Influence of HLA Class II Polymorphism on Predicted Cellular Immunity Against SARS-CoV-2 at the Population and Individual Level
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Abstract
Development of adaptive immunity after COVID-19 and after vaccination against SARS-CoV-2 is predicated on recognition of viral peptides, presented on HLA class II molecules, by CD4+ T-cells. We capitalised on extensive high-resolution HLA data on twenty five human race/ethnic populations to investigate the role of HLA polymorphism on SARS-CoV-2 immunogenicity at the population and individual level. Within populations, we identify wide inter-individual variability in predicted peptide presentation from structural, non-structural and accessory SARS-CoV-2 proteins, according to individual HLA genotype. However, we find similar potential for anti-SARS-CoV-2 cellular immunity at the population level suggesting that HLA polymorphism is unlikely to account for observed disparities in clinical outcomes after COVID-19 among different race/ethnic groups. Our findings provide important insight on the potential role of HLA polymorphism on development of protective immunity after SARS-CoV-2 infection and after vaccination and a firm basis for further experimental studies in this field.
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SciScore for 10.1101/2020.12.24.424326: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement Consent: US population categories were developed based on a race/ethnicity questionnaire included on the donor consent form. Randomization Following Hardy-Weinberg equilibrium proportions, multi-locus HLA Class II genotypes were generated by randomly sampling two haplotypes from the same population HLA haplotype frequency distribution. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Population comparisons of peptide scores were performed by calculating the mean and standard deviation using NumPy in Python, and between repeat population simulations using … SciScore for 10.1101/2020.12.24.424326: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement Consent: US population categories were developed based on a race/ethnicity questionnaire included on the donor consent form. Randomization Following Hardy-Weinberg equilibrium proportions, multi-locus HLA Class II genotypes were generated by randomly sampling two haplotypes from the same population HLA haplotype frequency distribution. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Population comparisons of peptide scores were performed by calculating the mean and standard deviation using NumPy in Python, and between repeat population simulations using statistics.shaipro (Shapiro-Wilk test for normality) and statistics.kruskal (Kruskal–Wallis one-way analysis of variance) both in Python. NumPysuggested: (NumPy, RRID:SCR_008633)Pythonsuggested: (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:It is important to acknowledge the limitations of our study. We used a computational approach to predict SARS-CoV-2 peptides presented by HLA class II molecules, however, peptide presentation does not always lead to CD4+ T-cell activation; peptide recognition is complex and incompletely understood and is influenced by many factors, including relative expression of individual viral proteins61. Nevertheless, NetMHCIIpan-4.0 is an established and validated algorithm for T-cell epitope prediction that has recently been updated resulting in improved performance21. Recent computational studies investigating SARS-CoV-2 vaccine immunogenicity have based their approach for T-cell epitope selection exclusively on peptide-HLA binding affinity incorporating different thresholds (e.g. 500nM or 50nM) and identified population coverage gaps in predicted cellular immunity15, 62, 63, 64. This approach is affected by inherent bias of certain HLA molecules towards higher or lower mean predicted affinities; thus, we show that the 50nM binding affinity threshold, one of the most commonly used, is heavily biased towards HLA-DR as the main SARS-CoV-2 peptide presenting locus with the majority of HLA-DQ and -DP molecules showing no peptide binding. Accordingly, using a 50nM binding affinity threshold for defining peptide immunogenicity resulted in very wide inter-individual variability in predicted CD4+ T-cell reactivity against SARS-CoV-2 proteins (supplementary Figure 4). To overcome this limitati...
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.
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- No protocol registration statement was detected.
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