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Systems-based approaches to cardiovascular disease

Nature Reviews Cardiology volume 9, pages 172–184 (2012)Cite this article

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Abstract

Common cardiovascular diseases, such as atherosclerosis and congestive heart failure, are exceptionally complex, involving a multitude of environmental and genetic factors that often show nonlinear interactions as well as being highly dependent on sex, age, and even the maternal environment. Although focused, reductionistic approaches have led to progress in elucidating the pathophysiology of cardiovascular diseases, such approaches are poorly powered to address complex interactions. Over the past decade, technological advances have made it possible to interrogate biological systems on a global level, raising hopes that, in combination with computational approaches, it may be possible to more fully address the complexities of cardiovascular diseases. In this Review, we provide an overview of such systems-based approaches to cardiovascular disease and discuss their translational implications.

Key Points

  • The cardiovascular system is a complex network of organs and cell types, each with specialized, but highly coordinated, functions; therefore, cardiovascular disease is complex in etiology and manifestation

  • A systems-based approach aims to reveal the architecture and the emerging properties of a complex network by uncovering the relationships among the constituents and establishing global governing principles

  • Current advances in bioinformatics, genomics, proteomics, and metabolomics offer an excellent opportunity to use systems-based analysis to dissect complex networks involved in cardiovascular physiology and diseases

  • In the study of cardiovascular diseases, systems biology compliments genetic analyses, such as genome-wide association studies, by establishing the underlying mechanisms and the functional significance of the candidate genes

  • 'Systems genetics' is a new approach based on the systems-based analysis of genetic variants and phenotypic spectra at various levels, spanning from gene expression to organ physiology

  • By establishing the molecular components and gene networks for the cardiovascular system, systems biology can help to develop effective and personalized diagnostic tools and therapies for cardiovascular diseases

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Figure 1: Construction of a protein-interaction network.
Figure 2: Principles of eQTL analysis.
Figure 3: Construction of a co-expression network.
Figure 4: Complex interactions in CAD.
Figure 5: Systems genetics for analysis of complex disease.
Figure 6: Network analysis provides context to hits in GWAS.

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References

  1. Schunkert, H. et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat. Genet. 43, 333–338 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Barabasi, A. L. Linked: The new science of networks (Penguin Books, London, 2002).

    Google Scholar 

  3. Bousquet, J. et al. Systems medicine and integrated care to combat chronic noncommunicable diseases. Genome Med. 3, 43 (2011).

    PubMed  PubMed Central  Google Scholar 

  4. Ideker, T., Galitski, T. & Hood, L. A new approach to decoding life: systems biology. Annu. Rev. Genomics Hum. Genet. 2, 343–372 (2001).

    CAS  PubMed  Google Scholar 

  5. Nadeau, J. H. & Subramaniam, S. Systems biology and medicine: a new take on an old paradigm. Wiley Interdiscip. Rev. Syst. Biol. Med. 1, 1–3 (2009).

    PubMed  Google Scholar 

  6. Nadeau, J. H. & Subramaniam, S. Systems biology—old wine in a new bottle or is the bottle changing the wine? Wiley Interdiscip. Rev. Syst. Biol. Med. 2, 1–2 (2010).

    PubMed  Google Scholar 

  7. Schadt, E. E. Molecular networks as sensors and drivers of common human diseases. Nature 461, 218–223 (2009).

    CAS  PubMed  Google Scholar 

  8. Schadt, E. E. & Lum, P. Y. Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Reverse engineering gene networks to identify key drivers of complex disease phenotypes. J. Lipid Res. 47, 2601–2613 (2006).

    CAS  PubMed  Google Scholar 

  9. Noble, D. The surprising heart: a review of recent progress in cardiac electrophysiology. J. Physiol. 353, 1–50 (1984).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Noble, D. Modeling the heart—from genes to cells to the whole organ. Science 295, 1678–1682 (2002).

