Elucidation of a dynamic interplay between a beta-2 adrenergic receptor, its agonist, and stimulatory G protein.

G protein-coupled receptors (GPCRs) represent the largest group of membrane receptors for transmembrane signal transduction. Ligand-induced activation of GPCRs triggers G protein activation followed by various signaling cascades. Understanding the structural and energetic determinants of ligand binding to GPCRs and GPCRs to G proteins is crucial to the design of pharmacological treatments targeting specific conformations of these proteins to precisely control their signaling properties. In this study, we focused on interactions of a prototypical GPCR, beta-2 adrenergic receptor (ß2AR), with its endogenous agonist, norepinephrine (NE), and the stimulatory G protein (Gs). Using molecular dynamics (MD) simulations, we demonstrated the stabilization of cationic NE, NE(+), binding to ß2AR by Gs protein recruitment, in line with experimental observations. We also captured the partial dissociation of the ligand from ß2AR and the conformational interconversions of Gs between closed and open conformations in the NE(+)-ß2AR-Gs ternary complex while it is still bound to the receptor. The variation of NE(+) binding poses was found to alter Gs α subunit (Gsα) conformational transitions. Our simulations showed that the interdomain movement and the stacking of Gsα α1 and α5 helices are significant for increasing the distance between the Gsα and ß2AR, which may indicate a partial dissociation of Gsα The distance increase commences when Gsα is predominantly in an open state and can be triggered by the intracellular loop 3 (ICL3) of ß2AR interacting with Gsα, causing conformational changes of the α5 helix. Our results help explain molecular mechanisms of ligand and GPCR-mediated modulation of G protein activation.

Advances in induced pluripotent stem cell-derived cardiac myocytes: technological breakthroughs, key discoveries and new applications.

A transformation is underway in precision and patient-specific medicine. Rapid progress has been enabled by multiple new technologies including induced pluripotent stem cell-derived cardiac myocytes (iPSC-CMs). Here, we delve into these advancements and their future promise, focusing on the efficiency of reprogramming techniques, the fidelity of differentiation into the cardiac lineage, the functional characterization of the resulting cardiac myocytes, and the many applications of in silico models to understand general and patient-specific mechanisms controlling excitation-contraction coupling in health and disease. Furthermore, we explore the current and potential applications of iPSC-CMs in both research and clinical settings, underscoring the far-reaching implications of this rapidly evolving field.

A multiscale predictive digital twin for neurocardiac modulation.

Cardiac function is tightly regulated by the autonomic nervous system (ANS). Activation of the sympathetic nervous system increases cardiac output by increasing heart rate and stroke volume, while parasympathetic nerve stimulation instantly slows heart rate. Importantly, imbalance in autonomic control of the heart has been implicated in the development of arrhythmias and heart failure. Understanding of the mechanisms and effects of autonomic stimulation is a major challenge because synapses in different regions of the heart result in multiple changes to heart function. For example, nerve synapses on the sinoatrial node (SAN) impact pacemaking, while synapses on contractile cells alter contraction and arrhythmia vulnerability. Here, we present a multiscale neurocardiac modelling and simulator tool that predicts the effect of efferent stimulation of the sympathetic and parasympathetic branches of the ANS on the cardiac SAN and ventricular myocardium. The model includes a layered representation of the ANS and reproduces firing properties measured experimentally. Model parameters are derived from experiments and atomistic simulations. The model is a first prototype of a digital twin that is applied to make predictions across all system scales, from subcellular signalling to pacemaker frequency to tissue level responses. We predict conditions under which autonomic imbalance induces proarrhythmia and can be modified to prevent or inhibit arrhythmia. In summary, the multiscale model constitutes a predictive digital twin framework to test and guide high-throughput prediction of novel neuromodulatory therapy. KEY POINTS: A multi-layered model representation of the autonomic nervous system that includes sympathetic and parasympathetic branches, each with sparse random intralayer connectivity, synaptic dynamics and conductance based integrate-and-fire neurons generates firing patterns in close agreement with experiment. A key feature of the neurocardiac computational model is the connection between the autonomic nervous system and both pacemaker and contractile cells, where modification to pacemaker frequency drives initiation of electrical signals in the contractile cells. We utilized atomic-scale molecular dynamics simulations to predict the association and dissociation rates of noradrenaline with the ß-adrenergic receptor. Multiscale predictions demonstrate how autonomic imbalance may increase proclivity to arrhythmias or be used to terminate arrhythmias. The model serves as a first step towards a digital twin for predicting neuromodulation to prevent or reduce disease.

