Natural Selection Footprint in Novel Coronavirus: A Genomic Perspective of SARS-COV2 Pandemic and Hypothesis for Peptide-Based Vaccine

Author(s): Mojtaba Shekarkar Azgomi, Leila Mohammadnezhad, Marco Pio La Manna, Francesco Dieli, Nadia Caccamo

We retrospective analyzed in silico the binding affinity of SARS-CoV-2 peptides to MHC class I HLA-A, -B, and –C molecules in different countries with high and low morbidity and mortality rates. We used the bioinformatics approach to screen 18260 SARS-CoV-2 epitopes that have significant affinity for different MHC class I alleles and found approximately five thousand predicted nonamers to bind different alleles. Those predicted epitopes show a different significant affinity for occurring MHC I alleles. regarding HLA frequencies within different populations that can vary due to differences in their evolutionary histories, we showed that those alleles have different correlations with SARS-CoV-2 pandemic in 22 countries based on different mortality and morbidity rate. There was a strong negative correlation between morbidity and mortality rates and the frequency of HLA-A*24, HLA-C*06, and HLA-B*5, while a strong positive correlation is detected between HLA-A*02, HLA-B*38, HLA-C*04, and HLA-C*08. We speculate that HLA class I polymorphism, by governing the set of viral peptides presented to CD8+ T cells, influences the outcome of SARS-Cov-2 infection. Finally, we were able to draw a footprint of natural selection on MHC I alleles based on the significantly different affinity of the predicted peptides for known alleles. Our data showed that the HLA class I genetic background and the study epitope prediction should be taken into account for the generation of epitope-based vaccine or diagnostic tools.