Gene Expression Profiling of Stress Resilient Mice within the Nucleus Accumbens

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Project Summary Chronic stress exposure increases vulnerability to a variety of mental illnesses including depression. However, not all individuals that experience stress develop depression. Furthermore, the mechanisms that lead to stress -resilience, the ability to avoid the negative social, physiological, and biological consequences of stress have yet to be identified. Here, we focus on understanding the transcriptional mechanisms of resilience to identify genes that may be protective against stress-related disorders to guide drug discovery efforts. The chronic social defeat stress (CSDS) paradigm in mice has proven to be a highly useful animal model for studying depression- related behavioral abnormalities. Following CSDS, a subset of mice exhibit depressive-like behavior and changes in gene expression that recapitulate changes seen in depression patients examined postmortem. Importantly, this paradigm allows for the distinction of animals that succumb to the effects of the stress, termed susceptible, from those that do not, termed resilient. Previously published work has identified a gene network in the Nucleus Accumbens (NAc) that is specific to resilient mice. In this proposal, I will establish a reproducible pipeline to characterize the regulatory dynamics of resilient-specific gene networks in order to better understand the transcriptional circuitry that exist within these networks. I will then activate these transcriptional circuits in stress-naïve mice before subjecting them to CSDS to test their influence on resilience. The proposed research will provide a framework to study the transcriptional mechanism(s) of resilience by providing mechanistic insight into the transcriptional circuitry regulating stress resilience in mice. Together these aims will, for the first time, establish a novel experimental approach for studying the control over resilience by a complex network of genes as opposed to standard approaches which interrogate one gene at a time. This approach is designed to be applicable to any brain region, animal model, or network-based bioinformatics approach.