    CAS  PubMed  Google Scholar 

  11. Noble, D. The music of life: biology beyond genes (Oxford University Press, Oxford, 2006).

    Google Scholar 

  12. Lusis, A. J. Atherosclerosis. Nature 407, 233–241 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Mudd, J. O. & Kass, D. A. Tackling heart failure in the twenty-first century. Nature 451, 919–928 (2008).

    CAS  PubMed  Google Scholar 

  14. Bondarenko, V. E., Szigeti, G. P., Bett, G. C., Kim, S. J. & Rasmusson, R. L. Computer model of action potential of mouse ventricular myocytes. Am. J. Physiol. Heart Circ. Physiol. 287, H1378–1403 (2004).

    CAS  PubMed  Google Scholar 

  15. Greenstein, J. L. & Winslow, R. L. An integrative model of the cardiac ventricular myocyte incorporating local control of Ca2 release. Biophys. J. 83, 2918–2945 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Shannon, T. R., Wang, F., Puglisi, J., Weber, C. & Bers, D. M. A mathematical treatment of integrated Ca dynamics within the ventricular myocyte. Biophys. J. 87, 3351–3371 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Weiss, J. N. et al. From pulsus to pulseless: the saga of cardiac alternans. Circ. Res. 98, 1244–1253 (2006).

    CAS  PubMed  Google Scholar 

  18. Takahashi, K. & Yamanaka, S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 126, 663–676 (2006).

    CAS  PubMed  Google Scholar 

  19. Lusis, A. J. & Weiss, J. N. Cardiovascular networks: systems-based approaches to cardiovascular disease. Circulation 121, 157–170 (2010).

    PubMed  PubMed Central  Google Scholar 

  20. Newman, M. E. J. Networks: an introduction (Oxford University Press, Oxford, 2010).

    Google Scholar 

  21. Shipley, B. Cause and correlation: a user's guide to path analysis, structural equations and causal inference (Cambridge University Press, Cambridge, 2002).

    Google Scholar 

  22. Subramaniam, S. & Nadeau, J. H. Systems medicine—viewed through the real and computing lenses. Wiley Interdiscip. Rev. Syst. Biol. Med. 2, 383–384 (2010).

    PubMed  Google Scholar 

  23. Langfelder, P. & Horvath, S. Eigengene networks for studying the relationships between co-expression modules. BMC Syst. Biol. 1, 54 (2007).

    PubMed  PubMed Central  Google Scholar 

  24. Ferrell, J. E. Jr, Tsai, T. Y. & Yang, Q. Modeling the cell cycle: why do certain circuits oscillate? Cell 144, 874–885 (2011).

    CAS  PubMed  Google Scholar 

  25. Yosef, N. & Regev, A. Impulse control: temporal dynamics in gene transcription. Cell 144, 886–896 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Edgar, R., Domrachev, M. & Lash, A. E. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 30, 207–210 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Giallourakis, C., Henson, C., Reich, M., Xie, X. & Mootha, V. K. Disease gene discovery through integrative genomics. Annu Rev. Genomics Hum. Genet. 6, 381–406 (2005).

    CAS  PubMed  Google Scholar 

  28. Stoll, M. et al. A genomic-systems biology map for cardiovascular function. Science 294, 1723–1726 (2001).

    CAS  PubMed  Google Scholar 

  29. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Keller, M. P. & Attie, A. D. Physiological insights gained from gene expression analysis in obesity and diabetes. Annu. Rev. Nutr. 30, 341–364 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Su, W. L., Kleinhanz, R. R. & Schadt, E. E. Characterizing the role of miRNAs within gene regulatory networks using integrative genomics techniques. Mol. Syst. Biol. 7, 490 (2011).

    PubMed  PubMed Central  Google Scholar 

  32. Zhong, H., Yang, X., Kaplan, L. M., Molony, C. & Schadt, E. E. Integrating pathway analysis and genetics of gene expression for genome-wide association studies. Am. J. Hum. Genet. 86, 581–591 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Deng, M. C. et al. Noninvasive discrimination of rejection in cardiac allograft recipients using gene expression profiling. Am. J. Transplant. 6, 150–160 (2006).

    CAS  PubMed  Google Scholar 

  34. Peri, S. et al. Development of human protein reference database as an initial platform for approaching systems biology in humans. Genome Res. 13, 2363–2371 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Gerszten, R. E., Asnani, A. & Carr, S. A. Status and prospects for discovery and verification of new biomarkers of cardiovascular disease by proteomics. Circ. Res. 109, 463–474 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Lewis, G. D. & Gerszten, R. E. Toward metabolomic signatures of cardiovascular disease. Circ. Cardiovasc. Genet. 3, 119–121 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Wang, T. J. et al. Metabolite profiles and the risk of developing diabetes. Nat. Med. 17, 448–453 (2011).

    PubMed  PubMed Central  Google Scholar 

  38. Wang, Z. et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 472, 57–63 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Small, K. S. et al. Identification of an imprinted master trans regulator at the KLF14 locus related to multiple metabolic phenotypes. Nat. Genet. 43, 561–564 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Lander, E. S. Genome-sequencing anniversary. The accelerator. Science 331, 1024 (2011).

    PubMed  Google Scholar 

  41. Neto, E. C., Keller, M. P., Attie, A. D. & Yandell, B. S. Causal graphical models in systems genetics: a unified framework for joint inference of causal network and genetic architecture for correlated phenotypes. Ann. Appl. Stat. 4, 320–339 (2010).

    PubMed  PubMed Central  Google Scholar 

  42. Vidal, M., Cusick, M. E. & Barabasi, A. L. Interactome networks and human disease. Cell 144, 986–998 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Barabasi, A. L. & Bonabeau, E. Scale-free networks. Sci. Am. 288, 60–69 (2003).

    PubMed  Google Scholar 

  44. Ravasz, E., Somera, A. L., Mongru, D. A., Oltvai, Z. N. & Barabasi, A. L. Hierarchical organization of modularity in metabolic networks. Science 297, 1551–1555 (2002).

    CAS  PubMed  Google Scholar 

  45. Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N. & Barabasi, A. L. The large-scale organization of metabolic networks. Nature 407, 651–654 (2000).

    CAS  PubMed  Google Scholar 

  46. Albert, R., Jeong, H. & Barabasi, A. L. Error and attack tolerance of complex networks. Nature 406, 378–382 (2000).

    CAS  PubMed  Google Scholar 

  47. Barabasi, A. L. Scale-free networks: a decade and beyond. Science 325, 412–413 (2009).

    CAS  PubMed  Google Scholar 

  48. Cai, C. et al. Is human blood a good surrogate for brain tissue in transcriptional studies? BMC Genomics 11, 589 (2010).

    PubMed  PubMed Central  Google Scholar 

  49. Miller, J. A., Horvath, S. & Geschwind, D. H. Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways. Proc. Natl Acad. Sci. USA 107, 12698–12703 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. van Nas, A. et al. Elucidating the role of gonadal hormones in sexually dimorphic gene coexpression networks. Endocrinology 150, 1235–1249 (2009).

    CAS  PubMed  Google Scholar 

  51. Langfelder, P., Luo, R., Oldham, M. C. & Horvath, S. Is my network module preserved and reproducible? PLoS Comput. Biol. 7, e1001057 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Loscalzo, J., Kohane, I. & Barabasi, A. L. Human disease classification in the postgenomic era: a complex systems approach to human pathobiology. Mol. Syst. Biol. 3, 124 (2007).

    PubMed  PubMed Central  Google Scholar 

  53. Hansson, G. K. & Libby, P. The immune response in atherosclerosis: a double-edged sword. Nat. Rev. Immunol. 6, 508–519 (2006).

    CAS  PubMed  Google Scholar 

  54. Libby, P. & Theroux, P. Pathophysiology of coronary artery disease. Circulation 111, 3481–3488 (2005).

    PubMed  Google Scholar 

  55. Jessup, M. & Brozena, S. Heart failure. N. Engl. J. Med. 348, 2007–2018 (2003).

    PubMed  Google Scholar 

  56. Creemers, E. E., Wilde, A. A. & Pinto, Y. M. Heart failure: advances through genomics. Nat. Rev. Genet. 12, 357–362 (2011).

