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Center for Cardiovascular Bioinformatics & Modeling 1 Center for Imaging Sciences 2, and

Systems Biology: A Necessary Methodology for Understanding the Mechanisms of Sudden Cardiac Death in Heart Failure. Raimond L. Winslow 1,3 , William Baumgartner Jr 1,3 , Patrick Helm 1,3 , Christina Yung 1,3 , Faisal Beg 2,3 and Michael I. Miller 2,3.

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Center for Cardiovascular Bioinformatics & Modeling 1 Center for Imaging Sciences 2, and

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  1. Systems Biology: A Necessary Methodology for Understanding the Mechanisms of Sudden Cardiac Death in Heart Failure Raimond L. Winslow1,3, William Baumgartner Jr1,3, Patrick Helm1,3, Christina Yung1,3, Faisal Beg2,3 and Michael I. Miller2,3 Center for Cardiovascular Bioinformatics & Modeling1 Center for Imaging Sciences2, and Whitaker Biomedical Engineering Institute3 The Johns Hopkins University School of Medicine and Whiting School of Engineering

  2. Heart Failure • Mechanical pump failure leading to reduced cardiac output • Cause unknown • Diverse origins • High blood pressure, artherosclerosis, MI, congenital heart defects, valve disease, alcohol abuse, viral infections, gene mutations • Common end-stage phenotype • The primary U.S. hospital discharge diagnosis • Incidence ~ 400,000/year, prevalence of ~ 4.5 million • prevalence increasing as population ages • 15% mortality at 1 Yr, 80% mortality at 6 Yr • leading cause of Sudden Cardiac Death in the US

  3. Organ Phenotype Cellular Phenotype Action Potentials Voltage Clamp Ca2+ Transients Normal HF O’Rourke et al (1999). Circ. Res. 84: 562 The Heart Failure Phenotype MR heart image pre- (A) and post- (B) tachycardia pacing

  4. The Heart Failure Phenotype (cont.) Myocyte Model Molecular Phenotype Gene Current Regulation Measurement 67 % KCND3 IKv4.3 whole-cell currents channel density mRNA 33 % KCNJ2 IKir2.1 50 % ATP2A2 Iserca2a whole-cell currents Protein level mRNA 200 % NCX1 INCX1 Kaab et al (1996). Circ. Res. 78: 262 O’Rourke et al (1999). Circ. Res. 84: 562

  5. NIH Specialized Center of Research in Sudden Cardiac Death(NIH P50 HL52307) Goal: To understand the molecular basis of sudden cardiac death in human heart failure Experiments (Human and Canine) Channel/ Transporter Function Cell Electro- physiology Gene/Protein Expression Ventricular Remodeling Ventricular Conduction Microarrays Protein Assays Histological Analyses MR Diffusion Imaging Electrode Arrays Recombinant Channels Somatic Gene Transfer Ca2+ & V NADH, FADH, Vmito, Ca2+mito Modeling & Data Analysis

  6. Topics • To what extent can known changes of gene/protein expression in HF account for altered cellular responses? • Develop and apply a new model of the cardiac ventricular myocyte • Model describes how “microscopic” interactions between individual ion channels influences macroscopic behavior of the myoycte • How can we best image, quantify and model changes of cardiac geometry and micro-anatomic structure that occur in HF? • Diffusion Tensor MR Imaging (DTMRI) and modeling of cardiac geometry and fiber orientation • Quantitative analysis of statistical variation of heart structure • To what extent can known changes of gene/protein expression in HF explain the origins of Sudden Cardiac Death? • Computational model of the cardiac ventricles • Possible origins of whole-heart arrythmias

  7. Common Pool Models Reconstruct the AP Model Ca2+ Release is “All-or-None” Experiment Model Data Model Models of the Myocyte • Models are system of ODEs describing channel gating, membrane transport and ion fluxes • “Common Pool” Models have a single Ca2+ compartment into which all ICa,L and IRyR is directed (Stern, MD (1992). Biophys. J. 63: 497-517) • Models reconstruct APs • Models cannot reconstruct graded Ca2+ release Wier et al (1994) J. Physiol. 474(3): 463-471

