Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

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

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.

Your institute does not have access to this article

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

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.

Accession codes

Accessions

BioProject

References

  1. Bérdy, J. Bioactive microbial metabolites. J. Antibiot. (Tokyo) 58, 1–26 (2005).

    Article  Google Scholar 

  2. Bérdy, J. Thoughts and facts about antibiotics: where we are now and where we are heading. J. Antibiot. (Tokyo) 65, 385–395 (2012).

    Article  Google Scholar 

  3. Bentley, S.D. et al. Complete genome sequence of the model actinomycete Streptomyces coelicolor A3(2). Nature 417, 141–147 (2002).

    Article  PubMed  Google Scholar 

  4. Lautru, S., Deeth, R.J., Bailey, L.M. & Challis, G.L. Discovery of a new peptide natural product by Streptomyces coelicolor genome mining. Nat. Chem. Biol. 1, 265–269 (2005).

    CAS  Article  PubMed  Google Scholar 

  5. Kersten, R.D. et al. A mass spectrometry–guided genome mining approach for natural product peptidogenomics. Nat. Chem. Biol. 7, 794–802 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. Ziemert, N. et al. The natural product domain seeker NaPDoS: a phylogeny based bioinformatic tool to classify secondary metabolite gene diversity. PLoS ONE 7, e34064 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  7. Medema, M.H. et al. antiSMASH: rapid identification, annotation and analysis of secondary metabolite biosynthesis gene clusters in bacterial and fungal genome sequences. Nucleic Acids Res. 39, W339–W346 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. Conway, K.R. & Boddy, C.N. ClusterMine360: a database of microbial PKS/NRPS biosynthesis. Nucleic Acids Res. 41, D402–D407 (2013).

    CAS  Article  PubMed  Google Scholar 

  9. Diminic, J. et al. Databases of the thiotemplate modular systems (CSDB) and their in silico recombinants (r-CSDB). J. Ind. Microbiol. Biotechnol. 40, 653–659 (2013).

    CAS  Article  PubMed  Google Scholar 

  10. Yadav, G., Gokhale, R.S. & Mohanty, D. SEARCHPKS: a program for detection and analysis of polyketide synthase domains. Nucleic Acids Res. 31, 3654–3658 (2003).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  11. Tae, H., Kong, E.-B. & Park, K. ASMPKS: an analysis system for modular polyketide synthases. BMC Bioinformatics 8, 327 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Ichikawa, N. et al. DoBISCUIT: a database of secondary metabolite biosynthetic gene clusters. Nucleic Acids Res. 41, D408–D414 (2013).

    CAS  Article  PubMed  Google Scholar 

  13. Caboche, S. et al. NORINE: a database of nonribosomal peptides. Nucleic Acids Res. 36, D326–D331 (2008).

    CAS  Article  PubMed  Google Scholar 

  14. Kim, J. & Yi, G.-S. PKMiner: a database for exploring type II polyketide synthases. BMC Microbiol. 12, 169 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. Fischbach, M.A. & Walsh, C. Assembly-line enzymology for polyketide and nonribosomal peptide antibiotics: logic, machinery, and mechanisms. Chem. Rev. 106, 3468–3496 (2006).

    CAS  Article  PubMed  Google Scholar 

  16. Raghupathy, N. & Durand, D. Gene cluster statistics with gene families. Mol. Biol. Evol. 26, 957–968 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. Wang, X. et al. Identification and characterization of the actinomycin G gene cluster of Streptomyces iakyrus. Mol. Biosyst. 9, 1286–1289 (2013).

    CAS  Article  PubMed  Google Scholar 

  18. Colwell, R.K. et al. Models and estimators linking individual-based and sample-based rarefaction, extrapolation and comparison of assemblages. J. Plant Ecol. 5, 3–21 (2012).

    Article  Google Scholar 

  19. Doroghazi, J.R. & Metcalf, W.W. Comparative genomics of actinomycetes with a focus on natural product biosynthetic genes. BMC Genomics 14, 611 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. Jensen, P.R., Williams, P.G., Oh, D.C., Zeigler, L. & Fenical, W. Species-specific secondary metabolite production in marine actinomycetes of the genus Salinispora. Appl. Environ. Microbiol. 73, 1146–1152 (2007).

