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The traditional cardiac model-building paradigm involves constructing a composite model using data collected from many cells. Equations are derived for each relevant cellular component (e.g., ion channel, exchanger) independently. After the equations for all components are combined to form the composite model, a subset of parameters is tuned, often arbitrarily and by hand, until the model output matches a target objective, such as an action potential. Unfortunately, such models often fail to accurately simulate behavior that is dynamically dissimilar (e.g., arrhythmia) to the simple target objective to which the model was fit. In this study, we develop a new approach in which data are collected via a series of complex electrophysiology protocols from single cardiac myocytes and then used to tune model parameters via a parallel fitting method known as a genetic algorithm (GA). The dynamical complexity of the electrophysiological data, which can only be fit by an automated method such as a GA, leads to more accurately parameterized models that can simulate rich cardiac dynamics. The feasibility of the method is first validated computationally, after which it is used to develop models of isolated guinea pig ventricular myocytes that simulate the electrophysiological dynamics significantly better than does a standard guinea pig model. In addition to improving model fidelity generally, this approach can be used to generate a cell-specific model. By so doing, the approach may be useful in applications ranging from studying the implications of cell-to-cell variability to the prediction of intersubject differences in response to pharmacological treatment.
Computational modeling of cardiac cellular electrophysiology has a long history, and many models are now available for different species, cell types, and experimental preparations. This success brings with it a challenge: how do we assess and compare the underlying hypotheses and emergent behaviors so that we can choose a model as a suitable basis for a new study or to characterize how a particular model behaves in different scenarios? We have created an online resource for the characterization and comparison of electrophysiological cell models in a wide range of experimental scenarios. The details of the mathematical model (quantitative assumptions and hypotheses formulated as ordinary differential equations) are separated from the experimental protocol being simulated. Each model and protocol is then encoded in computer-readable formats. A simulation tool runs virtual experiments on models encoded in CellML, and a website (https://chaste.cs.ox.ac.uk/WebLab) provides a friendly interface, allowing users to store and compare results. The system currently contains a sample of 36 models and 23 protocols, including current-voltage curve generation, action potential properties under steady pacing at different rates, restitution properties, block of particular channels, and hypo-/hyperkalemia. This resource is publicly available, open source, and free, and we invite the community to use it and become involved in future developments. Investigators interested in comparing competing hypotheses using models can make a more informed decision, and those developing new models can upload them for easy evaluation under the existing protocols, and even add their own protocols.
Is cardiac electrophysiology complete? What are the challenges that are to be met in cardiac electrophysiology and how can we best engage these? These questions will be addressed in view of the progressing subspecialization of the field. A suggested answer lies in multidisciplinary and extradisciplinary approaches.
The electrophysiological properties of the heart include cardiac automaticity, excitation (i.e., depolarization and repolarization of action potential) of individual cardiomyocytes, and highly coordinated electrical propagation through the whole heart. An abnormality in any of these properties can cause arrhythmias. MicroRNAs (miRs) have been recognized as essential regulators of gene expression through the conventional RNA interference (RNAi) mechanism and are involved in a variety of biological events. Recent evidence has demonstrated that miRs regulate the electrophysiology of the heart through fine regulation by the conventional RNAi mechanism of the expression of ion channels, transporters, intracellular Ca2+-handling proteins, and other relevant factors. Recently, a direct interaction between miRs and ion channels has also been reported in the heart, revealing a biophysical modulation by miRs of cardiac electrophysiology. These advanced discoveries suggest that miR controls cardiac electrophysiology through two distinct mechanisms: immediate action through biophysical modulation and long-term conventional RNAi regulation. Here, we review the recent research progress and summarize the current understanding of how miR manipulates the function of ion channels to maintain the homeostasis of cardiac electrophysiology.
Chaste is an open-source C++ library for computational biology that has well-developed cardiac electrophysiology tissue simulation support. In this paper, we introduce the features available for performing cardiac electrophysiology action potential simulations using a wide range of models from the Physiome repository. The mathematics of the models are described in CellML, with units for all quantities. The primary idea is that the model is defined in one place (the CellML file), and all model code is auto-generated at compile or run time; it never has to be manually edited. We use ontological annotation to identify model variables describing certain biological quantities (membrane voltage, capacitance, etc.) to allow us to import any relevant CellML models into the Chaste framework in consistent units and to interact with them via consistent interfaces. This approach provides a great deal of flexibility for analysing different models of the same system. Chaste provides a wide choice of numerical methods for solving the ordinary differential equations that describe the models. Fixed-timestep explicit and implicit solvers are provided, as discussed in previous work. Here we introduce the Rush-Larsen and Generalized Rush-Larsen integration techniques, made available via symbolic manipulation of the model equations, which are automatically rearranged into the forms required by these approaches. We have also integrated the CVODE solvers, a 'gold standard' for stiff systems, and we have developed support for symbolic computation of the Jacobian matrix, yielding further increases in the performance and accuracy of CVODE. We discuss some of the technical details of this work and compare the performance of the available numerical methods. Finally, we discuss how this is generalized in our functional curation framework, which uses a domain-specific language for defining complex experiments as a basis for comparison of model behavior.
