Assessment of the predictive accuracy of five in silico prediction tools, alone or in combination, and two metaservers to classify long QT syndrome gene mutations

The Competitive Counterflow Assay for Identifying Drugs Transported by Solute Carriers: Principle, Applications, Challenges/Limits, and Perspectives

The identification of substrates for solute carriers (SLCs) handling drugs is an important challenge, owing to the major implication of these plasma membrane transporters in pharmacokinetics and drug-drug interactions. In this context, the competitive counterflow (CCF) assay has been proposed as a practical and less expensive approach than the reference functional uptake assays for discriminating SLC substrates and non-substrates. The present article was designed to summarize and discuss key-findings about the CCF assay, including its principle, applications, challenges and limits, and perspectives. The CCF assay is based on the decrease of the steady-state accumulation of a tracer substrate in SLC-positive cells, caused by candidate substrates. Reviewed data highlight the fact that the CCF assay has been used to identify substrates and non-substrates for organic cation transporters (OCTs), organic anion transporters (OATs), and organic anion transporting polypeptides (OATPs). The performance values of the CCF assay, calculated from available CCF study data compared with reference functional uptake assay data, are, however, rather mitigated, indicating that the predictability of the CCF method for assessing SLC-mediated transportability of drugs is currently not optimal. Further studies, notably aimed at standardizing the CCF assay and developing CCF-based high-throughput approaches, are therefore required in order to fully precise the interest and relevance of the CCF assay for identifying substrates and non-substrates of SLCs.

Assessing the Performance of In silico Tools and Molecular Dynamics Simulations for Predicting Pharmacogenetic Variant Impact

The ability of freely available in silico tools to predict the effect of non-synonymous single nucleotide polymorphisms (nsSNPs) in pharmacogenes on protein function is not well defined. We assessed the performance of seven sequence-based (SIFT, PolyPhen2, mutation accessor, FATHMM, PhD-SNP, MutPred2, and SNPs & Go) and five structure-based (mCSM, SDM, DDGun, CupSat, and MAESTROweb) tools in predicting the impact of 118 nsSNPs in the CYP2C19, CYP2C9, CYP2B6, CYP2D6, and DPYD genes with known function (24 normal, one increased, 42 decreased, and 51 no-function). Sequence-based tools had a higher median (IQR) positive predictive value (89% [89-94%] vs. 12% [10-15%], P < 0.001) and lower negative predictive value (30% [24-34%] vs. 90% [80-93%], P < 0.001) than structure-based tools. Accuracy did not significantly differ between sequence-based (59% [37-67%]) and structure-based (34% [23-44%]) tools (P = 0.070). Notably, the no-function CYP2C9*3 allele and decreased function CYP2C9*8 allele were predicted incorrectly as tolerated by 100% of sequenced-based tools and as stabilizing by 60% and 20% of structure-based tools, respectively. As a case study, we performed mutational analysis for the CYP2C9*1, *3 (I359L), and *8 (R150H) proteins through molecular dynamic (MD) simulations using S-warfarin as the substrate. The I359L variant increased the distance of the major metabolic site of S-warfarin to the oxy-ferryl center of CYP2C9, and I359L and R150H caused shifts in the conformation of S-warfarin to a position less favorable for metabolism. These data suggest that MD simulations may better capture the impact of nsSNPs in pharmacogenes than other tools.

Using computational approaches to enhance the interpretation of missense variants in the PAX6 gene

The PAX6 gene encodes a highly-conserved transcription factor involved in eye development. Heterozygous loss-of-function variants in PAX6 can cause a range of ophthalmic disorders including aniridia. A key molecular diagnostic challenge is that many PAX6 missense changes are presently classified as variants of uncertain significance. While computational tools can be used to assess the effect of genetic alterations, the accuracy of their predictions varies. Here, we evaluated and optimised the performance of computational prediction tools in relation to PAX6 missense variants. Through inspection of publicly available resources (including HGMD, ClinVar, LOVD and gnomAD), we identified 241 PAX6 missense variants that were used for model training and evaluation. The performance of ten commonly used computational tools was assessed and a threshold optimization approach was utilized to determine optimal cut-off values. Validation studies were subsequently undertaken using PAX6 variants from a local database. AlphaMissense, SIFT4G and REVEL emerged as the best-performing predictors; the optimized thresholds of these tools were 0.967, 0.025, and 0.772, respectively. Combining the prediction from these top-three tools resulted in lower performance compared to using AlphaMissense alone. Tailoring the use of computational tools by employing optimized thresholds specific to PAX6 can enhance algorithmic performance. Our findings have implications for PAX6 variant interpretation in clinical settings.

MLe-KCNQ2: An Artificial Intelligence Model for the Prognosis of Missense KCNQ2 Gene Variants

Despite the increasing availability of genomic data and enhanced data analysis procedures, predicting the severity of associated diseases remains elusive in the absence of clinical descriptors. To address this challenge, we have focused on the KV7.2 voltage-gated potassium channel gene (KCNQ2), known for its link to developmental delays and various epilepsies, including self-limited benign familial neonatal epilepsy and epileptic encephalopathy. Genome-wide tools often exhibit a tendency to overestimate deleterious mutations, frequently overlooking tolerated variants, and lack the capacity to discriminate variant severity. This study introduces a novel approach by evaluating multiple machine learning (ML) protocols and descriptors. The combination of genomic information with a novel Variant Frequency Index (VFI) builds a robust foundation for constructing reliable gene-specific ML models. The ensemble model, MLe-KCNQ2, formed through logistic regression, support vector machine, random forest and gradient boosting algorithms, achieves specificity and sensitivity values surpassing 0.95 (AUC-ROC > 0.98). The ensemble MLe-KCNQ2 model also categorizes pathogenic mutations as benign or severe, with an area under the receiver operating characteristic curve (AUC-ROC) above 0.67. This study not only presents a transferable methodology for accurately classifying KCNQ2 missense variants, but also provides valuable insights for clinical counseling and aids in the determination of variant severity. The research context emphasizes the necessity of precise variant classification, especially for genes like KCNQ2, contributing to the broader understanding of gene-specific challenges in the field of genomic research. The MLe-KCNQ2 model stands as a promising tool for enhancing clinical decision making and prognosis in the realm of KCNQ2-related pathologies.

Identification of High-Risk Single Nucleotide Polymorphisms in the Human CYB5R3 Gene Responsible for Recessive Congenital Methemoglobinemia: A Computational Approach

Introduction

NADH-cytochrome b5 reductase deficiency due to pathogenic variants in the CYB5R3 gene causes recessive congenital methemoglobinemia (RCM) type I or type II. In type I, cyanosis from birth is the only major symptom, and the enzyme deficiency is restricted only to erythrocytes. Whereas in type II, cyanosis is associated with severe neurological manifestations, and the enzyme deficiency is generalized to all tissues.

Methods

In this study, several computational methods (SIFT, Polyphen-2, PROVEAN, Mutation Assessor, Panther, Phd-SNP, SNPs&GO, SNAP2, Align, GVGD, MutPred2, I-Mutant 2.0, MUpro, Duet, ConSurf and Netsurf-2.0 tools) were used to find the most deleterious nsSNPs in the CYB5R3 gene. Furthermore, structural analysis by Swiss-PDB viewer, protein-ligand docking using FTSite, and protein-protein interaction using STRING were carried out to evaluate the impact of these nsSNPs on the protein structure and function.

Results

Our in silico analysis suggested that out of 339 nsSNPs of the CYB5R3 gene, 17 (L47H, L47P, R61P, L73R G76D, G76C, P96H, G104C, S128P, G144D, P145S, L149P, Y151H, M177T, I178T, I216N, and G251V), are the most deleterious. Among them, two (P96H and S128P) were reported to be associated with the severe form RCM type II, six are related to RCM type I (G104C, G144D, P145S, L149P, M177T, and I178T), and the remaining nine high-risk nsSNPs have not yet been reported in RCM patients.

