Abstract

Salvia miltiorrhiza (SM) and Panax notoginseng (PN) in combination (SMPN) have been widely used primarily in Traditional Chinese Medicine (TCM), for the treatment of coronary heart disease, and its pharmacological activity should be complicated because of its multiple components. Here, we combine text mining with bioinformatics to predict functional networks for the combination. 53 genes related with SMPN were found with text mining. Protein-protein interaction information for these genes from databases and Literature data was searched. Eight highly-connected regions were detected by IPCA algorithm to infer significant complexes or pathways in this network. Over-represented Gene Ontology categories of highly-connected regions by biological network gene ontology tool involved in small GTPase mediated signal transduction, apoptosis, regulation of immune effector process, phosphorylation about enzyme linked receptor protein signaling pathway, positive regulation of biological process. Integrate expression data from six microarray experiments about coronary heart disease into the SMPN network, and use the jActiveModules tool to find active subnetworks in differential expression conditions. The most relevant functions and pathways extracted from these subnetworks were related to proliferation and apoptosis of endothelial cell, apoptosis of arterial smooth muscle cell, apoptosis and regulation of immune system process within macrophages during foam cell formation, cardiocyte apoptosis. Analysis of the subnetwork composition indicated that there were in each subnetworks, and in the most subnetworks were dominant, the nodes came from SM network more than from PN network. It was suggested that, therapeutic efficacies of SMPN should be results of interaction between SM and PN in the multiple pathways and biological processes, and SM maybe play a principal role and PN serve as adjuvant one to assist the effects during the treatment of coronary heart disease. Key words: Panax notoginseng, salvia miltiorrhiza, herbal combination, bioinformatics, text mining, pharamcological activity.

Full Text
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