Mechanistic Links Between Na+ Channel (SCN5A) Mutations and Impaired Cardiac Pacemaking in Sick Sinus Syndrome
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, INa. However, the functional role of INa 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 INa changes induced by 2 groups of SCN5A gene mutations (affecting the activation and inactivation of INa, 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.
Sick sinus syndrome (SSS) denotes a collection of cardiac arrhythmias associated with dysfunction of the sinoatrial node (SAN) that commonly lead to disorders in cardiac rhythm and conduction.1 Such arrhythmias include intermittent sinus bradycardia, sinus arrest, sinus pause, slow SAN-atrium conduction, sinus exit block or alternating bradycardia and atrial tachycardia.1–4 The symptoms of SSS vary, with patients presenting with syncope, presyncope, palpitations, or dizziness.1,5 Many SSS patients have to be fitted with an electronic pacemaker.
Mechanisms underlying the pathogenesis for sinus node dysfunction in SSS patients are unclear. SSS can occur in elderly and pediatric patients. It can also occur in healthy people without any evident structural heart disease2,4 but with genetic defects. Recent studies have identified several gene mutations in congenital SSS patients.2,4,6,7 Among them are the SCN5A gene mutations that alter the structure of the pore-forming α-subunit of the cardiac Na+ channel. In congenital SSS families, Benson et al2 identified several SCN5A mutants that include threonine in place of isoleucine at position 220 (T220I), leucine in place of proline at position 1298 (P1298L), and in-frame deletion (delF1617) mutations. Functional analysis of the expression of the wild-type (WT) and mutant Na+ channels in cultured mammalian cell line (tsA201) revealed that these mutations resulted in reduced INa current density, together with impaired fast inactivation (ie, a shift in the voltage-dependent steady-state inactivation curve to a more hyperpolarizing potential) in comparison to the WT channel.2 Smits et al6 identified another SCN5A mutation associated with SSS: replacement of glutamine by lysine at position 161 (E161K). Functional analysis of the E161K mutation also revealed reduced INa density. However, in contrast to T220I, P1298L, and delF1617 mutations, the E161K mutation caused a shift in the steady-state activation curve to a more depolarized potential. Although all these SCN5A mutations share in common a reduction in INa, they are intrinsically different in their kinetic effects and can be classified into 2 distinctive groups, one altering INa inactivation (G1: T220I, P1298L and delF1617) and the other altering INa activation properties (G2: E161K). At the macroscopic level, a negative shift in voltage-dependent inactivation or a positive shift of voltage-dependent activation may have similar consequences in terms of reducing the INa “window” current. However, they may modulate differently cardiac excitability and, thus, differentially affect the ability of the SAN to pace and drive the surrounding atrial muscle.
The loss-of-function of INa caused by SCN5A mutations is also associated with other cardiac diseases that include Brugada syndrome and progressive cardiac conduction system disease.6,8,9 However, the mechanistic link between loss-of-function of INa arising from these mutations and impaired SAN function has not been elucidated. Primarily, whether and how these mutations compromise the ability of the SAN to drive the surrounding atrial muscle has not been investigated, as the existing experimental studies are limited to molecular and ionic levels.2,6,10,11 Furthermore, the role of Na+ channels in cardiac pacemaking is still controversial,7,12–14 because the channels are expressed in peripheral SAN cells, but either completely absent or dramatically less-expressed in central SAN cells which mainly control the cardiac rhythm.7
Computational models provide a powerful way to study the functional effects of reduced INa in initiation and conduction of the pacemaking action potential (AP), an emergent dynamic behavior of coordinated ion channels and intercellular electric coupling.6,14–17 Smits et al6 have shown that the E161K mutation slows down the pacemaking rate in a peripheral SAN cell model,15 and AP conduction in a 1D atrial strand model. Both effects were enhanced by acetylcholine (ACh),6 the neurotransmitter released by the vagal nerves. However, the mechanisms by which the loss-of-function of INa slows down the heart rate in the intact SAN-atrium (in which the heart rate is mainly controlled by central SAN cells with little or no INa, rather than peripheral SAN cells) and impairs AP conduction from the SAN to the atrial tissue (ie, the ability of the SAN to drive the surrounding atrium) have not been fully elucidated. It is also unclear whether or not the 2 distinctive mutation groups (G1 and G2) exhibit similarity or difference in their functional impacts. The aim of this study was to address these issues.
