ArticleBMC genomics2026
Unraveling rare codon bias in Actinomycetota: lineage-specific and 5' terminal enrichment across 1936 genomes.
Article in BMC genomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
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7 authors.
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Abstract
backgroundActinomycetota are a diverse phylum of major ecological, medical, and industrial importance, best known for producing antibiotics and other secondary metabolites. While some regulatory mechanisms of secondary metabolism are understood, many remain unresolved. Codon usage bias, the preferential use of synonymous codons, represents one potential layer of regulation, as it is known to influence translation efficiency and timing. The availability of thousands of high-quality genomes now enables codon usage to be examined across this phylum at unprecedented scale.
resultsWe analyzed codon usage across 1936 high- and medium-quality genomes from 11 genera. The most common codon across the dataset was GCC, particularly enriched in Streptomyces albidoflavus. In contrast, UUA codon was consistently rare and showed variable distribution across genera. AGA was identified as another rare codon with especially strong enrichment at 5′ termini. Both UUA and AGA codons were enriched in functional categories such as replication, transcription, and secondary metabolism, and were significantly overrepresented in biosynthetic gene clusters, particularly within biosynthetic and regulatory genes.
conclusionsThese results show that rare codon usage in Actinomycetota reflects both evolutionary history and nonrandom positional enrichment, particularly at 5′ termini, where it may fine-tune translation timing. This positional bias likely represents a conserved mechanism for coordinating gene expression that has been demonstrated in many bacteria and is here shown to be prevalent across Actinomycetota. Beyond providing biological insight, our findings highlight the practical value of codon analysis for rational gene design, optimization of heterologous expression, metabolic engineering, and for generating testable hypotheses about the regulation and expression of biosynthetic gene clusters.
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