Over the last decade, mouse models have become a popular instrument for studying cardiac arrhythmias. This review assesses in which respects a mouse heart is a miniature human heart, a suitable model for studying mechanisms of cardiac arrhythmias in humans and in which respects human and murine hearts differ. Section I considers the issue of scaling of mammalian cardiac (electro) physiology to body mass. Then, we summarize differences between mice and humans in cardiac activation (section II) and the currents underlying the action potential in the murine working myocardium (section III). Changes in cardiac electrophysiology in mouse models of heart disease are briefly outlined in section IV, while section V discusses technical considerations pertaining to recording cardiac electrical activity in mice. Finally, section VI offers general considerations on the influence of cardiac size on the mechanisms of tachy-arrhythmias.
Telocytes (TCs) with exceptionally long cellular processes of telopodes have been described in human epicardium to act as structural supporting cells in the heart. We examined myocardial chamber-specific TCs identified in atrial and ventricular fibroblast culture using immunocytochemistry and studied their electrophysiological property by whole-cell patch clamp. Atrial and ventricular TCs with extended telopodes and alternating podoms and podomers that expressed CD34, c-Kit and PDGFR-β were identified. These cells expressed large conductance Ca²⁺-activated K⁺ current (BK(Ca)) and inwardly rectifying K⁺ current (IK(ir)), but not transient outward K⁺ current (I(to)) and ATP-sensitive potassium current (K(ATP)). The active channels were functionally competent with demonstrated modulatory response to H2 S and transforming growth factor (TGF)-β1 whereby H₂S significantly inhibited the stimulatory effect of TGF-β1 on current density of both BKCa and IK(ir). Furthermore, H₂S attenuated TGF-β1-stimulated KCa1.1/Kv1.1 (encode BK(Ca)) and Kir2.1 (encode IK(ir)) expression in TCs. Our results show that functionally competent K⁺ channels are present in human atrial and ventricular TCs and their modulation may have significant implications in myocardial physiopathology.
Biological networks are typically comprised of many parts whose interactions are governed by nonlinear dynamics. This potentially imbues them with the ability to support multiple attractors, and therefore to exhibit correspondingly distinct patterns of behavior. In particular, multiple attractors have been demonstrated for the electrical activity of the diseased heart in situations where cardioversion is able to convert a reentrant arrhythmia to a stable normal rhythm. Healthy hearts, however, are typically resilient to abnormal rhythms. This raises the question as to how a healthy cardiac cell network must be altered so that it can support multiple distinct behaviors. Here we demonstrate how anatomic defects can give rise to multi-stability in the heart as a function of the electrophysiological properties of the cardiac tissue and the timing of activation of ectopic foci. This leads to a form of hysteretic behavior, which we call dynamic entrapment, whereby the heart can become trapped in aberrant attractor as a result of a transient change in tissue properties. We show that this can lead to a highly inconsistent relationship between clinical symptoms and underlying pathophysiology, which raises the possibility that dynamic entrapment may underlie other forms of chronic idiopathic illness.
The variability observed in action potential (AP) cardiomyocyte measurements is the consequence of many different sources of randomness. Often ignored, this variability may be studied to gain insight into the cell ionic properties. In this paper, we focus on the study of ionic channel conductances and describe a methodology to estimate their probability density function (PDF) from AP recordings. The method relies on the matching of observable statistical moments and on the maximum entropy principle. We present four case studies using synthetic and sets of experimental AP measurements from human and canine cardiomyocytes. In each case, the proposed methodology is applied to infer the PDF of key conductances from the exhibited variability. The estimated PDFs are discussed and, when possible, compared to the true distributions. We conclude that it is possible to extract relevant information from the variability in AP measurements and discuss the limitations and possible implications of the proposed approach.
