KvSNP: accurately predicting the effect of genetic variants in voltage-gated potassium channels

Ligand distances as key predictors of pathogenicity and function in NMDA receptors

Genetic variants in the 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 and neurodevelopmental disorders, including early onset epilepsy, developmental and epileptic encephalopathy, intellectual disability, and autism spectrum disorders. 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 of missense variants are therefore crucial for therapeutic applications. We assembled 223 missense variants from patients, 631 control variants from the general population, and 160 missense variants characterized by electrophysiological readouts that show whether they can enhance or reduce the function of the receptor. This includes new functional data from 33 variants reported here, for the first time. By mapping these variants onto the NMDAR protein structures, 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 a key predictive feature for both variant pathogenicity and molecular functional consequences. Leveraging distances from ligands, we developed two machine-learning based predictors for NMDA variants: a pathogenicity predictor which outperforms currently available predictors and the first molecular function (increase/decrease) predictor. Our findings can have direct application to patient care by improving diagnostic yield for genetic neurodevelopmental disorders and by guiding personalized treatment informed by the knowledge of the molecular disease mechanism.

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.

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).

Hydropathicity-based prediction of pain-causing NaV1.7 variants

Background

Mutation-induced variations in the functional architecture of the NaV1.7 channel protein are causally related to a broad spectrum of human pain disorders. Predicting in silico the phenotype of NaV1.7 variant is of major clinical importance; it can aid in reducing costs of in vitro pathophysiological characterization of NaV1.7 variants, as well as, in the design of drug agents for counteracting pain-disease symptoms.

Results

In this work, we utilize spatial complexity of hydropathic effects toward predicting which NaV1.7 variants cause pain (and which are neutral) based on the location of corresponding mutation sites within the NaV1.7 structure. For that, we analyze topological and scaling hydropathic characteristics of the atomic environment around NaV1.7’s pore and probe their spatial correlation with mutation sites. We show that pain-related mutation sites occupy structural locations in proximity to a hydrophobic patch lining the pore while clustering at a critical hydropathic-interactions distance from the selectivity filter (SF). Taken together, these observations can differentiate pain-related NaV1.7 variants from neutral ones, i.e., NaV1.7 variants not causing pain disease, with 80.5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}% sensitivity and 93.7\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}% specificity [area under the receiver operating characteristics curve = 0.872].

Conclusions

Our findings suggest that maintaining hydrophobic NaV1.7 interior intact, as well as, a finely-tuned (dictated by hydropathic interactions) distance from the SF might be necessary molecular conditions for physiological NaV1.7 functioning. The main advantage for using the presented predictive scheme is its negligible computational cost, as well as, hydropathicity-based biophysical rationalization.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04119-2.

Functional testing for variant prioritization in a family with long QT syndrome

Next-generation sequencing platforms are being increasingly applied in clinical genetic settings for evaluation of families with suspected heritable disease. These platforms potentially improve the diagnostic yield beyond that of disease-specific targeted gene panels, but also increase the number of rare or novel genetic variants that may confound precise diagnostics. Here, we describe a functional testing approach used to interpret the results of whole exome sequencing (WES) in a family presenting with syncope and sudden death. One individual had a prolonged QT interval on electrocardiogram (ECG) and carried a diagnosis of long QT syndrome (LQTS), but a second individual did not meet criteria for LQTS. Filtering WES results for uncommon variants with arrhythmia association identified four for further analyses. In silico analyses indicated that two of these variants, KCNH2 p.(Cys555Arg) and KCNQ1 p.(Arg293Cys), were likely to be causal in this family's LQTS. We subsequently performed functional characterization of these variants in a heterologous expression system. The expression of KCNQ1-Arg293Cys did not show a deleterious phenotype but KCNH2-Cys555Arg demonstrated a loss-of-function phenotype that was partially dominant. Our stepwise approach identified a precise genetic etiology in this family, which resulted in the establishment of a LQTS diagnosis in the second individual as well as an additional asymptomatic family member, enabling personalized clinical management. Given its ability to aid in the diagnosis, the application of functional characterization should be considered as a value adjunct to in silico analyses of WES.

