Integrated Long Noncoding RNA and Messenger RNA Expression Analysis Identifies Molecules Specifically Associated With Resiliency and Susceptibility to Depression and Antidepressant Response

Depression involves maladaptive processes impairing an individual's ability to interface with the environment appropriately. Long-non-coding RNAs (lncRNAs) are gaining traction for their role in higher-order brain functioning. Recently, we reported that lncRNA co-expression modules may underlie abnormal responses to stress in rats showing depression-like behavior. The current study explored the global expression regulation of lncRNAs and mRNAs in the hippocampus of rats showing susceptibility (learned helplessness [LH]) or resiliency (non-learned helplessness [NLH]) to depression and fluoxetine response to LH (LH+FLX). Multiple comparisons analysis was performed with an ANOVA via the aov and summary function in the R platform to identify the differential expression of mRNAs and lncRNAs among LH, NLH, tested controls (TC), and LH+FLX groups. WGCNA analysis was used to identify distinctive modules and pathways associated with each phenotype. A machine learning analysis was conducted to screen the critical target genes. Based on the combined analysis, the regulatory effects of lncRNAs on mRNA expression were explored. Multiple comparison analyses revealed differentially expressed mRNAs and lncRNAs with each phenotype. Integrated bioinformatics analysis identified novel transcripts, specific modules, and regulatory pairs of mRNA-lncRNA in each phenotype. Additionally, the machine-learning approach predicted lncRNA-regulated Spp2 and Olr25 genes in developing LH behavior, whereas joint analysis of mRNA_lncRNA pairs identified Mboat7, Lmod1, IL18, and Rfx5 genes in depression-like behavior and Adam6 and Tpra1 in antidepressant response. The study shows a novel role for lncRNAs in the development of specific depression phenotypes and in identifying newer targets for therapeutic development.