    CAS  PubMed  Google Scholar 

  57. Romanoski, C. E. et al. Systems genetics analysis of gene-by-environment interactions in human cells. Am. J. Hum. Genet. 86, 399–410 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Ramsey, S. A. et al. Uncovering a macrophage transcriptional program by integrating evidence from motif scanning and expression dynamics. PLoS Comput. Biol. 4, e1000021 (2008).

    PubMed  PubMed Central  Google Scholar 

  59. Ideker, T., Dutkowski, J. & Hood, L. Boosting signal-to-noise in complex biology: prior knowledge is power. Cell 144, 860–863 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Lander, A. D. Pattern, growth, and control. Cell 144, 955–969 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Becker, L. et al. A macrophage sterol-responsive network linked to atherogenesis. Cell Metab. 11, 125–135 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Brehme, M. & Vidal, M. A global protein-lipid interactome map. Mol. Syst. Biol. 6, 443 (2010).

    PubMed  PubMed Central  Google Scholar 

  63. Nadeau, J. H. et al. Pleiotropy, homeostasis, and functional networks based on assays of cardiovascular traits in genetically randomized populations. Genome Res. 13, 2082–2091 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Nadeau, J. H. & Dudley, A. M. Genetics. Systems genetics. Science 331, 1015–1016 (2011).

    PubMed  PubMed Central  Google Scholar 

  65. Ghazalpour, A. et al. Genomic analysis of metabolic pathway gene expression in mice. Genome Biol. 6, R59 (2005).

    PubMed  PubMed Central  Google Scholar 

  66. Chen, Y. et al. Variations in DNA elucidate molecular networks that cause disease. Nature 452, 429–435 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Ghazalpour, A. et al. Integrating genetic and network analysis to characterize genes related to mouse weight. PLoS Genet. 2, e130 (2006).

    PubMed  PubMed Central  Google Scholar 

  68. Keller, M. P. et al. A gene expression network model of type 2 diabetes links cell cycle regulation in islets with diabetes susceptibility. Genome Res. 18, 706–716 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Meng, H. et al. Identification of Abcc6 as the major causal gene for dystrophic cardiac calcification in mice through integrative genomics. Proc. Natl Acad. Sci. USA 104, 4530–4535 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Mungrue, I. N., Pagnon, J., Kohannim, O., Gargalovic, P. S. & Lusis, A. J. CHAC1/MGC4504 is a novel proapoptotic component of the unfolded protein response, downstream of the ATF4-ATF3-CHOP cascade. J. Immunol. 182, 466–476 (2009).

    CAS  PubMed  Google Scholar 

  71. Schadt, E. E. et al. An integrative genomics approach to infer causal associations between gene expression and disease. Nat. Genet. 37, 710–717 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Wang, S. S. et al. Identification of pathways for atherosclerosis in mice: integration of quantitative trait locus analysis and global gene expression data. Circ. Res. 101, e11–e30 (2007).

    CAS  PubMed  Google Scholar 

  73. Yang, X. et al. Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks. Nat. Genet. 41, 415–423 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Hagg, S. et al. Multi-organ expression profiling uncovers a gene module in coronary artery disease involving transendothelial migration of leukocytes and LIM domain binding 2: the Stockholm Atherosclerosis Gene Expression (STAGE) study. PLoS Genet. 5, e1000754 (2009).

    PubMed  PubMed Central  Google Scholar 

  75. Dobrin, R. et al. Multi-tissue coexpression networks reveal unexpected subnetworks associated with disease. Genome Biol. 10, R55 (2009).

