Understanding Sudden Cardiac Death through Integrative Data Modeling

1. Information Flow at the Systems Level:Organization and Modeling of Experimental Data Across Multiple Scales of Biological AnalysisRaimond L. WinslowCenter for Cardiovascular Bioinformatics & ModelingJohns Hopkins University Whiting School of Engineering andSchool of Medicine(www.ccbm.jhu.edu)

2. Outline • Objective • develop new methods for risk stratification and treatment of Sudden Cardiac Death (SCD) • Data Collection from the Molecular to Organ level • Data Organization • Integrative Modeling • A tool for understanding the relationships between molecular events (e.g., changes in gene/protein expression, post-translational modifications of proteins) and function at the cellular and whole-heart levels

3. Heart Failure is the Leading Cause of SCD MR Imaging of Canine Heart Pre- and Post- Failure Heart Failure • Mechanical pump failure leading to reduced cardiac output • Diverse origins • Common end-stage phenotype • The primary U.S. hospital discharge diagnosis • Incidence ~ 400,000/year, prevalence of ~ 4.5 million • 15% mortality at 1 Yr, 80% mortality at 6 Yr • leading cause of Sudden Cardiac Death in the US Chamber Dilation Wall Thinning

4. Data Collection Goal: To understand the molecular basis of sudden cardiac death in human heart failure Experiments (Human, Canine, Rabbit) Cell Membrane Transporter Function Cell Electro- Physiology Gene/Protein Expression Cardiac Imaging Ventricular Conduction Patient Data Microarrays 2D PAGE Mass Spec (MALDI-TOF, TOF-TOF, SELDI) MR Diffusion Tensor Imaging Spin-Tagging Electrode Arrays Heterologous Expression Systems Whole Cell & Patch-Clamp Recording Ca2+, Na+ & V NADH, FADH, Vmito, Ca2+mito Modeling & Data Analysis

5. Web Services Integration (IBM MinelinkTM) Data Organization HTML SOAP IBM WebsphereTM (Not Completed) SQL SOAP SOAP Database Federation Software (IBM Information Integrator) Data Analysis & Visualization Models (HIPPA) Protein-DB2 MAGE-DB2 IMAGING CLINICAL

6. ~ 10 nm From Katz (1992) Physiology of the Heart Bers (2002) Nature 415: 198-205 } • The “Calcium Release Unit” (CaRUs) • ~ 10 L-Type Channels and 50 RyR • ~5,000 such units in the myocyte • ~ independendent Vm { Integrative Modeling:Relating Molecular Mechanisms of Excitation-Contraction Coupling to Cellular and Whole-Heart Function Ca2+ Release Channels (RyR) 10 nm Ca2+ Ca2+ L-Type Ca2+ Channel • Ca2+-I >> Voltage-I Trigger Ca2+ Release Ca2+

7. Common Pool Models Reconstruct the AP Experiment Model Common Pool Models of the Myocyte Existing Myocyte Models • Existing myocyte models lump all 5,000 CaRUs into single compartment • => “common pool” models • Described as systems of ODEs • Reconstruct properties of the AP Iserca2a

8. When Ca2+-I >> Voltage-I Lack of Graded Release Experiment Model Linz & Meyer (1998) J. Physiol. 513(pt 2): 425-442 Membrane Potential Total Ca2+ Release EC Coupling and Common Pool Models Mechanism Unstable APs RyR Ca2+ { Ca2+ LCCs Model Prediction Unstable APs (Alternans) The Common Ca2+ Pool

9. 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) Integrating from Channels to the Cell:The Local-Control Myocyte Model Greenstein, J. L. and Winslow, R. L. (2002) Biophys. J. 83: 2918-2945 Ca2+ Release Unit • 1 ICaL : 5 RyR per Functional Unit • 4 functional units coupled via Ca2+ diffusion per Calcium Release Unit (CaRU) • ~ 12,500 independent CaRU’s per myocyte (=> ~ 50,000 LCCs per cell) • Numerically integrate the ODEs defining the myocyte model over steps Dt, while simulating stochastic dynamics of the CaRUs within each Dt

10. 12,500 CaRUs RyR Open Fraction Stochastic Simulation Algorithm • Improved pseudo-random number generator (MT19937) with longer period and improved performance • Dynamic allocation algorithm for controlling number of CaRUs • Parallel implementation, ~ linear scaling • ~1 minute per 1 Sec of activity • Model can relate channel level events (e.g., phosphorylation) to whole-cell behavior

11. Experiment 4 Model 40 Wier et al (1994) J. Physiol. 474(3): 463-471 Local Control Myocyte Model Exhibits Graded Release and Stable APs Action Potentials