The consequences of the impaired INa channel function caused by several SCN5A mutations (T220I, P1298L, delF1617, and E161K) in generating sinus node dysfunction were investigated by using (1) previously developed electrophysiologically detailed mathematical models of the central and peripheral SAN15 cells; (2) two-dimensional anatomic models of the intact SAN-atrium tissue, incorporating accurate single-cell models of the SAN15 and the right atrium (RA),18 and histologically reconstructed tissue geometry19; and (3) multielectrode extracellular potential recordings of the activation pattern from isolated intact rabbit SAN-atrium tissue to verify model predictions.
A full list of the model equations and parameters used for the central and peripheral SAN cells and RA cells under control conditions, SCN5A mutation conditions, and ACh conditions, as well as 2D tissue model of the intact SAN-atrium, is provided in the Online Data Supplement, available at http://circres.ahajournals.org (electronic copies of the respective codes are also available on request).
Two-Dimensional Slice Model
The 2D model of intact SAN-atrium tissue used in this study was based on histologically reconstructed19 geometry of a single slice of the rabbit RA, which was cut through the atrial muscle of the crista terminalis (CT) and the intercaval region with central and peripheral SAN areas (Figure 1A). The geometry presents a high spatial resolution (40 μm, which corresponds to 2 to 4 diameters of a cardiac myocyte) regular Cartesian grid of 210×45 nodes. For each node, a flag variable was used to identify whether it belongs to the SAN or RA cell type based on immunohistochemistry mapping data.19 The SAN was modeled by the Zhang et al equations15 for central and peripheral cells, and the RA was modeled by the Aslanidi et al equations18 for atrial cells, with each model producing different AP morphology (Figure 1A).
The model also incorporated an experimentally observed19,20 nonconductive region (“block zone”) next to the SAN toward the atrial septum, where cells have low excitability. The AP conduction in the 2D tissue model attributable to intercellular electric coupling via gap junctions was modeled through the diffusion coefficient, D. In the model, we considered the regional differences in the cellular electrophysiological properties and gap junctional coupling between the SAN center, periphery and atrial tissue as observed experimentally.19 A spatial gradient of D (Figure 1F) was introduced as in our previous study.17 The gradient distributions of current densities from the central to the peripheral SAN were modeled by correlating them to cell membrane capacitance, Cm, which was assumed to be small in the center and large in the periphery.12,15,17 Spatial variations in all ion channel conductances were defined as a function of Cm as found experimentally.15 To incorporate the effects of ACh into the cell models,16 (i) the ACh-activated K+ current, IK,ACh , was introduced, (ii) the L-type Ca2+ current, ICa,L, was partially inhibited, and (iii) the activation curve of the hyperpolarization-activated current, If, was shifted toward more negative potentials. Full equations are presented in the Online Data Supplement.
The single-cell models for central and peripheral SAN cells and RA cells have been validated in our previous studies.15,18 The developed 2D anatomic model of the intact SAN-atrium was validated by its ability to reproduce the correct sequence of AP initiation and conduction through the rabbit SAN-atrium, as observed experimentally.20,21 In the 2D model, the pacemaking AP was first initiated in the center of the SAN (Figure 1B and 1C), and it then propagated preferentially toward the CT; AP conduction toward the atrial septum was relatively slow. The space–time plot of the AP profiles recorded from cells along a line across the SAN-atrium (Figure 1D) showed continuous AP initiation and conduction. The computed activation time of the CT (1.5-mm distance from the center of the SAN) matched quantitatively to experimental data (Figure 1C), which validates the intercellular electric coupling used in the model. Simulation results with the 2D model were also validated against respective experimental data from isolated rabbit SAN-atrium (see below).
SCN5A Mutation Model
To model SCN5A mutations from G1 (T220I, P1298L, and delF1617), parameters of the fast Na+ current, INa, were changed to reproduce experimental data (Figure 2). Note that prior experimental data2 were recorded from tsA201 cells transfected with a recombinant human Na+ channel cDNA (hH1), whereas our models are for the native current in rabbit SAN cells. Thus, in simulations, we shifted the steady-state inactivation curve by the same amount as observed experimentally (Figure 2C and 2D) and implemented the same percentage changes of the fast- and slow-inactivation time constants as observed experimentally (Figure 2A). We scaled the maximal INa channel conductance by a factor SCD (see Online Table I) to reproduce the normalized current–voltage relationship (Figure 2E) and the reduction in INa as observed experimentally (Figure 2F). The heterozygous E161K mutation was modeled by the same method as used by Smits et al6: INa was divided into 2 components: one for the WT INa and another for the E161K mutant INa. The WT component had a maximum conductance of 50% of control, whereas the mutant component had a maximum conductance of 20% of control. The steady-state activation curve of the mutant component was also shifted by 11.9 mV (Figure 2B). Full equations are presented in the Online Data Supplement.