Rate-dependent effects of state-specific sodium channel blockers in cardiac tissue: Insights from idealized models.

A common side effect of pharmaceutical drugs is an increased propensity for cardiac arrhythmias. Many drugs bind to cardiac ion-channels in a state-specific manner, which alters the ionic conductances in complicated ways, making it difficult to identify the mechanisms underlying pro-arrhythmic drug effects. To better understand the fundamental mechanisms underlying the diverse effects of state-dependent sodium (Na+) channel blockers on cellular excitability, we consider two canonical motifs of drug-ion-channel interactions and compare the effects of Na+ channel blockers on the rate-dependence of peak upstroke velocity, conduction velocity, and vulnerable window size. In the literature, both motifs are referred to as "guarded receptor," but here we distinguish between state-specific binding that does not alter channel gating (referred to here as "guarded receptor") and state-specific binding that blocks certain gating transitions ("gate immobilization"). For each drug binding motif, we consider drugs that bind to the inactivated state and drugs that bind to the non-inactivated state of the Na+ channel. Exploiting the idealized nature of the canonical binding motifs, we identify the fundamental mechanisms underlying the effects on excitability of the various binding interactions. Specifically, we derive the voltage-dependence of the drug binding time constants and the equilibrium fractions of channels bound to drug, and we then derive a formula that incorporates these time constants and equilibrium fractions to elucidate the fundamental mechanisms. In the case of charged drug, we find that drugs that bind to inactivated channels exhibit greater rate-dependence than drugs that bind to non-inactivated channels. For neutral drugs, the effects of guarded receptor interactions are rate-independent, and we describe a novel mechanism for reverse rate-dependence resulting from neutral drug binding to non-inactivated channels via the gate immobilization motif.

Selectivity filter modalities and rapid inactivation of the hERG1 channel.

The human ether-á-go-go-related gene (hERG1) channel conducts small outward K+ currents that are critical for cardiomyocyte membrane repolarization. The gain-of-function mutation N629D at the outer mouth of the selectivity filter (SF) disrupts inactivation and K+-selective transport in hERG1, leading to arrhythmogenic phenotypes associated with long-QT syndrome. Here, we combined computational electrophysiology with Markov state model analysis to investigate how SF-level gating modalities control selective cation transport in wild-type (WT) and mutant (N629D) hERG1 variants. Starting from the recently reported cryogenic electron microscopy (cryo-EM) open-state channel structure, multiple microseconds-long molecular-dynamics (MD) trajectories were generated using different cation configurations at the filter, voltages, electrolyte concentrations, and force-field parameters. Most of the K+ permeation events observed in hERG1-WT simulations occurred at microsecond timescales, influenced by the spontaneous dehydration/rehydration dynamics at the filter. The SF region displayed conductive, constricted, occluded, and dilated states, in qualitative agreement with the well-documented flickering conductance of hERG1. In line with mutagenesis studies, these gating modalities resulted from dynamic interaction networks involving residues from the SF, outer-mouth vestibule, P-helices, and S5-P segments. We found that N629D mutation significantly stabilizes the SF in a state that is permeable to both K+ and Na+, which is reminiscent of the SF in the nonselective bacterial NaK channel. Increasing the external K+ concentration induced "WT-like" SF dynamics in N629D, in qualitative agreement with the recovery of flickering currents in experiments. Overall, our findings provide an understanding of the molecular mechanisms controlling selective transport in K+ channels with a nonconventional SF sequence.

