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1000
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Abstract/Summary
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Response to antidepressant treatment in major depressive disorder (MDD) cannot be predicted currently, leading to uncertainty in medication selection, increasing costs, and prolonged suffering for many patients. Despite tremendous efforts in identifying response-associated genes in large genome-wide association studies, the results have been fairly modest, underlining the need to establish conceptually novel strategies. For the identification of transcriptome signatures that can distinguish between treatment responders and nonresponders, we herein submit a novel animal experimental approach focusing on extreme phenotypes. We utilized the large variance in response to antidepressant treatment occurring in DBA/2J mice, enabling sample stratification into subpopulations of good and poor treatment responders to delineate response-associated signature transcript profiles in peripheral blood samples. As a proof of concept, we translated our murine data to the transcriptome data of a clinically relevant human cohort. A cluster of 259 differentially regulated genes was identified when peripheral transcriptome profiles of good and poor treatment responders were compared in the murine model. Differences in expression profiles from baseline to week 12 of the human orthologues selected on the basis of the murine transcript signature allowed prediction of response status with an accuracy of 76% in the patient population. Finally, we show that glucocorticoid receptor (GR)-regulated genes are significantly enriched in this cluster of antidepressant-response genes. Our findings point to the involvement of GR sensitivity as a potential key mechanism shaping response to antidepressant treatment and support the hypothesis that antidepressants could stimulate resilience-promoting molecular mechanisms. Our data highlight the suitability of an appropriate animal experimental approach for the discovery of treatment response-associated pathways across species. AUTHOR SUMMARY: Major depression is the second leading cause of disability worldwide. However, only one-third of patients with depression benefit from the first antidepressant compound they are prescribed. It is a fundamental problem that the outcomes of individual antidepressant treatments are still highly unpredictable. In clinical studies, discovery of biomarkers for antidepressant response is hampered by confounding factors such as the heterogeneity of the disease phenotype and additional environmental factors, e.g., previous life events and different schedules of psychopharmacological treatment, which reduce the power to detect true response biomarkers. To overcome some of these limitations, we have established a conceptually novel approach that allows the selection of extreme phenotypes in an antidepressant-responsive mouse strain. In the first step, we identify signatures in the transcriptome of peripheral blood associated with responses following stratification into good and poor treatment responders. As proof of concept, we translate the murine data to a population of depressed patients. We show that differences in expression profiles from baseline to week 12 of the human orthologues predict response status in patients. We finally provide evidence that sensitivity of the glucocorticoid receptor could be a potential key mechanism shaping response to antidepressant treatment.
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