Brain · 2019 · 64 citations · 53 references
NeurogenomicsGeneticsGene Expression ChangesNeurophysiological BiomarkersDisease Gene IdentificationCommon Epilepsy SyndromeEpigeneticsGenome-wide Association StudyNeurologyGenetic AnalysesPotential Causal DriversNeurogeneticsIntractable Partial EpilepsyStatistical GeneticsGenetic FactorFunctional GenomicsImaging GenomicsNeuroimaging BiomarkersNeuroscienceSystems BiologyMedicine
Mesial temporal lobe epilepsy with hippocampal sclerosis represents the most common epilepsy syndrome in adult patients with medically intractable partial epilepsy. Mesial temporal lobe epilepsy is usually regarded as a polygenic and complex disorder, still poorly understood but probably caused and perpetuated by dysregulation of numerous biological networks and cellular functions. The study of gene expression changes by single nucleotide polymorphisms in regulatory elements (expression quantitative trait loci, eQTLs) has been shown to be a powerful complementary approach to the detection and understanding of risk loci by genome-wide association studies. We performed a whole (gene and exon-level) transcriptome analysis on cortical tissue samples (Brodmann areas 20 and 21) from 86 patients with mesial temporal lobe epilepsy with hippocampal sclerosis and 75 neurologically healthy controls. Genome-wide genotyping data from the same individuals (patients and controls) were analysed and paired with the transcriptome data. We report potential epilepsy-risk eQTLs, some of which are specific to tissue from patients with mesial temporal lobe epilepsy with hippocampal sclerosis. We also found large transcriptional and splicing deregulation in mesial temporal lobe epilepsy with hippocampal sclerosis tissue as well as gene networks involving neuronal and glial mechanisms that provide new insights into the cause and maintenance of the seizures. These data (available via the 'Seizubraineac' web-tool resource, www.seizubraineac.org) will facilitate the identification of new therapeutic targets and biomarkers as well as genetic risk variants that could influence epilepsy and pharmacoresistance.
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WGCNA: an R package for weighted correlation network analysis
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De novo mutations in epileptic encephalopathies
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