Integrating Clinical and Genomic Analyses of Hippocampal-Prefrontal Circuit Disorder in Depression

Major depressive disorder (MDD) is a prevalent, devastating and recurrent mental disease. Hippocampus (HIP)-prefrontal cortex (PFC) neural circuit abnormalities have been confirmed to exist in MDD; however, the gene-related molecular features of this circuit in the context of depression remain unclear. To clarify this issue, we performed gene set enrichment analysis (GSEA) to comprehensively analyze the genetic characteristics of the two brain regions and used weighted gene correlation network analysis (WGCNA) to determine the main depression-related gene modules in the HIP-PFC network. To clarify the regional differences and consistency for MDD, we also compared the expression patterns and molecular functions of the key modules from the two brain regions. The results showed that candidate modules related to clinical MDD of HIP and PFC, which contained with 363 genes and 225 genes, respectively. Ninety-five differentially expressed genes (DEGs) were identified in the HIP candidate module, and 51 DEGs were identified in the PFC candidate module, with only 11 overlapping DEGs in these two regional modules. Combined with the enrichment results, although there is heterogeneity in the molecular functions in the HIP-PFC network of depression, the regulation of the MAPK cascade, Ras protein signal transduction and Ephrin signaling were significantly enriched in both brain regions, indicating that these biological pathways play important roles in MDD pathogenesis. Additionally, the high coefficient protein-protein interaction (PPI) network was constructed via STRING, and the top-10 coefficient genes were identified as hub genes via the cytoHubba algorithm. In summary, the present study reveals the gene expression characteristics of MDD and identifies common and unique molecular features and patterns in the HIP-PFC network. Our results may provide novel clues from the gene function perspective to explain the pathogenic mechanism of depression and to aid drug development. Further research is needed to confirm these findings and to investigate the genetic regulation mechanisms of different neural networks in depression.