Details

Autor(en) / Beteiligte

• Hervé, Mylène
• Bergon, Aurélie
• Le Guisquet, Anne-Marie
• Leman, Samuel
• Consoloni, Julia-Lou
• Fernandez-Nunez, Nicolas
• Lefebvre, Marie-Noëlle
• El-Hage, Wissam
• Belzeaux, Raoul
• Belzung, Catherine
• Ibrahim, El Chérif

Titel

Translational Identification of Transcriptional Signatures of Major Depression and Antidepressant Response

Ist Teil von

• Frontiers in molecular neuroscience, 2017-08, Vol.10

Ort / Verlag

Frontiers Media

Erscheinungsjahr

2017

Link zum Volltext

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Major depressive disorder (MDD) is a highly prevalent mental illness whose therapy management remains uncertain, with more than 20% of patients who do not achieve response to antidepressants. Therefore, identification of reliable biomarkers to predict response to treatment will greatly improve MDD patient medical care. Due to the inaccessibility and lack of brain tissues from living MDD patients to study depression, researches using animal models have been useful in improving sensitivity and specificity of identifying biomarkers. In the current study, we used the unpredictable chronic mild stress (UCMS) model and correlated stress-induced depressive-like behavior (n = 8 unstressed vs. 8 stressed mice) as well as the fluoxetine-induced recovery (n = 8 stressed and fluoxetine-treated mice vs. 8 unstressed and fluoxetine-treated mice) with transcriptional signatures obtained by genome-wide microarray profiling from whole blood, dentate gyrus (DG), and the anterior cingulate cortex (ACC). Hierarchical clustering and rank-rank hypergeometric overlap (RRHO) procedures allowed us to identify gene transcripts with variations that correlate with behavioral profiles. As a translational validation, some of those transcripts were assayed by RT-qPCR with blood samples from 10 severe major depressive episode (MDE) patients and 10 healthy controls over the course of 30 weeks and four visits. Repeated-measures ANOVAs revealed candidate trait biomarkers (ARHGEF1, CMAS, IGHMBP2, PABPN1 and TBC1D10C), whereas univariate linear regression analyses uncovered candidates state biomarkers (CENPO, FUS and NUBP1), as well as prediction biomarkers predictive of antidepressant response (CENPO, NUBP1). These data suggest that such a translational approach may offer new leads for clinically valid panels of biomarkers for MDD.