|
Basic Characteristics of Mutations
|
|
Mutation Site
|
P323L |
|
Mutation Site Sentence
|
The first viral genome/proteome change associated with a significant change in viral properties (the P323L substitution in NSP12 and D614G substitution in Spike) took 3 months to increase in frequency in the UK (and worldwide) from minor variant genomes to become the dominant sequence. |
|
Mutation Level
|
Amino acid level |
|
Mutation Type
|
Nonsynonymous substitution |
|
Gene/Protein/Region
|
NSP12 |
|
Standardized Encoding Gene
|
ORF1b
|
|
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
|
UK |
|
Literature Information
|
|
PMID
|
39970290
|
|
Title
|
Using minor variant genomes and machine learning to study the genome biology of SARS-CoV-2 over time
|
|
Author
|
Dong X,Matthews DA,Gallo G,Darby A,Donovan-Banfield I,Goldswain H,MacGill T,Myers T,Orr R,Bailey D,Carroll MW,Hiscox JA
|
|
Journal
|
Nucleic acids research
|
|
Journal Info
|
2025 Feb 8;53(4):gkaf077
|
|
Abstract
|
In infected individuals, viruses are present as a population consisting of dominant and minor variant genomes. Most databases contain information on the dominant genome sequence. Since the emergence of SARS-CoV-2 in late 2019, variants have been selected that are more transmissible and capable of partial immune escape. Currently, models for projecting the evolution of SARS-CoV-2 are based on using dominant genome sequences to forecast whether a known mutation will be prevalent in the future. However, novel variants of SARS-CoV-2 (and other viruses) are driven by evolutionary pressure acting on minor variant genomes, which then become dominant and form a potential next wave of infection. In this study, sequencing data from 96 209 patients, sampled over a 3-year period, were used to analyse patterns of minor variant genomes. These data were used to develop unsupervised machine learning clusters to identify amino acids that had a greater potential for mutation than others in the Spike protein. Being able to identify amino acids that may be present in future variants would better inform the design of longer-lived medical countermeasures and allow a risk-based evaluation of viral properties, including assessment of transmissibility and immune escape, thus providing candidates with early warning signals for when a new variant of SARS-CoV-2 emerges.
|
|
Sequence Data
|
-
|
|
|