ABSTRACT
BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new emerging coronavirus that causes coronavirus disease 2019 (COVID-19). Whole-genome tracking of the SARS-CoV-2 enhanced our understanding of the mechanism of disease, control, and prevent COVID-19 infections. Materials and MethodsIn the current study, we investigated 1221 SARS-CoV-2 protein sequences of Iranian SARS-CoV-2 in the public database of the GISAID from January 2019 to April 2022. Prepare a list of suitable samples and preprocess performed by python programming language. To compare and identify mutation patterns SARS-CoV-2 genome was aligned to the Wuhan-Hu-1 as a reference sequence. ResultsOur investigation revealed that spike-P323L, ORF9c-G50N, NSP14-I42V, spike-D614G, NSP4-T492I, nucleocapsid-R203K, nucleocapsid-G204R, membrane-A63T, membrane-Q19E, NSP5-P132H, envelope-T9I, NSP3-G489S, ORF3a-T24I, membrane-D3G, spike-S477N, Spike-D478K, nucleocapsid-S235F, spike-N501Y, nucleocapsid-D3L, and spike-P861H as the most frequent mutations among the Iranian SARS-COV-2 sequences. Furthermore, it was observed that more than 95 % of the SARS-CoV-2 genome, including NSP7, NSP8, NSP9, NSP10, NSP11, and ORF8, had no mutation when compared to the Wuhan-Hu-1. Finally, our data indicated the ORF3a-T24I, NSP3-G489S, NSP5-P132H, NSP14-I42V, envelope-T9I, nucleocapsid-D3L, membrane-Q19E, and membrane-A63T mutations might be one of the responsible factors for the surge in the SARS-CoV-2 omicron variant wave in Iran. DiscussionOur results highlight the value of real-time genomic surveillance that help to identify novel SARS-CoV-2 variants and could be applied to update SARS-CoV-2 diagnostic tools, vaccine design, and understanding of the mechanisms of adaptation to a new host environment.
ABSTRACT
BackgroundThe Coronavirus 2019 (COVID-19) was named by the World Health Organization (WHO) due to its rapid transmittable potential and high mortality rate. Based on the critical role of None Structural Proteins (NSP), NSP3, NSP4, and NSP6 in COVID-19, this study attempts to investigate the superior natural selection mutations and Epistasis among these none structural proteins. MethodsApproximately 6.5 million SARS-CoV-2 protein sequences of each NSP3, NSP4, and NSP6 nonstructural protein were analyzed from January 2020 to January 2022. Python programming language was utilized to preprocess and apply inclusion criteria on the FASTA file to prepare a list of suitable samples. NSP3, NSP4, and NSP6 were aligned to the reference sequence to compare and identify mutation patterns categorized based on frequency, geographical zone distribution, and date. To discover epistasis situations, linear regression between mutation frequency and date among candidate genes was performed to determine correlations. ResultsThe rate of NSP3, NSP4, and NSP6 mutations in divided geographical areas was different. Based on continental studies, P1228L (54.48%), P1469S (54.41%), and A488S (53.86%) mutations in NSP3, T492I (54.84%), and V167L (52.81%) in NSP4 and T77A (69.85%) mutation in NSP6 increased over time, especially in recent months. For NSP3, Europe had the highest P1228L, P1469S, and A488S mutations. For NSP4, Oceania had the highest T492I and V167L mutations, and for NSP6, Europe had the highest T77A mutation. Hot spot regions for NSP3, NSP4, and NSP6 were 1358 to 1552 AA, 150 to 200 AA, and 58 to 87 AA, respectively. Our results showed a significant correlation and co-occurrence between NSP3, NSP4, and NSP6 mutations. ConclusionWe conclude that the effect of mutations on virus stability and replication can be predicted by examining the amino acid changes of P1228L, P1469S, A488S, T492I, V167L and T77A mutations. Also, these mutations can possibly be effective on the function of proteins and their targets in the host cell.
