Skip to main content

Genomic Identification and Analysis of Specialized Metabolite Biosynthetic Gene Clusters in Plants Using PlantiSMASH

Part of the Methods in Molecular Biology book series (MIMB,volume 1795)

Abstract

Plants produce a vast diversity of specialized metabolites, which play important roles in the interactions with their microbiome, as well as with animals and other plants. Many such molecules have valuable biological activities that render them (potentially) useful as medicines, flavors and fragrances, nutritional ingredients, or cosmetics. Recently, plant scientists have discovered that the genes for many biosynthetic pathways for the production of such specialized metabolites are physically clustered on the chromosome within biosynthetic gene clusters (BGCs). The Plant Secondary Metabolite Analysis Shell (plantiSMASH) allows for the automated identification of such plant BGCs, facilitates comparison of BGCs across genomes, and helps users to predict the functional interactions of pairs of genes within and between BGCs based on coexpression analysis. In this chapter, we provide a detailed protocol on how to install and run plantiSMASH, and how to interpret its results to draw biological conclusions that are supported by the data.

Key words

  • Specialized metabolite
  • Secondary metabolite
  • Biosynthetic gene cluster
  • Biosynthetic pathway
  • Plant
  • Genomic
  • Bioinformatics

This is a preview of subscription content, access via your institution.

Buying options

Protocol
USD   49.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-1-4939-7874-8_15
  • Chapter length: 16 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   119.00
Price excludes VAT (USA)
  • ISBN: 978-1-4939-7874-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   159.99
Price excludes VAT (USA)
Hardcover Book
USD   249.99
Price excludes VAT (USA)
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

References

  1. Medema MH, Osbourn A (2016) Computational genomic identification and functional reconstitution of plant natural product biosynthetic pathways. Nat Prod Rep 33:951–962. https://doi.org/10.1039/c6np00035e

    CrossRef  PubMed  PubMed Central  CAS  Google Scholar 

  2. Nützmann H-W, Osbourn A (2014) Gene clustering in plant specialized metabolism. Curr Opin Biotechnol 26:91–99. https://doi.org/10.1016/j.copbio.2013.10.009

    CrossRef  PubMed  CAS  Google Scholar 

  3. Boycheva S, Daviet L, Wolfender J-L, Fitzpatrick TB (2014) The rise of operon-like gene clusters in plants. Trends Plant Sci 19:447–459. https://doi.org/10.1016/j.tplants.2014.01.013

    CrossRef  PubMed  CAS  Google Scholar 

  4. Nützmann HW, Huang A, Osbourn A (2016) Plant metabolic gene clusters—from genetics to genomics. New Phytol 211:771–789. https://doi.org/10.1111/nph.13981

    CrossRef  PubMed  PubMed Central  Google Scholar 

  5. Kautsar SA, Suarez Duran HG, Blin K et al (2017) plantiSMASH: automated identification, annotation and expression analysis of plant biosynthetic gene clusters. Nucleic Acids Res 45:W55–W63. https://doi.org/10.1093/nar/gkx305

    CrossRef  PubMed  PubMed Central  CAS  Google Scholar 

  6. Schläpfer P, Zhang P, Wang C et al (2017) Genome-wide prediction of metabolic enzymes, pathways, and gene clusters in plants. Plant Physiol 173:2041–2059. https://doi.org/10.1104/pp.16.01942

    CrossRef  PubMed  PubMed Central  CAS  Google Scholar 

  7. Töpfer N, Fuchs L-M, Aharoni A (2017) The PhytoClust tool for metabolic gene clusters discovery in plant genomes. Nucleic Acids Res 45:7049–7063. https://doi.org/10.1093/nar/gkx404

    CrossRef  PubMed  PubMed Central  CAS  Google Scholar 

  8. Wisecaver JH, Borowsky AT, Tzin V et al (2017) A global coexpression network approach for connecting genes to specialized metabolic pathways in plants. Plant Cell 29:944–959. https://doi.org/10.1105/tpc.17.00009

