Early cross-coronavirus reactive signatures of protective humoral immunity against COVID-19

  1. Paulina Kaplonek
  2. Chuangqi Wang
  3. Yannic Bartsch
  4. Stephanie Fischinger
  5. Matthew J. Gorman
  6. Kathryn Bowman
  7. Jaewon Kang
  8. Diana Dayal
  9. Patrick Martin
  10. Radoslaw Nowak
  11. Ching-Lin Hsieh
  12. Jared Feldman
  13. Boris Julg
  14. Eric J. Nilles
  15. Elon R. Musk
  16. Anil S. Menon
  17. Eric S. Fischer
  18. Jason S. McLellan
  19. Aaron Schmidt
  20. Marcia B. Goldberg
  21. Michael Filbin
  22. Nir Hacohen
  23. Douglas A Lauffenburger
  24. Galit Alter

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Abstract

The introduction of vaccines has inspired new hope in the battle against SARS-CoV-2. However, the emergence of viral variants, in the absence of potent antivirals, has left the world struggling with the uncertain nature of this disease. Antibodies currently represent the strongest correlate of immunity against COVID-19, thus we profiled the earliest humoral signatures in a large cohort of severe and asymptomatic COVID-19 individuals. While a SARS-CoV-2-specific immune response evolved rapidly in survivors of COVID-19, non-survivors exhibited blunted and delayed humoral immune evolution, particularly with respect to S2-specific antibody evolution. Given the conservation of S2 across β-coronaviruses, we found the early development of SARS-CoV-2-specific immunity occurred in tandem with pre-existing common β-coronavirus OC43 humoral immunity in survivors, which was selectively also expanded in individuals that develop paucisymptomatic infection. These data point to the importance of cross-coronavirus immunity as a correlate of protection against COVID-19.

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    SciScore for 10.1101/2021.05.11.443609: (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
    Patient cohort and clinical data collection: Luminex: SARS-CoV-2 and eCoV-specific antibody subclass/isotype and Fcγ-receptor (FcγR) binding levels were assessed using a 384-well based customized multiplexed Luminex assay, as previously described (59).
    eCoV-specific
    suggested: None
    Unbound antibodies were washed away, and antigen-bound antibodies were detected by using a PE-coupled detection antibody for each subclass and isotype (IgG1, IgG2, IgG3, IgG4, IgA1, and IgM; Southern Biotech), and Fcγ-receptors were fluorescently labeled with PE before addition to immune complexes (FcγR2A, FcγR2B, FcγR3A, FcγR3B; Duke Protein Production facility).
    antigen-bound
    suggested: None
    IgA1
    suggested: None
    PE median fluorescent intensity (MFI) is reported as a readout for antigen-specific antibody titers.
    antigen-specific
    suggested: None

    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.

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  2. Version published to 10.1101/2021.05.11.443609v1 on bioRxiv