Citations

Background: Previous studies in the SCN5A- encoded Na channel showed that amino acid substitutions at one position affected function of a Na channel containing a pathogenic mutation elsewhere. The first and most commonly used SCN5A cDNA clone (hH1) is the minor alternatively spliced transcript with an extra glutamine at position 1077 (Q1077), and also differs from the Na channel found in nearly all humans by virtue of a rare amino acid substitution (R1027Q). We hypothesized that R1027Q in previous studies using hH1 may have increased expression levels of the Brugada syndrome (BrS)-associated mutation (R1512W) and the common polymorphism (H558R). Methods: We generated mutations in the alternatively-spliced SCN5A transcript containing Q1077 (as in the hH1 clone) as the wild type (WT) channel to generate channels with the following amino acid substitutions in addition to Q1077ins: H558R, H558R/R1027Q, R1512W, and R1027Q/R1512W. These channels were expressed in HEK-293 cells and current densities measured by whole cell voltage clamp. Results: The current density for H558R was reduced dramatically (−7±2 pA/pF) whereas H558R/R1027Q exhibited normal current density (−297±56 pA/pF) comparable to WT (−310±52 pA/pF). The BrS mutation, R1512W-hH1, was characterized originally as having only subtle inactivation defects but normal current density. However, when we expressed R1512W in the patient’s true WT background (i.e. without R1027Q), the current density was attenuated significantly (−79±19 pA/pF) whereas the double mutant (R1027Q/R1512W) which replicated R1512W-hH1 showed nearly normal density (−180±21 pA/pF, −253±35 pA/pF for hH1, n=10–25 cells). Conclusions: R1027Q, the rare genetic variant present in the hH1 cDNA clone, rescues expression of Na current for channels with H558R and the BrS1-associated mutation, R1512W. Using the hH1 clone which contains R1027Q as background (as is the case for nearly all previous studies of SCN5A mutations) may yield erroneous conclusions regarding the functional impact assigned to putative pathogenic mutations. It may be critical to study mutations in either true WT clones or in the patient’s own genetic background. This research has received full or partial funding support from the American Heart Association, AHA Midwest Affiliate (Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, South Dakota & Wisconsin).

Introduction: Annually, thousands of sudden deaths in individuals under the age of 35 years remain unexplained following a medico-legal autopsy and are termed autopsy negative sudden unexplained death in the young (SUDY). Cardiomyopathies, channelopathies, and metabolic disorders may underlie a significant number of SUDY cases. Previously, we demonstrated that 25% of autopsy-negative SUDY cases had mutations in the 4 major cardiac ion channel genes ( KCNQ1, KCNH2, SCN5A , and RYR2 ). However, over 100 sudden death-susceptibility genes have been discovered and may be implicated in SUDY. Objective: We explored the utility of whole exome sequencing (WES) followed by gene-specific surveillance as an efficient and effective means of performing post-mortem genetic testing in SUDY. Methods: Postmortem WES was performed on 14 consecutively-referred white SUDY victims (57% men; average age at death 17.4 ± 8.6 years) using the Agilent SureSelect Human All Exon V4+UTR capture kit and an Illumina HiSeq 2000 sequencer. Following variant alignment (hg19) and annotation, 117 cardiac channelopathy-, cardiomyopathy-, and metabolic disorder-susceptibility genes were surveyed to identify putative SUDY-associated mutations. Potentially pathogenic variants had to be non-synonymous and ultra-rare [i.e. absent in all 3 evaluated exome databases (1,000 Genome Project, the NHLBI GO Exome Sequencing Project, and Exome Chip Design)]. Results: On average, each SUDY case had 12,758 ± 2016 non-synonymous variants, of which 79 ± 15 localized to the 117 evaluated genes. Overall, 8 unique, ultra-rare variants (7 missense, 1 in-frame insertion) identified in 6 genes (3 in TTN ; 1 each in CACNA1C, JPH2, MYH7, VCL, RYR2 ) were detected in 7 of 14 cases (50%). Of the 7 missense alterations, 2 (T171M- CACNA1C , I22160T- TTN ) were predicted damaging by 3 in-silico tools. Conclusions: Although WES and gene-specific surveillance is an efficient and effective strategy to detect rare, potentially lethal, genetic variants, the accurate interpretation of each variant is daunting. Importantly, rarity, even ultra-rarity, does not equal pathogenicity even when the ultra-rare variant resides within a so-called sudden death-susceptibility gene.