Discussion

This study highlighted the potential pathogenic nsSNPs of the CYB5R3 gene. To comprehend how these most harmful nsSNPs contribute to disease, it is crucial to experimentally validate their functional effects.

In silico versus functional characterization of genetic variants: lessons from muscle channelopathies

Accurate determination of the pathogenicity of missense genetic variants of uncertain significance is a huge challenge for implementing genetic data in clinical practice. In silico predictive tools are used to score variants' pathogenicity. However, their value in clinical settings is often unclear, as they have not usually been validated against robust functional assays. We compared nine widely used in silico predictive tools, including more recently developed tools (EVE and REVEL) with detailed cell-based electrophysiology, for 126 CLCN1 variants discovered in patients with the skeletal muscle channelopathy myotonia congenita. We found poor accuracy for most tools. The highest accuracy was obtained with MutationTaster (84.58%) and REVEL (82.54%). Both of these scores showed poor specificity, although specificity was better using EVE. Combining methods based on concordance improved performance overall but still lacked specificity. Our calculated statistics for the predictive tools were different to reported values for other genes in the literature, suggesting that the utility of the tools varies between genes. Overall, current predictive tools for this chloride channel are not reliable for clinical use, and tools with better specificity are urgently required. Improving the accuracy of predictive tools is a wider issue and a huge challenge for effective clinical implementation of genetic data.

In Silico-Based Structural Evaluation to Categorize the Pathogenicity of Mutations Identified in the RAD Class of Proteins

RAD genes, known as double-strand break repair proteins, play a major role in maintaining the genomic integrity of a cell by carrying out essential DNA repair functions via double-strand break repair pathways. Mutations in the RAD class of proteins show high susceptibility to breast and ovarian cancers; however, adequate research on the mutations identified in these genes has not been extensively reported for their deleterious effects. Changes in the folding pattern of RAD proteins play an important role in protein-protein interactions and also functions. Missense mutations identified from four cancer databases, cBioPortal, COSMIC, ClinVar, and gnomAD, cause aberrant conformations, which may lead to faulty DNA repair mechanisms. It is therefore necessary to evaluate the effects of pathogenic mutations of RAD proteins and their subsequent role in breast and ovarian cancers. In this study, we have used eight computational prediction servers to analyze pathogenic mutations and understand their effects on the protein structure and function. A total of 5122 missense mutations were identified from four different cancer databases, of which 1165 were predicted to be pathogenic using at least five pathogenicity prediction servers. These mutations were characterized as high-risk mutations based on their location in the conserved domains and subsequently subjected to structural stability characterization. The mutations included in the present study were selected from clinically relevant mutants in breast cancer pedigrees. Comparative folding patterns and intra-atomic interaction results showed alterations in the structural behavior of RAD proteins, specifically RAD51C triggered by mutations G125V and L138F and RAD51D triggered by mutations S207L and E233G.

Computational refinement identifies functional destructive single nucleotide polymorphisms associated with human retinoid X receptor gene

Alterations in the nuclear retinoid X receptor (RXRs) signalling have been implicated in neurodegenerative disorders such as Alzheimer's disease, Parkinson's disease, stroke, multiple sclerosis and glaucoma. Single nucleotide polymorphisms (SNPs) are the main cause underlying single nucleic acid variations which in turn determine heterogeneity within various populations. These genetic polymorphisms have been suggested to associate with various degenerative disorders in population-wide analysis. This bioinformatics study was designed to investigate, search, retrieve and identify deleterious SNPs which may affect the structure and function of various RXR isoforms through a computational and molecular modelling approach. Amongst the 1,813 retrieved SNPs several were found to be deleterious with rs140464195_G139R, rs368400425_R358W and rs368586400_L383F RXRα mutant variants being the most detrimental ones causing changes in the interatomic interactions and decreasing the flexibility of the mutant proteins. Molecular genetics analysis identified seven missense mutations in RXRα/β/γ isoforms. Two novel mutations SNP IDs (rs1588299621 and rs1057519958) were identified in RXRα isoform. We used several in silico prediction tools such as SIFT, PolyPhen, I-Mutant, Protein Variation Effect Analyzer (PROVEAN), PANTHER, SNP&Go, PhD-SNP and SNPeffect to predict pathogenicity and protein stability associated with RXR mutations. The structural assessment by DynaMut tool revealed that hydrogen bonds were affected along with hydrophobic and carbonyl interactions resulting in reduced flexibility at the mutated residue positions but ultimately stabilizing the molecule as a whole. Summarizing, analysis of the missense mutations in RXR isoforms showed a mix of conclusive and inconclusive genotype-phenotype correlations suggesting the use of sophisticated computational analysis tools for studying RXR variants.Communicated by Ramaswamy H. Sarma.

Assessing the relationship between the in silico predicted consequences of 97 missense mutations mapping to 68 genes related to lipid metabolism and their association with porcine fatness traits

In general, the relationship between the predicted functional consequences of missense mutations mapping to genes known to be involved in human diseases and the severity of disease manifestations is weak. In this study, we tested in pigs whether missense single nucleotide polymorphisms (SNPs), predicted to have consequences on the function of genes related to lipid metabolism are associated with lipid phenotypes. Association analysis demonstrated that nine out of 72 nominally associated SNPs were classified as "highly" or "very highly consistent" in silico-predicted functional mutations and did not show association with lipid traits expected to be affected by inactivation of the corresponding gene. Although the lack of endophenotypes and the limited sample size of certain genotypic classes might have limited to some extent the reach of the current study, our data indicate that present-day bioinformatic tools have a modest ability to predict the impact of missense mutations on complex phenotypes.

In Silico and Structure-Based Assessment of Similar Variants Discovered in Tandem Repeats of BRCT Domains of BRCA1 and BARD1 To Characterize the Folding Pattern

BRCA1 and BARD1 are important proteins in the homologous DNA damage repair pathways. Different genetic variants identified in these proteins have been clinically correlated with the occurrence of hereditary breast and ovarian cancer (HBOC). Variants of unknown significance (VUS) reported in the BRCT domains of BRCA1 and BARD1 substantiate the importance of BRCT domain-containing proteins for genomic integrity. To classify the pathogenicity of variants, in silico, structural and molecular dynamics (MD)-based approaches were explored. Different variants reported in the BRCT region were retrieved from cBioPortal, LOVD3, BRCA Exchange, and COSMIC databases to evaluate the pathogenicity. Multiple sequence alignment and superimposition of the structures of BRCA1 BRCT and BARD1 BRCT domains were performed to compare alterations in folding patterns. From 11 in silico predictions servers, variants reported to be pathogenic by 70% of the servers were considered for structural analysis. To our observations, four residue pairs of both the proteins were reported, harboring 11 variants, H1686Y, W1718L, P1749L, P1749S, and W1837L variants for BRCA1 BRCT and H606D, H606N, W635L, P657L, P657S, and W762F for BARD1 BRCT. MD simulations of the BRCT repeat regions of these variants and wild-type proteins were performed to evaluate the differences of folding patterns. Root mean square deviation (RMSD), R _g, solvent-accessible surface area (SASA), and root mean square fluctuation (RMSF) of variants showed slight differences in the folding patterns from the wild-type proteins. Furthermore, principal components analysis of H1686Y, P1749S, and W1718L variants of BRCA1 showed less flexibility than the wild type, whereas that of H606D, W635L, and W762F of BARD1 showed more flexibility than the wild type. Normal mode analysis of the energy minima from the simulation trajectories revealed that most of the variants do not show much differences in the flexibility compared to the wild-type proteins, except for the discrete regions in the BRCT repeats, most prominently in the 1798-1801 amino acid region of BRCA1 and at the residue 744 in BARD1.