Variation benchmark datasets: update, criteria, quality and applications

Development of new computational methods and testing their performance has to be carried out using experimental data. Only in comparison to existing knowledge can method performance be assessed. For that purpose, benchmark datasets with known and verified outcome are needed. High-quality benchmark datasets are valuable and may be difficult, laborious and time consuming to generate. VariBench and VariSNP are the two existing databases for sharing variation benchmark datasets used mainly for variation interpretation. They have been used for training and benchmarking predictors for various types of variations and their effects. VariBench was updated with 419 new datasets from 109 papers containing altogether 329 014 152 variants; however, there is plenty of redundancy between the datasets. VariBench is freely available at http://structure.bmc.lu.se/VariBench/. The contents of the datasets vary depending on information in the original source. The available datasets have been categorized into 20 groups and subgroups. There are datasets for insertions and deletions, substitutions in coding and non-coding region, structure mapped, synonymous and benign variants. Effect-specific datasets include DNA regulatory elements, RNA splicing, and protein property for aggregation, binding free energy, disorder and stability. Then there are several datasets for molecule-specific and disease-specific applications, as well as one dataset for variation phenotype effects. Variants are often described at three molecular levels (DNA, RNA and protein) and sometimes also at the protein structural level including relevant cross references and variant descriptions. The updated VariBench facilitates development and testing of new methods and comparison of obtained performances to previously published methods. We compared the performance of the pathogenicity/tolerance predictor PON-P2 to several benchmark studies, and show that such comparisons are feasible and useful, however, there may be limitations due to lack of provided details and shared data.

Database URL: http://structure.bmc.lu.se/VariBench

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.

Predicting the Functional Impact of KCNQ1 Variants of Unknown Significance

BACKGROUND

An emerging standard-of-care for long-QT syndrome uses clinical genetic testing to identify genetic variants of the KCNQ1 potassium channel. However, interpreting results from genetic testing is confounded by the presence of variants of unknown significance for which there is inadequate evidence of pathogenicity.

METHODS AND RESULTS

In this study, we curated from the literature a high-quality set of 107 functionally characterized KCNQ1 variants. Based on this data set, we completed a detailed quantitative analysis on the sequence conservation patterns of subdomains of KCNQ1 and the distribution of pathogenic variants therein. We found that conserved subdomains generally are critical for channel function and are enriched with dysfunctional variants. Using this experimentally validated data set, we trained a neural network, designated Q1VarPred, specifically for predicting the functional impact of KCNQ1 variants of unknown significance. The estimated predictive performance of Q1VarPred in terms of Matthew's correlation coefficient and area under the receiver operating characteristic curve were 0.581 and 0.884, respectively, superior to the performance of 8 previous methods tested in parallel. Q1VarPred is publicly available as a web server at http://meilerlab.org/q1varpred.

CONCLUSIONS

Although a plethora of tools are available for making pathogenicity predictions over a genome-wide scale, previous tools fail to perform in a robust manner when applied to KCNQ1. The contrasting and favorable results for Q1VarPred suggest a promising approach, where a machine-learning algorithm is tailored to a specific protein target and trained with a functionally validated data set to calibrate informatics tools.

Functional Invalidation of Putative Sudden Infant Death Syndrome-Associated Variants in the KCNH2-Encoded Kv11.1 Channel

BACKGROUND

Heterologous functional validation studies of putative long-QT syndrome subtype 2-associated variants clarify their pathological potential and identify disease mechanism(s) for most variants studied. The purpose of this study is to clarify the pathological potential for rare nonsynonymous KCNH2 variants seemingly associated with sudden infant death syndrome.

METHODS

Genetic testing of 292 sudden infant death syndrome cases identified 9 KCNH2 variants: E90K, R181Q, A190T, G294V, R791W, P967L, R1005W, R1047L, and Q1068R. Previous studies show R181Q-, P967L-, and R1047L-Kv11.1 channels function similar to wild-type Kv11.1 channels, whereas Q1068R-Kv11.1 channels accelerate inactivation gating. We studied the biochemical and biophysical properties for E90K-, G294V-, R791W-, and R1005W-Kv11.1 channels expressed in human embryonic kidney 293 cells; examined the electronic health records of patients who were genotype positive for the sudden infant death syndrome-linked KCNH2 variants; and simulated their functional impact using computational models of the human ventricular action potential.

RESULTS

Western blot and voltage-clamping analyses of cells expressing E90K-, G294V-, R791W-, and R1005W-Kv11.1 channels demonstrated these variants express and generate peak Kv11.1 current levels similar to cells expressing wild-type-Kv11.1 channels, but R791W- and R1005W-Kv11.1 channels accelerated deactivation and activation gating, respectively. Electronic health records of patients with the sudden infant death syndrome-linked KCNH2 variants showed that the patients had median heart rate-corrected QT intervals <480 ms and none had been diagnosed with long-QT syndrome or experienced cardiac arrest. Simulating the impact of dysfunctional gating variants predicted that they have little impact on ventricular action potential duration.

