Abstract
Investigation of the potential therapeutic mechanisms of drug candidates is an essential step in the process of new drug discovery. With the rapid development of systems biology, recent network analyses of proteins, drugs, and diseases have enabled great progress in delineating the molecule mechanisms of drug candidates. However, most analyses perform a direct association between gene/protein and disease levels without considering the intermediate biological pathways regulated by the drugs. Given that a protein performs its biological roles through pathways, we propose using a novel pathway-pathway network analysis to investigate the potential therapeutic functions of the drug candidates. Many studies have demonstrated that salvianolic acid B (Sal B) of Salvia miltiorrhiza is an effective therapy for cardiovascular diseases (CVD). Using molecular docking methods to identify direct interacting targets of Sal B, we collected all Sal B-regulated proteins with supporting experimental evidence in PubMed abstracts. FDA-approved CVD drugs and their corresponding targets were also collected. From a traditional drug-protein network analysis, we found that Sal B could affect ACE and REN of the renin-angiotensin-aldosterone system to relax vessels and alleviate hypertension. Subsequent pathway-pathway network analysis was attempted to study the mechanisms of Sal B in treating CVD, and demonstrated that Sal B regulates immunity/inflammation, apoptosis, ion transport and basic metabolism processes in the treatment of CVD. Regulating the immune/inflammation process may be the major mechanism of Sal B. We believe that pathway-pathway network analysis is a novel method for studying the therapeutic mechanisms of herbal ingredients.
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Zhu M, Gao L, Li X, et al. The analysis of the drug-targets based on the topological properties in the human protein-protein interaction network. J Drug Targeting, 2009, 17: 524–532
Chen J Y, Yan Z, Shen C, et al. A systems biology approach to the study of cisplatin drug resistance in ovarian cancers. J Bioinform Comput Biol, 2007, 5: 383–405
Chu L, Chen B. Construction of a cancer-perturbed protein-protein interaction network for discovery of apoptosis drug targets. BMC Syst Biol, 2008, 2: 56
Yang L, Chen J, Shi L, et al. Identifying unexpected therapeutic targets via chemical-protein interactome. PLoS One, 2010, 5: e9568
Lamb J, Crawford E D, Peck D, et al. The connectivity map: Using gene-expression signatures to connect small molecules, genes, and disease. Science, 2006, 313: 1929–1935
Yue Q X, Cao Z W, Guan S H, et al. Proteomics characterization of the cytotoxicity mechanism of ganoderic acid D and computer-automated estimation of the possible drug target network. Mol Cell Proteomics, 2008, 7: 949–961
Barabási A, Oltvai Z N. Network biology: Understanding the cell’s functional organization. Nat Rev Genet, 2004, 5: 101–113
Kanehisa M, Goto S, Furumichi M, et al. KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res, 2010, 38: D355–D360
Zhao J, Jiang P, Zhang W. Molecular networks for the study of TCM pharmacology. Brief Bioinform, 2010, 11: 417–430
Sucher N J. Insights from molecular investigations of traditional Chinese herbal stroke medicines: Implications for neuroprotective epilepsy therapy. Epilepsy Behav, 2006, 8: 350–362
Wang L, Zhou G, Liu P, et al. Dissection of mechanisms of Chinese medicinal formula Realgar-Indigo naturalis as an effective treatment for promyelocytic leukemia. Proc Natl Acad Sci USA, 2008, 105: 4826–4831
Lu Y, Liu X, Liang X, et al. Metabolomic strategy to study therapeutic and synergistic effects of tanshinone IIA, salvianolic acid B and ginsenoside Rb1 in myocardial ischemia rats. J Ethnopharmacol, 2011, 134: 45–49
Yang T, Lin F, Chen Y, et al. Salvianolic acid B inhibits low-density lipoprotein oxidation and neointimal hyperplasia in endothelium-denuded hypercholesterolaemic rabbits. J Sci Food Agric, 2011, 91: 134–141
Hartwell L H, Hopfield J J, Leibler S, et al. From molecular to modular cell biology. Nature, 1999, 402: C47–C52
Kuntz I D. Structure-based strategies for drug design and discovery. Science, 1992, 257: 1078–1082
Goodsell D S, Morris G M, Olson A J. Automated docking of flexible ligands: Applications of AutoDock. J Mol Recognit, 1996, 9: 1–5
Rarey M, Kramer B, Lengauer T, et al. A fast flexible docking method using an incremental construction algorithm. J Mol Biol, 1996, 261: 470–489
Chen Y Z, Zhi D G. Ligand-protein inverse docking and its potential use in the computer search of protein targets of a small molecule. Proteins, 2001, 43: 217–226
Ashburner M, Ball C A, Blake J A, et al. Gene ontology: Tool for the unification of biology. The gene ontology consortium. Nat Genet, 2000, 25: 25–29
Guimerà R, Nunes A L A. Functional cartography of complex metabolic networks. Nature, 2005, 433: 895–900
Newman M E J, Girvan M. Finding and evaluating community structure in networks. Phys Rev E Stat Nonlin Soft Matter Phys, 2004, 69: 026113
Zhang R, Xu X, Chen T, et al. An assay for angiotensin-converting enzyme using capillary zone electrophoresis. Anal Biochem, 2000, 280: 286–290
Gao X, Xu D, Deng Y, et al. Screening of angiotensin converting enzyme inhibitors from Salvia miltiorrhizae (in Chinese). China Journal of Chinese Materia Medica, 2004, 29: 359–362
Cline M S, Smoot M, Cerami E, et al. Integration of biological networks and gene expression data using Cytoscape. Nat Protoc, 2007, 2: 2366–2382
Hunter J J, Chien K R. Signaling pathways for cardiac hypertrophy and failure. N Engl J Med, 1999, 341: 1276–1283
Muslin A J. MAPK signalling in cardiovascular health and disease: Molecular mechanisms and therapeutic targets. Clin Sci, 2008, 115: 203–218
Saarikangas J, Zhao H, Lappalainen P. Regulation of the actin cytoskeleton-plasma membrane interplay by phosphoinositides. Phys Rev, 2010, 90: 259–289
Hansson G K, Hermansson A. The immune system in atherosclerosis. Nat Immunol, 2011, 12: 204–212
Lee Y, Gustafsson A B. Role of apoptosis in cardiovascular disease. Apoptosis, 2009, 14: 536–548
Rosenfeld M E. Cellular mechanisms in the development of atherosclerosis. Diabetes Res Clin Pract, 1996, 30: 1–11
Chen Y H, Lin S J, Ku H H, et al. Salvianolic acid B attenuates VCAM-1 and ICAM-1 expression in TNF-alpha-treated human aortic endothelial cells. J Cell Biochem, 2001, 82: 512–521
Shi C, Huang H, Wu H, et al. Salvianolic acid B modulates hemostasis properties of human umbilical vein endothelial cells. J Cell Biochem, 2007, 119: 769–775
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Ye, L., He, Y., Ye, H. et al. Pathway-pathway network-based study of the therapeutic mechanisms by which salvianolic acid B regulates cardiovascular diseases. Chin. Sci. Bull. 57, 1672–1679 (2012). https://doi.org/10.1007/s11434-012-5142-y
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DOI: https://doi.org/10.1007/s11434-012-5142-y