A genetical metabolomics approach for bioprospecting plant biosynthetic gene clusters

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Objective: Plants produce a plethora of specialized metabolites to defend themselves against pathogens and insects, to attract pollinators and to communicate with other organisms. Many of these are also applied in the clinic and in agriculture. Genes encoding the enzymes that drive the biosynthesis of these metabolites are sometimes physically grouped on the chromosome, in regions called biosynthetic gene clusters (BGCs). Several algorithms have been developed to identify plant BGCs, but a large percentage of predicted gene clusters upon further inspection do not show coexpression or do not encode a single functional biosynthetic pathway. Hence, further prioritization is needed. Results: Here, we introduce a strategy to systematically evaluate potential functions of predicted BGCs by superimposing their locations on metabolite quantitative trait loci (mQTLs). We show the feasibility of such an approach by integrating automated BGC prediction with mQTL datasets originating from a recombinant inbred line (RIL) population of Oryza sativa and a genome-wide association study (GWAS) of Arabidopsis thaliana. In these data, we identified several links for which the enzyme content of the BGCs matches well with the chemical features observed in the metabolite structure, suggesting that this method can effectively guide bioprospecting of plant BGCs.

LanguageEnglish
Article number194
JournalBMC Research Notes
Volume12
Issue number1
DOIs
Publication statusPublished - 2 Apr 2019

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Plant Genes
Metabolomics
Multigene Family
Genes
Metabolites
Quantitative Trait Loci
Genome-Wide Association Study
Biosynthetic Pathways
Enzymes
Gene encoding
Bioprospecting
Agriculture
Biosynthesis
Arabidopsis
Pathogens
Chromosomes
Insects
Inspection
Association reactions
Population

Keywords

  • Bioinformatics
  • Comparative genomics
  • Gene cluster
  • Genetics
  • GWAS
  • Mass spectrometry
  • Metabolomics
  • Natural products
  • QTL
  • Specialized metabolism

Cite this

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title = "A genetical metabolomics approach for bioprospecting plant biosynthetic gene clusters",
abstract = "Objective: Plants produce a plethora of specialized metabolites to defend themselves against pathogens and insects, to attract pollinators and to communicate with other organisms. Many of these are also applied in the clinic and in agriculture. Genes encoding the enzymes that drive the biosynthesis of these metabolites are sometimes physically grouped on the chromosome, in regions called biosynthetic gene clusters (BGCs). Several algorithms have been developed to identify plant BGCs, but a large percentage of predicted gene clusters upon further inspection do not show coexpression or do not encode a single functional biosynthetic pathway. Hence, further prioritization is needed. Results: Here, we introduce a strategy to systematically evaluate potential functions of predicted BGCs by superimposing their locations on metabolite quantitative trait loci (mQTLs). We show the feasibility of such an approach by integrating automated BGC prediction with mQTL datasets originating from a recombinant inbred line (RIL) population of Oryza sativa and a genome-wide association study (GWAS) of Arabidopsis thaliana. In these data, we identified several links for which the enzyme content of the BGCs matches well with the chemical features observed in the metabolite structure, suggesting that this method can effectively guide bioprospecting of plant BGCs.",
keywords = "Bioinformatics, Comparative genomics, Gene cluster, Genetics, GWAS, Mass spectrometry, Metabolomics, Natural products, QTL, Specialized metabolism",
author = "Lotte Witjes and Rik Kooke and {Van Der Hooft}, {Justin J.J.} and {De Vos}, {Ric C.H.} and Keurentjes, {Joost J.B.} and Medema, {Marnix H.} and Harm Nijveen",
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A genetical metabolomics approach for bioprospecting plant biosynthetic gene clusters. / Witjes, Lotte; Kooke, Rik; Van Der Hooft, Justin J.J.; De Vos, Ric C.H.; Keurentjes, Joost J.B.; Medema, Marnix H.; Nijveen, Harm.

In: BMC Research Notes, Vol. 12, No. 1, 194, 02.04.2019.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - A genetical metabolomics approach for bioprospecting plant biosynthetic gene clusters

AU - Witjes, Lotte

AU - Kooke, Rik

AU - Van Der Hooft, Justin J.J.

AU - De Vos, Ric C.H.

AU - Keurentjes, Joost J.B.

AU - Medema, Marnix H.

AU - Nijveen, Harm

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N2 - Objective: Plants produce a plethora of specialized metabolites to defend themselves against pathogens and insects, to attract pollinators and to communicate with other organisms. Many of these are also applied in the clinic and in agriculture. Genes encoding the enzymes that drive the biosynthesis of these metabolites are sometimes physically grouped on the chromosome, in regions called biosynthetic gene clusters (BGCs). Several algorithms have been developed to identify plant BGCs, but a large percentage of predicted gene clusters upon further inspection do not show coexpression or do not encode a single functional biosynthetic pathway. Hence, further prioritization is needed. Results: Here, we introduce a strategy to systematically evaluate potential functions of predicted BGCs by superimposing their locations on metabolite quantitative trait loci (mQTLs). We show the feasibility of such an approach by integrating automated BGC prediction with mQTL datasets originating from a recombinant inbred line (RIL) population of Oryza sativa and a genome-wide association study (GWAS) of Arabidopsis thaliana. In these data, we identified several links for which the enzyme content of the BGCs matches well with the chemical features observed in the metabolite structure, suggesting that this method can effectively guide bioprospecting of plant BGCs.

AB - Objective: Plants produce a plethora of specialized metabolites to defend themselves against pathogens and insects, to attract pollinators and to communicate with other organisms. Many of these are also applied in the clinic and in agriculture. Genes encoding the enzymes that drive the biosynthesis of these metabolites are sometimes physically grouped on the chromosome, in regions called biosynthetic gene clusters (BGCs). Several algorithms have been developed to identify plant BGCs, but a large percentage of predicted gene clusters upon further inspection do not show coexpression or do not encode a single functional biosynthetic pathway. Hence, further prioritization is needed. Results: Here, we introduce a strategy to systematically evaluate potential functions of predicted BGCs by superimposing their locations on metabolite quantitative trait loci (mQTLs). We show the feasibility of such an approach by integrating automated BGC prediction with mQTL datasets originating from a recombinant inbred line (RIL) population of Oryza sativa and a genome-wide association study (GWAS) of Arabidopsis thaliana. In these data, we identified several links for which the enzyme content of the BGCs matches well with the chemical features observed in the metabolite structure, suggesting that this method can effectively guide bioprospecting of plant BGCs.

KW - Bioinformatics

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KW - Natural products

KW - QTL

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