A big data approach to myocyte membrane analysis: using populations of models to understand the cellular causes of heart failure

Experimentally-calibrated populations of models (ePoMs) allow to elucidate the trends hidden behind large amounts of data, but they have never been used as a means to monitor the temporal evolution of a heart's electrical activity. This work aimed at using ePoMs to understand the cell membrane anomalies that lead to heart failure. A population of the Tusscher-Noble-Noble-Panfilov model was calibrated to the activation-recovery intervals measured from epicardial electrograms acquired during a Physioheart experiment. A Mann-Whitney-Wilcoxon U-test was performed on the statistical distributions of the calibrated parameters, at different time points during the experiment, to elucidate the physiology changes that would have led to the resulting ePoMs. The methodology developed in this paper could detect the specific pathological ion dynamics responsible for the abnormal electrical behavior observed during the experiment. Furthermore, the analysis of the electrical activities was capable of detection of pathologies at an earlier stage when compared to the analysis of the cardiac output alone. The use of big data analytics proved to be more effective than the traditional signal analysis approach in predicting heart failure; additionally, this approach accounts for variabilities in both the healthy and the pathological conditions

High heart rate associated early repolarization causes J‐waves in both zebra finch and mouse

Animal science

Genome-wide association analyses identify new Brugada syndrome risk loci and highlight a new mechanism of sodium channel regulation in disease susceptibility

Brugada syndrome (BrS) is a cardiac arrhythmia disorder associated with sudden death in young adults. With the exception of SCN5A, encoding the cardiac sodium channel NaV1.5, susceptibility genes remain largely unknown. Here we performed a genome-wide association meta-analysis comprising 2,820 unrelated cases with BrS and 10,001 controls, and identified 21 association signals at 12 loci (10 new). Single nucleotide polymorphism (SNP)-heritability estimates indicate a strong polygenic influence. Polygenic risk score analyses based on the 21 susceptibility variants demonstrate varying cumulative contribution of common risk alleles among different patient subgroups, as well as genetic associations with cardiac electrical traits and disorders in the general population. The predominance of cardiac transcription factor loci indicates that transcriptional regulation is a key feature of BrS pathogenesis. Furthermore, functional studies conducted on MAPRE2, encoding the microtubule plus-end binding protein EB2, point to microtubule-related trafficking effects on NaV1.5 expression as a new underlying molecular mechanism. Taken together, these findings broaden our understanding of the genetic architecture of BrS and provide new insights into its molecular underpinnings

Low-Cost Optical Mapping Systems for Panoramic Imaging of Complex Arrhythmias and Drug-Action in Translational Heart Models

Panoramic optical mapping is the primary method for imaging electrophysiological activity from the entire outer surface of Langendorff-perfused hearts. To date, it is the only method of simultaneously measuring multiple key electrophysiological parameters, such as transmembrane voltage and intracellular free calcium, at high spatial and temporal resolution. Despite the impact it has already had on the fields of cardiac arrhythmias and whole-heart computational modeling, present-day system designs precludes its adoption by the broader cardiovascular research community because of their high costs. Taking advantage of recent technological advances, we developed and validated low-cost optical mapping systems for panoramic imaging using Langendorff-perfused pig hearts, a clinically-relevant model in basic research and bioengineering. By significantly lowering financial thresholds, this powerful cardiac electrophysiology imaging modality may gain wider use in research and, even, teaching laboratories, which we substantiated using the lower-cost Langendorff-perfused rabbit heart model

H3K27ac acetylome signatures reveal the epigenomic reorganization in remodeled non-failing human hearts

BACKGROUND: H3K27ac histone acetylome changes contribute to the phenotypic response in heart diseases, particularly in end-stage heart failure. However, such epigenetic alterations have not been systematically investigated in remodeled non-failing human hearts. Therefore, valuable insight into cardiac dysfunction in early remodeling is lacking. This study aimed to reveal the acetylation changes of chromatin regions in response to myocardial remodeling and their correlations to transcriptional changes of neighboring genes. RESULTS: We detected chromatin regions with differential acetylation activity (DARs; Padj. < 0.05) between remodeled non-failing patient hearts and healthy donor hearts. The acetylation level of the chromatin region correlated with its RNA polymerase II occupancy level and the mRNA expression level of its adjacent gene per sample. Annotated genes from DARs were enriched in disease-related pathways, including fibrosis and cell metabolism regulation. DARs that change in the same direction have a tendency to cluster together, suggesting the well-reorganized chromatin architecture that facilitates the interactions of regulatory domains in response to myocardial remodeling. We further show the differences between the acetylation level and the mRNA expression level of cell-type-specific markers for cardiomyocytes and 11 non-myocyte cell types. Notably, we identified transcriptome factor (TF) binding motifs that were enriched in DARs and defined TFs that were predicted to bind to these motifs. We further showed 64 genes coding for these TFs that were differentially expressed in remodeled myocardium when compared with controls. CONCLUSIONS: Our study reveals extensive novel insight on myocardial remodeling at the DNA regulatory level. Differences between the acetylation level and the transcriptional level of cell-type-specific markers suggest additional mechanism(s) between acetylome and transcriptome. By integrating these two layers of epigenetic profiles, we further provide promising TF-encoding genes that could serve as master regulators of myocardial remodeling. Combined, our findings highlight the important role of chromatin regulatory signatures in understanding disease etiology

Genome-wide association analyses identify new Brugada syndrome risk loci and highlight a new mechanism of sodium channel regulation in disease susceptibility.

