Network-centered view of coronary artery disease

References

• Helgadottir A, Thorleifsson G, Manolescu A et al. A common variant on chromosome 9p21 affects the risk of myocardial infarction. Science316(5830), 1491–1493 (2007).
   PubMed Web of Science ®Google Scholar
• McPherson R, Pertsemlidis A, Kavaslar N et al. A common allele on chromosome 9 associated with coronary heart disease. Science316(5830), 1488–1491 (2007).
   PubMed Web of Science ®Google Scholar
• Drake TA, Schadt EE, Lusis AJ. Integrating genetic and gene expression data: application to cardiovascular and metabolic traits in mice. Mamm. Genome17(6), 466–479 (2006).
   PubMedGoogle Scholar
• Ghazalpour A, Doss S, Yang X et al. Thematic review series: the pathogenesis of atherosclerosis. Toward a biological network for atherosclerosis. J. Lipid Res.45(10), 1793–1805 (2004).
   PubMed Web of Science ®Google Scholar
• Weiss JN, Yang L, Qu Z. Systems biology approaches to metabolic and cardiovascular disorders: network perspectives of cardiovascular metabolism. J. Lipid Res.47(11), 2355–2366 (2006).
   PubMed Web of Science ®Google Scholar
• Reilly MP, Rader DJ. The metabolic syndrome: more than the sum of its parts? Circulation108(13), 1546–1551 (2003).
   PubMed Web of Science ®Google Scholar
• Aitman TJ, Glazier AM, Wallace CA et al. Identification of Cd36 (Fat) as an insulin-resistance gene causing defective fatty acid and glucose metabolism in hypertensive rats. Nat. Genet.21(1), 76–83 (1999).
   PubMed Web of Science ®Google Scholar
• Alizadeh AA, Eisen MB, Davis RE et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature403(6769), 503–511 (2000).
   PubMed Web of Science ®Google Scholar
• Mootha VK, Lindgren CM, Eriksson KF et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet.34(3), 267–273 (2003).
   PubMed Web of Science ®Google Scholar
• Ghazalpour A, Doss S, Sheth SS et al. Genomic analysis of metabolic pathway gene expression in mice. Genome Biol.6(7), R59 (2005).
   Google Scholar
• Rockman MV, Kruglyak L. Genetics of global gene expression. Nat. Rev. Genet.7(11), 862–872 (2006).
   PubMed Web of Science ®Google Scholar
• Meng H, Vera I, Che N et al. Identification of Abcc6 as the major causal gene for dystrophic cardiac calcification in mice through integrative genomics. Proc. Natl Acad. Sci. USA104(11), 4530–4535 (2007).
   PubMedGoogle Scholar
• Schadt EE, Lamb J, Yang X et al. An integrative genomics approach to infer causal associations between gene expression and disease. Nat. Genet.37(7), 710–717 (2005).
   PubMed Web of Science ®Google Scholar
• Strogatz SH. Exploring complex networks. Nature410(6825), 268–276 (2001).
   PubMed Web of Science ®Google Scholar
• Barabasi AL, Oltvai ZN. Network biology: understanding the cell’s functional organization. Nat. Rev. Genet.5(2), 101–113 (2004).
   PubMed Web of Science ®Google Scholar
• Gargalovic PS, Gharavi NM, Clark MJ et al. The unfolded protein response is an important regulator of inflammatory genes in endothelial cells. Arterioscler. Thromb. Vasc. Biol.26(11), 2490–2496 (2006).
   PubMed Web of Science ®Google Scholar
• Gargalovic PS, Imura M, Zhang B et al. Identification of inflammatory gene modules based on variations of human endothelial cell responses to oxidized lipids. Proc. Natl Acad. Sci. USA103(34), 12741–12746 (2006).
   PubMed Web of Science ®Google Scholar
• Cohen R, Havlin S. Scale-free networks are ultrasmall. Phys. Rev. Lett.90(5), 058701 (2003).
   PubMed Web of Science ®Google Scholar
• Barabasi AL, Albert R. Emergence of scaling in random networks. Science286(5439), 509–512 (1999).
   PubMed Web of Science ®Google Scholar
• Jeong H, Tombor B, Albert R, Oltvai ZN, Barabasi AL. The large-scale organization of metabolic networks. Nature407(6804), 651–654 (2000).
   PubMed Web of Science ®Google Scholar
• Jeong H, Mason SP, Barabasi AL, Oltvai ZN. Lethality and centrality in protein networks. Nature411(6833), 41–42 (2001).
   PubMed Web of Science ®Google Scholar
• Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol.4, (2005) (Article 17).
   PubMedGoogle Scholar
• Sporns O, Tononi G, Kotter R. The human connectome: a structural description of the human brain. PLoS Comput. Biol.1(4), e42 (2005).