    PubMed  PubMed Central  Google Scholar 

  76. Beaumont, M. A. & Rannala, B. The Bayesian revolution in genetics. Nat. Rev. Genet. 5, 251–261 (2004).

    CAS  PubMed  Google Scholar 

  77. Needham, C. J., Bradford, J. R., Bulpitt, A. J. & Westhead, D. R. A primer on learning in Bayesian networks for computational biology. PLoS Comput. Biol. 3, e129 (2007).

    PubMed  PubMed Central  Google Scholar 

  78. Emilsson, V. et al. Genetics of gene expression and its effect on disease. Nature 452, 423–428 (2008).

    CAS  PubMed  Google Scholar 

  79. Ferrara, C. T. et al. Genetic networks of liver metabolism revealed by integration of metabolic and transcriptional profiling. PLoS Genet. 4, e1000034 (2008).

    PubMed  PubMed Central  Google Scholar 

  80. Gargalovic, P. S. et al. Identification of inflammatory gene modules based on variations of human endothelial cell responses to oxidized lipids. Proc. Natl Acad. Sci. USA 103, 12741–12746 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Romanoski, C. E. et al. Network for activation of human endothelial cells by oxidized phospholipids: a critical role of heme oxygenase 1. Circ. Res. 109, e27–e41 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. Petretto, E. et al. Integrated genomic approaches implicate osteoglycin (Ogn) in the regulation of left ventricular mass. Nat. Genet. 40, 546–552 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Sarwar, R. & Cook, S. A. Genomic analysis of left ventricular remodeling. Circulation 120, 437–444 (2009).

    PubMed  Google Scholar 

  84. Coronary Artery Disease (C4D) Genetics Consortium. A genome-wide association study in Europeans and South Asians identifies five new loci for coronary artery disease. Nat. Genet. 43, 339–344 (2011).

  85. Manolio, T. A. Genomewide association studies and assessment of the risk of disease. N. Engl. J. Med. 363, 166–176 (2010).

    CAS  PubMed  Google Scholar 

  86. Musunuru, K. et al. From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus. Nature 466, 714–719 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Goh, K. I. et al. The human disease network. Proc. Natl Acad. Sci. USA 104, 8685–8690 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Horwitz, P. A. et al. Detection of cardiac allograft rejection and response to immunosuppressive therapy with peripheral blood gene expression. Circulation 110, 3815–3821 (2004).

    CAS  PubMed  Google Scholar 

  89. Pham, M. X. et al. Gene-expression profiling for rejection surveillance after cardiac transplantation. N. Engl. J. Med. 362, 1890–1900 (2010).

    CAS  PubMed  Google Scholar 

  90. Eagle, K. A. et al. Identifying patients at high risk of a cardiovascular event in the near future: current status and future directions: report of a national heart, lung, and blood institute working group. Circulation 121, 1447–1454 (2010).

    PubMed  PubMed Central  Google Scholar 

  91. Bloss, C. S., Schork, N. J. & Topol, E. J. Effect of direct-to-consumer genomewide profiling to assess disease risk. N. Engl. J. Med. 364, 524–534 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Ginsburg, G. S., Donahue, M. P. & Newby, L. K. Prospects for personalized cardiovascular medicine: the impact of genomics. J. Am. Coll. Cardiol. 46, 1615–1627 (2005).

    PubMed  Google Scholar 

  93. Nadeau, J. H. & Topol, E. J. The genetics of health. Nat. Genet. 38, 1095–1098 (2006).

    CAS  PubMed  Google Scholar 

  94. Anderson, J. L. et al. Randomized trial of genotype-guided versus standard warfarin dosing in patients initiating oral anticoagulation. Circulation 116, 2563–2570 (2007).

    CAS  PubMed  Google Scholar 

  95. Krynetskiy, E. & McDonnell, P. Building individualized medicine: prevention of adverse reactions to warfarin therapy. J. Pharmacol. Exp. Ther. 322, 427–434 (2007).

    CAS  PubMed  Google Scholar 

  96. Connolly, S. J. et al. Dabigatran versus warfarin in patients with atrial fibrillation. N. Engl. J. Med. 361, 1139–1151 (2009).

