Biological noise is a key determinant of the reproducibility and adaptability of cardiac pacemaking and EC coupling

Abstract

Each heartbeat begins with the generation of an action potential in pacemaking cells in the sinoatrial node. This signal triggers contraction of cardiac muscle through a process termed excitation-contraction (EC) coupling. EC coupling is initiated in dyadic structures of cardiac myocytes, where ryanodine receptors in the junctional sarcoplasmic reticulum come into close apposition with clusters of CaV1.2 channels in invaginations of the sarcolemma. Cooperative activation of CaV1.2 channels within these clusters causes a local increase in intracellular Ca2+ that activates the juxtaposed ryanodine receptors. A salient feature of healthy cardiac function is the reliable and precise beat-to-beat pacemaking and amplitude of Ca2+ transients during EC coupling. In this review, we discuss recent discoveries suggesting that the exquisite reproducibility of this system emerges, paradoxically, from high variability at subcellular, cellular, and network levels. This variability is attributable to stochastic fluctuations in ion channel trafficking, clustering, and gating, as well as dyadic structure, which increase intracellular Ca2+ variance during EC coupling. Although the effects of these large, local fluctuations in function and organization are sometimes negligible at the macroscopic level owing to spatial-temporal summation within and across cells in the tissue, recent work suggests that the "noisiness" of these intracellular Ca2+ events may either enhance or counterintuitively reduce variability in a context-dependent manner. Indeed, these noisy events may represent distinct regulatory features in the tuning of cardiac contractility. Collectively, these observations support the importance of incorporating experimentally determined values of Ca2+ variance in all EC coupling models. The high reproducibility of cardiac contraction is a paradoxical outcome of high Ca2+ signaling variability at subcellular, cellular, and network levels caused by stochastic fluctuations in multiple processes in time and space. This underlying stochasticity, which counterintuitively manifests as reliable, consistent Ca2+ transients during EC coupling, also allows for rapid changes in cardiac rhythmicity and contractility in health and disease.

Figure 1.

Time and frequency profiles of Gaussian and pink noise. (a) Example of white noise. Data in this panel were reproduced from Krogh-Madsen et al. (2017) and show the time course of interburst intervals (left column) recorded from a chick ventricular cardiomyocyte and corresponding power spectral density (right column). (b) Example of pink noise. Time course of RR interval from a 53-yr-old man (left column) obtained from Irurzun et al. (2021). The power spectral distribution of this trace is shown in the right column.

Figure 2.

Increases in the amplitude of Gaussian noise diminish signal detection and discrimination. (a–c) In this figure, an arbitrary sinusoidal signal (black trace, left column) is superimposed on the composite of the original signal added to Gaussian white noise of increasing variance (colored traces). Summation of Gaussian white noise with a mean of 0 and SD of 1 (a), 10 (b), and 100 (c) to an arbitrary sinusoidal signal (y = 10 sin [x/2]; black line). The power spectral density plot of the composite signal and noise values is shown in the right column.

Figure 3.

Entrainment of SA node (SAN) myocytes. (a–c) Simultaneous records of spontaneous action potentials from slow- and fast-firing myocytes in an intact SA node under control conditions (a), during high sucrose (b), and after returning to control conditions (c). Data are from Jalife, 1984.

Figure 4.

Depiction of the stochastic resonance model of cardiac pacemaking. (a) Left: Confocal image of HCN4⁺ myocytes in a mouse SA node (SAN). The superior section of the node is densely populated by HCN4-expressing myocytes. The inferior SAN has a lower myocyte density. Right: Membrane potential records from representative superior and inferior SAN myocytes. Superior SAN myocytes (top) fire action potentials at a higher frequency than inferior myocytes (bottom). The majority of inferior SAN myocytes fire random action potentials or subthreshold voltage fluctuations (arrows). (b) Stochastic resonance model, in which the superior node functions as a periodic oscillator and the inferior as a noise generator. Subthreshold superior oscillations, when they occur with simultaneous random noise input from the inferior, could exhibit stochastic resonance and produce more robust spiking. Data are from Grangier et al., 2021.

Figure 5.

Predicted noise–performance relationship for a stochastic resonance model of pacemaking activity. Hypothetical plots of the relationship between noise and pacemaking periodicity under control conditions and during activation of sympathetic nervous system (SNS) and parasympathetic nervous system (PNS) signaling. (a) Plot assumes that sympathetic and parasympathetic stimulation only increase peak performance, leaving the response to the noise amplitude unchanged. (b) Plot assumes that sympathetic and parasympathetic stimulation shift the noise amplitude relationship toward a preference for lower values in the case of SNS activation and larger values for PNS activation, while increasing peak performance. ISO, isoproterenol.

Figure 6.

SR Ca²⁺ release is the largest source of beat-to-beat [Ca²⁺]_i variability in adult and neonatal ventricular myocytes. (a) Average peak amplitude of action potential–evoked (1-Hz) global [Ca²⁺]_i transients from adult and neonatal ventricular myocytes, measured at 1-min intervals for 5 min, and the corresponding COV among adult and neonatal myocyte populations. The dashed line represents peak [Ca²⁺]_i signal averaged over 5 min. * denotes statistical significance. (b) Representative averaged [Ca²⁺]_i transient and associated signal variance of adult and neonatal ventricular myocytes and the distribution of peak amplitude [Ca²⁺]_i variance (nM²) of adult ventricular myocytes in the presence and absence of the SERCA inhibitor thapsigargin (1 μM). Figure from Vega et al., 2011.

Figure 7.

SR Ca²⁺ release is the largest source of beat-to-beat [Ca²⁺]_i variability in adult rabbit ventricular myocytes. (a and b) Representative averaged [Ca²⁺]_i transient and associated signal variance of adult rabbit ventricular myocytes and the distribution of peak amplitude [Ca²⁺]_i variance (nM²) of adult ventricular myocytes under control conditions (a) and in the presence of the SERCA inhibitor thapsigargin (1 μM; b). (c) Population data for the amplitude of [Ca²⁺]_i variance during EC coupling (control, n = 23 cells; thapsigargin, n = 20 cells; ****, P < 0.0001).

Figure 8.

Increasing the number of SR Ca²⁺-release units decreases cardiac EC coupling variability. (a and b) Simulations of action potentials and [Ca²⁺]_i transients using a model with a small (a) or large (b) number of SR Ca²⁺-release units. (c) Beat-to-beat action potential duration (APD; left) and peak [Ca²⁺]_i transient fluctuations (right) versus 1/√N, where N is the number of SR Ca²⁺-release units.

Figure 9.

Noise has a limited impact on arrhythmogenesis in cases where ventricular myocytes have a high repolarization reserve. (a–c) Action potential simulations using zero (a), low (b), and high (c) Gaussian noise levels.

Figure 10.

Noise increases the probability of EADs when repolarization reserve is decreased by 50%. (a–c) Action potential simulations using zero (a), low (b), and high (c) Gaussian noise levels.

Figure 11.

Noise facilitates development of alternans. Normalized alternans amplitude (average of 20 simulations) versus beat number. Initial conditions and parameters are the same except the number of SERCA pumps. If the number of SERCA pumps is large (10,000), noise is small (black).