Cell-to-cell heterogeneity in ion channel conductance impacts substrate vulnerability to arrhythmia.

Determining whether an ectopic depolarization will lead to a self-perpetuating arrhythmia is of critical importance in determining arrhythmia risk, so it is necessary to understand what factors impact substrate vulnerability. This study sought to explore the impact of cell-to-cell heterogeneity in ion channel conductance on substrate vulnerability to arrhythmia by measuring the duration of the vulnerable window in computational models of 1-dimensional cables of ventricular cardiomyocytes. We began by using a population of uniform cable models to determine the mechanisms underlying the vulnerable window phenomenon. We found that in addition to the known importance of GNa, the conductances GCaL and GKr also play a minor role in determining the vulnerable window duration. We also found that a steeper slope of the repolarizing action potential during the vulnerable window correlated with a shorter vulnerable window duration in uniform cables. We applied our understanding from these initial simulations to an investigation of the vulnerable window in heterogeneous cable models. The heterogeneous cables displayed a great deal of intra-cable variation in vulnerable window duration, highly sensitive to the cardiomyocytes in the local environment of the ectopic stimulus. Coupling strength modulated not only the magnitude of the vulnerable window duration but also the extent of intra-tissue variability in vulnerable window duration.

HRAS-Mutant Cardiomyocyte Model of Multifocal Atrial Tachycardia.

BACKGROUND: Germline HRAS gain-of-function pathogenic variants cause Costello syndrome (CS). During early childhood, 50% of patients develop multifocal atrial tachycardia, a treatment-resistant tachyarrhythmia of unknown pathogenesis. This study investigated how overactive HRAS activity triggers arrhythmogenesis in atrial-like cardiomyocytes (ACMs) derived from human-induced pluripotent stem cells bearing CS-associated HRAS variants. METHODS: HRAS Gly12 mutations were introduced into a human-induced pluripotent stem cells-ACM reporter line. Human-induced pluripotent stem cells were generated from patients with CS exhibiting tachyarrhythmia. Calcium transients and action potentials were assessed in induced pluripotent stem cell-derived ACMs. Automated patch clamping assessed funny currents. HCN inhibitors targeted pacemaker-like activity in mutant ACMs. Transcriptomic data were analyzed via differential gene expression and gene ontology. Immunoblotting evaluated protein expression associated with calcium handling and pacemaker-nodal expression. RESULTS: ACMs harboring HRAS variants displayed higher beating rates compared with healthy controls. The hyperpolarization activated cyclic nucleotide gated potassium channel inhibitor ivabradine and the Nav1.5 blocker flecainide significantly decreased beating rates in mutant ACMs, whereas voltage-gated calcium channel 1.2 blocker verapamil attenuated their irregularity. Electrophysiological assessment revealed an increased number of pacemaker-like cells with elevated funny current densities among mutant ACMs. Mutant ACMs demonstrated elevated gene expression (ie, ISL1, TBX3, TBX18) related to intracellular calcium homeostasis, heart rate, RAS signaling, and induction of pacemaker-nodal-like transcriptional programming. Immunoblotting confirmed increased protein levels for genes of interest and suppressed MAPK (mitogen-activated protein kinase) activity in mutant ACMs. CONCLUSIONS: CS-associated gain-of-function HRASG12 mutations in induced pluripotent stem cells-derived ACMs trigger transcriptional changes associated with enhanced automaticity and arrhythmic activity consistent with multifocal atrial tachycardia. This is the first human-induced pluripotent stem cell model establishing the mechanistic basis for multifocal atrial tachycardia in CS.

Creating cell-specific computational models of stem cell-derived cardiomyocytes using optical experiments.

