A standardised hERG phenotyping pipeline to evaluate <i>KCNH2</i> genetic variant pathogenicity

Oliveira-Mendes, Bárbara Bruna Ribeiro; Féliciangéli, Sylvain; Ménard, Mélissa; Chatelain, Franck C.; Alameh, Malak; Montnach, Jérôme; Nicolas, Sébastien; Ollivier, Béatrice; Barc, Julien; Baró, Isabelle; Schott, Jean‐Jacques; Probst, Vincent; Kyndt, Florence; Denjoy, Isabelle; Lesage, Florian; Loussouarn, Gildas; Waard, Michel De

doi:10.1002/ctm2.609

Cited by 8 publications

(19 citation statements)

References 47 publications

(53 reference statements)

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“…Fewer ML-based studies aiming to link KCNH2 variants to LQTS have been published. In a recent study, nearly 15 variants from the Kv11.1 channel PAS, CNBD, or transmembrane domains were classified by their potential to influence trafficking [OliveiraMendes2021]. In addition to their in vivo approaches, the study incorporated structural information from the Wang et al .…”

Section: Discussionmentioning

confidence: 99%

Prediction of Kv11.1 potassium channel PAS-domain variants trafficking via machine learning

Immadisetty¹,

Kekenes‐Huskey²

2021

Preprint

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1AbstractLong QT syndrome (LQTS) is a cardiac condition characterized by a prolonged QT-interval. This prolongation can contribute to fatal arrhythmias despite otherwise normal metrics of cardiac function. Hence, genetic screening of individuals remains a standard for initial identification of individuals susceptible to LQTS. Several genes, including KCNH2 that encodes for the Kv11.1 channel are known to cause LQTS2, however, only a small subset of variants found in the human population are established as pathogenic. Hence, the majority of its missense mutations are known to be benign or are variant of unknown significance (VUS). Here we use molecular dynamics (MD) simulations and machine learning (ML) to determine the propensity for Kv11.1 channel variants to present loss-of-function behavior. Specifically, we use these computational techniques to correlate structural and dynamic changes in an important Kv11.1 subdomain, the PAS-domain (PASD), with the channel’s ability to traffic normally to the plasma membrane. With these techniques, we have identified several molecular features, namely, number of hydrating waters and intra-PASD hydrogen bonds, as moderately predictive of trafficking. Together with bioinformatics data including sequence conservation and folding energies, we are able to predict with reasonable accuracy (≈75%) the ability of VUS’ to traffic. Additionally, we compared two ML algorithms i.e., Decision tree (DT)s and Random forest (RF) for their robustness, and we report that RF performed moderately better than DTs’. Features derived from MD trajectories particularly help improve the prediction of trafficking deficient variants by both ML techniques.

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Section: Discussionmentioning

confidence: 99%

Prediction of Kv11.1 potassium channel PAS-domain variants trafficking via machine learning

Immadisetty¹,

Kekenes‐Huskey²

2021

Preprint

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“… (A) Family pedigree harboring the c.2504G>C p.Arg835Pro KCNH2 mutation that leads to hERG loss of function ( Oliveira-Mendes et al, 2021 ). The proband (arrow) presented a sudden cardiac arrest (SCA) due to ventricular fibrillation at 45 during an episode of hypokalemia requiring an implantable cardiac defibrillator (ICD).…”

Section: Herg Phenotyping Why?mentioning

confidence: 99%

“…For example, expression of the LQTS variant R534C, in X. laevis oocytes, surprisingly leads to a shift in the activation curve to negative value, predicting thus a shortening in the action potential by a computer model ( Nakajima, 1999 ). In contrast, in mammalian CHO cells, kept at 37°C, R534C is associated with a major decrease in maximal current amplitude that is due to reduced trafficking ( Oliveira-Mendes et al, 2021 ). To reconcile these data, it was shown in HEK293 cells that this reduced trafficking is rescued by incubating the cells at 27°C during the expression time course ( Anderson et al, 2006 ).…”

Section: Experimental Temperatures Used For Functional Studiesmentioning

confidence: 99%

A need for exhaustive and standardized characterization of ion channels activity. The case of KV11.1

Alameh

Oliveira-Mendes²,

Kyndt³

et al. 2023

Front. Physiol.

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hERG, the pore-forming subunit of the rapid component of the delayed rectifier K+ current, plays a key role in ventricular repolarization. Mutations in the KCNH2 gene encoding hERG are associated with several cardiac rhythmic disorders, mainly the Long QT syndrome (LQTS) characterized by prolonged ventricular repolarization, leading to ventricular tachyarrhythmias, sometimes progressing to ventricular fibrillation and sudden death. Over the past few years, the emergence of next-generation sequencing has revealed an increasing number of genetic variants including KCNH2 variants. However, the potential pathogenicity of the majority of the variants remains unknown, thus classifying them as variants of uncertain significance or VUS. With diseases such as LQTS being associated with sudden death, identifying patients at risk by determining the variant pathogenicity, is crucial. The purpose of this review is to describe, on the basis of an exhaustive examination of the 1322 missense variants, the nature of the functional assays undertaken so far and their limitations. A detailed analysis of 38 hERG missense variants identified in Long QT French patients and studied in electrophysiology also underlies the incomplete characterization of the biophysical properties for each variant. These analyses lead to two conclusions: first, the function of many hERG variants has never been looked at and, second, the functional studies done so far are excessively heterogeneous regarding the stimulation protocols, cellular models, experimental temperatures, homozygous and/or the heterozygous condition under study, a context that may lead to conflicting conclusions. The state of the literature emphasizes how necessary and important it is to perform an exhaustive functional characterization of hERG variants and to standardize this effort for meaningful comparison among variants. The review ends with suggestions to create a unique homogeneous protocol that could be shared and adopted among scientists and that would facilitate cardiologists and geneticists in patient counseling and management.

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“…Applications of machine learning to map genotype to electrophysiological cellular phenotype have been fewer in number. Along these lines, a recent study classified many different KCNH2 variants by their potential to influence trafficking, in part by categorizing how an amino acid substitution might impact the site's occupied volume, polarity, and steric interactions [157]. The classification of the variants included structural information that leveraged by the Kv11.1 channel structure resolved by cryo-electron microscopy [145].…”

Section: Lqt2-linked Mutation Dysfunctional Phenotypes: Trafficking I...mentioning

confidence: 99%

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

Kekenes‐Huskey

Burgess

Sun

et al. 2022

IJMS

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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.

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A standardised hERG phenotyping pipeline to evaluate KCNH2 genetic variant pathogenicity

Cited by 8 publications

References 47 publications

Prediction of Kv11.1 potassium channel PAS-domain variants trafficking via machine learning

Prediction of Kv11.1 potassium channel PAS-domain variants trafficking via machine learning

A need for exhaustive and standardized characterization of ion channels activity. The case of KV11.1

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

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