ReviewEssays in biochemistry2026
Bacterial biosynthetic superclusters: emerging drivers of synergistic natural products against antimicrobial resistance.
Review in Essays in biochemistry, 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- Bacterial biosynthetic gene clusters.Essays in biochemistry · 2026Review
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Authors and funding
3 authors.
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Abstract
Antimicrobial resistance (AMR) is a critical threat to global health and the economy. A promising strategy to address this challenge is the exploration of synergistic combinations of natural antimicrobials, which can broaden antibiotic spectra, enhance efficacy, and reduce the likelihood of resistance development. Such combinations often arise in bacteria through complex metabolic pathways encoded by biosynthetic superclusters (BSs)-genomic arrangements in which multiple biosynthetic gene clusters (BGCs) co-localize within the same genomic region, promoting co-regulation and co-production of specialized metabolites with complementary activities. This review provides an overview of current knowledge on biosynthetic gene superclusters in bacteria, focusing on their genomic architectures, regulatory connectivity, and the synergistic interaction profiles of the encoded metabolites as shaped by evolutionary pressures. Finally, we highlight how integrative genome-metabolome mining can be applied to discover, validate, and reprogram supercluster genes, positioning them as promising, naturally evolved platforms for the rational design of modular antimicrobial combinations to help combat AMR.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.