DNA Repair Mechanisms, Protein Interactions and Therapeutic Targeting of the MRN Complex

Repair of a DNA double-strand break relies upon a pathway of proteins to identify damage, regulate cell cycle checkpoints, and repair the damage. This process is initiated by a sensor protein complex, the MRN complex, comprised of three proteins-MRE11, RAD50, and NBS1. After a double-stranded break, the MRN complex recruits and activates ATM, in-turn activating other proteins such as BRCA1/2, ATR, CHEK1/2, PALB2 and RAD51. These proteins have been the focus of many studies for their individual roles in hereditary cancer syndromes and are included on several genetic testing panels. These panels have enabled us to acquire large amounts of genetic data, much of which remains a challenge to interpret due to the presence of variants of uncertain significance (VUS). While the primary aim of clinical testing is to accurately and confidently classify variants in order to inform medical management, the presence of VUSs has led to ambiguity in genetic counseling. Pathogenic variants within MRN complex genes have been implicated in breast, ovarian, prostate, colon cancers and gliomas; however, the hundreds of VUSs within MRE11, RAD50, and NBS1 precludes the application of these data in genetic guidance of carriers. In this review, we discuss the MRN complex's role in DNA double-strand break repair, its interactions with other cancer predisposing genes, the variants that can be found within the three MRN complex genes, and the MRN complex's potential as an anti-cancer therapeutic target.

Study of the frequency and clinical features of maturity-onset diabetes in the young in the pediatric and adolescent diabetes population in Iran

OBJECTIVES

Maturity-onset diabetes of the young (MODY), an autosomal dominant disease, is frequently misdiagnosed as type 1 or 2 diabetes. Molecular diagnosis is essential to distinguish them. This study was done to investigate the prevalence of MODY subtypes and patients' clinical characteristics.

METHODS

A total of 43 out of 230 individuals with diabetes were selected based on the age of diagnosis >6 months, family history of diabetes, absence of marked obesity, and measurable C-peptide. Next-generation and direct SANGER sequencing was performed to screen MODY-related mutations. The variants were interpreted using the Genome Aggregation Database (genomAD), Clinical Variation (ClinVar), and pathogenicity prediction tools.

RESULTS

There were 23 males (53.5%), and the mean age at diabetes diagnosis was 6.7 ± 3.6 years. Sixteen heterozygote single nucleotide variations (SNVs) from 14 patients (14/230, 6%) were detected, frequently GCK (37.5%) and BLK (18.7%). Two novel variants were identified in HNF4A and ABCC8. Half of the detected variants were categorized as likely pathogenic. Most prediction tools predicted Ser28Cys in HNF4A as benign and Tyr123Phe in ABCC8 as a pathogenic SNV. Six cases (42.8%) with positive MODY SNVs had islet autoantibodies. At diagnosis, age, HbA_1c, and C-peptide level were similar between SNV-positive and negative patients.

CONCLUSIONS

This is the first study investigating 14 variants of MODY in Iran. The results recommend genetic screening for MODY in individuals with unusual type 1 or 2 diabetes even without family history. Treatment modifies depending on the type of patients' MODY and is associated with the quality of life.

Evolutionary coupling analysis guides identification of mistrafficking-sensitive variants in cardiac K+ channels: Validation with hERG

Loss of function (LOF) mutations of voltage sensitive K+ channel proteins hERG (Kv11.1) and KCNQ1 (Kv7.1) account for the majority of instances of congenital Long QT Syndrome (cLQTS) with the dominant molecular phenotype being a mistrafficking one resulting from protein misfolding. We explored the use of Evolutionary Coupling (EC) analysis, which identifies evolutionarily conserved pairwise amino acid interactions that may contribute to protein structural stability, to identify regions of the channels susceptible to misfolding mutations. Comparison with published experimental trafficking data for hERG and KCNQ1 showed that the method strongly predicts "scaffolding" regions of the channel membrane domains and has useful predictive power for trafficking phenotypes of individual variants. We identified a region in and around the cytoplasmic S2-S3 loop of the hERG Voltage Sensor Domain (VSD) as susceptible to destabilising mutation, and this was confirmed using a quantitative LI-COR ® based trafficking assay that showed severely attenuated trafficking in eight out of 10 natural hERG VSD variants selected using EC analysis. Our analysis highlights an equivalence in the scaffolding structures of the hERG and KCNQ1 membrane domains. Pathogenic variants of ion channels with an underlying mistrafficking phenotype are likely to be located within similar scaffolding structures that are identifiable by EC analysis.

Comprehensive In Silico Functional Prediction Analysis of CDKL5 by Single Amino Acid Substitution in the Catalytic Domain

Cyclin-dependent kinase-like 5 (CDKL5) is a serine/threonine protein kinase whose pathological mutations cause CDKL5 deficiency disorder. Most missense mutations are concentrated in the catalytic domain. Therefore, anticipating whether mutations in this region affect CDKL5 function is informative for clinical diagnosis. This study comprehensively predicted the pathogenicity of all 5700 missense substitutions in the catalytic domain of CDKL5 using in silico analysis and evaluating their accuracy. Each missense substitution was evaluated as “pathogenic” or “benign”. In silico tools PolyPhen-2 HumDiv mode/HumVar mode, PROVEAN, and SIFT were selected individually or in combination with one another to determine their performance using 36 previously reported mutations as a reference. Substitutions predicted as pathogenic were over 88.0% accurate using each of the three tools. The best performance score (accuracy, 97.2%; sensitivity, 100%; specificity, 66.7%; and Matthew’s correlation coefficient (MCC), 0.804) was achieved by combining PolyPhen-2 HumDiv, PolyPhen-2 HumVar, and PROVEAN. This provided comprehensive information that could accurately predict the pathogenicity of the disease, which might be used as an aid for clinical diagnosis.

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.

Genetic Analysis of ABCB4 Mutations and Variants Related to the Pathogenesis and Pathophysiology of Low Phospholipid-Associated Cholelithiasis

Clinical studies have revealed that the ABCB4 gene encodes the phospholipid transporter on the canalicular membrane of hepatocytes, and its mutations and variants are the genetic basis of low phospholipid-associated cholelithiasis (LPAC), a rare type of gallstone disease caused by a single-gene mutation or variation. The main features of LPAC include a reduction or deficiency of phospholipids in bile, symptomatic cholelithiasis at <40 years of age, intrahepatic sludge and microlithiasis, mild chronic cholestasis, a high cholesterol/phospholipid ratio in bile, and recurrence of biliary symptoms after cholecystectomy. Needle-like cholesterol crystals, putatively “anhydrous” cholesterol crystallization at low phospholipid concentrations in model and native bile, are characterized in ABCB4 knockout mice, a unique animal model for LPAC. Gallbladder bile with only trace amounts of phospholipids in these mice is supersaturated with cholesterol, with lipid composition plotting in the left two-phase zone of the ternary phase diagram, consistent with “anhydrous” cholesterol crystallization. In this review, we summarize the molecular biology and physiological functions of ABCB4 and comprehensively discuss the latest advances in the genetic analysis of ABCB4 mutations and variations and their roles in the pathogenesis and pathophysiology of LPAC in humans, based on the results from clinical studies and mouse experiments. To date, approximately 158 distinct LPAC-causing ABCB4 mutations and variants in humans have been reported in the literature, indicating that it is a monogenic risk factor for LPAC. The elucidation of the ABCB4 function in the liver, the identification of ABCB4 mutations and variants in LPAC patients, and the exploration of gene therapy for ABCB4 deficiency in animal models can help us to better understand the cellular, molecular, and genetic mechanisms underlying the onset of the disease, and will pave the way for early diagnosis and prevention of susceptible subjects and effective intervention for LPAC in patients.

Genome interpretation using in silico predictors of variant impact

Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.

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.

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.

