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A roadmap for natural product discovery based on large-scale genomics and metabolomics

Nature Chemical Biology volume 10, pages 963–968 (2014)Cite this article

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Abstract

Actinobacteria encode a wealth of natural product biosynthetic gene clusters, whose systematic study is complicated by numerous repetitive motifs. By combining several metrics, we developed a method for the global classification of these gene clusters into families (GCFs) and analyzed the biosynthetic capacity of Actinobacteria in 830 genome sequences, including 344 obtained for this project. The GCF network, comprising 11,422 gene clusters grouped into 4,122 GCFs, was validated in hundreds of strains by correlating confident mass spectrometric detection of known small molecules with the presence or absence of their established biosynthetic gene clusters. The method also linked previously unassigned GCFs to known natural products, an approach that will enable de novo, bioassay-free discovery of new natural products using large data sets. Extrapolation from the 830-genome data set reveals that Actinobacteria encode hundreds of thousands of future drug leads, and the strong correlation between phylogeny and GCFs frames a roadmap to efficiently access them.

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Figure 1: Similarity metrics for NPGC comparisons.
Figure 2: Genomic NPGC content and extrapolation.
Figure 3: GCF conservation over genetic distance.
Figure 4: MS-GCF correlations.

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Acknowledgements

J.R.D. was funded through an Institute for Genomic Biology fellowship. This work was supported in part by US National Institutes of Health grants GM PO1 GM077596 and GM 067725 (N.L.K.) and an Institute for Genomic Biology Proof of Concept grant. D.P.L. and the Agricultural Research Service (ARS) Culture Collection Current Research Information System project is funded through ARS National Program 301.

Author information

Author notes

  1. James R Doroghazi and Jessica C Albright: These authors contributed equally to this work.

Authors and Affiliations

  1. Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA

    James R Doroghazi, Kou-San Ju & William W Metcalf

  2. Department of Chemistry, Northwestern University, Evanston, Illinois, USA

    Jessica C Albright, Anthony W Goering & Neil L Kelleher

  3. Department of Molecular Biosciences, Northwestern University, Evanston, Illinois, USA

    Jessica C Albright, Anthony W Goering & Neil L Kelleher

  4. Feinberg School of Medicine, Northwestern University, Evanston, Illinois, USA

    Jessica C Albright, Anthony W Goering & Neil L Kelleher

  5. Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA

    Robert R Haines, Konstantin A Tchalukov & William W Metcalf

  6. US Department of Agriculture, Bacterial Foodborne Pathogens and Mycology Research, Agricultural Research Service, National Center for Agricultural Utilization Research, Peoria, Illinois, USA

    David P Labeda

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Contributions

J.R.D. designed and performed bioinformatic analyses. J.R.D., R.R.H. and K.A.T. produced microbial extracts. J.C.A. designed LC/MS experiments, and J.C.A. and A.W.G. collected and analyzed LC/MS data. K.-S.J. received and processed germplasm and contributed to genomic library preparation. D.P.L. selected and provided germplasm from the ARS Culture Collection. W.W.M. and N.L.K. designed and directed the work. J.R.D., J.C.A., W.W.M. and N.L.K. wrote the manuscript. Draft genomes sequenced as part of this project are available through NCBI BioProject PRJNA238534.

Corresponding authors

Correspondence to Neil L Kelleher or William W Metcalf.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Results, Supplementary Tables 1–3, Supplementary Figures 1–6 and Supplementary Note. (PDF 2814 kb)

Supplementary Data Set 1

A spreadsheet that lists the locus tags and corresponding organism for each genome in this study (XLSX 33 kb)

Supplementary Data Set 2

All gene cluster families that have at least one characterized natural product are listed in this Excel spreadsheet. (XLSX 50 kb)

Supplementary Data Set 3

A spreadsheet containing raw MS data for the known compounds identified in this study. (XLSX 33 kb)

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Doroghazi, J., Albright, J., Goering, A. et al. A roadmap for natural product discovery based on large-scale genomics and metabolomics. Nat Chem Biol 10, 963–968 (2014). https://doi.org/10.1038/nchembio.1659

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