Introduction: Vectorcardiography (VCG) has clinical prognostic and diagnostic value in various disease states. Hypertrophic cardiomyopathy is a disease state which can lead to sudden cardiac death, where genotype status has been shown to predict worse outcomes.. Hypothesis: We set out to determine the utility of vectorcardiographic parameters in one of the largest genotyped cohorts of patients with hypertrophic cardiomyopathy (HCM) and to determine whether they can identify genotype status. Methods: Between 1997 and 2007, research based genetic testing was performed on 1053 patients with HCM (60% males; mean age at diagnosis 44.4 years); yield of genetic testing was 34%. Of these, 967 had ECG’s appropriate for computerized derivation of VCG parameters including the spatial mean and peaks QRS-T angles, spatial ventricular gradient and the spatial QRS, QT, and Tpeak-Tend (TpTe) intervals. Results were compared against those from a group of healthy control subjects (N=418).Subsequent genotype positive versus negative comparisons were performed using paired student T-tests or Mann-Whitney U-tests as appropriate. Results: Spatial peaks and spatial mean QRS-T angles were significantly higher in HCM patients (129.3±26.4 and 121.8±38.6 degrees, respectively) than in controls (25.0±15.0 and 43.0±-21.0 degrees, respectively, p<0.001). Sensitivities for HCM, based on cutoff values of 2 standard deviations above the mean values reported for control patients, were 84.5% and 81.5% for spatial peaks and spatial mean QRS-T angles, respectively. VCG predicted genotype status as genotype positive patients had significantly higher spatial peaks (132.4±-29.2 vs. 127.9±24.8 degrees; p = 0.02) and spatial mean (129.7±33.8 vs. 117.6±40.3 degrees; p<0.001) QRS-T angles, spatial TpTe (118.0±40.8 vs. 107.9±48.7 ms; p<0.001) as well as significantly lower spatial ventricular gradients (0.059±0.044 vs. 0.067±0.04; p<0.001). Conclusions: Patients with HCM can be distinguished from normal subjects through the study of the spatial QRS-T angle. Whether incorporation of spatial peaks QRS-T angle to pre-existing genetic test prediction tools will further improve prediction of genotype status remains to be determined.

Background: Prior epidemiological studies demonstrated that the p.D85N-Potassium voltage-gated channel subfamily E member 1 (KCNE1) common variant reduces repolarization reserve and predisposes to drug-induced QT prolongation/torsades de pointes. We sought to develop a cellular model for drug-induced long QT syndrome using a patient-specific induced pluripotent stem cell-derived cardiomyocyte (iPSC-CM). Methods: p.D85N-KCNE1 iPSCs were generated from a 23-year-old female with an exaggerated heart rate-corrected QT interval response to metoclopramide (ΔQTc of 160 ms). Clustered regularly interspaced short palindromic repeats-associated 9 technology was used to generate gene-corrected isogenic iPSCs. Field potential duration and action potential duration (APD) were measured from iPSC-CMs. Results: At baseline, p.D85N-KCNE1 iPSC-CMs displayed significantly longer field potential duration (281±15 ms, n=13 versus 223±8.6 ms, n=14, P <0.01) and action potential duration at 90% repolarization (APD90; 579±22 ms, n=24 versus 465±33 ms, n=26, P <0.01) than isogenic-control iPSC-CMs. Dofetilide at a concentration of 2 nM increased significantly field potential duration (379±20 ms, n=13, P <0.01) and APD90 (666±11 ms, n=46, P <0.01) in p.D85N-KCNE1 iPSC-CMs but not in isogenic-control. The effect of dofetilide on APD90 (616±54 ms, n=7 versus 526±54 ms, n=10, P <0.05) was confirmed by Patch-clamp. Interestingly, treatment of p.D85N-KCNE1 iPSC-CMs with estrogen at a concentration of 1 nM exaggerated further dofetilide-induced APD90 prolongation (696±9 ms, n=81, P <0.01) and caused more early afterdepolarizations (11.7%) compared with isogenic control (APD90: 618±8 ms, n=115 and early afterdepolarizations: 2.6%, P <0.05). Conclusions: This iPSC-CM study provides further evidence that the p.D85N-KCNE1 common variant in combination with environmental factors such as QT prolonging drugs and female sex is proarrhythmic.

