Common genes associated with antidepressant response in mouse and man identify key role of glucocorticoid receptor sensitivity

• Published in: PLoS biology 2017-12, Vol.15 (12), p.e2002690-e2002690
• Main Authors: Carrillo-Roa, Tania, Labermaier, Christiana, Weber, Peter, Herzog, David P, Lareau, Caleb, Santarelli, Sara, Wagner, Klaus V, Rex-Haffner, Monika, Harbich, Daniela, Scharf, Sebastian H, Nemeroff, Charles B, Dunlop, Boadie W, Craighead, W Edward, Mayberg, Helen S, Schmidt, Mathias V, Uhr, Manfred, Holsboer, Florian, Sillaber, Inge, Binder, Elisabeth B, Müller, Marianne B
• Format: Article
• Language: English
• Subjects: Animal models; Animal research; Animals; Antidepressants; Antidepressive Agents - pharmacology; Antidepressive Agents - therapeutic use; Authorship; Behavioral sciences; Biology and Life Sciences; Biomarkers; Biomarkers, Pharmacological; Brain - metabolism; Brain research; Care and treatment; Clusters; Corticosteroids; Corticosterone - blood; Depressive Disorder, Major - drug therapy; Dosage and administration; Drugs; Gene expression; Gene Expression Profiling; Gene Expression Regulation; Genes; Genetic aspects; Genome-wide association studies; Genomes; Genomics; Glucocorticoids; Health aspects; Humans; Hypotheses; Major depressive disorder; Medicine; Medicine and Health Sciences; Mental depression; Mice; Mice, Inbred DBA; Molecular modelling; Multigene Family; Neurobiology; Neurosciences; Paroxetine - metabolism; Paroxetine - pharmacology; Paroxetine - therapeutic use; Peripheral blood; Predictions; Psychiatry; Psychotherapy; Receptor mechanisms; Receptors, Glucocorticoid - genetics; Receptors, Glucocorticoid - metabolism; Receptors, Glucocorticoid - physiology; Research and Analysis Methods; Sensitivity; Subpopulations; Supervision; Transcription; Trends
• ISSN: 1545-7885; 1544-9173; 1545-7885
• DOI: 10.1371/journal.pbio.2002690

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.