Comparative in silico prediction of P-glycoprotein-mediated transport for 2010-2020 US FDA-approved drugs using six Web-tools

P-glycoprotein (P-gp) is an efflux pump implicated in pharmacokinetics and drug-drug interactions. The identification of its substrates is consequently an important issue, notably for drugs under development. For such a purpose, various in silico methods have been developed, but their relevance remains to be fully established. The present study was designed to get insight about this point, through determining the performance values of six freely accessible Web-tools (ADMETlab, AdmetSAR2.0, PgpRules, pkCSM, SwissADME and vNN-ADMET), computationally predicting P-gp-mediated transport. Using an external test set of 231 marketed drugs, approved over the 2010-2020 period by the US Food and Drug Administration and fully in vitro characterized for their P-gp substrate status, various performance parameters (including sensitivity, specificity, accuracy, Matthews correlation coefficient and area under the receiver operating characteristics curve) were determined. They were found to rather poorly meet criteria commonly required for acceptable prediction, whatever the Web-tools were used alone or in combination. Predictions of being P-gp substrate or non-substrate by these online in silico methods may therefore be considered with caution.

Variant Reinterpretation in Survivors of Cardiac Arrest With Preserved Ejection Fraction (the Cardiac Arrest Survivors With Preserved Ejection Fraction Registry) by Clinicians and Clinical Commercial Laboratories

BACKGROUND

Following an unexplained cardiac arrest, clinical genetic testing is increasingly becoming standard of care. Periodic review of variant classification is required, as reinterpretation can change the diagnosis, prognosis, and management of patients and their relatives.

METHODS

This study aimed to develop and validate a standardized algorithm to facilitate clinical application of the 2015 American College of Medical Genetics and Association for Molecular Pathology guidelines for the interpretation of genetic variants. The algorithm was applied to genetic results in the Cardiac Arrest Survivors With Preserved Ejection Fraction Registry, to assess the rate of variant reclassification over time. Variant classifications were then compared with the classifications of 2 commercial laboratories to determine the rate and identify sources of variant interpretation discordance.

RESULTS

Thirty-one percent of participants (40 of 131) had at least 1 genetic variant with a clinically significant reclassification over time. Variants of uncertain significance were more likely to be downgraded (73%) to benign than upgraded to pathogenic (27%; P=0.03). For the second part of the study, 50% (70 of 139) of variants had discrepant interpretations (excluding benign variants), provided by at least 1 team.

CONCLUSIONS

Periodic review of genetic variant classification is a key component of follow-up care given rapidly changing information in the field. There is potential for clinical care gaps with discrepant variant interpretations, based on the interpretation and application of current guidelines. The development of gene- and disease-specific guidelines and algorithms may provide an opportunity to further standardize variant interpretation reporting in the future. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT00292032.

Improving the clinical interpretation of missense variants in X linked genes using structural analysis

BACKGROUND

Improving the clinical interpretation of missense variants can increase the diagnostic yield of genomic testing and lead to personalised management strategies. Currently, due to the imprecision of bioinformatic tools that aim to predict variant pathogenicity, their role in clinical guidelines remains limited. There is a clear need for more accurate prediction algorithms and this study aims to improve performance by harnessing structural biology insights. The focus of this work is missense variants in a subset of genes associated with X linked disorders.

METHODS

We have developed a protein-specific variant interpreter (ProSper) that combines genetic and protein structural data. This algorithm predicts missense variant pathogenicity by applying machine learning approaches to the sequence and structural characteristics of variants.

RESULTS

ProSper outperformed seven previously described tools, including meta-predictors, in correctly evaluating whether or not variants are pathogenic; this was the case for 11 of the 21 genes associated with X linked disorders that met the inclusion criteria for this study. We also determined gene-specific pathogenicity thresholds that improved the performance of VEST4, REVEL and ClinPred, the three best-performing tools out of the seven that were evaluated; this was the case in 11, 11 and 12 different genes, respectively.

CONCLUSION

ProSper can form the basis of a molecule-specific prediction tool that can be implemented into diagnostic strategies. It can allow the accurate prioritisation of missense variants associated with X linked disorders, aiding precise and timely diagnosis. In addition, we demonstrate that gene-specific pathogenicity thresholds for a range of missense prioritisation tools can lead to an increase in prediction accuracy.

Performance of mutation pathogenicity prediction tools on missense variants associated with 46,XY differences of sex development

OBJECTIVES

Single nucleotide variants (SNVs) are the most common type of genetic variation among humans. High-throughput sequencing methods have recently characterized millions of SNVs in several thousand individuals from various populations, most of which are benign polymorphisms. Identifying rare disease-causing SNVs remains challenging, and often requires functional in vitro studies. Prioritizing the most likely pathogenic SNVs is of utmost importance, and several computational methods have been developed for this purpose. However, these methods are based on different assumptions, and often produce discordant results. The aim of the present study was to evaluate the performance of 11 widely used pathogenicity prediction tools, which are freely available for identifying known pathogenic SNVs: Fathmn, Mutation Assessor, Protein Analysis Through Evolutionary Relationships (Phanter), Sorting Intolerant From Tolerant (SIFT), Mutation Taster, Polymorphism Phenotyping v2 (Polyphen-2), Align Grantham Variation Grantham Deviation (Align-GVGD), CAAD, Provean, SNPs&GO, and MutPred.

METHODS

We analyzed 40 functionally proven pathogenic SNVs in four different genes associated with differences in sex development (DSD): 17β-hydroxysteroid dehydrogenase 3 (HSD17B3), steroidogenic factor 1 (NR5A1), androgen receptor (AR), and luteinizing hormone/chorionic gonadotropin receptor (LHCGR). To evaluate the false discovery rate of each tool, we analyzed 36 frequent (MAF>0.01) benign SNVs found in the same four DSD genes. The quality of the predictions was analyzed using six parameters: accuracy, precision, negative predictive value (NPV), sensitivity, specificity, and Matthews correlation coefficient (MCC). Overall performance was assessed using a receiver operating characteristic (ROC) curve.

RESULTS

Our study found that none of the tools were 100% precise in identifying pathogenic SNVs. The highest specificity, precision, and accuracy were observed for Mutation Assessor, MutPred, SNP, and GO. They also presented the best statistical results based on the ROC curve statistical analysis. Of the 11 tools evaluated, 6 (Mutation Assessor, Phanter, SIFT, Mutation Taster, Polyphen-2, and CAAD) exhibited sensitivity >0.90, but they exhibited lower specificity (0.42-0.67). Performance, based on MCC, ranged from poor (Fathmn=0.04) to reasonably good (MutPred=0.66).

CONCLUSION

Computational algorithms are important tools for SNV analysis, but their correlation with functional studies not consistent. In the present analysis, the best performing tools (based on accuracy, precision, and specificity) were Mutation Assessor, MutPred, and SNPs&GO, which presented the best concordance with functional studies.

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

GJB4 and GJC3 variants in non-syndromic hearing impairment in Ghana

IMPACT STATEMENT

Although connexins are known to be the major genetic factors associated with HI, only a few studies have investigated GJB4 and GJC3 variants among hearing-impaired patients. This study is the first to report GJB4 and GJC3 variants from an African HI cohort. We have demonstrated that GJB4 and GJC3 genes may not contribute significantly to HI in Ghana, hence these genes should not be considered for routine clinical screening in Ghana. However, it is important to study a larger population to determine the association of GJB4 and GJC3 variants with HI.

Using an integrative machine learning approach utilising homology modelling to clinically interpret genetic variants: CACNA1F as an exemplar

Advances in DNA sequencing technologies have revolutionised rare disease diagnostics and have led to a dramatic increase in the volume of available genomic data. A key challenge that needs to be overcome to realise the full potential of these technologies is that of precisely predicting the effect of genetic variants on molecular and organismal phenotypes. Notably, despite recent progress, there is still a lack of robust in silico tools that accurately assign clinical significance to variants. Genetic alterations in the CACNA1F gene are the commonest cause of X-linked incomplete Congenital Stationary Night Blindness (iCSNB), a condition associated with non-progressive visual impairment. We combined genetic and homology modelling data to produce CACNA1F-vp, an in silico model that differentiates disease-implicated from benign missense CACNA1F changes. CACNA1F-vp predicts variant effects on the structure of the CACNA1F encoded protein (a calcium channel) using parameters based upon changes in amino acid properties; these include size, charge, hydrophobicity, and position. The model produces an overall score for each variant that can be used to predict its pathogenicity. CACNA1F-vp outperformed four other tools in identifying disease-implicated variants (area under receiver operating characteristic and precision recall curves = 0.84; Matthews correlation coefficient = 0.52) using a tenfold cross-validation technique. We consider this protein-specific model to be a robust stand-alone diagnostic classifier that could be replicated in other proteins and could enable precise and timely diagnosis.