Abnormalities on cardiac imaging (cardiac magnetic resonance imaging [CMR] or positron emission tomography [PET]), left ventricular ejection fraction (LVEF), and electrophysiology study (EPS) all predict ventricular arrhythmias (VA) in patients with cardiac sarcoidosis (CS). We sought to assess the utility of EPS in patients with CS and abnormal cardiac imaging, focusing on those with LVEF >35%.
We describe a sequence of methods to produce a partial differential equation model of the electrical activation of the ventricles. In our framework, we incorporate the anatomy and cardiac microstructure obtained from magnetic resonance imaging and diffusion tensor imaging of a New Zealand White rabbit, the Purkinje structure and the Purkinje-muscle junctions, and an electrophysiologically accurate model of the ventricular myocytes and tissue, which includes transmural and apex-to-base gradients of action potential characteristics. We solve the electrophysiology governing equations using the finite element method and compute both a 6-lead precordial electrocardiogram (ECG) and the activation wavefronts over time. We are particularly concerned with the validation of the various methods used in our model and, in this regard, propose a series of validation criteria that we consider essential. These include producing a physiologically accurate ECG, a correct ventricular activation sequence, and the inducibility of ventricular fibrillation. Among other components, we conclude that a Purkinje geometry with a high density of Purkinje muscle junctions covering the right and left ventricular endocardial surfaces as well as transmural and apex-to-base gradients in action potential characteristics are necessary to produce ECGs and time activation plots that agree with physiological observations.
Left-ventricular (LV) conduction disturbances are common in heart-failure patients and a left bundle-branch block (LBBB) electrocardiogram (ECG) type is often seen. The precise cause of this pattern is uncertain and is probably variable between patients, ranging from proximal interruption of the left bundle branch to diffuse distal conduction disease in the working myocardium. Using realistic numerical simulation methods and patient-tailored model anatomies, we investigated different hypotheses to explain the observed activation order on the LV endocardium, electrogram morphologies, and ECG features in two patients with heart failure and LBBB ECG.
Features of the QRS complex of the electrocardiogram, reflecting ventricular depolarisation, associate with various physiologic functions and several pathologic conditions. We test 32.5 million variants for association with ten measures of the QRS complex in 12 leads, using 405,732 electrocardiograms from 81,192 Icelanders. We identify 190 associations at 130 loci, the majority of which have not been reported before, including associations with 21 rare or low-frequency coding variants. Assessment of genes expressed in the heart yields an additional 13 rare QRS coding variants at 12 loci. We find 51 unreported associations between the QRS variants and echocardiographic traits and cardiovascular diseases, including atrial fibrillation, complete AV block, heart failure and supraventricular tachycardia. We demonstrate the advantage of in-depth analysis of the QRS complex in conjunction with other cardiovascular phenotypes to enhance our understanding of the genetic basis of myocardial mass, cardiac conduction and disease.
Myocardial infarction (MI) can result in sympathetic nerve loss in the infarct region. However, the contribution of hypo-innervation to electrophysiological remodeling, independent from MI-induced ischemia and fibrosis, has not been comprehensively investigated. We present a novel mouse model of regional cardiac sympathetic hypo-innervation utilizing a targeted-toxin (dopamine beta-hydroxylase antibody conjugated to saporin, DBH-Sap), and measure resulting electrophysiological and Ca2+ handling dynamics. Five days post-surgery, sympathetic nerve density was reduced in the anterior left ventricular epicardium of DBH-Sap hearts compared to control. In Langendorff-perfused hearts, there were no differences in mean action potential duration (APD80) between groups; however, isoproterenol (ISO) significantly shortened APD80 in DBH-Sap but not control hearts, resulting in a significant increase in APD80 dispersion in the DBH-Sap group. ISO also produced spontaneous diastolic Ca2+ elevation in DBH-Sap but not control hearts. In innervated hearts, sympathetic nerve stimulation (SNS) increased heart rate to a lesser degree in DBH-Sap hearts compared to control. Additionally, SNS produced APD80 prolongation in the apex of control but not DBH-Sap hearts. These results suggest that hypo-innervated hearts have regional super-sensitivity to circulating adrenergic stimulation (ISO), while having blunted responses to SNS, providing important insight into the mechanisms of arrhythmogenesis following sympathetic nerve loss.
