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, andWhitaker Biomedical Engineering Institute3

The Johns Hopkins University School of Medicine and Whiting School of Engineering

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

Heart Failure

The Heart Failure Phenotype

O’Rourke et al (1999). Circ. Res. 84: 562

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Mem

bran

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tial

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V)

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Normal

HF

Action Potentials

Ca2+ Transients

Voltage Clamp

Cellular Phenotype

MR heart image pre- (A) and post- (B) tachycardia pacing

Organ Phenotype

The Heart Failure Phenotype (cont.)

KCND3 IKv4.3

KCNJ2 IKir2.1

NCX1 INCX1

ATP2A2 Iserca2a

Molecular Phenotype

Gene Current Regulation Measurement

67 %

33 %

50 %

200 %

O’Rourke et al (1999). Circ. Res. 84: 562

Kaab et al (1996). Circ. Res. 78: 262

whole-cell currentschannel densitymRNA

whole-cell currentsProtein levelmRNA

Myocyte Model

NIH Specialized Center of Research in Sudden Cardiac Death(NIH P50 HL52307)

Gene/ProteinExpression

Channel/Transporter

Function

CellElectro-

physiology

VentricularRemodeling

VentricularConduction

Experiments (Human and Canine)

Microarrays

Protein Assays

RecombinantChannels

SomaticGene Transfer

Ca2+ & V

NADH, FADH,

Vmito, Ca2+

mito

HistologicalAnalyses

MR DiffusionImaging

ElectrodeArrays

Modeling & Data Analysis

Goal: To understand the molecular basis of sudden cardiac death in human heart failure

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

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

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Experiment

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Model

Common Pool ModelsReconstruct the AP

Data

Model

Model Ca2+ Release is “All-or-None”

Wier et al (1994) J. Physiol. 474(3): 463-471

The Structural Basis of Excitation-Contraction Coupling

~ 10 nm

Adapted from Fig. 1ABers (2000) Circ. Res. 87: 275

Ca2+L-Type Ca2+

Channel

Ca2+ ReleaseChannels (RyR)

10 nm

Voltage-Dependent Inactivation slow and weak

Ca2+-Mediated Inactivation Fast and strong

Model PredictionUnstable APs (Alternans)

Formulation of a Myocyte Model IncorporatingLocal-Control of Ca2+ Release

Greenstein, J. L. and Winslow, R. L. (2002) Biophys. J. 83: 2918-2945

Ca2+ Flux from NSR

(Jtr)

Ca2+ Flux to Cytosol

(Jxfer)RyRs(Jrel)

JSR

LCC

(ICaL)ClCh

(Ito2)

Functional Unit

Jxfer,i,4

Jxfer,i,2

Jxfer,i,3

Jiss,i,1,4 Jiss,i,2,

3

Jiss,i,3,

4

Jiss,i,1,

2

Jxfer,i,1

Ca2+ Release Unit

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 t

Within each t, simulate stochastic gating of each CaRU

Total Ca2+ flux is determined by the ensemble behavior of independent CaRUs

Local Control Myocyte Model Exhibits Graded Release, Stable APs, and Predicts Cellular Phenotype of HF

40

4

Experiment Model

Wier et al (1994) J. Physiol.474(3): 463-471

Graded Release

Experiment

Model

Normal

Normal

Failing

Failing

Stable APs & Reproduction ofthe Heart Failure Phenotype

Mechanisms Regulating AP Duration in HF

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[Ca i ]

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B

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Model

Normal

CHF

IKv4.3 66% serca2a (62%)

IKir2.1 32% NCX1 (75%)

Normal

Normal

CHF

Normal

CHF

Winslow et al (1999). Circ. Res. 84: 571

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I CaL

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Mechanism of HF AP Duration Prolongation: Model Interpretations

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i] (M

)

Time (sec)

