ArticleMetabolites2026
Wheat Bran Stir-Frying Reshapes the Metabolome of
Article in Metabolites, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
background
methodsAn integrated strategy combining UPLC-MS/MS-based metabolomics, network pharmacology, and molecular docking was employed. First, differential metabolites between raw and processed Euryales Semen were identified and quantified. Second, a "metabolite-target-disease" network was constructed to predict core targets and pathways. The docking protocol was validated by re-docking of co-crystallized ligands, with all RMSD values below 2.0 Å. Finally, molecular docking was used to predict the binding interactions between key metabolites and potential protein targets.
resultsA total of 1597 metabolites were identified, and 244 were defined as differentially expressed (VIP > 1, |log2(FC)| ≥ 1). Processing significantly upregulated 19 of the 22 key metabolites identified, with Urolithin B showing the most dramatic increase (168-fold), which was identified with Level 1 confidence using an authentic standard. Network pharmacology analysis identified 10 core targets (e.g., MMP2, MMP9, AKR1B1) primarily associated with tumors, diabetes, and other chronic diseases. Molecular docking predicted strong binding affinities (<-9.0 kcal·mol
conclusionsThis study suggests that wheat bran stir-frying fundamentally reshapes the metabolic profile of Euryales Semen, leading to the enrichment of bioactive metabolites (e.g., Urolithin B) and suggesting a potential for enhanced tonifying, anti-tumor, and anti-diabetic activities that warrants further experimental investigation. The dramatic increase in the content of Urolithin B, a metabolite not commonly found in plants, raises the hypothesis of a processing-induced chemical transformation, which warrants further experimental validation, and highlights its potential as a critical component for the herb's efficacy. The findings offer a preliminary computational basis for understanding traditional processing practices, linking traditional knowledge with modern scientific evidence.
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