Systems Biomedicine and Human Disease: A New Approach to Pathobiology and Therapeutics

1. Systems Biomedicine andHuman Disease: A New Approach to Pathobiology and Therapeutics Joseph Loscalzo Brigham and Women’s Hospital Harvard Medical School Boston, MA

2. Changing Biomedical Paradigm • Conventional scientific approach: hold all variables constant except the one of interest, and deduce its importance (Cartesian reductionism). • Inductive generalization can follow. • Reductionist approach often oversimplifies biomedical systems and leads to linear thinking with erroneous outcomes. A timely example will follow….

3. Homocysteine Theory of Atherosclerosis • First proposed by McCully (Am.J.Path. 1969; 56:111) • Evidence from over 30 studies suggests that even mild-to-moderate elevations of plasma homocysteine confer a significant, independent risk for atherothrombosis. • Hyperhomocysteinemia found in 20-40% of patients with vascular disease, but in only 2% of unaffected individuals.

4. Homocysteine and Atherothrombosis Homocysteine & CAD Survival --Nygard et al., NEJM 1997;337:230

5. Homocysteine Metabolism Methionine THF SAM DMG 5,10-Methylene THF 3 Me-B12 1 Betaine Homocysteine 5-Methyl-THF SAH 2 B6 4 Cystathionine 1: Methionine Synthase 2: MTHF Reductase 3: Betaine-homocysteine Methyltransferase 4: Cystathionine-beta- Synthase Cysteine Sulfate

6. Prospective Vitamin Trials in Hyperhomocysteinemia Folate-unfortified Population CHD CHAOS-2, WENBIT, NORVIT, SEARCH CVA Su.Fol.Om3, VITATOPS Folate-fortified Population CHDHOPE-2, WACS CVA VISP Renal Dis. FAVORIT, VA Trial

7. Vitamin Rx, Homocysteine, & CV Risk NORVIT Trial HOPE-2 Trial 3749 pts. s/p AMI 5522 pts. w/ CVD or diabetes 2.5 mg Folic Acid 1.0 mg B12 50 mg pyridoxine Folic Acid (mg) 0.8 0.8 0 0 B12 (mg) 0.4 0.4 0 0 Pyridoxine (mg) 40 0 40 0 --Bonaa KH, et al., NEJM, 2006 --HOPE-2 Investigators, NEJM, 2006

8. Folate, B12, and Homocysteine Methionine Folate, B12 Homocysteine

9. Folate, B12, and Homocysteine DNA Synthesis Cell Proliferation Methionine DHF THF SAM dTMP Acceptor DMG dUMP Me-B12 Methyl- Acceptor 5,10 Betaine N -CH2-THF Homocysteine 5 SAH N -CH3-THF Modify Gene Expression Impair NO Synthesis/EC Function CpG DNA Methylation ADMA Synthesis

10. Folate and Restenosis --Lange et al., NEJM 2004;350:2673

11. Changing Scientific Paradigm • Most biological systems respond to multiple inputs that vary simultaneously and can interact—i.e., these are complex systems. • New quantitative approaches can be used to examine complex system responses, and are now increasingly relevant to biomedicine.

12. Systems Biomedicine Systems biomedicine is the science of integrating genetic, genomic, biochemical, cellular, physiological, and clinical data to create a network that can be used to model predictively (patho)biological events that underlie health or disease states. --Morel et al., Mayo Clin Proc 2004;79:651-8

13. Systems Biology and Pathobiology Genomics Proteomics Metabolomics Metabonomics Phenomics Pathophenomics Native Modified Transcriptomics Functional Genomics/Proteomics Environmental Perturbations Systems Biology/Pathobiology

14. Genome-Proteome Relationship Genome implies phenotype. Proteome is phenotype. Genome Nascent Proteome Mature Proteome PTM, OPTM Environmental Determinants

15. Systems Analysis Paradigm Biological Question & Experimental Design Data Acquisition Biological Engineering Output: Predictive Understanding Data Processing & Integration Modeling & Simulation Output: Biological Knowledge Data Correlation & Causality Biological Validation Component Identification & Knowledge Assembly

16. General Analytical Paradigm Obtain individual tissue samples isogenic controls and genetic variants

17. Analysis of Complex Biomedical Systems • Correlation analysis can suggest, but does not prove, causation. • System perturbations in space and time can be used to discern associations that may be causal. • Ideally, one needs to define the key regulatory elements in a network that define the system response. • Quantitative approaches to do so are evolving rapidly.

