Antibody Binding to SARS-CoV-2 S Glycoprotein Correlates with but Does Not Predict Neutralization

  1. Shilei Ding
  2. Annemarie Laumaea
  3. Mehdi Benlarbi
  4. Guillaume Beaudoin-Bussières
  5. Romain Gasser
  6. Halima Medjahed
  7. Marie Pancera
  8. Leonidas Stamatatos
  9. Andrew McGuire
  10. Renée Bazin
  11. Andrés Finzi

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Abstract

Convalescent plasma from SARS-CoV-2 infected individuals and monoclonal antibodies were shown to potently neutralize viral and pseudoviral particles carrying the S glycoprotein. However, a non-negligent proportion of plasma samples from infected individuals, as well as S-specific monoclonal antibodies, were reported to be non-neutralizing despite efficient interaction with the S glycoprotein in different biochemical assays using soluble recombinant forms of S or when expressed at the cell surface. How neutralization relates to the binding of S glycoprotein in the context of viral particles remains to be established. Here, we developed a pseudovirus capture assay (VCA) to measure the capacity of plasma samples or antibodies immobilized on ELISA plates to bind to membrane-bound S glycoproteins from SARS-CoV-2 expressed at the surface of lentiviral particles. By performing VCA, ELISA, and neutralization assays, we observed a strong correlation between these parameters. However, while we found that plasma samples unable to capture viral particles did not neutralize, capture did not guarantee neutralization, indicating that the capacity of antibodies to bind to the S glycoprotein at the surface of pseudoviral particles is required but not sufficient to mediate neutralization. Altogether, our results highlight the importance of better understanding the inactivation of S by plasma and neutralizing antibodies.

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  1. ScreenIT

    SciScore for 10.1101/2020.09.08.287482: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Ethics statement: All subjects gave their informed consent for inclusion before they participated in the study.
    IRB: The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of CHUM (19.381, approved on March 25, 2020).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    The 293T-ACE2 cell line was previously reported [14].
    293T-ACE2
    suggested: RRID:CVCL_YZ65)
    Briefly, pseudoviral particles were produced by transfecting 2×106 HEK293T cells with pNL4.3 Luc R-E-(
    HEK293T
    suggested: NCBI_Iran Cat# C498, RRID:CVCL_0063)
    Briefly, 293T cells were transfected by the calcium phosphate method with the lentiviral vector pNL4.3 R-E-Luc (NIH AIDS Reagent Program) and a plasmid encoding for SARS-CoV-2 Spike, SARS-CoV-1 Spike at a ratio of 5:4.
    293T
    suggested: NCBI_Iran Cat# C498, RRID:CVCL_0063)

    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 10.3390/v12111214
  3. Version published to 10.1101/2020.09.08.287482 on bioRxiv