- Research article
- Open Access
Clarifying off-target effects for torcetrapib using network pharmacology and reverse docking approach
© Fan et al.; licensee BioMed Central Ltd. 2012
- Received: 31 July 2012
- Accepted: 4 December 2012
- Published: 10 December 2012
Torcetrapib, a cholesteryl ester transfer protein (CETP) inhibitor which raises high-density lipoprotein (HDL) cholesterol and reduces low-density lipoprotein (LDL) cholesterol level, has been documented to increase mortality and cardiac events associated with adverse effects. However, it is still unclear the underlying mechanisms of the off-target effects of torcetrapib.
In the present study, we developed a systems biology approach by combining a human reassembled signaling network with the publicly available microarray gene expression data to provide unique insights into the off-target adverse effects for torcetrapib. Cytoscape with three plugins including BisoGenet, NetworkAnalyzer and ClusterONE was utilized to establish a context-specific drug-gene interaction network. The DAVID functional annotation tool was applied for gene ontology (GO) analysis, while pathway enrichment analysis was clustered by ToppFun. Furthermore, potential off-targets of torcetrapib were predicted by a reverse docking approach. In general, 10503 nodes were retrieved from the integrative signaling network and 47660 inter-connected relations were obtained from the BisoGenet plugin. In addition, 388 significantly up-regulated genes were detected by Significance Analysis of Microarray (SAM) in adrenal carcinoma cells treated with torcetrapib. After constructing the human signaling network, the over-expressed microarray genes were mapped to illustrate the context-specific network. Subsequently, three conspicuous gene regulatory networks (GRNs) modules were unearthed, which contributed to the off-target effects of torcetrapib. GO analysis reflected dramatically over-represented biological processes associated with torcetrapib including activation of cell death, apoptosis and regulation of RNA metabolic process. Enriched signaling pathways uncovered that IL-2 Receptor Beta Chain in T cell Activation, Platelet-Derived Growth Factor Receptor (PDGFR) beta signaling pathway, IL2-mediated signaling events, ErbB signaling pathway and signaling events mediated by Hepatocyte Growth Factor Receptor (HGFR, c-Met) might play decisive characters in the adverse cardiovascular effects associated with torcetrapib. Finally, a reverse docking algorithm in silico between torcetrapib and transmembrane receptors was conducted to identify the potential off-targets. This screening was carried out based on the enriched signaling network analysis.
Our study provided unique insights into the biological processes of torcetrapib-associated off-target adverse effects in a systems biology visual angle. In particular, we highlighted the importance of PDGFR, HGFR, IL-2 Receptor and ErbB1tyrosine kinase might be direct off-targets, which were highly related to the unfavorable adverse effects of torcetrapib and worthy of further experimental validation.
- Hepatocyte Growth Factor
- Cholesteryl Ester Transfer Protein
- Cholesteryl Ester Transfer Protein Inhibitor
- ErbB Signaling Pathway
Cardiovascular disease remains to be the most unexceptional cause of morbidity over the past few years in spite of the usage of hydroxymethylglutaryl coenzyme A (HMG CoA) reductase inhibitors (statins) that lower low-density lipoprotein (LDL) cholesterol . Elevated LDL or lowered high-density lipoprotein (HDL) cholesterol level is a crucial risk factor for cardiovascular ailments [2, 3]. Accordingly, raising HDL induced by cholesteryl ester transfer protein (CETP) inhibition is an attractive tactic for anti-atherosclerosis, which may reduce the residual risk of cardiovascular events .
With the rapid development of high-throughput screen (HTS) technology such as microarray, the superiority of systems biology and network pharmacology gradually embodied [7, 8]. Reconstructing networks of biological organism through integrating diverse sources are crucial for comprehending biological processes associated with pathema. Computational biology provides profitable patronage to address the scientific suspense through pragmatic modeling and theoretical exploration, which furnish a brand-new network poly-pharmacology approach for drug identification and discovery . Based on systems biology, it affords a rewarding assistance to improve drug potency and forecast the unwanted off-target effects at a higher efficiency and lower attrition, especially for a new generation of known drugs . In addition, as a crucial technology in drug discovery, reverse docking approach also revealed a prominent performance in understanding the basis of a drug and receptors which provided benignant avails in drug target identification .
To better expound the unfavorable adverse reactions of torcetrapib, a novel network systems approach was proposed by integrating high quality manually curated data with microarray gene expression profiling into a context-specific network, which allowed us to explicate the off-target adverse effects of torcetrapib in a different angle. Detailed illustrations are as follows.
