The overarching goal of this project is to identify hypothalamic-pituitary-adrenal (HPA)-axis-related parameters as potential predictors and/or biomarkers of disease progression and response to treatment for major depression in treatment-naTve patients. We will develop candidate parameters related to the HPA-axis, and more specifically, to glucocorticoid receptor (GR) function. Those parameters may include genotypes or haplotypes at GR-related loci, differences in expression of GR chaperone genes, measurements of GR function in vivo and in vitro, or some combination of these. Once our genetic investigations have identified putative predictors of treatment response, they will be integrated with data from neuro-imaging, transporteroccupancy studies, clinical assessments and other data for inclusion in an overall model of response predictors to be developed in the Special Scientific Procedures Core.
The specific aims of this project include examination of relationships among HPA-axis-associated markers, measured at the genotypic, mRNA-expression, biochemical and systemic levels. More specifically we will investigate how sequence variation at the genetic level in GR receptor- regulating genes associate with mRNA expression in monocytes isolated from patients at multiple timepoints, and to glucocorticoid receptor function measured in vitro (i.e., in monocytes) as well as in vivo (using the combined dexamethasone suppression/CRH stimulation (DEX-CRH) test). Integrating multiple levels of analysis may help to identify genetic and molecular mechanisms for HPAaxis dysregulation and its normalization in response to anti-depressant treatment, thereby suggesting specific predictors for response to antidepressant treatments or disease progression. The elucidation of molecular mechanisms for the normalization of HPA-axis hyperactivity that accompanies successful antidepressant treatment may also be an important step in the development of novel antidepressants. This project will interact closely with the Operations and Clinical Assessment Core by coordinating all necessary blood draws and endocrine challenge tests and through a shared database integrating genetic and phenotypic data, the Research Methods Core by genotyping polymorphisms in all candidate genes relevant for this project and providing detailed information on their population-specific haplotypic structure, and the Special Scientific Procedures Core by generating multi-level HPA-axis related data for inclusion in the overall predictive model for treatment outcome.
|Kelley, Mary E; Dunlop, Boadie W; Nemeroff, Charles B et al. (2018) Response rate profiles for major depressive disorder: Characterizing early response and longitudinal nonresponse. Depress Anxiety 35:992-1000|
|Ahmed, Ahmed T; Frye, Mark A; Rush, A John et al. (2018) Mapping depression rating scale phenotypes onto research domain criteria (RDoC) to inform biological research in mood disorders. J Affect Disord 238:1-7|
|Dunlop, Boadie W; Cole, Steven P; Nemeroff, Charles B et al. (2018) Differential change on depressive symptom factors with antidepressant medication and cognitive behavior therapy for major depressive disorder. J Affect Disord 229:111-119|
|O'Connell, Chloe P; Goldstein-Piekarski, Andrea N; Nemeroff, Charles B et al. (2018) Antidepressant Outcomes Predicted by Genetic Variation in Corticotropin-Releasing Hormone Binding Protein. Am J Psychiatry 175:251-261|
|Syed, Shariful A; Beurel, Eléonore; Loewenstein, David A et al. (2018) Defective Inflammatory Pathways in Never-Treated Depressed Patients Are Associated with Poor Treatment Response. Neuron 99:914-924.e3|
|Berg, Joanna M; Kennedy, Jamie C; Dunlop, Boadie W et al. (2017) The Structure of Personality Disorders within a Depressed Sample: Implications for Personalizing Treatment. Pers Med Psychiatry 1-2:59-64|
|Ramirez-Mahaluf, Juan P; Roxin, Alexander; Mayberg, Helen S et al. (2017) A Computational Model of Major Depression: the Role of Glutamate Dysfunction on Cingulo-Frontal Network Dynamics. Cereb Cortex 27:660-679|
|Dunlop, Boadie W; Rajendra, Justin K; Craighead, W Edward et al. (2017) Functional Connectivity of the Subcallosal Cingulate Cortex And Differential Outcomes to Treatment With Cognitive-Behavioral Therapy or Antidepressant Medication for Major Depressive Disorder. Am J Psychiatry 174:533-545|
|Dunlop, Boadie W; Kelley, Mary E; Aponte-Rivera, Vivianne et al. (2017) Effects of Patient Preferences on Outcomes in the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) Study. Am J Psychiatry 174:546-556|
|Drysdale, Andrew T; Grosenick, Logan; Downar, Jonathan et al. (2017) Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med 23:28-38|
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