  8. ~ 10 nm Model Prediction Unstable APs (Alternans) The Structural Basis of Excitation-Contraction Coupling • Voltage-Dependent Inactivation • slow and weak • Ca2+-Mediated Inactivation • Fast and strong Adapted from Fig. 1A Bers (2000) Circ. Res. 87: 275 Ca2+ Release Channels (RyR) L-Type Ca2+ Channel Ca2+ 10 nm

  9. Functional Unit Ca2+ Release Unit Ca2+ Flux from NSR (Jtr) Jxfer,i,1 Jxfer,i,2 Jiss,i,1,2 Ca2+ Flux to Cytosol Jiss,i,1,4 Jiss,i,2,3 JSR RyRs (Jxfer) (Jrel) Jxfer,i,4 Jxfer,i,3 Jiss,i,3,4 ClCh LCC (ICaL) (Ito2) Formulation of a Myocyte Model IncorporatingLocal-Control of Ca2+ ReleaseGreenstein, J. L. and Winslow, R. L. (2002) Biophys. J. 83: 2918-2945 • 1 ICaL : 5 RyR per Functional Unit • 4 functional units coupled via Ca2+ diffusion per Calcium Release Unit (CaRU) • ~ 12,500 CaRU’s per myocyte • Integrate ODEs defining the model over time steps Dt • Within each Dt, simulate stochastic gating of each CaRU • Total Ca2+ flux is determined by the ensemble behavior of independent CaRUs

  10. 4 40 Local Control Myocyte Model Exhibits Graded Release, Stable APs, and Predicts Cellular Phenotype of HF Stable APs & Reproduction of the Heart Failure Phenotype Graded Release Experiment Model Experiment Failing Normal Model Failing Normal Wier et al (1994) J. Physiol. 474(3): 463-471

  11. Normal IKv4.3 66%serca2a (62%) IKir2.1 32%NCX1 (75%) Normal Normal CHF CHF Mechanisms Regulating AP Duration in HF Model Normal CHF Experiment Winslow et al (1999). Circ. Res. 84: 571

  12. Stimulus Duration Mechanism of HF AP Duration Prolongation: Model Interpretations NCX1 ATP2A2 Decreased JSR Ca2+ Decreased JSR Ca2+ Release Prolonged AP Duration Increased L-Type Ca2+ Current Winslow et al (1999) Circ. Res. 84: 571

  13. Ca2+-Mediated Inactivation of ICaL is a Major Factor Regulating AP Duration: Effects of Ablation Model Experiment Mutant CaM1234 disables Ca Sensor for Cainactivation Alseikhan et al (2002). Biophys J. 82:358a

  14. Topics • To what extent can known changes of gene/protein expression in HF account for altered cellular responses? • Develop and apply a new model of the cardiac ventricular myocyte • Model describes how “microscopic” interactions between individual ion channels influences macroscopic behavior of the myoycte • How can we best image, quantify and model changes of cardiac geometry and micro-anatomic structure that occur in HF? • Diffusion Tensor MR Imaging (DTMRI) of cardiac geometry and fiber orientation • Quantitative analysis of statistical variation of heart structure • To what extent can known changes of gene/protein expression in HF explain the origins of Sudden Cardiac Death? • Computational model of the cardiac ventricles • Possible origins of whole-heart arrythmias

  15. Diffusion Tensor MR Imaging (DTMRI) x • DTMRI  3x3 diffusion tensor Mi(x) • Hypothesis – The principle eigenvector of Mi(x) is aligned with fiber direction at point x Fox and Hutchins (1972). Johns Hopkins Med. J. 130(5): 289-299 Imaging Procedure • fixed Myocardium • 3-D FSE DTMRI • 256 x 256 x 100 imaging volume • 350 mm in-plane, 900 mm out-of-plane resolution • Fiber orientation estimates at ~ 1-3 * 106 voxels • 60 hr imaging time Structural Remodeling in End-Stage Heart Failure Imaging Heart Geometry and Fiber Structure DTMRI Fiber Angles In Cross Section DTMRI vs HISTO Fiber Angles Holmes, A. et al (2000). Magn. Res. Med., 44:157 Scollan et al (2000). Ann. Biomed. Eng., 28(8): 934-944.