Pore opening, not voltage sensor movement, underpins the voltage-dependence of facilitation by a hERG blocker.

A drug that blocks the cardiac myocyte voltage-gated K+ channels encoded by the human Ether-à-go-go-Related Gene (hERG) carries a potential risk of long QT syndrome and life-threatening cardiac arrhythmia, including Torsade de Points Interestingly, certain hERG blockers can also facilitate hERG activation to increase hERG currents, which may reduce proarrhythmic potential. However, the molecular mechanism involved in the facilitation effect of hERG blockers remains unclear. The hallmark feature of the facilitation effect by hERG blockers is that a depolarizing preconditioning pulse shifts voltage-dependence of hERG activation to more negative voltages. Here we utilize a D540K hERG mutant to study the mechanism of the facilitation effect. D540K hERG is activated by not only depolarization but also hyperpolarization. This unusual gating property enables tests of the mechanism by which voltage induces facilitation of hERG by blockers. With D540K hERG, we find that nifekalant, a hERG blocker and Class III antiarrhythmic agent, blocks and facilitates not only current activation by depolarization but also current activation by hyperpolarization, suggesting a shared gating process upon depolarization and hyperpolarization. Moreover, in response to hyperpolarizing conditioning pulses, nifekalant facilitates D540K hERG currents but not wild-type currents. Our results indicate that induction of facilitation is coupled to pore opening, not voltage per se We propose that gated access to the hERG central cavity underlies the voltage-dependence of induction of facilitation. This study identifies hERG channel pore gate opening as the conformational change facilitated by nifekalant, a clinically important antiarrhythmic agent. Significance Statement Nifekalant is a clinically important antiarrhythmic agent and a hERG blocker which can also facilitate voltage-dependent activation of hERG channels after a preconditioning pulse. Here we show that the mechanism of action of the preconditioning pulse is to open a conductance gate to enable drug access to a facilitation site. Moreover, we find that facilitation increases hERG currents by altering pore dynamics, rather than acting through voltage sensors.

A Computational Pipeline to Predict Cardiotoxicity: From the Atom to the Rhythm.

RATIONALE: Drug-induced proarrhythmia is so tightly associated with prolongation of the QT interval that QT prolongation is an accepted surrogate marker for arrhythmia. But QT interval is too sensitive a marker and not selective, resulting in many useful drugs eliminated in drug discovery. OBJECTIVE: To predict the impact of a drug from the drug chemistry on the cardiac rhythm. METHODS AND RESULTS: In a new linkage, we connected atomistic scale information to protein, cell, and tissue scales by predicting drug-binding affinities and rates from simulation of ion channel and drug structure interactions and then used these values to model drug effects on the hERG channel. Model components were integrated into predictive models at the cell and tissue scales to expose fundamental arrhythmia vulnerability mechanisms and complex interactions underlying emergent behaviors. Human clinical data were used for model framework validation and showed excellent agreement, demonstrating feasibility of a new approach for cardiotoxicity prediction. CONCLUSIONS: We present a multiscale model framework to predict electrotoxicity in the heart from the atom to the rhythm. Novel mechanistic insights emerged at all scales of the system, from the specific nature of proarrhythmic drug interaction with the hERG channel, to the fundamental cellular and tissue-level arrhythmia mechanisms. Applications of machine learning indicate necessary and sufficient parameters that predict arrhythmia vulnerability. We expect that the model framework may be expanded to make an impact in drug discovery, drug safety screening for a variety of compounds and targets, and in a variety of regulatory processes.

Rate-dependent effects of lidocaine on cardiac dynamics: Development and analysis of a low-dimensional drug-channel interaction model.