Although statins had been well characterized as the best studied contemporary cardiovascular therapies over the past few years, the optimal approach to LDL reduction remained to be controversial. Meanwhile, the prejudice of low levels of HDL cholesterol in cardiovascular system became increasingly prominent, which had a tight consanguinity with myocardial infarction and death from coronary heart disease (CHD). Thus, strategies targeting HDL had been a therapeutic tactic for anti-atherosclerosis. As a novel CETP inhibitor, torcetrapib had been recognized as one of the auspicious foremost candidates for elevating HDL. However, owing to its high risk of mortality, torcetrapib experienced the battle of “Waterloo”, which overshadowed the entire prospect of anti-cholesterol drugs.
With the speedy development of bioinformatics, organization of knowledge on drug, disease and target inaugurated a brand-new era in drug target identification and discovery. Network pharmacology comprehended the complexity of biological processes by integrating network biology and poly-pharmacological perspective to create predictive models . Network reconstruction and integration of aberrant genes involved in drugs could uncover the capital gene regulatory networks (GRNs) modules which led to the dysfunction of regular biological systems.
After integrating HPRD (Human Protein Reference Database, http://www.hprd.org/) with a manually curated human signaling network acquired from Cui et al. , the over-expressed microarray data originated from human adrenal carcinoma cells treated with torcetrapib were mapped to construct the context-specific network. Cytoscape (http://www.cytoscape.org/), an open source package for visualizing complex networks and integrating diverse types of resources, is an indispensable platform for bioinformatics, social network analysis and network pharmacology . The drug-gene interaction network of torcetrapib was established utilizing three plugins, including BisoGenet , NetworkAnalyzer and ClusterONE . Molecular relations (protein-protein and protein/DNA interactions) were connected based on SysBiomics platform (http://biomine.cigb.edu.cu/sysbiomics/). GRNs communities, which reflected the situation of torcetrapib-associated over-expressed genes, were detected in MCODE algorithm. The DAVID functional annotation tool (http://david.abcc.ncifcrf.gov/) [17, 18] and ToppFun web server (http://toppgene.cchmc.org/enrichment.jsp)  were employed freely to identify the significantly-represented biological processes and the enriched signaling pathways, respectively.
An in silico drug target reverse searching method was applied for screening potential off-targets of torcetrapib. Reverse docking, a flexible ligand-receptors inverse docking program, conducted computer-automated search of potential targets of a small molecule by docking it to a cavity of each receptor. To optimize docking parameter, an accurate docking module in Discovery Studio (version 2.5, Accelrys) named CDOCKER was employed. The cavity of each protein was derived from the three dimensional structures of Protein Data Bank (PDB, http://www.rcsb.org/) based on the enriched pathways. Proteins with high binding affinity with torcetrapib were considered to be the most potential direct off-targets.
Torcetrapib-associated signaling map construction
GRNs modules excavation
Gene ontology (GO) analysis
Pathway enrichment analysis
Main enriched signaling pathways of torcetrapib related to its adverse reactions (FDR<0.05)
BioCarta: IL-2 Receptor Beta Chain in T cell Activation
NCI-Nature Curated: PDGFR-beta signaling pathway
NCI-Nature Curated: IL2-mediated signaling events
KEGG pathway: ErbB signaling pathway
NCI-Nature Curated: Signaling events mediated by Hepatocyte Growth Factor Receptor (c-Met)
Reactome: Genes involved in mRNA Decay by 3’ to 5’ Exoribonuclease
Reactome: Genes involved in Metabolism of mRNA
Reactome: Genes involved in Metabolism of RNA
Reverse docking analysis
Off-targets candidates for torcetrapib identified by reverse docking procedure
Target details (PDB Code)
Binding score (kcal/mol)
IL-2 receptor (4HCV)
compound 13 J
IL2-mediated signaling events and activation of T cell receptor pathway mediated by IL-2 gave rise to the unwanted effects for torcetrapib
Among the myriad of intra-cellular signaling networks that governed the pathogenesis of cardiovascular event, activation of T cell receptor signaling mediated by IL-2 awoke our concern. Recently, numerous evidences illustrated that the pathological proceeding of atherosclerosis had an intimate relation with chronic inflammation . As a primary regulator of immune cell, the characteristics of T cell receptor pathway mediated by IL-2 in atherosclerosis had been certificated [22–25]. Lipid deposition and infiltration of inflammatory cells were responsible for the formation of atherosclerosis and a variety of cells such as T lymphocytes, monocytes, macrophages, endothelial cells, platelet and vascular smooth muscle cells were engaged in the occurrence and progression of atherosclerosis. Meanwhile, leukocyte adhesion molecules and inflammatory chemokines were other elements which facilitated the accumulation of plaques. T cells activated by IL-2 in the arterial vessel played a momentous function in atherosclerosis, which induced apoptosis of vascular smooth muscle cells and facilitated the formation of plaques .