Brugada syndrome (BrS) is a cardiac arrhythmia disorder associated with sudden death in young adults. With the exception of SCN5A, encoding the cardiac sodium channel Na1.5, susceptibility genes remain largely unknown. Here we performed a genome-wide association meta-analysis comprising 2,820 unrelated cases with BrS and 10,001 controls, and identified 21 association signals at 12 loci (10 new). Single nucleotide polymorphism (SNP)-heritability estimates indicate a strong polygenic influence. Polygenic risk score analyses based on the 21 susceptibility variants demonstrate varying cumulative contribution of common risk alleles among different patient subgroups, as well as genetic associations with cardiac electrical traits and disorders in the general population. The predominance of cardiac transcription factor loci indicates that transcriptional regulation is a key feature of BrS pathogenesis. Furthermore, functional studies conducted on MAPRE2, encoding the microtubule plus-end binding protein EB2, point to microtubule-related trafficking effects on Na1.5 expression as a new underlying molecular mechanism. Taken together, these findings broaden our understanding of the genetic architecture of BrS and provide new insights into its molecular underpinnings

Evaluating the Risks of Arrhythmia through Big Data: Automatic Processing and Neural Networks to Classify Epicardial Electrograms

Arrhythmic behaviors are a major risk to the population. These are diverse and can have their origin in cellular dynamics that affect the functioning of the heart. When trying to understand the mechanisms behind arrhythmogenesis the epicardial electrograms present themselves as a useful measurement because they reflect the electrical behavior of the cells surrounding the electrodes. Nevertheless, there is a lack of methods in the literature to automatically process and analyze these signals. In this paper, an algorithm to automatically detect the R, S and T wave peaks in epicardial electrogram signals is presented. This algorithm uses the derivative of the signal to find the activation and recovery times, and uses these as fiducial points to find the desired features. These features are then used as inputs to an artificial neural network, trained to classify individual beats into `healthy' and `pathological'. After optimization, both the detector and the neural network showed good performance in their tasks; furthermore, the robustness and amenability to real-time implementation of the methods here presented make them ideal for monitoring patients or experimental platforms when epicardial electrograms can be measured

Using Populations of Models to Navigate Big Data in Electrophysiology: Evaluation of Parameter Sensitivity of Action Potential Models

Experimentally-calibrated populations of models (ePoM) for cardiac electrophysiology can be used as a means to elucidate the cellular dynamics that lead to pathologies observed in organ-level measurements, while taking into account the variability inherent to living creatures. Notwithstanding, the results obtained through ePoM will depend on the capabilities of the template model, and not one model can accurately reproduce all pathologies. The objective of this work was to show how using different models, within an ePoM framework, can be advantageous when looking for the causes for a pathological behavior observed in experimental data. Populations of the ten Tusscher (2006) and the O’Hara-Rudy model were calibrated to activation-recovery intervals measured during an ex-vivo porcine heart experiment; a pathological reduction in ARI was observed as the experiment progressed in time. The ePoM approach predicted a reduction in calcium uptake via L-type channels, using the TP06 model, and an increased potassium concentration in blood, using the ORd model, as the causes for the reduction in ARI; these findings were then confirmed by other experimental data. This approach can also accommodate different biomarkers or more mathematical models to further increase its predictive capabilities

Bridging Organ- and Cellular-Level Behavior in Ex Vivo Experimental Platforms Using Populations of Models of Cardiac Electrophysiology

The inability to discern between pathology and physiological variability is a key issue in cardiac electrophysiology since this prevents the use of minimally invasive acquisitions to predict early pathological behavior. The goal of this work is to demonstrate how experimentally calibrated populations of models (ePoM) may be employed to inform which cellular-level pathologies are responsible for abnormalities observed in organ-level acquisitions while accounting for intersubject variability; this will be done through an exemplary computational and experimental approach. Unipolar epicardial electrograms (EGM) were acquired during an ex vivo porcine heart experiment. A population of the Ten Tusscher 2006 model was calibrated to activation–recovery intervals (ARI), measured from the electrograms, at three representative times. The distributions of the parameters from the resulting calibrated populations were compared to reveal statistically significant pathological variations. Activation–recovery interval reduction was observed in the experiments, and the comparison of the calibrated populations of models suggested a reduced L-type calcium conductance and a high extra-cellular potassium concentration as the most probable causes for the abnormal electrograms. This behavior was consistent with a reduction in the cardiac output (CO) and was confirmed by other experimental measurements. A proof of concept method to infer cellular pathologies by means of organ-level acquisitions is presented, allowing for an earlier detection of pathology than would be possible with current methods. This novel method that uses mathematical models as a tool for formulating hypotheses regarding the cellular causes of observed organ-level behaviors, while accounting for physiological variability has been unexplored