bioRxivBioinformaticsPreprint
Emerging SARS-CoV-2 mutation hotspots associated with clinical outcomes
Xianwu Pang, Pu LiiD, Lifeng ZhangiD, Lusheng QueiD, Min Dong, Qihui WangiD, Yinfeng Wei, Bo XieiD, Xing XieiD, Lanxiang LiiD, Chunyue Yin, Liuchun Wei, Qingniao ZhouiD, Yingfang LiiD, Lei YuiD, Weidong LiiD, Zengnan MoiD, Jing LengiD, Yanling HuiD
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the ongoing coronavirus disease 2019 (COVID-19) pandemic. Understanding the influence of mutations in the SARS-CoV-2 gene on clinical outcomes and related factors is critical for treatment and prevention. Here, we analyzed 209,551 high-coverage complete virus sequences and 321 RNA-seq samples to mine the mutations associated with clinical outcome in the SARS-CoV-2 genome. Several important hotspot variants were found to be associated with severe clinical outcomes. Q57H variant in ORF3a protein were found to be associated with higher mortality rate, and was high proportion in severe cases (39.36%) and 501Y.V2 strains (100%) but poorly proportional to asymptomatic cases (10.04%). T265I could change nsp2 structure and mitochondrial permeability, and evidently higher in severe cases (20.12%) and 501Y.V2 strains (100%) but lower in asymptomatic cases (1.43%). Additionally, R203K and G204R could decrease the flexibility and immunogenic property of N protein with high frequency among severe cases, VUI 202012/01 and 484K.V2 strains. Interestingly, the SARS-CoV-2 genome was more susceptible to mutation because of the high frequency of nt14408 mutation (which located in RNA polymerase) and the high expression levels of ADAR and APOBEC in severe clinical outcomes. In conclusion, several important mutation hotspots in the SARS-CoV-2 genome associated with clinical outcomes was found in our study, and that might correlate with different SARS-CoV-2 mortality rates.
- assay: whole-genome sequencing
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: variant effect prediction
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
- 1
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