Background: Arrhythmogenic cardiomyopathy (ACM) is an inherited cardiac disease characterized by fibrofatty replacement of the right and left ventricle, often causing ventricular dysfunction and life-threatening arrhythmias. Variants in desmosomal genes account for up to 60% of cases. Our objective was to establish the prevalence and clinical features of ACM stemming from pathogenic variants in the nondesmosomal cadherin 2 (CDH2), a novel genetic substrate of ACM. Methods: A cohort of 500 unrelated patients with a definite diagnosis of ACM and no disease-causing variants in the main ACM genes was assembled. Genetic screening of CDH2 was performed through next-generation or Sanger sequencing. Whenever possible, cascade screening was initiated in the families of CDH2 -positive probands, and clinical evaluation was performed. Results: Genetic screening of CDH2 led to the identification of 7 rare variants: 5, identified in 6 probands, were classified as pathogenic or likely pathogenic. The previously established p.D407N pathogenic variant was detected in 2 additional probands. Probands and family members with pathogenic/likely pathogenic variants in CDH2 were clinically evaluated, and along with previously published cases, altogether contributed to the identification of gene-specific features (13 cases from this cohort and 11 previously published, for a total of 9 probands and 15 family members). Ventricular arrhythmic events occurred in most CDH2 -positive subjects (20/24, 83%), while the occurrence of heart failure was rare (2/24, 8.3%). Among probands, sustained ventricular tachycardia and sudden cardiac death occurred in 5/9 (56%). Conclusions: In this worldwide cohort of previously genotype-negative ACM patients, the prevalence of probands with CDH2 pathogenic/likely pathogenic variants was 1.2% (6/500). Our data show that this cohort of CDH2 -ACM patients has a high incidence of ventricular arrhythmias, while evolution toward heart failure is rare.

Background: Heart rate–corrected QT interval (QTc) prolongation, whether secondary to drugs, genetics including congenital long QT syndrome, and/or systemic diseases including SARS-CoV-2–mediated coronavirus disease 2019 (COVID-19), can predispose to ventricular arrhythmias and sudden cardiac death. Currently, QTc assessment and monitoring relies largely on 12-lead electrocardiography. As such, we sought to train and validate an artificial intelligence (AI)–enabled 12-lead ECG algorithm to determine the QTc, and then prospectively test this algorithm on tracings acquired from a mobile ECG (mECG) device in a population enriched for repolarization abnormalities. Methods: Using >1.6 million 12-lead ECGs from 538 200 patients, a deep neural network (DNN) was derived (patients for training, n = 250 767; patients for testing, n = 107 920) and validated (n = 179 513 patients) to predict the QTc using cardiologist-overread QTc values as the “gold standard”. The ability of this DNN to detect clinically-relevant QTc prolongation (eg, QTc ≥500 ms) was then tested prospectively on 686 patients with genetic heart disease (50% with long QT syndrome) with QTc values obtained from both a 12-lead ECG and a prototype mECG device equivalent to the commercially-available AliveCor KardiaMobile 6L. Results: In the validation sample, strong agreement was observed between human over-read and DNN-predicted QTc values (−1.76±23.14 ms). Similarly, within the prospective, genetic heart disease–enriched dataset, the difference between DNN-predicted QTc values derived from mECG tracings and those annotated from 12-lead ECGs by a QT expert (−0.45±24.73 ms) and a commercial core ECG laboratory [10.52±25.64 ms] was nominal. When applied to mECG tracings, the DNN’s ability to detect a QTc value ≥500 ms yielded an area under the curve, sensitivity, and specificity of 0.97, 80.0%, and 94.4%, respectively. Conclusions: Using smartphone-enabled electrodes, an AI DNN can predict accurately the QTc of a standard 12-lead ECG. QTc estimation from an AI-enabled mECG device may provide a cost-effective means of screening for both acquired and congenital long QT syndrome in a variety of clinical settings where standard 12-lead electrocardiography is not accessible or cost-effective.