Predicting the Functional Impact of KCNQ1 Variants of Unknown Significance

Distances from ligands as main predictive features for pathogenicity and functional effect of variants in NMDA receptors

Genetic variants in genes GRIN1 , GRIN2A , GRIN2B , and GRIN2D , which encode subunits of the N-methyl-D-aspartate receptor (NMDAR), have been associated with severe and heterogeneous neurologic diseases. Missense variants in these genes can result in gain or loss of the NMDAR function, requiring opposite therapeutic treatments. Computational methods that predict pathogenicity and molecular functional effects are therefore crucial for accurate diagnosis and therapeutic applications. We assembled missense variants: 201 from patients, 631 from general population, and 159 characterized by electrophysiological readouts showing whether they can enhance or reduce the receptor function. This includes new functional data from 47 variants reported here, for the first time. We found that pathogenic/benign variants and variants that increase/decrease the channel function were distributed unevenly on the protein structure, with spatial proximity to ligands bound to the agonist and antagonist binding sites being key predictive features. Leveraging distances from ligands, we developed two independent machine learning-based predictors for NMDAR missense variants: a pathogenicity predictor which outperforms currently available predictors (AUC=0.945, MCC=0.726), and the first binary predictor of molecular function (increase or decrease) (AUC=0.809, MCC=0.523). Using these, we reclassified variants of uncertain significance in the ClinVar database and refined a previous genome-informed epidemiological model to estimate the birth incidence of molecular mechanism-defined GRIN disorders. Our findings demonstrate that distance from ligands is an important feature in NMDARs that can enhance variant pathogenicity prediction and enable functional prediction. Further studies with larger numbers of phenotypically and functionally characterized variants will enhance the potential clinical utility of this method.

Applying Artificial Intelligence for Phenotyping of Inherited Arrhythmia Syndromes

Inherited arrhythmia disorders account for a significant proportion of sudden cardiac death, particularly among young individuals. Recent advances in our understanding of these syndromes have improved patient diagnosis and care, yet certain clinical gaps remain, particularly within case ascertainment, access to genetic testing, and risk stratification. Artificial intelligence (AI), specifically machine learning and its subset deep learning, present promising solutions to these challenges. The capacity of AI to process vast amounts of patient data and identify disease patterns differentiates them from traditional methods, which are time- and resource-intensive. To date, AI models have shown immense potential in condition detection (including asymptomatic/concealed disease) and genotype and phenotype identification, exceeding expert cardiologists in these tasks. Additionally, they have exhibited applicability for general population screening, improving case ascertainment in a set of conditions that are often asymptomatic such as left ventricular dysfunction. Third, models have shown the ability to improve testing protocols; through model identification of disease and genotype, specific clinical testing (eg, drug challenges or further diagnostic imaging) can be avoided, reducing health care expenses, speeding diagnosis, and possibly allowing for more incremental or targeted genetic testing approaches. These significant benefits warrant continued investigation of AI, particularly regarding the development and implementation of clinically applicable screening tools. In this review we summarize key developments in AI, including studies in long QT syndrome, Brugada syndrome, hypertrophic cardiomyopathy, and arrhythmogenic cardiomyopathies, and provide direction for effective future AI implementation in clinical practice.

Computational Study on Effect of KCNQ1 P535T Mutation in a Cardiac Ventricular Tissue

Heart diseases such as arrhythmia are the main causes of sudden death. Arrhythmias are typically caused by mutations in specific genes, damage in the cardiac tissue, or due to some chemical exposure. Arrhythmias caused due to mutation is called inherited arrhythmia. Induced arrhythmias are caused due to tissue damage or chemical exposure. Mutations in genes that encode ion channels of the cardiac cells usually result in (dysfunction) improper functioning of the channel. Improper functioning of the ion channel may lead to major changes in the action potential (AP) of the cardiac cells. This further leads to distorted electrical activity of the heart. Distorted electrical activity will affect the ECG that results in arrhythmia. KCNQ1 P535T mutation is one such gene mutation that encodes the potassium ion channel (KV7.1) of the cardiac ventricular tissue. Its clinical significance is not known. This study aims to perform a simulation study on P535T mutation in the KCNQ1 gene that encodes the potassium ion channel KV7.1 in the ventricular tissue grid. The effect of P535T mutation on transmural tissue grids for three genotypes (wild type, heterozygous, and homozygous) of cells are studied and the generated pseudo-ECGs are compared. Results show the delayed repolarization in the cells of ventricular tissue grid. Slower propagation of action potential in the transmural tissue grid is observed in the mutated (heterozygous and homozygous) genotypes. Longer QT interval is also observed in the pseudo-ECG of heterozygous and homozygous genotype tissue grids. From the pseudo-ECGs, it is observed that KCNQ1 P535T mutation leads to Long QT Syndrome (LQTS) which may result in life-threatening arrhythmias, such as Torsade de Pointes (TdP), Jervell and Lange-Nielsen syndrome (JLNS), and Romano-Ward syndrome (RWS).

