ABSTRACT
As one of the most common clinical cardiovascular diseases (CVDs), coronary heart disease (CHD) is the most common cause of death in the world. It has been confirmed that Zhishi Xiebai Guizhi decoction (ZXGD), a classical prescription of the traditional Chinese medicine (TCM), has achieved certain effects in the treatment of CHD; however, the mechanism still remains controversial. In this paper, an integrated approach, including UPLC-UESI-Q Exactive Focus, gene expression profiling, network pharmacology, and experimental validation, was introduced to systematically investigate the mechanism of ZXGD in the treatment of CHD. First, UPLC-UESI-Q Exactive Focus was applied to identify the chemical compounds of ZXGD. Then, the targets of the components for ZXGD were predicted by MedChem Studio software embed in the integrative pharmacology-based research platform of TCM, and the differentially expressed genes (DEGs) of CHD were obtained by gene expression profiling in gene expression omnibus database. The common genes of the above two genes were obtained by Venn analysis as the targets of GXGD in treatment with CHD. Third, the core targets were screened out by protein-protein interaction network analysis, and the kyoto encyclopedia of genes and genomes pathway enrichment analysis was performed by the database for annotation, visualization, and integrated discovery bioinformatics resources. After that, the formula-herb-compound-target-pathway network was constructed to explore the mechanism of ZXGD in the treatment of CHD. Finally, molecular docking and the vitro experiment were carried out to validate some key targets. As a result, a total of 39 compounds, 12 core targets, and 4 pathways contributed to ZXGD for the treatment of CHD. This study preliminarily provided a foundation for the study on the mechanism against CHD for ZXGD and may be a reference for the compatibility mechanism and the extended application of TCM compound prescription.
ABSTRACT
Toutongning capsule (TTNC) is an effective and safe traditional Chinese medicine used in the treatment of migraine. In this present study, a multiscale strategy was used to systematically investigate the mechanism of TTNC in treating migraine, which contained UPLC-UESI-Q Exactive Focus network pharmacology and experimental verification. First, 88 compounds were identified by the UPLC-UESI-Q Exactive Focus method for TTNC. Then, the target fishing for these compounds was performed by means of an efficient drug similarity search tool. Third, a series of network pharmacology experiments were performed to predict the key compounds, targets, and pathways. They were protein-protein interaction (PPI), KEGG pathway enrichment analysis, and herbs-compounds-targets-pathways (H-C-T-P) network construction. As a result, 18 potential key compounds, 20 potential key targets, and 6 potential signaling pathways were obtained for TTNC in treatment with migraine. Finally, molecular docking and experimental were carried out to verify the key targets. In short, the results showed that TTNC is able to treat migraine through multiple components, multiple targets, and multiple pathways. This work may provide a theoretical basis for further research on the molecular mechanism of TTNC in the treatment of migraine.
ABSTRACT
Coprinus comatus, a novel cultivated edible mushroom, has a various of pharmacological effects due to its many active components. In this study, agaricoglycerides, a new class of fungal secondary metabolites that have strong activity against neurolysin, were isolated from C. comatus mycelia. Simultaneously, a 3-level Box-Behnken factorial design was used, combined with response surface methodology, to optimize the precursor composition of agaricoglycerides for the production of agaricoglyceride A. The model estimated that a maximal yield of agaricoglyceride A (20.105 mg/L) could be obtained when the concentrations of 4-hydroxybenzoic acid, glycerol, and methanol (MeOH) were set at 75 mg/L, 0.75 mL/L, and 0.75 mL/L, respectively. The verified experiments showed that the model was significantly consistent with the model prediction. These results showed that appropriately adding the precursors could increase the production of agaricoglyceride A.