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

BACKGROUND

Depression involves maladaptive processes impairing an individual's ability to interface with the environment appropriately. Long noncoding RNAs (lncRNAs) are gaining traction for their role in higher-order brain functioning. Recently, we reported that lncRNA coexpression modules may underlie abnormal responses to stress in rats showing depression-like behavior. The current study explored the global expression regulation of lncRNAs and messenger RNAs (mRNAs) in the hippocampus of rats showing susceptibility (learned helplessness [LH]) or resiliency (non-LH) to depression and fluoxetine response to LH (LH+FLX).

METHODS

Multiple comparison analysis was performed with an analysis of variance via the and function in the R platform to identify the differential expression of mRNAs and lncRNAs among LH, non-LH, tested control, and LH+FLX groups. Weighted gene coexpression network 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.

RESULTS

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. In addition, the machine learning approach predicted lncRNA-regulated and genes in developing LH behavior, whereas joint analysis of mRNA-lncRNA pairs identified , , , and genes in depression-like behavior and and in antidepressant response.

CONCLUSIONS

The study shows a novel role for lncRNAs in the development of specific depression phenotypes and in identifying newer targets for therapeutic development.