   PubMed Web of Science ®Google Scholar
• Romashko DN, Marban E, O’Rourke B. Subcellular metabolic transients and mitochondrial redox waves in heart cells. Proc. Natl Acad. Sci. USA95(4), 1618–1623 (1998).
   PubMed Web of Science ®Google Scholar
• Keating MT, Sanguinetti MC. Molecular and cellular mechanisms of cardiac arrhythmias. Cell104(4), 569–580 (2001).
   PubMed Web of Science ®Google Scholar
• Aon MA, Cortassa S, Marban E, O’Rourke B. Synchronized whole cell oscillations in mitochondrial metabolism triggered by a local release of reactive oxygen species in cardiac myocytes. J.Biol. Chem.278(45), 44735–44744 (2003).
   PubMed Web of Science ®Google Scholar
• Aon MA, Cortassa S, O’Rourke B. Percolation and criticality in a mitochondrial network. Proc. Natl Acad. Sci. USA101(13), 4447–4452 (2004).
   PubMed Web of Science ®Google Scholar
• Akar FG, Aon MA, Tomaselli GF, O’Rourke B. The mitochondrial origin of postischemic arrhythmias. J. Clin. Invest.115(12), 3527–3535 (2005).
   PubMedGoogle Scholar
• Chou CC, Zhou S, Hayashi H et al. Remodelling of action potential and intracellular calcium cycling dynamics during subacute myocardial infarction promotes ventricular arrhythmias in Langendorff-perfused rabbit hearts. J. Physiol.580(Pt 3), 895–906 (2007).
   PubMedGoogle Scholar
• Sato D, Shiferaw Y, Garfinkel A, Weiss JN, Qu Z, Karma A. Spatially discordant alternans in cardiac tissue: role of calcium cycling. Circ. Res.99(5), 520–527 (2006).
   PubMedGoogle Scholar
• Aebersold R, Mann M. Mass spectrometry-based proteomics. Nature422(6928), 198–207 (2003).
   PubMed Web of Science ®Google Scholar
• Collins SR, Kemmeren P, Zhao XC et al. Toward a comprehensive atlas of the physical interactome of Saccharomyces cerevisiae. Mol. Cell. Proteomics6(3), 439–450 (2007).
   PubMed Web of Science ®Google Scholar
• Drake TA, Ping P. Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Proteomics approaches to the systems biology of cardiovascular diseases. J. Lipid Res.48(1), 1–8 (2007).
   PubMed Web of Science ®Google Scholar
• Buerke M, Schwertz H, Langin T et al. Proteome analysis of myocardial tissue following ischemia and reperfusion – effects of complement inhibition. Biochim. Biophys. Acta1764(10), 1536–1545 (2006).
   PubMedGoogle Scholar
• Kim N, Lee Y, Kim H et al. Potential biomarkers for ischemic heart damage identified in mitochondrial proteins by comparative proteomics. Proteomics6(4), 1237–1249 (2006).
   PubMed Web of Science ®Google Scholar
• Zhou SG, Zhou SF, Huang HQ, Chen JW, Huang M, Liu PQ. Proteomic analysis of hypertrophied myocardial protein patterns in renovascularly hypertensive and spontaneously hypertensive rats. J. Proteome Res.5(11), 2901–2908 (2006).
   PubMed Web of Science ®Google Scholar
• Forner F, Foster LJ, Campanaro S, Valle G, Mann M. Quantitative proteomic comparison of rat mitochondria from muscle, heart, and liver. Mol. Cell. Proteomics5(4), 608–619 (2006).
   PubMed Web of Science ®Google Scholar
• Johnson DT, Harris RA, Blair PV, Balaban RS. Functional consequences of mitochondrial proteome heterogeneity. Am. J. Physiol. Cell. Physiol.292(2), C698–C707 (2007).
   Google Scholar
• Johnson DT, Harris RA, French S et al. Tissue heterogeneity of the mammalian mitochondrial proteome. Am. J. Physiol. Cell. Physiol.292(2), C689–C697 (2007).
   PubMed Web of Science ®Google Scholar
• Balaban RS. Maintenance of the metabolic homeostasis of the heart: developing a systems analysis approach. Ann. NY Acad. Sci.1080, 140–153 (2006).
   PubMedGoogle Scholar
• Hopper RK, Carroll S, Aponte AM et al. Mitochondrial matrix phosphoproteome: effect of extra mitochondrial calcium. Biochemistry45(8), 2524–2536 (2006).
   PubMed Web of Science ®Google Scholar
• Lamb J, Crawford ED, Peck D et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science313(5795), 1929–1935 (2006).
   PubMed Web of Science ®Google Scholar
• Farber CR, Lusis AJ. Integrating Global Gene Expression Analysis and Genetics. Rao DC (Ed.). Elsevier, Inc., DC, USA (2007).
   Google Scholar