The BeatBox simulation environment combines flexible script language user interface with the robust computational tools, in order to setup cardiac electrophysiology in-silico experiments without re-coding at low-level, so that cell excitation, tissue/anatomy models, stimulation protocols may be included into a BeatBox script, and simulation run either sequentially or in parallel (MPI) without re-compilation. BeatBox is a free software written in C language to be run on a Unix-based platform. It provides the whole spectrum of multi scale tissue modelling from 0-dimensional individual cell simulation, 1-dimensional fibre, 2-dimensional sheet and 3-dimensional slab of tissue, up to anatomically realistic whole heart simulations, with run time measurements including cardiac re-entry tip/filament tracing, ECG, local/global samples of any variables, etc. BeatBox solvers, cell, and tissue/anatomy models repositories are extended via robust and flexible interfaces, thus providing an open framework for new developments in the field. In this paper we give an overview of the BeatBox current state, together with a description of the main computational methods and MPI parallelisation approaches.
Simulating the cardiac function requires the numerical solution of multi-physics and multi-scale mathematical models. This underscores the need for streamlined, accurate, and high-performance computational tools. Despite the dedicated endeavors of various research teams, comprehensive and user-friendly software programs for cardiac simulations, capable of accurately replicating both normal and pathological conditions, are still in the process of achieving full maturity within the scientific community.
Dravet syndrome is a severe form of intractable pediatric epilepsy with a high incidence of SUDEP: Sudden Unexpected Death in epilepsy. Cardiac arrhythmias are a proposed cause for some cases of SUDEP, yet the susceptibility and potential mechanism of arrhythmogenesis in Dravet syndrome remain unknown. The majority of Dravet syndrome patients have de novo mutations in SCN1A, resulting in haploinsufficiency. We propose that, in addition to neuronal hyperexcitability, SCN1A haploinsufficiency alters cardiac electrical function and produces arrhythmias, providing a potential mechanism for SUDEP.
Cardiac arrhythmias are a major cause of morbidity and mortality worldwide. Although recent advances in cell-based models, including human-induced pluripotent stem cell-derived cardiomyocytes (iPSC-CM), are contributing to our understanding of electrophysiology and arrhythmia mechanisms, preclinical animal studies of cardiovascular disease remain a mainstay. Over the past several decades, animal models of cardiovascular disease have advanced our understanding of pathological remodeling, arrhythmia mechanisms, and drug effects and have led to major improvements in pacing and defibrillation therapies. There exist a variety of methodological approaches for the assessment of cardiac electrophysiology and a plethora of parameters may be assessed with each approach. This guidelines article will provide an overview of the strengths and limitations of several common techniques used to assess electrophysiology and arrhythmia mechanisms at the whole animal, whole heart, and tissue level with a focus on small animal models. We also define key electrophysiological parameters that should be assessed, along with their physiological underpinnings, and the best methods with which to assess these parameters.
Background The red blood cell (RBC) storage lesion is a series of morphological, functional, and metabolic changes that RBCs undergo following collection, processing, and refrigerated storage for clinical use. Since the biochemical attributes of the RBC unit shifts with time, transfusion of older blood products may contribute to cardiac complications, including hyperkalemia and cardiac arrest. We measured the direct effect of storage age on cardiac electrophysiology and compared it with hyperkalemia, a prominent biomarker of storage lesion severity. Methods and Results Donor RBCs were processed using standard blood-banking techniques. The supernatant was collected from RBC units, 7 to 50 days after donor collection, for evaluation using Langendorff-heart preparations (rat) or human induced pluripotent stem cell-derived cardiomyocytes. Cardiac parameters remained stable following exposure to "fresh" supernatant from red blood cell units (day 7: 5.8±0.2 mM K+), but older blood products (day 40: 9.3±0.3 mM K+) caused bradycardia (baseline: 279±5 versus day 40: 216±18 beats per minute), delayed sinus node recovery (baseline: 243±8 versus day 40: 354±23 ms), and increased the effective refractory period of the atrioventricular node (baseline: 77±2 versus day 40: 93±7 ms) and ventricle (baseline: 50±3 versus day 40: 98±10 ms) in perfused hearts. Beating rate was also slowed in human induced pluripotent stem cell-derived cardiomyocytes after exposure to older supernatant from red blood cell units (-75±9%, day 40 versus control). Similar effects on automaticity and electrical conduction were observed with hyperkalemia (10-12 mM K+). Conclusions This is the first study to demonstrate that "older" blood products directly impact cardiac electrophysiology, using experimental models. These effects are likely caused by biochemical alterations in the supernatant from red blood cell units that occur over time, including, but not limited to hyperkalemia. Patients receiving large volume and/or rapid transfusions may be sensitive to these effects.
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