State-dependent sodium channel blockers are often prescribed to treat cardiac arrhythmias, but many sodium channel blockers are known to have pro-arrhythmic side effects. While the anti and proarrhythmic potential of a sodium channel blocker is thought to depend on the characteristics of its rate-dependent block, the mechanisms linking these two attributes are unclear. Furthermore, how specific properties of rate-dependent block arise from the binding kinetics of a particular drug is poorly understood. Here, we examine the rate-dependent effects of the sodium channel blocker lidocaine by constructing and analyzing a novel drug-channel interaction model. First, we identify the predominant mode of lidocaine binding in a 24 variable Markov model for lidocaine-sodium channel interaction by Moreno et al. Specifically, we find that (1) the vast majority of lidocaine bound to sodium channels is in the neutral form, i.e., the binding of charged lidocaine to sodium channels is negligible, and (2) neutral lidocaine binds almost exclusively to inactivated channels and, upon binding, immobilizes channels in the inactivated state. We then develop a novel 3-variable lidocaine-sodium channel interaction model that incorporates only the predominant mode of drug binding. Our low-dimensional model replicates an extensive amount of the voltage-clamp data used to parameterize the Moreno et al. model. Furthermore, the effects of lidocaine on action potential upstroke velocity and conduction velocity in our model are similar to those predicted by the Moreno et al. model. By exploiting the low-dimensionality of our model, we derive an algebraic expression for level of rate-dependent block as a function of pacing frequency, restitution properties, diastolic and plateau potentials, and drug binding rate constants. Our model predicts that the level of rate-dependent block is sensitive to alterations in restitution properties and increases in diastolic potential, but it is insensitive to variations in the shape of the action potential waveform and lidocaine binding rates.

Structural basis for antiarrhythmic drug interactions with the human cardiac sodium channel.

The human voltage-gated sodium channel, hNaV1.5, is responsible for the rapid upstroke of the cardiac action potential and is target for antiarrhythmic therapy. Despite the clinical relevance of hNaV1.5-targeting drugs, structure-based molecular mechanisms of promising or problematic drugs have not been investigated at atomic scale to inform drug design. Here, we used Rosetta structural modeling and docking as well as molecular dynamics simulations to study the interactions of antiarrhythmic and local anesthetic drugs with hNaV1.5. These calculations revealed several key drug binding sites formed within the pore lumen that can simultaneously accommodate up to two drug molecules. Molecular dynamics simulations identified a hydrophilic access pathway through the intracellular gate and a hydrophobic access pathway through a fenestration between DIII and DIV. Our results advance the understanding of molecular mechanisms of antiarrhythmic and local anesthetic drug interactions with hNaV1.5 and will be useful for rational design of novel therapeutics.

Mechanisms of flecainide induced negative inotropy: An in silico study.

It is imperative to develop better approaches to predict how antiarrhythmic drugs with multiple interactions and targets may alter the overall electrical and/or mechanical function of the heart. Safety Pharmacology studies have provided new insights into the multi-target effects of many different classes of drugs and have been aided by the addition of robust new in vitro and in silico technology. The primary focus of Safety Pharmacology studies has been to determine the risk profile of drugs and drug candidates by assessing their effects on repolarization of the cardiac action potential. However, for decades experimental and clinical studies have described substantial and potentially detrimental effects of Na+ channel blockers in addition to their well-known conduction slowing effects. One such side effect, associated with administration of some Na+ channel blocking drugs is negative inotropy. This reduces the pumping function of the heart, thereby resulting in hypotension. Flecainide is a well-known example of a Na+ channel blocking drug, that exhibits strong rate-dependent block of INa and may cause negative cardiac inotropy. While the phenomenon of Na+ channel suppression and resulting negative inotropy is well described, the mechanism(s) underlying this effect are not. Here, we set out to use a modeling and simulation approach to reveal plausible mechanisms that could explain the negative inotropic effect of flecainide. We utilized the Grandi-Bers model [1] of the cardiac ventricular myocyte because of its robust descriptions of ion homeostasis in order to characterize and resolve the relative effects of QRS widening, flecainide off-target effects and changes in intracellular Ca2+ and Na+ homeostasis. The results of our investigations and predictions reconcile multiple data sets and illustrate how multiple mechanisms may play a contributing role in the flecainide induced negative cardiac inotropic effect.

Molecular determinants of pro-arrhythmia proclivity of d- and l-sotalol via a multi-scale modeling pipeline.

