bioRxivEvolutionary Biology
Phylodynamic Pattern of Genetic Clusters, Paradigm Shift on Spatio-temporal Distribution of Clades, and Impact of Spike Glycoprotein Mutations of SARS-CoV-2 Isolates from India
Abstract
Background: The COVID-19 pandemic is associated with high morbidity and mortality, with the emergence of numerous variants. The dynamics of SARS-CoV-2 with respect to clade distribution is uneven, unpredictable and fast changing. Aims: Retrieving the complete genomes of SARS-CoV-2 from India and subjecting them to analysis on phylogenetic clade diversity, Spike (S) protein mutations and their functional consequences such as immune escape features and impact on infectivity. Methods: Whole genome of SARS-CoV-2 isolates (n=4,326) deposited from India during the period from January 2020 to December 2020 is retrieved from GISAID and various analyses performed using in silico tools. Results: Notable clade dynamicity is observed indicating the emergence of diverse SARS-CoV-2 variants across the country. GR clade is predominant over the other clades and the distribution pattern of clades is uneven. D614G is the commonest and predominant mutation found among the S-protein followed by L54F. Mutation score prediction analyses reveal that there are several mutations in S-protein including the RBD and NTD regions that can influence the virulence of virus. Besides, mutations having immune escape features as well as impacting the immunogenicity and virulence through changes in the glycosylation patterns are identified. Conclusions: The study has revealed emergence of variants with shifting of clade dynamics within a year in India. It is shown uneven distribution of clades across the nation requiring timely deposition of SARS-CoV-2 sequences. Functional evaluation of mutations in S-protein reveals their significance in virulence, immune escape features and disease severity besides impacting therapeutics and prophylaxis.
- organism: virus
- assay: whole-genome sequencing
- research focus: variant calling
- research focus: variant effect prediction
- approach: statistical model
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