HLA‐dependent variation in SARS‐CoV‐2 CD8  + T cell cross‐reactivity with human coronaviruses

  1. Paul R. Buckley
  2. Chloe H. Lee
  3. Mariana Pereira Pinho
  4. Rosana Ottakandathil Babu
  5. Jeongmin Woo
  6. Agne Antanaviciute
  7. Alison Simmons
  8. Graham Ogg
  9. Hashem Koohy

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

The conditions and extent of cross‐protective immunity between severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) and common‐cold human coronaviruses (HCoVs) remain open despite several reports of pre‐existing T cell immunity to SARS‐CoV‐2 in individuals without prior exposure. Using a pool of functionally evaluated SARS‐CoV‐2 peptides, we report a map of 126 immunogenic peptides with high similarity to 285 MHC‐presented peptides from at least one HCoV. Employing this map of SARS‐CoV‐2‐non‐homologous and homologous immunogenic peptides, we observe several immunogenic peptides with high similarity to human proteins, some of which have been reported to have elevated expression in severe COVID‐19 patients. After combining our map with SARS‐CoV‐2‐specific TCR repertoire data from COVID‐19 patients and healthy controls, we show that public repertoires for the majority of convalescent patients are dominated by TCRs cognate to non‐homologous SARS‐CoV‐2 peptides. We find that for a subset of patients, >50% of their public SARS‐CoV‐2‐specific repertoires consist of TCRs cognate to homologous SARS‐CoV‐2‐HCoV peptides. Further analysis suggests that this skewed distribution of TCRs cognate to homologous or non‐homologous peptides in COVID‐19 patients is likely to be HLA‐dependent. Finally, we provide 10 SARS‐CoV‐2 peptides with known cognate TCRs that are conserved across multiple coronaviruses and are predicted to be recognized by a high proportion of the global population. These findings may have important implications for COVID‐19 heterogeneity, vaccine‐induced immune responses, and robustness of immunity to SARS‐CoV‐2 and its variants.

Article activity feed

  1. Version published to 10.1111/imm.13451
  2. ScreenIT

    SciScore for 10.1101/2021.07.17.452778: (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

    Software and Algorithms
    SentencesResources
    BLOSUM62 local-global alignment scores (local or global would produce the same score for a pairwise alignment of lengths N vs N) were computed using the pairwiseAlignment function from the R package Biostrings, with high gap penalties (opening and extension of both 100).
    Biostrings
    suggested: (Biostrings, RRID:SCR_016949)
    Gathering human proteome sequence: The human proteome was downloaded in fasta format from UniProt
    UniProt
    suggested: (UniProtKB, RRID:SCR_004426)
    All graphs were exported from iGraph into Cytoscape v3.82 using the R function createNetworkFromIgraph from package RCy3.
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)
    From cytoscape, all graphs were exported as .graphml files and read into Gephi.
    Gephi
    suggested: (Gephi, RRID:SCR_004293)
    Python code for the IEDB tool to compute the population coverage was downloaded from http://tools.iedb.org/population/download on 24-11-20.
    Python
    suggested: (IPython, RRID:SCR_001658)

    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: We detected the following sentences addressing limitations in the study:
    There is conflicting evidence surrounding the existence of memory SARS-CoV-2 cross-reactive CD8+ T cells in unexposed individuals15,16,40, and a limitation of our work is that we could not to provide a direct link to pre-existing immunity, because from healthy donors the MIRA dataset only evaluated expanded naïve T cells and did not examine anti-viral efficacy of the responding T cells. Indeed, although we cannot determine the cause or timeframe of this selection of sCoV-2-HCoV peptides in this subset of individuals, the potential implications are interesting. It is plausible that these patients may exhibit more robust protection against SARS-CoV-2 variants, HCoVs or even future emerging coronavirus strains. Future work should explore any immunity benefit of infection-induced cross-reactive T cell responses, and in addition, it will be interesting to examine whether vaccination against SARS-CoV-2 can induce T cell memory that is cross-reactive with SARS-CoV-2 variants and/or wider coronaviruses in such individuals. Furthermore, by our identification of a set of 10 potentially cross-reactive peptides with broad population coverage, it is possible that these peptides could be employed to test which patients exhibit cross-reactive phenotypes e.g., after vaccination with relevant antigens. More broadly, data are beginning to demonstrate distinct vaccine-induced responses linked to differential patient exposure to SARS-CoV-2 3,4. In turn, it is possible that COVID-19 vaccine boost...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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.

    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.

  3. Version published to 10.1101/2021.07.17.452778v1 on bioRxiv