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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
-100-80-60-40-2002040
0 100 200 300 400 500 600 700
Mem
bran
e P
oten
tial
(m
V)
Time (mSec)
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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0
20
40
0 0.1 0.2 0.3 0.4 0.5
Experiment
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20
40
0 100 200 300 400 5000 0.1 0.2 0.3 0.4 0.5
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
100200300400500600700800900
0 200 400 600 800 1000
[Ca i ]
(nM
)
Time (mSec)
0
100
200
300
400
500
600
0 200 400 600 800 1000
Model
[Ca i ]
(nM
)
Time (mSec)
B
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0 100 200 300 400 500 600 700
Mem
bran
e P
oten
tial
(m
V)
Time (mSec)
-100
-50
0
50
0 100 200 300 400 500 600 700
Mem
bran
e P
ote
nti
al
(mV
)
Time (Sec)Experiment
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
-3
-2.5
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0
0.5
0 0.05 0.1 0.15 0.2 0.25
I CaL
(pA
/pF
)
Time (sec)
Mechanism of HF AP Duration Prolongation: Model Interpretations
0
0.1
0.2
0.3
0.4
0.5
0 0.1 0.2 0.3 0.4 0.5 0.6
[Ca
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
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
-1
-0.5
0
0.5
1
1.5
-500 0 500 1000 1500 2000 2500
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