ReviewCurrent research in microbial sciences2026
From predicted bacteriocins to ecological function.
Review in Current research in microbial sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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3 authors.
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Abstract
Genome and metagenome mining have uncovered large repertoires of predicted bacteriocin loci, but ecological interpretation has lagged behind discovery. This review uses bacteriocins as a focused model for a broader problem in antimicrobial gene prediction: sequence identifies encoded potential, but it does not establish expression, product deployment, target engagement, or community-level consequence. We distinguish four forms of bacteriocin "silence": transcriptional silence, where loci are not detectably expressed under tested conditions; conditional silence, where expression or activity emerges only under specific environmental or social cues; phenotypic silence, where expression or product formation does not yield detectable activity in routine assays; and ecological silence, where activity is observed but its consequences for coexistence, exclusion, colonization, or community assembly remain unresolved. We also clarify how this framework extends previous reviews of bacteriocin diversity and microbiome-shaping roles by providing an inference-centered taxonomy that separates encoded potential from demonstrated ecological function. Finally, we propose a function-first roadmap for bacteriocin research, including condition-resolved transcriptomics and proteomics, promoter and cue-dissection experiments, expanded target panels, spatially structured assays, defined consortia and microcosms, producer/non-producer comparisons, and explicit tests of resistance, immunity, and fitness consequences. Treating predicted bacteriocin loci as hypotheses rather than conclusions will improve how the field moves from gene catalogs to causal ecological understanding.
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