Systems-based approaches to cardiovascular disease

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.

[1]  T. Klingler,et al.  Noninvasive Discrimination of Rejection in Cardiac Allograft Recipients Using Gene Expression Profiling , 2006, American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons.

[2]  Peter Langfelder,et al.  Eigengene networks for studying the relationships between co-expression modules , 2007, BMC Systems Biology.

[3]  E. Schadt Molecular networks as sensors and drivers of common human diseases , 2009, Nature.

[4]  V. Mootha,et al.  Metabolite profiles and the risk of developing diabetes , 2011, Nature Network Boston.

[5]  Trey Ideker,et al.  Boosting Signal-to-Noise in Complex Biology: Prior Knowledge Is Power , 2011, Cell.

[6]  Yigal M. Pinto,et al.  Heart failure: advances through genomics , 2011, Nature Reviews Genetics.

[7]  Omid Kohannim,et al.  CHAC1/MGC4504 Is a Novel Proapoptotic Component of the Unfolded Protein Response, Downstream of the ATF4-ATF3-CHOP Cascade1 , 2009, The Journal of Immunology.

[8]  P. Libby,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. , 2010, Circulation.

[9]  Y. Pao,et al.  Pleiotropy, homeostasis, and functional networks based on assays of cardiovascular traits in genetically randomized populations. , 2003, Genome research.

[10]  E. Schadt,et al.  Identification of Abcc6 as the major causal gene for dystrophic cardiac calcification in mice through integrative genomics , 2007, Proceedings of the National Academy of Sciences.

[11]  Bill Shipley,et al.  Cause and Correlation in Biology: A User''s Guide to Path Analysis , 2016 .

[12]  Peter Libby,et al.  The immune response in atherosclerosis: a double-edged sword , 2006, Nature Reviews Immunology.

[13]  Aiqing He,et al.  Identification of inflammatory gene modules based on variations of human endothelial cell responses to oxidized lipids , 2006, Proceedings of the National Academy of Sciences.

[14]  W. Kraus,et al.  Lack of association between adrenergic receptor genotypes and survival in heart failure patients treated with carvedilol or metoprolol. , 2008, Journal of the American College of Cardiology.

[15]  Brian J. Bennett,et al.  Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease , 2011, Nature.

[16]  M. Vidal,et al.  A global protein–lipid interactome map , 2010, Molecular systems biology.

[17]  Mark I. McCarthy,et al.  A genome-wide association study in Europeans and South Asians identifies five new loci for coronary artery disease , 2011, Nature Genetics.

[18]  Eric E Schadt,et al.  Cycle Regulation in Islets with Diabetes Susceptibility a Gene Expression Network Model of Type 2 Diabetes Links Cell P

, 2008 .

[19]  A. Marian,et al.  Pharmacogenetic study of statin therapy and cholesterol reduction. , 2005, Current atherosclerosis reports.

[20]  Eric E Schadt,et al.  Identification of Pathways for Atherosclerosis in Mice: Integration of Quantitative Trait Locus Analysis and Global Gene Expression Data , 2007, Circulation research.

[21]  S. Cook,et al.  Genomic Analysis of Left Ventricular Remodeling , 2009, Circulation.

[22]  B. Yandell,et al.  CAUSAL GRAPHICAL MODELS IN SYSTEMS GENETICS: A UNIFIED FRAMEWORK FOR JOINT INFERENCE OF CAUSAL NETWORK AND GENETIC ARCHITECTURE FOR CORRELATED PHENOTYPES. , 2010, The annals of applied statistics.

[23]  Olle Melander,et al.  From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus , 2010, Nature.

[24]  Andrew J. Bulpitt,et al.  A Primer on Learning in Bayesian Networks for Computational Biology , 2007, PLoS Comput. Biol..

[25]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[26]  Aldons J. Lusis,et al.  Atherosclerosis : Vascular biology , 2000 .

