Mapping the Immunodominance Landscape of SARS-CoV-2 Spike Protein for the Design of Vaccines against COVID-19

  1. Bao-zhong Zhang
  2. Ye-fan Hu
  3. Lin-lei Chen
  4. Yi-gang Tong
  5. Jing-chu Hu
  6. Jian-piao Cai
  7. Kwok-Hung Chan
  8. Ying Dou
  9. Jian Deng
  10. Hua-rui Gong
  11. Chaiyaporn Kuwentrai
  12. Wenjun Li
  13. Xiao-lei Wang
  14. Hin Chu
  15. Cai-hui Su
  16. Ivan Fan-Ngai Hung
  17. Thomas Chung Cheung Yau
  18. Kelvin Kai-Wang To
  19. Kwok Yung Yuen
  20. Jian-Dong Huang

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Abstract

The ongoing coronavirus disease 2019 (COVID-19) pandemic is a serious threat to global public health, and imposes severe burdens on the entire human society. The severe acute respiratory syndrome (SARS) coronavirus-2 (SARS-CoV-2) can cause severe respiratory illness and death. Currently, there are no specific antiviral drugs that can treat COVID-19. Several vaccines against SARS-CoV-2 are being actively developed by research groups around the world. The surface S (spike) protein and the highly expressed internal N (nucleocapsid) protein of SARS-CoV-2 are widely considered as promising candidates for vaccines. In order to guide the design of an effective vaccine, we need experimental data on these potential epitope candidates. In this study, we mapped the immunodominant (ID) sites of S protein using sera samples collected from recently discharged COVID-19 patients. The SARS-CoV-2 S protein-specific antibody levels in the sera of recovered COVID-19 patients were strongly correlated with the neutralising antibody titres. We used epitope mapping to determine the landscape of ID sites of S protein, which identified nine linearized B cell ID sites. Four out of the nine ID sites were found in the receptor-binding domain (RBD). Further analysis showed that these ID sites are potential high-affinity SARS-CoV-2 antibody binding sites. Peptides containing two out of the nine sites were tested as vaccine candidates against SARS-CoV-2 in a mouse model. We detected epitope-specific antibodies and SARS-CoV-2-neutralising activity in the immunised mice. This study for the first time provides human serological data for the design of vaccines against COVID-19.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.23.056853: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Twenty-six patients who were discharged from hospital provided written informed consent under UW 13-265.
    IRB: This study was approved by the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster (UW 13-372, UW 13-265, and UW 19-470).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableSerum specimens from COVID-19 patients: Serum samples were collected from 39 patients with COVID-19 (22 males and 17 females) between Jan 26, 2020, and March 18, 2020.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Briefly, splenocytes harvested from sacrificed mice were washed and immediately transferred to anti-IFN-γ antibody pre-coated filter plates.
    anti-IFN-γ
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    After 1 h of incubation at 37°C, 35 µL of the virus–serum mixture was added to Vero-E6 cell monolayer for SARS-CoV-2 infection in 96-well microtitre plates in quadruplicate.
    Vero-E6
    suggested: None
    Experimental Models: Organisms/Strains
    SentencesResources
    Animal experiments: The SPF BALB/c mice were supplied by the Laboratory Animal Unit of the University of Hong Kong.
    BALB/c
    suggested: RRID:IMSR_ORNL:BALB/cRl)

    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 10.1101/2020.04.23.056853v2 on bioRxiv
  3. Version published to 10.1101/2020.04.23.056853v1 on bioRxiv