Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany.
Max Planck Institute of Psychiatry, Munich, Germany.
PLoS Biol. 2017 Dec 28;15(12):e2002690. doi: 10.1371/journal.pbio.2002690. eCollection 2017 Dec.
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.
目前,无法预测重度抑郁症(MDD)患者对抗抑郁治疗的反应,这导致药物选择不确定、治疗费用增加,许多患者遭受不必要的痛苦。尽管在全基因组关联研究中做出了巨大努力来鉴定与反应相关的基因,但结果相当有限,这突显了需要建立新概念性策略。为了鉴定能够区分治疗反应者和无反应者的转录组特征,我们提出了一种新的动物实验方法,该方法侧重于极端表型。我们利用 DBA/2J 小鼠中抗抑郁治疗反应的巨大差异,将样本分为治疗反应良好和不良的亚群,以描绘外周血样本中与反应相关的特征转录谱。作为概念验证,我们将我们的鼠类数据转化为临床相关人类队列的转录组数据。在鼠模型中比较治疗反应良好和不良者的外周转录组谱时,鉴定出了一组 259 个差异调节基因。基于鼠转录特征选择的人类同源物从基线到第 12 周的表达谱差异允许以 76%的准确率预测患者人群中的反应状态。最后,我们表明糖皮质激素受体(GR)调节基因在这个抗抑郁治疗反应基因簇中显著富集。我们的研究结果表明,GR 敏感性的参与可能是决定抗抑郁治疗反应的潜在关键机制,并支持抗抑郁药可能刺激促进恢复的分子机制的假说。我们的数据突出了适当的动物实验方法在跨物种发现与治疗反应相关的途径中的适用性。