Translational Identification of Transcriptional Signatures of Major Depression and Antidepressant Response.

摘要: 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 reliable biomarkers predict treatment will greatly improve MDD patient medical care. Due the inaccessibility and lack brain tissues from living study depression, researches using animal models have been useful in improving sensitivity specificity identifying biomarkers. In current study, we used unpredictable chronic mild stress (UCMS) model correlated stress-induced depressive-like behavior (n = 8 unstressed vs. stressed mice) as well fluoxetine-induced recovery fluoxetine-treated mice transcriptional signatures obtained by genome-wide microarray profiling whole blood, dentate gyrus (DG), anterior cingulate cortex (ACC). Hierarchical clustering rank-rank hypergeometric overlap (RRHO) procedures allowed us identify gene transcripts variations that correlate behavioral profiles. As translational validation, some those were assayed RT-qPCR blood samples 10 severe major episode (MDE) healthy controls over course 30 weeks four visits. Repeated-measures ANOVAs revealed candidate trait (ARHGEF1, CMAS, IGHMBP2, PABPN1 TBC1D10C), whereas univariate linear regression analyses uncovered candidates state (CENPO, FUS NUBP1), prediction predictive antidepressant NUBP1). These data suggest such approach may offer new leads for clinically valid panels MDD.