18. Laws of Real Network Evolution • Each real network starts from a small nucleus and expands with addition of new nodes. • Growth: Networks are assembled one node at a time. • Preferential Attachment: In scale-free networks, new nodes, when forming links, prefer nodes that have more links (Pareto’s rule). --Barabasi et al., Science 1999;286:509-12

19. Generic Network Structures Random Network Clustered or “Scale-free” Network P(k) = e-k P(k) = k-g m = g Poisson Distribution Power Law Distribution

20. Cellular Metabolism and Scale-free Networks • Substrates and metabolites define nodes. • Reaction complexes (which may be enzyme-catalyzed) define links or connections. • The most connected nodes are hubs. • Goals of analysis are to: • establish the topology of the network (random or scale-free); • determine its connectivity parameters; and • analyze system behavior with perturbations.

21. Complex Metabolic Networks Scale-free Network Modular Random Network Scale-free Modular Network --Ravasz et al., Science 2002;297:1551-1555

22. NO Network Citrulline Creatine GSNO Agmatine 8 Guanidinoacetate 9 O2 O2 7 FAD FMN NO3- NO2- NO Arginine Argininosuccinate 1 H2O 10 O2 O2-. O2 BH2 BH4 11 12 urea 13 2 Ornithine Citrulline OONO- H2O2 14 GSH Glutamate-5-semialdehyde 3 5 15 GSSG NADP+ Glutamate G6P 16 4 H2O NADPH Oxo-glutarate 1,6 6PG O2-. O2

23. NO Network Connectivity Distribution <k> = 6.4 + 10.4 [<k>random = 0.01 + 2.7]

24. Endothelial NO Synthesis Capillary Zone Electrophoretogram of EC Lysate

25. NO System Responses after L-arginine

26. Biological Implications of Scale-free Networks --Facilitate chemical diversity at minimal energy cost --Recapitulate natural selection and evolution --Define difference between mutable nodes that engender diversity and facilitate natural selection (weakly connected), and immutable nodes (hubs), the loss of which is lethal for the organism --Minimize transition time between (metabolic) states --Accommodate pertubations to the network with minimal effect on critical functions of the organism --Minimize consequences of most biochemical errors

27. Biomedicine and Network Analysis • Characterization of allelic interactions in disease susceptibility and pathogenesis • Analysis of associations among genomic, proteomic, and metabolomic data sets (Basso et al., Nature Genetics 2005;37:382) • Identification of predictive biomarker sets • Control of epidemics (Dybiec et al., Phys Rev 2004;70, e-pub; Eubank et al., Nature 2004;429:180). • Identification of regulatory elements in metabolic and genetic pathways • Development of individualized treatments

28. Systems Pathobiology:A New Way to Define Disease Genome-Transcriptome-Proteome Environmental Perturbations Inflammation Thrombosis Hemorrhage Fibrosis Proliferation Apoptosis Necrosis Intermediate Pathophenotypes Distinct Pathophenotypes: Clinical Syndromes & Diseases --Loscalzo et al., Mol.Syst.Biol., in press

29. Determinants of the Pathophenome Environmental Determinants Pathophysiological States E5 En PS5 PSn Primary Disease Subgenome E1 E2 PS1 PS2 A5 An E3 E4 PS3 PS4 A1 A2 A3 A4 I5 In I1 I2 M5 Mn I3 I4 P5 Pn M1 M2 P1 P2 Intermediate Phenotype M3 M4 P3 P4 Secondary Disease Subgenome PATHOPHENOME --Loscalzo et al., Mol.Syst.Biol., in press

30. Sickle Cell Anemia

31. HbC HbF b-Thal G6PD TGF-b Apoptosis/ Necrosis Hypoxia HbS Inflammation Dehydration Infective Agent Immune Response Stroke Thrombosis Aplastic Anemia Hemolytic Anemia Acute Chest Syndrome Bone Infarct Painful Crisis Sickle Cell Anemia Disease-modifying Genes Environmental Determinants Intermediate Phenotypes Pathophenotype --Loscalzo et al., Mol.Syst.Biol., in press

32. Pulmonary Arterial Hypertension

33. Modular Representation of Disease Example: Hypertrophic Cardiomyopathy --Loscalzo et al., Mol.Syst.Biol., in press

34. Network Approach to Prostate Cancer --MNI method used to infer a model of gene regulation. --Comparative gene expression data filtered based on model to identify key candidates in metastasis. Expression Change Analysis Mode-of-Action by Network ID --Ergun et al., Mol Syst Biol 2007; e-publ.