Drug isomers may differ in their proarrhythmia risk. An interesting example is the drug sotalol, an antiarrhythmic drug comprising d- and l- enantiomers that both block the hERG cardiac potassium channel and confer differing degrees of proarrhythmic risk. We developed a multi-scale in silico pipeline focusing on hERG channel - drug interactions and used it to probe and predict the mechanisms of pro-arrhythmia risks of the two enantiomers of sotalol. Molecular dynamics (MD) simulations predicted comparable hERG channel binding affinities for d- and l-sotalol, which were validated with electrophysiology experiments. MD derived thermodynamic and kinetic parameters were used to build multi-scale functional computational models of cardiac electrophysiology at the cell and tissue scales. Functional models were used to predict inactivated state binding affinities to recapitulate electrocardiogram (ECG) QT interval prolongation observed in clinical data. Our study demonstrates how modeling and simulation can be applied to predict drug effects from the atom to the rhythm for dl-sotalol and also increased proarrhythmia proclivity of d- vs. l-sotalol when accounting for stereospecific beta-adrenergic receptor blocking.

Mechanisms of cAMP compartmentation in cardiac myocytes: experimental and computational approaches to understanding.

The small diffusible second messenger 3',5'-cyclic adenosine monophosphate (cAMP) is found in virtually every cell in our bodies, where it mediates responses to a variety of different G protein coupled receptors (GPCRs). In the heart, cAMP plays a critical role in regulating many different aspects of cardiac myocyte function, including gene transcription, cell metabolism, and excitation-contraction coupling. Yet, not all GPCRs that stimulate cAMP production elicit the same responses. Subcellular compartmentation of cAMP is essential to explain how different receptors can utilize the same diffusible second messenger to elicit unique functional responses. However, the mechanisms contributing to this behaviour and its significance in producing physiological and pathological responses are incompletely understood. Mathematical modelling has played an essential role in gaining insight into these questions. This review discusses what we currently know about cAMP compartmentation in cardiac myocytes and questions that are yet to be answered.

Dynamics of adrenergic signaling in cardiac myocytes and implications for pharmacological treatment.

Dense innervation of the heart by the sympathetic nervous system (SNS) allows cardiac output to respond appropriately to the needs of the body under varying conditions, but occasionally the abrupt onset of SNS activity can trigger cardiac arrhythmias. Sympathetic activity leads to the release of norepinephrine (NE) onto cardiomyocytes, activating ß1-adrenergic receptors (ß1-ARs) and leading to the production of the second messenger cyclic AMP (cAMP). Upon sudden activation of ß1-ARs in experiments, intracellular cAMP can transiently rise to a high concentration before converging to a steady state level. Although changes to cellular cAMP concentration are important in modulating the overall cardiovascular response to sympathetic tone, the underlying mechanisms of the cAMP transients and the parameters that control their magnitude are unclear. We reduce a detailed computational model of the ß1-adrenergic signaling cascade to a system of two differential equations by eliminating extraneous variables and applying quasi-steady state approximation. The structure of the reduced model reveals that the large cAMP transients associated with abrupt ß1-AR activation are generated by the interplay of production/degradation of cAMP and desensitization/resensitization of ß1-ARs. The reduced model is used to predict how the dynamics of intracellular cAMP depend on the concentrations of norepinephrine (NE), phosphodiesterases 3 and 4 (PDE3,4), G-protein coupled receptor kinase 2 (GRK2), and ß1-AR, in healthy conditions and a simple model of early stages of heart failure. The key findings of the study are as follows: 1) Applying a reduced model of the dynamics of cardiac sympathetic signaling we show that the concentrations of two variables, cAMP and non-desensitized ß1-AR, capture the overall dynamics of sympathetic signaling; 2) The key factors influencing cAMP production are AC activity and PDE3,4 activity, while those that directly impact ß1-AR phosphorylation are GRK2 and PKA1. Thus, disease states that affect sympathetic control of the heart can be thoroughly assessed by studying AC activity, PDE3,4, GRK2 and PKA activity, as these factors directly impact cAMP production/degradation and ß1-AR (de) phosphorylation and are therefore predicted to comprise the most effective pharmaceutical targets in diseases affecting cardiac ß1-adrenergic signaling.

