Predicting changes to INa from missense mutations in human SCN5A

Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation

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

Role of Machine Learning and Artificial Intelligence in Arrhythmias and Electrophysiology

Machine learning (ML), a subset of artificial intelligence (AI) centered on machines learning from extensive datasets, stands at the forefront of a technological revolution shaping various facets of society. Cardiovascular medicine has emerged as a key domain for ML applications, with considerable efforts to integrate these innovations into routine clinical practice. Within cardiac electrophysiology, ML applications, especially in the automated interpretation of electrocardiograms, have garnered substantial attention in existing literature. However, less recognized are the diverse applications of ML in cardiac electrophysiology and arrhythmias, spanning basic science research on arrhythmia mechanisms, both experimental and computational, as well as contributions to enhanced techniques for mapping cardiac electrical function and translational research related to arrhythmia management. This comprehensive review delves into various ML applications within the scope of this journal, organized into 3 parts. The first section provides a fundamental understanding of general ML principles and methodologies, serving as a foundational resource for readers interested in exploring ML applications in arrhythmia research. The second part offers an in-depth review of studies in arrhythmia and electrophysiology that leverage ML methodologies, showcasing the broad potential of ML approaches. Each subject is thoroughly outlined, accompanied by a review of notable ML research advancements. Finally, the review delves into the primary challenges and future perspectives surrounding ML-driven cardiac electrophysiology and arrhythmias research.

A cardiologist's guide to machine learning in cardiovascular disease prognosis prediction

A modern-day physician is faced with a vast abundance of clinical and scientific data, by far surpassing the capabilities of the human mind. Until the last decade, advances in data availability have not been accompanied by analytical approaches. The advent of machine learning (ML) algorithms might improve the interpretation of complex data and should help to translate the near endless amount of data into clinical decision-making. ML has become part of our everyday practice and might even further change modern-day medicine. It is important to acknowledge the role of ML in prognosis prediction of cardiovascular disease. The present review aims on preparing the modern physician and researcher for the challenges that ML might bring, explaining basic concepts but also caveats that might arise when using these methods. Further, a brief overview of current established classical and emerging concepts of ML disease prediction in the fields of omics, imaging and basic science is presented.

Continuous Bayesian variant interpretation accounts for incomplete penetrance among Mendelian cardiac channelopathies

PURPOSE

The congenital Long QT Syndrome (LQTS) and Brugada Syndrome (BrS) are Mendelian autosomal dominant diseases that frequently precipitate fatal cardiac arrhythmias. Incomplete penetrance is a barrier to clinical management of heterozygotes harboring variants in the major implicated disease genes KCNQ1, KCNH2, and SCN5A. We apply and evaluate a Bayesian penetrance estimation strategy that accounts for this phenomenon.

METHODS

We generated Bayesian penetrance models for KCNQ1-LQT1 and SCN5A-LQT3 using variant-specific features and clinical data from the literature, international arrhythmia genetic centers, and population controls. We analyzed the distribution of posterior penetrance estimates across 4 genotype-phenotype relationships and compared continuous estimates with ClinVar annotations. Posterior estimates were mapped onto protein structure.

RESULTS

Bayesian penetrance estimates of KCNQ1-LQT1 and SCN5A-LQT3 are empirically equivalent to 10 and 5 clinically phenotype heterozygotes, respectively. Posterior penetrance estimates were bimodal for KCNQ1-LQT1 and KCNH2-LQT2, with a higher fraction of missense variants with high penetrance among KCNQ1 variants. There was a wide distribution of variant penetrance estimates among identical ClinVar categories. Structural mapping revealed heterogeneity among "hot spot" regions and featured high penetrance estimates for KCNQ1 variants in contact with calmodulin and the S6 domain.

CONCLUSIONS

Bayesian penetrance estimates provide a continuous framework for variant interpretation.

Familial atrial fibrillation mutation M1875T-SCN5A increases early sodium current and dampens the effect of flecainide

AIMS

Atrial fibrillation (AF) is the most common cardiac arrhythmia. Pathogenic variants in genes encoding ion channels are associated with familial AF. The point mutation M1875T in the SCN5A gene, which encodes the α-subunit of the cardiac sodium channel Nav1.5, has been associated with increased atrial excitability and familial AF in patients.

