In humans, the dimeric receptor complex IFNAR2-IFNAR1 accelerates cellular response triggered by type I interferon (IFN) family proteins in response to viral infection including Coronavirus infection. Studies have revealed the association of the IFNAR2 gene with severe illness in Coronavirus infection and indicated the association of genomic variants, i.e. single nucleotide polymorphisms (SNPs). However, comprehensive analysis of SNPs of the IFNAR2 gene has not been performed in both coding and non-coding region to find the causes of loss of function of IFNAR2 in COVID-19 patients. In this study, we have characterized coding SNPs (nsSNPs) of IFNAR2 gene using different bioinformatics tools and identified deleterious SNPs. We found 9 nsSNPs as pathogenic and disease-causing along with a decrease in protein stability. We employed molecular docking analysis that showed 5 nsSNPs to decrease binding affinity to IFN. Later, MD simulations showed that P136R mutant may destabilize crucial binding with the IFN molecule in response to COVID-19. Thus, P136R is likely to have a high impact on disrupting the structure of the IFNAR2 protein. GTEx portal analysis predicted 14 sQTLs and 5 eQTLs SNPs in lung tissues hampering the post-transcriptional modification (splicing) and altering the expression of the IFNAR2 gene. sQTLs and eQTLs SNPs potentially explain the reduced IFNAR2 production leading to severe diseases. These mutants in the coding and non-coding region of the IFNAR2 gene can help to recognize severe illness due to COVID 19 and consequently assist to develop an effective drug against the infection.Communicated by Ramaswamy H. Sarma.
Keywords: COVID-19; GTEx; IFNAR2; SNPs; molecular docking; molecular dynamics simulation; protein–protein interaction.