Novel SERPINC1 missense mutation (Cys462Tyr) causes disruption of the 279Cys-462Cys disulfide bond and leads to type Ⅰ hereditary antithrombin deficiency

BACKGROUND

Antithrombin (AT) is the primary physiological anticoagulant of normal hemostasis. Hereditary AT deficiency, an autosomal dominant thrombotic disease caused by mutations in the AT gene (SERPINC1), is associated with venous thromboembolism.

OBJECTIVE

We investigated the phenotypes, genotypes, and pathogenesis of hereditary AT deficiency in a 12-year-old boy (proband) who developed a pulmonary embolism and a subsequent deep vein thrombosis.

METHODS

The AT activity and AT antigen level of the proband and his family members were measured. Mutation sites in all seven exons of SERPINC1 were identified. Analysis of conserved regions around codon 462 of the SERPINC1 gene and functional predictions were performed using bioinformatics tools.

RESULTS

The proband, his father, and his paternal grandmother demonstrated reduced AT activity and antigen levels consistent with Type I AT deficiency. A novel heterozygous missense mutation, c.1385G>A (Cys462Tyr) was identified in all three symptomatic family members. This missense mutation causes disruption of the 279Cys-462Cys disulfide bond and leads to type Ⅰ hereditary AT deficiency.

CONCLUSION

A SERPINC1 missense mutation (Cys462Tyr) causing damage to the 279Cys-462Cys disulfide bond of the AT protein appears to be the cause of Type I AT deficiency in this family. These findings indicate one pathological mechanism associated with hereditary AT deficiency.

A Systematic Literature Review of Whole Exome and Genome Sequencing Population Studies of Genetic Susceptibility to Cancer

The application of next-generation sequencing (NGS) technologies in cancer research has accelerated the discovery of somatic mutations; however, progress in the identification of germline variation associated with cancer risk is less clear. We conducted a systematic literature review of cancer genetic susceptibility studies that used NGS technologies at an exome/genome-wide scale to obtain a fuller understanding of the research landscape to date and to inform future studies. The variability across studies on methodologies and reporting was considerable. Most studies sequenced few high-risk (mainly European) families, used a candidate analysis approach, and identified potential cancer-related germline variants or genes in a small fraction of the sequenced cancer cases. This review highlights the importance of establishing consensus on standards for the application and reporting of variants filtering strategies. It also describes the progress in the identification of cancer-related germline variation to date. These findings point to the untapped potential in conducting studies with appropriately sized and racially diverse families and populations, combining results across studies and expanding beyond a candidate analysis approach to advance the discovery of genetic variation that accounts for the unexplained cancer heritability.

Brugada Syndrome: Oligogenic or Mendelian Disease?

Brugada syndrome (BrS) is diagnosed by a coved-type ST-segment elevation in the right precordial leads on the electrocardiogram (ECG), and it is associated with an increased risk of sudden cardiac death (SCD) compared to the general population. Although BrS is considered a genetic disease, its molecular mechanism remains elusive in about 70-85% of clinically-confirmed cases. Variants occurring in at least 26 different genes have been previously considered causative, although the causative effect of all but the SCN5A gene has been recently challenged, due to the lack of systematic, evidence-based evaluations, such as a variant's frequency among the general population, family segregation analyses, and functional studies. Also, variants within a particular gene can be associated with an array of different phenotypes, even within the same family, preventing a clear genotype-phenotype correlation. Moreover, an emerging concept is that a single mutation may not be enough to cause the BrS phenotype, due to the increasing number of common variants now thought to be clinically relevant. Thus, not only the complete list of genes causative of the BrS phenotype remains to be determined, but also the interplay between rare and common multiple variants. This is particularly true for some common polymorphisms whose roles have been recently re-evaluated by outstanding works, including considering for the first time ever a polygenic risk score derived from the heterozygous state for both common and rare variants. The more common a certain variant is, the less impact this variant might have on heart function. We are aware that further studies are warranted to validate a polygenic risk score, because there is no mutated gene that connects all, or even a majority, of BrS cases. For the same reason, it is currently impossible to create animal and cell line genetic models that represent all BrS cases, which would enable the expansion of studies of this syndrome. Thus, the best model at this point is the human patient population. Further studies should first aim to uncover genetic variants within individuals, as well as to collect family segregation data to identify potential genetic causes of BrS.

Optimization of in silico tools for predicting genetic variants: individualizing for genes with molecular sub-regional stratification

Genes are unique in functional role and differ in their sensitivities to genetic defects, but with difficulties in pathogenicity prediction. This study attempted to improve the performance of existing in silico algorithms and find a common solution based on individualization strategy. We initiated the individualization with the epilepsy-related SCN1A variants by sub-regional stratification. SCN1A missense variants related to epilepsy were retrieved from mutation databases, and benign missense variants were collected from ExAC database. Predictions were performed by using 10 traditional tools with stepwise optimizations. Model predictive ability was evaluated using the five-fold cross-validations on variants of SCN1A, SCN2A, and KCNQ2. Additional validation was performed in SCN1A variants of damage-confirmed/familial epilepsy. The performance of commonly used predictors was less satisfactory for SCN1A with accuracy less than 80% and varied dramatically by functional domains of Nav1.1. Multistep individualized optimizations, including cutoff resetting, domain-based stratification, and combination of predicting algorithms, significantly increased predictive performance. Similar improvements were obtained for variants in SCN2A and KCNQ2. The predictive performance of the recently developed ensemble tools, such as Mendelian clinically applicable pathogenicity, combined annotation-dependent depletion and Eigen, was also improved dramatically by application of the strategy with molecular sub-regional stratification. The prediction scores of SCN1A variants showed linear correlations with the degree of functional defects and the severity of clinical phenotypes. This study highlights the need of individualized optimization with molecular sub-regional stratification for each gene in practice.

A computational approach for explaining the effect of the prl gene polymorphism on prolactin structure and biological activity in Japanese quails

Prolactin is a versatile hormone with multiple activities, including a negative control on egg production. This study was conducted to genotype all the coding portions of the prl gene using PCR-SSCP-sequencing, and to investigate the effects of amino acid substitutions of the prl gene on the structure and function of prolactin in quails using in silico approach. Though all genotyped exons exerted homogenous PCR-SSCP patterns, a total of 12 novel SNPs were detected in the investigated exons, including three SNPs in exon-1, 8 SNPs in exon-2, and one SNP in exon-4. Three adjacent missense SNPs were detected in exon-2, namely H69P, T70P, and S71F. Computational tools indicated obvious deleterious effects of T70P, with less extent to H69P and S71F on prolactin functions and activity, which may lead to limited participation of this hormone in the negative control of egg production. In conclusion, the introduction of in silico prediction has suggested an alternative solution for the breeders to evaluate the effect of each witnessed nsSNP in protein structure and function. The current study suggests three nsSNPs, T70P, T70P, and S71F as strong candidates for the negative effect on prolactin biological activity with a consequent reversal positive effect on egg productivity traits.

Epigenetic signatures in overgrowth syndromes: Translational opportunities

In recent years, numerous overgrowth syndromes have been found to be caused by pathogenic DNA sequence variants in "epigenes," genes that encode proteins that function in epigenetic regulation. Epigenetic marks, including DNA methylation (DNAm), histone modifications and chromatin conformation, have emerged as a vital genome-wide regulatory mechanism that modulate the transcriptome temporally and spatially to drive normal developmental and cellular processes. Evidence suggests that epigenetic marks are layered and engage in crosstalk, in that disruptions of any one component of the epigenetic machinery impact the others. This interdependence of epigenetic marks underpins the recent identification of gene-specific DNAm signatures for a variety of disorders caused by pathogenic variants in epigenes. Here, we discuss the power of DNAm signatures with respect to furthering our understanding of disease pathophysiology, enhancing the efficacy of molecular diagnostics and identifying new targets for therapeutics of overgrowth syndromes. These findings highlight the promise of the field of epigenomics to provide unprecedented insights into disease mechanisms generating a host of opportunities to advance precision medicine.