A computational model of induced pluripotent stem-cell derived cardiomyocytes for high throughput risk stratification of KCNQ1 genetic variants.

In the last decade, there has been tremendous progress in identifying genetic anomalies linked to clinical disease. New experimental platforms have connected genetic variants to mechanisms underlying disruption of cellular and organ behavior and the emergence of proarrhythmic cardiac phenotypes. The development of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) signifies an important advance in the study of genetic disease in a patient-specific context. However, considerable limitations of iPSC-CM technologies have not been addressed: 1) phenotypic variability in apparently identical genotype perturbations, 2) low-throughput electrophysiological measurements, and 3) an immature phenotype which may impact translation to adult cardiac response. We have developed a computational approach intended to address these problems. We applied our recent iPSC-CM computational model to predict the proarrhythmic risk of 40 KCNQ1 genetic variants. An IKs computational model was fit to experimental data for each mutation, and the impact of each mutation was simulated in a population of iPSC-CM models. Using a test set of 15 KCNQ1 mutations with known clinical long QT phenotypes, we developed a method to stratify the effects of KCNQ1 mutations based on proarrhythmic markers. We utilized this method to predict the severity of the remaining 25 KCNQ1 mutations with unknown clinical significance. Tremendous phenotypic variability was observed in the iPSC-CM model population following mutant perturbations. A key novelty is our reporting of the impact of individual KCNQ1 mutant models on adult ventricular cardiomyocyte electrophysiology, allowing for prediction of mutant impact across the continuum of aging. This serves as a first step toward translating predicted response in the iPSC-CM model to predicted response of the adult ventricular myocyte given the same genetic mutation. As a whole, this study presents a new computational framework that serves as a high throughput method to evaluate risk of genetic mutations based-on proarrhythmic behavior in phenotypically variable populations.

A demonstration of modularity, reuse, reproducibility, portability and scalability for modeling and simulation of cardiac electrophysiology using Kepler Workflows.

Multi-scale computational modeling is a major branch of computational biology as evidenced by the US federal interagency Multi-Scale Modeling Consortium and major international projects. It invariably involves specific and detailed sequences of data analysis and simulation, often with multiple tools and datasets, and the community recognizes improved modularity, reuse, reproducibility, portability and scalability as critical unmet needs in this area. Scientific workflows are a well-recognized strategy for addressing these needs in scientific computing. While there are good examples if the use of scientific workflows in bioinformatics, medical informatics, biomedical imaging and data analysis, there are fewer examples in multi-scale computational modeling in general and cardiac electrophysiology in particular. Cardiac electrophysiology simulation is a mature area of multi-scale computational biology that serves as an excellent use case for developing and testing new scientific workflows. In this article, we develop, describe and test a computational workflow that serves as a proof of concept of a platform for the robust integration and implementation of a reusable and reproducible multi-scale cardiac cell and tissue model that is expandable, modular and portable. The workflow described leverages Python and Kepler-Python actor for plotting and pre/post-processing. During all stages of the workflow design, we rely on freely available open-source tools, to make our workflow freely usable by scientists.

When Does the IC50 Accurately Assess the Blocking Potency of a Drug?

