Glucuronic acid metabolites of phenolic acids target AKT-PH domain to improve glucose metabolism

Combination of in silico prediction and convolutional neural network framework for targeted screening of metabolites from LC-HRMS fingerprints: A case study of "Pericarpium Citri Reticulatae - FructusAurantii"

In this study, a novel approach is introduced, merging in silico prediction with a Convolutional Neural Network (CNN) framework for the targeted screening of in vivo metabolites in Liquid Chromatography-High Resolution Mass Spectrometry (LC-HRMS) fingerprints. Initially, three predictive tools, supplemented by literature, identify potential metabolites for target prototypes derived from Traditional Chinese Medicines (TCMs) or functional foods. Subsequently, a CNN is developed to minimize false positives from CWT-based peak detection. The Extracted Ion Chromatogram (EIC) peaks are then annotated using MS-FINDER across three levels of confidence. This methodology focuses on analyzing the metabolic fingerprints of rats administered with "Pericarpium Citri Reticulatae - Fructus Aurantii" (PCR-FA). Consequently, 384 peaks in positive mode and 282 in negative mode were identified as true peaks of probable metabolites. By contrasting these with "blank serum" data, EIC peaks of adequate intensity were chosen for MS/MS fragment analysis. Ultimately, 14 prototypes (including flavonoids and lactones) and 40 metabolites were precisely linked to their corresponding EIC peaks, thereby providing deeper insight into the pharmacological mechanism. This innovative strategy markedly enhances the chemical coverage in the targeted screening of LC-HRMS metabolic fingerprints.

Deciphering the metabolic signatures of Trigonella microgreens as a function of photoperiod and temperature using targeted compound analysis and non-targeted UHPLC-QTOF-IMS based approach

Trigonella foenum-graecum L. (Fenugreek) is an annual herb that belongs to Fabaceae family. The compositional make-up of microgreens depends on prevailing environmental conditions. So, Trigonella microgreens were cultivated under different photoperiod and temperature conditions and evaluated for plant height, total chlorophyll content (TCC), targeted compound analysis and non-targeted UHPLC-QTOF-IMS based metabolomic profile. The plant height and TCC of Trigonella microgreens increased by approximately 22 % and 20 %, respectively under T1 conditions (longer photoperiod of 22 h with 22 °C in light and 17 °C in dark). The targeted phenolic profile analysis revealed the dominant presence of gallic acid, p-coumaric acid and apigenin in Trigonella microgreens. Also, the concentration of p-coumaric acid concentration raised from 3.51 mg/g to 5.83 mg/g as a response of T1 conditions. The sugar profile revealed augmented concentration of myo-inositol, glucose, fructose, xylose, maltose, and sucrose in longer photoperiod with T1 conditions. The microgreens were also rich in amino acids like aspartic acid, glutamic acid, leucine, isoleucine, and phenylalanine. Notably, the concentration of proline increased from 10.40 mg/g to 16.92 mg/g as a response to T1 growth conditions. The concentration of these metabolites varied significantly under different photoperiod and temperature conditions. The comprehensive non-targeted UHPLC-QTOF-IMS analysis of microgreens revealed different class of metabolites like organic compounds, alkaloids, coumarin-derivatives, phenolic and flavonoid derivatives, terpenoids, sugars, amino acids and few nucleic acid derivatives. The multivariate PLS-DA explained different expression level of metabolites under different growing conditions. The T1 growing condition resulted in the increased biosynthesis of phenolic compounds and various metabolites. The expression level of terpenoid derivatives specifically of Trigonelloside C and Trigoneoside XIIa/b increased under T1 conditions. The substantial alteration in the metabolites due to growing conditions may alter the microgreen's dietary benefits. So, additional research may be warranted.