Mechanism of Chinese Medicine Ingredients Combined with Glibenclamide from Xiaoke Pill for the Treatment of Diabetes Mellitus Based on Biomolecules Network / 中国药学杂志

OBJECTIVE: To explore the synergistic molecular network mechanism of Chinese medicine ingredients and glibenclamide from Xiaoke pill for the treatment of diabetes mellitus. METHODS: After obtained diabetic-related genes of Xiaoke pill, STRING software was used to construct biomolecular network, DAVID software was used to identify KEGG pathways, Cytoscape software was used to construct the network for Xiaoke pill ingredients-key target-disease related pathway. RESULTS: The results showed that Xiaoke pill targeted 123 diabetic-related genes. Glibenclamide mainly affected pathways involved in type 2 diabetes, whereas Chinese medicine ingredients from Xiaoke pill was associated with inflammatory-related pathways such as TNF, Jak-STAT, NF-kB and cytokine pathway, islet β cell-related pathways such as PI3K/Akt, mTOR and MAPK pathway, AGE-RAGE and metabolic pathways. By combining Chinese medicine ingredients and glibenclamide, Xiaoke pill widely targeted diabetic-related pathways. CONCLUSION: It indicates that Xiaoke pill, a combination of traditional Chinese and Western medicine, can prevente diabetes and diabetic syndrome possibly by improving insulin resistance and protecting β cells and inhibiting inflammatory response.

Identification of biomarkers to response of Tripterygium Glycosides Tablets acting on rheumatoid arthritis by integrating transcriptional data mining and biomolecular network analysis / 中国中药杂志

Growing clinical evidence shows that a partial rheumatoid arthritis( RA) patient treated with Tripterygium Glycosides Tablets( TGT) may fail to achieve clinical improvement. It is of great clinical significance to predict the therapeutic effect of TGT in RA. Therefore,the aim of the current study was to identify potential biomarkers for TGT treatment in RA. Affymetrix EG1.0 arrays were applied to detect gene expression in peripheral blood mononuclear cells obtained from 6 RA patients( 3 responders and 3 non-responders) treated with TGT. By integrating differential expression data analysis and biomolecular network analysis,360 mRNAs( 185 up-regulated and 175 down-regulated) and 24 miRNAs( 7 up-regulated and 17 down-regulated) which were differentially expressed between TGT responder and non-responder groups were identified. A total of 206 candidate target genes for the differentially expressed miRNAs were obtained based on miRanada and Target Scan databases,and then the miRNA target gene coexpression network and miRNA-mediated gene signal transduction network were constructed. Following the network analyses,three candidate miRNAs biomarkers( hsa-miR-4720-5 p,hsa-miR-374 b-5 p,hsa-miR-185-3 p) were identified as candidate biomarkers predicting individual response to TGT. Partialleast-squares( PLS) was applied to construct a model for predicting response to TGT based on the expression levels of the candidate gene biomarkers in RA patients. The five-fold cross-validation showed that the prediction accuracy( ACC) of this PLS-based model efficacy was 100.00%,100.00%,100.00%,66.67% and 66.67% respectively,and all the area under the receiver operating characteristic curve( AUC) were 1.00,indicating the highly predictive efficiency of this PLS-based model. In conclusion,the integrating transcription data mining and biomolecular network investigation show that hsa-mir-4720-5 p,hsa-mir-374 b-5 p and hsa-mir-185-3 p may be candidate biomarkers predicting individual response to TGT. In addition,the PLS model based on the expression levels of these candidate biomarkers may be helpful for the clinical screen of RA patients,which potentially benefit individualized therapy of RA in a daily clinical setting.

Mechanism of Salvianolate injection combined with aspirin in treatment of stable angina pectoris based on biomolecules network / 中国中药杂志

Biomolecular network analysis was used to predict the mechanism of Salvianolate injection combined with aspirin for the treatment of stable angina pectoris(SAP). Related genes of Salvianolate injection, aspirin and SAP were obtained from Genecards, STITCH and DisGeNET databases. Agilent literature search software was used to construct biomolecular network; modules were identified by AP, MCODE and MCL methods. DAVID software was used for identification of related KEGG pathways. Results showed that Salvianolate injection and aspirin had a coverage rate of 45.92%, 62.56% respectively for SAP molecular network, and the coverage rate was 71.64% in combined use. The top 10 important nodes of SAP overlapped with Salvianolate injection and aspirin included MAPK14, MAPK8, IL-6 and IL-8. The important SAP nodes overlapped with Salvianolate injection alone included AKT1 and IFNG, and the important SAP nodes overlapped with aspirin included EPHB2 and TP53. Related SAP signaling pathways with combined Salvianolate injection and aspirin included Jak-STAT signaling pathway and MAPK signaling pathway. Related SAP signaling pathways with Salvianolate injection alone included VEGF signaling pathway and type 1 diabetes signaling pathway. Related SAP signaling pathways with aspirin alone included AA metabolism, linoleic acid metabolism signaling pathway, etc. The results showed that Salvianolate injection and aspirin combination had an enhancement effect in treatment of SAP through anti-inflammatory reaction and inhibition of atherosclerosis development; in addition, the combination use may have an additive effect through the antiplatelet aggregation, protecting endothelial cells, regulating blood lipid and regulating glucose metabolism.