SARS-CoV-2 Mutation Detail Information

Virus Mutation SARS-CoV-2 Mutation D614G


Basic Characteristics of Mutations
Mutation Site D614G
Mutation Site Sentence Some mutations such as D614G and V1176F were shown to be associated with viral infectivity.
Mutation Level Amino acid level
Mutation Type Nonsynonymous substitution
Gene/Protein/Region
Standardized Encoding Gene
Genotype/Subtype -
Viral Reference NC_045512.2
Functional Impact and Mechanisms
Disease COVID-19    
Immune -
Target Gene -
Clinical and Epidemiological Correlations
Clinical Information -
Treatment -
Location -
Literature Information
PMID 35743837
Title Identifying COVID-19 Severity-Related SARS-CoV-2 Mutation Using a Machine Learning Method
Author Huang F,Chen L,Guo W,Zhou X,Feng K,Huang T,Cai Y
Journal Life (Basel, Switzerland)
Journal Info 2022 May 28;12(6):806
Abstract SARS-CoV-2 shows great evolutionary capacity through a high frequency of genomic variation during transmission. Evolved SARS-CoV-2 often demonstrates resistance to previous vaccines and can cause poor clinical status in patients. Mutations in the SARS-CoV-2 genome involve mutations in structural and nonstructural proteins, and some of these proteins such as spike proteins have been shown to be directly associated with the clinical status of patients with severe COVID-19 pneumonia. In this study, we collected genome-wide mutation information of virulent strains and the severity of COVID-19 pneumonia in patients varying depending on their clinical status. Important protein mutations and untranslated region mutations were extracted using machine learning methods. First, through Boruta and four ranking algorithms (least absolute shrinkage and selection operator, light gradient boosting machine, max-relevance and min-redundancy, and Monte Carlo feature selection), mutations that were highly correlated with the clinical status of the patients were screened out and sorted in four feature lists. Some mutations such as D614G and V1176F were shown to be associated with viral infectivity. Moreover, previously unreported mutations such as A320V of nsp14 and I164ILV of nsp14 were also identified, which suggests their potential roles. We then applied the incremental feature selection method to each feature list to construct efficient classifiers, which can be directly used to distinguish the clinical status of COVID-19 patients. Meanwhile, four sets of quantitative rules were set up, which can help us to more intuitively understand the role of each mutation in differentiating the clinical status of COVID-19 patients. Identified key mutations linked to virologic properties will help better understand the mechanisms of infection and will aid in the development of antiviral treatments.
Sequence Data -
Mutation Information
Note
Basic Characteristics of Mutations
  • Mutation Site: The specific location in a gene or protein sequence where a change occurs.
  • Mutation Level: The level at which a mutation occurs, including the nucleotide or amino acid level.
  • Mutation Type: The nature of the mutation, such as missense mutation, nonsense mutation, synonymous mutation, etc.
  • Gene/Protein/Region: Refers to the specific region of the virus where the mutation occurs. Including viral genes, viral proteins, or a specific viral genome region. If the article does not specifically indicate the relationship between the mutation and its correspondence, the main
  • Gene/Protein/Region studied in the article is marked.
  • Genotype/Subtype: Refers to the viral genotype or subtype where the mutation occurs. If the article does not specifically indicate the relationship between the mutation and its correspondence, the main Genotype/Subtype studied in the article is marked.
  • Viral Reference: Refers to the standard virus strain used to compare and analyze viral sequences.
Functional Impact and Mechanisms
  • Disease: An abnormal physiological state with specific symptoms and signs caused by viral infection.
  • Immune: The article focuses on the study of mutations and immune.
  • Target Gene: Host genes that viral mutations may affect.
Clinical and Epidemiological Correlations
  • Clinical Information: The study is a clinical or epidemiological study and provides basic information about the population.
  • Treatment: The study mentioned a certain treatment method, such as drug resistance caused by mutations. If the study does not specifically indicate the relationship between mutations and their correspondence treatment, the main treatment studied in the article is marked.
  • Location: The source of the research data.
Literature Information
  • Sequence Data: The study provides the data accession number.