Mechanistic links between Na+ channel (SCN5A) mutations and impaired cardiac pacemaking in sick sinus syndrome

Abstract

Rationale: Familial sick sinus syndrome (SSS) has been linked to loss-of-function mutations of the SCN5A gene, which result in decreased inward Na(+) current, I(Na). However, the functional role of I(Na) in cardiac pacemaking is controversial, and mechanistic links between mutations and sinus node dysfunction in SSS are unclear.

Objective: To determine mechanisms by which the SCN5A mutations impair cardiac pacemaking.

Methods and results: Action potential (AP) models for rabbit sinoatrial node (SAN) cells were modified to incorporate experimentally reported I(Na) changes induced by 2 groups of SCN5A gene mutations (affecting the activation and inactivation of I(Na), respectively). The cell models were incorporated into an anatomically detailed 2D model of the intact SAN-atrium. Effects of the mutations and vagal nerve activity on cardiac pacemaking at the single-cell and tissue levels were studied. Multielectrode extracellular potential recordings of activation pattern from intact SAN-atrium preparations were performed to test predictions of the models. At the single-cell level, the mutations slowed down pacemaking rates in peripheral, but not in central SAN cells that control the heart rhythm. However, in tissue simulations, the mutations not only slowed down pacemaking, but also compromised AP conduction across the SAN-atrium, leading to a possible SAN exit block or sinus arrest, the major features of SSS. Simulated vagal nerve activity amplified the bradycardiac effects of the mutations. Two groups of SCN5A mutations showed subtle differences in impairing the ability of the SAN to drive the surrounding atrium, primarily attributable to their differential effects on atrial excitability and conduction safety. Experimental data with tetrodotoxin and carbachol confirmed the simulation outcomes.

Conclusions: Our study substantiates the causative link between SCN5A gene mutations and SSS and illustrates mechanisms by which the mutations impair the driving ability of the SAN.

Figure 1

Model of the rabbit SAN and surrounding atrial tissue. A: colour-coded distribution of cell types throughout the 2D tissue slice. Insets show respective single cell APs. B: spatial distribution of the activation time during normal AP conduction through the 2D slice (rainbow palette). Lines are isochrones and numbers are activation time in ms. C: activation time profile through the middle of the 2D slice; an open circle shows respective experimental data. D: AP profiles during conduction through the slice. E: gradient in cell capacitance along the slice. F: gradient in the diffusion coefficient along the slice.

Figure 2

Effects of the SCN5A mutations on Na⁺ current in SAN cells. A: percentage increase of the fast and slow Na⁺ channel inactivation time constants from the WT channel to T220I, P1298L, delF1617 and E161K mutant channels. B: simulated steady-state activation curve for Na⁺ channel. C: simulated steady-state inactivation curves for Na⁺ channel. D: relative (to the WT channel) shift of the steady-state inactivation curve from experimental data. E: normalised simulated Na⁺ channel current density. E: percentage reduction in Na⁺ current density from the WT channel. For mutation parameter values see Online Table I.

Figure 3

Effects of the SCN5A mutations on the SAN pacemaking rate. Simulated voltage and current recordings from central (i) and peripheral (ii) SAN cells. Effects of the mutation on APs (A), I_Na (B), I_Ca,L (C), I_K,r (D) and PCL (E) shown.

Figure 4

Effects of ACh on the SAN pacemaking rate. Simulated voltage and current recordings from central (i) and peripheral (ii) SAN cells. Effects of the SCN5A mutations and [ACh] = 1.5 ×10⁻⁸ mol/L on APs (A), I_K,ACh (B), I_Ca,L (C), I_f (D), I_Na (E) and PCL (F) shown.

Figure 5

Effects of ACh and the SCN5A mutations on the SAN pacemaking rate. A-F: effects of varying ACh concentrations on AP generation in SAN cells with the WT (left) and mutant (right) channels. G: dependence of PCL on ACh concentration in single cell simulations. H: dependence of PCL on ACh concentration in 2D simulations.

Figure 6

Effects of ACh and the SCN5A mutations on AP conduction. AP profiles in the 2D tissue with [ACh] = 0 (left) and [ACh] = 1.5 ×10⁻⁸ mol/L (right) are shown. A, B: tissue with the WT channel. C, D: tissue with the P1298L mutant channel. E, F: tissue with the E161K mutant channel.

Figure 7

Effects of ACh and the SCN5A mutations on AP conduction parameters: activation times with the E161K mutation and ACh (A) and the P1298L mutation (B) as compared to the WT channel; conduction velocities with the E161K mutation and ACh (C) and the P1298L mutation (D) as compared to the WT channel; dV/dt_max with the E161K mutation and ACh (E) and the P1298L mutation (F) as compared to the WT channel. [ACh] = 1.5 ×10⁻⁸ mol/L.

Figure 8

Experimental validation of the SAN-atrium tissue model. Isolated SAN-atrium tissue with superimposed activation maps (top) show increase of the conduction time from the SAN into the RA due to effects of TTX, CCh and their combination (see panel labels). AP conduction velocities and PCLs under all conditions considered match the respective values simulated with the 2D tissue model (bottom). SVC – superior vena cava, IVC – inferior vena cava. The SAN leading pacemaker cite in control is shown with an asterisk.

Figure 8

Experimental validation of the SAN-atrium tissue model. Isolated SAN-atrium tissue with superimposed activation maps (top) show increase of the conduction time from the SAN into the RA due to effects of TTX, CCh and their combination (see panel labels). AP conduction velocities and PCLs under all conditions considered match the respective values simulated with the 2D tissue model (bottom). SVC – superior vena cava, IVC – inferior vena cava. The SAN leading pacemaker cite in control is shown with an asterisk.

Figure 8

Experimental validation of the SAN-atrium tissue model. Isolated SAN-atrium tissue with superimposed activation maps (top) show increase of the conduction time from the SAN into the RA due to effects of TTX, CCh and their combination (see panel labels). AP conduction velocities and PCLs under all conditions considered match the respective values simulated with the 2D tissue model (bottom). SVC – superior vena cava, IVC – inferior vena cava. The SAN leading pacemaker cite in control is shown with an asterisk.

Figure 8

Experimental validation of the SAN-atrium tissue model. Isolated SAN-atrium tissue with superimposed activation maps (top) show increase of the conduction time from the SAN into the RA due to effects of TTX, CCh and their combination (see panel labels). AP conduction velocities and PCLs under all conditions considered match the respective values simulated with the 2D tissue model (bottom). SVC – superior vena cava, IVC – inferior vena cava. The SAN leading pacemaker cite in control is shown with an asterisk.

Figure 8

Experimental validation of the SAN-atrium tissue model. Isolated SAN-atrium tissue with superimposed activation maps (top) show increase of the conduction time from the SAN into the RA due to effects of TTX, CCh and their combination (see panel labels). AP conduction velocities and PCLs under all conditions considered match the respective values simulated with the 2D tissue model (bottom). SVC – superior vena cava, IVC – inferior vena cava. The SAN leading pacemaker cite in control is shown with an asterisk.

Figure 8

Experimental validation of the SAN-atrium tissue model. Isolated SAN-atrium tissue with superimposed activation maps (top) show increase of the conduction time from the SAN into the RA due to effects of TTX, CCh and their combination (see panel labels). AP conduction velocities and PCLs under all conditions considered match the respective values simulated with the 2D tissue model (bottom). SVC – superior vena cava, IVC – inferior vena cava. The SAN leading pacemaker cite in control is shown with an asterisk.