Similarly, hypertension is also considered to be an inflammatory pathema [27, 28]. Considerable documents illustrated that T cells could stimulate the release of cytokines and inflammatory factors, which resulted in hypertension and myocardial fibrosis. As a vasoactive peptide, angiotensin II (AngII) was identified as a crucial factor in the development of hypertension. Activated T cells mediated by IL-2 had been authenticated to be rich in AngII receptor, which could promote the migration of dendritic cells  and amplify inflammation through autocrine [30, 31]. More and more evidences attested the relations between experimental hypertension and T cell immune activation. Guzik et al.  found that mice continuously infiltrated with AngII exhibited extraordinary abnormalities of T cell. Further studies disclosed that AngII significantly increased the amount of T cell in the perivascular adipose tissue via enrichment of CD69/CD44 or activation of Chemokines (C-C motif) receptor 5, which subsequently elevated the level of T lymphocytes in the peripheral circulatory system. Thus, the off-target prediction was applied by docking torcetrapib to IL-2 receptor.
PDGFR-beta signaling pathway and the adverse effects of torcetrapib
Platelet derived growth factor (PDGF), a 24ku cationic glycoprotein, mainly indwelt in platelet alpha granule, impaired endothelial cell, macrophages, smooth muscle cells, fibroblasts and mesangia cells, which mediated multiple interactions between tissues and endothelial cells through releasing PDGF in an autocrine and paracrine chain amplificated reaction forms [33, 34]. A variety of mechanisms involved in the development of atherosclerosis had been reported to be highly associated with PDGF. Cagnin et al.  discovered that a high level of PDGF and interleukin was detected in patients with atherosclerosis, suggesting that PDGF could influence the proceeding of atherosclerosis in association with inflammatory factors. Additionally, Cha et al.  also observed proliferation and migration in smooth muscle cell after PDGF treatment in cultured human aortic smooth muscle cells in vitro, which indicated that PDGF could facilitate the formation of atherosclerosis via accelerating the migration and proliferation of plaque.
Despite the fact that percutaneous coronary intervention (PCI) was one of the most effective therapeutic approaches for CHD by far, restenosis after stenting was still unavoidable, which affected the long term efficacy. Li and colleagues  disclosed that the increased expression of PDGF mRNA was found on carotid artery balloon dilatation rat. Experimental results suggested that PDGF could activate its upstream pathways via directly binding PDGFR-β, which initiated intermediate signal protein, activated mitogen activated protein kinase pathway (MAPK) cascade afterwards and promoted proliferation, migration and angiogenesis in smooth muscle cells through dimerization and autophosphorylation of tyrosine residues phosphorylated [38, 39]. Chintalgattu’s research  uncovered an elevation of PDGFR-β in cardiac pressure overload mice, implicating that PDGFR-β was a compensatory reaction in heart under pressure load, which depicted the intimate relationship between the activation of PDGFR signaling pathway and cardiovascular diseases.
Aberrant ErbB pathway was associated with the off-target effects for torcetrapib
Except for cancer, the ErbB family of four receptor tyrosine kinases (ErbB1, ErbB2, ErbB3 and ErbB4) also engaged in certain non-neoplastic pathologies, such as hypertension , infectious diseases  and chronic renal dysfunction . More recent studies have demonstrated that neuregulins (NRGs)/ErbB1 signaling pathway was essential for normal myocardial development and pathological vasoconstriction, especially in cardiac smooth muscle . One such momentous NRGs was heparin-binding (HB)-EGF. Hao et al.  reported that the activation of ErbB1 receptor mediated by HB-EGF played a significant role in cardio-vasculature and hypertension, which facilitated the formation of atherosclerotic plaque and vascular stenosis.
