Abstract
Abstract
A major bottleneck in microbial biodiscovery is not only detecting bioactivity, but deciding which candidates should advance to resource-intensive downstream characterization. This challenge becomes particularly relevant when antimicrobial phenotypes vary across culture conditions and target pathogens. Here, we developed a layered, pathogen-oriented prioritization framework using 62 filamentous actinomycetes recovered from ecologically distinctive, oligotrophic systems of the Cuatro Ciénegas Basin. Isolates were screened against clinically relevant
Candida
species through co-culture, monoculture, and liquid-culture assays. Antifungal activity was strongly context-dependent: inhibition was widespread under interaction-based co-culture conditions, decreased markedly under monoculture, and was undetectable in liquid-culture supernatants.
Candida auris
imposed the most stringent biological filter, with only four of 19 isolates retaining measurable activity under monoculture. A pathogen-weighted performance index (WPI) was then applied to standardized monoculture inhibition data to explicitly incorporate pathogen-specific clinical relevance into candidate ranking. PB7 and PR34 emerged as the highest-ranked candidates, whereas PR69 was retained as an internal benchmark representing conventional broad-spectrum activity-driven selection. Comparative genomics revealed distinct phylogenomic and biosynthetic profiles among the selected strains, while preliminary LC–MS/MS profiling of the PB7 extract revealed substantial spectral space that remained unassigned under the applied library-search criteria. Together, these findings show that experimental context, pathogen choice, and the decision rule used to rank activity can substantially shape biodiscovery outcomes. By making these choices explicit, the proposed framework provides a transparent and reproducible strategy for prioritizing microbial candidates before costly downstream genomic and chemical characterization.