Preclinical assessment of drug-induced proarrhythmicity is typically evaluated by the potency of the drug to block the potassium human ether-à-go-go-related gene (hERG) channels, which is currently quantified by the IC50. However, channel block depends on the experimental conditions. Our aim is to improve the evaluation of the blocking potency of drugs by designing experimental stimulation protocols to measure the IC50 that will help to decide whether the IC50 is representative enough. We used the state-of-the-art mathematical models of the cardiac electrophysiological activity to design three stimulation protocols that enhance the differences in the probabilities to occupy a certain conformational state of the channel and, therefore, the potential differences in the blocking effects of a compound. We simulated an extensive set of 144 in silico IKr blockers with different kinetics and affinities to conformational states of the channel and we also experimentally validated our key predictions. Our results show that the IC50 protocol dependency relied on the tested compounds. Some of them showed no differences or small differences on the IC50 value, which suggests that the IC50 could be a good indicator of the blocking potency in these cases. However, others provided highly protocol dependent IC50 values, which could differ by even 2 orders of magnitude. Moreover, the protocols yielding the maximum IC50 and minimum IC50 depended on the drug, which complicates the definition of a "standard" protocol to minimize the influence of the stimulation protocol on the IC50 measurement in safety pharmacology. As a conclusion, we propose the adoption of our three-protocol IC50 assay to estimate the potency to block hERG in vitro. If the IC50 values obtained for a compound are similar, then the IC50 could be used as an indicator of its blocking potency, otherwise kinetics and state-dependent binding properties should be accounted.

A computational model of induced pluripotent stem-cell derived cardiomyocytes incorporating experimental variability from multiple data sources.

KEY POINTS: Induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) capture patient-specific genotype-phenotype relationships, as well as cell-to-cell variability of cardiac electrical activity Computational modelling and simulation provide a high throughput approach to reconcile multiple datasets describing physiological variability, and also identify vulnerable parameter regimes We have developed a whole-cell model of iPSC-CMs, composed of single exponential voltage-dependent gating variable rate constants, parameterized to fit experimental iPSC-CM outputs We have utilized experimental data across multiple laboratories to model experimental variability and investigate subcellular phenotypic mechanisms in iPSC-CMs This framework links molecular mechanisms to cellular-level outputs by revealing unique subsets of model parameters linked to known iPSC-CM phenotypes ABSTRACT: There is a profound need to develop a strategy for predicting patient-to-patient vulnerability in the emergence of cardiac arrhythmia. A promising in vitro method to address patient-specific proclivity to cardiac disease utilizes induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). A major strength of this approach is that iPSC-CMs contain donor genetic information and therefore capture patient-specific genotype-phenotype relationships. A cited detriment of iPSC-CMs is the cell-to-cell variability observed in electrical activity. We postulated, however, that cell-to-cell variability may constitute a strength when appropriately utilized in a computational framework to build cell populations that can be employed to identify phenotypic mechanisms and pinpoint key sensitive parameters. Thus, we have exploited variation in experimental data across multiple laboratories to develop a computational framework for investigating subcellular phenotypic mechanisms. We have developed a whole-cell model of iPSC-CMs composed of simple model components comprising ion channel models with single exponential voltage-dependent gating variable rate constants, parameterized to fit experimental iPSC-CM data for all major ionic currents. By optimizing ionic current model parameters to multiple experimental datasets, we incorporate experimentally-observed variability in the ionic currents. The resulting population of cellular models predicts robust inter-subject variability in iPSC-CMs. This approach links molecular mechanisms to known cellular-level iPSC-CM phenotypes, as shown by comparing immature and mature subpopulations of models to analyse the contributing factors underlying each phenotype. In the future, the presented models can be readily expanded to include genetic mutations and pharmacological interventions for studying the mechanisms of rare events, such as arrhythmia triggers.

Potassium currents in the heart: functional roles in repolarization, arrhythmia and therapeutics.

This is the second of the two White Papers from the fourth UC Davis Cardiovascular Symposium Systems Approach to Understanding Cardiac Excitation-Contraction Coupling and Arrhythmias (3-4 March 2016), a biennial event that brings together leading experts in different fields of cardiovascular research. The theme of the 2016 symposium was 'K+ channels and regulation', and the objectives of the conference were severalfold: (1) to identify current knowledge gaps; (2) to understand what may go wrong in the diseased heart and why; (3) to identify possible novel therapeutic targets; and (4) to further the development of systems biology approaches to decipher the molecular mechanisms and treatment of cardiac arrhythmias. The sessions of the Symposium focusing on the functional roles of the cardiac K+ channel in health and disease, as well as K+ channels as therapeutic targets, were contributed by Ye Chen-Izu, Gideon Koren, James Weiss, David Paterson, David Christini, Dobromir Dobrev, Jordi Heijman, Thomas O'Hara, Crystal Ripplinger, Zhilin Qu, Jamie Vandenberg, Colleen Clancy, Isabelle Deschenes, Leighton Izu, Tamas Banyasz, Andras Varro, Heike Wulff, Eleonora Grandi, Michael Sanguinetti, Donald Bers, Jeanne Nerbonne and Nipavan Chiamvimonvat as speakers and panel discussants. This article summarizes state-of-the-art knowledge and controversies on the functional roles of cardiac K+ channels in normal and diseased heart. We endeavour to integrate current knowledge at multiple scales, from the single cell to the whole organ levels, and from both experimental and computational studies.