HGFR pathway contributed to the unfavorable effects of torcetrapib
As a heparin binding glycoprotein originated from mesenchymal cells, hepatocyte growth factor (HGF) possessed various biological activities including regulating mitosis, morphogenesis, hematopoiesis, myocardial hypertrophy, angiogenesis, fibrosis and tissue regeneration, which were took effect via binding HGF specific receptor kinase (c-Met) . HGF promoted mitosis and revealed anti-apoptosis effect on vascular endothelial cells. Meanwhile, there was no stimulation of HGF on the growth of smooth muscle cells, suggesting that it was a specific endothelial cell growth factor and injury repaired factor . Previously, we found that HGF played profitable prothetic roles in the pathogenesis of CHD, especially for atherosclerosis. The autocrine or paracrine mechanisms of HGF was reduced by high concentration of transforming growth factor β (TGF-β) and AngII after endothelial damage in atherosclerosis, which resulted in the elevation of serum HGF produced by lung, liver and kidney to regulate the proliferation or migration of vascular endothelial and smooth muscle cells [47, 48].
As a CETP inhibitor, torcetrapib could activate relevant signaling pathways mentioned above through directly binding PDGFR, HGFR, IL-2 Receptor and ErbB1tyrosine kinase and up-regulating CBL, SOCS1, JAK1, JUN, TGFBR2 and EXOSC6 afterward, which subsequently exerted the exacerbation of endothelium injury and increased cardiovascular events . Thus, a synergetic combination of anti-hypertensive drugs such as angiotensin converting enzyme inhibitors (ACEIs) was proposed to be an effective and beneficial strategy to decrease torcetrapib-associated off-target unfavorable effects in cardiovascular system .
A whole genomic drug-gene interaction network based on the integrative manually curated signaling network and microarray profiles was established to explicate the potential off-target effects for torcetrapib. Totally, three momentous GRNs modules which might have a close relationship with the unwanted effects of torcetrapib were mined. Meanwhile, enriched analysis was carried out and certain significant enriched pathways were detected, which had been reported to have a definite correlation with cardiovascular maladjustment. In particular, we highlighted the importance of IL-2 Receptor Beta Chain in T cell Activation, PDGFR-beta signaling pathway, IL2-mediated signaling events, ErbB signaling pathway and signaling events mediated by HGFR (c-Met) and revealed that PDGFR, HGFR, IL-2 Receptor and ErbB1tyrosine kinase were direct off-targets for torcetrapib.
Taken together, these findings suggested that the network off-target effects prediction methods in silico were profitable for illustrating the relationship between drug and disease related off-targets for interventions. However, due to the false positive connection and noises in the reassembled network, the predictive model in this study was still far more completed. We proposed that our study on the off-target effects of torcetrapib based on network pharmacology will provide beneficial insights for further experimental validations.
Microarray data analysis
The microarray gene expression profiling associated with torcetrapib was acquired from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) database under the accession number GDS3556 . This data set was derived from a study on H295 adrenal carcinoma cells treated with blank solvent, AngII and torcetrapib. Analysis of differently expression gene was performed by Significance Analysis of Microarray (SAM) . If the fold change>1.2 and False Discovery Rate (FDR)<0.05, gene expression was considered significantly different.
Human signaling network construction
To establish a comprehensive human signaling network, we manually curated the cellular signaling molecules which integrated diverse pathways resources including BioCarta, literature-mined network, Cancer Cell Map  and HPRD. An open source platform for complex network analysis and visualization named Cytoscape was freely utilized to assemble the drug-gene interaction network . Molecular inter-relations in the integrative network were added using BisoGenet plugin from various databases including BIND, HPRD, MINT, DPI, BIOGRID and INTACT .
Functional enrichment analysis
Functional enrichment analysis was applied to identify primary biological processes, which provided clues to the underlying molecular mechanisms related to the adverse effects of torcetrapib. Significant clustering of genes was mined by MCODE algorithm . All GRNs modules were classified by DAVID functional annotation tool [17, 18] to perform GO analysis on the basis of “GOTERM_BP_FAT”, whilst pathway enrichment analysis was clustered by ToppFun .
Chemical structures of all ligands utilized in reverse docking protocol were generated by CambridgeSoft ChemOffice 2008. Ligands were prepared by adding charges, hydrogen and applying force field in Discovery Studio environment. Energy was also minimized with ChARMm force field before performing docking. The random conformations search of torcetrapib was conducted utilizing a high temperature simulated annealing dynamics scheme. Ligands were heated to 700 K in 2000 steps, followed by annealing to 300 K in 5000 steps. Ten random conformations were generated and a final minimization was introduced to each docking poses.
