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. 2022 Aug 31;10(4):e0091422.
doi: 10.1128/spectrum.00914-22. Epub 2022 Jul 19.

Contrasting Distribution of SARS-CoV-2 Lineages across Multiple Rounds of Pandemic Waves in West Bengal, the Gateway of East and North-East States of India

Animesh K Singh #  1 , Rezwanuzzaman Laskar #  1 , Anindita Banerjee #  1 , Rajiv Kumar Mondal #  1 , Bishal Gupta  2 , Sonia Deb  2 , Shreelekha Dutta  1 , Subrata Patra  1 , Trinath Ghosh  1 , Sumanta Sarkar  1 , Shekhar Ghosh  1 , Sabyasachi Bhattacharya  1 , Debojyoti Roy  1 , Ankita Chakraborty  1 , Meghna Chowdhury  1 , Surajit Mahaptra  1 , Antara Paul  1 , Anup Mazumder  1 , Aparna Chowdhury  2 , Shiv Sekhar Chatterjee  3 , Arunabha Sarkar  4 , Raja Ray  5 , Kuhu Pal  6 , Angshuman Jana  7 , Goutam Barik  8 , Swagata Ganguly  9 , Mitali Chatterjee  10 , Dipankar Majhi  11 , Bhaswati Bandopadhyay  2 , Saumitra Das  1 , Arindam Maitra  1 , Nidhan K Biswas  1
Affiliations

Contrasting Distribution of SARS-CoV-2 Lineages across Multiple Rounds of Pandemic Waves in West Bengal, the Gateway of East and North-East States of India

Animesh K Singh et al. Microbiol Spectr. .

Abstract

The evolution of viral variants and their impact on viral transmission have been an area of considerable importance in this pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We analyzed the viral variants in different phases of the pandemic in West Bengal, a state in India that is important geographically, and compared the variants with other states like Delhi, Maharashtra, and Karnataka, located in other regions of the country. We have identified 57 pango-lineages in 3,198 SARS-CoV-2 genomes, alteration in their distribution, as well as contrasting profiles of amino acid mutational dynamics across different waves in different states. The evolving characteristics of Delta (B.1.617.2) sublineages and alterations in hydrophobicity profiles of the viral proteins caused by these mutations were also studied. Additionally, implications of predictive host miRNA binding/unbinding to emerging spike or nucleocapsid mutations were highlighted. Our results throw considerable light on interesting aspects of the viral genomic variation and provide valuable information for improved understanding of wave-defining mutations in unfolding the pandemic. IMPORTANCE Multiple waves of infection were observed in many states in India during the coronavirus disease 2019 (COVID19) pandemic. Fine-scale evolution of major SARS-CoV-2 lineages and sublineages during four wave-window categories: Pre-Wave 1, Wave 1, Pre-Wave 2, and Wave 2 in four major states of India: Delhi (North), Maharashtra (West), Karnataka (South), and West Bengal (East) was studied using large-scale virus genome sequencing data. Our comprehensive analysis reveals contrasting molecular profiles of the wave-defining mutations and their implications in host miRNA binding/unbinding of the lineages in the major states of India.

Keywords: SARS-CoV-2; West Bengal; coronavirus; epidemiology; evolution; lineage; miRNA; mutation; pandemic.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

FIG 1
FIG 1
(a) Numbers of new daily SARS-CoV-2 infected cases (blue) and 7-day rolling average (red). (b) Map of the state of West Bengal with district-wise cumulative new infection distribution is highlighted. (c) Comparison between circulating lineages (top: red) and new infection cases (gray) in pandemic wave windows in the state of West Bengal. (d) Circulating lineage with pangolin nomenclature across four wave-windows. (e) Phylogenetic time tree of circulating SARS sequences sampled from West Bengal state during the study period.
FIG 2
FIG 2
The normalized mutation rate in each of the SARS-CoV-2 proteins per wave window for each of the four states.
FIG 3
FIG 3
Unrooted phylogenetic tree of the Delta sublineages for sequences sampled from the state of West Bengal (a), Maharashtra (b), Karnataka (c), and (d) Delhi (d).
FIG 4
FIG 4
Hydrophobicity plots of the epitope regions show altered hydrophobicity due to the mentioned mutations: spike protein (a to f); nucleocapsid protein (g to i); and membrane protein (j).
FIG 5
FIG 5
Selection of wave-window time periods based on the daily infection counts from the state of West Bengal.
See this image and copyright information in PMC

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