Isolated Tissue Experiments
Five male adult rabbits (2 to 3 months old) were used for experimental study. The sinoatrial preparations were set up as described previously from animals killed by anesthetic overdose with venous injection of sodium phenobarbital (in accord with UK Home Office Legislation).22 After excision of the SAN and surrounding atrial muscle, the preparation was placed endocardial surface upwards in a tissue bath and superfused with modified Tyrode’s solution (in mmol/L: NaCl 120, NaHCO3 25.2, NaH2PO4 1.2, MgCl2 1.3, glucose 5, KCl 4.0, CaCl2 1.8, gassed with 95% O2/5% CO2) at 37°C and at a flow rate of ≈5 mL/min. Electric signals were obtained from the surface of this preparation by apposition of a custom-made extracellular multielectrode array that allowed electrograms to be monitored at multiple sites in the tissue as excitation passed under the array. The electrode array held 30 separate silver electrodes in a 5×6 configuration (a detailed description of the electrodes is presented in the Online Data Supplement). The 30 recording electrodes were connected through shielded wires to a 32-channel amplifier (SCXI-1102C, National Instruments Corporation UK Ltd, Newbury, UK). The sampling frequency for each channel was set at 1 kHz. The signals were continuously sampled and stored on disk and displayed on screen using a custom-developed program, written in Labview 7.0 (National Instruments Corporation UK Ltd). Experiments were performed under the conditions of control, application of tetrodotoxin (TTX) (0.5 μmol/L; IC50≈0.1 μmol/L23) or a nondegrading ACh equivalent, carbachol (CCh) (200 nmol/L; IC50≈100 nmol/L24), and a combination of TTX (0.5 μmol/L) and CCh (200 nmol/L). Propagation maps were then derived during off-line analysis. The signals were displayed on screen in sets of 8 to 16 electrograms. The activation time was denoted as the point of maximal negative slope and marked with a cursor. After marking all significant waveforms in all leads, the activation times were then displayed in a grid representing the layout of the original recording array. All activation times, in milliseconds, were related to the timing of the first detected waveform.
Mutation Effects on Single Cells
At the single-cell level, the central and peripheral SAN cell models were used to investigate the functional effects of the SCN5A mutations (G1: T220I, P1298L, and delF1617; G2: E161K) on AP generation under control and ACh conditions. Figure 3A through 3D shows effects of the P1298L mutation, and Figure 3E summarizes quantitative effects of all mutations. Figure 3A through 3D shows simulated AP with the WT and P1298L mutant channels, along with major underlying ionic currents in the central (left graphs) and peripheral (right graphs) cells. These simulations illustrate that the P1298L mutation did not affect the central cell (Figure 3A, i), but it dramatically slowed down pacemaking in the peripheral cell (Figure 3A, ii); the pacemaking cycle length (PCL) increased from 170 to 204 ms, an increase of 19.7%. This is similar to experimental observations by Honjo et al,12 who reported that blocking INa with TTX did not affect central cells, but slowed pacemaking of peripheral cells in the rabbit SAN. In the model, the resultant slowing of the pacemaking was primarily attributable to the mutation-induced decrease in INa (Figure 3B, ii), because other major underlying currents (such as ICa,L and IKr) were not greatly affected (Figure 3C [ii] and 3D [ii]). Functional effects of other mutations were similar: there was a slowing of pacemaking in peripheral cells and an increase of the PCL by 7.1%, 10.8%, 19.1%, and 7.1% for the T220I, delF1617, and E161K mutations, respectively (Figure 3E, ii).
ACh Effects on Single Cells
Application of ACh slowed down pacemaking by increasing the PCL in both central and peripheral SAN cells, with a larger effect on the central cells.16 Simulating effects of 5×10−8 mol/L ACh resulted in the PCL increasing from 338 ms to 429 ms (27.1% increase) for the central SAN cell, and from 171 ms to 194 ms (13.7% increase) for the peripheral SAN cell. The negative chronotropic effect of ACh was attributable to an integral action of IK,ACh (Figure 4B) and changes in ICa,L (Figure 4C) and If (Figure 4D). When the P1298L mutation was considered, the negative chronotropic effect of ACh was amplified for the peripheral cell model, but it remained the same for the central cell model (Figure 4A). The augmented negative chronotropic effects of ACh with the mutant channel can be attributed to the reduced inward depolarizing INa during the diastolic depolarization period, which counterbalances the ACh-activated outward repolarizing current, IK,ACh, leading to a greater slowing of the diastolic depolarization. Such behavior was also observed for other SCN5A mutations. With the same 5×10−8 mol/L ACh concentration, the PCL in the peripheral cell model increased by 26.0%, 50.4%, 45.3%, and 23.4% with the T220I, P1298L, delF1617, and E161K mutations respectively, which is markedly greater than with the WT channel (13.7%).
