Dataset Information

Differential DNA methylation patterns define status epilepticus and epileptic tolerance.

ABSTRACT: Prolonged seizures (status epilepticus) produce pathophysiological changes in the hippocampus that are associated with large-scale, wide-ranging changes in gene expression. Epileptic tolerance is an endogenous program of cell protection that can be activated in the brain by previous exposure to a non-harmful seizure episode before status epilepticus. A major transcriptional feature of tolerance is gene downregulation. Here, through methylation analysis of 34,143 discrete loci representing all annotated CpG islands and promoter regions in the mouse genome, we report the genome-wide DNA methylation changes in the hippocampus after status epilepticus and epileptic tolerance in adult mice. A total of 321 genes showed altered DNA methylation after status epilepticus alone or status epilepticus that followed seizure preconditioning, with >90% of the promoters of these genes undergoing hypomethylation. These profiles included genes not previously associated with epilepsy, such as the polycomb gene Phc2. Differential methylation events generally occurred throughout the genome without bias for a particular chromosomal region, with the exception of a small region of chromosome 4, which was significantly overrepresented with genes hypomethylated after status epilepticus. Surprisingly, only few genes displayed differential hypermethylation in epileptic tolerance. Nevertheless, gene ontology analysis emphasized the majority of differential methylation events between the groups occurred in genes associated with nuclear functions, such as DNA binding and transcriptional regulation. The present study reports select, genome-wide DNA methylation changes after status epilepticus and in epileptic tolerance, which may contribute to regulating the gene expression environment of the seizure-damaged hippocampus.

SUBMITTER: Miller-Delaney SF

PROVIDER: S-EPMC6703365 | biostudies-literature | 2012 Feb

REPOSITORIES: biostudies-literature

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Differential DNA methylation patterns define status epilepticus and epileptic tolerance.

Miller-Delaney Suzanne F C SF Das Sudipto S Sano Takanori T Jimenez-Mateos Eva M EM Bryan Kenneth K Buckley Patrick G PG Stallings Raymond L RL Henshall David C DC

The Journal of neuroscience : the official journal of the Society for Neuroscience 20120201 5

Prolonged seizures (status epilepticus) produce pathophysiological changes in the hippocampus that are associated with large-scale, wide-ranging changes in gene expression. Epileptic tolerance is an endogenous program of cell protection that can be activated in the brain by previous exposure to a non-harmful seizure episode before status epilepticus. A major transcriptional feature of tolerance is gene downregulation. Here, through methylation analysis of 34,143 discrete loci representing all an ...[more]

PMID: 22302800

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Project description:BackgroundDNA methylation is a key epigenetic regulator contributing to cancer development. To understand the role of DNA methylation in tumorigenesis, it is important to investigate and compare differential methylation (DM) patterns between normal and case samples across different cancer types. However, current pan-cancer analyses call DM separately for each cancer, which suffers from lower statistical power and fails to provide a comprehensive view for patterns across cancers.MethodsIn this work, we propose a rigorous statistical model, PanDM, to jointly characterize DM patterns across diverse cancer types. PanDM uses the hidden correlations in the combined dataset to improve statistical power through joint modeling. PanDM takes summary statistics from separate analyses as input and performs methylation site clustering, differential methylation detection, and pan-cancer pattern discovery. We demonstrate the favorable performance of PanDM using simulation data. We apply our model to 12 cancer methylome data collected from The Cancer Genome Atlas (TCGA) project. We further conduct ontology- and pathway-enrichment analyses to gain new biological insights into the pan-cancer DM patterns learned by PanDM.ResultsPanDM outperforms two types of separate analyses in the power of DM calling in the simulation study. Application of PanDM to TCGA data reveals 37 pan-cancer DM patterns in the 12 cancer methylomes, including both common and cancer-type-specific patterns. These 37 patterns are in turn used to group cancer types. Functional ontology and biological pathways enriched in the non-common patterns not only underpin the cancer-type-specific etiology and pathogenesis but also unveil the common environmental risk factors shared by multiple cancer types. Moreover, we also identify PanDM-specific DM CpG sites that the common strategy fails to detect.ConclusionsPanDM is a powerful tool that provides a systematic way to investigate aberrant methylation patterns across multiple cancer types. Results from real data analyses suggest a novel angle for us to understand the common and specific DM patterns in different cancers. Moreover, as PanDM works on the summary statistics for each cancer type, the same framework can in principle be applied to pan-cancer analyses of other functional genomic profiles. We implement PanDM as an R package, which is freely available at http://www.sta.cuhk.edu.hk/YWei/PanDM.html .

Dataset Information

Differential DNA methylation patterns define status epilepticus and epileptic tolerance.

Publications

Differential DNA methylation patterns define status epilepticus and epileptic tolerance.

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