    CAS  Article  PubMed  Google Scholar 

  21. Dunbar, K.L., Melby, J.O. & Mitchell, D.A. YcaO domains use ATP to activate amide backbones during peptide cyclodehydrations. Nat. Chem. Biol. 8, 569–575 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. Charlop-Powers, Z., Owen, J.G., Reddy, B.V.B., Ternei, M.A. & Brady, S.F. Chemical-biogeographic survey of secondary metabolism in soil. Proc. Natl. Acad. Sci. USA 111, 3757–3762 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. Bunge, J., Willis, A. & Walsh, F. Estimating the number of species in microbial diversity studies. Annual Review of Statistics and Its Application 1, 427–445 (2014).

    Article  Google Scholar 

  24. Nguyen, D.D. et al. MS/MS networking guided analysis of molecule and gene cluster families. Proc. Natl. Acad. Sci. USA 110, E2611–E2620 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. Cote, R. in ATCC Bacteria and Bacteriophages 19th edn (eds. Pienta, P., Tang, J. & Cote, R.) 484 (American Type Culture Collection, 1996).

  26. Zerbino, D.R. & Birney, E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 18, 821–829 (2008).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  27. Li, R. et al. De novo assembly of human genomes with massively parallel short read sequencing. Genome Res. 20, 265–272 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. Chaisson, M.J., Brinza, D. & Pevzner, P.A. De novo fragment assembly with short mate-paired reads: does the read length matter? Genome Res. 19, 336–346 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. Margulies, M. et al. Genome sequencing in microfabricated high-density picolitre reactors. Nature 437, 376–380 (2005); erratum Nature 4, 120 (2006).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  30. Boetzer, M., Henkel, C.V., Jansen, H.J., Butler, D. & Pirovano, W. Scaffolding pre-assembled contigs using SSPACE. Bioinformatics 27, 578–579 (2011).

    CAS  Article  PubMed  Google Scholar 

  31. Peng, Y., Leung, H.C., Yiu, S.-M. & Chin, F.Y. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 28, 1420–1428 (2012).

    CAS  Article  PubMed  Google Scholar 

  32. Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Li, L., Stoeckert, C.J. & Roos, D.S. OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res. 13, 2178–2189 (2003).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  34. Kurtz, S. et al. Versatile and open software for comparing large genomes. Genome Biol. 5, R12 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Edgar, R.C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).

    CAS  Article  PubMed  Google Scholar 

  36. Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. A density-based algorithm for discovering clusters in large spatial databases with noise. in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96) (eds. Simoudis, E., Han, J. & Fayyad, U.) 226–231 (AAAI Press, 1996).

  37. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  38. Edgar, R.C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Price, M.N., Dehal, P.S. & Arkin, A.P. FastTree 2–approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Zhang, Q., Yu, Y., Vélasquez, J.E. & van der Donk, W.A. Evolution of lanthipeptide synthetases. Proc. Natl. Acad. Sci. USA 109, 18361–18366 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  41. Yutin, N., Puigbò, P., Koonin, E.V. & Wolf, Y.I. Phylogenomics of prokaryotic ribosomal proteins. PLoS ONE 7, e36972 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  42. Larkin, M.A. et al. Clustal W and clustal X version 2.0. Bioinformatics 23, 2947–2948 (2007).

    CAS  Article  PubMed  Google Scholar 

  43. Huerta-Cepas, J., Dopazo, J. & Gabaldón, T. ETE: a python Environment for Tree Exploration. BMC Bioinformatics 11, 24 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res. 19, 1639–1645 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  45. El-Nakeeb, M.A. & Lechevalier, H.A. Selective isolation of aerobic actinomycetes. Appl. Microbiol. 11, 75–77 (1963).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Smith, S.E. et al. Comparative genomic and phylogenetic approaches to characterize the role of genetic recombination in mycobacterial evolution. PLoS ONE 7, e50070 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

Download references

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

Authors and Affiliations

Authors

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.

Ethics declarations

Competing interests

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)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nchembio.1659

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing