Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Caesar LK

PROVIDER: S-EPMC10313767 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

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

Caesar Lindsay K LK Butun Fatma A FA Robey Matthew T MT Ayon Navid J NJ Gupta Raveena R Dainko David D Bok Jin Woo JW Nickles Grant G Stankey Robert J RJ Johnson Don D Mead David D Cank Kristof B KB Earp Cody E CE Raja Huzefa A HA Oberlies Nicholas H NH Keller Nancy P NP Kelleher Neil L NL

Nature chemical biology 20230306 7

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 ...[more]

PMID: 36879060

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Dataset Information

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

Publications

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

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