Co-Expression Network Modeling Identifies Specific Inflammation and Neurological Disease-Related Genes mRNA Modules in Mood Disorder.

Mood disorders are a kind of serious mental illness, although their molecular factors involved in the pathophysiology remain unknown. One approach to examine the molecular basis of mood disorders is co-expression network analysis (WGCNA), which is expected to further divide the set of differentially expressed genes into subgroups (i.e., modules) in a more (biologically) meaningful way, fascinating the downstream enrichment analysis. The aim of our study was to identify hub genes in modules in mood disorders by using WGCNA. Microarray data for expression values of 4,311,721 mRNA in peripheral blood mononuclear cells drawn from 21 MDD, 8 BD, and 24 HC individuals were obtained from GEO (GSE39653); data for genes with expression in the bottom third for 80% or more of the samples were removed. Then, the top 70% most variable genes/probs were selected for WGCNA: 27,884 probes representing 21,840 genes; correlation between module genes and mood disorder (MDD+BD vs. HC) was evaluated. About 52% of 27,765 genes were found to form 50 co-expression modules with sizes 42-3070. Among the 50 modules, the eigengenes of two modules were significantly correlated with mood disorder ( < 0.05). The saddlebrown module was found in one of the meta-modules in the network of the 50 eigengenes along with mood disorder, 6 (IER5, NFKBIZ, CITED2, TNF, SERTAD1, ADM) out of 12 differentially expressed genes identified in Savitz et al. were found in the saddlebrown module. We found a significant overlap for 6 hub genes (ADM, CITED2, IER5, NFKBIZ, SERTAD1, TNF) with similar co-expression and dysregulation patterns associated with mood disorder. Overall, our findings support other reports on molecular-level immune dysfunction in mood disorder and provide novel insights into the pathophysiology of mood disorder.