bioRxivGenomicsPreprint
Surveillance of genetic diversity and evolution in locally transmitted SARS-CoV-2 in Pakistan during the first wave of the COVID-19 pandemic
M. Shakeel, M. Irfan, Z. Nisa, M. Rashid, S. K. Ansari, I. A. Khan
Abstract
Surveillance of genetic diversity in the SARS-CoV-2 is extremely important to detect the emergence of more infectious and deadly strains of the virus. In this study, we monitored mutational events in the SARS-CoV-2 genome through whole genome sequencing. The samples (n=48) were collected from the hot spot regions of the metropolitan city Karachi, Pakistan during the four months (May 2020 to August 2020) of first wave of the COVID-19 pandemic. The data analysis highlighted 122 mutations, including 120 single nucleotide variations (SNV), and 2 deletions. Among the 122 mutations, there were 71 singletons, and 51 recurrent mutations. A total of 16 mutations, including 5 nonsynonymous mutations, were detected in spike protein. Notably, the spike protein missense mutation D614G was observed in 31 genomes. The phylogenetic analysis revealed majority of the genomes (36) classified as B lineage, where 2 genomes were from B.6 lineage, 5 genomes from B.1 ancestral lineage and remaining from B.1 sub-lineages. It was noteworthy that three clusters of B.1 sub-lineages were observed, including B.1.36 lineage (10 genomes), B.1.160 lineage (11 genomes), and B.1.255 lineage (5 genomes), which represent independent events of SARS-CoV-2 transmission within the city. The sub-lineage B.1.36 had higher representation from the Asian countries and the UK, B.1.160 correspond to the European countries with highest representation from the UK, Denmark, and lesser representation from India, Saudi Arabia, France and Switzerland, and the third sub-lineage (B.1.255) correspond to the USA. Collectively, our study provides meaningful insight into the evolution of SARS-CoV-2 lineages in spatio-temporal local transmission during the first wave of the pandemic.
- organism: virus
- assay: whole-genome sequencing
- research focus: phylogenetics / evolution
- approach: statistical model
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