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Updated: Oct 14, 2025

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
Published on: November 10, 2023
Emerging Severe Acute Respiratory Syndrome Coronavirus 2 Mutation Hotspots Associated With Clinical Outcomes and
Xianwu Pang1, Pu Li2, Lifeng Zhang3
1Guangxi Collaborative Innovation Center for Biomedicine, Guangxi Medical University, Nanning, China.
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 is critical for treatment and prevention. Here, we analyzed all high-coverage complete SARS-CoV-2 sequences from GISAID database from January 1, 2020, to January 1, 2021, to mine the mutation hotspots associated with clinical outcome and developed a model to predict the clinical outcome in different epidemic strains. Exploring the cause of mutation based on RNA-dependent RNA polymerase (RdRp) and RNA-editing enzyme, mutation was more likely to occur in severe and mild cases than in asymptomatic cases, especially A > G, C > T, and G > A mutations. The mutations associated with asymptomatic outcome were mainly in open reading frame 1ab (ORF1ab) and N genes; especially R6997P and V30L mutations occurred together and were correlated with asymptomatic outcome with high prevalence. D614G, Q57H, and S194L mutations were correlated with mild and severe outcome with high prevalence. Interestingly, the single-nucleotide variant (SNV) frequency was higher with high percentage of nt14408 mutation in RdRp in severe cases. The expression of ADAR and APOBEC was associated with clinical outcome. The model has shown that the asymptomatic percentage has increased over time, while there is high symptomatic percentage in Alpha, Beta, and Gamma. These findings suggest that mutation in the SARS-CoV-2 genome may have a direct association with clinical outcomes and pandemic. Our result and model are helpful to predict the prevalence of epidemic strains and to further study the mechanism of mutation causing severe disease.
Insights
SARS-CoV-2 mutations influence COVID-19 severity. Specific mutations correlate with asymptomatic, mild, or severe outcomes, aiding in predicting epidemic strain prevalence and understanding disease mechanisms.
Area of Science:
- Virology
- Genomics
- Epidemiology
Background:
- The COVID-19 pandemic, caused by SARS-CoV-2, necessitates understanding genetic variations.
- Mutations in SARS-CoV-2 genes are critical for effective treatment and prevention strategies.
Purpose of the Study:
- To analyze SARS-CoV-2 mutations and their association with clinical outcomes (asymptomatic, mild, severe).
- To develop a predictive model for clinical outcomes based on identified mutation patterns in different epidemic strains.
Main Methods:
- Analysis of high-coverage complete SARS-CoV-2 sequences from the GISAID database (Jan 2020 - Jan 2021).
- Identification of mutation hotspots and correlation with clinical outcomes.
- Development of a predictive model incorporating mutation data and enzyme expression (ADAR, APOBEC).
Main Results:
- Mutations were more frequent in severe and mild cases than asymptomatic ones, with specific types (A>G, C>T, G>A) being prominent.
- Asymptomatic outcomes linked to mutations in ORF1ab and N genes (e.g., R6997P, V30L).
- Mild/severe outcomes associated with D614G, Q57H, S194L mutations; higher SNV frequency and nt14408 mutation in RdRp observed in severe cases.
- Increased asymptomatic cases over time; Alpha, Beta, and Gamma strains showed high symptomatic percentages.
- ADAR and APOBEC expression levels correlated with clinical outcomes.
Conclusions:
- SARS-CoV-2 genomic mutations are directly associated with clinical outcomes and pandemic dynamics.
- The developed model can predict epidemic strain prevalence and aid in studying mutation-driven severe disease mechanisms.
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