CONCLUSIONS

We conclude that these rare Kv11.1 missense variants are not long-QT syndrome subtype 2-causative variants and therefore do not represent the pathogenic substrate for sudden infant death syndrome in the variant-positive infants.

Recent advances in understanding and prevention of sudden cardiac death

There have been tremendous advances in the diagnosis and treatment of heart disease over the last 50 years. Nevertheless, it remains the number one cause of death. About half of heart-related deaths occur suddenly, and in about half of these cases the person was unaware that they had underlying heart disease. Genetic heart disease accounts for only approximately 2% of sudden cardiac deaths, but as it typically occurs in younger people it has been a particular focus of activity in our quest to not only understand the underlying mechanisms of cardiac arrhythmogenesis but also develop better strategies for earlier detection and prevention. In this brief review, we will highlight trends in the recent literature focused on sudden cardiac death in genetic heart diseases and how these studies are contributing to a broader understanding of sudden death in the community.

The structural effects of mutations can aid in differential phenotype prediction of beta-myosin heavy chain (Myosin-7) missense variants

MOTIVATION

High-throughput sequencing platforms are increasingly used to screen patients with genetic disease for pathogenic mutations, but prediction of the effects of mutations remains challenging. Previously we developed SAAPdap (Single Amino Acid Polymorphism Data Analysis Pipeline) and SAAPpred (Single Amino Acid Polymorphism Predictor) that use a combination of rule-based structural measures to predict whether a missense genetic variant is pathogenic. Here we investigate whether the same methodology can be used to develop a differential phenotype predictor, which, once a mutation has been predicted as pathogenic, is able to distinguish between phenotypes-in this case the two major clinical phenotypes (hypertrophic cardiomyopathy, HCM and dilated cardiomyopathy, DCM) associated with mutations in the beta-myosin heavy chain (MYH7) gene product (Myosin-7).

RESULTS

A random forest predictor trained on rule-based structural analyses together with structural clustering data gave a Matthews' correlation coefficient (MCC) of 0.53 (accuracy, 75%). A post hoc removal of machine learning models that performed particularly badly, increased the performance (MCC = 0.61, Acc = 79%). This proof of concept suggests that methods used for pathogenicity prediction can be extended for use in differential phenotype prediction.

AVAILABILITY AND IMPLEMENTATION

Analyses were implemented in Perl and C and used the Java-based Weka machine learning environment. Please contact the authors for availability.

CONTACTS

andrew@bioinf.org.uk or andrew.martin@ucl.ac.uk

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

An Interdomain KCNH2 Mutation Produces an Intermediate Long QT Syndrome

Hereditary long QT syndrome is caused by deleterious mutation in one of several genetic loci, including locus LQT2 that contains the KCNH2 gene (or hERG, human ether-a-go-go related gene), causing faulty cardiac repolarization. Here, we describe and characterize a novel mutation, p.Asp219Val in the hERG channel, identified in an 11-year-old male with syncope and prolonged QT interval. Genetic sequencing showed a nonsynonymous variation in KCNH2 (c.656A>T: amino acid p.Asp219Val). p.Asp219Val resides in a region of the channel predicted to be unstructured and flexible, located between the PAS (Per-Arnt-Sim) domain and its interaction sites in the transmembrane domain. The p.Asp219Val hERG channel produced K(+) current that activated with modest changes in voltage dependence. Mutant channels were also slower to inactivate, recovered from inactivation more readily and demonstrated a significantly accelerated deactivation rate compared with the slow deactivation of wild-type channels. The intermediate nature of the biophysical perturbation is consistent with the degree of severity in the clinical phenotype. The findings of this study demonstrate a previously unknown role of the proximal N-terminus in deactivation and support the hypothesis that the proximal N-terminal domain is essential in maintaining slow hERG deactivation.

Using common variants to indicate cancer genes

The catalogue of tumour-specific somatic mutations (SMs) is growing rapidly owing to the advent of next-generation sequencing. Identifying those mutations responsible for the development and progression of the disease, so-called driver mutations, will increase our understanding of carcinogenesis and provide candidates for targeted therapeutics. The phenotypic consequence(s) of driver mutations cause them to be selected for within the tumour environment, such that many approaches aimed at distinguishing drivers are based on finding significantly somatically mutated genes. Currently, these methods are designed to analyse, or be specifically applied to, nonsynonymous mutations: those that alter an encoded protein. However, growing evidence suggests the involvement of noncoding transcripts in carcinogenesis, mutations in which may also be disease-driving. We wished to test the hypothesis that common DNA variation rates within humans can be used as a baseline from which to score the rate of SMs, irrespective of coding capacity. We preliminarily tested this by applying it to a dataset of 159,498 SMs and using the results to rank genes. This resulted in significant enrichment of known cancer genes, indicating that the approach has merit. As additional data from cancer sequencing studies are made publicly available, this approach can be refined and applied to specific cancer subtypes. We named this preliminary version of our approach PRISMAD (polymorphism rates indicate somatic mutations as drivers) and have made it publicly accessible, with scripts, via a link at www.precancer.leeds.ac.uk/software-and-datasets.