The dose-dependent effects of ACh on pacemaking APs with the WT and mutant channels were also simulated. Figure 5 presents results obtained from the peripheral cell model for different ACh concentrations with the WT (Figure 5A, 5C, and 5E) and the P1298L mutant (Figure 5B, 5D, and 5F) channels. It shows that increase in ACh concentration led to a greater increase in PCL, and the negative chronotropic effect was amplified by the P1298L mutation at all ACh concentrations. At an ACh concentration of 15×10−8 mol/L (Figure 5E), the cell with the WT channel still exhibited pacemaking (though with prolonged PCL), but the cell with the mutant channel became quiescent. Figure 5G summarizes the simulated dose-dependent effect of ACh on pacemaking APs with the WT and various mutant channels. It shows that all the mutations shifted the dose dependence of the PCL leftward, indicating a more suppressive effect of ACh on SAN cells incorporating ‘mutant’ INa. The dose-dependent effects of ACh on pacemaking at the 2D tissue level are illustrated in Figure 5H.
Two-Dimensional Tissue Effects
We investigated further the functional consequences of the mutations on AP conduction across the intact SAN-atrium under control and ACh conditions. Results are shown in Figure 6, which presents the spatial (running horizontally) and temporal (running vertically) profiles of APs recorded from representative cells across the SAN-atrium model in Figure 1A. Figure 6A presents the normal conduction in the normal tissue (with the WT channel and no ACh). Simulation of ACh addition (1.5×10−8 mol/L) slowed down the pacemaking rate (ie, led to an increase in the PCL) (Figure 6B). Figure 6C shows the AP profile with the P1298L mutation, a representative of the G1 group. Without ACh, the mutation slowed down the pacemaking rate, but did not impair the ability of the SAN to drive the atrium. However, with ACh the SAN pacemaker activity was abolished (Figure 6D), and, thus, the SAN lost the power to drive the atrium. Figure 6E and 6F show the effects of the G2 E161K mutation. Without ACh, the mutation slowed down pacemaking and also produced a conduction block in the direction toward the atrial septum; however, the AP conduction in the direction toward the CT was sustained. With an ACh concentration of 1.5×10−8 mol/L, the SAN was able to generate spontaneous activity but failed to drive the surrounding atrium; conduction exit block occurred in both directions.
Detailed simulations were also performed to investigate the effects of the mutations and ACh on the characteristics of AP initiation and conduction in the intact SAN-atrium tissue model. Figure 7A presents the measured activation timing sequence across the tissue with the WT and E161K mutant channels, with and without ACh. In the model, simulated application of ACh or the E161K mutation alone increased the time required for the AP to propagate from the center of the SAN to the RA, ie, the SAN conduction time (Figure 7A). The E161K mutation alone resulted in AP conduction failure in the direction toward the atrial septum. Furthermore, when ACh addition was simulated, there was a dramatic delay in AP initiation and also conduction block both toward the CT and the septum. However, the P1298L mutation markedly delayed AP initiation (Figure 7B), but did not increase the SAN conduction time; addition of ACh completely abolished the AP.
The measured AP conduction velocity in SAN-atrium tissue simulations also reflected the above observations. In the normal tissue with the WT channel, addition of ACh reduced the conduction velocity by ≈12% in the SAN (on average) and by ≈7% in the RA (Figure 7C). In tissue with the E161K mutant channel, the conduction velocity was decreased by ≈46% in the SAN (on average) and by ≈45% in the RA (Figure 7C). Subsequent addition of ACh to the tissue with the E161K mutant channel further reduced the AP conduction velocity in the SAN, and resulted in a complete conduction block in the atrium. In tissue with the P1298L mutant channel, the AP conduction velocity was decreased by ≈8% in the SAN (on average), and by ≈8% in the RA (Figure 7D); subsequent addition of ACh abolished pacemaking (Figure 7D).
