelli Farhadian
  25. Adam V. Wisnewski
  26. Charles Dela Cruz
  27. Saad Omer
  28. Albert I. Ko
  29. Aaron Ring
  30. Akiko Iwasaki

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Abstract

Recent studies have provided insights into innate and adaptive immune dynamics in coronavirus disease 2019 (COVID-19). Yet, the exact feature of antibody responses that governs COVID-19 disease outcomes remain unclear. Here, we analysed humoral immune responses in 209 asymptomatic, mild, moderate and severe COVID-19 patients over time to probe the nature of antibody responses in disease severity and mortality. We observed a correlation between anti-Spike (S) IgG levels, length of hospitalization and clinical parameters associated with worse clinical progression. While high anti-S IgG levels correlated with worse disease severity, such correlation was time-dependent. Deceased patients did not have higher overall humoral response than live discharged patients. However, they mounted a robust, yet delayed response, measured by anti-S, anti-RBD IgG, and neutralizing antibody (NAb) levels, compared to survivors. Delayed seroconversion kinetics correlated with impaired viral control in deceased patients. Finally, while sera from 89% of patients displayed some neutralization capacity during their disease course, NAb generation prior to 14 days of disease onset emerged as a key factor for recovery. These data indicate that COVID-19 mortality does not correlate with the cross-sectional antiviral antibody levels per se , but rather with the delayed kinetics of NAb production.

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    SciScore for 10.1101/2020.12.18.20248331: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Ethics statement: This study was approved by Yale Human Research Protection Program Institutional Review Boards (FWA00002571, protocol ID 2000027690).
    Consent: Informed consent was obtained from all enrolled patients and healthcare workers.
    Randomizationnot detected.
    BlindingCytokines and flow cytometry analyses were blinded.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.
    Cell Line AuthenticationContamination: The cell line was obtained from the ATCC and has been tested negative for contamination with mycoplasma.

    Table 2: Resources

    Antibodies
    SentencesResources
    Plates were washed three times with PBS-T (PBS with 0.1% Tween-20) and 50 μl of HRP anti-Human IgG Antibody (GenScript #A00166, 1:5,000) or anti-Human IgM-Peroxidase
    anti-Human IgG
    suggested: None
    anti-Human IgM-Peroxidase
    suggested: None
    Flow cytometry: Antibody clones and vendors were as follows: BB515 anti-hHLA-DR (G46-6) (1:400
    anti-hHLA-DR ( G46-6 )
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    Viral titers were measured by standard plaque assay using Vero E6 cells.
    Vero E6
    suggested: RRID:CVCL_XD71)
    Software and Algorithms
    SentencesResources
    Ethics statement: This study was approved by Yale Human Research Protection Program Institutional Review Boards (FWA00002571, protocol ID 2000027690).
    Yale Human Research Protection Program
    suggested: None
    The clinical data were collected using EPIC EHR and REDCap 9.3.6 software.
    REDCap
    suggested: (REDCap, RRID:SCR_003445)
    Data were analysed using FlowJo software version 10.6 software (Tree Star).
    FlowJo
    suggested: (FlowJo, RRID:SCR_008520)
    Statistical analysis: All analysis of patient samples was conducted using Matlab 2020a
    Matlab
    suggested: (MATLAB, RRID:SCR_001622)
    , GraphPad Prism 9, and JMP 15.
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)

    Results from OddPub: Thank you for sharing your data.

    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 10.1101/2020.12.18.20248331v1 on medRxiv