Molecular damage in Fabry disease: characterization and prediction of alpha-galactosidase A pathological mutations

Loss-of-function mutations of the enzyme alpha-galactosidase A (GLA) causes Fabry disease (FD), that is a rare and potentially fatal disease. Identification of these pathological mutations by sequencing is important because it allows an early treatment of the disease. However, before taking any treatment decision, if the mutation identified is unknown, we first need to establish if it is pathological or not. General bioinformatic tools (PolyPhen-2, SIFT, Condel, etc.) can be used for this purpose, but their performance is still limited. Here we present a new tool, specifically derived for the assessment of GLA mutations. We first compared mutations of this enzyme known to cause FD with neutral sequence variants, using several structure and sequence properties. Then, we used these properties to develop a family of prediction methods adapted to different quality requirements. Trained and tested on a set of known Fabry mutations, our methods have a performance (Matthews correlation: 0.56-0.72) comparable or better than that of the more complex method, Polyphen-2 (Matthews correlation: 0.61), and better than those of SIFT (Matthews correl.: 0.54) and Condel (Matthews correl.: 0.51). This result is validated in an independent set of 65 pathological mutations, for which our method displayed the best success rate (91.0%, 87.7%, and 73.8%, for our method, PolyPhen-2 and SIFT, respectively). These data confirmed that our specific approach can effectively contribute to the identification of pathological mutations in GLA, and therefore enhance the use of sequence information in the identification of undiagnosed Fabry patients.

Phylogenetic and physicochemical analyses enhance the classification of rare nonsynonymous single nucleotide variants in type 1 and 2 long-QT syndrome

BACKGROUND

Hundreds of nonsynonymous single nucleotide variants (nsSNVs) have been identified in the 2 most common long-QT syndrome-susceptibility genes (KCNQ1 and KCNH2). Unfortunately, an ≈3%

BACKGROUND

and KCNH2 nsSNVs amongst healthy individuals complicates the ability to distinguish rare pathogenic mutations from similarly rare yet presumably innocuous variants.

METHODS AND RESULTS

In this study, 4 tools [(1) conservation across species, (2) Grantham values, (3) sorting intolerant from tolerant, and (4) polymorphism phenotyping] were used to predict pathogenic or benign status for nsSNVs identified across 388 clinically definite long-QT syndrome cases and 1344 ostensibly healthy controls. From these data, estimated predictive values were determined for each tool independently, in concert with previously published protein topology-derived estimated predictive values, and synergistically when ≥3 tools were in agreement. Overall, all 4 tools displayed a statistically significant ability to distinguish between case-derived and control-derived nsSNVs in KCNQ1, whereas each tool, except Grantham values, displayed a similar ability to differentiate KCNH2 nsSNVs. Collectively, when at least 3 of the 4 tools agreed on the pathogenic status of C-terminal nsSNVs located outside the KCNH2/Kv11.1 cyclic nucleotide-binding domain, the topology-specific estimated predictive value improved from 56% to 91%.

CONCLUSIONS

Although in silico prediction tools should not be used to predict independently the pathogenicity of a novel, rare nSNV, our results support the potential clinical use of the synergistic utility of these tools to enhance the classification of nsSNVs, particularly for Kv11.1's difficult to interpret C-terminal region.

Bioinformatics for personal genome interpretation

An international consortium released the first draft sequence of the human genome 10 years ago. Although the analysis of this data has suggested the genetic underpinnings of many diseases, we have not yet been able to fully quantify the relationship between genotype and phenotype. Thus, a major current effort of the scientific community focuses on evaluating individual predispositions to specific phenotypic traits given their genetic backgrounds. Many resources aim to identify and annotate the specific genes responsible for the observed phenotypes. Some of these use intra-species genetic variability as a means for better understanding this relationship. In addition, several online resources are now dedicated to collecting single nucleotide variants and other types of variants, and annotating their functional effects and associations with phenotypic traits. This information has enabled researchers to develop bioinformatics tools to analyze the rapidly increasing amount of newly extracted variation data and to predict the effect of uncharacterized variants. In this work, we review the most important developments in the field--the databases and bioinformatics tools that will be of utmost importance in our concerted effort to interpret the human variome.