中国科学院心理健康重点实验室知识库

BackgroundPost-traumatic stress disorder (PTSD) is a complex psychiatric condition that emerges following exposure to trauma and significantly affects daily functioning. Current research is focused on identifying effective treatments for PTSD. Advances in bioinformatics provide opportunities to elucidate the underlying mechanisms of PTSD.MethodsRNA sequencing (RNA-seq) datasets were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using GEO2R. Weighted gene co-expression network analysis (WGCNA) was employed to examine gene correlation patterns. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed for functional annotation and enrichment analysis, respectively. The MCODE plugin in Cytoscape software was utilized to analyze the protein-protein interaction (PPI) network. Anxiety and depression in a mice stress model were assessed using the open-field test (OFT), elevated plus maze test (EPMT), and forced swimming test (FST). Real-time quantitative PCR (qRT-PCR) was conducted to validate key genes in stress-exposed models.ResultsA total of 157 common upregulated DEGs and 53 common downregulated DEGs were identified in the amygdala (AMY) and the hippocampus (HIP). Notably enriched pathways included neuroactive ligand-receptor interaction, mechanistic target of rapamycin (mTOR) signaling pathway, nicotine addiction, and dopaminergic synapse. The PPI network identified four hub genes, with key pathways associated with nicotine addiction and dopaminergic synapse. qRT-PCR validation confirmed that the expression trends of these four genes were consistent with microarray data. Behavioral tests (OFT, EMPT, and FST) revealed significant changes.ConclusionThis study utilized bioinformatics and in vitro experiments to identify genes and pathways potentially crucial for PTSD development. Key genes were validated in a mouse model, providing insights into potential target genes for PTSD treatment.