Human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) have gained traction as a powerful model in cardiac disease and therapeutics research, since iPSCs are self-renewing and can be derived from healthy and diseased patients without invasive surgery. However, current iPSC-CM differentiation methods produce cardiomyocytes with immature, fetal-like electrophysiological phenotypes, and the variety of maturation protocols in the literature results in phenotypic differences between labs. Heterogeneity of iPSC donor genetic backgrounds contributes to additional phenotypic variability. Several mathematical models of iPSC-CM electrophysiology have been developed to help understand the ionic underpinnings of, and to simulate, various cell responses, but these models individually do not capture the phenotypic variability observed in iPSC-CMs. Here, we tackle these limitations by developing a computational pipeline to calibrate cell preparation-specific iPSC-CM electrophysiological parameters. We used the genetic algorithm (GA), a heuristic parameter calibration method, to tune ion channel parameters in a mathematical model of iPSC-CM physiology. To systematically optimize an experimental protocol that generates sufficient data for parameter calibration, we created simulated datasets by applying various protocols to a population of in silico cells with known conductance variations, and we fitted to those datasets. We found that calibrating models to voltage and calcium transient data under 3 varied experimental conditions, including electrical pacing combined with ion channel blockade and changing buffer ion concentrations, improved model parameter estimates and model predictions of unseen channel block responses. This observation held regardless of whether the fitted data were normalized, suggesting that normalized fluorescence recordings, which are more accessible and higher throughput than patch clamp recordings, could sufficiently inform conductance parameters. Therefore, this computational pipeline can be applied to different iPSC-CM preparations to determine cell line-specific ion channel properties and understand the mechanisms behind variability in perturbation responses.

Predicting individual-specific cardiotoxicity responses induced by tyrosine kinase inhibitors.

Introduction: Tyrosine kinase inhibitor drugs (TKIs) are highly effective cancer drugs, yet many TKIs are associated with various forms of cardiotoxicity. The mechanisms underlying these drug-induced adverse events remain poorly understood. We studied mechanisms of TKI-induced cardiotoxicity by integrating several complementary approaches, including comprehensive transcriptomics, mechanistic mathematical modeling, and physiological assays in cultured human cardiac myocytes. Methods: Induced pluripotent stem cells (iPSCs) from two healthy donors were differentiated into cardiac myocytes (iPSC-CMs), and cells were treated with a panel of 26 FDA-approved TKIs. Drug-induced changes in gene expression were quantified using mRNA-seq, changes in gene expression were integrated into a mechanistic mathematical model of electrophysiology and contraction, and simulation results were used to predict physiological outcomes. Results: Experimental recordings of action potentials, intracellular calcium, and contraction in iPSC-CMs demonstrated that modeling predictions were accurate, with 81% of modeling predictions across the two cell lines confirmed experimentally. Surprisingly, simulations of how TKI-treated iPSC-CMs would respond to an additional arrhythmogenic insult, namely, hypokalemia, predicted dramatic differences between cell lines in how drugs affected arrhythmia susceptibility, and these predictions were confirmed experimentally. Computational analysis revealed that differences between cell lines in the upregulation or downregulation of particular ion channels could explain how TKI-treated cells responded differently to hypokalemia. Discussion: Overall, the study identifies transcriptional mechanisms underlying cardiotoxicity caused by TKIs, and illustrates a novel approach for integrating transcriptomics with mechanistic mathematical models to generate experimentally testable, individual-specific predictions of adverse event risk.

Coupling and heterogeneity modulate pacemaking capability in healthy and diseased two-dimensional sinoatrial node tissue models.

Both experimental and modeling studies have attempted to determine mechanisms by which a small anatomical region, such as the sinoatrial node (SAN), can robustly drive electrical activity in the human heart. However, despite many advances from prior research, important questions remain unanswered. This study aimed to investigate, through mathematical modeling, the roles of intercellular coupling and cellular heterogeneity in synchronization and pacemaking within the healthy and diseased SAN. In a multicellular computational model of a monolayer of either human or rabbit SAN cells, simulations revealed that heterogenous cells synchronize their discharge frequency into a unique beating rhythm across a wide range of heterogeneity and intercellular coupling values. However, an unanticipated behavior appeared under pathological conditions where perturbation of ionic currents led to reduced excitability. Under these conditions, an intermediate range of intercellular coupling (900-4000 MΩ) was beneficial to SAN automaticity, enabling a very small portion of tissue (3.4%) to drive propagation, with propagation failure occurring at both lower and higher resistances. This protective effect of intercellular coupling and heterogeneity, seen in both human and rabbit tissues, highlights the remarkable resilience of the SAN. Overall, the model presented in this work allowed insight into how spontaneous beating of the SAN tissue may be preserved in the face of perturbations that can cause individual cells to lose automaticity. The simulations suggest that certain degrees of gap junctional coupling protect the SAN from ionic perturbations that can be caused by drugs or mutations.

