Pressured cytotoxic T cell epitope strength among SARS‐CoV ‐2 variants correlates with COVID ‐19 severity

  1. Vishal Rao
  2. Ushashi Banerjee
  3. Narmada Sambaturu
  4. Sneha Chunchanur
  5. R. Ambica
  6. Nagasuma Chandra

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Abstract

Heterogeneity in susceptibility among individuals to COVID‐19 has been evident through the pandemic worldwide. Cytotoxic T lymphocyte (CTL) responses generated against pathogens in certain individuals are known to impose selection pressure on the pathogen, thus driving emergence of new variants. In this study, we probe the role played by host genetic heterogeneity in terms of HLA‐genotypes in determining differential COVID‐19 severity in patients. We use bioinformatic tools for CTL epitope prediction to identify epitopes under immune pressure. Using HLA‐genotype data of COVID‐19 patients from a local cohort, we observe that the recognition of pressured epitopes from the parent strain Wuhan‐Hu‐1 correlates with COVID‐19 severity. We also identify and rank list HLA‐alleles and epitopes that offer protectivity against severe disease in infected individuals. Finally, we shortlist a set of 6 pressured and protective epitopes that represent regions in the viral proteome that are under high immune pressure across SARS‐CoV‐2 variants. Identification of such epitopes, defined by the distribution of HLA‐genotypes among members of a population, could potentially aid in prediction of indigenous variants of SARS‐CoV‐2 and other pathogens.

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  1. Version published to 10.1111/tan.15071
  2. ScreenIT

    SciScore for 10.1101/2021.12.06.21267084: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: COVID-19 sample collection: Ethical approval for this study was obtained from the Institutional Ethics Committee at Bangalore Medical College and Research Institute, Bangalore, India (BMCRI/PS/02/2021-21), and IISc (1-26062020), Bangalore, India.
    Consent: Written informed consent was obtained from all study participants before sample collection.
    Sex as a biological variableA total of 36 Covid-19 patients (median age = 52 + 16, both male and female participants), volunteered their samples for this study.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line AuthenticationAuthentication: The total epitope pool of 2022 epitopes in case of the SARS-CoV-2 Wuhan strain could not be covered completely due to binding of some epitopes to indigenous HLA alleles, which have not been validated experimentally in previous studies.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    A total of 1827 HLA Class-I alleles were obtained from 240 ethnic groups with HLA-allele frequency data from the Allele Frequency Net Database25 (AFND) (http://www.allelefrequencies.net/).
    http://www.allelefrequencies.net/
    suggested: (Allele Frequencies in Worldwide Populations, RRID:SCR_007259)
    The raw sequencing data was subjected to adapter trimming with the tool TrimGalore (version 0.6.6) and Class-I HLAs were predicted from the RNA sequencing reads using the software arcasHLA v0.2.030 with standard settings.
    TrimGalore
    suggested: None

    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:
    We acknowledge that many of the results presented here might be influenced by some of the limitations that accompany the approaches undertaken. Some of these include – i) the TAP proteasomal pathway, vesicular trafficking of HLA bound peptides from the ER to the cell surface and absence of TCRs specific to viral epitopes which resemble self-peptides due to thymic negative selection, are further aspects which need to be considered while quantifying CTL responses based on epitope presentation. ii) High affinity binding of certain epitopes could not be validated due to lack of previously available experimental binding affinity studies of indigenous HLA alleles considered in our study iii) COVID-19 is known to present a wide range of immunopathologies in patients6 due to which CTL response need not be a major contributing factor in determining disease severity in some cases. Thus, our hypothesis of lower CTL responses in severe disease states might not be applicable to all the COVID-19 patient samples considered in our study. Despite these and the small sample size in each category, a clear trend appears to be there. In the future, our framework allows incorporation of some of these aspects into our analysis once the aforementioned data is made available. Our current analysis lays emphasis on the role played by host genetic heterogeneity in determining COVID-19 disease outcome and driving evolution of new SARS-CoV-2 variants. The results obtained in our study provide deeper evolu...

    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.
    • 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.

  3. Version published to 10.1101/2021.12.06.21267084 on medRxiv