Network Pharmacology and Molecular Docking Study on the Potential Mechanism of Yi-Qi-Huo-Xue-Tong-Luo Formula in Treating Diabetic Peripheral Neuropathy.
To investigate the potential mechanism of action of Yi-Qi-Huo-Xue-Tong-Luo formula (YQHXTLF) in the treatment of diabetic peripheral neuropathy (DPN).
Network pharmacology and molecular docking techniques were used in this study. Firstly, the active ingredients and the corresponding targets of YQHXTLF were retrieved using the Traditional Chinese Medicine Systems Pharmacology (TCMSP) platform; subsequently, the targets related to DPN were retrieved using GeneCards, Online Mendelian Inheritance in Man (OMIM), Pharmgkb, Therapeutic Target Database (TTD) and Drugbank databases; the common targets of YQHXTLF and DPN were obtained by Venn diagram; afterwards, the "YQHXTLF Pharmacodynamic Component-DPN Target" regulatory network was visualized using Cytoscape 3.6.1 software, and Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on the potential targets using R 3.6.3 software. Finally, molecular docking of the main chemical components in the PPI network with the core targets was verified by Autodock Vina software.
A total of 86 active ingredients and 229 targets in YQHXTLF were screened, and 81 active ingredients and 110 targets were identified to be closely related to diabetic peripheral neuropathy disease. PPI network mapping identified TP53, MAPK1, JUN, and STAT3 as possible core targets. KEGG pathway analysis showed that these targets are mostly involved in AGE-RAGE signaling pathway in diabetic complications, TNF signaling pathway, and MAPK signaling pathway. The molecular docking results showed that the main chemical components of YQHXTLF have a stable binding activity to the core pivotal targets.
YQHXTLF may act on TP53, MAPK1, JUN, and STAT3 to regulate inflammatory response, apoptosis, or proliferation as a molecular mechanism for the treatment of diabetic peripheral neuropathy, reflecting its multitarget and multipathway action, and providing new ideas to further uncover its pharmacological basis and mechanism of action.
Lin Y
，Shen C
，Wang F
，Fang Z
，Shen G
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Investigation of the mechanism of Prunella vulgaris in treatment of papillary thyroid carcinoma based on network pharmacology integrated molecular docking and experimental verification.
To analyze the molecular mechanism of Prunella vulgaris L. (PV) in the treatment of papillary thyroid carcinoma (PTC) by using network pharmacology combined with molecular docking verification. Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform database was used to predict the main active components of PV, Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, PubChem, and Swiss Target Prediction databases were used to obtain the corresponding targets of all active components. Targets collected for PTC treatment through Gene Cards, Digest and Online Mendelian Inheritance in Man databases respectively. The Search Tool for the Retrieval of Interaction Gene/Protein database was used to obtain the interaction information between proteins, and the topology analysis and visualization were carried out through Cytoscape 3.7.2 software (https://cytoscape.org/). The R package cluster profiler was used for gene ontology and Kyoto encyclopedia of genes and genomes analysis. The "active ingredient-target-disease" network was constructed by using Cyto scape 3.7.2, and topological analysis was carried out to obtain the core compound. The molecular docking was processed by using Discovery Studio 2019 software, and the core target and active ingredient were verified. The inhibition rate was detected by CCK8 method. Western blot was used to detect the expression levels of kaempferol anti-PTC related pathway proteins. A total of 11 components and 83 corresponding targets in the component target network of PV, of which 6 were the core targets of PV in the treatment of PTC. It was showed that quercetin, luteolin, beta (β)-sitosterol, kaempferol may be the core components of PV in the treatment of PTC. vascular endothelial growth factor A, tumor protein p53, transcription factor AP-1, prostaglandin endoperoxidase 2, interleukin 6, and IL-1B may be important targets for the treatment of PTC. The main biological processes mainly including response to nutrient levels, response to xenobiotic stimulus, response to extracellular stimulus, external side of plasma membrane, membrane raft, membrane microdomain, serine hydrolase activity, serine-type endopeptidase activity, antioxidant activity, etc IL-17 signaling pathway, and PI3K-Akt signaling pathway may affect the recurrence and metastasis of PTC. Kaempferol may significantly reduce the activity of Papillary cells of human thyroid carcinoma bcpap cell lines cells compared with quercetin, luteolin, β-sitosterol. Kaempferol may reduce the protein expression levels of interleukin 6, vascular endothelial growth factor A, transcription factor AP-1, tumor protein p53, 1L-1B and prostaglandin endoperoxidase 2, respectively. PV has the characteristics of multi-components, multi-targets and multi- pathways in the treatment of PTC, which network pharmacology help to provides a theoretical basis for the screening of effective components of PV and further research.
Zhu X
，Li Y
，Wang X
，Huang Y
，Mao J
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Systematic Evaluation of the Mechanisms of Mulberry Leaf (Morus alba Linne) Acting on Diabetes Based on Network Pharmacology and Molecular Docking.
Diabetes mellitus is one of the most common endocrine metabolic disorder- related diseases. The application of herbal medicine to control glucose levels and improve insulin action might be a useful approach in the treatment of diabetes. Mulberry leaves (ML) have been reported to exert important activities of anti-diabetic.
In this work, we aimed to explore the multi-targets and multi-pathways regulatory molecular mechanism of Mulberry leaves (ML, Morus alba Linne) acting on diabetes.
Identification of active compounds of Mulberry leaves using Traditional Chinese Medicine Systems Pharmacology (TCMSP) database was carried out. Bioactive components were screened by FAF-Drugs4 website (Free ADME-Tox Filtering Tool). The targets of bioactive components were predicted from SwissTargetPrediction website, and the diabetes related targets were screened from GeneCards database. The common targets of ML and diabetes were used for Gene Ontology (GO) and pathway enrichment analysis. The visualization networks were constructed by Cytoscape 3.7.1 software. The biological networks were constructed to analyze the mechanisms as follows: (1) compound-target network; (2) common target-compound network; (3) common targets protein interaction network; (4) compound-diabetes protein-protein interactions (ppi) network; (5) target-pathway network; and (6) compound-target-pathway network. At last, the prediction results of network pharmacology were verified by molecular docking method.
17 active components were obtained by TCMSP database and FAF-Drugs4 website. 51 potential targets (11 common targets and 40 associated indirect targets) were obtained and used to build the PPI network by the String database. Furthermore, the potential targets were used for GO and pathway enrichment analysis. Eight key active compounds (quercetin, Iristectorigenin A, 4- Prenylresveratrol, Moracin H, Moracin C, Isoramanone, Moracin E and Moracin D) and 8 key targets (AKT1, IGF1R, EIF2AK3, PPARG, AGTR1, PPARA, PTPN1 and PIK3R1) were obtained to play major roles in Mulberry leaf acting on diabetes. And the signal pathways involved in the mechanisms mainly include AMPK signaling pathway, PI3K-Akt signaling pathway, mTOR signaling pathway, insulin signaling pathway and insulin resistance. The molecular docking results show that the 8 key active compounds have good affinity with the key target of AKT1, and the 5 key targets (IGF1R, EIF2AK3, PPARG, PPARA and PTPN1) have better affinity than AKT1 with the key compound of quercetin.
Based on network pharmacology and molecular docking, this study provided an important systematic and visualized basis for further understanding of the synergy mechanism of ML acting on diabetes.
Wu Q
，Hu Y
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ResearchGate
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钛学术
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