Time-regulated transcripts with the potential to modulate human pluripotent stem cell-derived cardiomyocyte differentiation.

BACKGROUND: Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM) are a promising disease model, even though hiPSC-CMs cultured for extended periods display an undifferentiated transcriptional landscape. MiRNA-target gene interactions contribute to fine-tuning the genetic program governing cardiac maturation and may uncover critical pathways to be targeted. METHODS: We analyzed a hiPSC-CM public dataset to identify time-regulated miRNA-target gene interactions based on three logical steps of filtering. We validated this process in silico using 14 human and mouse public datasets, and further confirmed the findings by sampling seven time points over a 30-day protocol with a hiPSC-CM clone developed in our laboratory. We then added miRNA mimics from the top eight miRNAs candidates in three cell clones in two different moments of cardiac specification and maturation to assess their impact on differentiation characteristics including proliferation, sarcomere structure, contractility, and calcium handling. RESULTS: We uncovered 324 interactions among 29 differentially expressed genes and 51 miRNAs from 20,543 transcripts through 120 days of hiPSC-CM differentiation and selected 16 genes and 25 miRNAs based on the inverse pattern of expression (Pearson R-values < - 0.5) and consistency in different datasets. We validated 16 inverse interactions among eight genes and 12 miRNAs (Person R-values < - 0.5) during hiPSC-CMs differentiation and used miRNAs mimics to verify proliferation, structural and functional features related to maturation. We also demonstrated that miR-124 affects Ca2+ handling altering features associated with hiPSC-CMs maturation. CONCLUSION: We uncovered time-regulated transcripts influencing pathways affecting cardiac differentiation/maturation axis and showed that the top-scoring miRNAs indeed affect primarily structural features highlighting their role in the hiPSC-CM maturation.

PPARdelta activation induces metabolic and contractile maturation of human pluripotent stem cell-derived cardiomyocytes.

Pluripotent stem-cell-derived cardiomyocytes (PSC-CMs) provide an unprecedented opportunity to study human heart development and disease, but they are functionally and structurally immature. Here, we induce efficient human PSC-CM (hPSC-CM) maturation through metabolic-pathway modulations. Specifically, we find that peroxisome-proliferator-associated receptor (PPAR) signaling regulates glycolysis and fatty acid oxidation (FAO) in an isoform-specific manner. While PPARalpha (PPARa) is the most active isoform in hPSC-CMs, PPARdelta (PPARd) activation efficiently upregulates the gene regulatory networks underlying FAO, increases mitochondrial and peroxisome content, enhances mitochondrial cristae formation, and augments FAO flux. PPARd activation further increases binucleation, enhances myofibril organization, and improves contractility. Transient lactate exposure, which is frequently used for hPSC-CM purification, induces an independent cardiac maturation program but, when combined with PPARd activation, still enhances oxidative metabolism. In summary, we investigate multiple metabolic modifications in hPSC-CMs and identify a role for PPARd signaling in inducing the metabolic switch from glycolysis to FAO in hPSC-CMs.

Proteomic cellular signatures of kinase inhibitor-induced cardiotoxicity.

Drug Toxicity Signature Generation Center (DToxS) at the Icahn School of Medicine at Mount Sinai is one of the centers for the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) program. Its key aim is to generate proteomic and transcriptomic signatures that can predict cardiotoxic adverse effects of kinase inhibitors approved by the Food and Drug Administration. Towards this goal, high throughput shotgun proteomics experiments (308 cell line/drug combinations +64 control lysates) have been conducted. Using computational network analyses, these proteomic data can be integrated with transcriptomic signatures, generated in tandem, to identify cellular signatures of cardiotoxicity that may predict kinase inhibitor-induced toxicity and enable possible mitigation. Both raw and processed proteomics data have passed several quality control steps and been made publicly available on the PRIDE database. This broad protein kinase inhibitor-stimulated human cardiomyocyte proteomic data and signature set is valuable for prediction of drug toxicities.

A library of induced pluripotent stem cells from clinically well-characterized, diverse healthy human individuals.

