ABSTRACT
Objective: To explore the mechanism of Shenqi Jiangtang Granules in treatment of type 2 diabetes mellitus by network pharmacology. Methods: The active components and targets of Shenqi Jiangtang Granules and targets associated with type 2 diabetes were obtained by literature mining and database. Enrichr database was used to analyze gene ontology enrichment and Kyoto gene and genome encyclopedia. In addition, Cytoscape 3.5.1 software was used to draw network interaction diagrams, and imageGP tool was used to draw GO and KEGG bubble diagrams. Results: A total of 43 key active ingredients and 30 key targets were obtained. GO enrichment analysis included 918 items, including 56 cell component (CC) entries, 99 molecular function (MF) entries, and 763 biological process (BP) entries. CC entries were related to axonal membrane, nuclear transcription factor complex, RNA polymerase Ⅱ transcription factor complex, nuclear euchromatin, and platelet α granule. MF entries were mainly related to MAP kinase activity, vascular endothelial growth factor receptor binding and other aspects. BP entries were related to biological processes, mainly including angiogenesis, foam cell differentiation, and cellular response to interleukin-21. The KEGG pathway enrichment analysis included 105 KEGG pathways, which were closely related to IL-17 signaling pathway, AGE-RAGE signaling pathway in diabetic complications, VEGF signaling pathway, mitophagy and other signaling pathways. Conclusion: Shenqi Jiangtang Granules may play a role in treatment of type 2 diabetes by acting on neuroendocrine disorders, insulin resistance, oxidative stress, inflammation, and other related protein targets and pathways.
ABSTRACT
Objective: To explore the mechanism of Shenqi Jiangtang Granules (SJG) in the treatment of diabetic microcirculation disorder based on network pharmacology. Methods: The targets of diabetic kidney disease, diabetic retinopathy, diabetic peripheral neuropathy, diabetic foot disease and small cerebral vascular disease were searched through Genecards database to be integrated as the targets of diabetic microcirculation disorder disease. The targets of SJG' active components for treatment of diabetic microcirculation disorder were screened and predicted by utilizing PubChem Search and Swiss target prediction online tool. Cytoscape 3.3.0 software was adopted to construct an active component-diabetic microcirculation disorder target network. The protein-protein interaction (PPI) network was established by using STRING database. Gene ontology (GO) biological process and Kyoto Encyclopedia of Genes and Gnomes (KEGG) pathway enrichment analysis were performed using Clue GO. Results: A total of 85 active components of SJG and 10 targets (ACE, VEGFA, TNF, IL6, STAT3, ALB, PON1, PTPN22, PPARG and NOS3) related to diabetic microcirculation disorder were screened. Through GO and KEGG pathway enrichment analysis, it was found that the active components of SJG in the treatment of diabetic microcirculation disorder may participate in multiple signaling pathways, such as AGE-RAGE signaling pathway in diabetic complications, HIF-1 signaling pathway, insulin resistance, hypertrophic cardiomyopathy, inflammatory bowel disease, rheumatoid arthritis, etc. Conclusion: This study reflects the characteristics of multi-components, multi-targets and multi-pathways of SJG, which provides new ideas and clues for further research on the mechanism of SJG in the treatment of diabetic microcirculation disorder.
ABSTRACT
Objective: To analyze the molecular biological mechanism of Shenqi Jiangtang Granules (SJG) in the treatment of lipid metabolism disorder based on network pharmacology and glucolipid metabolic disorders (GLMD) theory. Methods: The targets of SJG's active components for treatment of lipid metabolism disorder were screened and predicted by utilizing PubChem Search, Genecards database and Swiss target prediction online tool. The protein-protein interaction (PPI) network was established by using STRING database. Cytoscape 3.3.0 software was adopted to construct a disease-active component-potential target network. Gene ontology (GO) biological process and Kyoto Encyclopedia of Genes and Gnomes (KEGG) pathway enrichment analysis were performed using Clue GO. Results: A total of 115 active components of SJG and 22 targets related to lipid metabolism disorder were screened. The active components of SJG regulated intermediate-density lipoprotein particle remodeling, glycolytic process by regulation of transcription from RNA polymerase II promoter and leukotriene production involved in inflammatory response, and participated in AGE-RAGE signaling pathway, NF-κB signaling pathway, TNF signaling pathway and bile secretion. Conclusion: This study reflects the characteristics of multi-components, multi-targets, and multi-pathways of SJG, and provides new ideas and clues for new application of SJG, which is consistent with the GLMD theory.