2025/12/17 by Garima Singh, Maonian Xu, Mitja M. Zdouc +12 · 1 voice
Agricultural and Biological Sciences · Medicine · #Bioprospecting #Comparative genomics #Drug discovery #Fungal Biology and Applications #Gene #Genome #Genomics #Lichen and fungal ecology #Metabolomics #Metagenomics #Microbial Natural Products and Biosynthesis
paper · doi:10.1099/mgen.0.001569
published in Microbial Genomics 11(12) (Microbiology Society)
openalex created_date 2025/12/17 · openalex publication_date 2025/12/17 · openalex updated_date 2026/08/01
The increasing demand for novel drug leads requires bioprospecting non-model taxa. Comparative genomics and correlative omics are a fast and efficient method for linking bioactive but genetically orphan natural products to their biosynthetic gene clusters (BGCs) and identifying potentially novel drug leads. Here we implement these approaches for the first systematic comparison of the BGC diversity in lichen-forming fungi (LFF) (comprising 20% of known fungi), prolific but underutilized producers of bioactive natural products. We first identified BGCs from all publicly available LFF genomes (111), encompassing 71 fungal genera and 23 families, and generated BGC similarity networks of each class. We recovered 5,541 BGCs grouped into 4,464 gene cluster families. We used mass spectrometry (MS) and correlative metabolomics to link five MS-identified metabolites - alectoronic acid, alpha-collatolic acid, evernic acid, stenosporic acid and perlatolic acid - to their putative BGCs. We subsequently used MS on an additional 80 species to explore the taxonomic breadth of common lichen compounds, uncovering a strong pattern between specific families and secondary metabolites. We found that (1) ~98% of the BGCs in LFF are putatively novel (uncharacterized to date), (2) lichen metabolic profiles contain a plethora of unidentified metabolites and (3) ribosomal peptide-related BGCs constitute about 20% of the LFF BGC landscape. Our study provides comprehensive insights into the BGC landscape of LFFs, highlighting unique, widespread and previously uncharacterized BGCs. We anticipate that the approach we describe will serve as a baseline for leveraging biosynthetic research in non-model organisms, inspiring further investigations into microbial dark matter.