A library of well-characterized human induced pluripotent stem cell (hiPSC) lines from clinically healthy human subjects could serve as a useful resource of normal controls for in vitro human development, disease modeling, genotype-phenotype association studies, and drug response evaluation. We report generation and extensive characterization of a gender-balanced, racially/ethnically diverse library of hiPSC lines from 40 clinically healthy human individuals who range in age from 22 to 61 years. The hiPSCs match the karyotype and short tandem repeat identities of their parental fibroblasts, and have a transcription profile characteristic of pluripotent stem cells. We provide whole-genome sequencing data for one hiPSC clone from each individual, genomic ancestry determination, and analysis of mendelian disease genes and risks. We document similar transcriptomic profiles, single-cell RNA-sequencing-derived cell clusters, and physiology of cardiomyocytes differentiated from multiple independent hiPSC lines. This extensive characterization makes this hiPSC library a valuable resource for many studies on human biology.

Plexin-B2 orchestrates collective stem cell dynamics via actomyosin contractility, cytoskeletal tension and adhesion.

During morphogenesis, molecular mechanisms that orchestrate biomechanical dynamics across cells remain unclear. Here, we show a role of guidance receptor Plexin-B2 in organizing actomyosin network and adhesion complexes during multicellular development of human embryonic stem cells and neuroprogenitor cells. Plexin-B2 manipulations affect actomyosin contractility, leading to changes in cell stiffness and cytoskeletal tension, as well as cell-cell and cell-matrix adhesion. We have delineated the functional domains of Plexin-B2, RAP1/2 effectors, and the signaling association with ERK1/2, calcium activation, and YAP mechanosensor, thus providing a mechanistic link between Plexin-B2-mediated cytoskeletal tension and stem cell physiology. Plexin-B2-deficient stem cells exhibit premature lineage commitment, and a balanced level of Plexin-B2 activity is critical for maintaining cytoarchitectural integrity of the developing neuroepithelium, as modeled in cerebral organoids. Our studies thus establish a significant function of Plexin-B2 in orchestrating cytoskeletal tension and cell-cell/cell-matrix adhesion, therefore solidifying the importance of collective cell mechanics in governing stem cell physiology and tissue morphogenesis.

Prediction of arrhythmia susceptibility through mathematical modeling and machine learning.

At present, the QT interval on the electrocardiographic (ECG) waveform is the most common metric for assessing an individual's susceptibility to ventricular arrhythmias, with a long QT, or, at the cellular level, a long action potential duration (APD) considered high risk. However, the limitations of this simple approach have long been recognized. Here, we sought to improve prediction of arrhythmia susceptibility by combining mechanistic mathematical modeling with machine learning (ML). Simulations with a model of the ventricular myocyte were performed to develop a large heterogenous population of cardiomyocytes (n = 10,586), and we tested each variant's ability to withstand three arrhythmogenic triggers: 1) block of the rapid delayed rectifier potassium current (IKr Block), 2) augmentation of the L-type calcium current (ICaL Increase), and 3) injection of inward current (Current Injection). Eight ML algorithms were trained to predict, based on simulated AP features in preperturbed cells, whether each cell would develop arrhythmic dynamics in response to each trigger. We found that APD can accurately predict how cells respond to the simple Current Injection trigger but cannot effectively predict the response to IKr Block or ICaL Increase. ML predictive performance could be improved by incorporating additional AP features and simulations of additional experimental protocols. Importantly, we discovered that the most relevant features and experimental protocols were trigger specific, which shed light on the mechanisms that promoted arrhythmia formation in response to the triggers. Overall, our quantitative approach provides a means to understand and predict differences between individuals in arrhythmia susceptibility.

Protein structure-based gene expression signatures.

Gene expression signatures (GES) connect phenotypes to differential messenger RNA (mRNA) expression of genes, providing a powerful approach to define cellular identity, function, and the effects of perturbations. The use of GES has suffered from vague assessment criteria and limited reproducibility. Because the structure of proteins defines the functional capability of genes, we hypothesized that enrichment of structural features could be a generalizable representation of gene sets. We derive structural gene expression signatures (sGES) using features from multiple levels of protein structure (e.g., domain and fold) encoded by the mRNAs in GES. Comprehensive analyses of data from the Genotype-Tissue Expression Project (GTEx), the all RNA-seq and ChIP-seq sample and signature search (ARCHS4) database, and mRNA expression of drug effects on cardiomyocytes show that sGES are useful for characterizing biological phenomena. sGES enable phenotypic characterization across experimental platforms, facilitates interoperability of expression datasets, and describe drug action on cells.