Changes in the measured conduction velocity across the SAN-atrium were attributed to the changed maximum upstroke velocity (dV/dtmax), as shown in Figure 7E and 7F. In the normal tissue with the WT channel, simulated addition of ACh reduced dV/dtmax in both the RA and the SAN, with a greater effect on the RA. In tissue with the E161K mutant channel, there was a decrease in dV/dtmax throughout the tissue (up to ≈27%), not only in the RA and periphery of SAN, but also in the center of the SAN; subsequent addition of ACh further reduced dV/dtmax in the SAN, with more dramatic effects in the SAN periphery, primarily at the border between the SAN and the atrium. This resulted in the SAN failing to drive the atrium, leading to SAN exit block. The P1298L mutation reduced dV/dtmax markedly in the SAN region, but only slightly in the RA region. Addition of ACh to tissue with the P1298L mutant channel (Figure 7F) abolished pacemaking and dV/dtmax was zero throughout the SAN (data not shown).
Effects on Excitability and Conduction Safety
We have measured the excitation threshold, a reciprocal measure of cardiac excitability, for atrial cells in WT and mutant cases. Both the G1 and G2 mutations reduced atrial excitability, but to different extents. For the P1298L and E161K mutations, the measured excitation threshold increased from 1.7 to 2.1 and 2.8 nA, respectively, for a stimulus pulse duration of 1 ms. With simulated ACh addition, the respective changes were from 2.8 with the WT channel to 3.0 and 4.1 nA with the P1298L and E161K mutant channels. Thus, both mutations and ACh increased the excitation threshold for atrial cells. The larger increase of the excitation threshold associated with the G2 group (E161K) was correlated with higher vulnerability of the tissue to AP conduction block at the junction of the SAN-atrium (see Online Figure I).
Large differences in the magnitude of the threshold increase can be explained by the different nature of the mutations: although both the P1298L and E161K mutations result in about the same reductions in the current density of INa (Figure 2E and 2F), but the underlying kinetic changes are different. P1298L represents a family of mutations that cause hyperpolarizing shifts in the steady-state inactivation curve (Figure 2C), whereas E161K causes a depolarizing shift in the steady-state activation curve (Figure 2B). Both shifts decrease the window current, but only E161K substantially increased the excitation threshold, which is sensitive primarily to the activation kinetics of INa. Results of a systematic analysis of effects of activation and inactivation shifts on excitability are presented in the Online Data Supplement (see Online Figure I).
Na+ channel defects that result in decreased tissue excitability have been associated with reduced AP conduction safety manifested as decreases in the safety factor (SF).25 Our simulations of SF changes (see the Online Data Supplement for details) attributable to effects of the SCN5A mutations and ACh suggested that the G2 (E161K) mutation results in a greater decrease of SF arising from greater decreased tissue excitability as compared to G1 mutations (Online Figure II). Importantly, the AP conduction block attributable to the effects of the E161K mutation and high ACh concentration can be localized close to the exit site from the CT toward the RA (see Online Figure II), where low SF emerges from a combination of both electrophysiological (high excitation threshold, low dV/dtmax) and geometric (high source-to-load mismatch attributable to narrowing of the tissue) factors.
Multielectrode extracellular recordings of AP initiation and conduction pattern in isolated rabbit SAN-atrium tissue were performed experimentally to verify simulation results with the 2D tissue slice model (Figure 6). Because of lack of phenotypically accurate rabbit models, defective INa due to the SCN5A variants was mimicked by reducing INa through the application of TTX. Figure 8 shows the electric activation maps of the intact SAN-atrium tissue reconstructed from extracellular potentials recorded from the endocardial surface of the SAN-atrium preparation (see Methods). In control, the excitation was generated in the SAN center and propagated toward the RA with an average AP conduction velocity in the direction transverse to the CT of 0.12 m/s. In terms of the AP conduction velocity, application of 0.5 μmol/L TTX alone slowed it down to 0.08 m/sec, whereas application of 200 nmol/L CCh alone did not have any significant effect on the velocity. However, combined application of 0.5 μmol/L TTX and 200 nmol/L CCh resulted in a much more substantial slowing of the AP conduction than TTX alone, down to 0.05 m/s. In terms of pacemaking rhythm, application of CCh or TTX alone resulted in an increase in the PCL from 340 to 430 and 380 ms, respectively. However, combined application of TTX and CCh increased the PCL to 490 ms. The experimentally measured changes in both the AP conduction velocity and PCL are in good agreement with the simulation results (Figure 8), thereby supporting the predicted roles of SCN5A variants and the accentuating effects of ACh on impairing the cardiac pacemaker.
