Meta-analysis of kindling-induced gene expression changes in the rat hippocampus

Abstract

Numerous studies have been performed to examine gene expression patterns in the rodent hippocampus in the kindling model of epilepsy. However, recent reviews of this literature have revealed limited agreement among studies. Because this conclusion was based on retrospective comparison of reported “hit lists” from individual studies, we hypothesized that re-analysis of the original expression data would help address this concern. In this paper, we reanalyzed four genome-wide expression studies of excitotoxin-induced kindling in rat and performed a statistical meta-analysis. The meta-analysis revealed over 800 genes which show significant change in expression 24 h after initial seizure induction, and 59 genes altered after 10 days. To evaluate our results in light of previous work, we assembled a reference list of genes formed from a consensus of the published literature. Our profiles include most of the genes in this reference list, and most of the additional genes are from pathways or biological processes previously recognized to be altered in kindling. In addition our results emphasized expression changes in lipid metabolism and protein degradation pathways. We conclude that a cautious re-analysis of published expression data can help illuminate genes and pathways underling kindling. Supplementary Material is available at

Integrated analysis of human transcriptome data for Rett syndrome finds a network of involved genes

“…Low overlap of previously known genes and identified by the data-driven approach was reported several times before (e.g. epilepsy study (Rogic & Pavlidis, 2009)) and all these studies revealed new genes which were not known to be involved in disease progression, yet (Ch'ng et al, 2015;Mistry et al, 2013;Raddatz et al, 2014;Roder et al, 2012).…”

“…The induced changes on the cellular level represent intricate biological networks including molecular interactions which finally lead to a specific disorder phenotype. By performing meta-or integrational analysis of published transcriptomics data, various studies of neurological disorders like autismspectrum disorder (Ch'ng et al, 2015;Li et al, 2014), schizophrenia (Mistry et al, 2013), multiple sclerosis (Raddatz et al, 2014), intracranial aneurysms (Roder et al, 2012), epilepsy (Rogic & Pavlidis, 2009), and aging and age-associated spatial learning impairment (Uddin & Singh, 2013) have obtained new insights. However, to our knowledge, this approach has not been applied yet in RTT.…”

Integrated analysis of human transcriptome data for Rett syndrome finds a network of involved genes

“…This method is nevertheless often misleading as the transcriptome shows a snapshot of the cells (or tissue) current status, including influences like individual genetic background, nutrition, stress, cell cycle or circadian rhythms (Zhang et al 2014), which may be larger than the change induced by a disorder. By performing meta-or integrational analysis of multiple transcriptomics datasets, various studies of neurological disorders like autism-spectrum disorder (Li et al 2014;Ch'ng et al 2015), schizophrenia (Mistry et al 2013), multiple sclerosis (Raddatz et al 2014), intracranial aneurysms (Roder et al 2012), epilepsy (Rogic and Pavlidis 2009), and aging and age-associated spatial learning impairment (Uddin and Singh 2013) have obtained new insights. However, this approach has not been applied in RTT.…”

Meta‐Analysis of Gene Expression in Autism Spectrum Disorder

“…Other ASD related meta‐analyses are geared toward examining pathogenic variations in whole exomes of individuals [Ben‐David & Shifman, ; Liu et al, ] not transcriptomes. As meta‐analysis techniques have been successfully applied in neuropsychiatry [Choi et al, ; Mistry, Gillis, & Pavlidis, ; Rogic & Pavlidis, ], a systematic integration of expression data across multiple independent ASD cohorts will add value to the existing data, and may yield novel insights.…”