Gene expression analysis in absence epilepsy using a monozygotic twin design
Transcriptomics Dataset
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General Information
AccessionGSE7486
TitleGene expression analysis in absence epilepsy using a monozygotic twin design
DescriptionObjective: To identify genes involved in idiopathic absence epilepsies by analysing gene expression using a monozygotic (MZ) twin design. Methods: Genome-wide gene expression in lymphoblastoid cell lines was determined using microarrays derived from five discordant and four concordant MZ twin pairs with idiopathic absence epilepsies and five unaffected MZ twin pairs. Gene expression was analysed using three strategies: discordant MZ twins were compared as matched pairs, MZ twins concordant for epilepsy were compared to control MZ twins, and a singleton design of affected versus unaffected MZ twin individuals was used irrespective of twin pairing. An overlapping gene list was generated from these analyses. Dysregulation of genes recognised from the microarray experiment were validated using quantitative real time PCR (qRT-PCR) in the twin sample and in an independent sample of 18 sporadic absence cases and 24 healthy controls. Results: Sixty-five probe sets were identified from the microarray analysis strategies. Sixteen genes were chosen for validation and nine of these genes confirmed by qRT-PCR in the twin sample. Differential expression of the immediate early gene EGR1 and RCN2, coding for the calcium-binding protein Reticulocalbin 2, was re-confirmed by qRT-PCR in the independent sample. Interpretation: Using a unique sample of discordant MZ twins, our study identified genes with altered expression, which suggest novel mechanisms in idiopathic absence epilepsy. Dysregulation of EGR1 and RCN2 might represent common transcriptional alterations in idiopathic absence epilepsy. Keywords: Childhood Absence Epilepsy, Juvenile Absence Epilepsy, Idiopathic Generalised Epilepsy, gene expression, twin study, monozygotic twins
Landing Pagehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE7486
RepositoryGene Expression Omnibus
Tools used to analyze the dataset
Keyword
Dataset Accession
Enrichr
Published May 2016
A comprehensive gene set enrichment analysis web server 2016 update
7759
65
Characteristic Direction
Published March 2014
A geometrical multivariate approach to identify differentially expressed genes
26829
L1000CDS2
Published August 2016
An ultra-fast LINCS L1000 Characteristic Direction signature search engine
7756
GeneMANIA
Published June 2010
Biological network integration for gene prioritization and predicting gene function
7435
PAEA
Published November 2015
Enrichment analysis tool implementing the principal angle method
3879
Canned Analyses generated with the dataset
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Dataset Accession
Tool Name
Direction
Geneset
Interaction network and enrichment analysis of genes downregulated in epilepsy syndrome
GeneMANIA, Characteristic Direction
The analysis explores the gene interaction network and pathway enrichment of the top 50 mo...
Interaction network and enrichment analysis of genes upregulated in epilepsy syndrome
GeneMANIA, Characteristic Direction
The analysis explores the gene interaction network and pathway enrichment of the top 50 mo...
Small molecules which mimic epilepsy syndrome
L1000CDS2, Characteristic Direction
The L1000 database was queried in order to identify small molecule perturbations which mim...
Small molecules which reverse epilepsy syndrome
L1000CDS2, Characteristic Direction
The L1000 database was queried in order to identify small molecule perturbations which rev...
Enrichment analysis of genes dysregulated in epilepsy syndrome
PAEA, Characteristic Direction
An enrichment analysis was performed on the top most dyresgulated genes determined by appl...
Enrichment analysis of genes downregulated in epilepsy syndrome
Enrichr, Characteristic Direction
An enrichment analysis was performed on the top 500 most downregulated genes identified by...
Enrichment analysis of genes upregulated in epilepsy syndrome
Enrichr, Characteristic Direction
An enrichment analysis was performed on the top 500 most upregulated genes identified by a...
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