Temporal Pattern of Mutation Accumulation in SARS-CoV-2 Proteins: Insights from Whole Genome Sequences Pan-India Using Data Mining Approach

Abstract

AbstractMutation is a fundamental factor that affects host-pathogen biology and consequently viral survival and spread. Close monitoring and observation of such mutation help decipher essential changes in the SARS Cov2 genome. A plethora of mutations have been documented owing to increased whole genomic sequencing. Understanding how conserved the specific mutations are and the temporal pattern of mutation accumulation is of paramount interest. Using an in-house data mining approach, pan-India data was mined and analysed for 26 proteins expressed by SARS-CoV-2 to understand the spread of mutations over 28 months (January 2021-April 2023). It was observed that proteins such as Nsp3, Nsp4, ORF9b, among others, acquired mutations over the period. In contrast, proteins such as Nsp6-10 were highly stable, with no detectable conserved mutations. Further, it was observed that many of the mutations that were highly prevalent in the delta variants were not observed in the omicron variants, which probably influenced the host-pathogen relationship. The study attempts to catalogue and focus on well-conserved mutations across all the SARS-CoV-2 proteins, highlighting the importance of understanding non-spike mutations.