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Genus- and host-associated codon usage bias patterns in coronavirus spike genes
1Faculty of Information Science and Technology, Multimedia University, Bukit Beruang, Melaka, Malaysia.
Plos One
|June 24, 2026
Summary
Codon usage bias in coronavirus spike genes differs between genera, influenced by host adaptation. Analysis reveals selection pressures shape viral evolution across Alphacoronavirus, Betacoronavirus, Gammacoronavirus, and Deltacoronavirus.
Area of Science:
- Virology
- Evolutionary Biology
- Genomics
Background:
- Codon usage bias (CUB) is shaped by mutation and selection, offering insights into viral evolution and host adaptation.
- Previous studies on CUB in coronaviruses are limited, especially focusing on the spike (S) gene across all four genera.
Purpose of the Study:
- To systematically analyze CUB in coronavirus spike genes across different genera and host groups.
- To investigate the influence of phylogenetic relationships and host-associated selection on CUB.
Main Methods:
- Evaluated codon usage indices: codon adaptation index (CAI), effective number of codons (ENC), and GC content at the third codon position (GC3s).
- Employed multivariate and clustering approaches: correspondence analysis, hierarchical clustering, heatmap visualization, and ENC-GC3s analysis.
- Compared CUB across coronavirus genera (Alphacoronavirus, Betacoronavirus, Gammacoronavirus, Deltacoronavirus) and host groups.
Main Results:
- Significant differences in CAI and ENC were observed among coronavirus genera, indicating genus-level organization of codon usage.
- Host-based comparisons revealed significant CAI variation, suggesting host-associated translational selection.
- Heatmap analysis showed enrichment of A/U-ending codons and suppression of CpG-containing codons across genomes.
- ENC-GC3s analysis indicated factors beyond mutational bias influence codon usage patterns.
Conclusions:
- Codon usage bias in coronavirus spike genes is shaped by both virus-intrinsic factors and host-associated selective pressures.
- Findings provide a gene-centric, cross-genera framework for understanding coronavirus evolution and host adaptation.
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