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.

Mutation-Specific Differences in Kv7.1 (KCNQ1) and Kv11.1 (KCNH2) Channel Dysfunction and Long QT Syndrome Phenotypes

The electrocardiogram (ECG) empowered clinician scientists to measure the electrical activity of the heart noninvasively to identify arrhythmias and heart disease. Shortly after the standardization of the 12-lead ECG for the diagnosis of heart disease, several families with autosomal recessive (Jervell and Lange-Nielsen Syndrome) and dominant (Romano-Ward Syndrome) forms of long QT syndrome (LQTS) were identified. An abnormally long heart rate-corrected QT-interval was established as a biomarker for the risk of sudden cardiac death. Since then, the International LQTS Registry was established; a phenotypic scoring system to identify LQTS patients was developed; the major genes that associate with typical forms of LQTS were identified; and guidelines for the successful management of patients advanced. In this review, we discuss the molecular and cellular mechanisms for LQTS associated with missense variants in KCNQ1 (LQT1) and KCNH2 (LQT2). We move beyond the "benign" to a "pathogenic" binary classification scheme for different KCNQ1 and KCNH2 missense variants and discuss gene- and mutation-specific differences in K+ channel dysfunction, which can predispose people to distinct clinical phenotypes (e.g., concealed, pleiotropic, severe, etc.). We conclude by discussing the emerging computational structural modeling strategies that will distinguish between dysfunctional subtypes of KCNQ1 and KCNH2 variants, with the goal of realizing a layered precision medicine approach focused on individuals.

Structural and electrophysiological basis for the modulation of KCNQ1 channel currents by ML277

The KCNQ1 ion channel plays critical physiological roles in electrical excitability and K⁺ recycling in organs including the heart, brain, and gut. Loss of function is relatively common and can cause sudden arrhythmic death, sudden infant death, epilepsy and deafness. Here, we report cryogenic electron microscopic (cryo-EM) structures of Xenopus KCNQ1 bound to Ca²⁺/Calmodulin, with and without the KCNQ1 channel activator, ML277. A single binding site for ML277 was identified, localized to a pocket lined by the S4-S5 linker, S5 and S6 helices of two separate subunits. Several pocket residues are not conserved in other KCNQ isoforms, explaining specificity. MD simulations and point mutations support this binding location for ML277 in open and closed channels and reveal that prevention of inactivation is an important component of the activator effect. Our work provides direction for therapeutic intervention targeting KCNQ1 loss of function pathologies including long QT interval syndrome and seizures.

KCNQ1 channels are active in heart, brain and gut. Functional loss causes epilepsy and sudden arrhythmic death. Here, authors describe a key activator drug binding site, explaining isoform and drug selectivity, and point the way for new drug design.

Predicting the functional effects of voltage-gated potassium channel missense variants with multi-task learning

BACKGROUND

Variants in genes encoding voltage-gated potassium channels are associated with a broad spectrum of neurological diseases including epilepsy, ataxia, and intellectual disability. Knowledge of the resulting functional changes, characterized as overall ion channel gain- or loss-of-function, is essential to guide clinical management including precision medicine therapies. However, for an increasing number of variants, little to no experimental data is available. New tools are needed to evaluate variant functional effects.

METHODS

We catalogued a comprehensive dataset of 959 functional experiments across 19 voltage-gated potassium channels, leveraging data from 782 unique disease-associated and synthetic variants. We used these data to train a taxonomy-based multi-task learning support vector machine (MTL-SVM), and compared performance to several baseline methods.

FINDINGS

MTL-SVM maintains channel family structure during model training, improving overall predictive performance (mean balanced accuracy 0·718 ± 0·041, AU-ROC 0·761 ± 0·063) over baseline (mean balanced accuracy 0·620 ± 0·045, AU-ROC 0·711 ± 0·022). We can obtain meaningful predictions even for channels with few known variants (KCNC1, KCNQ5).

