Real-time relationship between PKA biochemical signal network dynamics and increased action potential firing rate in heart pacemaker cells: Kinetics of PKA activation in heart pacemaker cells

Abstract

cAMP-PKA protein kinase is a key nodal signaling pathway that regulates a wide range of heart pacemaker cell functions. These functions are predicted to be involved in regulation of spontaneous action potential (AP) generation of these cells. Here we investigate if the kinetics and stoichiometry of increase in PKA activity match the increase in AP firing rate in response to β-adrenergic receptor (β-AR) stimulation or phosphodiesterase (PDE) inhibition, that alters the AP firing rate of heart sinoatrial pacemaker cells. In cultured adult rabbit pacemaker cells infected with an adenovirus expressing the FRET sensor AKAR3, the EC50 in response to graded increases in the intensity of β-AR stimulation (by Isoproterenol) the magnitude of the increases in PKA activity and the spontaneous AP firing rate were similar (0.4±0.1nM vs. 0.6±0.15nM, respectively). Moreover, the kinetics (t1/2) of the increases in PKA activity and spontaneous AP firing rate in response to β-AR stimulation or PDE inhibition were tightly linked. We characterized the system rate-limiting biochemical reactions by integrating these experimentally derived data into a mechanistic-computational model. Model simulations predicted that phospholamban phosphorylation is a potent target of the increase in PKA activity that links to increase in spontaneous AP firing rate. In summary, the kinetics and stoichiometry of increases in PKA activity in response to a physiological (β-AR stimulation) or pharmacological (PDE inhibitor) stimuli match those of changes in the AP firing rate. Thus Ca(2+)-cAMP/PKA-dependent phosphorylation limits the rate and magnitude of increase in spontaneous AP firing rate.

Keywords: Computational modeling; Coupled-clock system; Pacemaker; Phosphorylation.

Figure 1. Schematic illustration of the coupled-clock pacemaker cell system

Basal Ca²⁺-calmodulin activation of adenylyl cyclases (AC) initiates cAMP-PKA-dependent phosphorylation signaling, and in parallel to AC, activates calmodulin-dependent kinase II (CaMKII) phosphorylation signaling. cAMP positively shifts the f-channel activation curve. Phosphodiesterases (PDE) degrade cAMP production, while protein phosphatase (PPT) degrades phosphorylation. PKA and CaMKII phosphorylate: sarcoplasmic reticulum (SR) Ca²⁺ cycling proteins (ryanodine receptor (RyR), phospholamban (PLB), which bind to and inhibit the sarcoplasmic reticulum Ca²⁺-ATPase (SERCA)); surface membrane ion channel proteins (L-type, K channel); and mitochondrial ATP production proteins to activate ATP production and sarcomeres to modulate contraction.

Figure 2. FRET based measurements of PKA activity

(A) Schematic of AKAR3. Phosphorylation of a PKA-specific substrate (LRRATLVD) by PKA leads to its association with the Forkhead association 1 (FHA1) domain bringing the cyan florescent protein (CFP) closer to yellow fluorescent protein (YFP) which results in increased FRET from YFP to CFP and hence an increase in the yellow (FRET)/cyan emission ratio. (B) Changes in PKA activity in response to the PDE inhibitor (IBMX) or to the PKA inhibitor (H-89) in a single cultured SANC. (C) Representative example of the PKA activity (AKAR3 emission ratio Y/C) in a single cultured SANC in response to cumulative increases in the ISO concentration. (D) Representative example of the increase in spontaneous AP firing in response to cumulative increases in the ISO concentration.

Figure 3. PKA activity in response to β-AR stimulation

(A) Average graded changes in PKA activity (emission ratio Y/C) and spontaneous AP firing rate in response to different ISO concentrations. (B) The relationship between the increase in PKA activity and the spontaneous AP firing rate. PKA activity was normalized to the minimum activity (the decrease in PKA in response to 10 µM H-89) and to the maximal activity (the increase in PKA in response to 100 µM IBMX). The dashed line represents the estimated basal level of PKA activity and spontaneous AP firing rate in freshly isolated SANC. (C) Half times (t_1/2) of the increase in PKA activity and spontaneous AP firing rate in response to different single concentrations of ISO.

Figure 4. Distribution of PKA amplitude and time response to β-adrenergic receptor stimulation

(A) Distributions of PKA activity gradients amplitude among different spatial regimes within the same cell and (B) the relationship between average PKA activity amplitude and PKA coefficient of variation in response to graded increases in isoproterenol (ISO) concentration. (C) Distributions of the t_1/2 among different spatial regimes within the same cell and (D) the relationship between average t_1/2 and t_1/2 coefficient of variation in response to graded increases in isoproterenol (ISO) concentration. (E) Coefficient of variation of PKA activity and t_1/2 gradients at different ISO concentrations.

Figure 5. Distribution of PKA amplitude and time response to PDE inhibition or PKA inhibition

Distributions of the (A) PKA amplitudes and (B) the time response to achieve the activity in different spatial regimes within the same cell in response to PDE inhibition (IBMX) following PKA inhibition (H-89).

Figure 6. PKA activity in response to β-AR stimulation or PDE inhibition

Representative example of time course of change in PKA activity (AKAR3 emission ratio Y/C) in response to (A) β-adrenergic receptor stimulation (ISO 100 nM) in the presence of 1 nM ISO and (B) PDE inhibition (IBMX 100 µM) in the presence of 1 nM ISO.*The estimated basal level of PKA activity (see text for details).

Figure 7. Model simulations of the synergism of different mechanisms in mediating SANC spontaneous AP firing rate

The relative role of each mechanism was tested at a constant cAMP, by determining the changes in AP firing rate when only the tested mechanism sensed the changes in cAMP (cAMP was “clamped” to basal level for the remaining other mechanisms) and comparing the resultant change in AP firing rate to that when all mechanisms (direct cAMP and PKA-dependent phosphorylation) were functional.

Figure 8. Kinetics and magnitude of cAMP/PKA-phospholamban phosphorylation and spontaneous AP firing rate in response to maximal β-adrenergic receptor stimulation or PDE inhibition

Model simulations in response to maximal β-adrenergic stimulation predict the rate and extent of changes in (A) PKA activity, (B) cAMP, and (C) PLB phosphorylation in response to maximal β-adrenergic receptor stimulation. (D) The simulations predict that the kinetics of the increase in PLB phosphorylation are similar to those of the increase in spontaneous AP firing rate. Model simulations in response to PDE inhibition predict the rate and extent of changes in (E) PKA activity, (F) cAMP, and (G) PLB phosphorylation in response to PDE inhibition. (H) The simulations predict that the kinetics of the increase in PKA are similar to those of the increase in spontaneous AP firing rate.