Gene expression analysis in untreated absence epilepsy demonstrates an inconsistent pattern.

Markus von Deimling, Robert Häsler, Verena Steinbach, Paul-Martin Holterhus, Sarah von Spiczak, Ulrich Stephani, Ingo Helbig, Hiltrud Muhle
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Epilepsy research : international multidisciplinary journal devoted to experimental and clinical epileptology
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Childhood and Juvenile Absence Epilepsy account for 30% of all genetic generalized epilepsies with a strong genetic contribution. At the current state the genetic background remains to be resolved. The aim of this study was to identify disease associated transcripts pinpointing potential underlying disease mechanisms in patients with CAE and JAE.We performed gene expression analysis from peripheral blood mononuclear cells (PBMCs) in 30 patients with newly-diagnosed absence epilepsy prior to initiating treatment and 30 healthy age - and gender-matched pediatric controls. In a first group (group 1), 10 patients and controls we performed genome-wide transcriptome analysis using the Affymetrix HG U133 2.0+ microarray. 75 differentially expressed genes were followed up by qRT-PCR in two independent groups of 10 patients and controls (group 2 and 3). Furthermore, we analyzed 18 candidate genes by qRT-PCR in groups 2 and 3, which had previously been considered strong candidates for genetic epilepsies.Genome-wide gene expression analysis in group 1 revealed 601 differentially regulated genes. Independent validation of 75 group 1-derived genes by qRT-PCR in groups 2 and 3 confirmed candidate genes with a consistent, but non-significant pattern of up- or down-regulation across all groups (ATP1B3, CAND1, PRPF6, TRIM8). Previously known genes including GABRA1, GABRB3, GABRG2, and RCN2 showed evidence for up- or down-regulation in individual experiments, but were not reliable across groups either.Gene expression analysis in absence epilepsy from PMBCs displayed a high degree of heterogeneity between different patient groups. Our study provides several potentially interesting candidate genes, while demonstrating the limits of using gene expression analysis from blood in the identification of novel pathogenic mechanisms. In particular, we found that gene expression levels vary in response to altered experimental conditions, representing a substantial challenge for the identification of disease-related gene expression signatures for neurological diseases from whole blood.