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

MCnebula analysis combined with alpha-glucosidase inhibitory screening reveals potential chemical contributors to efficacy enhancement of natural products after processing

Natural products often show enhanced therapeutic effects after processing, largely due to changes in their chemical profiles. However, identifying the key chemical classes and components responsible for these improvements remains a challenge. In this study, we present a novel workflow for mass spectrometry data analysis, based on our previously developed MCnebula, combined with in vivo and in vitro testing to identify contributors to the anti-diabetic effects of processed natural products. Cornus officinalis (CO), a traditional food and medicinal plant, showed strong α-glucosidase inhibition, particularly its steamed and wine-steamed products, outperforming acarbose in both in vitro and in vivo testing. MCnebula analysis revealed that flavonoids underwent the most significant changes during processing. Further validation of selected flavonoid compounds, such as quercetin and kaempferol, demonstrated their α-glucosidase inhibitory effects to be 207 and 263 times more potent than acarbose, respectively. The combined use of MCnebula analysis and bioactivity validation revealed the key compounds that contribute to the enhanced anti-glucose effects of CO after processing, offering insights into the chemical transformations with bioactive potential as anti-diabetic dietary supplements and agents.

Targeted and non-targeted metabolic characteristics of 6,7-dihydroxy-2,4- dimethoxyphenanthrene during simulated gastrointestinal digestion

6,7-dihydroxy-2,4-dimethoxyphenanthrene (PC4), isolated and identified from Chinese yam, was one of the important characteristic active ingredients. The present study established simulated gastrointestinal digestion and caco-2 intestinal epithelial models to investigate the digestive and metabolic characteristics of PC4. The results indicated that PC4 was mainly digested and metabolized in the intestine, with a digestion rate of 94 %, producing active characteristic metabolites including 2,6-dimethoxy-4-phenanthrenol (MC1) and 1-methoxyphenanthrene (MC2). Molecular docking indicated that the COX-2 enzyme inhibitory activity of MC1 might be superior to that of the prototype PC4. During 24 h co-incubation of PC4 with caco-2 monolayer epithelial, the signal pathway related to lipid decomposition was up-regulated within 0-12 h, while the diabetes complications AGE-RAGE was inhibited just in 0-6 h period significantly. The research database provided scientific basis for the digestion and metabolism of PC4, and laid theoretical foundation for the scientific development of Chinese yam functional foods.

Software-aided efficient identification of the components of compound formulae and their metabolites in rats by UHPLC/IM-QTOF-MS and an in-house high-definition MS2 library: Sishen formula as a case

Identifying the compound formulae-related xenobiotics in bio-samples is full of challenges. Conventional strategies always exhibit the insufficiencies in overall coverage, analytical efficiency, and degree of automation, and the results highly rely on the personal knowledge and experience. The goal of this work was to establish a software-aided approach, by integrating ultra-high performance liquid chromatography/ion-mobility quadrupole time-of-flight mass spectrometry (UHPLC/IM-QTOF-MS) and in-house high-definition MS2 library, to enhance the identification of prototypes and metabolites of the compound formulae in vivo, taking Sishen formula (SSF) as a template. Seven different MS2 acquisition methods were compared, which demonstrated the potency of a hybrid scan approach (namely high-definition data-independent/data-dependent acquisition (HDDIDDA)) in the identification precision, MS1 coverage, and MS2 spectra quality. The HDDIDDA data for 55 reference compounds, four component drugs, and SSF, together with the rat bio-samples (e.g., plasma, urine, feces, liver, and kidney), were acquired. Based on the UNIFI™ platform (Waters), the efficient data processing workflows were established by combining mass defect filtering (MDF)-induced classification, diagnostic product ions (DPIs), and neutral loss filtering (NLF)-dominated structural confirmation. The high-definition MS2 spectral libraries, dubbed in vitro-SSF and in vivo-SSF, were elaborated, enabling the efficient and automatic identification of SSF-associated xenobiotics in diverse rat bio-samples. Consequently, 118 prototypes and 206 metabolites of SSF were identified, with the identification rate reaching 80.51% and 79.61%, respectively. The metabolic pathways mainly involved the oxidation, reduction, hydrolysis, sulfation, methylation, demethylation, acetylation, glucuronidation, and the combined reactions. Conclusively, the proposed strategy can drive the identification of compound formulae-related xenobiotics in vivo in an intelligent manner.

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.