Inflammation as a Risk Factor in Cardiotoxicity: An Important Consideration for Screening During Drug Development.

Numerous commonly prescribed drugs, including antiarrhythmics, antihistamines, and antibiotics, carry a proarrhythmic risk and may induce dangerous arrhythmias, including the potentially fatal Torsades de Pointes. For this reason, cardiotoxicity testing has become essential in drug development and a required step in the approval of any medication for use in humans. Blockade of the hERG K+ channel and the consequent prolongation of the QT interval on the ECG have been considered the gold standard to predict the arrhythmogenic risk of drugs. In recent years, however, preclinical safety pharmacology has begun to adopt a more integrative approach that incorporates mathematical modeling and considers the effects of drugs on multiple ion channels. Despite these advances, early stage drug screening research only evaluates QT prolongation in experimental and computational models that represent healthy individuals. We suggest here that integrating disease modeling with cardiotoxicity testing can improve drug risk stratification by predicting how disease processes and additional comorbidities may influence the risks posed by specific drugs. In particular, chronic systemic inflammation, a condition associated with many diseases, affects heart function and can exacerbate medications' cardiotoxic effects. We discuss emerging research implicating the role of inflammation in cardiac electrophysiology, and we offer a perspective on how in silico modeling of inflammation may lead to improved evaluation of the proarrhythmic risk of drugs at their early stage of development.

In vitro and In silico Models to Study SARS-CoV-2 Infection: Integrating Experimental and Computational Tools to Mimic "COVID-19 Cardiomyocyte".

The rapid dissemination of SARS-CoV-2 has made COVID-19 a tremendous social, economic, and health burden. Despite the efforts to understand the virus and treat the disease, many questions remain unanswered about COVID-19 mechanisms of infection and progression. Severe Acute Respiratory Syndrome (SARS) infection can affect several organs in the body including the heart, which can result in thromboembolism, myocardial injury, acute coronary syndromes, and arrhythmias. Numerous cardiac adverse events, from cardiomyocyte death to secondary effects caused by exaggerated immunological response against the virus, have been clinically reported. In addition to the disease itself, repurposing of treatments by using "off label" drugs can also contribute to cardiotoxicity. Over the past several decades, animal models and more recently, stem cell-derived cardiomyocytes have been proposed for studying diseases and testing treatments in vitro. In addition, mechanistic in silico models have been widely used for disease and drug studies. In these models, several characteristics such as gender, electrolyte imbalance, and comorbidities can be implemented to study pathophysiology of cardiac diseases and to predict cardiotoxicity of drug treatments. In this Mini Review, we (1) present the state of the art of in vitro and in silico cardiomyocyte modeling currently in use to study COVID-19, (2) review in vitro and in silico models that can be adopted to mimic the effects of SARS-CoV-2 infection on cardiac function, and (3) provide a perspective on how to combine some of these models to mimic "COVID-19 cardiomyocytes environment.".

The role of ß-adrenergic system remodeling in human heart failure: A mechanistic investigation.

ß-adrenergic receptor antagonists (ß-blockers) are extensively used to improve cardiac performance in heart failure (HF), but the electrical improvements with these clinical treatments are not fully understood. The aim of this study was to analyze the electrophysiological effects of ß-adrenergic system remodeling in heart failure with reduced ejection fraction and the underlying mechanisms. We used a combined mathematical model that integrated ß-adrenergic signaling with electrophysiology and calcium cycling in human ventricular myocytes. HF remodeling, both in the electrophysiological and signaling systems, was introduced to quantitatively analyze changes in electrophysiological properties due to the stimulation of ß-adrenergic receptors in failing myocytes. We found that the inotropic effect of ß-adrenergic stimulation was reduced in HF due to the altered Ca2+ dynamics resulting from the combination of structural, electrophysiological and signaling remodeling. Isolated cells showed proarrhythmic risk after sympathetic stimulation because early afterdepolarizations appeared, and the vulnerability was greater in failing myocytes. When analyzing coupled cells, ß-adrenergic stimulation reduced transmural repolarization gradients between endocardium and epicardium in normal tissue, but was less effective at reducing these gradients after HF remodeling. The comparison of the selective activation of ß-adrenergic isoforms revealed that the response to ß2-adrenergic receptors stimulation was blunted in HF while ß1-adrenergic receptors downstream effectors regulated most of the changes observed after sympathetic stimulation. In conclusion, this study was able to reproduce an altered ß-adrenergic activity on failing myocytes and to explain the mechanisms involved. The derived predictions could help in the treatment of HF and guide in the design of future experiments.