[27]  R. Collins,et al.  SLCO1B1 variants and statin-induced myopathy--a genomewide study. , 2008, The New England journal of medicine.

[28]  R. Gerszten,et al.  Toward metabolomic signatures of cardiovascular disease. , 2010, Circulation. Cardiovascular genetics.

[29]  Bethan Hughes,et al.  2009 FDA drug approvals , 2010, Nature Reviews Drug Discovery.

[30]  H. Stefánsson,et al.  Genetics of gene expression and its effect on disease , 2008, Nature.

[31]  A. Garfinkel,et al.  Novel approaches to identifying antiarrhythmic drugs. , 2003, Trends in cardiovascular medicine.

[32]  A. Lander Pattern, Growth, and Control , 2011, Cell.

[33]  J. Castle,et al.  An integrative genomics approach to infer causal associations between gene expression and disease , 2005, Nature Genetics.

[34]  Albert-László Barabási,et al.  Linked: The New Science of Networks , 2002 .

[35]  Xia Yang,et al.  Integrating pathway analysis and genetics of gene expression for genome-wide association studies. , 2010, American journal of human genetics.

[36]  J. Paulauskis,et al.  An association study of 43 SNPs in 16 candidate genes with atorvastatin response , 2005, The Pharmacogenomics Journal.

[37]  Alex E. Lash,et al.  Gene Expression Omnibus: NCBI gene expression and hybridization array data repository , 2002, Nucleic Acids Res..

[38]  Eric J Topol,et al.  The genetics of health , 2006, Nature Genetics.

[39]  Donald M Bers,et al.  A mathematical treatment of integrated Ca dynamics within the ventricular myocyte. , 2004, Biophysical journal.

[40]  Bin Li,et al.  Uncovering a Macrophage Transcriptional Program by Integrating Evidence from Motif Scanning and Expression Dynamics , 2008, PLoS Comput. Biol..

[41]  Teri A Manolio,et al.  Genomewide association studies and assessment of the risk of disease. , 2010, The New England journal of medicine.

[42]  A. Barabasi,et al.  Hierarchical Organization of Modularity in Metabolic Networks , 2002, Science.

[43]  G. Ginsburg,et al.  Prospects for personalized cardiovascular medicine: the impact of genomics. , 2005, Journal of the American College of Cardiology.

[44]  B. Rothermel,et al.  Autophagy in Hypertensive Heart Disease* , 2010, The Journal of Biological Chemistry.

[45]  Rui Luo,et al.  Is My Network Module Preserved and Reproducible? , 2011, PLoS Comput. Biol..

[46]  Xiaohui S. Xie,et al.  Disease gene discovery through integrative genomics. , 2005, Annual review of genomics and human genetics.

[47]  G. Bett,et al.  Computer model of action potential of mouse ventricular myocytes. , 2004, American journal of physiology. Heart and circulatory physiology.

[48]  A. Regev,et al.  Impulse Control: Temporal Dynamics in Gene Transcription , 2011, Cell.

[49]  Evgeny Krynetskiy,et al.  Building Individualized Medicine: Prevention of Adverse Reactions to Warfarin Therapy , 2007, Journal of Pharmacology and Experimental Therapeutics.

[50]  S. Gygi,et al.  Network organization of the human autophagy system , 2010, Nature.

[51]  S. Yamanaka,et al.  Induction of Pluripotent Stem Cells from Mouse Embryonic and Adult Fibroblast Cultures by Defined Factors , 2006, Cell.

[52]  Mark I. McCarthy,et al.  Identification of an imprinted master trans-regulator at the KLF14 locus related to multiple metabolic phenotypes , 2011, Nature Genetics.

[53]  Leon Poller,et al.  Dabigatran versus warfarin in patients with atrial fibrillation. , 2009, The New England journal of medicine.

[54]  Shankar Subramaniam,et al.  Systems medicine—viewed through the real and computing lenses , 2010, Wiley interdisciplinary reviews. Systems biology and medicine.