INTERPRETATION

Our model enables functional variant prediction for voltage-gated potassium channels. It may assist in tailoring current and future precision therapies for the increasing number of patients with ion channel disorders.

FUNDING

This work was supported by intramural funding of the Medical Faculty, University of Tuebingen (PATE F.1315137.1), the Federal Ministry for Education and Research (Treat-ION, 01GM1907A/B/G/H) and the German Research Foundation (FOR-2715, Le1030/16-2, He8155/1-2).

Towards generalizable predictions for G protein-coupled receptor variant expression

Missense mutations that compromise the plasma membrane expression (PME) of integral membrane proteins are the root cause of numerous genetic diseases. Differentiation of this class of mutations from those that specifically modify the activity of the folded protein has proven useful for the development and targeting of precision therapeutics. Nevertheless, it remains challenging to predict the effects of mutations on the stability and/ or expression of membrane proteins. In this work, we utilize deep mutational scanning data to train a series of artificial neural networks to predict the PME of transmembrane domain variants of G protein-coupled receptors from structural and/ or evolutionary features. We show that our best-performing network, which we term the PME predictor, can recapitulate mutagenic trends within rhodopsin and can differentiate pathogenic transmembrane domain variants that cause it to misfold from those that compromise its signaling. This network also generates statistically significant predictions for the relative PME of transmembrane domain variants for another class A G protein-coupled receptor (β₂ adrenergic receptor) but not for an unrelated voltage-gated potassium channel (KCNQ1). Notably, our analyses of these networks suggest structural features alone are generally sufficient to recapitulate the observed mutagenic trends. Moreover, our findings imply that networks trained in this manner may be generalizable to proteins that share a common fold. Implications of our findings for the design of mechanistically specific genetic predictors are discussed.

Integration of Protein Structure and Population-Scale DNA Sequence Data for Disease Gene Discovery and Variant Interpretation

The experimental and computational techniques for capturing information about protein structures and genetic variation within the human genome have advanced dramatically in the past 20 years, generating extensive new data resources. In this review, we discuss these advances, along with new approaches for determining the impact a genetic variant has on protein function. We focus on the potential of new methods that integrate human genetic variation into protein structures to discover relationships to disease, including the discovery of mutational hotspots in cancer-related proteins, the localization of protein-altering variants within protein regions for common complex diseases, and the assessment of variants of unknown significance for Mendelian traits. We expect that approaches that integrate these data sources will play increasingly important roles in disease gene discovery and variant interpretation. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Congenital Long QT Syndrome

Congenital long QT syndrome (LQTS) encompasses a group of heritable conditions that are associated with cardiac repolarization dysfunction. Since its initial description in 1957, our understanding of LQTS has increased dramatically. The prevalence of LQTS is estimated to be ∼1:2,000, with a slight female predominance. The diagnosis of LQTS is based on clinical, electrocardiogram, and genetic factors. Risk stratification of patients with LQTS aims to identify those who are at increased risk of cardiac arrest or sudden cardiac death. Factors including age, sex, QTc interval, and genetic background all contribute to current risk stratification paradigms. The management of LQTS involves conservative measures such as the avoidance of QT-prolonging drugs, pharmacologic measures with nonselective β-blockers, and interventional approaches such as device therapy or left cardiac sympathetic denervation. In general, most forms of exercise are considered safe in adequately treated patients, and implantable cardioverter-defibrillator therapy is reserved for those at the highest risk. This review summarizes our current understanding of LQTS and provides clinicians with a practical approach to diagnosis and management.

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.

Mutation location and IKs regulation in the arrhythmic risk of long QT syndrome type 1: the importance of the KCNQ1 S6 region

AIMS

Mutation type, location, dominant-negative IKs reduction, and possibly loss of cyclic adenosine monophosphate (cAMP)-dependent IKs stimulation via protein kinase A (PKA) influence the clinical severity of long QT syndrome type 1 (LQT1). Given the malignancy of KCNQ1-p.A341V, we assessed whether mutations neighbouring p.A341V in the S6 channel segment could also increase arrhythmic risk.

