Association of HLA Class I Genotypes With Severity of Coronavirus Disease-19

  1. Maxim Shkurnikov
  2. Stepan Nersisyan
  3. Tatjana Jankevic
  4. Alexei Galatenko
  5. Ivan Gordeev
  6. Valery Vechorko
  7. Alexander Tonevitsky

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Abstract

Human leukocyte antigen (HLA) class I molecules play a crucial role in the development of a specific immune response to viral infections by presenting viral peptides at the cell surface where they will be further recognized by T cells. In the present manuscript, we explored whether HLA class I genotypes can be associated with the critical course of Coronavirus Disease-19 by searching possible connections between genotypes of deceased patients and their age at death. HLA-A, HLA-B, and HLA-C genotypes of n = 111 deceased patients with COVID-19 (Moscow, Russia) and n = 428 volunteers were identified with next-generation sequencing. Deceased patients were split into two groups according to age at the time of death: n = 26 adult patients aged below 60 and n = 85 elderly patients over 60. With the use of HLA class I genotypes, we developed a risk score (RS) which was associated with the ability to present severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) peptides by the HLA class I molecule set of an individual. The resulting RS was significantly higher in the group of deceased adults compared to elderly adults [ p = 0.00348, area under the receiver operating characteristic curve ( AUC ROC = 0.68)]. In particular, presence of HLA-A * 01:01 allele was associated with high risk, while HLA-A * 02:01 and HLA-A * 03:01 mainly contributed to low risk. The analysis of patients with homozygosity strongly highlighted these results: homozygosity by HLA-A * 01:01 accompanied early deaths, while only one HLA-A * 02:01 homozygote died before 60 years of age. Application of the constructed RS model to an independent Spanish patients cohort ( n = 45) revealed that the score was also associated with the severity of the disease. The obtained results suggest the important role of HLA class I peptide presentation in the development of a specific immune response to COVID-19.

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  1. Version published to 10.3389/fimmu.2021.641900
  2. ScreenIT

    SciScore for 10.1101/2020.11.19.20234567: (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
    HLA-A, HLA-B and HLA-C genes were sequenced with MiSeq platform (Illumina, San Diego, CA, USA) through exons 2 to 4 in both directions using reagent kit HLA-Expert (DNA-Technology LLC, Moscow, Russia) and annotated using the database of the human major histocompatibility complex sequences IMGT/HLA v3.41.0 [12].
    MiSeq
    suggested: (A5-miseq, RRID:SCR_012148)
    IMGT/HLA
    suggested: (IMGT/HLA, RRID:SCR_002971)
    Clustal Omega v1.2.4 was used to construct multiple sequence alignment for each viral protein [14].
    Clustal Omega
    suggested: (Clustal Omega, RRID:SCR_001591)
    Specifically, for each amino acid of each viral protein we assessed the probability of proteasomal cleavage in the considered site using NetChop v3.1 [16].
    NetChop
    suggested: None
    Binding affinities were predicted using netMHCpan v4.1 [17] for all viral peptides (n = 15314) and HLA alleles present in our cohorts of deceased and control patients (n = 107).
    netMHCpan
    suggested: (NetMHCpan Server, RRID:SCR_018182)
    The following functions from scipy.stats Python module [18] were used to conduct statistical testing: fisher exact for Fisher’s exact test, mannwhitneyu for Mann-Whitney U test.
    scipy
    suggested: (SciPy, RRID:SCR_008058)
    Python
    suggested: (IPython, RRID:SCR_001658)
    Principal component analysis was conducted with scikit-learn Python module [19].
    scikit-learn
    suggested: (scikit-learn, RRID:SCR_002577)
    Plots were constructed with Seaborn and Matplotlib [20].
    Matplotlib
    suggested: (MatPlotLib, RRID:SCR_008624)

    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.

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  3. Version published to 10.1101/2020.11.19.20234567v1 on medRxiv
  4. Version published to 10.1101/2020.11.19.20234567v2 on medRxiv