A Comprehensive Metabolomics and Lipidomics Atlas for the Legumes Common Bean, Chickpea, Lentil and Lupin.
Mustafa BulutRegina WendenburgElena BitocchiElisa BellucciMagdalena KrocTania GioiaKarolina SusekRoberto PapaAlisdair Robert FernieSaleh AlseekhPublished in: The Plant journal : for cell and molecular biology (2023)
Legumes represent an important component of human and livestock diets; they are rich in macro- and micronutrients such as proteins, dietary fibers and polyunsaturated fatty acids. Whilst, several health-promoting and anti-nutritious properties have been associated with grain content, in-depth metabolomics characterization of major legume species remains elusive. In this article, we used both gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) to assess the metabolic diversity in the five legume species commonly grown in Europe, including common bean, chickpea, lentil, white and pearl lupin at the tissue-specific level. We were able to detect and quantify over 3,430 metabolites covering major nutritional and anti-nutritional compounds. Specifically, the metabolomics atlas includes 224 derivatized metabolites, 2283 specialized metabolites, and 923 lipids. The data generated here will serve the community as a basis for future integration to metabolomics-assisted crop breeding and facilitate the metabolite-based genome-wide association study (mGWAS) to dissect the genetic and biochemical bases of metabolism in legume species.
Keyphrases
- mass spectrometry
- liquid chromatography
- gas chromatography
- gas chromatography mass spectrometry
- ms ms
- high resolution mass spectrometry
- genome wide association study
- high performance liquid chromatography
- healthcare
- capillary electrophoresis
- tandem mass spectrometry
- solid phase extraction
- single cell
- mental health
- endothelial cells
- palliative care
- high resolution
- simultaneous determination
- climate change
- electronic health record
- genome wide
- induced pluripotent stem cells
- fatty acid
- big data
- artificial intelligence
- health promotion