Microbial genomics · 2025
Biodiscovery of Actinomycetota through metabolo-genomics reveals functional diversity across contrasting Mexican ecosystems.
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Rodríguez-Orduña Lorena, Aguilar César, Hernández-Melgar Alan Gerardo, Garza Héctor F Arocha, Verdel-Aranda Karina, Rodríguez Augusto Vázquez, López-Ribot José L, Moreno-Ulloa Aldo, Licona-Cassani Cuauhtémoc
Аннотация
Discovery of novel bioactive natural products from Actinomycetota has decreased, motivating the exploration of rare ecosystems. In this work, we integrated genomics and untargeted metabolomics to profile the biosynthetic capacity of Streptomyces and Nocardia strains isolated from two contrasting, Ramsar-listed Mexican biomes with well-defined ecological features: Cuatro Ciénegas, a semi-arid, oligotrophic karst basin with high microbial endemism, and Calakmul, a tropical rainforest characterized by high biodiversity, seasonal wetlands and limestone-derived alkaline soils. We hypothesized that bacteria isolated from contrasting environments would encode for novel and distinctive subsets of secondary metabolites. Using different metabolo-genomic approaches, a diverse collection of Streptomyces and Nocardia strains was identified, including several potentially novel species. Genome mining revealed a large repertoire of biosynthetic gene clusters (BGCs), many without matches to known metabolites, while molecular networking and dereplication (GNPS, SNAP-MS) exposed extensive chemical diversity within the isolates. Targeted synteny/ortholog analyses (CORASON) linked subsets of metabolites to BGCs, confirming actinomycin and collismycin and identifying komodoquinone-, nocardiopsistin- and rubiginone-like clusters. Notably, bioactivity assays of crude extracts demonstrated effective antifungal effects against Candida albicans SC5314 biofilms and planktonic growth, suggesting their potential therapeutic use. These findings reveal a significant untapped chemical space encoded by Actinomycetota from Ramsar sites, while reinforcing the need for improved tools to connect genomic and metabolomic data for natural product discovery.