Biology subjects

Laskar, R.

Publications and source records attributed to Laskar, R..

3 recordsLinked to original sources

bioRxiv2021

Differential mutational profile of SARS-CoV-2 proteins across deceased and asymptomatic patients

The SARS-CoV-2 infection spread at an alarming rate with many places showed multiple peaks in incidence. Present study involves a total of 332 SARS-CoV-2 sequences from 114 Asymptomatic and 218 Deceased patients from twenty-one different countries. The mining of mutations was done using the GISAID CoVSurver (www.gisaid.org/epiflu-applications/covsurver-mutations-app) with the reference sequence hCoV-19/Wuhan/WIV04/2019 present in NCBI with Accession number NC-045512.2. The impact of the mutations on SARS-CoV-2 proteins mutation was predicted using PredictSNP1(loschmidt.chemi.muni.cz/predictsnp1) which is a meta-server integrating six predictor tools: SIFT, PhD-SNP, PolyPhen-1, PolyPhen-2, MAPP and SNAP. The iStable integrated server (predictor.nchu.edu.tw/iStable) was used to predict shifts in the protein stability due to mutations. A total of 372 variants were observed in the 332 SARS-CoV-2 sequences with several variants incident in multiple patients accounting for a total of 1596 incidences. Asymptomatic and Deceased specific mutants constituted 32% and 62% of the repertoire respectively indicating their exclusivity. However, the most prevalent mutations were those present in both. Though some parts of the genome are more variable than others but there was clear difference between incidence and prevalence. NSP3 with 68 variants had total occurrence of only 105 whereas Spike protein had 346 occurrences with just 66 variants. For Deleterious variants, NSP3 had the highest incidence of 25 followed by NSP2 (16), ORF3a (14) and N (14). Spike protein had just 7 Deleterious variants out of 66. Deceased patients have more Deleterious than Neutral variants as compared to the symptomatic ones. Further, it appears that the Deleterious variants which decrease protein stability are more significant in pathogenicity of SARS-CoV-2.

bioinformatics↗

bioRxiv2020

Mutational analysis and assessment of its impact on proteins of SARS-CoV-2 genomes from India

The ongoing global pandemic of SARS-CoV-2 implies a corresponding accumulation of mutations. Herein the mutational status of 611 genomes from India along with their impact on proteins was ascertained. After excluding gaps and ambiguous sequences, a total of 493 variable sites (152 parsimony informative and 341 singleton) were observed. The most prevalent reference nucleotide was C (209) and substituted one was T (293). NSP3 had the highest incidence of 101 sites followed by S protein (74 sites), NSP12b (43 sites) and ORF3a (31 sites). The average number of mutations per sample for males and females was 2.56 and 2.88 respectively suggesting a higher contribution of mutations from females. Non-uniform geographical distribution of mutations implied by Odisha (30 samples, 109 mutations) and Tamil Nadu (31 samples, 40 mutations) suggests that sequences in some regions are mutating faster than others. There were 281 mutations (198 Neutral and 83 Disease) affecting amino acid sequence. NSP13 has a maximum of 14 Disease variants followed by S protein and ORF3a with 13 each. Further, constitution of Disease mutations in genomes from asymptomatic people was mere 11% but those from deceased patients was over three folds higher at 38% indicating contribution of these mutations to the pathophysiology of the SARS-CoV-2.

microbiology↗

bioRxiv2020

Phylo-geo-network and haplogroup analysis of 611 novel Coronavirus (nCov-2019) genomes from India

The novel Coronavirus from Wuhan China discovered in December 2019 (nCOV-2019) has since developed into a global epidemic with major concerns about the possibility of the virus evolving into something even more sinister. In the present study we constructed the phylo-geo-network of nCOV-2019 genomes from across India to understand the viral evolution in the country. A total of 611 full length genomes were extracted from different states of India from the EpiCov repository of GISAID initiative and NCBI. Their alignment uncovered 270 parsimony informative sites. Further, 339 genomes were divided into 51 haplogroups. The network revealed the core haplogroup as that of reference sequence NC_045512.2 (Haplogroup A1) with 157 identical sequences present across 16 states. The rest were having not more than ten identical sequences across not more than three locations. Interestingly, some locations with fewer samples have more haplogroups and most haplogroups (41) are localized exclusively to any one state only, suggesting the local evolution of viruses. The two most common lineages are B6 and B1 (Pangolin) whereas clade A2a (Covidex) appears to be the most predominant in India. However, since the pandemic is still emerging, the final outcome will be clear later only.

evolutionary biology↗