Correlative metabologenomics of 110 fungi reveals metabolite-gene cluster pairs.

Natural products research increasingly applies -omics technologies to guide molecular discovery. While the combined analysis of genomic and metabolomic datasets has proved valuable for identifying natural products and their biosynthetic gene clusters (BGCs) in bacteria, this integrated approach lacks application to fungi. Because fungi are hyper-diverse and underexplored for new chemistry and bioactivities, we created a linked genomics-metabolomics dataset for 110 Ascomycetes, and optimized both gene cluster family (GCF) networking parameters and correlation-based scoring for pairing fungal natural products with their BGCs. Using a network of 3,007 GCFs (organized from 7,020 BGCs), we examined 25 known natural products originating from 16 known BGCs and observed statistically significant associations between 21 of these compounds and their validated BGCs. Furthermore, the scalable platform identified the BGC for the pestalamides, demystifying its biogenesis, and revealed more than 200 high-scoring natural product-GCF linkages to direct future discovery.

Bioactivity-driven fungal metabologenomics identifies antiproliferative stemphone analogs and their biosynthetic gene cluster.

INTRODUCTION: Fungi biosynthesize chemically diverse secondary metabolites with a wide range of biological activities. Natural product scientists have increasingly turned towards bioinformatics approaches, combining metabolomics and genomics to target secondary metabolites and their biosynthetic machinery. We recently applied an integrated metabologenomics workflow to 110 fungi and identified more than 230 high-confidence linkages between metabolites and their biosynthetic pathways. OBJECTIVES: To prioritize the discovery of bioactive natural products and their biosynthetic pathways from these hundreds of high-confidence linkages, we developed a bioactivity-driven metabologenomics workflow combining quantitative chemical information, antiproliferative bioactivity data, and genome sequences. METHODS: The 110 fungi from our metabologenomics study were tested against multiple cancer cell lines to identify which strains produced antiproliferative natural products. Three strains were selected for further study, fractionated using flash chromatography, and subjected to an additional round of bioactivity testing and mass spectral analysis. Data were overlaid using biochemometrics analysis to predict active constituents early in the fractionation process following which their biosynthetic pathways were identified using metabologenomics. RESULTS: We isolated three new-to-nature stemphone analogs, 19-acetylstemphones G (1), B (2) and E (3), that demonstrated antiproliferative activity ranging from 3 to 5 µM against human melanoma (MDA-MB-435) and ovarian cancer (OVACR3) cells. We proposed a rational biosynthetic pathway for these compounds, highlighting the potential of using bioactivity as a filter for the analysis of integrated-Omics datasets. CONCLUSIONS: This work demonstrates how the incorporation of biochemometrics as a third dimension into the metabologenomics workflow can identify bioactive metabolites and link them to their biosynthetic machinery.