    CAS  PubMed  Google Scholar 

  97. Baudhuin, L. M. et al. Relation of ADRB1, CYP2D6, and UGT1A1 polymorphisms with dose of, and response to, carvedilol or metoprolol therapy in patients with chronic heart failure. Am. J. Cardiol. 106, 402–408 (2010).

    CAS  PubMed  Google Scholar 

  98. Sehnert, A. J. et al. Lack of association between adrenergic receptor genotypes and survival in heart failure patients treated with carvedilol or metoprolol. J. Am. Coll. Cardiol. 52, 644–651 (2008).

    CAS  PubMed  Google Scholar 

  99. Sehrt, D., Meineke, I., Tzvetkov, M., Gultepe, S. & Brockmöller, J. Carvedilol pharmacokinetics and pharmacodynamics in relation to CYP2D6 and ADRB pharmacogenetics. Pharmacogenomics 12, 783–795 (2011).

    CAS  PubMed  Google Scholar 

  100. SEARCH Collaborative Group et al. SLCO1B1 variants and statin-induced myopathy—a genomewide study. N. Engl. J. Med. 359, 789–799 (2008).

  101. Chasman, D. I. et al. Pharmacogenetic study of statin therapy and cholesterol reduction. JAMA 291, 2821–2827 (2004).

    CAS  PubMed  Google Scholar 

  102. Thompson, J. F. et al. An association study of 43 SNPs in 16 candidate genes with atorvastatin response. Pharmacogenomics J. 5, 352–358 (2005).

    CAS  PubMed  Google Scholar 

  103. Shu, Y. et al. Evolutionary conservation predicts function of variants of the human organic cation transporter, OCT1. Proc. Natl Acad. Sci. USA 100, 5902–5907 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. Reitman, M. L. & Schadt, E. E. Pharmacogenetics of metformin response: a step in the path toward personalized medicine. J. Clin. Invest. 117, 1226–1229 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  105. Hughes, B. 2009 FDA drug approvals. Nat. Rev. Drug Discov. 9, 89–92 (2010).

    CAS  PubMed  Google Scholar 

  106. Weiss, J. N., Garfinkel, A. & Chen, P. S. Novel approaches to identifying antiarrhythmic drugs. Trends Cardiovasc. Med. 13, 326–330 (2003).

    CAS  PubMed  Google Scholar 

  107. Garfinkel, A. et al. Preventing ventricular fibrillation by flattening cardiac restitution. Proc. Natl Acad. Sci. USA 97, 6061–6066 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  108. Baggs, J. E., Hughes, M. E. & Hogenesch, J. B. The network as the target. Wiley Interdiscip. Rev. Syst. Biol. Med. 2, 127–133 (2010).

    PubMed  Google Scholar 

  109. Behrends, C., Sowa, M. E., Gygi, S. P. & Harper, J. W. Network organization of the human autophagy system. Nature 466, 68–76 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  110. Wang, Z. V., Rothermel, B. A. & Hill, J. A. Autophagy in hypertensive heart disease. J. Biol. Chem. 285, 8509–8514 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. Cardiovascular Research Laboratories, University of California Los Angeles, 675 Charles E. Young Drive South, MRL 3645, University of California Los Angeles, Los Angeles, 90095–1760, CA, USA

    W. Robb MacLellan

  2. Department of Anesthesiology, BH–569 CHS, University of California Los Angeles, Los Angeles, 90095–7115, CA, USA

    Yibin Wang

  3. David Geffen School of Medicine, University of California Los Angeles, 695 Charles E. Young Drive South, 6524A Gonda Building, Los Angeles, 90095–1679, CA, USA

    Aldons J. Lusis

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All authors contributed equally to all aspects of the article, including researching data, discussion of content, and writing, reviewing and editing the manuscript before submission.

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Correspondence to Aldons J. Lusis.

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MacLellan, W., Wang, Y. & Lusis, A. Systems-based approaches to cardiovascular disease. Nat Rev Cardiol 9, 172–184 (2012). https://doi.org/10.1038/nrcardio.2011.208

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