The three dimensional structures of proteins were obtained from PDB, which contains information about experimentally-determined structures of proteins, nucleic acids and complex assemblies. Drug targets were downloaded with high resolution and without mutation or missing residues around the active site. Ligands, oligomeric chains, water molecules or solvent were spilt from proteins. All proteins were remedied through the “Prepare Protein” command in Discovery Studio protocols, which added hydrogen, fixed the missing side chains, corrected connectivity or bond orders and adjusted residue protonation states to PH 7.0.
Binding site analysis
For binding site identification, a ligand-based approach was used for identifying the potential binding sites via “Define and Edit Binding Site” tool in Discovery Studio. Ligand-based similarity search method, a strategy utilizing compounds that are known to bind to the desired targets to identify the targets of other compounds with similar properties, is an indispensable technology that is gaining increasing usage in drug discovery. In the present study, search was performed on the global surface of the protein by similarity and substructure searching , and the automatic identification of binding sphere was considered as highly significant.
A reverse docking algorithm, the opposite of a “direct” docking approach, was conducted by CDOCKER to hunt for potential targets of torcetrapib based on the enriched signaling pathways. CDOCKER, an implementation protocol in Discovery Studio environment, is a grid-based simulated annealing (several cycles) docking method through CHARMm force field docking tool . Docking was performed using the default setting, which can avoid a potential reduction in docking accuracy.
This work was supported by the National Natural Science Foundation of China (No. 91129727, 81020108031, 30973558, 30901815, 30901803, 81270049), the Major Specialized Research Fund from the Ministry of Science and Technology in China (No. 2009ZX09103-144, No. 2009ZX09301-010) and Research Fund from Ministry of Education of China (111 Projects No.B07001).
- Bays H, Stein EA: Pharmacotherapy for dyslipidaemia–current therapies and future agents. Expert Opin Pharmacother. 2003, 4 (11): 1901-1938. 10.1517/14656522.214.171.1241.View ArticleGoogle Scholar
- Assmann G, Gotto AJ: HDL cholesterol and protective factors in atherosclerosis. Circulation. 2004, 109 (23 Suppl 1): I8-I14.Google Scholar
- Demarin V, Lisak M, Morovic S, Cengic T: Low high-density lipoprotein cholesterol as the possible risk factor for stroke. Acta Clin Croat. 2010, 49 (4): 429-439.Google Scholar
- Shinkai H: Cholesteryl ester transfer-protein modulator and inhibitors and their potential for the treatment of cardiovascular diseases. Vasc Health Risk Manag. 2012, 8: 323-331.View ArticleGoogle Scholar
- Clark RW, Sutfin TA, Ruggeri RB, Willauer AT, Sugarman ED, Magnus-Aryitey G, Cosgrove PG, Sand TM, Wester RT, Williams JA, Perlman ME, Bamberger MJ: Raising high-density lipoprotein in humans through inhibition of cholesteryl ester transfer protein: an initial multidose study of torcetrapib. Arterioscler Thromb Vasc Biol. 2004, 24 (3): 490-497. 10.1161/01.ATV.0000118278.21719.17.View ArticleGoogle Scholar
- Barter PJ, Rye KA, Tardif JC, Waters DD, Boekholdt SM, Breazna A, Kastelein JJ: Effect of torcetrapib on glucose, insulin, and hemoglobin A1c in subjects in the investigation of lipid level management to understand its impact in atherosclerotic events (ILLUMINATE) trial. Circulation. 2011, 124 (5): 555-562. 10.1161/CIRCULATIONAHA.111.018259.View ArticleGoogle Scholar
- Hopkins AL: Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol. 2008, 4 (11): 682-690. 10.1038/nchembio.118.View ArticleGoogle Scholar