METHODS AND RESULTS

We designed a new murine model carrying the Scn5a-M1875T mutation enabling us to study the effects of the Nav1.5 mutation in detail in vivo and in vitro using patch clamp and microelectrode recording of atrial cardiomyocytes, optical mapping, electrocardiogram, echocardiography, gravimetry, histology, and biochemistry. Atrial cardiomyocytes from newly generated adult Scn5a-M1875T+/- mice showed a selective increase in the early (peak) cardiac sodium current, larger action potential amplitude, and a faster peak upstroke velocity. Conduction slowing caused by the sodium channel blocker flecainide was less pronounced in Scn5a-M1875T+/- compared to wildtype atria. Overt hypertrophy or heart failure in Scn5a-M1875T+/- mice could be excluded.

CONCLUSION

The Scn5a-M1875T point mutation causes gain-of-function of the cardiac sodium channel. Our results suggest increased atrial peak sodium current as a potential trigger for increased atrial excitability.

Functional genomics of OCTN2 variants informs protein-specific variant effect predictor for Carnitine Transporter Deficiency

Genetic variants in SLC22A5, encoding the membrane carnitine transporter OCTN2, cause the rare metabolic disorder Carnitine Transporter Deficiency (CTD). CTD is potentially lethal but actionable if detected early, with confirmatory diagnosis involving sequencing of SLC22A5. Interpretation of missense variants of uncertain significance (VUSs) is a major challenge. In this study, we sought to characterize the largest set to date (n = 150) of OCTN2 variants identified in diverse ancestral populations, with the goals of furthering our understanding of the mechanisms leading to OCTN2 loss-of-function (LOF) and creating a protein-specific variant effect prediction model for OCTN2 function. Uptake assays with 14C-carnitine revealed that 105 variants (70%) significantly reduced transport of carnitine compared to wild-type OCTN2, and 37 variants (25%) severely reduced function to less than 20%. All ancestral populations harbored LOF variants; 62% of green fluorescent protein (GFP)-tagged variants impaired OCTN2 localization to the plasma membrane of human embryonic kidney (HEK293T) cells, and subcellular localization significantly associated with function, revealing a major LOF mechanism of interest for CTD. With these data, we trained a model to classify variants as functional (>20% function) or LOF (<20% function). Our model outperformed existing state-of-the-art methods as evaluated by multiple performance metrics, with mean area under the receiver operating characteristic curve (AUROC) of 0.895 ± 0.025. In summary, in this study we generated a rich dataset of OCTN2 variant function and localization, revealed important disease-causing mechanisms, and improved upon machine learning-based prediction of OCTN2 variant function to aid in variant interpretation in the diagnosis and treatment of CTD.

How Functional Genomics Can Keep Pace With VUS Identification

Over the last two decades, an exponentially expanding number of genetic variants have been identified associated with inherited cardiac conditions. These tremendous gains also present challenges in deciphering the clinical relevance of unclassified variants or variants of uncertain significance (VUS). This review provides an overview of the advancements (and challenges) in functional and computational approaches to characterize variants and help keep pace with VUS identification related to inherited heart diseases.

Absolute Quantification of Nav1.5 Expression by Targeted Mass Spectrometry

Nav1.5 is the pore forming α-subunit of the cardiac voltage-gated sodium channel that initiates cardiac action potential and regulates the human heartbeat. A normal level of Nav1.5 is crucial to cardiac function and health. Over- or under-expression of Nav1.5 can cause various cardiac diseases ranging from short PR intervals to Brugada syndromes. An assay that can directly quantify the protein amount in biological samples would be a priori to accurately diagnose and treat Nav1.5-associated cardiac diseases. Due to its large size (>200 KD), multipass transmembrane domains (24 transmembrane passes), and heavy modifications, Nav1.5 poses special quantitation challenges. To date, only the relative quantities of this protein have been measured in biological samples. Here, we describe the first targeted and mass spectrometry (MS)-based quantitative assay that can provide the copy numbers of Nav1.5 in cells with a well-defined lower limit of quantification (LLOQ) and precision. Applying the developed assay, we successfully quantified transiently expressed Nav1.5 in as few as 1.5 million Chinese hamster ovary (CHO) cells. The obtained quantity was 3 ± 2 fmol on the column and 3 ± 2 × 104 copies/cell. To our knowledge, this is the first absolute quantity of Nav1.5 measured in a biological sample.