METHODS AND RESULTS

Clinical and genetic data were obtained from 1316 LQT1 patients [450 families, 166 unique KCNQ1 mutations, including 277 p.A341V-positive subjects, 139 patients with p.A341-neighbouring mutations (91 missense, 48 non-missense), and 900 other LQT1 subjects]. A first cardiac event represented the primary endpoint. S6 segment missense variant characteristics, particularly cAMP stimulation responses, were analysed by cellular electrophysiology. p.A341-neighbouring mutation carriers had a QTc shorter than p.A341V carriers (477 ± 33 vs. 490 ± 44 ms) but longer than the remaining LQT1 patient population (467 ± 41 ms) (P < 0.05 for both). Similarly, the frequency of symptomatic subjects in the p.A341-neighbouring subgroup was intermediate between the other two groups (43% vs. 73% vs. 20%; P < 0.001). These differences in clinical severity can be explained, for p.A341V vs. p.A341-neighbouring mutations, by the p.A341V-specific impairment of IKs regulation. The differences between the p.A341-neighbouring subgroup and the rest of LQT1 mutations may be explained by the functional importance of the S6 segment for channel activation.

CONCLUSION

KCNQ1 S6 segment mutations surrounding p.A341 increase arrhythmic risk. p.A341V-specific loss of PKA-dependent IKs enhancement correlates with its phenotypic severity. Cellular studies providing further insights into IKs-channel regulation and knowledge of structure-function relationships could improve risk stratification. These findings impact on clinical management.

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.

Compound Heterozygous KCNQ1 Mutations Causing Recessive Romano-Ward Syndrome: Functional Characterization by Mutant Co-expression

Next Generation Sequencing has identified many KCNQ1 genetic variants associated with type 1 long QT or Romano-Ward syndrome, most frequently inherited in an autosomal dominant fashion, although recessive forms have been reported. Particularly in the case of missense variants, functional studies of mutants are of aid to establish variant pathogenicity and to understand the mechanistic basis of disease. Two compound heterozygous KCNQ1 mutations (p.A300T and p.P535T) were previously found in a child who suffered sudden death. To provide further insight into the clinical significance and basis for pathogenicity of these variants, different combinations of wildtype, A300T and P535T alleles were co-expressed with the accessory β-subunit minK in HEK293 cells, to analyze colocalization with the plasma membrane and some biophysical phenotypes of homo and heterotetrameric channels using the patch-clamp technique. A300T homotetrameric channels showed left-shifted activation V_1/2 as previously observed in Xenopus oocytes, decreased maximum conductance density, slow rise-time_300ms, and a characteristic use-dependent response. A300T slow rise-time_300ms and use-dependent response behaved as dominant biophysical traits for all allele combinations. The P535T variant significantly decreased maximum conductance density and Kv7.1-minK-plasma membrane colocalization. P535T/A300T heterotetrameric channels showed decreased colocalization with plasma membrane, slow rise-time_300ms and the A300T characteristic use-dependent response. While A300T left shifted activation voltage dependence behaved as a recessive trait when co-expressed with WT alleles, it was dominant when co-expressed with P535T alleles. Conclusions: The combination of P535T/A300T channel biophysical properties is compatible with recessive Romano Ward syndrome. Further analysis of other biophysical traits may identify other mechanisms involved in the pathophysiology of this disease.

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.

High-Throughput Reclassification of SCN5A Variants

Partial or complete loss-of-function variants in SCN5A are the most common genetic cause of the arrhythmia disorder Brugada syndrome (BrS1). However, the pathogenicity of SCN5A variants is often unknown or disputed; 80% of the 1,390 SCN5A missense variants observed in at least one individual to date are variants of uncertain significance (VUSs). The designation of VUS is a barrier to the use of sequence data in clinical care. We selected 83 variants: 10 previously studied control variants, 10 suspected benign variants, and 63 suspected Brugada syndrome-associated variants, selected on the basis of their frequency in the general population and in individuals with Brugada syndrome. We used high-throughput automated patch clamping to study the function of the 83 variants, with the goal of reclassifying variants with functional data. The ten previously studied controls had functional properties concordant with published manual patch clamp data. All 10 suspected benign variants had wild-type-like function. 22 suspected BrS variants had loss of channel function (<10% normalized peak current) and 22 variants had partial loss of function (10%-50% normalized peak current). The previously unstudied variants were initially classified as likely benign (n = 2), likely pathogenic (n = 10), or VUSs (n = 61). After the patch clamp studies, 16 variants were benign/likely benign, 45 were pathogenic/likely pathogenic, and only 12 were still VUSs. Structural modeling identified likely mechanisms for loss of function including altered thermostability and disruptions to alpha helices, disulfide bonds, or the permeation pore. High-throughput patch clamping enabled reclassification of the majority of tested VUSs in SCN5A.