- Kitano H: Systems biology: a brief overview. Science. 2002, 295 (5560): 1662-1664. 10.1126/science.1069492.View ArticleGoogle Scholar
- Kortagere S, Lill M, Kerrigan J: Role of computational methods in pharmaceutical sciences. Methods Mol Biol. 2012, 929: 21-48. 10.1007/978-1-62703-050-2_3.View ArticleGoogle Scholar
- Xie L, Xie L, Bourne PE: Structure-based systems biology for analyzing off-target binding. Curr Opin Struct Biol. 2011, 21 (2): 189-199. 10.1016/j.sbi.2011.01.004.View ArticleGoogle Scholar
- Chen YZ, Zhi DG: Ligand-protein inverse docking and its potential use in the computer search of protein targets of a small molecule. Proteins. 2001, 43 (2): 217-226. 10.1002/1097-0134(20010501)43:2<217::AID-PROT1032>3.0.CO;2-G.View ArticleGoogle Scholar
- Zhao S, Iyengar R: Systems pharmacology: network analysis to identify multiscale mechanisms of drug action. Annu Rev Pharmacol Toxicol. 2012, 52: 505-521. 10.1146/annurev-pharmtox-010611-134520.View ArticleGoogle Scholar
- Cui Q, Ma Y, Jaramillo M, Bari H, Awan A, Yang S, Zhang S, Liu L, Lu M, O’Connor-McCourt M, Purisima EO, Wang E: A map of human cancer signaling. Mol Syst Biol. 2007, 3: 152-View ArticleGoogle Scholar
- Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13 (11): 2498-2504. 10.1101/gr.1239303.View ArticleGoogle Scholar
- Martin A, Ochagavia ME, Rabasa LC, Miranda J, Fernandez-de-Cossio J, Bringas R: BisoGenet: a new tool for gene network building, visualization and analysis. BMC Bioinforma. 2010, 11: 91-10.1186/1471-2105-11-91.View ArticleGoogle Scholar
- Nepusz T, Yu H, Paccanaro A: Detecting overlapping protein complexes in protein-protein interaction networks. Nat Methods. 2012, 9 (5): 471-472. 10.1038/nmeth.1938.View ArticleGoogle Scholar
- Huang DW, Sherman BT, Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009, 4 (1): 44-57.View ArticleGoogle Scholar
- Huang DW, Sherman BT, Lempicki RA: Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009, 37 (1): 1-13. 10.1093/nar/gkn923.View ArticleGoogle Scholar
- Chen J, Bardes EE, Aronow BJ, Jegga AG: ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 2009, 37 (Web Server issue): W305-W311.View ArticleGoogle Scholar
- Davidson E, Levin M: Gene regulatory networks. Proc Natl Acad Sci U S A. 2005, 102 (14): 4935-10.1073/pnas.0502024102.View ArticleGoogle Scholar
- Tousoulis D, Davies G, Stefanadis C, Toutouzas P, Ambrose JA: Inflammatory and thrombotic mechanisms in coronary atherosclerosis. Heart. 2003, 89 (9): 993-997. 10.1136/heart.89.9.993.View ArticleGoogle Scholar
- Mottaghi A, Salehi E, Sezavar H, Keshavarz SA, Eshraghian MR, Rezaei N, Rejali L, Saboor-Yaraghi AA: The in vitro effect of oxidized LDL and PHA on proliferation and gene expression of regulatory T cells in patients with atherosclerosis. Iran J Allergy Asthma Immunol. 2012, 11 (3): 217-223.Google Scholar
- Dinh TN, Kyaw TS, Kanellakis P, To K, Tipping P, Toh BH, Bobik A, Agrotis A: Cytokine therapy with interleukin-2/anti-interleukin-2 monoclonal antibody complexes expands CD4 + CD25 + Foxp3+ regulatory T cells and attenuates development and progression of atherosclerosis. Circulation. 2012, 126 (10): 1256-1266. 10.1161/CIRCULATIONAHA.112.099044.View ArticleGoogle Scholar
- Xiong YS, Wu AL, Lin QS, Yu J, Li C, Zhu L, Zhong RQ: Contribution of monocytes Siglec-1 in stimulating T cells proliferation and activation in atherosclerosis. Atherosclerosis. 2012, 224 (1): 58-65. 10.1016/j.atherosclerosis.2012.06.063.View ArticleGoogle Scholar
- Ammirati E, Monaco C, Norata GD: Antigen-dependent and antigen-independent pathways modulate CD4 + CD28 null T-cells during atherosclerosis. Circ Res. 2012, 111 (2): e48-e51. 10.1161/CIRCRESAHA.112.271627.View ArticleGoogle Scholar