GENESIS: Gene-Specific Machine Learning Models for Variants of Uncertain Significance Found in Catecholaminergic Polymorphic Ventricular Tachycardia and Long QT Syndrome-Associated Genes

BACKGROUND

Cardiac channelopathies such as catecholaminergic polymorphic tachycardia and long QT syndrome predispose patients to fatal arrhythmias and sudden cardiac death. As genetic testing has become common in clinical practice, variants of uncertain significance (VUS) in genes associated with catecholaminergic polymorphic ventricular tachycardia and long QT syndrome are frequently found. The objective of this study was to predict pathogenicity of catecholaminergic polymorphic ventricular tachycardia-associated RYR2 VUS and long QT syndrome-associated VUS in KCNQ1, KCNH2, and SCN5A by developing gene-specific machine learning models and assessing them using cross-validation, cellular electrophysiological data, and clinical correlation.

METHODS

The GENe-specific EnSemble grId Search framework was developed to identify high-performing machine learning models for RYR2, KCNQ1, KCNH2, and SCN5A using variant- and protein-specific inputs. Final models were applied to datasets of VUS identified from ClinVar and exome sequencing. Whole cell patch clamp and clinical correlation of selected VUS was performed.

RESULTS

The GENe-specific EnSemble grId Search models outperformed alternative methods, with area under the receiver operating characteristics up to 0.87, average precisions up to 0.83, and calibration slopes as close to 1.0 (perfect) as 1.04. Blinded voltage-clamp analysis of HEK293T cells expressing 2 predicted pathogenic variants in KCNQ1 each revealed an ≈80% reduction of peak Kv7.1 current compared with WT. Normal Kv7.1 function was observed in KCNQ1-V241I HEK cells as predicted. Though predicted benign, loss of Kv7.1 function was observed for KCNQ1-V106D HEK cells. Clinical correlation of 9/10 variants supported model predictions.

CONCLUSIONS

Gene-specific machine learning models may have a role in post-genetic testing diagnostic analyses by providing high performance prediction of variant pathogenicity.

Machine learning techniques for arrhythmic risk stratification: a review of the literature

Ventricular arrhythmias (VAs) and sudden cardiac death (SCD) are significant adverse events that affect the morbidity and mortality of both the general population and patients with predisposing cardiovascular risk factors. Currently, conventional disease-specific scores are used for risk stratification purposes. However, these risk scores have several limitations, including variations among validation cohorts, the inclusion of a limited number of predictors while omitting important variables, as well as hidden relationships between predictors. Machine learning (ML) techniques are based on algorithms that describe intervariable relationships. Recent studies have implemented ML techniques to construct models for the prediction of fatal VAs. However, the application of ML study findings is limited by the absence of established frameworks for its implementation, in addition to clinicians’ unfamiliarity with ML techniques. This review, therefore, aims to provide an accessible and easy-to-understand summary of the existing evidence about the use of ML techniques in the prediction of VAs. Our findings suggest that ML algorithms improve arrhythmic prediction performance in different clinical settings. However, it should be emphasized that prospective studies comparing ML algorithms to conventional risk models are needed while a regulatory framework is required prior to their implementation in clinical practice.

The Role of Artificial Intelligence and Machine Learning in Clinical Cardiac Electrophysiology

In recent years, artificial intelligence (AI) has found numerous applications in cardiology due in part to large digitized datasets and the evolution of high performance computing. In the discipline of cardiac electrophysiology (EP), a number of clinical, imaging, and electrical waveform data are considered in the diagnosis, prognostication and management of arrhythmias, which lend themselves well to automation through AI. But equally relevant, AI offers a unique opportunity to discover novel EP concepts and improve clinical care through its inherent, hierarchical tenets of self-learning. This review will focus on the application of AI in clinical EP and summarize state-of-the art, large, clinical studies in the following key domains: (1) ECG-based arrhythmia and disease classification, (2) atrial fibrillation source detection, (3) substrate and risk assessment for atrial fibrillation and ventricular tachyarrhythmias, and (4) predicting outcomes after cardiac resynchronization therapy. Many are small, single-center, proof-of-concept investigations, but they still demonstrate groundbreaking performance of deep learning, a subdomain of AI, which surpasses traditional statistical analysis. Larger studies, for instance classifying arrhythmias from ECG recordings, have further provided external validation of their high accuracy. Ultimately, the performance of AI is dependent on the quality of the input data and the rigor of algorithm development. The field is still nascent and several barriers will need to be overcome, including prospective validation in large, well-labelled datasets and more seamless information technology-based data collection/integration, before AI can be adopted into broader clinical EP practice. This review will conclude with a discussion of these challenges and future work.

