Bioinformatic analysis identifies potential key genes of epilepsy

RNA Sequencing Demonstrates Ex Vivo Neocortical Transcriptomic Changes Induced by Epileptiform Activity in Male and Female Mice

Seizures are generally associated with epilepsy but may also be a symptom of many other neurological conditions. A hallmark of a seizure is the intensity of the local neuronal activation, which can drive large-scale gene transcription changes. Such changes in the transcriptional profile likely alter neuronal function, thereby contributing to the pathological process. Therefore, there is a strong clinical imperative to characterize how gene expression is changed by seizure activity. To this end, we developed a simplified ex vivo technique for studying seizure-induced transcriptional changes. We compared the RNA sequencing profile in mouse neocortical tissue with up to 3 h of epileptiform activity induced by 4-aminopyridine (4AP) relative to control brain slices not exposed to the drug. We identified over 100 genes with significantly altered expression after 4AP treatment, including multiple genes involved in MAPK, TNF, and neuroinflammatory signaling pathways, all of which have been linked to epilepsy previously. Notably, the patterns in male and female brain slices were almost identical. Various immediate early genes were among those showing the largest upregulation. The set of down-regulated genes included ones that might be expected either to increase or to decrease neuronal excitability. In summary, we found the seizure-induced transcriptional profile complex, but the changes aligned well with an analysis of published epilepsy-associated genes. We discuss how simple models may provide new angles for investigating seizure-induced transcriptional changes.

Impact of copy number variants in epilepsy plus neurodevelopment disorders

INTRODUCTION

Epilepsy, a neurological disorder characterized by recurring unprovoked seizures due to excessive neuronal excitability, is primarily attributed to genetic factors, accounting for an estimated 70 % of cases. Array-comparative genomic hybridization (aCGH) is a crucial genetic test for detecting copy number variants (CNVs) associated with epilepsy. This study aimed to analyze a cohort of epilepsy patients with CNVs detected through aCGH to enhance our understanding of the genetic underpinnings of epilepsy.

METHODS

A retrospective cross-sectional study was conducted using the aCGH database from the Genetics Department of the Faculty of Medicine of the University of Porto, encompassing 146 patients diagnosed with epilepsy, epileptic encephalopathy, or seizures. Clinical data were collected, and aCGH was performed following established guidelines. CNVs were classified based on ACMG standards, and patients were categorized into four groups according to their clinical phenotype.

RESULTS

Among the 146 included patients, 94 (64 %) had at least one CNV, with 22 (15.1 %) classified as pathogenic or likely pathogenic. Chromosomes 1, 2, 16, and X were frequently implicated, with Xp22.33 being the most reported region (8 CNVs). The phenotype "Epilepsy and global developmental delay/intellectual disability" showed the highest prevalence of clinically relevant CNVs. Various CNVs were identified across different groups, suggesting potential roles in epilepsy.

CONCLUSIONS

This study highlights the significance of aCGH in unraveling the genetic basis of epilepsy and tailoring treatment strategies. It contributes valuable insights to the expanding knowledge in the field, emphasizing the need for research to elucidate the diverse genetic causes of epilepsy.

Identification of hub genes significantly linked to subarachnoid hemorrhage and epilepsy via bioinformatics analysis

Background

Although epilepsy has been linked to subarachnoid hemorrhage (SAH), the underlying mechanism has not been fully elucidated. This study aimed to further explore the potential mechanisms in epilepsy and SAH through genes.

Methods

Gene expression profiles for subarachnoid hemorrhage (GSE36791) and epilepsy (GSE143272) were downloaded from the Gene Expression Omnibus (GEO) database. Differential expression analysis was performed to identify the common differentially expressed genes (DEGs) to epilepsy and SAH, which were further analyzed by functional enrichment analysis. Single-sample gene set enrichment analysis (ssGSEA) and weighted correlation network analysis (WGCNA) were used to identify common module genes related to the infiltration of immune cells in epilepsy and SAH. Hub module genes were identified using a protein-protein interaction (PPI) network. Finally, the most relevant genes were obtained by taking the intersection points between the DEGs and hub module genes. We performed validation by retrospectively analyzing the RT-PCR levels of the most relevant genes in patients with pure SAH and patients with SAH complicated with epilepsy. Our experiments verified that the SAH and SAH+epilepsy groups were significantly different from the normal control group. In addition, significant differences were observed between the SAH and SAH+epilepsy groups.

Results

In total, 159 common DEGs-85 downregulated genes and 74 upregulated genes-were identified. Functional analysis emphasized that the immune response was a common feature to epilepsy and SAH. The results of ssGSEA and WGCNA revealed changes in immunocyte recruitment and the related module genes. Finally, MMP9 and C3aR1 were identified as hub genes, and RT-PCR confirmed that the expression levels of the hub genes were higher in epilepsy and SAH samples than in normal samples.

Conclusions

Our study revealed the pathogenesis of SAH complicated with epilepsy and identified hub genes that might provide new ideas for further mechanistic studies.

Potential Pleiotropic Genes and Shared Biological Pathways in Epilepsy and Depression Based on GWAS Summary Statistics

Current epidemiological and experimental studies have indicated the overlapping genetic foundation of epilepsy and depression. However, the detailed pleiotropic genetic etiology and neurobiological pathways have not been well understood, and there are many variants with underestimated effect on the comorbidity of the two diseases. Utilizing genome-wide association study (GWAS) summary statistics of epilepsy (15,212 cases and 29,677 controls) and depression (170,756 cases and 329,443 controls) from large consortia, we assessed the integrated gene-based association with both diseases by Multimarker Analysis of Genomic Annotation (MAGMA) and Fisher's meta-analysis. On the one hand, shared genes with significantly altered transcripts in Gene Expression Omnibus (GEO) data sets were considered as possible pleiotropic genes. On the other hand, the pathway enrichment analysis was conducted based on the gene lists with nominal significance in the gene-based association test of each disease. We identified a total of two pleiotropic genes (CD3G and SLCO3A1) with gene expression analysis validated and interpreted twenty-five common biological process supported with literature mining. This study indicates the potentially shared genes associated with both epilepsy and depression based on gene expression, meta-data analysis, and pathway enrichment strategy along with traditional GWAS and provides insights into the possible intersecting pathways that were not previously reported.