- Gotsman I, Sharpe AH, Lichtman AH: T-cell costimulation and coinhibition in atherosclerosis. Circ Res. 2008, 103 (11): 1220-1231. 10.1161/CIRCRESAHA.108.182428.View ArticleGoogle Scholar
- El CH, Hassoun PM: Immune and inflammatory mechanisms in pulmonary arterial hypertension. Prog Cardiovasc Dis. 2012, 55 (2): 218-228. 10.1016/j.pcad.2012.07.006.View ArticleGoogle Scholar
- Morillas P, de Andrade H, Castillo J, Quiles J, Bertomeu-Gonzalez V, Cordero A, Tarazon E, Rosello E, Portoles M, Rivera M, Bertomeu-Martínez V: Inflammation and apoptosis in hypertension. Relevance of the extent of target organ damage. Rev Esp Cardiol. 2012, 65 (9): 819-825. 10.1016/j.recesp.2012.03.020.View ArticleGoogle Scholar
- Hernandez-Presa MA, Bustos C, Ortego M, Tunon J, Ortega L, Egido J: ACE inhibitor quinapril reduces the arterial expression of NF-kappaB-dependent proinflammatory factors but not of collagen I in a rabbit model of atherosclerosis. Am J Pathol. 1998, 153 (6): 1825-1837. 10.1016/S0002-9440(10)65697-0.View ArticleGoogle Scholar
- Hoch NE, Guzik TJ, Chen W, Deans T, Maalouf SA, Gratze P, Weyand C, Harrison DG: Regulation of T-cell function by endogenously produced angiotensin II. Am J Physiol Regul Integr Comp Physiol. 2009, 296 (2): R208-R216.View ArticleGoogle Scholar
- Nataraj C, Oliverio MI, Mannon RB, Mannon PJ, Audoly LP, Amuchastegui CS, Ruiz P, Smithies O, Coffman TM: Angiotensin II regulates cellular immune responses through a calcineurin-dependent pathway. J Clin Invest. 1999, 104 (12): 1693-1701. 10.1172/JCI7451.View ArticleGoogle Scholar
- Guzik TJ, Hoch NE, Brown KA, McCann LA, Rahman A, Dikalov S, Goronzy J, Weyand C, Harrison DG: Role of the T cell in the genesis of angiotensin II induced hypertension and vascular dysfunction. J Exp Med. 2007, 204 (10): 2449-2460. 10.1084/jem.20070657.View ArticleGoogle Scholar
- Korpisalo P, Karvinen H, Rissanen TT, Kilpijoki J, Marjomaki V, Baluk P, McDonald DM, Cao Y, Eriksson U, Alitalo K, Ylä-Herttuala S: Vascular endothelial growth factor-A and platelet-derived growth factor-B combination gene therapy prolongs angiogenic effects via recruitment of interstitial mononuclear cells and paracrine effects rather than improved pericyte coverage of angiogenic vessels. Circ Res. 2008, 103 (10): 1092-1099. 10.1161/CIRCRESAHA.108.182287.View ArticleGoogle Scholar
- Yamamoto S, Fukumoto E, Yoshizaki K, Iwamoto T, Yamada A, Tanaka K, Suzuki H, Aizawa S, Arakaki M, Yuasa K, Oka K, Chai Y, Nonaka K, Fukumoto S: Platelet-derived growth factor receptor regulates salivary gland morphogenesis via fibroblast growth factor expression. J Biol Chem. 2008, 283 (34): 23139-23149. 10.1074/jbc.M710308200.View ArticleGoogle Scholar
- Cagnin S, Biscuola M, Patuzzo C, Trabetti E, Pasquali A, Laveder P, Faggian G, Iafrancesco M, Mazzucco A, Pignatti PF, Lanfranchi G: Reconstruction and functional analysis of altered molecular pathways in human atherosclerotic arteries. BMC Genomics. 2009, 10: 13-10.1186/1471-2164-10-13.View ArticleGoogle Scholar
- Cha BY, Shi WL, Yonezawa T, Teruya T, Nagai K, Woo JT: An inhibitory effect of chrysoeriol on platelet-derived growth factor (PDGF)-induced proliferation and PDGF receptor signaling in human aortic smooth muscle cells. J Pharmacol Sci. 2009, 110 (1): 105-110. 10.1254/jphs.08282FP.View ArticleGoogle Scholar
- Li D, Ma S, Yang Y, Yang D, Li G, Zhang X, Zhu J, Sun M, Tang B: BTEB2 knockdown suppresses neointimal hyperplasia in a rat artery balloon injury model. Mol Med Report. 2011, 4 (3): 413-417.Google Scholar
- Shim AH, Liu H, Focia PJ, Chen X, Lin PC, He X: Structures of a platelet-derived growth factor/propeptide complex and a platelet-derived growth factor/receptor complex. Proc Natl Acad Sci U S A. 2010, 107 (25): 11307-11312. 10.1073/pnas.1000806107.View ArticleGoogle Scholar
