化学
根(腹足类)
代谢组学
传统医学
生物碱
毛茛科
计算生物学
立体化学
植物
生物
色谱法
医学
作者
Rongrong Li,Xiaolin Wu,Xinyi Jiao,Xue Zhang,Chenxi Wang,Lifeng Han,Meifang Song,Yanlong Zhang,Guixiang Pan,Zhonglian Zhang
标识
DOI:10.1016/j.jpba.2023.115747
摘要
Radix et Rhizoma Thalictri Foliolosi (RRTF) belongs to one of the alkaloid-rich traditional Chinese medicines in Ranunculaceae, which possesses anti-inflammatory, anti-tumor, and several other pharmacological activities. However, due to lack of research on chemical composition, serious confusion in the origin, and ambiguity in pharmacological mechanisms, it is quite urgent to establish quality control standards based on modern research and to increase the widespread usage. Aiming to clarify the differential compounds among three species of RRTF (TFD, TFB, and TCW), targeted and untargeted acquisition strategies based on high resolution mass spectrometry were established. Plant metabolomics analysis and multivariate statistical analysis were accomplished to screen out differential markers which were answerable for categorizing different species of RRTF. A network pharmacology analysis was further performed to predict the bioactive constituents and pharmacological mechanisms. Moreover, multi-components quantitative analysis under multiple reaction monitoring mode and multiple logistic regression analysis were conducted to estimate the rationality of the quality markers (Q-markers). Ultimately, the targeted alkaloid detection list was built as premise relying on alkaloid cleavage pathway, and a total 87 compounds were identified. The 25 representative differential metabolites were screened out successfully and divided into three categories to differentiate TFD, TFB, and TCW. 14 active components and 25 presumptive targets of RRTF were found to play a central role according to network pharmacology analysis. The abundance of screened 12 Q-marker showed significant differences in the three varieties. In conclusion, the study systematically investigated the material basis of RRTF, distinguished and evaluated the quality of RRTF effectively, and predicted its pharmacodynamic material basis.
科研通智能强力驱动
Strongly Powered by AbleSci AI