A Bayesian method to estimate variant-induced disease penetrance

A major challenge emerging in genomic medicine is how to assess best disease risk from rare or novel variants found in disease-related genes. The expanding volume of data generated by very large phenotyping efforts coupled to DNA sequence data presents an opportunity to reinterpret genetic liability of disease risk. Here we propose a framework to estimate the probability of disease given the presence of a genetic variant conditioned on features of that variant. We refer to this as the penetrance, the fraction of all variant heterozygotes that will present with disease. We demonstrate this methodology using a well-established disease-gene pair, the cardiac sodium channel gene SCN5A and the heart arrhythmia Brugada syndrome. From a review of 756 publications, we developed a pattern mixture algorithm, based on a Bayesian Beta-Binomial model, to generate SCN5A penetrance probabilities for the Brugada syndrome conditioned on variant-specific attributes. These probabilities are determined from variant-specific features (e.g. function, structural context, and sequence conservation) and from observations of affected and unaffected heterozygotes. Variant functional perturbation and structural context prove most predictive of Brugada syndrome penetrance.

Structures Illuminate Cardiac Ion Channel Functions in Health and in Long QT Syndrome

The cardiac action potential is critical to the production of a synchronized heartbeat. This electrical impulse is governed by the intricate activity of cardiac ion channels, among them the cardiac voltage-gated potassium (Kv) channels KCNQ1 and hERG as well as the voltage-gated sodium (Nav) channel encoded by SCN5A. Each channel performs a highly distinct function, despite sharing a common topology and structural components. These three channels are also the primary proteins mutated in congenital long QT syndrome (LQTS), a genetic condition that predisposes to cardiac arrhythmia and sudden cardiac death due to impaired repolarization of the action potential and has a particular proclivity for reentrant ventricular arrhythmias. Recent cryo-electron microscopy structures of human KCNQ1 and hERG, along with the rat homolog of SCN5A and other mammalian sodium channels, provide atomic-level insight into the structure and function of these proteins that advance our understanding of their distinct functions in the cardiac action potential, as well as the molecular basis of LQTS. In this review, the gating, regulation, LQTS mechanisms, and pharmacological properties of KCNQ1, hERG, and SCN5A are discussed in light of these recent structural findings.

High-Throughput Functional Evaluation of KCNQ1 Decrypts Variants of Unknown Significance

BACKGROUND

The explosive growth in known human gene variation presents enormous challenges to current approaches for variant classification that have implications for diagnosis and treatment of many genetic diseases. For disorders caused by mutations in cardiac ion channels as in congenital arrhythmia syndromes, in vitro electrophysiological evidence has high value in discriminating pathogenic from benign variants, but these data are often lacking because assays are cost, time, and labor intensive.

METHODS

We implemented a strategy for performing high-throughput functional evaluations of ion channel variants that repurposed an automated electrophysiological recording platform developed previously for drug discovery.

RESULTS

We demonstrated the success of this approach by evaluating 78 variants in KCNQ1, a major gene involved in genetic disorders of cardiac arrhythmia susceptibility. We benchmarked our results with traditional electrophysiological approaches and observed a high level of concordance. This strategy also enabled studies of dominant-negative behavior of variants exhibiting severe loss-of-function. Overall, our results provided functional data useful for reclassifying >65% of the studied KCNQ1 variants.

CONCLUSIONS

Our results illustrate an efficient and high-throughput paradigm linking genotype to function for a human cardiac ion channel that will enable data-driven classification of large numbers of variants and create new opportunities for precision medicine.

