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Correlative metabologenomics of 110 fungi reveals metabolite–gene cluster pairs

Nature Chemical Biology (2023)Cite this article

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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.

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Fig. 1: Workflow for the metabologenomics approach for natural products discovery in fungi.
Fig. 2: Comparison of GCF-natural product score distributions using different GCF grouping parameters.
Fig. 3: Compiled metabolite–GCF correlations using optimized GCF network.
Fig. 4: Heterologous expression of pestalamide B in Aspergillus nidulans.
Fig. 5: Proposed biosynthesis of pestalamide B.

Data availability

All genomes that were sequenced for this work 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. Additionally, we have included Supplementary Data 1, which includes.html files for all MIBiG-anchored GCFs with detected metabolites, as well as the pestalamide GCF discovered in this work. The processed MZmine peak list that we used for correlations (generated using the publicly available .mzXML files) is provided as Supplementary Data 2.

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Acknowledgements

Genome sequencing for this project was conducted at the Roy J. Carver Biotechnology Center at the University of Illinois-Urbana-Champaign. This research was supported in part by the National Institutes of Health grant nos. F32 GM132679 to L.K.C., R01 GM112739-05A1 to N.P.K., T32 GM135066 to G.N., R44 AI140943-03 to J.W.B., P01 CA125066 to N.H.O. and 2R01 AT009143 to N.L.K. This work also made use of the IMSERC NMR facility at Northwestern University, which has received support from the Soft and Hybrid Nanotechnology Experimental Resource (grant no. NSF ECCS-2025633) grant. Figures 1, 4 and 5 were created using BioRender.com.

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Authors and Affiliations

  1. Department of Chemistry, Northwestern University, Evanston, IL, USA

    Lindsay K. Caesar, Fatma A. Butun, Navid J. Ayon, Raveena Gupta, David Dainko & Neil L. Kelleher

  2. Department of Molecular Biosciences, Northwestern University, Evanston, IL, USA

    Matthew T. Robey & Neil L. Kelleher

  3. Department of Medical Microbiology and Immunology, University of Wisconsin-Madison, Madison, WI, USA

    Jin Woo Bok, Grant Nickles & Nancy P. Keller

  4. Terra Bioforge, Madison, WI, USA

    Robert J. Stankey, Don Johnson & David Mead

  5. Department of Chemistry and Biochemistry, University of North Carolina at Greensboro, Greensboro, NC, USA

    Kristof B. Cank, Cody E. Earp, Huzefa A. Raja & Nicholas H. Oberlies

  6. Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA

    Nancy P. Keller

  7. Proteomics Center of Excellence, Northwestern University, Evanston, IL, USA

    Neil L. Kelleher

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Contributions

L.K.C. led the project, organized data collection and analyzed data for both large scale correlations and targeted biosynthetic studies. F.A.B. was responsible for fungal culture and DNA extraction and helped prepare and run MS samples. M.T.R. assembled genomes and conducted bioinformatic analysis for both GCF networking and metabologenomics correlations. N.J.A. grew fungi, extracted metabolomes and ran MS samples. R.G. and D.D. prepared and ran MS samples and assisted with metabolomics analysis. J.W.B. completed fungal transformations for heterologous expression and knockout studies. G.N. compiled correlations plots and assisted with GCF optimization. R.J.S., D.J. and D.M. designed, cloned and validated plasmids for heterologous expression. K.B.C., C.E.E. and N.H.O. assisted with metabolite dereplication and NMR analysis. H.A.R. provided expertise for fungal growth and extraction and taxonomic identification of fungal strains. N.P.K. and N.L.K. supervised the project after its initiation by N.L.K. The manuscript was written by L.K.C. and N.L.K., with all authors providing substantial edits and commentary throughout.

Corresponding author

Correspondence to Neil L. Kelleher.

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Competing interests

The authors declare financial conflicts of interest with MicroMGx (N.L.K.), Varigen Biosciences (D.M.) and Terra Bioforge (N.L.K., D.M., M.T.R. and N.P.K.). Further, N.L.K. is a consultant for Thermo Fisher Scientific focusing on the use of Fourier-transform Mass Spectrometry in multi-Omics research. Finally, N.H.O. and H.A.R. are on the Scientific Advisory Board of Clue Genetics, and N.H.O. is on the Scientific Advisory Board of Mycosynthetix. The remaining authors declare no competing interests.

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Nature Chemical Biology thanks Hosein Mohimani and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Tables 1–15, Figs. 1–34 and uncropped gel images associated with Supplementary Figs. 31 and 32.

Reporting Summary

Supplementary Data 1

Folder containing .html files for 29 GCFs discussed in this paper. Each .html file includes gene cluster arrow diagrams for all BGCs belonging to a specific GCF and individual arrows are linked to their genetic sequences.

Supplementary Data 2

Filtered MzMine peak list used for metabologenomics analysis.

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Caesar, L.K., Butun, F.A., Robey, M.T. et al. Correlative metabologenomics of 110 fungi reveals metabolite–gene cluster pairs. Nat Chem Biol (2023). https://doi.org/10.1038/s41589-023-01276-8

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