A genetical metabolomics approach for bioprospecting plant biosynthetic gene clusters

• Published in: BMC research notes Vol. 12; no. 1; p. 194
• Main Authors: Witjes, Lotte, Kooke, Rik, van der Hooft, Justin J J, de Vos, Ric C H, Keurentjes, Joost J B, Medema, Marnix H, Nijveen, Harm
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 02-04-2019; BioMed Central; BMC
• Subjects: Algorithms; Analysis; Arabidopsis; Arabidopsis thaliana; Bioinformatics; Bioprospecting; Biosynthesis; Chromosomes; Comparative genomics; Computational biology; Datasets; Enzymes; Flavonoids; Gene cluster; Gene clusters; Genes; Genetic aspects; Genetics; Genome-wide association studies; Genomes; Genomics; GWAS; Inbreeding; Insects; Mass spectrometry; Metabolism; Metabolites; Metabolomics; Natural products; Pathogenic microorganisms; Plant genetics; Plant metabolites; Pollinators; QTL; Quantitative genetics; Quantitative trait loci; Research Note; Specialized metabolism; Spectroscopy; Structure; Netherlands; United States > US; Gene cluster; Metabolomics; Specialized metabolism; GWAS; Natural products; Comparative genomics; QTL; Genetics; Bioinformatics; Mass spectrometry
• ISSN: 1756-0500; 1756-0500
• DOI: 10.1186/s13104-019-4222-3

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.
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.