Upgraded molecular models of the human KCNQ1 potassium channel

The voltage-gated potassium channel KCNQ1 (K_V7.1) assembles with the KCNE1 accessory protein to generate the slow delayed rectifier current, I_KS, which is critical for membrane repolarization as part of the cardiac action potential. Loss-of-function (LOF) mutations in KCNQ1 are the most common cause of congenital long QT syndrome (LQTS), type 1 LQTS, an inherited genetic predisposition to cardiac arrhythmia and sudden cardiac death. A detailed structural understanding of KCNQ1 is needed to elucidate the molecular basis for KCNQ1 LOF in disease and to enable structure-guided design of new anti-arrhythmic drugs. In this work, advanced structural models of human KCNQ1 in the resting/closed and activated/open states were developed by Rosetta homology modeling guided by newly available experimentally-based templates: X. leavis KCNQ1 and various resting voltage sensor structures. Using molecular dynamics (MD) simulations, the capacity of the models to describe experimentally established channel properties including state-dependent voltage sensor gating charge interactions and pore conformations, PIP2 binding sites, and voltage sensor–pore domain interactions were validated. Rosetta energy calculations were applied to assess the utility of each model in interpreting mutation-evoked KCNQ1 dysfunction by predicting the change in protein thermodynamic stability for 50 experimentally characterized KCNQ1 variants with mutations located in the voltage-sensing domain. Energetic destabilization was successfully predicted for folding-defective KCNQ1 LOF mutants whereas wild type-like mutants exhibited no significant energetic frustrations, which supports growing evidence that mutation-induced protein destabilization is an especially common cause of KCNQ1 dysfunction. The new KCNQ1 Rosetta models provide helpful tools in the study of the structural basis for KCNQ1 function and can be used to generate hypotheses to explain KCNQ1 dysfunction.

SCN5A (NaV1.5) Variant Functional Perturbation and Clinical Presentation: Variants of a Certain Significance

BACKGROUND

Accurately predicting the impact of rare nonsynonymous variants on disease risk is an important goal in precision medicine. Variants in the cardiac sodium channel SCN5A (protein Na_V1.5; voltage-dependent cardiac Na+ channel) are associated with multiple arrhythmia disorders, including Brugada syndrome and long QT syndrome. Rare SCN5A variants also occur in ≈1% of unaffected individuals. We hypothesized that in vitro electrophysiological functional parameters explain a statistically significant portion of the variability in disease penetrance.

METHODS

From a comprehensive literature review, we quantified the number of carriers presenting with and without disease for 1712 reported SCN5A variants. For 356 variants, data were also available for 5 Na_V1.5 electrophysiological parameters: peak current, late/persistent current, steady-state V1/2 of activation and inactivation, and recovery from inactivation.

RESULTS

We found that peak and late current significantly associate with Brugada syndrome (P<0.001; ρ=-0.44; Spearman rank test) and long QT syndrome disease penetrance (P<0.001; ρ=0.37). Steady-state V1/2 activation and recovery from inactivation associate significantly with Brugada syndrome and long QT syndrome penetrance, respectively. Continuous estimates of disease penetrance align with the current American College of Medical Genetics classification paradigm.

CONCLUSIONS

Na_V1.5 in vitro electrophysiological parameters are correlated with Brugada syndrome and long QT syndrome disease risk. Our data emphasize the value of in vitro electrophysiological characterization and incorporating counts of affected and unaffected carriers to aid variant classification. This quantitative analysis of the electrophysiological literature should aid the interpretation of Na_V1.5 variant electrophysiological abnormalities and help improve Na_V1.5 variant classification.

Interfaces Between Alpha-helical Integral Membrane Proteins: Characterization, Prediction, and Docking

Protein-protein interaction (PPI) is an essential mechanism by which proteins perform their biological functions. For globular proteins, the molecular characteristics of such interactions have been well analyzed, and many computational tools are available for predicting PPI sites and constructing structural models of the complex. In contrast, little is known about the molecular features of the interaction between integral membrane proteins (IMPs) and few methods exist for constructing structural models of their complexes. Here, we analyze the interfaces from a non-redundant set of complexes of α-helical IMPs whose structures have been determined to a high resolution. We find that the interface is not significantly different from the rest of the surface in terms of average hydrophobicity. However, the interface is significantly better conserved and, on average, inter-subunit contacting residue pairs correlate more strongly than non-contacting pairs, especially in obligate complexes. We also develop a neural network-based method, with an area under the receiver operating characteristic curve of 0.75 and a Pearson correlation coefficient of 0.70, for predicting interface residues and their weighted contact numbers (WCNs). We further show that predicted interface residues and their WCNs can be used as restraints to reconstruct the structure α-helical IMP dimers through docking for fourteen out of a benchmark set of sixteen complexes. The RMSD100 values of the best-docked ligand subunit to its native structure are <2.5 Å for these fourteen cases. The structural analysis conducted in this work provides molecular details about the interface between α-helical IMPs and the WCN restraints represent an efficient means to score α-helical IMP docking candidates.

