Chronic opioid use leads to the structural reorganization of neuronal networks, involving genetic reprogramming in neurons and glial cells. Our previous in vivo studies have revealed that a significant fraction of the morphine-induced alterations to the striatal transcriptome included glucocorticoid (GC) receptor (GR)-dependent genes. Additional analyses suggested glial cells to be the locus of these changes. In the current study, we aimed to differentiate the direct transcriptional effects of morphine and a GR agonist on primary striatal neurons and astrocytes. Whole-genome transcriptional profiling revealed that while morphine had no significant effect on gene expression in both cell types, dexamethasone significantly altered the transcriptional profile in astrocytes but not neurons. We obtained a complete dataset of genes undergoing the regulation, which includes genes related to glucose metabolism (Pdk4), circadian activity (Per1) and cell differentiation (Sox2). There was also an overlap between morphine-induced transcripts in striatum and GR-dependent transcripts in cultured astrocytes. We further analyzed the regulation of expression of one gene belonging to both groups, serum and GC regulated kinase 1 (Sgk1). We identified two transcriptional variants of Sgk1 that displayed selective GR-dependent upregulation in cultured astrocytes but not neurons. Moreover, these variants were the only two that were found to be upregulated in vivo by morphine in a GR-dependent fashion. Our data suggest that the morphine-induced, GR-dependent component of transcriptome alterations in the striatum is confined to astrocytes. Identification of this mechanism opens new directions for research on the role of astrocytes in the central effects of opioids. V V C 2013 Wiley Periodicals, Inc.

The molecular mechanisms underlying the systemic response to subarachnoid hemorrhage (SAH) from ruptured intracranial aneurysms (RAs) are not fully understood. We investigated whether the analysis of gene expression in peripheral blood could provide clinically relevant information regarding the biologic consequences of SAH. Transcriptomics were performed using Illumina HumanHT-12v4 microarrays for 43 RA patients and 18 controls (C). Differentially expressed transcripts were analyzed for overrepresented functional groups and blood cell type-specific gene expression. The set of differentially expressed transcripts was validated using quantitative polymerase chain reaction in an independent group of subjects (15 RA patients and 14 C). There were 135 differentially expressed genes (false discovery rate 1%, absolute fold change 1.7): the abundant levels of 78 mRNAs increased and 57 mRNAs decreased. Among RA patients, transcripts specific to T lymphocyte subpopulations were downregulated, whereas those related to monocytes and neutrophils were upregulated. Expression profiles of a set of 16 genes and lymphocyte-to-monocyte-and-neutrophil gene expression ratios distinguished RA patients from C. These results indicate that SAH from RAs strongly influences the transcription profiles of blood cells. A specific pattern of these changes suggests suppression in lymphocyte response and enhancements in monocyte and neutrophil activities. This is probably related to the immunodepression observed in SAH.

BackgroundDespite 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.ResultsTo 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.ConclusionsThe 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.