Wfs1 and Related Molecules as Key Candidate Genes in the Hippocampus of Depression - Yang, Jing; Chen, Chaoqin; Jin, Xiaoyuan; Liu, Lu; Lin, Jiajia; Kang, Xianhui; Zhu, Shengmei (Frontiers in Genetics, 2021)
Abstract Background: Depression is a mental illness that seriously harms a human’s physical and mental health. The pathogenesis of depression is still unclear. However, the hippocampus is one of the brain regions closely related to depression, and a comprehensive molecular study on it under chronic stress could be extremely beneficial. This study aims to reveal the differentially expressed genes (DEG) in the hippocampus from the chronic stress (CUMS) model and identify specific and meaningful genetic targets for the diagnosis and treatment of depression. Method: The study obtained GSE84183 from the GEO database. The R language screened the differential expression genes (DEG) in the hippocampus tissue of depressed mice, and the enrichment pathways of DEGs were analyzed. A protein-protein interaction (PPI) network was constructed in the STRING database and visualized in Cytoscape software. MicroRNAs for these DEGs were obtained from TarBase and miRTarBase databases, and transcription factors (TF) related to DEG were predicted from the ENCODE database. Both networks used the visual analysis platform NetworkAnayst. Finally, the microRNA-TF network is integrated based on the above two networks and imported into Cytoscape for further analysis. Results: In total, this study screened 325 differentially expressed genes, downregulated 42 genes, and upregulated 283 genes. The real-time polymerase chain reaction was used to verify the top ten DEG’s (Cplx2, COX3, Ptgds, Hspa8, Rgs7bp, Raver1, Gm4832, Rpl4, Mettl7a2, Wfs1) in CUMS mouse hippocampal tissue. The results showed a significant change in Wfs1 after chronic stress stimulation. DEGs enriched significantly in biological processes that mainly involved circadian rhythm, cell cycle, and cytokines. DEGs PPI networks consist of 102 nodes and 546 edges. The modules with the highest scores (Serpind1, Ckap4, Wfs1, Notum, Serpina1e, Apol9a) were screened by MCODE analysis. Therefore, combined with the bioinformatics analysis above, Wfs1 can be used as a prognosis and treatment target for depression. The microRNA-TF regulatory network analysis predicts that key microRNA (mmu-mir-17-5p, mmu-mir-7b-5p) and TF (UBTF) are closely related to the pathological process of depression.