[55]  A. Garfinkel,et al.  From Pulsus to Pulseless: The Saga of Cardiac Alternans , 2006, Circulation research.

[56]  Dudley J Pennell,et al.  Integrated genomic approaches implicate osteoglycin (Ogn) in the regulation of left ventricular mass , 2008, Nature Genetics.

[57]  Joseph H Nadeau,et al.  Systems Genetics , 2011, Science.

[58]  E. Lander The Accelerator , 2011, Science.

[59]  Hanno Steen,et al.  Development of human protein reference database as an initial platform for approaching systems biology in humans. , 2003, Genome research.

[60]  J. Epstein,et al.  Detection of Cardiac Allograft Rejection and Response to Immunosuppressive Therapy With Peripheral Blood Gene Expression , 2004, Circulation.

[61]  James Yee,et al.  Gene-expression profiling for rejection surveillance after cardiac transplantation. , 2010, The New England journal of medicine.

[62]  S. Horvath,et al.  Variations in DNA elucidate molecular networks that cause disease , 2008, Nature.

[63]  D. Noble The music of life : biology beyond genes , 2008 .

[64]  R. Albert,et al.  The large-scale organization of metabolic networks , 2000, Nature.

[65]  J. Ferrell,et al.  Modeling the Cell Cycle: Why Do Certain Circuits Oscillate? , 2011, Cell.

[66]  L. Hood,et al.  Systems medicine and integrated care to combat chronic noncommunicable diseases , 2011, Genome Medicine.

[67]  A. Attie,et al.  Physiological insights gained from gene expression analysis in obesity and diabetes. , 2010, Annual review of nutrition.

[68]  E. Wijsman,et al.  A macrophage sterol-responsive network linked to atherogenesis. , 2010, Cell metabolism.

[69]  Aiqing He,et al.  Systems genetics analysis of gene-by-environment interactions in human cells. , 2010, American journal of human genetics.

[70]  Eric E Schadt,et al.  Elucidating the role of gonadal hormones in sexually dimorphic gene coexpression networks. , 2009, Endocrinology.

[71]  J. Nadeau,et al.  Systems biology—old wine in a new bottle or is the bottle changing the wine? , 2010, Wiley interdisciplinary reviews. Systems biology and medicine.

[72]  B. Rannala,et al.  The Bayesian revolution in genetics , 2004, Nature Reviews Genetics.

[73]  V. Bajic,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 , 2009, PLoS genetics.

[74]  Peter Langfelder,et al.  Is human blood a good surrogate for brain tissue in transcriptional studies? , 2010, BMC Genomics.

[75]  Martin H. Levinson Linked: The New Science of Networks , 2004 .

[76]  A. Barabasi,et al.  The human disease network , 2007, Proceedings of the National Academy of Sciences.

[77]  P. Libby,et al.  Pathophysiology of Coronary Artery Disease , 2005, Circulation.

[78]  Aldons J. Lusis,et al.  Network for Activation of Human Endothelial Cells by Oxidized Phospholipids: A Critical Role of Heme Oxygenase 1 , 2011, Circulation research.

[79]  N. Schork,et al.  Effect of direct-to-consumer genomewide profiling to assess disease risk. , 2011, The New England journal of medicine.

[80]  Albert-László Barabási,et al.  Scale-Free Networks: A Decade and Beyond , 2009, Science.

[81]  Aldons J. Lusis,et al.  A thematic review series: systems biology approaches to metabolic and cardiovascular disorders , 2006, Journal of Lipid Research.

[82]  Systems Biology and Medicine: A New Take on an Old Paradigm , 2009, Wiley interdisciplinary reviews. Systems biology and medicine.

[83]  Eric E Schadt,et al.  Multi-tissue coexpression networks reveal unexpected subnetworks associated with disease. , 2009 .

[84]  N. Mehta Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. , 2011, Circulation. Cardiovascular genetics.

[85]  J. Baggs,et al.  The network as the target , 2010, Wiley interdisciplinary reviews. Systems biology and medicine.