Molecular modelling and dynamics of CA2 missense mutations causative to carbonic anhydrase 2 deficiency syndrome

Carbonic anhydrase 2 (CA2) enzyme deficiency caused by CA2 gene mutations is an inherited disorder characterized by symptoms like osteopetrosis, renal tubular acidosis, and cerebral calcification. This study has collected the CA2 deficiency causal missense mutations and assessed their pathogenicity using diverse computational programs. The 3D protein models for all missense mutations were built, and analyzed for structural divergence, protein stability, and molecular dynamics properties. We found M-CAP as the most sensitive prediction method to measure the deleterious potential of CA2 missense mutations. Free energy dynamics of tertiary structure models of CA2 mutants with DUET, mCSM, and SDM based consensus methods predicted only 50% of the variants as destabilizing. Superimposition of native and mutant CA2 models revealed the minor structural fluctuations at the amino acid residue level but not at the whole protein structure level. Near native molecular dynamic simulation analysis indicated that CA2 causative missense variants result in residue level fluctuation pattern in the protein structure. This study expands the understanding of genotype-protein phenotype correlations underlying CA2 variant pathogenicity and presents a potential avenue for modifying the CA2 deficiency by targeting biophysical structural features of CA2 protein. Communicated by Ramaswamy H. Sarma.

New insights into DNA methylation signatures: SMARCA2 variants in Nicolaides-Baraitser syndrome

BACKGROUND

Nicolaides-Baraitser syndrome (NCBRS) is a neurodevelopmental disorder caused by pathogenic sequence variants in SMARCA2 which encodes the catalytic component of the chromatin remodeling BAF complex. Pathogenic variants in genes that encode epigenetic regulators have been associated with genome-wide changes in DNA methylation (DNAm) in affected individuals termed DNAm signatures.

METHODS

Genome-wide DNAm was assessed in whole-blood samples from the individuals with pathogenic SMARCA2 variants and NCBRS diagnosis (n = 8) compared to neurotypical controls (n = 23) using the Illumina MethylationEPIC array. Differential methylated CpGs between groups (DNAm signature) were identified and used to generate a model enabling classification variants of uncertain significance (VUS; n = 9) in SMARCA2 as "pathogenic" or "benign". A validation cohort of NCBRS cases (n = 8) and controls (n = 96) demonstrated 100% model sensitivity and specificity.

RESULTS

We identified a DNAm signature of 429 differentially methylated CpG sites in individuals with NCBRS. The genes to which these CpG sites map are involved in cell differentiation, calcium signaling, and neuronal function consistent with NCBRS pathophysiology. DNAm model classifications of VUS were concordant with the clinical phenotype; those within the SMARCA2 ATPase/helicase domain classified as "pathogenic". A patient with a mild neurodevelopmental NCBRS phenotype and a VUS distal to the ATPase/helicase domain did not score as pathogenic, clustering away from cases and controls. She demonstrated an intermediate DNAm profile consisting of one subset of signature CpGs with methylation levels characteristic of controls and another characteristic of NCBRS cases; each mapped to genes with ontologies consistent with the patient's unique clinical presentation.

CONCLUSIONS

Here we find that a DNAm signature of SMARCA2 pathogenic variants in NCBRS maps to CpGs relevant to disorder pathophysiology, classifies VUS, and is sensitive to the position of the variant in SMARCA2. The patient with an intermediate model score demonstrating a unique genotype-epigenotype-phenotype correlation underscores the potential utility of this signature as a functionally relevant VUS classification system scalable beyond binary "benign" versus "pathogenic" scoring. This is a novel feature of DNAm signatures that could enable phenotypic predictions from genotype data. Our findings also demonstrate that DNAm signatures can be domain-specific, highlighting the precision with which they can reflect genotypic variation.

In silico analysis of missense mutations in exons 1-5 of the F9 gene that cause hemophilia B

Background

Missense mutations in the first five exons of F9, which encodes factor FIX, represent 40% of all mutations that cause hemophilia B. To address the ongoing debate regarding in silico identification of disease-causing mutations at these exons, we analyzed 215 missense mutations from www.factorix.org using six in silico prediction tools, which are the most common used programs for analysis prediction of impact of mutations on the protein structure and function, with further advantage of using similar approaches. We developed different algorithms to integrate multiple predictions from such tools. In order to approach a structural analysis on FIX we performed a modeling of five selected pathogenic mutations.

Results

SIFT, PolyPhen-2 HumDiv, SNAP2, and MutationAssessor were the most successful in identifying true non-causative and causative mutations. A proposed function integrating these algorithms (wgP4) was the most sensitive (90.1%), specific (22.6%), and accurate (87%) than similar functions, and identified 187 variants as deleterious. Clinical phenotype was significantly associated with predicted causative mutations at all five exons. However, PolyPhen-2 HumDiv was more successful in linking clinical severity to specific exons, while functions that integrate 4–6 predictions were more successful in linking phenotype to genotypes at the light chain (exons 3–5). The most important value of integrating multiple predictions is the inclusion of scores derived from different approaches. Modeling of protein structure showed the effects of pathogenic nsSNPs on structure and function of FIX.

Conclusions

A simple function that integrates information from different in silico programs yields the best prediction of mutated phenotypes. However, the specificity, sensitivity, and accuracy of genotype-phenotype predictions depend on specific characteristics of the protein domain and the disease of interest as we validated by the structural analysis of selected pathogenic F9 mutations. The proposed function integrating algorithm (wgP4) might be useful for the analysis of nsSNPs impact on other genes.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2919-x) contains supplementary material, which is available to authorized users.

Identification of genetic variants associated with tacrolimus metabolism in kidney transplant recipients by extreme phenotype sampling and next generation sequencing

An extreme phenotype sampling (EPS) model with targeted next-generation sequencing (NGS) identified genetic variants associated with tacrolimus (Tac) metabolism in subjects from the Deterioration of Kidney Allograft Function (DeKAF) Genomics cohort which included 1,442 European Americans (EA) and 345 African Americans (AA). This study included 48 subjects separated into 4 groups of 12 (AA high, AA low, EA high, EA low). Groups were selected by the extreme phenotype of dose-normalized Tac trough concentrations after adjusting for common genetic variants and clinical factors. NGS spanned >3 Mb of 28 genes and identified 18,661 genetic variants (3,961 previously unknown). A group of 125 deleterious variants, by SIFT analysis, were associated with Tac troughs in EAs (burden test, p=0.008), CYB5R2 was associated with Tac troughs in AAs (SKAT, p=0.00079). In CYB5R2, rs61733057 (increased allele frequency in AAs) was predicted to disrupt protein function by SIFT and PolyPhen2 analysis. The variants merit further validation.

Kir2.2 p.Thr140Met: a genetic susceptibility to sporadic periodic paralysis

INTRODUCTION

Periodic paralyses (PP) are recurrent episodes of flaccid limb muscle weakness. Next to autosomal dominant forms, sporadic PP (SPP) cases are known but their genetics are unclear.

METHODS

In a patient with hypokalemic SPP, we performed exome sequencing to identify a candidate gene. We sequenced this gene in 263 unrelated PP patients without any known causative mutations. Then we performed functional analysis of all variants found and molecular modelling for interpretation.

