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.