A multiscale computational modelling approach predicts mechanisms of female sex risk in the setting of arousal-induced arrhythmias.

KEY POINTS: This study represents a first step toward predicting mechanisms of sex-based arrhythmias that may lead to important developments in risk stratification and may inform future drug design and screening. We undertook simulations to reveal the conditions (i.e. pacing, drugs, sympathetic stimulation) required for triggering and sustaining reentrant arrhythmias. Using the recently solved cryo-EM structure for the Eag-family channel as a template, we revealed potential interactions of oestrogen with the pore loop hERG mutation (G604S). Molecular models suggest that oestrogen and dofetilide blockade can concur simultaneously in the hERG channel pore. ABSTRACT: Female sex is a risk factor for inherited and acquired long-QT associated torsade de pointes (TdP) arrhythmias, and sympathetic discharge is a major factor in triggering TdP in female long-QT syndrome patients. We used a combined experimental and computational approach to predict 'the perfect storm' of hormone concentration, IKr block and sympathetic stimulation that induces arrhythmia in females with inherited and acquired long-QT. More specifically, we developed mathematical models of acquired and inherited long-QT syndrome in male and female ventricular human myocytes by combining effects of a hormone and a hERG blocker, dofetilide, or hERG mutations. These 'male' and 'female' model myocytes and tissues then were used to predict how various sex-based differences underlie arrhythmia risk in the setting of acute sympathetic nervous system discharge. The model predicted increased risk for arrhythmia in females when acute sympathetic nervous system discharge was applied in the settings of both inherited and acquired long-QT syndrome. Females were predicted to have protection from arrhythmia induction when progesterone is high. Males were protected by the presence of testosterone. Structural modelling points towards two plausible and distinct mechanisms of oestrogen action enhancing torsadogenic effects: oestradiol interaction with hERG mutations in the pore loop containing G604 or with common TdP-related blockers in the intra-cavity binding site. Our study presents findings that constitute the first evidence linking structure to function mechanisms underlying female dominance of arousal-induced arrhythmias.

Mechanisms of Calmodulin Regulation of Different Isoforms of Kv7.4 K+ Channels.

Calmodulin (CaM), a Ca(2+)-sensing protein, is constitutively bound to IQ domains of the C termini of human Kv7 (hKv7, KCNQ) channels to mediate Ca(2+)-dependent reduction of Kv7 currents. However, the mechanism remains unclear. We report that CaM binds to two isoforms of the hKv7.4 channel in a Ca(2+)-independent manner but that only the long isoform (hKv7.4a) is regulated by Ca(2+)/CaM. Ca(2+)/CaM mediate reduction of the hKv7.4a channel by decreasing the channel open probability and altering activation kinetics. We took advantage of a known missense mutation (G321S) that has been linked to progressive hearing loss to further examine the inhibitory effects of Ca(2+)/CaM on the Kv7.4 channel. Using multidisciplinary techniques, we demonstrate that the G321S mutation may destabilize CaM binding, leading to a decrease in the inhibitory effects of Ca(2+) on the channels. Our study utilizes an expression system to dissect the biophysical properties of the WT and mutant Kv7.4 channels. This report provides mechanistic insights into the critical roles of Ca(2+)/CaM regulation of the Kv7.4 channel under physiological and pathological conditions.