Clinical and Applied Genomics (CAG) Laboratory, Department of Biological Sciences, Aliah University, Kolkata, India.
Chem Biol Interact. 2021 Sep 25;347:109598. doi: 10.1016/j.cbi.2021.109598. Epub 2021 Jul 23.
The 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.
The 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.
A 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.
Deceased 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.
SARS-CoV-2 感染以惊人的速度传播，许多地方的发病率出现了多次高峰。本研究分析了来自 21 个不同国家的 114 例无症状和 218 例死亡患者的 332 个 SARS-CoV-2 基因组序列，以评估其中的突变谱，从而确定临床状况与观察到的突变之间的相关性。
使用 GISAID CoVSurver（www.gisaid.org/epiflu-applications/covsurver-mutations-app）对突变进行挖掘，该工具使用 NCBI 中存在的参考序列“hCoV-19/Wuhan/WIV04/2019”，其访问号为 NC-045512.2。使用 PredictSNP1(loschmidt.chemi.muni.cz/predictsnp1)预测突变对 SARS-CoV-2 蛋白突变的影响，这是一个整合了六个预测工具的元服务器：SIFT、PhD-SNP、PolyPhen-1、PolyPhen-2、MAPP 和 SNAP。使用 iStable 集成服务器（predictor.nchu.edu.tw/iStable）预测突变引起的蛋白质稳定性变化。
在 332 个 SARS-CoV-2 序列中观察到 372 个变体，其中多个变体存在于多个患者中，总共有 1596 例。无症状和死亡特异性突变体分别占库的 32%和 62%，表明它们具有部分排他性。然而，最常见的突变是同时存在于两者中的突变。虽然基因组的某些部分比其他部分更具变异性，但发病率和患病率之间存在明显差异。非结构蛋白 3（NSP3）有 68 个变体，总共有 105 例，而刺突蛋白有 346 个变体，只有 66 个变体。在有害变体中，NSP3 的发病率最高，为 25%，其次是 NSP2（16%）、ORF3a（14%）和 N（14%）。刺突蛋白只有 66 个变体中有 7 个有害变体。
与无症状患者相比，死亡患者的有害变异比中性变异更多。此外，似乎降低蛋白质稳定性的有害变异在 SARS-CoV-2 的致病性中更为重要。
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