Microarray analysis of the chronic escape deficit model of depression: Effects of escitalopram treatment in hippocampus

Objective: Currently, the biological bases of depression and the molecular mechanisms underlying antidepressant action are not completely understood. Behavioural models of depression and genome-wide gene expression analysis can be relevant to better understand the pathophysiology of this disease. Chronic escape deficit is a valid and useful model of depression and is based on the induction of an escape deficit after exposure of rats to unavoidable stress. This behavioural model allows to evaluate the capacity of a treatment to revert the escape deficit. The majority of antidepressant drugs need to be administered for at least 3−4 weeks in order to revert the escape deficit. In this study, we demonstrated that only one week of treatment with Escitalopram, a widely used SSRI, is effective in the chronic escape deficit model of depression. Also, our study demonstrated that only 50% of the animals receiving ESC responded to the treatment. The mechanisms underlying the action of escitalopram are still poorly understood and the molecular targets and pathways involved remain to be identified. In order to identify the biological target involved in the response to escitalopram, we performed a microarray experiment using the chronic escape deficit model of depression after a 7 day treatment with escitalopram. Methods: Gene expression patterns in the rat hippocampus were analyzed using Affymetrix GeneChip Rat Exon 1.0 ST evaluating both gene-level and exon-level expression profiling on the whole genome. Total RNA extracted from hippocampus of each treated animal was utilized to chipping a single array using the Affymetrix protocols. 20 single arrays were utilized for data analysis and divided into five replicates for each experimental group (naive, stress, escitalopram responders and not responders). With two parallel analyses (gene level and exon level) of raw data files carried out in Expression Console software using iterPLIER algorithms, we identified various transcripts that were differentially regulated in each pairwise comparison. In order to identify biological processes and signalling networks regulated by escitalopram response, we performed a functional analysis using Ingenuity web tool. Results: Functional annotation of selected genes reflected interesting different biological features between escitalopram responders and not responders. More specifically, the biological functions regard cellular growth and proliferation, gene expression and signal transduction. Conclusion: We believe that this pharmacogenomic approach will be helpful to understand the molecular mechanisms involved in the pathogenesis of depression as well as in the response to antidepressant drugs.