Binary transgenic (BT) mice with doxycycline (DOX)-suppressible cardiac-specific over-expression of endothelin 1 (ET 1) exhibit progressive heart failure, QRS prolongation, and death following DOX withdrawal. However, the molecular basis and reversibility of the electrophysiological abnormalities in this model were not known. Here we assess the mechanisms underlying ET 1 mediated electrical remodelling, and its role in heart failure. Prior attempts to prevent this model of ET-1 induced cardiomyopathy with ET receptor antagonism were not beneficial. We now propose to evaluate the effectiveness of blocking the synthesis of ET-1 with CGS 26303, a dual inhibitor of endothelin converting enzyme (ECE) and neutral endopeptidase. BT vs. littermate control mice were withdrawn from DOX and serially studied with ultrasound biomicroscopy, octapolar catheters, multi-electrode epicardial mapping, histopathology, Western blot, immunohistochemistry and qRT-PCR. Prolonged ventricular activation and depressed rate of ventricular activation were detected as early as 4 wks after transgene activation, when structure and function of the heart remained unaffected. By 8 wks of ET-1 over-expression, biventricular systolic and diastolic dysfunction, myocardial fibrosis, cardiomyocyte hypertrophy, prolonged ventricular activation and repolarization, depressed rate of ventricular activation, and abnormal atrioventricular nodal function were observed. Within 4 wks of ET-1 induction, reduction were observed in connexin-43 mRNA, protein, and phosphorylation, Nav1.5 mRNA and protein, Na+ conductance, K+ channel interacting protein-2 mRNA and Kv4.2 mRNA. Chromatin immunoprecipitation revealed that nuclear factor κB preferentially binds to Cx43 and Nav1.5 promoters. Importantly, the associated electrophysiological abnormalities at this time point were reversible upon suppression of ET 1 over-expression and completely prevented the development of structural and functional remodelling. Treatment with CGS-26303 (5 mg/kg/day) failed to improve survival, or hemodynamic and contractile decline. ET-1-mediated ventricular conduction delays correlates with gap junction and ion channel remodelling, and precedes heart failure. The sequence and reversibility of this phenotype suggest that a primary abnormality in electrical remodelling may contribute to the pathogenesis of heart failure. CGS 26303 failed to prevent this cardiomyopathic phenotype. These data suggest that chronically high levels of bigET-1, as seen in heart failure, may induce increased ECE activity and/or non-ECE ET-1 synthesis, thus circumventing the efficacy of ECE blockade in this model.

Atrial fibrillation (AF) is a complex, multifaceted arrhythmia. Pathogenesis of AF is associated with multiple aetiologies and the mechanisms by which it is sustained and perpetuated are similarly diverse. In particular, regional heterogeneity in the electrophysiological properties of normal and pathological tissue plays a critical role in the occurrence of AF. Understanding AF in the context of electrophysiological heterogeneity requires cell-specific ionic models of electrical activity which can then be incorporated into models on larger temporal and spatial scales. Biophysically-based models have typically dominated the study of cellular excitability providing detailed and precise descriptions in the form of complex mathematical formulations. However, such models have limited applicability in multidimensional simulations as the computational expense is too prohibitive. Simplified mathematical models of cardiac cell electrical activity are an alternative approach to these traditional biophysically-detailed models. Utilizing this approach enables the embodiment of cellular excitation characteristics at minimal computational cost such that simulations of arrhythmogensis in atrial tissue are conceivable. In this thesis, a simplified, generic mathematical model is proposed that characterizes and reproduces the action potential waveforms of individual cardiac myocytes. It incorporates three time-dependent ionic currents and an additional time-independent leakage current. The formulation of the three time-dependent ionic currents is based on 4-state Markov schemes with state transition rates expressed as nonlinear sigmoidal functions of the membrane potential. Parameters of the generic model were optimized to fit the action potential waveforms of the Beeler-Reuter model, and, experimental recordings from atrial and sinoatrial cells of rabbits. A nonlinear least-squares optimization routine was employed for the parameter fits. The model was successfully fitted to the Beeler-Reuter waveform (RMS error: 1.4999 mV) and action potentials recorded from atrial tissue (RMS error: 1.3398 mV) and cells of the peripheral (RMS error: 2.4821 mV) and central (RMS error: 2.3126 mV) sinoatrial node. Thus, the model presented here is a mathematical framework by which a wide variety of cell-specific AP morphologies can be reproduced. Such a model offers the potential for insights into possible mechanisms that contribute to heterogeneity and/or arrhythmia.

