Dataset Information

Novel glutamatergic agents for major depressive disorder and bipolar disorder.

ABSTRACT: Mood disorders such as major depressive disorder (MDD) and bipolar disorder (BPD) are common, chronic, recurrent mental illnesses that affect the lives and functioning of millions of individuals worldwide. Growing evidence suggests that the glutamatergic system is central to the neurobiology and treatment of these disorders. Here, we review data supporting the involvement of the glutamatergic system in the pathophysiology of mood disorders as well as the efficacy of glutamatergic agents as novel therapeutics.

SUBMITTER: Machado-Vieira R

PROVIDER: S-EPMC3253881 | biostudies-literature | 2012 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Novel glutamatergic agents for major depressive disorder and bipolar disorder.

Machado-Vieira Rodrigo R Ibrahim Lobna L Henter Ioline D ID Zarate Carlos A CA

Pharmacology, biochemistry, and behavior 20110925 4

Mood disorders such as major depressive disorder (MDD) and bipolar disorder (BPD) are common, chronic, recurrent mental illnesses that affect the lives and functioning of millions of individuals worldwide. Growing evidence suggests that the glutamatergic system is central to the neurobiology and treatment of these disorders. Here, we review data supporting the involvement of the glutamatergic system in the pathophysiology of mood disorders as well as the efficacy of glutamatergic agents as novel ...[more]

PMID: 21971560

Similar Datasets

Project description:Background: Major Depressive Disorder (MDD) is a moderately heritable disorder with a high lifetime prevalence. At present, laboratory blood tests to support MDD diagnosis are not available. Methods: We used a classifier approach on blood gene expression profiles of a unique set of non-medicated subjects (MDD patients and controls) to select genes of which expression is predictive for disease status. To reveal blood gene expression changes related to MDD disease, we applied a powerful ex vivo stimulus to the blood, i.e. incubation with lipopolysaccharide (LPS; 10 ng/ml blood). Results: Based on LPS-stimulated blood gene expression using whole-genome microarrays in 42 subjects (primary cohort; 21 MDD patients (mean age 42.3 years), 21 healthy controls (mean age 41.9 years)), we identified a set of genes (CAPRIN1, CLEC4A, KRT23, MLC1, PLSCR1, PROK2, ZBTB16) that serves as a molecular signature of MDD. These findings were validated for the primary cohort using an independent quantitative PCR method (P = 0.007). The difference between depressive patients and controls was confirmed (P = 0.019) in a replication cohort of 13 patients with MDD (mean age 42.8 years) and 14 controls (mean age 45.6 years). The MDD-signature score comprised of expression levels of 7 genes could discriminate depressive patients from controls with sensitivity of 76.9% and specificity of 71.8%. Conclusions: We show for the first time that molecular analysis of stimulated blood cells can be used as an endophenotype for MDD diagnosis, which is a milestone in establishing biomarkers for neuropsychiatric disorders with moderate heritability in general. Our results may provide a new entry point for following and predicting treatment outcome, as well as prediction of severity and recurrence of MDD. In total, 33 MDD patients and 34 healthy controls were analyzed using basal gene expression in whole blood, and gene expression from whole blood that was stimulated with LPS for 5-6 h, using microarrays. Patients were arbitrarily selected from all patients to serve as primary cohort (nMDD = 21 (MDD01-MDD21); nControls = 21 (Con01-Con21)), or replication cohort (nMDD = 12 (MDD22-MDD35); nControls = 13 (Con22-Con37)) using microarrays. This submission does not include Samples CON21_LPS or CON30_LPS.

Dataset Information

Novel glutamatergic agents for major depressive disorder and bipolar disorder.

Publications

Novel glutamatergic agents for major depressive disorder and bipolar disorder.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure