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Introduction Fungi biosynthesize chemically diverse secondary metabolites with a wide range of biological activities. Natu- ral 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 bio- synthetic 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 metabolog- enomics workflow can identify bioactive metabolites and link them to their biosynthetic machinery.
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This content will become publicly available on May 1, 2026
1H-NMR Guided Isolation of Bioactive Compounds from Species of the Genus Piper
The discovery of bioactive natural products is often challenged by the complexity of isolating and characterizing active compounds within diverse mixtures. Previously, we introduced a 1H NMR-based weighted gene correlation network analysis (WGCNA) approach to identify spectral features linked to growth inhibitory activity of Piper (Piperaceae) leaf extracts against model plant, fungal, and bacterial organisms. This method enabled us to prioritize specific spectral features linked to bioactivity, offering a targeted approach to natural product discovery. In this study, we validate the predictive capacity of the WGCNA by isolating the compounds responsible for the bioactivity-associated resonances and confirming their antifungal efficacy. Using growth inhibition assays, we verified that the isolated compounds, including three novel antifungal agents, exhibited significant bioactivity. Notably, one of these compounds contains a rare imidazolium heterocyclic motif, marking a new structural class in Piper. These findings substantiate the 1H NMR-based WGCNA as a reliable tool for identifying structural types associated with biological activity, streamlining the process of discovering bioactive natural products in complex extracts.
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- Award ID(s):
- 2133818
- PAR ID:
- 10623972
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Molecules
- Volume:
- 30
- Issue:
- 9
- ISSN:
- 1420-3049
- Page Range / eLocation ID:
- 2020
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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