RESUMEN
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.
RESUMEN
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.