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Multi-tissue integration of transcriptomic and specialized metabolite profiling provides tools for assessing the common bean (Phaseolus vulgaris) metabolome

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de Souza,  L. P.
Central Metabolism, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

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Scossa,  F.
Central Metabolism, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

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Proost,  Sebastian
BioinformaticsCRG, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

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Tohge,  T.
Central Metabolism, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

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Fernie,  A. R.
Central Metabolism, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

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Citation

de Souza, L. P., Scossa, F., Proost, S., Bitocchi, E., Papa, R., Tohge, T., et al. (2019). Multi-tissue integration of transcriptomic and specialized metabolite profiling provides tools for assessing the common bean (Phaseolus vulgaris) metabolome. The Plant Journal, 97(6), 1132-1153. doi:10.1111/tpj.14178.


Cite as: http://hdl.handle.net/21.11116/0000-0003-555F-4
Abstract
Summary Common bean (Phaseolus vulgaris L.) is an important legume species with a rich natural diversity of landraces that originated from the wild forms following multiple independent domestication events. After the publication of its genome, several resources for this relevant crop have been made available. A comprehensive characterization of specialized metabolism in P. vulgaris, however, is still lacking. In this study, we used a metabolomics approach based on liquid chromatography-mass spectrometry to dissect the chemical composition at a tissue-specific level in several accessions of common bean belonging to different gene pools. Using a combination of literature search, mass spectral interpretation, 13C-labeling, and correlation analyses, we were able to assign chemical classes and/or putative structures for approximately 39% of all measured metabolites. Additionally, we integrated this information with transcriptomics data and phylogenetic inference from multiple legume species to reconstruct the possible metabolic pathways and identify sets of candidate genes involved in the biosynthesis of specialized metabolites. A particular focus was given to flavonoids, triterpenoid saponins and hydroxycinnamates, as they represent metabolites involved in important ecological interactions and they are also associated with several health-promoting benefits when integrated into the human diet. The data are presented here in the form of an accessible resource that we hope will set grounds for further studies on specialized metabolism in legumes.