ESC working group on cardiac cellular electrophysiology position paper: relevance, opportunities, and limitations of experimental models for cardiac electrophysiology research

Cardiac arrhythmias are a major cause of death and disability. A large number of experimental cell and animal models have been developed to study arrhythmogenic diseases. These models have provided important insights into the underlying arrhythmia mechanisms and translational options for their therapeutic management. This position paper from the ESC Working Group on Cardiac Cellular Electrophysiology provides an overview of (i) currently available in vitro, ex vivo, and in vivo electrophysiological research methodologies, (ii) the most commonly used experimental (cellular and animal) models for cardiac arrhythmias including relevant species differences, (iii) the use of human cardiac tissue, induced pluripotent stem cell (hiPSC)-derived and in silico models to study cardiac arrhythmias, and (iv) the availability, relevance, limitations, and opportunities of these cellular and animal models to recapitulate specific acquired and inherited arrhythmogenic diseases, including atrial fibrillation, heart failure, cardiomyopathy, myocarditis, sinus node, and conduction disorders and channelopathies. By promoting a better understanding of these models and their limitations, this position paper aims to improve the quality of basic research in cardiac electrophysiology, with the ultimate goal to facilitate the clinical translation and application of basic electrophysiological research findings on arrhythmia mechanisms and therapies.

Computational models of atrial fibrillation: achievements, challenges, and perspectives for improving clinical care

Despite significant advances in its detection, understanding and management, atrial fibrillation (AF) remains a highly prevalent cardiac arrhythmia with a major impact on morbidity and mortality of millions of patients. AF results from complex, dynamic interactions between risk factors and comorbidities that induce diverse atrial remodelling processes. Atrial remodelling increases AF vulnerability and persistence, while promoting disease progression. The variability in presentation and wide range of mechanisms involved in initiation, maintenance and progression of AF, as well as its associated adverse outcomes, make the early identification of causal factors modifiable with therapeutic interventions challenging, likely contributing to suboptimal efficacy of current AF management. Computational modelling facilitates the multilevel integration of multiple datasets and offers new opportunities for mechanistic understanding, risk prediction and personalized therapy. Mathematical simulations of cardiac electrophysiology have been around for 60 years and are being increasingly used to improve our understanding of AF mechanisms and guide AF therapy. This narrative review focuses on the emerging and future applications of computational modelling in AF management. We summarize clinical challenges that may benefit from computational modelling, provide an overview of the different in silico approaches that are available together with their notable achievements, and discuss the major limitations that hinder the routine clinical application of these approaches. Finally, future perspectives are addressed. With the rapid progress in electronic technologies including computing, clinical applications of computational modelling are advancing rapidly. We expect that their application will progressively increase in prominence, especially if their added value can be demonstrated in clinical trials.

Opportunities and challenges for the computational interpretation of rare variation in clinically important genes

Genome sequencing is enabling precision medicine-tailoring treatment to the unique constellation of variants in an individual's genome. The impact of recurrent pathogenic variants is often understood, however there is a long tail of rare genetic variants that are uncharacterized. The problem of uncharacterized rare variation is especially acute when it occurs in genes of known clinical importance with functionally consequential variants and associated mechanisms. Variants of uncertain significance (VUSs) in these genes are discovered at a rate that outpaces current ability to classify them with databases of previous cases, experimental evaluation, and computational predictors. Clinicians are thus left without guidance about the significance of variants that may have actionable consequences. Computational prediction of the impact of rare genetic variation is increasingly becoming an important capability. In this paper, we review the technical and ethical challenges of interpreting the function of rare variants in two settings: inborn errors of metabolism in newborns and pharmacogenomics. We propose a framework for a genomic learning healthcare system with an initial focus on early-onset treatable disease in newborns and actionable pharmacogenomics. We argue that (1) a genomic learning healthcare system must allow for continuous collection and assessment of rare variants, (2) emerging machine learning methods will enable algorithms to predict the clinical impact of rare variants on protein function, and (3) ethical considerations must inform the construction and deployment of all rare-variation triage strategies, particularly with respect to health disparities arising from unbalanced ancestry representation.