35. Network Analysis of Ataxias --Analysis of network of 54 proteins involved in Purkinje cell degeneration. --Incorporated genetic determinants of 23 know inherited ataxias. --Identified 770 mostly novel protein- protein interactions using a stringent yeast two-hybrid screen. --75 paired interactions verified in mammalian cell screen. --Many ataxia-causing proteins share interacting partners, a subset of which affect neurodegeneration. --Lim et al., Cell 2006;125:801-14

36. Asthma: Biological Interaction Network and Gene Expression Red: Upregulated Genes Blue: Downregulated Genes • --Analyzed gene expression • in experimental asthma. • --Construct biological • interaction network. • --Map differentially • expressed genes • onto network. • --Analyze topological • characteristics of • modulated genes. • --Analyze correlation • between topology and • biological function using • Gene Ontology classifications. • --Highly connected genes (hubs) • have little change in expression. --Lu et al., Mol Syst Biol 2007;3:98

37. Applications of Systems Pathobiology to Personalized Medicine • Define molecular determinants of disease risk and pathogenesis • Identify environmental factors that interact with –omic determinants • Define biomarker panels comprehensively to quantify risk, assess prognosis, and determine response to therapy • Disease is the result of interactions among a modular collection of –omic and environmental networks unique to the individual. • Individualize therapy based on this analysis (“personalizing” medicine)

38. Approaches to Clinical Care Personalized Medicine Benefit Population Medicine Population Size

39. 50 y.o. man presents with 2 hr of substernal chest pain. PMH: untreated hypertension and 100 pack-year smoking history PE is notable for P95 and BP 155/85, clear chest, S4. ECG: 2 mm ST elevation in V2-5 CXR: mild pulmonary vascular redistribution Case History

40. Care Paradigm: 2007 • Aspirin, atenolol, abciximab, and atorvastatin administered. • Coronary angiography reveals total occlusion of the mid-LAD with thrombus. • Angioplasty performed and drug-eluting stent placed with good result. • D/C on aspirin, clopidogrel, atenolol, atorvastatin, and enalapril • Expected survival based on average survival from population studies of myocardial infarction All patients are treated identically according to uniform guidelines and clinical pathways.

41. Care Paradigm: 2027 • His genomic screen reveals: • proinflammatory genotype (haplotype) • prothrombotic genotype (haplotype) • His leukocyte expression array discloses: • robust inflammatory response • increased propensity for cholesteryl ester formation • Proteomic and metabolomic analyses of plasma show: • increased oxidant stress • prothrombotic state • proinflammatory state • Pharmacogenomic analysis (including CYP3A4, CYP2C19, and CYP2D6 polymorphisms) shows that he will rapidly metabolize thienopyridines.

42. Care Paradigm: 2027 • This genomic, proteomic, and metabolomic signature defines the patient’s molecular phenotype and is used to guide therapy and inform prognosis. • On the basis of this analysis, he is treated with aspirin, abciximab, celecoxib (high-dose), dexamethasone • On the basis of this analysis, he is expected to survive the event for at least 10 years. Each patient is treated with individualized therapy.

43. Drug Development Paradigms Population Medicine Personalized Medicine Therapeutic Decision Conventional Phenotypes Molecular Phenotypes vs. based on: Discovery And Development Largest Patient Population or Unmet Need Individual MolecularMechanism based on: vs. Individual Targeted Therapy Product Blockbuster Drug vs.

44. Pharmacoeconomics and Clinical Trials Attrition Rate Cumulative Cost N=100 Preclinical N=40 N=30 Phase I Phase 2 N=14 N=9 Phase 3 NDA 2.8 Yr 1.5 Yr 2.3 Yr 2.3 Yr --DeMasi et al., J Health Econ, 2003

45. How to Effect the Transition to Personalized Medicine • Begin with small steps. • “Systematic effort should be made to include biomarker data in [new drug] labeling.” –LJ Lesko, Center for Drug Evaluation and Research, November 15, 2005 • Development of accessible, comprehensive (inter)national database for genotype-phenotype analysis • Define minimal bioinformatic data set to optimize risk assessment and therapeutic outcome. • Provide commercialization incentives accordingly (What is the minimal –omic unit for cost-effective drug development?).

46. “It is the last lesson of modern science that the highest simplicity of structure is produced not by a few elements, but by the highest complexity.” --Ralph Waldo Emerson, 1850