The biosynthetic gene cluster landscape of the oral microbiome across health and dental caries

Specialized metabolites encoded by biosynthetic gene clusters (BGCs) in the oral microbiome remain largely unexplored in the context of oral health and disease. Previous genome-centric surveys have identified hundreds of uncharacterized BGCs in the oral cavity associated with health and disease, but these studies relied on reference genomes and did not capture strain-level variation or the native distribution of BGCs. Here, we assembled three independently sourced metagenomic datasets from healthy and dental caries samples, extracted BGCs, and quantified their metagenomic abundance and transcriptional activity. We found that aryl polyene, ribosomally synthesized and post-translationally modified peptide (RiPP), and nonribosomal peptide (NRP) encoding BGCs were the most prominent BGCs identified across the three metagenomic datasets. We grouped the identified BGCs into homology-based gene cluster families (GCFs) and found that specific GCFs were consistently associated with either health or caries across diverse taxa, suggesting that some specialized metabolites may perform conserved ecological functions. Conversely, other BGCs showed more restricted taxonomic distributions and were linked to disease-associated taxa, such as Propionibacterium acidifaciens, suggesting niche-specific biosynthetic capacities within the oral environment. Applying elastic-net regression to the metatranscriptomic dataset further identified a subset of 51 BGCs out > 3 000 that distinguished healthy from caries samples, reinforcing the discriminatory power of BGC expression patterns. Together, these results demonstrate that BGCs provide functional resolution beyond taxonomic profiling and that BGC expression, rather than genomic presence alone, differentiates oral microbial community states. This underscores the relevance of specialized metabolism to oral health and supports the use of BGC-centric analyses to interrogate microbial interactions underlying community stability and disease-associated shifts.