Machine Learning in Arrhythmia and Electrophysiology

Machine learning (ML), a branch of artificial intelligence, where machines learn from big data, is at the crest of a technological wave of change sweeping society. Cardiovascular medicine is at the forefront of many ML applications, and there is a significant effort to bring them into mainstream clinical practice. In the field of cardiac electrophysiology, ML applications have also seen a rapid growth and popularity, particularly the use of ML in the automatic interpretation of ECGs, which has been extensively covered in the literature. Much lesser known are the other aspects of ML application in cardiac electrophysiology and arrhythmias, such as those in basic science research on arrhythmia mechanisms, both experimental and computational; in the development of better techniques for mapping of cardiac electrical function; and in translational research related to arrhythmia management. In the current review, we examine comprehensively such ML applications as they match the scope of this journal. The current review is organized in 3 parts. The first provides an overview of general ML principles and methodologies that will afford readers of the necessary information on the subject, serving as the foundation for inviting further ML applications in arrhythmia research. The basic information we provide can serve as a guide on how one might design and conduct an ML study. The second part is a review of arrhythmia and electrophysiology studies in which ML has been utilized, highlighting the broad potential of ML approaches. For each subject, we outline comprehensively the general topics, while reviewing some of the research advances utilizing ML under the subject. Finally, we discuss the main challenges and the perspectives for ML-driven cardiac electrophysiology and arrhythmia research.

Multiple arrhythmic and cardiomyopathic phenotypes associated with an SCN5A A735E mutation

BACKGROUND

SCN5A mutations are associated with multiple arrhythmic and cardiomyopathic phenotypes including Brugada syndrome (BrS), sinus node dysfunction (SND), atrioventricular block, supraventricular tachyarrhythmias (SVTs), long QT syndrome (LQTS), dilated cardiomyopathy and left ventricular noncompaction. Several single SCN5A mutations have been associated with overlap of some of these phenotypes, but never with overlap of all the phenotypes.

OBJECTIVE

We encountered two pedigrees with multiple arrhythmic phenotypes with or without cardiomyopathic phenotypes, and sought to identify a responsible mutation and reveal its functional abnormalities.

METHODS

Target panel sequencing of 72 genes, including inherited arrhythmia syndromes- and cardiomyopathies-related genes, was employed in two probands. Cascade screening was performed by Saner sequencing. Wild-type or identified mutant SCN5A were expressed in tsA201 cells, and whole-cell sodium currents (I_Na) were recorded using patch-clamp techniques.

RESULTS

We identified an SCN5A A735E mutation in these probands, but did not identify any other mutations. All eight mutation carriers exhibited at least one of the arrhythmic phenotypes. Two patients exhibited multiple arrhythmic phenotypes: one (15-year-old girl) exhibited BrS, SND, and exercise and epinephrine-induced QT prolongation, the other (4-year-old boy) exhibited BrS, SND, and SVTs. Another one (30-year-old male) exhibited all arrhythmic and cardiomyopathic phenotypes, except for LQTS. One male suddenly died at age 22. Functional analysis revealed that the mutant did not produce functional I_Na.

CONCLUSIONS

A non-functional SCN5A A735E mutation could be associated with multiple arrhythmic and cardiomyopathic phenotypes, although there remains a possibility that other unidentified factors may be involved in the phenotypic variability of the mutation carriers.

Predicting functional effects of missense variants in voltage-gated sodium and calcium channels

Malfunctions of voltage-gated sodium and calcium channels (encoded by SCNxA and CACNA1x family genes, respectively) have been associated with severe neurologic, psychiatric, cardiac, and other diseases. Altered channel activity is frequently grouped into gain or loss of ion channel function (GOF or LOF, respectively) that often corresponds not only to clinical disease manifestations but also to differences in drug response. Experimental studies of channel function are therefore important, but laborious and usually focus only on a few variants at a time. On the basis of known gene-disease mechanisms of 19 different diseases, we inferred LOF (n = 518) and GOF (n = 309) likely pathogenic variants from the disease phenotypes of variant carriers. By training a machine learning model on sequence- and structure-based features, we predicted LOF or GOF effects [area under the receiver operating characteristics curve (ROC) = 0.85] of likely pathogenic missense variants. Our LOF versus GOF prediction corresponded to molecular LOF versus GOF effects for 87 functionally tested variants in SCN1/2/8A and CACNA1I (ROC = 0.73) and was validated in exome-wide data from 21,703 cases and 128,957 controls. We showed respective regional clustering of inferred LOF and GOF nucleotide variants across the alignment of the entire gene family, suggesting shared pathomechanisms in the SCNxA/CACNA1x family genes.