12. Normal and Failing APs Experiment Failing Normal Model Failing Normal Altered Expression of EC Coupling Proteins and the Cellular Phenotype of Heart Failure Altered Gene Expression in End-Stage Canine and Human Heart Failure Kaab et al (1996). Circ. Res. 78(2): 262 Yung et al (2003). Genomics. in press online Genes Encoding K+ Currents Genes Encoding EC Coupling Proteins KCND3 (Ito1) ~ 66%ATP2A2 ~ (62%) KCNJ12 (IK1) ~ 32%NCX1 ~ (75%) { { Little Effect on AP and Ca2+ Transient Major Effect on AP and Ca2+ Transient Greenstein & Winslow (2002). Biophys. J. 83(6): 2918

13. Relating Effects of PKA-Mediated Phosphorylation of EC-Coupling Proteins to Cellular Function • EC-coupling proteins are believed to be hyper-phosphorylated in the failing heart • Targets and actions of PKA-mediated phosphorylation ( 1mM ISO) • L-Type Ca2+ Channels (LCCs) • Increase LCC availability (~ 2 – 2.5x) • Mode-1, 2 re-distribution (~ 15% Mode-2, ~85% Mode-1) • Increased mean channel open time in Mode-2 (~.5 to 5.0 mSec) • Serca2a Pump (ATP2A2) • Serca2a up-regulated by ~ 3x (Simmerman & Jones Physiol. Rev. 78: 921) • IKr • Increased through reduced inactivation (Heath & Terrar J. Physiol. 522: 391) • IKs • Increased ~ 2x (Kathofer et al J. Biol. Chem. 275: 26743) • Use the local-control model to understand consequences of this hyper-phosphorylation at the cellular level

14. Control Iso (1 mM) Develop Model Using Data on b1-Adrenergic AgonistsEffects on APs and Ca2+ Transients Ca2+ Transients Action Potentials • “Baseline Model” • Serca2a and K+ current changes • Mode-1,2 redistribution • Increased availability

15. Early After-Depolarizations in Response to LCC Phosphorylation EAD Frequency • Early After-Depolarizations (EADs) are thought to trigger polymorphic ventricular tachycardia • Rate of occurrence of EADs is increased in myocytes isolated from failing hearts • No EADs in the absence of Mode 2 gating • => rate of EAD generation increases with increased Mode-2 gating % Mode 2 # EADs # APs

16. EAD Generation is Stochastic • Identical initial conditions, but different random number seeds produces different realizations of LCC and RyR state transitions • => stochastic gating of LCCs triggers EADs

17. Mode 2 Current Mode 1 Current Initiation of Stochastic EADs by Increased Mode-2 Gating • Long Mode-2 open time increases likelihood of clustered random Mode-2 LCC openings • Spontaneous, near simultaneous openings of a sufficient number of LCCs gating in Mode 2 generates inward current • Resulting depolarization re-activates LCCs gating in Mode 1, producing an EAD • Novel hypothesis regarding generation of EADs

18. 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, 800 mm out-of-plane resolution • Fiber orientation estimates at ~ 3 * 106 voxels Integrating from Cell to Ventricular Function:DTMR Imaging of Ventricular Anatomic 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.

19. Finite Element Models of Cardiac Ventricular Anatomy • 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.ccmb.jhu.edu Endocardial Fibers – FEM Model Epicardial Fibers – FEM Model

20. { { From Ionic Models From DTMRI Modeling Electrical Conduction in the Cardiac VentriclesEADs Can Trigger Ventricular Arrhythmias Reaction-Diffusion Equation EADs Trigger Reentry and Polymorpic VT Winslow et al (2000). Ann. Rev. Biomed. Eng., 2: 119-155

21. “Closing the Loop” on Whole-Heart Experimentsand Models 256 Epicardial Electrode Array MR Image and Model Ventricular Anatomy Measure Electrode Positions

22. Experiment Model “Closing the Loop” on Whole-Heart Experiments and Models (cont.) • Electrically mapped and DTMR imaged 4 normal and 3 failing canine hearts • 256-electrode sock array, ~ 5mm electrode spacing • Complete anatomical and electrical reconstruction performed on one normal canine heart Winslow et al. (2002). Novartis Foundation Symposium 247: In Silico Simulation of Biological Processes, pgs. 129-150, John Wiley & Sons, Ltd. 2002.

23. Summary • Use of a “hierarchy of models”, each developed to address problems at different levels of biological organization, is important • Individual stochastically gating channels • Cell models • Tissue/whole heart models • The detailed spatial arrangement of ion channels in the cardiac myocyte has a profound effect on cell and whole heart function • Stochastic effects at low molecule copy number • ~10 – 100 free Ca2+ ions in the diadic space at the peak of the Ca2+ transient • Continuum models may not be valid • Dynamics of Ca2+ ions become important • Importance of the interplay between modeling and experiment • Whole heart models have been used exclusively in the “predictive” mode • Methods now exist for coupling whole-heart experiments and models

24. Acknowledgements Supported by the NIH (HL60133, HL70894, HL61711, HL72488, P50 HL52307, NO1-HV-28180, ), the Falk Medical Trust, the Whitaker Foundation, the D. W Reynolds Foundation and IBM Corporation