Aim: To investigate the role of HLA in SARS-Cov-2 infection severity
Methods: In silico peptide binding prediction (NetMHCpan 4.1) was used to measure peptide binding of viral peptide binding to class I and II HLA molecules. High resolution HLA typing was performed using paired high-resolution HLA typing (GenDx) and SARS-CoV-2 genomic sequencing for 60 patients presenting to Michigan Medicine. Clinical response to infection using the Sequential Organ Failure Assessment (SOFA) scoring system as an indicator of disease severity.
Results: Using a multiple linear regression-based approach that accounts for HLA class I genotype and demographic factors, we identify a significant association between disproportionate HLA-C-mediated peptide binding and severely symptomatic infection. Furthermore, molecular modeling and unsupervised machine learning techniques were used to provide structural and functional insight into differential peptide binding by individual HLA-C molecules correlated with disease severity.
Conclusion: To our knowledge, this is the first study addressing both HLA type and whole viral genome sequences on a per patient basis. Surprisingly, the results suggest, in this cohort, that severe disease is associated with a disproportionate number of SARS-CoV-2 structural and non-structural protein epitopes binding to HLA-C. Furthermore, we modeled how structural characteristics of HLA-C molecules relate to differential promiscuity in SARS-CoV-2 peptide binding. The aim of the research is to gain a better understanding of how individual HLA types impact interactions with novel antigens which may potentially impact clinical outcomes, both in SARS-CoV-2 pathogenesis and novel pathogens.
