Inspired by the nutritional and biological attributes of mung beans, the current work aims to monitor metabolome patterns of different mung species and their entanglements on antidiabetic potential using NIR-UPLC-MS/MS multiplex approach combined with chemometrics. In this regard, a total of 71 chromatographic peaks spanning sugars, amino acids, flavonoids, fatty acids and their lipid derivatives, and phytosterols were chemically profiled. Coincidently, OPLS-DA underscored an obvious discrimination among the green, red and black mung species suggesting their chemical discrepancies where eriodictyol-O-glucoside, caffeic acid, formononetin-O-glucoside, viniferal and genistin serve as focal discriminators of green mung beans while lysoPC 18:2, lanosterol, gallocatechin, tyramine, petunidin 3-O-glucoside, biochanin A, vigvexin A, vignatic acid B, lysoPC 16:0 and phaseollin were the determining metabolites of red ones. Successively, the differential markers enriched in black mung samples included 10-formyltetrahydrofolate, stearidonic acid, hydroxylinoleic acid, vignatic acid A, campestrol, arachidonic acid and PG (18:2/18:1). Experimentally speaking, all mung samples exerted noteworthy dose-dependent inhibitory potential towards α-amylase and α-glucosidase enzymes. OPLS coefficient plots highlighted gamma-aminobutyric acid (GABA), gallic acid and beta-sitosterol as possible efficacy metabolites harmoniously mediated antidiabetic potential. Equally important, NIR spectroscopic analysis coupled with PLS-R model quantitively predicted the bio-efficient markers from various mung bean samples with a significant level of experimental reliability. These findings pursue concept of nutritional therapy and provide a fresh perspective to probe into mung beans bioactive molecules which might serve as referenced templates for mitigating diabetes. However, future work should be explored to uncover muti-target mechanisms of mung beans-derived compounds and strengthen their relevance.
Multi-omics for unveiling potential antidiabetic markers from red, green and black mung beans using NIR-UPLC-MS/MS multiplex approach.
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作者:Ghallab Dina S, Ibrahim Reham S, Ghareeb Doaa A, El Newehy Nihal M
| 期刊: | Scientific Reports | 影响因子: | 3.900 |
| 时间: | 2025 | 起止号: | 2025 Jun 1; 15(1):19213 |
| doi: | 10.1038/s41598-025-03911-x | ||
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