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ARS Home » Northeast Area » Beltsville, Maryland (BHNRC) » Beltsville Human Nutrition Research Center » Methods and Application of Food Composition Laboratory » Research » Publications at this Location » Publication #405466

Research Project: Advanced Technology for Rapid Comprehensive Analysis of the Chemical Components

Location: Methods and Application of Food Composition Laboratory

Title: An untargeted metabolomics approach to study the variation between wild and cultivated soybeans

Author
item TAREQ, FAKIR - University Of Maryland
item KOTHA, RAGHAVENDHAR - University Of Maryland
item Natarajan, Savithiry - Savi
item Sun, Jianghao
item Luthria, Devanand - Dave

Submitted to: Molecules
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/12/2023
Publication Date: 7/19/2023
Citation: Tareq, F.S., Kotha, R.R., Natarajan, S.S., Sun, J., Luthria, D.L. 2023. An untargeted metabolomics approach to study the variation between wild and cultivated soybeans. Molecules. 28: 5507. https://doi.org/10.3390/molecules28145507.
DOI: https://doi.org/10.3390/molecules28145507

Interpretive Summary: Soybean is one of the major food produced and consumed across the globe. Soybeans provide an excellent source of protein, oil, and carbohydrates. Soybeans are the world's largest source of animal protein feed and the second-largest source of vegetable oil. The United States is the leading soybean producer and exporter. In order to develop improved-quality soybeans, it is important to investigate their phytochemical composition. In the present study, we performed nontargeted HPLC-HRMS analysis of 14 soybean genotypes belonging to wild (Glycine soja) and cultivated (Glycine max) soybeans. These samples represented a wide range of genetic diversity. The UHPLC-HRMS analysis resulted in the putative identification of 99 metabolites belonging to several classes of phytochemicals, including isoflavones, organic acids, lipids, sugars, amino acids, saponins, and other compounds. In addition, unsupervised learning algorithms were applied to mine the generated data and to pinpoint metabolites differentiating wild and cultivated soybeans. The key identified metabolites differentiating wild and cultivated soybeans were isoflavonoids, amino acids, and fatty acids. Catechin analogs, cynaroside, hydroxylated unsaturated fatty acid derivatives, amino acid, and uridine diphosphate-N-acetylglucosamine were upregulated in the methanol extract of wild soybeans. In contrast, isoflavonoids and other minor compounds were downregulated in the same soybean extract. This metabolic information will benefit breeders and biotechnology professionals to develop value-added soybeans with improved quality traits.

Technical Abstract: The differential metabolite profiles of four wild and ten cultivated soybeans genotypes were explored using an untargeted metabolomics approach. Potential marker metabolites causing the differences between wild and cultivated soybean samples were identified using multivariate analysis. Ground samples were extracted with methanol and water, and metabolic features were obtained using ultra-high-performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS) in both positive and negative ion modes. The UHPLC-HRMS analysis resulted in the putative identification of 99 metabolites belonging to several classes of phytochemicals, including isoflavones, organic acids, lipids, sugars, amino acids, saponins, and other compounds. In addition, unsupervised learning algorithms were applied to mine the generated data and to pinpoint metabolites differentiating wild and cultivated soybeans. The key identified metabolites differentiating wild and cultivated soybeans were isoflavonoids, amino acids, and fatty acids. Catechin analogs, cynaroside, hydroxylated unsaturated fatty acid derivatives, amino acid, and uridine diphosphate-N-acetylglucosamine were upregulated in the methanol extract of wild soybeans. In contrast, isoflavonoids and other minor compounds were downregulated in the same soybean extract. This metabolic information will benefit breeders and biotechnology professionals to develop value-added soybeans with improved quality traits.