Department of Molecular Neuropharmacology, Institute of Pharmacology Polish Academy of Sciences, Smętna 12, PL 31-343, Kraków, Poland.
BMC Genomics. 2013 Sep 8;14:606. doi: 10.1186/1471-2164-14-606.
Despite their widespread use, the biological mechanisms underlying the efficacy of psychotropic drugs are still incompletely known; improved understanding of these is essential for development of novel more effective drugs and rational design of therapy. Given the large number of psychotropic drugs available and their differential pharmacological effects, it would be important to establish specific predictors of response to various classes of drugs.
To identify the molecular mechanisms that may initiate therapeutic effects, whole-genome expression profiling (using 324 Illumina Mouse WG-6 microarrays) of drug-induced alterations in the mouse brain was undertaken, with a focus on the time-course (1, 2, 4 and 8 h) of gene expression changes produced by eighteen major psychotropic drugs: antidepressants, antipsychotics, anxiolytics, psychostimulants and opioids. The resulting database is freely accessible at http://www.genes2mind.org. Bioinformatics approaches led to the identification of three main drug-responsive genomic networks and indicated neurobiological pathways that mediate the alterations in transcription. Each tested psychotropic drug was characterized by a unique gene network expression profile related to its neuropharmacological properties. Functional links that connect expression of the networks to the development of neuronal adaptations (MAPK signaling pathway), control of brain metabolism (adipocytokine pathway), and organization of cell projections (mTOR pathway) were found.
The comparison of gene expression alterations between various drugs opened a new means to classify the different psychoactive compounds and to predict their cellular targets; this is well exemplified in the case of tianeptine, an antidepressant with unknown mechanisms of action. This work represents the first proof-of-concept study of a molecular classification of psychoactive drugs.
尽管精神类药物应用广泛，但这些药物发挥疗效的生物学机制仍不完全清楚；为了开发更有效的新药和合理设计治疗方法，深入了解这些机制至关重要。鉴于目前有大量精神类药物，且它们具有不同的药理学作用，如果能确定各种药物类别的具体反应预测因子，将具有重要意义。
为了确定可能引发治疗效果的分子机制，我们采用了全基因组表达谱分析（使用 324 个 Illumina Mouse WG-6 微阵列），重点研究了 18 种主要精神类药物（抗抑郁药、抗精神病药、抗焦虑药、精神兴奋剂和阿片类药物）诱导的大脑基因表达变化的时程（1、2、4 和 8 小时）。由此产生的数据库可在 http://www.genes2mind.org 免费获取。通过生物信息学方法，确定了三个主要的药物反应基因组网络，并指出了介导转录变化的神经生物学途径。每种经过测试的精神类药物都具有与其神经药理学特性相关的独特基因网络表达谱。将网络的表达与神经元适应性的发展（MAPK 信号通路）、大脑代谢的控制（脂肪细胞因子途径）和细胞突起的组织（mTOR 通路）联系起来的功能联系也被发现。
不同药物之间的基因表达变化的比较为分类不同的精神活性化合物和预测其细胞靶标提供了新的手段；在具有未知作用机制的抗抑郁药噻奈普汀的案例中，这一点得到了很好的例证。这项工作代表了精神活性药物分子分类的首个概念验证研究。
