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.

Frontiers in Microbiology
|November 4, 2021
PubMed

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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