RESULTS

Exome sequencing in the proband yielded three heterozygous variants predicted to be linked to disease. These encoded p.Thr140Met in the Kir2.2 potassium channel, p.Asp229Asn in protein kinase C theta, and p.Thr15943Ile in titin. Since all hitherto known causative PP genes code for ion channels, we studied the Kir2.2-encoding gene, KCNJ12, for involvement in PP pathogenesis. KCNJ12 screening in 263 PP patients revealed three further variants, each in a single individual and coding for p.Gly419Ser, p.Cys75Tyr, and p.Ile283Val. All four Kir2.2 variants were functionally expressed. Only p.Thr140Met displayed relevant functional alterations, i.e. homo-tetrameric channels produced almost no current, and hetero-tetrameric channels suppressed co-expressed wildtype Kir2.1 in a dominant-negative manner. Molecular modelling showed Kir2.2 p.Thr140Met to reduce movement of potassium ions towards binding sites in the hetero-tetramer pore compatible with a reduced maximal current. MD simulations revealed loss of hydrogen bonding with the p.Thr140Met substitution.

DISCUSSION

The electrophysiological findings of p.Thr140Met are similar to those found in thyrotoxic PP caused by Kir2.6 mutations. Also, the homologous Thr140 residue is mutated in Kir2.6. This supports the idea that Kir2.2 p.Thr140Met conveys susceptibility to SPP and should be included in genetic screening.

Predicting changes to INa from missense mutations in human SCN5A

Mutations in SCN5A can alter the cardiac sodium current I_Na and increase the risk of potentially lethal conditions such as Brugada and long-QT syndromes. The relation between mutations and their clinical phenotypes is complex, and systems to predict clinical severity of unclassified SCN5A variants perform poorly. We investigated if instead we could predict changes to I_Na, leaving the link from I_Na to clinical phenotype for mechanistic simulation studies. An exhaustive list of nonsynonymous missense mutations and resulting changes to I_Na was compiled. We then applied machine-learning methods to this dataset, and found that changes to I_Na could be predicted with higher sensitivity and specificity than most existing predictors of clinical significance. The substituted residues’ location on the protein correlated with channel function and strongly contributed to predictions, while conservedness and physico-chemical properties did not. However, predictions were not sufficiently accurate to form a basis for mechanistic studies. These results show that changes to I_Na, the mechanism through which SCN5A mutations create cardiac risk, are already difficult to predict using purely in-silico methods. This partly explains the limited success of systems to predict clinical significance of SCN5A variants, and underscores the need for functional studies of I_Na in risk assessment.

γ2 GABAAR Trafficking and the Consequences of Human Genetic Variation

GABA type A receptors (GABA_ARs) mediate the majority of fast inhibitory neurotransmission in the central nervous system (CNS). Most prevalent as heteropentamers composed of two α, two β, and a γ2 subunit, these ligand-gated ionotropic chloride channels are capable of extensive genetic diversity (α1-6, β1-3, γ1-3, δ, 𝜀, 𝜃, π, ρ1-3). Part of this selective GABA_AR assembly arises from the critical role for γ2 in maintaining synaptic receptor localization and function. Accordingly, mutations in this subunit account for over half of the known epilepsy-associated genetic anomalies identified in GABA_ARs. Fundamental structure-function studies and cellular pathology investigations have revealed dynamic GABA_AR trafficking and synaptic scaffolding as critical regulators of GABAergic inhibition. Here, we introduce in vitro and in vivo findings regarding the specific role of the γ2 subunit in receptor trafficking. We then examine γ2 subunit human genetic variation and assess disease related phenotypes and the potential role of altered GABA_AR trafficking. Finally, we discuss new-age imaging techniques and their potential to provide novel insight into critical regulatory mechanisms of GABA_AR function.

Navigating the nuances of clinical sequence variant interpretation in Mendelian disease

Understanding clinical genetic test results in the era of next-generation sequencing has become increasingly complex, necessitating clear and thorough guidelines for sequence variant interpretation. To meet this need the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) published guidelines for a systematic approach for sequence variant interpretation in 2015. This framework is intended to be adaptable to any Mendelian condition, promoting transparency and consistency in variant interpretation, yet its comprehensive nature yields important challenges and caveats that end users must understand. In this review, we address some of these nuances and discuss the evolving efforts to refine and adapt this framework. We also consider the added complexity of distinguishing between variant-level interpretations and case-level conclusions, particularly in the context of the large gene panel approach to clinical diagnostics.

Protein phenotype diagnosis of autosomal dominant calmodulin mutations causing irregular heart rhythms

The life-threatening group of irregular cardiac rhythmic disorders also known as Cardiac Arrhythmias (CA) are caused by mutations in highly conserved Calmodulin (CALM/CaM) genes. Herein, we present a multidimensional approach to diagnose changes in phenotypic, stability, and Ca²⁺ ion binding properties of CA-causing mutations. Mutation pathogenicity was determined by diverse computational machine learning approaches. We further modeled the mutations in 3D protein structure and analyzed residue level phenotype plasticity. We have also examined the influence of torsion angles, number of H-bonds, and free energy dynamics on the stability, near-native simulation dynamic potential of residue fluctuations in protein structures, Ca²⁺ ion binding potentials, of CaM mutants. Our study recomends to use M-CAP method for measuring the pathogenicity of CA causing CaM variants. Interestingly, most CA-causing variants we analyzed, exists in either third (V/H-96, S/I-98, V-103) or fourth (G/V-130, V/E/H-132, H-134, P-136, G-141, and L-142) EF-hands located in carboxyl domains of the CaM molecule. We observed that the minor structural fluctuations caused by these variants are likely tolerable owing to the highly flexible nature of calmodulin's globular domains. However, our molecular docking results supports that these variants disturb the affinity of CaM toward Ca²⁺ ions and corroborate previous findings from functional studies. Taken together, these computational findings can explain the molecular reasons for subtle changes in structure, flexibility, and stability aspects of mutant CaM molecule. Our comprehensive molecular scanning approach demonstrates the utility of computational methods in quick preliminary screening of CA- CaM mutations before undertaking time consuming and complicated functional laboratory assays.

Functional Assays Are Essential for Interpretation of Missense Variants Associated with Variable Expressivity

Missense DNA variants have variable effects upon protein function. Consequently, interpreting their pathogenicity is challenging, especially when they are associated with disease variability. To determine the degree to which functional assays inform interpretation, we analyzed 48 CFTR missense variants associated with variable expressivity of cystic fibrosis (CF). We assessed function in a native isogenic context by evaluating CFTR mutants that were stably expressed in the genome of a human airway cell line devoid of endogenous CFTR expression. 21 of 29 variants associated with full expressivity of the CF phenotype generated <10% wild-type CFTR (WT-CFTR) function, a conservative threshold for the development of life-limiting CF lung disease, and five variants had moderately decreased function (10% to ∼25% WT-CFTR). The remaining three variants in this group unexpectedly had >25% WT-CFTR function; two were higher than 75% WT-CFTR. As expected, 14 of 19 variants associated with partial expressivity of CF had >25% WT-CFTR function; however, four had minimal to no effect on CFTR function (>75% WT-CFTR). Thus, 6 of 48 (13%) missense variants believed to be disease causing did not alter CFTR function. Functional studies substantially refined pathogenicity assignment with expert annotation and criteria from the American College of Medical Genetics and Genomics and Association for Molecular Pathology. However, four algorithms (CADD, REVEL, SIFT, and PolyPhen-2) could not differentiate between variants that caused severe, moderate, or minimal reduction in function. In the setting of variable expressivity, these results indicate that functional assays are essential for accurate interpretation of missense variants and that current prediction tools should be used with caution.

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.

Performance of in silico prediction tools for the classification of rare BRCA1/2 missense variants in clinical diagnostics

Background

The use of next-generation sequencing approaches in clinical diagnostics has led to a tremendous increase in data and a vast number of variants of uncertain significance that require interpretation. Therefore, prediction of the effects of missense mutations using in silico tools has become a frequently used approach. Aim of this study was to assess the reliability of in silico prediction as a basis for clinical decision making in the context of hereditary breast and/or ovarian cancer.