In this study, we have developed a family of SAN cell models with WT and mutant SCN5A channels, and incorporated them into an electrophysiologically and anatomically detailed 2D computer model of the rabbit SAN and surrounding atrium. Using the model, we investigated the functional effects of 2 groups of SCN5A mutations (G1 and G2) in impairing the ability of the SAN to pace and drive the surrounding atrial muscle. Functional differences between the 2 mutation groups were also studied. Our major findings are as follows: (1) At the intact SAN-atrium tissue level, INa reduction attributable to the SCN5A mutations slowed down pacemaking and compromised the AP conduction across the SAN-atrium, leading to a possible sinus arrest or SAN exit block, which are major features of SSS. Our simulation results provide evidence substantiating the causative link between the identified gene mutations and familial SSS. (2) The functional impacts of reduced INa at the isolated single-cell level are different to those seen in the intact SAN-atrium tissue level. Such a discrepancy is attributable to electrotonic interaction between the SAN and atrium. Thus, single-cell simulations cannot be relied on solely, and multicellular tissue models must be used to evaluate the functional role of INa in cardiac pacemaking. (3) The bradycardiac effects of the SCN5A gene mutations are likely to be amplified by vagal nerve activity: simulated addition of ACh to the tissue with a mutant channel not only slowed pacemaking and AP conduction, but also compromised the ability of the SAN to pace and drive the atrium, leading to a higher probability of sinus arrest and SAN exit block than with the mutations or ACh alone. This finding agrees with that of Smits et al6 and may provide further insight into the mechanism underlying high risk of cardiac arrest in SSS patients at night, when vagal tone is greater.26,27 (4) The 2 groups of SCN5A mutations (G1 and G2) show common features in impairing the pacemaking ability of the SAN and the AP conduction across the SAN-atrium, but have subtle differences in affecting the ability of the SAN to drive the surrounding atrium. This is attributable to their different impact on atrial excitability. (5) SAN exit block occurred because of the combined effect of SCN5A mutations and ACh, which produces a decreased SF in the local region close to the exit site from the CT toward the RA, where the source-to-load mismatch is the largest because of both electrophysiological and geometric factors. (6) Our simulation results are supported by experimental data, collectively indicating an important role for cholinergic activity in accentuating the deleterious effects of defective Na+ channels on cardiac pacemaking. Taken together, these findings enhance current understanding of the mechanistic links between the studied SCN5A gene mutations and the resultant anomalies of cardiac pacemaker.
SCN5A Mutations and Pacemaker Abnormalities
Our study has shown that, at the isolated single-cell level, all the considered SCN5A mutations slowed pacemaking of peripheral SAN cells, but not central SAN cells that normally initiate and control the heart rhythm. However, at the intact SAN-atrium tissue level, the SCN5A mutations slowed down the heart rate by slowing down pacemaking in the central cells, which have no INa. This can be attributed to the electrotonic interaction between the SAN and the atrium. Because of a hyperpolarized resting potential (more negative than the maximal diastolic potential of the SAN) and lack of pacemaking activity, the atrium acts as an electric load suppressing the pacemaker activity of the SAN. Such a suppressive action can be mapped to the center of the SAN through cell-to-cell electric coupling, although its effects here are weaker than in the periphery. With the mutations, the suppressive action becomes greater as less INa is available to counterbalance the suppressive effect of adjacent atrial tissue.28
Our simulations also showed that the mutations slowed AP conduction across the SAN-atrium. As shown by the activation time profiles (Figure 6), the AP conduction through the SAN is slow with all mutations, as often reported in SSS patients or patients with other cardiac conditions linked to mutations in the SCN5A gene.6,10 This is considered a sign of potential conduction failure risks, eg, sinus exit block.1 Conduction block was observed in our simulations with the E161K mutation (Figure 6E), in agreement with clinical observations.6 The block can be accounted for by a combination of decreased atrial excitability (Online Figure I) and conduction safety (Online Figure II), which are most prominent with the E161K mutation.
Addition of ACh slowed pacemaking in both the single-cell and 2D simulations, as did CCh in experiments; the negative chronotropic effect of ACh was greater with the SCN5A mutant channels than with the WT channel. The augmented bradycardiac effect of ACh can be attributed to the associated reduction of If (Figure 4). Without ACh, reduced INa leads to a more hyperpolarized membrane potential in SAN cells, which activates more If. This increase in If counterbalances the reduced INa in the diastolic phase. However, as If is reduced by ACh, its counterbalancing effect on INa is also reduced, which results in the observed augmented negative chronotropic effect.
