Kangfuxiaoyan suppository (KFXYS) is a commonly used traditional Chinese medicine (TCM) preparation for the treatment of chronic pelvic inflammatory disease (CPID) clinically, and its safety and effectiveness have been well verified. However, the potential mechanism remains unclear. The integrated strategy of metabolomics and network pharmacology was employed in the study to reveal the potential mechanism of KFXYS in the treatment of CPID. Our research consists of five steps. First, the effect of KFXYS in reversing uterine inflammation indexes was verified. Second, based on the comprehensive characterization of 123 chemical ingredients of KFXYS, the ingredients of KFXYS absorbed into blood were identified by UPLC-Q-TOF/MS, then ADME research was carried out on the main ingredients. Third, the differential metabolites with significant correlation to inflammatory indexes were discovered by metabolomics and correlation analysis. Fourth, the potential targets and pathways of KFXYS in treating CPID were predicted by network pharmacology based on the ingredients which had good ADME behavior. Fifth, the proteins in common pathways of metabolomics and network pharmacology were used to screen the key targets from the potential targets of network pharmacology, and the potential mechanism of KFXYS in treating CPID was clarified. As a result, KFXYS significantly reversed the uterine inflammation indexes, including IL-1 and IL-6. The ingredients absorbed into blood including matrine, sophocarpine, aloin, esculetin-O-glucuronide, 7,4′-dihydroxyisoflavone-O-glucuronide, and 4′-methoxyisoflavone-7-O-glucuronide had good ADME behavior in vivo. Among the differential metabolites, Leukotriene A4, 5-Hydroxyindoleacetic acid, Ornithine, Arginine, and PC (20:1 (11Z)/20:4 (8Z,11Z,14Z,17Z)) were significant correlation to inflammation indexes. The integration analysis of metabolomics and network pharmacology shows that KFXYS may regulate the key targets including ARG1, NOS2, NOS3, etc. We speculate that ingredients of KFXYS, such as matrine, sophocarpine, aloin etc. act on the key proteins including ARG1, NOS2, and NOS3, to exert anti-inflammatory effect.
Cognitive dysfunction is a frequent complication of type 2 diabetes mellitus (T2DM), usually accompanied by metabolic disorders. However, the metabolic changes in diabetic cognitive dysfunction (DCD) patients, especially compared to T2DM groups, are not fully understood. Due to the subtle differences in metabolic alterations between DCD groups and T2DM groups, the comprehensive detection of the untargeted metabolic profiles of hippocampus and urine samples of rats was conducted by LC–MS, considering the different ionization modes and polarities of the examined compounds, and feature-based molecular networking (FBMN) was performed to help identify differential metabolites from a comprehensive perspective in this study. In addition, an association analysis of the differential metabolites in hippocampus and urine was conducted by the O2PLS model. Finally, a total of 71 hippocampal tissue differential metabolites and 179 urine differential metabolites were identified. The pathway enrichment results showed that glutamine and glutamate metabolism, alanine, aspartate, and glutamate metabolism, glycerol phospholipid metabolism, TCA cycle, and arginine biosynthesis in the hippocampus of DCD animals were changed. Seven metabolites (AUC > 0.9) in urine appeared as key differential metabolites that might reflect metabolic changes in the target tissue of DCD rats. This study showed that FBMN facilitated the comprehensive identification of differential metabolites in DCD rats. The differential metabolites may suggest an underlying DCD and be considered as potential biomarkers for DCD. Large samples and clinical experiments are needed for the subsequent elucidation of the possible mechanisms leading to these alterations and the verification of potential biomarkers.
Quality marker (Q-marker) serves as an important driver for the standardization of quality control in traditional Chinese medicine (TCM) formulas. However, it is still challenging to discover comprehensive and representative Q-markers. This study aimed to identify Q-markers of Hugan tablet (HGT), a famous TCM formula with ideal clinical effects in liver diseases. Here, we proposed a funnel-type stepwise filtering strategy that integrated secondary metabolites characterization, characteristic chromatogram, quantitative analysis, literature mining, biotransformation rules and network analysis. Firstly, the strategy of “secondary metabolites-botanical drugs-TCM formula” was applied to comprehensively identify the secondary metabolites of HGT. Then, the secondary metabolites with specificity and measurability in each botanical drug were identified by HPLC characteristic chromatogram, biosynthesis pathway and quantitative analysis. Based on literature mining, the effectiveness of botanical metabolites that met the above conditions was evaluated. Furthermore, the metabolism of the above metabolites in vivo was studied to reveal their biotransformation forms, which were used for network analysis. At last, according to biotransformation rules of the prototype drugs in vivo, the secondary metabolites were traced and preliminarily chosen as Q-markers. As a result, 128 plant secondary metabolites were identified in HGT, and 11 specific plant secondary metabolites were screened out. Then, the content of specific plant secondary metabolites in 15 batches of HGT was determined, which confirmed their measurability. And the results of literature mining showed that eight secondary metabolites had therapeutic effects in treating liver disease at the in vivo level, and three secondary metabolites inhibited liver disease-related indicators at the in vitro level. After that, 26 compounds absorbed into the blood (11 specific plant metabolites and their 15 metabolites in vivo) were detected in rats. Moreover, 14 compounds, including prototype components and their metabolites, were selected as Q-marker candidates by the “TCM formula-botanical drugs-compounds-targets-pathways” network. Finally, 9 plant secondary metabolites were defined as comprehensive and representative Q-markers. Our study not only provides a scientific basis for the improvement and secondary development of the quality standard of HGT, but also proposes a reference method for discovering and identifying Q-markers of TCM preparations.
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