ABSTRACT
Objective: To rapidly identify the two morphologies and chemical properties of similar herbal medicines, Blumea riparia and B. megacephala as the basis for chemical constituent analysis. Methods: UPLC-Q-Exactive-MS/MS was utilized for profiling and identification of the constituents in B. riparia and B. megacephala. Chemical pattern recognition (CPR) was further used to compare and distinguish the two herbs and to identify their potential characteristic markers. Then, an HPLC method was established for quality evaluation. Results: A total of 93 constituents are identified, including 54 phenolic acids, 35 flavonoids, two saccharides, one phenolic acid glycoside, and one other constituent, of which 67 were identified in B. riparia and B. megacephala for the first time. CPR indicates that B. riparia and B. megacephala samples can be distinguished from each other based on the LC-MS data. The isochlorogenic acid A to cryptochlorogenic acid peak area ratio calculated from the HPLC chromatograms was proposed as a differentiation index for distinguishing and quality control of B. riparia and B. megacephala. Conclusion: This study demonstrates significant differences between B. riparia and B. megacephala in terms of chemical composition. The results provide a rapid and simple strategy for the comparison and evaluation of the quality of B. riparia and B. megacephala.
ABSTRACT
In this study, UPLC-Q-Exactive-MS/MS was used to rapidly analyze the chemical constituents of Meconopsis quintupli-nervia, and the anti-liver fibrosis mechanism of M. quintuplinervia was preliminarily analyzed by network pharmacology, molecular docking, and cell experiments. The chemical constituents of M. quintuplinervia were identified according to the information of MS~1 and MS~2, as well as the data in the literature and databases. SwissTargetPrediction and TargetNet were used to predict the potential targets. The targets related to liver fibrosis were collected from GeneCards and OMIM. The protein-protein interaction(PPI) network was constructed by STRING. Cytoscape 3.6.1 was used to construct and analyze the "constituent-target-disease" network to obtain key targets and their corresponding constituents in the network. DAVID 6.8 was used for GO analysis and KEGG signaling pathway enrichment analysis. Finally, the preliminary verification was carried out by molecular docking and cell experiments. As a result, 106 chemical constituents were identified from M. quintuplinervia, including 66 flavonoids, 16 alkaloids, 18 phenolic acids, 1 anthocyanin, and 5 other constituents. Among them, 3 constituents were identified as potential new compounds, and 59 constituents were reported in M. quintuplinervia for the first time. Network pharmacology analysis showed that M. quintuplinervia presumably acted on AKT1, SRC, JUN, EGFR, STAT3, HSP90 AA1, MAPK3, and other core targets through luteolin, isorhamnetin, quercetin, apigenin, kaempferide, amurine, 2-methylflavinantine, allocryptopine, the multi and other active compounds, thereby regulating the PI3 K/AKT signaling pathway, pathways in cancer, proteoglycans in cancer, FoxO signaling pathway, and other pathways to exert anti-liver fibrosis effects. M. quintuplinervia extract(MQE) could significantly down-regulate PI3 K and AKT protein levels in the HSC-T6 cell model induced by TGF-ß1, suggesting that MQE may have the ability to regulate the PI3 K/AKT signaling pathway. The findings of this study indicated that the anti-liver fibrosis effect of M. quintuplinervia had multi-constituent, multi-target, and multi-pathway characteristics, which may provide a scientific basis for the research on the pharmacodynamic materials, action mechanism, and quality markers of M. quintupli-nervia.