Note that cholinergic receptor stimulation by ACh also reduces ICa,L, the effect ascribed to a protein kinase A–dependent reduction in L-type Ca2+ channel phosphorylation, which can occur either during β-adrenoreceptor stimulation,29 or in its absence.30 However, it remains controversial whether ACh inhibitory effects on ICa,L do occur in the absence of concurrent β-adrenoreceptor stimulation. Recent findings24 have shown that in the absence of β-adrenergic stimulation CCh reduced the ICa,L amplitude by 20%, but only at a 10-fold higher [CCh] than that required to stop pacemaking; effects of lower [CCh], corresponding to physiologically feasible ACh levels, were much smaller. In our model, the description of the dose dependence of ICa,L on [ACh] has been based on similar data.16,30 Consequently, modulation of ICa,L by “physiological” [ACh] considered in our study is weak; removing this modulation from the model has not changed qualitative results of our simulations (quantitative changes to the pacemaking rate and AP conduction velocity were <10%). Obviously, in the evenings when effects of the sympathetic nervous system on the heart rate (primarily, the pacemaking rate and AP conduction velocity) via β-adrenoreceptor are reduced, modulation of ICa,L by ACh will be weaker than during daytime. However, effects of such a reduction of ICa,L modulation on the heart will be much smaller that those attributable to ACh activation of IK,ACh.
Our simulations demonstrate that ACh not only slows down pacemaking as observed in single-cell studies2,6 but also impairs the ability of the SAN to drive the surrounding atrial muscle, as revealed by our 2D simulations (Figure 6) and verified in isolated SAN-atrium tissue experiments (Figure 8). Thus, increased vagal nerve activity (leading to greater ACh release onto the SAN) can be anticipated to augment the bradycardic effects of the SCN5A mutations. This result is also validated experimentally (Figure 8) and is consistent with clinical observations that significantly lower heart rates occur at night, when vagal tone is high.
Previous Modeling Studies
Note that our study of the conditions of compromised AP conduction in the SAN-atrium tissue and their mechanistic links with the SSS is related to, but distinct from, previous results by Kurata et al.31 That study focused on bifurcation structures of several single-cell models: (1) SAN cells under high [ACh] effects; (2) SAN cells electrotonically coupled to atrial cells via conductance GC; and (3) models 1 and 2 with blocked INa. When INa was not blocked, the cells exhibited robust pacemaking over broad ranges of [ACh] and GC values; PCLs increased as [ACh] and GC increased.31 However, INa block dramatically shrunk the [ACh] and GC parametric regions corresponding to spontaneous pacemaking,31 suggesting that INa is important for maintaining the relatively high structural stability of the system consisting of coupled SAN and atrial cells. Although distinct, these observations are consistent with our results, primarily on the effects of INa impairment, ACh and electrotonic load from the atrium on the pacemaking rate (Figure 5). Besides, simulations of our 2D tissue model show that the loss of structural stability in the coupled-cell model31 corresponds to failure of the SAN to pace and drive atrial tissue (Figure 6), the effect being attributable to a combination of SCN5A Na+ channel mutations and high [ACh]. The AP conduction from the SAN into the RA is compromised as the combined effects of INa impairment and activation of IK,ACh substantially increase the tissue excitation threshold (Online Figure I). These results are verified experimentally (see Figure 8): although CCh alone did not have a significant effect on the AP conduction velocity, a combination of TTX (which suppresses INa) and high [CCh] resulted in much slower conduction in SAN-atrium preparations than TTX alone.
Limitations of cardiac cell and tissue models are well documented.15,18 In our single-cell model development, we modified the equations for INa based on available experimental data from cultured tsA201 cells.2 Because our models are for rabbit cells and tissue, relative shifts between the WT and mutant parameters (such as the current density, the inactivation time constants and the steady-state inactivation curve of INa) were calculated from the experimental data and applied in the rabbit model. Here, we made an explicit assumption that rabbit Na+ channel characteristics would show changes in response to SNC5A mutations that would be proportionately similar to those for human Na+ channels. The fact that our model uses cross-species data are not unusual; this is a common practice in cardiac modeling in situations where complete data from a single species are often unavailable. Note also that data by Benson et al2 have been recorded from patients with heterozygous SCN5A mutations. Such mutations can be modeled by the same method as used by Smits et al,6 with INa divided into 2 components: one for the WT and another for the mutant INa, and each component having a maximum conductance of 50% of control. We have observed that such models of the heterozygous SCN5A mutations from G1 group produce results (not shown) that are qualitatively similar to those reported above. Another notable feature of the present study is that, because of the lack of phenotypically accurate rabbit models, in our in vitro experiments defective Na+ channel function with SCN5A variants was mimicked pharmacologically, by reducing INa through the application of TTX. Nevertheless, there was good concordance between experimental and simulation findings (see Figure 8).
