Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Draelos RL

PROVIDER: S-EPMC9018586 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Publications

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

Draelos Rachel L RL Ezekian Jordan E JE Zhuang Farica F Moya-Mendez Mary E ME Zhang Zhushan Z Rosamilia Michael B MB Manivannan Perathu K R PKR Henao Ricardo R Landstrom Andrew P AP

Circulation. Arrhythmia and electrophysiology 20220331 4

<h4>Background</h4>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 ventric ...[more]

PMID: 35357185

Similar Datasets

Project description:RationaleAutosomal-dominant mutations in ryanodine receptor type 2 ( RYR2) are responsible for ≈60% of all catecholaminergic polymorphic ventricular tachycardia. Dysfunctional RyR2 subunits trigger inappropriate calcium leak from the tetrameric channel resulting in potentially lethal ventricular tachycardia. In vivo CRISPR/Cas9-mediated gene editing is a promising strategy that could be used to eliminate the disease-causing Ryr2 allele and hence rescue catecholaminergic polymorphic ventricular tachycardia.ObjectiveTo determine if somatic in vivo genome editing using the CRISPR/Cas9 system delivered by adeno-associated viral (AAV) vectors could correct catecholaminergic polymorphic ventricular tachycardia arrhythmias in mice heterozygous for RyR2 mutation R176Q (R176Q/+).Methods and resultsGuide RNAs were designed to specifically disrupt the R176Q allele in the R176Q/+ mice using the SaCas9 ( Staphylococcus aureus Cas9) genome editing system. AAV serotype 9 was used to deliver Cas9 and guide RNA to neonatal mice by single subcutaneous injection at postnatal day 10. Strikingly, none of the R176Q/+ mice treated with AAV-CRISPR developed arrhythmias, compared with 71% of R176Q/+ mice receiving control AAV serotype 9. Total Ryr2 mRNA and protein levels were significantly reduced in R176Q/+ mice, but not in wild-type littermates. Targeted deep sequencing confirmed successful and highly specific editing of the disease-causing R176Q allele. No detectable off-target mutagenesis was observed in the wild-type Ryr2 allele or the predicted putative off-target site, confirming high specificity for SaCas9 in vivo. In addition, confocal imaging revealed that gene editing normalized the enhanced Ca2+ spark frequency observed in untreated R176Q/+ mice without affecting systolic Ca2+ transients.ConclusionsAAV serotype 9-based delivery of the SaCas9 system can efficiently disrupt a disease-causing allele in cardiomyocytes in vivo. This work highlights the potential of somatic genome editing approaches for the treatment of lethal autosomal-dominant inherited cardiac disorders, such as catecholaminergic polymorphic ventricular tachycardia.

Dataset Information

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

Background

Methods

Results

Conclusions

Publications

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

Similar Datasets

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