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.

Genetic Testing for Inherited Cardiac Arrhythmias: Current State-of-the-Art and Future Avenues

The seminal discovery that sequence variation in genes encoding cardiac ion channels was behind the inherited cardiac arrhythmic syndromes has led to major advances in understanding the functional biological mechanisms of cardiomyocyte depolarization and repolarization. The cost and speed with which these genes can now be sequenced have allowed for genetic testing to become a major component of clinical care and have led to important ramifications, yet interpretation of specific variants needs to be performed within the context of the clinical findings in the proband and extended family. As technology continues to advance, the promise of therapeutic manipulation of certain genetic pathways grows ever more real.

Genome Editing of Induced Pluripotent Stem Cells to Decipher Cardiac Channelopathy Variant

BACKGROUND

The long QT syndrome (LQTS) is an arrhythmogenic disorder of QT interval prolongation that predisposes patients to life-threatening ventricular arrhythmias such as Torsades de pointes and sudden cardiac death. Clinical genetic testing has emerged as the standard of care to identify genetic variants in patients suspected of having LQTS. However, these results are often confounded by the discovery of variants of uncertain significance (VUS), for which there is insufficient evidence of pathogenicity.

OBJECTIVES

The purpose of this study was to demonstrate that genome editing of patient-specific induced pluripotent stem cells (iPSCs) can be a valuable approach to delineate the pathogenicity of VUS in cardiac channelopathy.

METHODS

Peripheral blood mononuclear cells were isolated from a carrier with a novel missense variant (T983I) in the KCNH2 (LQT2) gene and an unrelated healthy control subject. iPSCs were generated using an integration-free Sendai virus and differentiated to iPSC-derived cardiomyocytes (CMs).

RESULTS

Whole-cell patch clamp recordings revealed significant prolongation of the action potential duration (APD) and reduced rapidly activating delayed rectifier K+ current (IKr) density in VUS iPSC-CMs compared with healthy control iPSC-CMs. ICA-105574, a potent IKr activator, enhanced IKr magnitude and restored normal action potential duration in VUS iPSC-CMs. Notably, VUS iPSC-CMs exhibited greater propensity to proarrhythmia than healthy control cells in response to high-risk torsadogenic drugs (dofetilide, ibutilide, and azimilide), suggesting a compromised repolarization reserve. Finally, the selective correction of the causal variant in iPSC-CMs using CRISPR/Cas9 gene editing (isogenic control) normalized the aberrant cellular phenotype, whereas the introduction of the homozygous variant in healthy control cells recapitulated hallmark features of the LQTS disorder.

CONCLUSIONS

The results suggest that the KCNH2T983I VUS may be classified as potentially pathogenic.

Mechanisms of KCNQ1 channel dysfunction in long QT syndrome involving voltage sensor domain mutations

Mutations that induce loss of function (LOF) or dysfunction of the human KCNQ1 channel are responsible for susceptibility to a life-threatening heart rhythm disorder, the congenital long QT syndrome (LQTS). Hundreds of KCNQ1 mutations have been identified, but the molecular mechanisms responsible for impaired function are poorly understood. We investigated the impact of 51 KCNQ1 variants with mutations located within the voltage sensor domain (VSD), with an emphasis on elucidating effects on cell surface expression, protein folding, and structure. For each variant, the efficiency of trafficking to the plasma membrane, the impact of proteasome inhibition, and protein stability were assayed. The results of these experiments combined with channel functional data provided the basis for classifying each mutation into one of six mechanistic categories, highlighting heterogeneity in the mechanisms resulting in channel dysfunction or LOF. More than half of the KCNQ1 LOF mutations examined were seen to destabilize the structure of the VSD, generally accompanied by mistrafficking and degradation by the proteasome, an observation that underscores the growing appreciation that mutation-induced destabilization of membrane proteins may be a common human disease mechanism. Finally, we observed that five of the folding-defective LQTS mutant sites are located in the VSD S0 helix, where they interact with a number of other LOF mutation sites in other segments of the VSD. These observations reveal a critical role for the S0 helix as a central scaffold to help organize and stabilize the KCNQ1 VSD and, most likely, the corresponding domain of many other ion channels.