Our 2D anatomic tissue model is only a portion of the whole atria of the rabbit. Three-dimensional geometry19 may ultimately be required to study patterns of AP generation and conduction in the SAN (eg, exact locations of the sinus exit block sites, including the established block zone toward the atrial septum), which may depend on details of the tissue spatial structure, 3D heterogeneity and anisotropy. The dimensions of our tissue model have been reduced from full 3D to 2D, because we were primarily interested in interaction between the center and periphery of the SAN during pacemaking and following AP conduction from the SAN across the CT into the RA. These phenomena can be studied within a slice, where main 3D geometric features of the SAN-atrium tissue are projected onto a 2D plane transversal to the CT. That our approach using a 2D slice is reasonable is borne out by experimental data verifying our simulation results (Figure 8).
Mechanisms of SSS
Our simulations show that different mutations of the same gene (SCN5A) can lead to different modes of SAN dysfunction: abnormally slow pacemaking (with all mutations; enhanced by ACh), failure of the SAN to pacemake (G1: with T220I, P1298L, and delF1617 mutations at high ACh concentrations), or sinus exit block (G2: with the E161K mutation). All of these behaviors are typical of SSS.1,4 Although an exaggeration of the normal ageing process through factors such as degenerative fibrosis could also be an important cause, it is clear that ion channel mutations underlie the familial disease.3 In addition to SCN5A mutations, other ionic channel defects (such as mutations in the HCN4 gene encoding the hyperpolarization-activated cyclic nucleotide gated channel generating the pacemaking current, If4) can also cause SSS. In cases where such mutations lead to abnormalities in AP rate and conduction, computational techniques similar to those used here may be used to provide mechanistic links between ion channel mutations and SSS.
We thank Prof. Wim Lammers at UAE University for developing extracellular mapping electrodes.
Sources of Funding
This work was supported by project grant BBS/B/1678X from the Biotechnology and Biological Sciences Research Council (UK), a Doctoral Training Account from the Engineering and Physical Sciences Research Council (UK), and a Wellcome Trust UK grant (WT/081809/Z/06/Z).
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Novelty and Significance
What Is Known?
Sick sinus syndrome (SSS) is a collection of cardiac arrhythmias associated with dysfunction of the cardiac primary pacemaker: the sinoatrial node (SAN).
Recent studies have identified several gene mutations in congenital SSS patients. Among them are mutations of the SCN5A cardiac Na+ channel.
It is still unclear how SCN5A mutations compromise the ability of the SAN to pace and drive the surrounding atrial muscle in SSS patients.
What New Information Does This Article Contribute?
At the single-cell level, SCN5A mutations slow down pacemaking rates in peripheral, but not in central, SAN cells that control the heart rhythm.
In the SAN-atrial tissue, the mutations not only slow down pacemaking, but also slow down the conduction across the SAN-atrium, leading to a possible SAN conduction exit block or sinus arrest, the major features of SSS.
Vagal nerve activity amplifies the bradycardiac effects of the SCN5A mutations; it also compromises the ability of the SAN to pace and drive the atrium, leading to a higher probability of sinus arrest or SAN exit block than with the mutations alone.
The role of SCN5A gene mutations in compromising the ability of the SAN to drive the surrounding atrial muscle in SSS patients has hitherto been unclear. We sought to address this issue using cell and tissue mathematical models. Our major findings are as follows. (1) Reduction of Na+ channel current caused by the SCN5A mutations slows down both pacemaking and conduction across the SAN-atrium, leading to a possible sinus arrest or SAN exit block, the major features of SSS. (2) The functional impacts of reduced Na+ current at the isolated single-cell level differ from those seen at the intact SAN-atrium tissue level, at which electrotonic interactions occur between the SAN and atrium. (3) The bradycardic effects of the SCN5A mutations are amplified by vagal activity, increasing the probability of sinus arrest and SAN exit block. (4) Simulation data were supported by experimental data from isolated rabbit SAN-atrium tissue. For the first time, these findings illustrate mechanisms by which the SCN5A gene mutations impair the driving ability of the SAN. Our results are consistent with clinical observations and may provide new insights into the mechanisms underlying high risk of cardiac arrest in SSS patients at night, when vagal tone is greater.
In March 2010, the average time from submission to first decision for all original research papers submitted to Circulation Research was 13.3 days.
↵*Both authors contributed equally to this article.
Original received October 27, 2009; resubmission received March 16, 2010; revised resubmission received April 23, 2010; accepted April 27, 2010.