  16. Structural Remodeling in End-Stage Heart FailureFinite Element Models of Cardiac Anatomy • As described in Nielsen et al AJP 260(4 Pt 2):H1365-78 • User selects number of volume elements/nodes • Matlab GUI for visual control of the fitting process • All imaging datasets, FE models, and FEM software are available at www.cmbl.jhu.edu Endocardial Fibers – FEM Model Epicardial Fibers – FEM Model

  17. Grenander and Miller (1998) Quart. Appl. Math. 56(4): 617-694 • Define transformations f() which move anatomical coordinates of template to target • Transformations: • include translation, rotation and expansion/contraction, large deformation landmark transformations, and high dimensional large deformation image matching transformations. • maintain global relationships between structures • Describe statistical variation of structures post-translation Template Target Structural Remodeling in End-Stage Heart FailureLarge-Deformation Transformations for Computational Anatomy:

  18. Quantifying Ventricular Deformation Deformation Metric Deformed Template Template (3 Views) Target

  19. Results • LV wall thinning • 17.5 2.9mm N • 12.9 2.8mm F • Septal thickening • 14.7 1.2mm N • 19.7 2.1mm F • Increased septal anisotropy • .71 .15 N, .82 .15 F • Fiber re-orientation Structural Remodeling in End-Stage Heart FailureDTMR Imaging Results (Canine Model) Fiber Anisotropy Fiber Inclination Angle Normal Canine Heart Failing Canine Heart

  20. Topics • To what extent can known changes of gene/protein expression in HF account for altered cellular responses? • Develop and apply a new model of the cardiac ventricular myocyte • Model describes how “microscopic” interactions between individual ion channels influences macroscopic behavior of the myoycte • How can we best image, quantify and modelchanges of cardiac geometry and micro-anatomic structure that occur in HF? • Diffusion Tensor MR Imaging (DTMRI) of cardiac geometry and fiber orientation • Quantitative analysis of statistical variation of heart structure • To what extent can known changes of gene/protein expression in HF explain the origins of Sudden Cardiac Death? • Computational model of the cardiac ventricles • Possible origins of whole-heart arrythmias

  21. Stochastic Local-Control Model Can EADs trigger arrhythmias in the heart? Test this hypothesis using an integrative model of the cardiac ventricles Possible Mechanism of Arrhythmia in HF Experiment Deterministic Common Pool Model EAD EAD

  22. { { From Ionic Models From DTMRI Possible Mechanism of Arrhythmia in HF (cont.) Reaction-Diffusion Equation Pak et al (1997). J Am Coll Cardiol 30: 576 Polymorphic Ventricular Tachycardia Winslow et al (2000). Ann. Rev. Biomed. Eng., 2: 119-155

  23. Can Ventricular Models Be Predictive? 128 Epicardial Electrode Array MR Image and Model Ventricular Anatomy Measure Electrode Positions

  24. Experiment Model Can Ventricular Models Be Predictive? (cont.) • Electrically mapped and DTMR imaged 4 normal and 3 failing canine hearts • 128-electrode sock array, ~ 7mm electrode spacing • Complete anatomical and electrical reconstruction performed on one normal canine heart

  25. Ongoing Efforts • Determine those genes/proteins that are differentially expressed in • End-stage human heart failure • The canine tachycardia pacing-induced model of HF • Measure changes over time • Correlate changes with cell electrophysiology • Continue to use computational models of the myocyte to infer the functional significance of changes in gene/protein expression • Model of cardiac mitochondrial metabolism (Cortassa et al. 2003. Biophys. J. 84: 2734-2755) • Incorporate data on changes of gene/protein expression into model to assess functional significance • Relate changes in geometry and micro-anatomic structure of the failing heart to risk for arrhythmia

  26. Modeling and Data Analysis Experiment Acknowledgements Paul Delmar Joseph Greenstein Alex Holmes Saleet Jafri Jeremy Rice David Scollan Antti Tanskanen Jiangyang Zhang Ion Hobai Eduardo Marban Brad Nuss Brian O’Rourke Suzanne Szak Gordon Tomaselli David Yue Supported by NIH RO1-HL60133, RO1-HL70894, RO1-HL72488, P50-HL52307, NO1-HV-28180, the Falk Medical Trust, the Whitaker Foundation and IBM Corporation

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