Identification of prototype compounds and their metabolites in rats' serum from Xuefu Zhuyu Decoction by UPLC-Q-TOF/MS

Two Omics Methods Expose Anti-Depression Mechanism of Raw and Vinegar-Baked Bupleurum Scorzonerifolium Willd

Bupleurum scorzonerifolium willd. (BS) and its vinegar-baked product (VBS) has been frequently utilized for depression management in clinical Chinese medicine. This paper aims to elucidate the antidepressant mechanism of BS and VBS from the perspectives of metabonomics and gut microbiota. A rat model of depression was established by CUMS combined with feeding alone to evaluate the antidepressant effects of BS and VBS. UPLC-Q-TOF-MS/MS-based metabolomics and 16S rRNA sequencing of rat feces were applied and the correlation of differential metabolic markers and intestinal floras was analyzed. The result revealed that BS and VBS significantly improved depression-like behaviors and the levels of monoamine neurotransmitters in CUMS rats. There were 27 differential endogenous metabolites between CUMS and normal rats, which were involved in 8 metabolic pathways. Whereas, BS and VBS could regulate 18 and 20 metabolites respectively, wherein fifteen of them were shared metabolites. On the genus level, BS and VBS could regulate twenty-five kinds of intestinal floras in CUMS rats, that is, they increased the abundance of beneficial bacteria and decreased the abundance of harmful bacteria. In conclusion, both BS and VBS exert excellent antidepressant effects by regulating various metabolic pathways and ameliorating intestinal microflora dysfunction.

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.