Stimulus Duration

NCX1 ATP2A2

Winslow et al (1999) Circ. Res. 84: 571

Decreased JSR Ca2+ Decreased JSR Ca2+ Release

Increased L-Type Ca2+ Current Prolonged AP Duration

Ca2+-Mediated Inactivation of ICaL is a Major Factor Regulating AP Duration: Effects of Ablation

Model

Experiment

Alseikhan et al (2002). Biophys J. 82:358a

Mutant CaM1234

disables Ca Sensor for Cainactivation

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

Fox and Hutchins (1972). Johns Hopkins Med. J. 130(5): 289-299

Structural Remodeling in End-Stage Heart Failure Imaging Heart Geometry and Fiber Structure

DTMRI 3x3 diffusion tensor Mi(x)Hypothesis – The principle eigenvector of Mi(x) is aligned with fiber direction at point x

Diffusion Tensor MR Imaging (DTMRI)

x

DTMRI vs HISTO Fiber Angles DTMRI Fiber AnglesIn Cross Section

Holmes, A. et al (2000). Magn. Res. Med., 44:157

Scollan et al (2000). Ann. Biomed. Eng., 28(8): 934-944.

fixed Myocardium3-D FSE DTMRI256 x 256 x 100 imaging volume350 m in-plane, 900 m out-of-plane resolutionFiber orientation estimates at ~ 1-3 * 106 voxels60 hr imaging time

Imaging Procedure

Structural Remodeling in End-Stage Heart FailureFinite Element Models of Cardiac Anatomy

Epicardial Fibers – FEM Model Endocardial Fibers – FEM Model

As described in Nielsen et al AJP 260(4 Pt 2):H1365-78 User selects number of volume elements/nodesMatlab GUI for visual control of the fitting processAll imaging datasets, FE models, and FEM software are available at www.cmbl.jhu.edu

Structural Remodeling in End-Stage Heart FailureLarge-Deformation Transformations for Computational Anatomy:

Grenander and Miller (1998) Quart. Appl. Math. 56(4): 617-694

Define transformations () 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

Quantifying Ventricular Deformation

Template (3 Views) TargetDeformedTemplate

DeformationMetric

Structural Remodeling in End-Stage Heart FailureDTMR Imaging Results (Canine Model)

Normal CanineHeart

Failing CanineHeart

Fiber Anisotropy Fiber Inclination Angle

2 2 2

1 2 1 3 2 3

2 2 2

1 2 3

( ) ( ) ( )A

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

Results

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

Experiment Deterministic Common Pool Model

EADEAD

Possible Mechanism of Arrhythmia in HF

Can EADs trigger arrhythmias in the heart?

Test this hypothesis using an integrative model of the cardiac ventricles

Stochastic Local-Control Model

Possible Mechanism of Arrhythmia in HF (cont.)

Reaction-Diffusion Equation

Winslow et al (2000). Ann. Rev. Biomed. Eng., 2: 119-155

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Nor

mal

ized

EC

G

Time (mSec)

Pak et al (1997). J Am Coll Cardiol 30: 576Polymorphic Ventricular Tachycardia

HxtxvxMtxItxvICt

txviappion

m

,),()(1

1),()),((

1),(

{From Ionic Models From DTMRI{

Can Ventricular Models Be Predictive?

128 Epicardial Electrode Array

Measure Electrode Positions

MR Image and ModelVentricular Anatomy

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

ModelExperiment

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

Acknowledgements

Supported by NIH RO1-HL60133, RO1-HL70894, RO1-HL72488, P50-HL52307, NO1-HV-28180, the Falk Medical Trust, the Whitaker Foundation and IBM Corporation

Modeling and Data Analysis Experiment

Paul DelmarJoseph GreensteinAlex HolmesSaleet JafriJeremy RiceDavid ScollanAntti TanskanenJiangyang Zhang

Ion HobaiEduardo MarbanBrad NussBrian O’RourkeSuzanne SzakGordon TomaselliDavid Yue