Investigational Treatments for COVID-19 may Increase Ventricular Arrhythmia Risk Through Drug Interactions.

Many drugs that have been proposed for treatment of coronavirus disease 2019 (COVID-19) are reported to cause cardiac adverse events, including ventricular arrhythmias. In order to properly weigh risks against potential benefits, particularly when decisions must be made quickly, mathematical modeling of both drug disposition and drug action can be useful for predicting patient response and making informed decisions. Here, we explored the potential effects on cardiac electrophysiology of four drugs proposed to treat COVID-19: lopinavir, ritonavir, chloroquine, and azithromycin, as well as combination therapy involving these drugs. Our study combined simulations of pharmacokinetics (PKs) with quantitative systems pharmacology (QSP) modeling of ventricular myocytes to predict potential cardiac adverse events caused by these treatments. Simulation results predicted that drug combinations can lead to greater cellular action potential prolongation, analogous to QT prolongation, compared with drugs given in isolation. The combination effect can result from both PK and pharmacodynamic drug interactions. Importantly, simulations of different patient groups predicted that women with pre-existing heart disease are especially susceptible to drug-induced arrhythmias, compared with diseased men or healthy individuals of either sex. Statistical analysis of population simulations revealed the molecular factors that make certain women with heart failure especially susceptible to arrhythmias. Overall, the results illustrate how PK and QSP modeling may be combined to more precisely predict cardiac risks of COVID-19 therapies.

Transcriptomic profiling of human cardiac cells predicts protein kinase inhibitor-associated cardiotoxicity.

Kinase inhibitors (KIs) represent an important class of anti-cancer drugs. Although cardiotoxicity is a serious adverse event associated with several KIs, the reasons remain poorly understood, and its prediction remains challenging. We obtain transcriptional profiles of human heart-derived primary cardiomyocyte like cell lines treated with a panel of 26 FDA-approved KIs and classify their effects on subcellular pathways and processes. Individual cardiotoxicity patient reports for these KIs, obtained from the FDA Adverse Event Reporting System, are used to compute relative risk scores. These are then combined with the cell line-derived transcriptomic datasets through elastic net regression analysis to identify a gene signature that can predict risk of cardiotoxicity. We also identify relationships between cardiotoxicity risk and structural/binding profiles of individual KIs. We conclude that acute transcriptomic changes in cell-based assays combined with drug substructures are predictive of KI-induced cardiotoxicity risk, and that they can be informative for future drug discovery.

Investigational treatments for COVID-19 may increase ventricular arrhythmia risk through drug interactions.

Many drugs that have been proposed for treatment of COVID-19 are reported to cause cardiac adverse events, including ventricular arrhythmias. In order to properly weigh risks against potential benefits, particularly when decisions must be made quickly, mathematical modeling of both drug disposition and drug action can be useful for predicting patient response and making informed decisions. Here we explored the potential effects on cardiac electrophysiology of 4 drugs proposed to treat COVID-19: lopinavir, ritonavir, chloroquine, and azithromycin, as well as combination therapy involving these drugs. Our study combined simulations of pharmacokinetics (PK) with quantitative systems pharmacology (QSP) modeling of ventricular myocytes to predict potential cardiac adverse events caused by these treatments. Simulation results predicted that drug combinations can lead to greater cellular action potential prolongation, analogous to QT prolongation, compared with drugs given in isolation. The combination effect can result from both pharmacokinetic and pharmacodynamic drug interactions. Importantly, simulations of different patient groups predicted that females with pre-existing heart disease are especially susceptible to drug-induced arrhythmias, compared males with disease or healthy individuals of either sex. Overall, the results illustrate how PK and QSP modeling may be combined to more precisely predict cardiac risks of COVID-19 therapies.