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Importation, circulation, and emergence of variants of SARS-CoV-2 in the South Indian state of Karnataka.

Chitra PattabiramanPramada PrasadAnson K GeorgeDarshan SreenivasRisha RasheedNakka Vijay Kiran ReddyAnita DesaiRavi Vasanthapuram
Published in: Wellcome open research (2021)
Background: As the coronavirus disease 2019 (COVID-19) pandemic continues, the selection of genomic variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) associated with higher transmission, more severe disease, re-infection, and immune escape are a cause for concern. Such variants have been reported from the UK (B.1.1.7), South Africa (B.1.351) and, Brazil (P.1/B.1.1.28). We performed this study to track the importation, spread, and emergence of variants locally. Methods: We sequenced whole genomes of SARS-CoV-2 from international travellers (n=75) entering Karnataka, South India, between Dec 22, 2020 and Jan 31, 2021, and from positive cases in the city of Bengaluru (n=108), between Nov 22, 2020- Jan 22, 2021, as well as a local outbreak. We present the lineage distribution and analysis of these sequences. Results: Genomes from the study group into 34 lineages. Variant B.1.1.7 was introduced by international travel (24/73, 32.9%). Lineage B.1.36 and B.1 formed a major fraction of both imported (B.136: 20/73, 27.4%; B.1: 14/73, 19.2%), and circulating viruses (B.1.36: 45/103; 43.7%, B.1: 26/103; 25.2%). The lineage B.1.36 was also associated with a local outbreak. We detected nine amino acid changes, previously associated with immune escape, spread across multiple lineages. The N440K change was detected in 45/162 (27.7%) of the sequences. Conclusions: Our data support the idea that variants of concern spread by travel. Viruses with amino acid replacements associated with immune escape are already circulating. It is critical to check transmission and monitor changes in SARS-CoV-2 locally.
Keyphrases
  • sars cov
  • respiratory syndrome coronavirus
  • copy number
  • coronavirus disease
  • amino acid
  • south africa
  • single cell
  • gene expression
  • genome wide
  • hepatitis c virus
  • cell fate
  • drug induced
  • big data