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. 2004 May:1015:16-27.
doi: 10.1196/annals.1302.002.

Modeling the actions of beta-adrenergic signaling on excitation--contraction coupling processes

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Modeling the actions of beta-adrenergic signaling on excitation--contraction coupling processes

Joseph L Greenstein et al. Ann N Y Acad Sci. 2004 May.

Abstract

Activation of the beta-adrenergic (beta-AR) signaling pathway enhances cardiac function through protein kinase A (PKA)-mediated phosphorylation of target proteins involved in the process of excitation-contraction (EC) coupling. Experimental studies of the effects of beta-AR stimulation on EC coupling have yielded complex results, including increased, decreased, or unchanged EC coupling gain. In this study, we extend a previously developed model of the canine ventricular myocyte describing local control of sarcoplasmic reticulum (SR) calcium (Ca(2+)) release to include the effects of beta-AR stimulation. Incorporation of phosphorylation-dependent effects on model membrane currents and Ca(2+)-cycling proteins yields changes of action potential (AP) and Ca(2+) transients in agreement with those measured experimentally in response to the nonspecific beta-AR agonist isoproterenol (ISO). The model reproduces experimentally observed alterations in EC coupling gain in response to beta-AR agonists and predicts the specific roles of L-type Ca(2+) channel (LCC) and SR Ca(2+) release channel phosphorylation in altering the amplitude and shape of the EC coupling gain function. The model also indicates that factors that promote mode 2 gating of LCCs, such as beta-AR stimulation or activation of the Ca(2+)/calmodulin-dependent protein kinase II (CaMKII), may increase the probability of occurrence of early after-depolarizations (EADs), due to the random, long-duration opening of LCC gating in mode 2.

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Figures

FIGURE 1
FIGURE 1
Schematic representation of the CaRU. (A) Trigger Ca2+ influx through LCCs enters the T-SR cleft (diadic space). The rise in local Ca2+ level promotes the opening of RyRs and chloride channels (ClChs). Local Ca2+ diffuses passively from the cleft into the cytosol, and JSR Ca2+ is refilled via passive diffusion from the NSR. (B) The T-SR cleft (shown in cross-section) is composed of four diadic subspace volumes arranged on a 2 × 2 grid, each containing 1 LCC, 1 ClCh, and 5 RyRs. Ca2+ in any subspace may diffuse to a neighboring subspace (Jiss) or to the cytosol (Jxfer). Jiss,i,j,l represents Ca2+ flux from the jth subspace to the lth subspace within the ith CaRU. Similarly Jxfer,i,j represents Ca2+ flux from the jth subspace to the cytosol from the ith CaRU.
FIGURE 2
FIGURE 2
Model and experimental APs and Ca2+ transients for control (black) and β-AR stimulation (gray). (A) Representative APs measured in isolated canine myocytes in control bath (black) and ISO (gray). (B) Indo-1 fluorescence ratio measured simultaneously with the APs in Fig. 1A (B. O’Rourke, unpublished data). (C) Control (black) and β-AR stimulated (gray) model APs. (D) Model cytosolic Ca2+ concentration (μM) corresponding to the APs in Fig. 1C.
FIGURE 3
FIGURE 3
Panels A–C show effects of β-AR stimulation using the baseline model on macroscopic peak LCC Ca2+ influx FLCC(max) (A), peak RyR Ca2+ release flux FRyR(max) (B), and EC-coupling gain defined as FRyR(max)/FLCC(max) (C) Responses include control (black circles), the baseline β-stimulated model (black triangles), and the β-stimulated model with initial control conditions for SR Ca2+ load (gray triangles). Panels D–F show effects of LCC open frequency and RyR Ca2+ sensitivity associated with β-AR stimulation on macroscopic peak LCC Ca2+ influx (D), peak RyR Ca2+ release flux (E), and EC coupling gain (F). Responses include control (circles), increased LCC open frequency (4 × f, triangles), and increased RyR Ca2+ sensitivity (10 × k12, squares).
FIGURE 4
FIGURE 4
(A) Membrane potential (ordinate; mV) as a function of time (abscissa; Sec) for the baseline β-AR model in response to 1 Hz pacing. (B) Membrane potential (ordinate; mV) as a function of time (abscissa; Sec) for three AP simulations in which model parameters, initial conditions on state variables governed by ordinary differential equations, and initial states of each channel are identical; however, the pseudorandom number generator seeds used to produce channel open times are initialized to different values prior to each simulation.

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