[86]  J. Brockmöller,et al.  Carvedilol pharmacokinetics and pharmacodynamics in relation to CYP2D6 and ADRB pharmacogenetics. , 2011, Pharmacogenomics.

[87]  A. Lusis,et al.  Cardiovascular networks: systems-based approaches to cardiovascular disease. , 2010, Circulation.

[88]  Karen A. Hartman,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. , 2010, The American journal of cardiology.

[89]  David A. Kass,et al.  Tackling heart failure in the twenty-first century , 2008, Nature.

[90]  Massimo Marchiori,et al.  Error and attacktolerance of complex network s , 2004 .

[91]  D. Noble,et al.  The surprising heart: a review of recent progress in cardiac electrophysiology. , 1984, The Journal of physiology.

[92]  T. Ideker,et al.  A new approach to decoding life: systems biology. , 2001, Annual review of genomics and human genetics.

[93]  M. Jessup,et al.  Heart failure. , 2003, The New England journal of medicine.

[94]  E. Schadt,et al.  Thematic review series: Systems Biology Approaches to Metabolic and Cardiovascular Disorders. Reverse engineering gene networks to identify key drivers of complex disease phenotypes Published, JLR Papers in Press, October 1, 2006. , 2006, Journal of Lipid Research.

[95]  S. Horvath,et al.  Evidence for anti-Burkitt tumour globulins in Burkitt tumour patients and healthy individuals. , 1967, British Journal of Cancer.

[96]  Bethan Hughes,et al.  2008 FDA drug approvals , 2009, Nature Reviews Drug Discovery.

[97]  A. Barabasi,et al.  Interactome Networks and Human Disease , 2011, Cell.

[98]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[99]  D. Noble Modeling the Heart--from Genes to Cells to the Whole Organ , 2002, Science.

[100]  Conrad C. Huang,et al.  Evolutionary conservation predicts function of variants of the human organic cation transporter, OCT1 , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[101]  Steven A Carr,et al.  Status and prospects for discovery and verification of new biomarkers of cardiovascular disease by proteomics. , 2011, Circulation research.

[102]  A. Garfinkel,et al.  Preventing ventricular fibrillation by flattening cardiac restitution. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[103]  M. DePamphilis,et al.  HUMAN DISEASE , 1957, The Ulster Medical Journal.

[104]  Elias Chaibub Neto,et al.  Genetic Networks of Liver Metabolism Revealed by Integration of Metabolic and Transcriptional Profiling , 2008, PLoS genetics.

[105]  R. Winslow,et al.  An integrative model of the cardiac ventricular myocyte incorporating local control of Ca2+ release. , 2002, Biophysical journal.

[106]  E. Schadt,et al.  Genomic analysis of metabolic pathway gene expression in mice , 2005, Genome Biology.

[107]  A. Barabasi,et al.  Human disease classification in the postgenomic era: A complex systems approach to human pathobiology , 2007, Molecular systems biology.

[108]  T. Spector,et al.  Corrigendum: Identification of an imprinted master trans regulator at the KLF14 locus related to multiple metabolic phenotypes , 2011, Nature Genetics.

[109]  N. Hollenberg,et al.  A genomic-systems biology map for cardiovascular function. , 2002, Current hypertension reports.

[110]  César A. Hidalgo,et al.  Scale-free networks , 2008, Scholarpedia.

[111]  B. Horne,et al.  Randomized Trial of Genotype-Guided Versus Standard Warfarin Dosing in Patients Initiating Oral Anticoagulation , 2007, Circulation.

[112]  E. Schadt,et al.  Characterizing the role of miRNAs within gene regulatory networks using integrative genomics techniques , 2011, Molecular systems biology.

[113]  E. Schadt,et al.  Pharmacogenetics of metformin response: a step in the path toward personalized medicine. , 2007, The Journal of clinical investigation.

[114]  S. Horvath,et al.  Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways , 2010, Proceedings of the National Academy of Sciences.