COVID-19-neutralizing antibodies predict disease severity and survival

  1. Wilfredo F. Garcia-Beltran
  2. Evan C. Lam
  3. Michael G. Astudillo
  4. Diane Yang
  5. Tyler E. Miller
  6. Jared Feldman
  7. Blake M. Hauser
  8. Timothy M. Caradonna
  9. Kiera L. Clayton
  10. Adam D. Nitido
  11. Mandakolathur R. Murali
  12. Galit Alter
  13. Richelle C. Charles
  14. Anand Dighe
  15. John A. Branda
  16. Jochen K. Lennerz
  17. Daniel Lingwood
  18. Aaron G. Schmidt
  19. A. John Iafrate
  20. Alejandro B. Balazs

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Abstract

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  1. ScreenIT

    SciScore for 10.1101/2020.10.15.20213512: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Patient samples: Use of patient samples for the development and validation of SARS-CoV-2 diagnostic tests was approved by Partners Institutional Review Board (protocol 2020P000895).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    SARS-CoV-2 receptor binding domain IgG, IgM, and IgA ELISA: To quantitatively detect IgG, IgM, and IgA antibodies to SARS-CoV-2 receptor binding domain (RBD), we developed an indirect ELISA using an anti-SARS-CoV and -CoV-2 monoclonal antibody (CR3022) with IgG1, IgM, and IgA1 isotypes (kindly provided by Galit Alter, Stephanie Fischinger, Caroline Atyeo, and Matt Slein in collaboration with Jeffrey Bernard at MassBiologics).
    SARS-CoV-2 receptor binding domain IgG
    suggested: None
    IgA
    suggested: None
    SARS-CoV-2 receptor binding domain (RBD)
    suggested: None
    -CoV-2
    suggested: (Imported from the IEDB Cat# CR3022, RRID:AB_2848080)
    IgG1
    suggested: None
    Human antibody isotypes were detected with specific antibodies (Bethyl) diluted as indicated: anti-human IgG-HRP (1:25,000), anti-human IgM-HRP (1:20,000), and anti-human IgA-HRP (1:5,000).
    anti-human IgG-HRP
    suggested: None
    anti-human IgM-HRP
    suggested: None
    anti-human IgA-HRP
    suggested: (SouthernBiotech Cat# 2050-05, RRID:AB_2687526)
    Anti-RBD antibody levels were calculated by interpolating onto the standard curve and correcting for sample dilution; one unit per mL (U/mL) was defined as the equivalent reactivity seen by 1 μg/mL of CR3022.
    Anti-RBD
    suggested: None
    Cells transfected with each vector were divided into 3 aliquots, stained with either PBS+, CR3022 SARS-CoV antibody (10 μg/mL in PBS+), or B38 SARS-CoV-2 antibody (10 μg/mL in PBS+) for 30 minutes at room temperature.
    CR3022
    suggested: (Imported from the IEDB Cat# CR3022, RRID:AB_2848080)
    SARS-CoV
    suggested: None
    Cells were then washed with 1 mL PBS+, spun at 1,150 x g, and stained with anti-human IgG AF647 polyclonal antibody (Invitrogen) at 2 μg/mL in PBS+ for 30 minutes at RT.
    anti-human IgG
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    Flow Cytometry: To quantify the pseudotyped lentiviral supernatants in terms of infectious units, we plated 400,000 of either 293T or 293T-ACE2 cells in 1 mL in a 12-well plate format (Corning).
    293T-ACE2
    suggested: RRID:CVCL_YZ65)
    To compare the relative surface expression of pseudovirus spike protein, we plated 400,000 293T cells per well in 1 mL in a 12-well plate.
    293T
    suggested: None
    Software and Algorithms
    SentencesResources
    Acquired images were analyzed using ImageJ to produce overlays.
    ImageJ
    suggested: (ImageJ, RRID:SCR_003070)
    Statistical and data analyses: Statistical and data analyses were performed using GraphPad Prism 8.4.3, JMP Pro 15.0.0 (SAS Institute), and R v4.0.2.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    Flow cytometry data was analyzed using FlowJo 10.6.2.
    FlowJo
    suggested: (FlowJo, RRID:SCR_008520)

    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 10.1016/j.cell.2020.12.015
  3. Version published to 10.1101/2020.10.15.20213512 on medRxiv