ArticleAccess microbiology2020
Differential transcription of expanded gene families in central carbon metabolism of
Article in Access microbiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 6 citations in OpenAlex.
- Considering internal conflict in the face of natural product biosynthesis and biosynthetic gene cluster evolution.Essays in biochemistry · 2026Review
- Comparative Genomics and Biosynthetic Cluster Analysis of Antifungal Secondary Metabolites of Three Strains ofMicroorganisms · 2024Article
- Functional connexion of bacterioferritin in antibiotic production and morphological differentiation in Streptomyces coelicolor.Microbial cell factories · 2024Article
- ActDES - a curated Actinobacterial Database for Evolutionary Studies.Microbial genomics · 2021Article
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundStreptomycete bacteria are prolific producers of specialized metabolites, many of which have clinically relevant bioactivity. A striking feature of their genomes is the expansion of gene families that encode the same enzymatic function. Genes that undergo expansion events, either by horizontal gene transfer or duplication, can have a range of fates: genes can be lost, or they can undergo neo-functionalization or sub-functionalization. To test whether expanded gene families in
resultsRNA-Seq analysis showed that two-thirds of genes within expanded gene families show transcriptional differences when strains were grown on tween compared to glucose. In addition, expression of specialized metabolite gene clusters (actinorhodin, isorenieratane, coelichelin and a cryptic NRPS) was also influenced by carbon source.
conclusionsExpression of genes encoding the same enzymatic function had transcriptional differences when grown on different carbon sources. This transcriptional divergence enables partitioning to function under different physiological conditions. These approaches can inform metabolic engineering of industrial
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