Dr. Xiao-Ning Zhang
Professor of biology
216 Walsh Science Center
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My lab is interested in studying how our environment regulates gene networks in order to modify our behavior. Specifically this research was initiated using genomic strategies to identify transcriptional pathways in the mouse brain that respond to food restriction (Guarnieri et al., 2012). Food restriction (FR) is known to promote learning and enhance motivation, yet my research identified stress-responsive genes activated in the brain upon FR. These genes appear to be regulated by the transcription factor known as the glucocorticoid receptor (GR), which is a fundamental regulator of our physiological stress response. Although the FR treatment used in this study was mild, these stress-responsive genes were up-regulated in various brain regions rapidly and this increase in transcription was persistent for over one week. This result was surprising since chronic stress is a key risk factor for many psychiatric illnesses and led me to wonder how an adaptive behavior such as FR differs from maladaptive chronic stress. In attempts to clarify the term ‘stress', we study the regulation of stress-responsive genes in neuronal circuits as well as peripheral tissues using cellular and animal models. Is this gene network activated in the periphery as well as the brain upon FR? This network may be neuron specific, or it may be a conserved pathway that is activated in most cells of the body.
In addition to this exciting line of research, my lab studies genes implicated in the mental illness known as bipolar disorder. By studying this potential gene network, we are trying to uncover a common pathway that may help better understand this challenging disorder and improve treatment options. All projects in my lab use a combination of molecular, cellular and bioinformatic approaches to these questions. Students who work in my lab during the summer should expect to become proficient in molecular techniques such as gene cloning, PCR, and quantitative PCR. We also use animal and cellular models to assess the regulation of these gene networks, providing experience with cell culture as well as handling mice to generate additional samples for molecular study. Finally we use bioinformatics to predict potential GR regulatory regions within this stress-responsive network, as well as the bioinformatic analysis of novel genomic data derived from recent RNA-sequencing projects on distinct animal models of stress.
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