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A genetics-free method for high-throughput discovery of cryptic microbial metabolites

Nature Chemical Biologyvolume 15pages161168 (2019) | Download Citation

Abstract

Bacteria contain an immense untapped trove of novel secondary metabolites in the form of ‘silent’ biosynthetic gene clusters (BGCs). These can be identified bioinformatically but are not expressed under normal laboratory growth conditions. Methods to access their products would dramatically expand the pool of bioactive compounds. We report a universal high-throughput method for activating silent BGCs in diverse microorganisms. Our approach relies on elicitor screening to induce the secondary metabolome of a given strain and imaging mass spectrometry to visualize the resulting metabolomes in response to ~500 conditions. Because it does not require challenging genetic, cloning, or culturing procedures, this method can be used with both sequenced and unsequenced bacteria. We demonstrate the power of the approach by applying it to diverse bacteria and report the discovery of nine cryptic metabolites with potentially therapeutic bioactivities, including a new glycopeptide chemotype with potent inhibitory activity against a pathogenic virus.

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Data availability

The data supporting the findings of this study are available within the paper and the supplementary material. NMR data used to characterize the cryptic metabolites are available from the corresponding author upon reasonable request. The DNA sequence of the ker gene cluster from A. keratiniphila has been deposited in GenBank (accession no. MH428036). The LAESI–IMS data for S. canus and A. keratiniphila, including the raw data for Figs. 3a and 4a as well as the source code used to generate the 3D plots, have been deposited in the Global Natural Products Social Molecular Networking (GNPS) database (MassIVE accession number MSV000082658).

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Acknowledgements

We thank the National Institutes of Health (DP2-AI-124786 to M.R.S.), the Burroughs Wellcome Fund, and the Princeton IP Accelerator Fund for support of this work. K.M.D. was supported by an Arnold O. Beckman Postdoctoral Fellowship. L.B.B. was supported by a National Science Foundation Graduate Research Fellowship.

Author information

Author notes

  1. These authors contributed equally: Fei Xu, Yihan Wu.

Affiliations

  1. Department of Chemistry, Princeton University, Princeton, NJ, USA

    • Fei Xu
    • , Yihan Wu
    • , Chen Zhang
    • , Katherine M. Davis
    • , Kyuho Moon
    • , Leah B. Bushin
    •  & Mohammad R. Seyedsayamdost
  2. Department of Molecular Biology, Princeton University, Princeton, NJ, USA

    • Mohammad R. Seyedsayamdost

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Contributions

F.X., Y.W., and M.R.S. designed the research; F.X., Y.W., and C.Z. carried out high-throughput elicitor screens and validations; Y.W. conducted the LAESI–IMS experiments and analyzed the results; F.X. purified and solved the structures of all secondary metabolites, with assistance from K.M.D. and K.M. Y.W. conducted structure calculations, with assistance from L.B.B. F.X. carried out bioinformatic analyses. M.R.S. wrote the manuscript, to which all authors contributed. F.X. and Y.W. contributed equally to this work.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Mohammad R. Seyedsayamdost.

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    Supplementary Tables 1–14 and Supplementary Figures 1–6

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https://doi.org/10.1038/s41589-018-0193-2