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