Antibody evasion by SARS-CoV-2 Omicron subvariants BA.2.12.1, BA.4 and BA.5

  1. Qian Wang
  2. Yicheng Guo
  3. Sho Iketani
  4. Manoj S. Nair
  5. Zhiteng Li
  6. Hiroshi Mohri
  7. Maple Wang
  8. Jian Yu
  9. Anthony D. Bowen
  10. Jennifer Y. Chang
  11. Jayesh G. Shah
  12. Nadia Nguyen
  13. Zhiwei Chen
  14. Kathrine Meyers
  15. Michael T. Yin
  16. Magdalena E. Sobieszczyk
  17. Zizhang Sheng
  18. Yaoxing Huang
  19. Lihong Liu
  20. David D. Ho

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Abstract

SARS-CoV-2 Omicron subvariants BA.2.12.1 and BA.4/5 have surged notably to become dominant in the United States and South Africa, respectively 1,2 . These new subvariants carrying further mutations in their spike proteins raise concerns that they may further evade neutralizing antibodies, thereby further compromising the efficacy of COVID-19 vaccines and therapeutic monoclonals. We now report findings from a systematic antigenic analysis of these surging Omicron subvariants. BA.2.12.1 is only modestly (1.8-fold) more resistant to sera from vaccinated and boosted individuals than BA.2. However, BA.4/5 is substantially (4.2-fold) more resistant and thus more likely to lead to vaccine breakthrough infections. Mutation at spike residue L452 found in both BA.2.12.1 and BA.4/5 facilitates escape from some antibodies directed to the so-called class 2 and 3 regions of the receptor-binding domain 3 . The F486V mutation found in BA.4/5 facilitates escape from certain class 1 and 2 antibodies but compromises the spike affinity for the viral receptor. The R493Q reversion mutation, however, restores receptor affinity and consequently the fitness of BA.4/5. Among therapeutic antibodies authorized for clinical use, only bebtelovimab retains full potency against both BA.2.12.1 and BA.4/5. The Omicron lineage of SARS-CoV-2 continues to evolve, successively yielding subvariants that are not only more transmissible but also more evasive to antibodies.

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  3. Version published to 10.1038/s41586-022-05053-w
  4. Version published to 10.1101/2022.05.26.493517v2 on bioRxiv
  5. Version published to 10.21203/rs.3.rs-1696532/v1 on Research Square
  6. ScreenIT

    SciScore for 10.1101/2022.05.26.493517: (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
    Spike proteins were captured through their C-terminal His-tag over an anti-His antibody surface.
    anti-His
    suggested: None
    For each residue within the RBD, the frequency of antibody recognition was calculated as the number of contact antibodies32.
    antibodies32
    suggested: None
    The structures of antibody-spike complexes for modeling were also obtained from PDB (7L5B (2-15), 6XDG (REGN10933), and 7KMG (LY-CoV555)).
    REGN10933
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    Heavy chain variable (VH) and light chain variable (VL) genes for each antibody were synthesized (GenScript), then transfected into Expi293 cells (Thermo Fisher Scientific), and purified from the supernatant by affinity purification using rProtein A Sepharose (GE).
    Expi293
    suggested: RRID:CVCL_D615)
    A14527); Vero-E6 cells were obtained from the ATCC (
    Vero-E6
    suggested: None
    CRL-1586); HEK293T cells were obtained from the ATCC (CRL-3216).
    HEK293T
    suggested: None
    Software and Algorithms
    SentencesResources
    Neutralization curves and IC50 values were derived by fitting a nonlinear five-parameter dose–response curve to the data in GraphPad Prism v.9.2.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    PyMOL v.
    PyMOL
    suggested: (PyMOL, RRID:SCR_000305)

    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.

    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.

  7. Version published to 10.1101/2022.05.26.493517v1 on bioRxiv