Accounting for variability in ion current recordings using a mathematical model of artefacts in voltage-clamp experiments

Mathematical models of ion channels, which constitute indispensable components of action potential models, are commonly constructed by fitting to whole-cell patch-clamp data. In a previous study, we fitted cell-specific models to hERG1a (Kv11.1) recordings simultaneously measured using an automated high-throughput system, and studied cell-cell variability by inspecting the resulting model parameters. However, the origin of the observed variability was not identified. Here, we study the source of variability by constructing a model that describes not just ion current dynamics, but the entire voltage-clamp experiment. The experimental artefact components of the model include: series resistance, membrane and pipette capacitance, voltage offsets, imperfect compensations made by the amplifier for these phenomena, and leak current. In this model, variability in the observations can be explained by either cell properties, measurement artefacts, or both. Remarkably, by assuming that variability arises exclusively from measurement artefacts, it is possible to explain a larger amount of the observed variability than when assuming cell-specific ion current kinetics. This assumption also leads to a smaller number of model parameters. This result suggests that most of the observed variability in patch-clamp data measured under the same conditions is caused by experimental artefacts, and hence can be compensated for in post-processing by using our model for the patch-clamp experiment. This study has implications for the question of the extent to which cell-cell variability in ion channel kinetics exists, and opens up routes for better correction of artefacts in patch-clamp data. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.

Biophysical defects of an SCN5A V1667I mutation associated with epinephrine-induced marked QT prolongation

BACKGROUND

The epinephrine infusion test (EIT) typically induces marked QT prolongation in LQT1, but not LQT3, while the efficacy of β-blocker therapy is established in LQT1, but not LQT3. We encountered an LQT3 family, with an SCN5A V1667I mutation, that exhibited epinephrine-induced marked QT prolongation.

METHODS

Wild-type (WT) or V1667I-SCN5A was transiently expressed into tsA-201 cells, and whole-cell sodium currents (I_Na ) were recorded using patch-clamp techniques. To mimic the effects of epinephrine, I_Na was recorded after the application of protein kinase A (PKA) activator, 8-CPT-cAMP (200 μM), for 10 minutes.

RESULTS

The peak density of V1667I-I_Na was significantly larger than WT-I_Na (WT: 469 ± 48 pA/pF, n = 20; V1667I: 690 ± 62 pA/pF, n = 19, P < .01). The steady-state activation (SSA) and fast inactivation rate of V1667I-I_Na were comparable to WT-I_Na . V1667I-I_Na displayed a significant depolarizing shift in steady-state inactivation (SSI) in comparison to WT-I_Na (V_1/2 -WT: -88.1 ± 0.8 mV, n = 17; V1667I: -82.5 ± 1.1 mV, n = 17, P < .01), which increases window currents. Tetrodotoxin (30 μM)-sensitive persistent V1667I-I_Na was comparable to WT-I_Na . However, the ramp pulse protocol (RPP) displayed an increased hump in V1667I-I_Na in comparison to WT-I_Na . Although 8-CPT-cAMP shifted SSA to hyperpolarizing potentials in WT-I_Na and V1667I-I_Na to the same extent, it shifted SSI to hyperpolarizing potentials much less in V1667I-I_Na than in WT-I_Na (V_1/2 -WT: -92.7 ± 1.3 mV, n = 6; V1667I: -85.3 ± 1.6 mV, n = 6, P < .01). Concordantly, the RPP displayed an increased hump in V1667I-I_Na , but not in WT-I_Na .

CONCLUSIONS

We demonstrated an increase of V1667I-I_Na by PKA activation, which may provide a rationale for the efficacy of β-blocker therapy in some cases of LQT3.

