ArticleChemico-biological interactions2021
Differential mutation profile of SARS-CoV-2 proteins across deceased and asymptomatic patients.
Article in Chemico-biological interactions, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 22 citations in OpenAlex.
- Genomic Characterization of SARS-CoV-2 NB.1.8.1 and PQ.2 from the Infants and Young Children with Gastrointestinal Symptoms.Infection and drug resistance · 2026Article
- A comprehensive review of current insights into the virulence factors of SARS-CoV-2.Journal of virology · 2025Review
- Characteristics of SARS-CoV-2 Omicron BA.5 variants in Shanghai after ending the zero-COVID policy in December 2022: a clinical and genomic analysis.Frontiers in microbiology · 2024Article
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- Predicting COVID-19 disease severity from SARS-CoV-2 spike protein sequence by mixed effects machine learning.Computers in biology and medicine · 2022Article
- Effect of Aryl-Cyclohexanones and their Derivatives on Macrophage Polarization In Vitro.Inflammation · 2022Article
- Phylogenetic analysis and comparative genomics of SARS-CoV-2 from survivor and non-survivor COVID-19 patients in Cordoba, Argentina.BMC genomics · 2022Article
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- Editorial: Decoding the genetics of viral evolution.Frontiers in genetics · 2022Article
- Analyzing the interaction of human ACE2 and RBD of spike protein of SARS-CoV-2 in perspective of Omicron variant.EXCLI journal · 2022Article
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2 authors at 1 institution in 1 country.
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Abstract
backgroundThe SARS-CoV-2 infection has spread at an alarming rate with many places showing multiple peaks in incidence. Present study analyzes a total of 332 SARS-CoV-2 genome sequences from 114 asymptomatic and 218 deceased patients from twenty-one different countries to assess the mutation profile therein in order to establish the correlation between the clinical status and the observed mutations.
methodsThe mining of mutations was carried out 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.
resultsA total of 372 variants were observed in the 332 SARS-CoV-2 sequences with several variants present 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 partial 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. Non-structural protein 3 (NSP3) with 68 variants had a total of only 105 incidences whereas Spike protein had 346 incidences with just 66 variants. Amongst the 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.
conclusionDeceased patients have more Deleterious than Neutral variants as compared to the asymptomatic ones. Further, it appears that the Deleterious variants which decrease protein stability are more significant in pathogenicity of SARS-CoV-2.
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