Dataset Information

Bioinformatic analysis identifies key transcriptome signatures in temporal lobe epilepsy.

ABSTRACT: Aims: To identify transcriptome signatures underlying epileptogenesis in temporal lobe epilepsy (TLE).

Methods: Robust rank aggregation analysis was used to integrate multiple microarrays in rodent models of TLE and determine differentially expressed genes (DEGs) in acute, latent, and chronic stages. Functional annotation and protein-protein interaction analysis were performed to explore the potential functions of the DEGs and identify hub genes with the highest intramodular connectivity. The association between hub genes and hippocampal sclerosis/seizure frequency was analyzed using publicly available RNA-sequencing datasets from TLE patients. We subsequently established a pilocarpine-induced status epilepticus (SE) model in rats and validated mRNA expression of hub genes by quantitative reverse transcription PCR (qRT-PCR).

Results: The DEGs in the acute, latent, and chronic phases of TLE in animal models were prominently enriched in inflammatory response. Hub genes identified in the acute phase mainly participated in biological processes including inflammation, blood-brain barrier damage, and cell adhesion. The hub genes in the latent phase were related to microglia/macrophage activation (Emr1 and Aif1) and phagocytosis (Cd68, Tyrobp, and Lyz). In the chronic phase, the hub genes were associated with activation of complements and microglia/macrophages. We further found that some hub genes identified in human TLE, such as Tlr2, Lgals3, and Stat3, were positively correlated with seizure frequency. Other hub genes, including Lgals3 and Serpine1, were associated with hippocampus sclerosis. qRT-PCR analysis confirmed that the mRNA levels of hub genes in rat hippocampus were significantly up-regulated after SE induction.

Conclusions: Our integrated analysis identified hub genes in different stages of epilepsy. The functional annotations suggest that the activation and phagocytic activities of microglia/macrophages may play critical roles in epileptogenesis of TLE.

SUBMITTER: Chen QL

PROVIDER: S-EPMC7702228 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Bioinformatic analysis identifies key transcriptome signatures in temporal lobe epilepsy.

Chen Qing-Lan QL Xia Lu L Zhong Shao-Ping SP Wang Qiang Q Ding Jing J Wang Xin X

CNS neuroscience & therapeutics 20201122 12

<h4>Aims</h4>To identify transcriptome signatures underlying epileptogenesis in temporal lobe epilepsy (TLE).<h4>Methods</h4>Robust rank aggregation analysis was used to integrate multiple microarrays in rodent models of TLE and determine differentially expressed genes (DEGs) in acute, latent, and chronic stages. Functional annotation and protein-protein interaction analysis were performed to explore the potential functions of the DEGs and identify hub genes with the highest intramodular connect ...[more]

PMID: 33225612

Similar Datasets

Project description:We previously described - studying transcriptional signatures of hippocampal CA3 explants - that febrile (FS) and afebrile (NFS) forms of refractory mesial temporal lobe epilepsy constitute two distinct genomic phenotypes. That network analysis was based on a limited number (hundreds) of differentially expressed genes (DE networks) among a large set of valid transcripts (close to two tens of thousands). Here we developed a methodology for complex network visualization (3D) and analysis that allows the categorization of network nodes according to distinct hierarchical levels of gene-gene connections (node degree) and of interconnection between node neighbors (concentric node degree). Hubs are highly connected nodes, VIPs have low node degree but connect only with hubs, and high-hubs have VIP status and high overall number of connections. Studying the whole set of CA3 valid transcripts we: i) obtained complete transcriptional networks (CO) for FS and NFS phenotypic groups; ii) examined how CO and DE networks are related; iii) characterized genomic and molecular mechanisms underlying FS and NFS phenotypes, identifying potential novel targets for therapeutic interventions. We found that: i) DE hubs and VIPs are evenly distributed inside the CO networks; ii) most DE hubs and VIPs are related to synaptic transmission and neuronal excitability whereas most CO hubs, VIPs and high hubs are related to neuronal differentiation, homeostasis and neuroprotection, indicating compensatory mechanisms. Complex network visualization and analysis is a useful tool for systems biology approaches to multifactorial diseases. Network centrality observed for hubs, VIPs and high hubs of CO networks, is consistent with the network disease model, where a group of nodes whose perturbation leads to a disease phenotype occupies a central position in the network. Conceivably, the chance for exerting therapeutic effects through the modulation of particular genes will be higher if these genes are highly interconnected in transcriptional networks.

Dataset Information

Bioinformatic analysis identifies key transcriptome signatures in temporal lobe epilepsy.

Publications

Bioinformatic analysis identifies key transcriptome signatures in temporal lobe epilepsy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets