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Lookup NU author(s): Lina Mardiana, Alexandra Longcake, Dr Michael HallORCiD, Professor Mike ProbertORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
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. 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. 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. 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. 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.
Author(s): Ayon NJ, Earp CE, Gupta R, Butun FA, Clements AE, Lee AG, Dainko D, Robey MT, Khin M, Mardiana L, Longcake A, Rangel-Grimaldo M, Hall MJ, Probert MR, Burdette JE, Keller NP, Raja HA, Oberlies NH, Kelleher NL, Caesar LK
Publication type: Article
Publication status: Published
Journal: Metabolomics
Year: 2024
Volume: 20
Issue: 5
Online publication date: 02/08/2024
Acceptance date: 16/07/2024
Date deposited: 05/08/2024
ISSN (print): 1573-3882
ISSN (electronic): 1573-3890
Publisher: Springer Nature
URL: https://doi.org/10.1007/s11306-024-02153-8
DOI: 10.1007/s11306-024-02153-8
Data Access Statement: All genomes that were sequenced for this work (as part of our previous publication) are available via NCBI under BioProject PRJNA852164. The metabolomics data (as.mzXML files) for the 110-strain dataset are available via the MassIVE repository under accession no. MSV000089848 and that for the 3 strains, the primary focus of this article, are available via the MassIVE repository under Accession No. MSV000094411. NMR data are available as an NP-MRD deposition under the ID numbers (NP0332825, NP0332827, and NP0332826 for compounds 1-3, respectively). The supplementary crystallographic data for this paper are provided under Cambridge Crystallographic Data Centre under CCDC 2303613. These data are provided free of charge by the joint Cambridge Crystallographic Data Centre and Fachinformationszentrum Karlsruhe Access Structures service. Additional data can be made available upon request.
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