    CrossRef  PubMed  PubMed Central  CAS  Google Scholar 

  9. Medema MH, Blin K, Cimermancic P et al (2011) antiSMASH: rapid identification, annotation and analysis of secondary metabolite biosynthesis gene clusters in bacterial and fungal genome sequences. Nucleic Acids Res 39:W339–W346. https://doi.org/10.1093/nar/gkr466

    CrossRef  PubMed  PubMed Central  CAS  Google Scholar 

  10. Mallona I, Peinado MA (2017) Truke, a web tool to check for and handle excel misidentified gene symbols. BMC Genomics 18:242. https://doi.org/10.1186/s12864-017-3631-8

    CrossRef  PubMed  PubMed Central  Google Scholar 

  11. Fu L, Niu B, Zhu Z et al (2012) CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28:3150–3152. https://doi.org/10.1093/bioinformatics/bts565

    CrossRef  PubMed  PubMed Central  CAS  Google Scholar 

  12. Serin EAR, Nijveen H, Hilhorst HWM, Ligterink W (2016) Learning from co-expression networks: possibilities and challenges. Front Plant Sci 7:444. https://doi.org/10.3389/fpls.2016.00444

    CrossRef  PubMed  PubMed Central  Google Scholar 

  13. Itkin M, Heinig U, Tzfadia O et al (2013) Biosynthesis of antinutritional alkaloids in solanaceous crops is mediated by clustered genes. Science 341:175–179. https://doi.org/10.1126/science.1240230

    CrossRef  PubMed  CAS  Google Scholar 

  14. Boutanaev AM, Moses T, Zi J et al (2015) Investigation of terpene diversification across multiple sequenced plant genomes. Proc Natl Acad Sci 112:E81–E88. https://doi.org/10.1073/pnas.1419547112

    CrossRef  PubMed  CAS  Google Scholar 

  15. Miyamoto K, Fujita M, Shenton MR et al (2016) Evolutionary trajectory of phytoalexin biosynthetic gene clusters in rice. Plant J 87:293–304. https://doi.org/10.1111/tpj.13200

    CrossRef  PubMed  CAS  Google Scholar 

  16. Finn RD, Coggill P, Eberhardt RY et al (2016) The Pfam protein families database: towards a more sustainable future. Nucleic Acids Res 44:D279–D285. https://doi.org/10.1093/nar/gkv1344

    CrossRef  PubMed  CAS  Google Scholar 

  17. Carver T, Harris SR, Berriman M et al (2012) Artemis: an integrated platform for visualization and analysis of high-throughput sequence-based experimental data. Bioinformatics 28:464–469. https://doi.org/10.1093/bioinformatics/btr703

    CrossRef  PubMed  CAS  Google Scholar 

  18. Thorvaldsdottir H, Robinson JT, Mesirov JP (2013) Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinform 14:178–192. https://doi.org/10.1093/bib/bbs017

    CrossRef  PubMed  CAS  Google Scholar 

Download references

Acknowledgments

This work was supported by a VENI grant [863.15.002 to M.H.M.] from The Netherlands Organization for Scientific Research (NWO) and by the Graduate School for Experimental Plant Sciences (EPS).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Marnix H. Medema .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2018 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Verify currency and authenticity via CrossMark

Cite this protocol

Kautsar, S.A., Suarez Duran, H.G., Medema, M.H. (2018). Genomic Identification and Analysis of Specialized Metabolite Biosynthetic Gene Clusters in Plants Using PlantiSMASH. In: Fauser, F., Jonikas, M. (eds) Plant Chemical Genomics. Methods in Molecular Biology, vol 1795. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7874-8_15

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-7874-8_15

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7873-1

  • Online ISBN: 978-1-4939-7874-8

  • eBook Packages: Springer Protocols