- Kim TJ, Lee JH, Lee JJ, Yu JY, Hwang BY, Ye SK, Shujuan L, Gao L, Pyo MY, Yun YP: Corynoxeine isolated from the hook of Uncaria rhynchophylla inhibits rat aortic vascular smooth muscle cell proliferation through the blocking of extracellular signal regulated kinase 1/2 phosphorylation. Biol Pharm Bull. 2008, 31 (11): 2073-2078. 10.1248/bpb.31.2073.View ArticleGoogle Scholar
- Chintalgattu V, Ai D, Langley RR, Zhang J, Bankson JA, Shih TL, Reddy AK, Coombes KR, Daher IN, Pati S, Patel SS, Pocius JS, Taffet GE, Buja LM, Entman ML, Khakoo AY: Cardiomyocyte PDGFR-beta signaling is an essential component of the mouse cardiac response to load-induced stress. J Clin Invest. 2010, 120 (2): 472-484. 10.1172/JCI39434.View ArticleGoogle Scholar
- Hao L, Du M, Lopez-Campistrous A, Fernandez-Patron C: Agonist-induced activation of matrix metalloproteinase-7 promotes vasoconstriction through the epidermal growth factor-receptor pathway. Circ Res. 2004, 94 (1): 68-76. 10.1161/01.RES.0000109413.57726.91.View ArticleGoogle Scholar
- Wang X, Huong SM, Chiu ML, Raab-Traub N, Huang ES: Epidermal growth factor receptor is a cellular receptor for human cytomegalovirus. Nature. 2003, 424 (6947): 456-461. 10.1038/nature01818.View ArticleGoogle Scholar
- Lautrette A, Li S, Alili R, Sunnarborg SW, Burtin M, Lee DC, Friedlander G, Terzi F: Angiotensin II and EGF receptor cross-talk in chronic kidney diseases: a new therapeutic approach. Nat Med. 2005, 11 (8): 867-874. 10.1038/nm1275.View ArticleGoogle Scholar
- Baliga RR, Pimental DR, Zhao YY, Simmons WW, Marchionni MA, Sawyer DB, Kelly RA: NRG-1-induced cardiomyocyte hypertrophy. Role of PI-3-kinase, p70(S6K), and MEK-MAPK-RSK. Am J Physiol. 1999, 277 (5 Pt 2): H2026-H2037.Google Scholar
- Nieder C, Andratschke N, Jeremic B, Molls M: Comparison of serum growth factors and tumor markers as prognostic factors for survival in non-small cell lung cancer. Anticancer Res. 2003, 23 (6D): 5117-5123.Google Scholar
- Taniyama Y, Morishita R, Aoki M, Hiraoka K, Yamasaki K, Hashiya N, Matsumoto K, Nakamura T, Kaneda Y, Ogihara T: Angiogenesis and antifibrotic action by hepatocyte growth factor in cardiomyopathy. Hypertension. 2002, 40 (1): 47-53. 10.1161/01.HYP.0000020755.56955.BF.View ArticleGoogle Scholar
- Zhu Y, Hojo Y, Ikeda U, Shimada K: Production of hepatocyte growth factor during acute myocardial infarction. Heart. 2000, 83 (4): 450-455. 10.1136/heart.83.4.450.View ArticleGoogle Scholar
- Morishita R, Nakamura S, Nakamura Y, Aoki M, Moriguchi A, Kida I, Yo Y, Matsumoto K, Nakamura T, Higaki J, Ogihara T: Potential role of an endothelium-specific growth factor, hepatocyte growth factor, on endothelial damage in diabetes. Diabetes. 1997, 46 (1): 138-142. 10.2337/diabetes.46.1.138.View ArticleGoogle Scholar
- Park JK, Mervaala EM, Muller DN, Menne J, Fiebeler A, Luft FC, Haller H: Rosuvastatin protects against angiotensin II-induced renal injury in a dose-dependent fashion. J Hypertens. 2009, 27 (3): 599-605. 10.1097/HJH.0b013e32831ef369.View ArticleGoogle Scholar
- Edgar R, Domrachev M, Lash AE: Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002, 30 (1): 207-210. 10.1093/nar/30.1.207.View ArticleGoogle Scholar
- Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A. 2001, 98 (9): 5116-5121. 10.1073/pnas.091062498.View ArticleGoogle Scholar
- Mestres J, Knegtel RMA: Similarity versus docking in 3D virtual screening. Perspect Drug Des Discov. 2000, 20: 191-207. 10.1023/A:1008789224614.View ArticleGoogle Scholar
- Wu G, Robertson DH, Brooks CR, Vieth M: Detailed analysis of grid-based molecular docking: a case study of CDOCKER-A CHARMm-based MD docking algorithm. J Comput Chem. 2003, 24 (13): 1549-1562. 10.1002/jcc.10306.View ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.