Diss. (sammanfattning) Göteborg : Göteborgs universitet, 2009. / Härtill 4 uppsatser.

The autonomic nervous system plays an integral role in the modulation of normal cardiac electrophysiology. This is achieved via a complex network of pre- and postganglionic sympathetic and parasympathetic fibers that synapse on extrinsic and intrinsic cardiac ganglia and ultimately directly innervate cardiac myocytes. Alterations in autonomic tone may induce changes in local cellular electrophysiology that may manifest clinically in a number of ways, ranging from changes in heart rate to changes in heart rhythm. These relationships between autonomic tone and the evolution of cardiac dysrhythmias are areas of evolving research, with increasing evidence for a key role for autonomic ganglia in the pathogenesis of atrial fibrillation and sympathetic nerves in the predilection toward ventricular tachycardia in areas of myocardial scar. In this review, we highlight what is known about the anatomy and physiology of the cardiac autonomic nervous system, the evidence supporting the relationship of autonomic tone to clinically significant arrhythmias, and a variety of mechanisms (eg, direct ion channel effects) and diagnostic tools that exist to help define this relationship. Further emphasized are potential future avenues of research needed to elucidate the relationship between changes in normal autonomic tone and the pathogenesis of cardiac arrhythmias.

No description available.

No description available.

Sudden cardiac death due to the development of lethal arrhythmias is the dominant cause of mortality in the UK, yet the mechanisms underlying their onset, maintenance and termination are still poorly understood. Therefore biomarkers are used to determine arrhythmic risk within patients and of new drug compounds. In recent years, the magnitude of variations in the length of successive beats, measured over a short period of time, has been shown to be a powerful predictor of arrhythmic risk. This beat-to-beat variability is thought to be the manifestation of the random opening and closing dynamics of individual ion channels that lie within the membrane of cardiac cells. Computational models have become an important tool in understanding the electrophysiology of the heart. However, current state-of-the-art electrophysiology models do not incorporate this intrinsic stochastic behaviour of ion channels. Those that do use computationally costly methods, restricting their use in complex tissue scale simulations, or employ stochastic simulation methods that result in negative numbers of channels and so are inaccurate. Therefore, using current stochastic modelling techniques to investigate the role of stochastic ion channel behaviour in beat-to-beat variability presents difficulties. In this thesis we take a mathematically rigorous and novel approach to develop accurate and computationally efficient models of stochastic ion channel dynamics that can be incorporated into existing electrophysiology models. Two different models of stochastic ion channel behaviour, both based on a system of stochastic differential equations (SDEs), are developed and compared. The first model is based on an existing SDE model from population dynamics called the Wright-Fisher model. The second approach incorporates boundary conditions into the SDE model of ion channel dynamics that is obtained in the limit from the discrete-state Markov chain model, and is called a reflected SDE. Of these two methods, the reflected SDE is found to more accurately capture the stochastic dynamics of the discrete-stateMarkov chain, seen as the ‘gold-standard’ model and also provides substantial computational speed up. Thus the reflected SDE is an accurate and efficient model of stochastic ion channel dynamics and so allows for detailed investigation into beat-to-beat variability using complex computational electrophysiology models. We illustrate the potential power of this method by incorporating it into a state-of-the-art canine cardiac cell electrophsyiology model so as to explore the effects of stochastic ion channel behaviour on beat-to-beat variability. The stochastic models presented in this thesis fulfil an important role in elucidating the effects of stochastic ion channel behaviour on beat-to-beat variability, a potentially important biomarker of arrhythmic risk.