Methods

We tested the performance of four prediction tools (Align-GVGD, SIFT, PolyPhen-2, MutationTaster2) using a set of 236 BRCA1/2 missense variants that had previously been classified by expert committees. However, a major pitfall in the creation of a reliable evaluation set for our purpose is the generally accepted classification of BRCA1/2 missense variants using the multifactorial likelihood model, which is partially based on Align-GVGD results. To overcome this drawback we identified 161 variants whose classification is independent of any previous in silico prediction. In addition to the performance as stand-alone tools we examined the sensitivity, specificity, accuracy and Matthews correlation coefficient (MCC) of combined approaches.

Results

PolyPhen-2 achieved the lowest sensitivity (0.67), specificity (0.67), accuracy (0.67) and MCC (0.39). Align-GVGD achieved the highest values of specificity (0.92), accuracy (0.92) and MCC (0.73), but was outperformed regarding its sensitivity (0.90) by SIFT (1.00) and MutationTaster2 (1.00). All tools suffered from poor specificities, resulting in an unacceptable proportion of false positive results in a clinical setting. This shortcoming could not be bypassed by combination of these tools. In the best case scenario, 138 families would be affected by the misclassification of neutral variants within the cohort of patients of the German Consortium for Hereditary Breast and Ovarian Cancer.

Conclusion

We show that due to low specificities state-of-the-art in silico prediction tools are not suitable to predict pathogenicity of variants of uncertain significance in BRCA1/2. Thus, clinical consequences should never be based solely on in silico forecasts. However, our data suggests that SIFT and MutationTaster2 could be suitable to predict benignity, as both tools did not result in false negative predictions in our analysis.

Electronic supplementary material

The online version of this article (10.1186/s12920-018-0353-y) contains supplementary material, which is available to authorized users.

MERRF Classification: Implications for Diagnosis and Clinical Trials

BACKGROUND

Given the etiologic heterogeneity of disease classification using clinical phenomenology, we employed contemporary criteria to classify variants associated with myoclonic epilepsy with ragged-red fibers (MERRF) syndrome and to assess the strength of evidence of gene-disease associations. Standardized approaches are used to clarify the definition of MERRF, which is essential for patient diagnosis, patient classification, and clinical trial design.

METHODS

Systematic literature and database search with application of standardized assessment of gene-disease relationships using modified Smith criteria and of variants reported to be associated with MERRF using modified Yarham criteria.

RESULTS

Review of available evidence supports a gene-disease association for two MT-tRNAs and for POLG. Using modified Smith criteria, definitive evidence of a MERRF gene-disease association is identified for MT-TK. Strong gene-disease evidence is present for MT-TL1 and POLG. Functional assays that directly associate variants with oxidative phosphorylation impairment were critical to mtDNA variant classification. In silico analysis was of limited utility to the assessment of individual MT-tRNA variants. With the use of contemporary classification criteria, several mtDNA variants previously reported as pathogenic or possibly pathogenic are reclassified as neutral variants.

CONCLUSIONS

MERRF is primarily an MT-TK disease, with pathogenic variants in this gene accounting for ~90% of MERRF patients. Although MERRF is phenotypically and genotypically heterogeneous, myoclonic epilepsy is the clinical feature that distinguishes MERRF from other categories of mitochondrial disorders. Given its low frequency in mitochondrial disorders, myoclonic epilepsy is not explained simply by an impairment of cellular energetics. Although MERRF phenocopies can occur in other genes, additional data are needed to establish a MERRF disease-gene association. This approach to MERRF emphasizes standardized classification rather than clinical phenomenology, thus improving patient diagnosis and clinical trial design.

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.

Next Generation Sequencing Methods for Diagnosis of Epilepsy Syndromes

Epilepsy is a neurological disorder characterized by an increased predisposition for seizures. Although this definition suggests that it is a single disorder, epilepsy encompasses a group of disorders with diverse aetiologies and outcomes. A genetic basis for epilepsy syndromes has been postulated for several decades, with several mutations in specific genes identified that have increased our understanding of the genetic influence on epilepsies. With 70-80% of epilepsy cases identified to have a genetic cause, there are now hundreds of genes identified to be associated with epilepsy syndromes which can be analyzed using next generation sequencing (NGS) techniques such as targeted gene panels, whole exome sequencing (WES) and whole genome sequencing (WGS). For effective use of these methodologies, diagnostic laboratories and clinicians require information on the relevant workflows including analysis and sequencing depth to understand the specific clinical application and diagnostic capabilities of these gene sequencing techniques. As epilepsy is a complex disorder, the differences associated with each technique influence the ability to form a diagnosis along with an accurate detection of the genetic etiology of the disorder. In addition, for diagnostic testing, an important parameter is the cost-effectiveness and the specific diagnostic outcome of each technique. Here, we review these commonly used NGS techniques to determine their suitability for application to epilepsy genetic diagnostic testing.

A molecular pathway analysis informs the genetic risk for arrhythmias during antipsychotic treatment

Arrhythmias are a frequent and potentially fatal side effect of antipsychotic treatment. Strict ECG monitoring and clinical interviews are the standards used to prevent arrhythmias. A biologic predictive tool is missing. The identification of a genetic makeup at risk of antipsychotic-induced arrhythmias is the aim of the present investigation. The aim of this study was to identify a molecular pathway enriched in single nucleotide polymorphisms associated with antipsychotic-induced QTc modifications. In total, 661 schizophrenic individuals from the CATIE study, M=486 (73.52%), mean age=40.92±11.02, were included. QTc variation was measured as a phase-specific change-created variable. A nested mixed regression for a repeated-measures model served in R for the analysis of the clinical and treatment-related covariates and molecular pathway analysis. Plink was used for the genetic genome-wide analysis. Quality checking was the standard (genotype call rate>0.95; minor allele frequency>0.01; Hardy-Weinberg equilibrium<0.0001) and the inflation factor was controlled by λ values. Quetiapine and perphenazine were associated with QTc variation during phase 1. No other significant association was detected. No significant inflation was detected. A number of molecular pathways were associated with QT variation at a conservative (adjusted) P value less than 0.05, including pathways related to neuronal wiring and collagen biosynthesis, along with pathways related to K+ currents and cardiac contraction. Pathways related to neuronal wiring, collagen biosynthesis, and ion currents were identified as possibly involved in QTc modifications during antispsychotic treatment in SKZ patients.

Using the genome aggregation database, computational pathogenicity prediction tools, and patch clamp heterologous expression studies to demote previously published long QT syndrome type 1 mutations from pathogenic to benign

BACKGROUND

Mutations in the KCNQ1-encoded Kv7.1 potassium channel cause long QT syndrome (LQTS) type 1 (LQT1). It has been suggested that ∼10%-20% of rare LQTS case-derived variants in the literature may have been published erroneously as LQT1-causative mutations and may be "false positives."

OBJECTIVE

The purpose of this study was to determine which previously published KCNQ1 case variants are likely false positives.

METHODS

A list of all published, case-derived KCNQ1 missense variants (MVs) was compiled. The occurrence of each MV within the Genome Aggregation Database (gnomAD) was assessed. Eight in silico tools were used to predict each variant's pathogenicity. Case-derived variants that were either (1) too frequently found in gnomAD or (2) absent in gnomAD but predicted to be pathogenic by ≤2 tools were considered potential false positives. Three of these variants were characterized functionally using whole-cell patch clamp technique.

RESULTS

Overall, there were 244 KCNQ1 case-derived MVs. Of these, 29 (12%) were seen in ≥10 individuals in gnomAD and are demotable. However, 157 of 244 MVs (64%) were absent in gnomAD. Of these, 7 (4%) were predicted to be pathogenic by ≤2 tools, 3 of which we characterized functionally. There was no significant difference in current density between heterozygous KCNQ1-F127L, -P477L, or -L619M variant-containing channels compared to KCNQ1-WT.

CONCLUSION

This study offers preliminary evidence for the demotion of 32 (13%) previously published LQT1 MVs. Of these, 29 were demoted because of their frequent sighting in gnomAD. Additionally, in silico analysis and in vitro functional studies have facilitated the demotion of 3 ultra-rare MVs (F127L, P477L, L619M).