HLA class I genotypes customize vaccination strategies in immune simulation to combat COVID-19

  1. Shouxiong Huang
  2. Ming Tan

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Abstract

Memory CD8 + T cells are associated with a better outcome in Coronavirus Disease 2019 (COVID-19) and recognized as promising vaccine targets against viral infections. This study determined the efficacy of population-dominant and infection-relevant human leukocyte antigens (HLA) class I proteins to present severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) peptides through calculating binding affinities and simulating CD8 + T cell responses. As a result, HLA class I proteins distinguished or shared various viral peptides derived from viruses. HLA class I supertypes clustered viral peptides through recognizing anchor and preferred residues. SARS-CoV-2 peptides overlapped significantly with SARS but minimally with common human coronaviruses. Immune simulation of CD8 + T cell activation using predicted SARS-CoV-2 peptide antigens depended on high-affinity peptide binding, anchor residue interaction, and synergistic presentation of HLA class I proteins in individuals. Results demonstrated that multi-epitope vaccination, employing a strong binding affinity, viral adjuvants, and heterozygous HLA class I genes, induced potent immune responses. Therefore, optimal CD8 + T cell responses can be achieved and customized contingent on HLA class I genotypes in human populations, supporting a precise vaccination strategy to combat COVID-19.

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  1. ScreenIT

    SciScore for 10.1101/2020.11.18.388983: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    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.
    • No funding statement was detected.
    • 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 10.1101/2020.11.18.388983 on bioRxiv