Modeling incomplete penetrance in long QT syndrome type 3 through ion channel heterogeneity: an in silico population study

Reconstructing ventricular cardiomyocyte dynamics and parameter estimation using data assimilation

Cardiac ventricular myocyte action potential dynamics are regulated by intricate and nonlinear interactions between the cell transmembrane potential and ionic currents and concentrations. Present technology limits the ability to measure transmembrane potential and multiple ionic currents simultaneously, which narrows the scope of experiments to provide a complete snapshot of the cardiac myocyte state. This limitation presents an obstacle for understanding how perturbations can trigger arrhythmias and more broadly how the myocyte responds to different conditions, such as changes in pacing rate or responses to drug treatment. In this study, we demonstrate that a data-assimilation approach can successfully reconstruct and predict the dynamics of a heterogeneous virtual cardiac ventricular myocyte population in the presence of parameter uncertainty. A population of heterogeneous cardiac ventricular myocytes is generated by varying ionic current conductance parameters, and additional observational uncertainty is mimicked by the addition of Gaussian noise to the transmembrane potential. We demonstrate that the data-assimilation approach accurately reconstructs transmembrane potential, with error less than the magnitude of the noise. Further, the data-assimilation approach successfully estimates the conductances of ionic currents generally with high accuracy and requiring low computational time. As a proof of concept, we apply the data-assimilation approach to reconstruct action potential dynamics from optical mapping experiments in an ex vivo isolated guinea pig heart. Critically, we demonstrate that the ionic conductance parameters estimated from a recording at one pacing frequency can accurately predict action potential dynamics at different rates.

Connecting transcriptomics with computational modeling to reveal developmental adaptations in pediatric human atrial tissue

Nearly 1% of babies are born with congenital heart disease-many of whom will require heart surgery within the first few years of life. A detailed understanding of cardiac maturation can help to expand our knowledge on cardiac diseases that develop during gestation, identify age-appropriate drug therapies, and inform clinical care decisions related to surgical repair and postoperative management. Yet, to date, our knowledge of the temporal changes that cardiomyocytes undergo during postnatal development is limited. In this study, we collected right atrial tissue samples from pediatric patients (n = 117) undergoing heart surgery. Patients were stratified into five age groups. We measured age-dependent adaptations in cardiac gene expression and used computational modeling to simulate action potential and calcium transients. Enrichment of differentially expressed genes revealed age-dependent changes in several key biological processes (e.g., cell cycle, structural organization), cardiac ion channels, and calcium handling genes. Gene-associated changes in ionic currents exhibited age-dependent trends, with changes in calcium handling (INCX) and repolarization (IK1) most strongly associated with an age-dependent decrease in the action potential plateau potential and increase in triangulation, respectively. We observed a shift in repolarization reserve, with lower IKr expression in younger patients, a finding potentially tied to an increased amplitude of IKs that could be triggered by elevated sympathetic activation in pediatric patients. Collectively, this study provides valuable insights into age-dependent changes in human cardiac gene expression and electrophysiology, shedding light on molecular mechanisms underlying cardiac maturation and function throughout development.NEW & NOTEWORTHY To date, our knowledge of the temporal changes that cardiomyocytes undergo during postnatal development is limited. In this study, we demonstrate age-dependent adaptations in the gene expression profile of >100 atrial tissue samples collected from congenital heart disease patients. We coupled transcriptomics datasets with computational modeling to simulate action potentials and calcium transients for different pediatric age groups.

Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation

The complexity of cardiac electrophysiology, involving dynamic changes in numerous components across multiple spatial (from ion channel to organ) and temporal (from milliseconds to days) scales, makes an intuitive or empirical analysis of cardiac arrhythmogenesis challenging. Multiscale mechanistic computational models of cardiac electrophysiology provide precise control over individual parameters, and their reproducibility enables a thorough assessment of arrhythmia mechanisms. This review provides a comprehensive analysis of models of cardiac electrophysiology and arrhythmias, from the single cell to the organ level, and how they can be leveraged to better understand rhythm disorders in cardiac disease and to improve heart patient care. Key issues related to model development based on experimental data are discussed, and major families of human cardiomyocyte models and their applications are highlighted. An overview of organ-level computational modeling of cardiac electrophysiology and its clinical applications in personalized arrhythmia risk assessment and patient-specific therapy of atrial and ventricular arrhythmias is provided. The advancements presented here highlight how patient-specific computational models of the heart reconstructed from patient data have achieved success in predicting risk of sudden cardiac death and guiding optimal treatments of heart rhythm disorders. Finally, an outlook toward potential future advances, including the combination of mechanistic modeling and machine learning/artificial intelligence, is provided. As the field of cardiology is embarking on a journey toward precision medicine, personalized modeling of the heart is expected to become a key technology to guide pharmaceutical therapy, deployment of devices, and surgical interventions.

Optimization of a cardiomyocyte model illuminates role of increased INa,L in repolarization reserve

Cardiac ion currents may compensate for each other when one is compromised by a congenital or drug-induced defect. Such redundancy contributes to a robust repolarization reserve that can prevent the development of lethal arrhythmias. Most efforts made to describe this phenomenon have quantified contributions by individual ion currents. However, it is important to understand the interplay between all major ion-channel conductances, as repolarization reserve is dependent on the balance between all ion currents in a cardiomyocyte. Here, a genetic algorithm was designed to derive profiles of nine ion-channel conductances that optimize repolarization reserve in a mathematical cardiomyocyte model. Repolarization reserve was quantified using a previously defined metric, repolarization reserve current, i.e., the minimum constant current to prevent normal action potential repolarization in a cell. The optimization improved repolarization reserve current up to 84% compared to baseline in a human adult ventricular myocyte model and increased resistance to arrhythmogenic insult. The optimized conductance profiles were not only characterized by increased repolarizing current conductances but also uncovered a previously unreported behavior by the late sodium current. Simulations demonstrated that upregulated late sodium increased action potential duration, without compromising repolarization reserve current. The finding was generalized to multiple models. Ultimately, this computational approach, in which multiple currents were studied simultaneously, illuminated mechanistic insights into how the metric's magnitude could be increased and allowed for the unexpected role of late sodium to be elucidated.NEW & NOTEWORTHY Genetic algorithms are typically used to fit models or extract desired parameters from data. Here, we use the tool to produce a ventricular cardiomyocyte model with increased repolarization reserve. Since arrhythmia mitigation is dependent on multiple cardiac ion-channel conductances, study using a comprehensive, unbiased, and systems-level approach is important. The use of this optimization strategy allowed us to find robust profiles that illuminated unexpected mechanistic determinants of key ion-channel conductances in repolarization reserve.