Analysis of the role of PANoptosis in seizures via integrated bioinformatics analysis and experimental validation.

BACKGROUND: Epilepsy is recognized as the most common chronic neurological condition among children, and hippocampal neuronal cell death has been identified as a crucial factor in the pathophysiological processes underlying seizures. In recent studies, PANoptosis, a newly characterized form of cell death, has emerged as a significant contributor to the development of various neurological disorders, including Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. PANoptosis involves the simultaneous activation of pyroptosis, apoptosis, and necroptosis within the same population of cells. However, its specific role in the context of seizures remains to be fully elucidated. Further investigation is required to uncover the precise involvement of PANoptosis in the pathogenesis of seizures and to better understand its potential implications for the development of targeted therapeutic approaches in epilepsy.

METHODS: In this study, the gene expression data of the hippocampus following the administration of kainic acid (KA) or NaCl was obtained from the Gene Expression Omnibus (GEO) database. The PANoptosis-related gene set was compiled from the GeneCards database and previous literature. Time series analysis was performed to analyze the temporal expression patterns of the PANoptosis-related genes. Gene set variation analysis (GSVA), Gene ontology (GO), and Kyoto encyclopedia of genes and genomes (KEGG) were employed to explore potential biological mechanisms underlying PANoptosis and its role in seizures. Weighted gene co-expression network analysis (WGCNA) and differential expression analysis were utilized to identify pivotal gene modules and PANoptosis-related genes associated with the pathophysiological processes underlying seizures. To validate the expression of PANoptosis-related genes, Western blotting or quantitative real-time polymerase chain reaction (qRT-PCR) assays were conducted. These experimental validations were performed in human blood samples, animal models, and cell models to verify the expression patterns of the PANoptosis-related genes and their relevance to epilepsy.

RESULTS: The GSVA analysis performed in this study demonstrated that PANoptosis-related genes have the potential to distinguish between the control group and KA-induced epileptic mice. This suggests that the expression patterns of these genes are significantly altered in response to KA-induced epilepsy. Furthermore, the Weighted gene co-expression network analysis (WGCNA) identified the blue module as being highly associated with epileptic phenotypes. This module consists of genes that exhibit correlated expression patterns specifically related to epilepsy. Within the blue module, 10 genes were further identified as biomarker genes for epilepsy. These genes include MLKL, IRF1, RIPK1, GSDMD, CASP1, CASP8, ZBP1, CASP6, PYCARD, and IL18. These genes likely play critical roles in the pathophysiology of epilepsy and could serve as potential biomarkers for diagnosing or monitoring the condition.

CONCLUSION: In conclusion, our study suggests that the hippocampal neuronal cell death in epilepsy may be closely related to PANoptosis, a novel form of cell death, which provides insights into the underlying pathophysiological processes of epilepsy and helps the development of novel therapeutic approaches for epilepsy.