Journal Article

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

Khin, Manead, Mardiana, Lina, Raja, Huzefa A, Longcake, Alexandra, Burdette, Joanna E, Ayon, Navid J, Clements, Ashley E, Keller, Nancy P, Hall, Michael J, Kelleher, Neil L, Lee, Alexa G, Oberlies, Nicholas H, Robey, Matthew T, Dainko, David, Probert, Michael R, Gupta, Raveena, Caesar, Lindsay K, Rangel-Grimaldo, Manuel, Butun, Fatma A, Earp, Cody E

2024

Overview

IntroductionFungi 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.ObjectivesTo 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.MethodsThe 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.ResultsWe 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.ConclusionsThis 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.

• Publisher: Springer Nature B.V
• Subject: Bioactive compounds / Bioinformatics / Biological activity / Fungi / Melanoma / Metabolites / Metabolomics / Natural products / Ovarian cancer / Secondary metabolites / Tumor cell lines