Sudden cardiac death kills 5-10 people per 10,000 population in Europe and the US each year. Individual propensity to arrhythmia and sudden cardiac death is typically assessed through clinical biomarkers. Variability in these biomarkers is a major challenge for risk stratification. Variability is observed at a wide range of spatio-temporal scales within the heart, from temporal fluctuations in ion channel behaviour, to inter-cell and inter-regional differences in ion channel expression, to structural differences between hearts. The extent to which variability manifests between spatial and temporal scales remains unclear but has a potentially crucial role in determining susceptibility to arrhythmia. In this dissertation we present a multi-scale study of the causes and consequences of variability in electrophysiology. At a sub-cellular level we demonstrate that, taking into account inter-individual variability in ion channel conductance, mRNA expression levels in failing human hearts predict the electrophysiological remodelling observed experimentally. On a tissue scale, we advocate the use of phenomenological models where information on subcellular processes is unavailable. We introduce a modification to a phenomenological model to capture beat-to-beat variability in action potential repolarisation recorded from four individual guinea pig myocytes. We demonstrate that, whilst temporal variability is dramatically reduced by inter-cell coupling, differences in their mean action potential duration may become apparent at a tissue level. The ventricular myocardium has a heterogeneous structure not captured by the simplified representation of conduction used above. In our final case study, we challenge a model of conduction by directly comparing simulations to optical mapping recordings of ventricular activation from failing and non-failing human hearts. We observe that good fits to experimental data are obtained only when endocardially bound structures are not in view, suggesting a role in conduction for these structures that are often ignored in cardiac simulations. Finally, we present future directions for the work presented. We make the case for reporting of inter-sample variability in experimental results and conclude that whilst variability may not always manifest across scales, its impact should be considered in both theoretical and experimental studies.

Cardiac tissue slices, a rarely used pseudo two-dimensional preparation, have gained increasing popularity for applications such as drug testing over the last ten years as they combine ease of handling with patho-physiologically relevant cell-type representation, distribution and inter-connection. The most well-established methods to measure electrophysiology in cardiac tissue are sharp electrodes and multi-electrode-arrays, techniques which are limited in spatial resolution or signal content. In this work, we have applied dual voltage Ca²⁺ optical mapping on cardiac slices, allowing us to record these two key parameters simultaneously at high spatio-temporal resolution, yielding better visualisation of conduction waves, spatial dispersion in action potential (AP) characteristics, and intracellular Ca²⁺ transient (CaT). The slice preparation method and the measurement protocols were refined to yield good reproducibility. Data analysis routines were developed to extract relevant parameters reliably. Despite being a promising candidate for drug testing, little is known about how slice and intact whole-heart AP properties are interrelated, and how to scale-up from observations in two dimensions (2D) to the three dimensional (3D) heart. In this thesis, we present a method to compare directly AP properties of intact whole-heart and tissue slices, and show the extent to which slices preserve AP characteristics. We have explored the suitability of tissue slices as an experimental model to study stretch induced changes in AP and CaT. During axial stretch, a dynamic profile of both AP and CaT was observed with an initial shortening of both AP and CaT duration, followed by a gradual recovery/prolongation. We have also used tissue slices to study spatial heterogeneity of AP and CaT properties in the rabbit left ventricular free wall. A transmural gradient can be captured in CaT and AP (with the longest APD and CaT durations being captured in the subendocardium). No large AP prolongation was found in the mid-myocardium. We conclude that the cardiac tissue slice preparation preserves some key functional parameters of the whole heart and is a promising model to study cardiac electrophysiology.

Atrial fibrillation (AF) is a troublesome disease often overlooked by more serious myocardial infarctions. Up until now, there has been very little or no use of high order spectral techniques in order to evaluate the organization of the atrium during AF. Cross-bicoherence algorithm can be used alongside a surrogate data threshold in order to determine significant phase coupling interactions, giving rise to an organizational metric. This proposed algorithm is used to show rotigaptide, a gap junction coupling drug, significantly increases the organization of the atria during episodes of AF due to improvement of cell-to-cell coupling.