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Exome sequencing identifies molecular diagnosis in children with drug-resistant epilepsy.

ABSTRACT: Objective:Early onset drug-resistant epilepsy is a neurologic disorder in which 2 antiepileptic drugs fail to maintain the seizure-free status of the patient. Heterogeneous clinical presentations make the diagnosis challenging. We aim to identify the underlying genetic causes of a pediatric cohort with drug-resistant epilepsy and evaluate whether the findings can provide information on patient management. Methods:We include patients with drug-resistant epilepsy onset before 18 years of age. Singleton clinical chromosomal microarray (CMA) followed by whole exome sequencing (WES) was performed using genomic DNA. In the first-tier analysis of the exome data, we aimed to identify disease-causing mutations in 546 genes known to cause, or to be associated with, epilepsy. For negative cases, we proceeded to exome-wide analysis. Rare coding variants were interrogated for pathogenicity based on the American College of Medical Genetics and Genomics (ACMG) guidelines. Results:We recruited 50 patients. We identified 6 pathogenic or likely pathogenic mutations, giving a diagnostic yield of 12%. Mutations were found in 6 different genes: SCN8A, SCN1A, MECP2, CDKL5, DEPDC5, and CHD2. The CDKL5 variant was found to be mosaic. One variant of unknown significance (VUS) in KCNT1 was found in a patient with compatible clinical features. Of note, a reported pathogenic SCN5A mutation known to contribute to Brugada syndrome, was also found in the patient with an SCN1A mutation. Significance:Our study suggests that singleton WES is an effective diagnostic tool for drug-resistant epilepsy. Genetic diagnosis can help to consolidate the clinical diagnosis, to facilitate phenotypic expansion, and to influence treatment and management options for seizure control in our patients. In our study, a significant portion of the genetic findings are known to be associated with an increased risk of sudden unexpected death in epilepsy (SUDEP). These findings could assist with more appropriate management in patients with epilepsy.

SUBMITTER: Tsang MH

PROVIDER: S-EPMC6398105 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Project description:Background: Epilepsy is a complex chronic disease of the nervous system which influences the health of approximately 70 million patients worldwide. In the past few decades, despite the development of novel antiepileptic drugs, around one-third of patients with epilepsy have developed drug-resistant epilepsy. We performed a bioinformatic analysis to explore the underlying diagnostic markers and mechanisms of drug-resistant epilepsy. Methods: Weighted correlation network analysis (WGCNA) was applied to genes in epilepsy samples downloaded from the Gene Expression Omnibus database to determine key modules. The least absolute shrinkage and selection operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms were used to screen the genes resistant to carbamazepine, phenytoin, and valproate, and sensitivity of the three-class classification SVM model was verified through the receiver operator characteristic (ROC) curve. A protein-protein interaction (PPI) network was utilized to analyze the protein interaction relationship. Finally, ingenuity pathway analysis (IPA) was adopted to conduct disease and function pathway and network analysis. Results: Through WGCNA, 72 genes stood out from the key modules related to drug resistance and were identified as candidate resistance genes. Intersection analysis of the results of the LASSO and SVM-RFE algorithms selected 11, 4, and 5 drug-resistant genes for carbamazepine, phenytoin, and valproate, respectively. Subsequent union analysis obtained 17 hub resistance genes to construct a three-class classification SVM model. ROC showed that the model could accurately predict patient resistance. Expression of 17 hub resistance genes in healthy subjects and patients was significantly different. The PPI showed that there are six resistance genes (CD247, CTSW, IL2RB, MATK, NKG7, and PRF1) that may play a central role in the resistance of epilepsy patients. Finally, IPA revealed that resistance genes (PRKCH and S1PR5) were involved in "CREB signaling in Neurons." Conclusion: We obtained a three-class SVM model that can accurately predict the drug resistance of patients with epilepsy, which provides a new theoretical basis for research and treatment in the field of drug-resistant epilepsy. Moreover, resistance genes PRKCH and S1PR5 may cooperate with other resistance genes to exhibit resistance effects by regulation of the cAMP-response element-binding protein (CREB) signaling pathway.

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Exome sequencing identifies molecular diagnosis in children with drug-resistant epilepsy.

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