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Harnessing gene expression networks to prioritize candidate epileptic encephalopathy genes.

ABSTRACT: We apply a novel gene expression network analysis to a cohort of 182 recently reported candidate Epileptic Encephalopathy genes to identify those most likely to be true Epileptic Encephalopathy genes. These candidate genes were identified as having single variants of likely pathogenic significance discovered in a large-scale massively parallel sequencing study. Candidate Epileptic Encephalopathy genes were prioritized according to their co-expression with 29 known Epileptic Encephalopathy genes. We utilized developing brain and adult brain gene expression data from the Allen Human Brain Atlas (AHBA) and compared this to data from Celsius: a large, heterogeneous gene expression data warehouse. We show replicable prioritization results using these three independent gene expression resources, two of which are brain-specific, with small sample size, and the third derived from a heterogeneous collection of tissues with large sample size. Of the nineteen genes that we predicted with the highest likelihood to be true Epileptic Encephalopathy genes, two (GNAO1 and GRIN2B) have recently been independently reported and confirmed. We compare our results to those produced by an established in silico prioritization approach called Endeavour, and finally present gene expression networks for the known and candidate Epileptic Encephalopathy genes. This highlights sub-networks of gene expression, particularly in the network derived from the adult AHBA gene expression dataset. These networks give clues to the likely biological interactions between Epileptic Encephalopathy genes, potentially highlighting underlying mechanisms and avenues for therapeutic targets.

SUBMITTER: Oliver KL

PROVIDER: S-EPMC4090166 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Harnessing gene expression networks to prioritize candidate epileptic encephalopathy genes.

Oliver Karen L KL Lukic Vesna V Thorne Natalie P NP Berkovic Samuel F SF Scheffer Ingrid E IE Bahlo Melanie M

PloS one 20140709 7

We apply a novel gene expression network analysis to a cohort of 182 recently reported candidate Epileptic Encephalopathy genes to identify those most likely to be true Epileptic Encephalopathy genes. These candidate genes were identified as having single variants of likely pathogenic significance discovered in a large-scale massively parallel sequencing study. Candidate Epileptic Encephalopathy genes were prioritized according to their co-expression with 29 known Epileptic Encephalopathy genes. ...[more]

PMID: 25014031

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Project description:Objective:Epileptic encephalopathy (EE) is a heterogeneous condition associated with deteriorations of cognitive, sensory and/or motor functions as a consequence of epileptic activity. The phenomenon is the most common and severe in infancy and early childhood. Genetic-based diagnosis in EE patients is challenging owing to genetic and phenotypic heterogeneity of numerous monogenic disorders and the fact that thousands of genes are involved in neurodevelopment. Therefore, high-throughput next-generation sequencing (NGS) was used to investigate the genetic causes of non-syndromic cryptogenic neonatal/infantile EE (NIEE). Methods:We have selected a cohort of 31 patients with seizure cryptogenic NIEE and seizure onset before 24 months. All investigations including metabolic work-up, were negative. Using NGS, we distinguished a panel of 430 epilepsy-associated genes by NGS was utilized to identify possible pathogenic variants in the patients. Segregation analysis and multiple silico analysis prediction tools were used for pathogenicity assessment. The identified variants were classified as "pathogenic," "likely pathogenic" and "uncertain significance," according to the American College of Medical Genetics (ACMG) guidelines. Results:Pathogenic or likely pathogenic variants were identified in six genes (ALG13 [1], CDKL5 [2], KCNQ2 [2], PNPO [1], SCN8A [1], SLC9A6 [2]) in 9 NIEE patients (9/31; 29%). Variants of uncertain significance (VUS) were found in DNM1 and TUBA8 in 2 NIEE patients (2/31; 6%). Most phenotypes in our cohort matched with those reported cases. Significance:The diagnostic rate (29%) of pathogenic and likely pathogenic variants was comparable to the recent studies of early-onset epileptic encephalopathy, indicating that gene panel analysis through NGS is a powerful tool to investigate cryptogenic NIEE in patients. Six percent of patients had neurometabolic disorders. Some of our diagnosed cases illustrated that successful molecular investigation may allow a better treatment strategy and avoid unnecessary and even invasive investigations. Functional analysis could be performed to further study the pathogenicity of the VUS identified in DNM1 and TUBA8.

Dataset Information

Harnessing gene expression networks to prioritize candidate epileptic encephalopathy genes.

Publications

Harnessing gene expression networks to prioritize candidate epileptic encephalopathy genes.

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