A distinct phylogenetic cluster of Indian SARS-CoV-2 isolates

Abstract

From an isolated epidemic, COVID-19 has now emerged as a global pandemic. The availability of genomes in the public domain following the epidemic provides a unique opportunity to understand the evolution and spread of the SARS-CoV-2 virus across the globe. The availability of whole genomes from multiple states in India prompted us to analyse the phylogenetic clusters of genomes in India. We performed whole-genome sequencing for 64 genomes making a total of 361 genomes from India, followed by phylogenetic clustering, substitution analysis, and dating of the different phylogenetic clusters of viral genomes. We describe a distinct phylogenetic cluster (Clade I / A3i) of SARS-CoV-2 genomes from India, which encompasses 41% of all genomes sequenced and deposited in the public domain from multiple states in India. Globally 3.5% of genomes, which till date could not be mapped to any distinct known cluster fall in this newly defined clade. The cluster is characterized by a core set of shared genetic variants -C6312A (T2016K), C13730T (A88V/A97V), C23929T, and C28311T (P13L). Further, the cluster is also characterized by a nucleotide substitution rate of 1.4 x 10 -3 variants per site per year, lower than the prevalent A2a cluster, and predominantly driven by variants in the E and N genes and relative sparing of the S gene. Epidemiological assessments suggest that the common ancestor emerged in the month of February 2020 and possibly resulted in an outbreak followed by countrywide spread, as evidenced by the low divergence of the genomes from across the country. To the best of our knowledge, this is the first comprehensive study characterizing the distinct and predominant cluster of SARS-CoV-2 in India.

Initial insights into the genetic epidemiology of SARS-CoV-2 isolates from Kerala suggest local spread from limited introductions

“…This also suggests that focussed testing, tracing and quarantine of expatriates and international travelers implemented during the epidemic would have been effective in curbing the spread from international travelers. The genome clusters also suggested polytomies, suggesting a recent outbreak [17] . Close follow-up of the cluster members confirmed the potential source of the outbreak, suggesting genetic epidemiology could be used in conjunction with case follow-ups to uncover potential outbreaks and possibly connect outbreaks which are apparently not related.…”

“…Further introductions into the state during the later days of the pandemic through international and regional travel could have contributed to the spread of the epidemic in the state and the pool of circulating genetic lineages or clades. While a number of studies on the genetic epidemiology of SARS-CoV-2 from different states in India have emerged [14][15][16][17] , there has been a paucity of information on the genetic architecture and epidemiology of SARS-CoV-2 isolates in the state of Kerala.…”

Population genetic analysis of Indian SARS-CoV-2 isolates reveals a unique phylogenetic cluster

“…This clade is defined by a set of four mutations; C28311T, C6312A, C13730T and C23929T. This clade was also identified in another Indian study 12 where they observed around 29% of their study population to be in this cluster and named it as Clade I/A3i (defined by a combination of same four variants). This study used viral genomes isolated from Telangana along with other available sequences to infer the phylogeny.…”

“…The geo-distribution of the mutations and therefore clades 11 across countries can have severe impact on the local evolution of the virus. In a previous study 12 , combination of four genetic variants were used to define a unique clade predominant in India.…”

What if we perceive SARS-CoV-2 genomes as documents? Topic modelling using Latent Dirichlet Allocation to identify mutation signatures and classify SARS-CoV-2 genomes

“…Interestingly, most of the high weight inferred signatures had a good overlap with the previously identified clades specific to various geographical regions (refer Table 1). For example, the signature-11, constituted predominantly by amino acid mutations N-P13L/ORF9b-P10S, ORF1a-L3606F, ORF1b-A88V, ORF1a-T2016K, was observed to dominate in India and other Asian regions 9 . Biology agnostic, data structure driven approaches for SARS-CoV-2 genome sequences may therefore have some merit in not only handling the large amount of genomic data, but also for identifying mutation signatures (and hence classifying genomes) that might be of interest to clinicians/ biologists 10 .…”