In this project, the candidate proposes to elucidate the genetic basis of important antidepressant response phenotypes in a pre-clinical system, recombinant-inbred intercrosses (RIX) from the Collaborative Cross (CC) mouse lines. Experiments will be performed using fluoxetine (Prozac), a highly prescribed antidepressant. While fluoxetine does not have major side effects, only about 50% of patients experience a therapeutic response. First, we will expose adult male RIX to human-like steady state concentrations of fluoxetine (250 exposed, 250 control cage mates) and assess changes in two phenotypes relevant to antidepressant response in rodents: behavioral despair in the tail suspension test and quantitative measures of hippocampal neurogenesis. For each of these traits, we will use existing genomic data to conduct genome-wide association mapping and pathway analysis. Second, we refine these associations using new and powerful ways to assess the hippocampal dentate gyrus transcriptome and methylome (next generation sequencing technologies). Third, we test the predictive validity of these genetic and molecular biomarkers by generating novel animals expected to show high or low fluoxetine sensitivity (N=10 RIX each). This pre-clinical systems pharmacogenomics project is powered to identify key genes regulating response to fluoxetine in mice. Follow- up work will then examine these candidate genes in human clinical trial samples.
The goal of this study is to understand how fluoxetine (Prozac) works. While it is among the most highly prescribed psychiatric drugs, unfortunately fluoxetine works in only half of all patients. If it were possible to know ahead of time that a patient would not respond to fluoxetine, say by a genetic test, the physician could prescribe a different drug. Since in humans it is very difficult to identify the key genes responsible for inter- individual differences in drug response, this project aims to use mice where conditions can be more tightly regulated. We also propose to use a modern set of technologies to get an in-depth understanding.
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