Profiling status epilepticus-induced changes in hippocampal RNA expression using high-throughput RNA sequencing

Status epilepticus (SE) is a life-threatening condition that can give rise to a number of neurological disorders, including learning deficits, depression, and epilepsy. Many of the effects of SE appear to be mediated by alterations in gene expression. To gain deeper insight into how SE affects the transcriptome, we employed the pilocarpine SE model in mice and Illumina-based high-throughput sequencing to characterize alterations in gene expression from the induction of SE, to the development of spontaneous seizure activity. While some genes were upregulated over the entire course of the pathological progression, each of the three sequenced time points (12-hour, 10-days and 6-weeks post-SE) had a largely unique transcriptional profile. Hence, genes that regulate synaptic physiology and transcription were most prominently altered at 12-hours post-SE; at 10-days post-SE, marked changes in metabolic and homeostatic gene expression were detected; at 6-weeks, substantial changes in the expression of cell excitability and morphogenesis genes were detected.  At the level of cell signaling, KEGG analysis revealed dynamic changes within the MAPK pathways, as well as in CREB-associated gene expression. Notably, the inducible expression of several noncoding transcripts was also detected. These findings offer potential new insights into the cellular events that shape SE-evoked pathology.  Overall design: cDNA from two animals was pooled into two independent biological replicates for each timepoint (ie. two sets of two animals per experimental group: control, 12 hours, 10 days, 6 weeks). Samples were sequenced using a Genome Analyzer II (GAII) at a concentration of 10pM in each lane. Base-calling was conducted with the standard Illumina Analysis Pipeline 1.0 (Firescrest-Bustard). Eight FASTQ sequence files (sequencing reads plus quality information) were generated and mapped to the mouse genome (UCSC mm9) using the Bowtie algorithm with default settings. A C++ program was used to count the number of uniquely mapped reads within exons of Ref-Seq genes (UCSC Genome Browser mm9 annotation).

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Choi YS, Lee B, Hansen KF, Aten S, Horning P, Wheaton KL, Impey S, Hoyt KR, Obrietan K