Enhanced closed-state inactivation of mutant cardiac sodium channels (SCN5A N1541D and R1632C) through different mechanisms

BACKGROUND

SCN5A variants can be associated with overlapping phenotypes such as Brugada syndrome (BrS), sinus node dysfunction and supraventricular tachyarrhythmias. Our genetic screening of SCN5A in 65 consecutive BrS probands revealed two patients with overlapping phenotypes: one carried an SCN5A R1632C (in domain IV-segment 4), which we have previously reported, the other carried a novel SCN5A N1541D (in domain IV-segment 1).

OBJECTIVE

We sought to reveal whether or not these variants are associated with the same biophysical defects.

METHODS

Wild-type (WT) or mutant SCN5A was expressed in tsA201-cells, and whole-cell sodium currents (hNa_v1.5/I_Na) were recorded using patch-clamp techniques.

RESULTS

The N1541D-I_Na density, when assessed from a holding potential of -150 mV, was not different from WT-I_Na as with R1632C-I_Na, indicating that SCN5A N1541D did not cause trafficking defects. The steady-state inactivation curve of N1541D-I_Na was markedly shifted to hyperpolarizing potentials in comparison to WT-I_Na (V_1/2-WT: -82.3 ± 0.9 mV, n = 15; N1541D: -108.8 ± 1.6 mV, n = 26, P < .01) as with R1632C-I_Na. Closed-state inactivation (CSI) was evaluated using prepulses of -90 mV for 1460 ms. Residual N1541D-I_Na and R1632C-I_Na were markedly reduced in comparison to WT-I_Na (WT: 63.8 ± 4.6%, n = 18; N1541D: 15.1 ± 2.3%, n = 19, P < .01 vs WT; R1632C: 5.3 ± 0.5%, n = 15, P < .01 vs WT). Entry into CSI of N1541D-I_Na was markedly accelerated, and that of R1632C-I_Na was weakly accelerated in comparison to WT-I_Na (tau-WT: 65.8 ± 7.4 ms, n = 18; N1541D: 13.7 ± 1.1 ms, n = 19, P < .01 vs WT; R1632C: 39.5 ± 2.9 ms, n = 15, P < .01 vs WT and N1541D). Although N1541D-I_Na recovered from closed-state fast inactivation at the same rate as WT-I_Na, R1632C-I_Na recovered very slowly (tau-WT: 1.90 ± 0.16 ms, n = 10; N1541D: 1.72 ± 0.12 ms, n = 10, P = .41 vs WT; R1632C: 53.0 ± 2.5 ms, n = 14, P < .01 vs WT and N1541D).

CONCLUSIONS

Both N1541D-I_Na and R1632C-I_Na exhibited marked enhancement of CSI, but through different mechanisms. The data provided a novel understanding of the mechanisms of CSI of I_Na. Clinically, the enhanced CSI of N1541D-I_Na leads to a severe loss-of-function of I_Na at voltages near the physiological resting membrane potential (~-90 mV) of cardiac myocytes; this can be attributable to the patient's phenotypic manifestations.

Protein structure aids predicting functional perturbation of missense variants in SCN5A and KCNQ1

Rare variants in the cardiac potassium channel K_V7.1 (KCNQ1) and sodium channel Na_V1.5 (SCN5A) are implicated in genetic disorders of heart rhythm, including congenital long QT and Brugada syndromes (LQTS, BrS), but also occur in reference populations. We previously reported two sets of Na_V1.5 (n = 356) and K_V7.1 (n = 144) variants with in vitro characterized channel currents gathered from the literature. Here we investigated the ability to predict commonly reported Na_V1.5 and K_V7.1 variant functional perturbations by leveraging diverse features including variant classifiers PROVEAN, PolyPhen-2, and SIFT; evolutionary rate and BLAST position specific scoring matrices (PSSM); and structure-based features including “functional densities” which is a measure of the density of pathogenic variants near the residue of interest. Structure-based functional densities were the most significant features for predicting Na_V1.5 peak current (adj. R² = 0.27) and K_V7.1 + KCNE1 half-maximal voltage of activation (adj. R² = 0.29). Additionally, use of structure-based functional density values improves loss-of-function classification of SCN5A variants with an ROC-AUC of 0.78 compared with other predictive classifiers (AUC = 0.69; two-sided DeLong test p = .01). These results suggest structural data can inform predictions of the effect of uncharacterized SCN5A and KCNQ1 variants to provide a deeper understanding of their burden on carriers.