Dataset Information

Microarray Analysis of the Major Depressive Disorder mRNA Profile Data.

ABSTRACT: OBJECTIVE:Major depressive disorder (MDD) is a common mood disorder associated with several psychophysiological changes like disturbances of sleep, appetite, or sexual desire, and it affects the patients' life seriously. We aimed to explore a genetic method to investigate the mechanism of MDD. METHODS:The mRNA expression profile (GSE53987) of MDD was downloaded from Gene Expression Omnibus database, including 105 samples of three brain regions in post-mortem tissue suffered from MDD and unaffected controls. Differentially expressed genes (DEGs) in MDD were identified using the Limma package in R. Gene Ontology functions and Kyoto Enrichment of Genes and Genomes pathways of the selected DEGs were enriched using Database for Annotation, Visualization and Integrated Discovery. Protein-protein interactive network of DEGs was constructed using the Cytoscape software. RESULTS:Totally, 241 DEGs in MDD-hip group, 218 DEGs in MDD-pfc group, and 327 DEGs in MDD-str group were identified. Also, different kinds of biological processes of DEGs in each group were enriched. Besides, glycan biosynthesis of DEGs in MDD-str group, RIG-I-like receptor signaling and pyrimidine metabolism of DEGs in the MDD-hip group were enriched, respectively. Moreover, several DEGs like PTK2, TDG and CETN2 in MDD-str group, DCT, AR and GNRHR in MDD-pfc group, and AKT1 and IRAK1 in MDD-hip group were selected from PPI network. CONCLUSION:Our data suggests that the brain striatum tissue may be greatly affected by MDD, and DEGs like PTK2, GALNT2 and GALNT2 in striatum, AR in prefrontal cortex and IRAK1 and IL12A in hippocampus may provide novel therapeutic basis for MDD treatment.

SUBMITTER: Gao L

PROVIDER: S-EPMC4504923 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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Microarray Analysis of the Major Depressive Disorder mRNA Profile Data.

Gao Lishu L Gao Yue Y Xu Enping E Xie Jian J

Psychiatry investigation 20150706 3

<h4>Objective</h4>Major depressive disorder (MDD) is a common mood disorder associated with several psychophysiological changes like disturbances of sleep, appetite, or sexual desire, and it affects the patients' life seriously. We aimed to explore a genetic method to investigate the mechanism of MDD.<h4>Methods</h4>The mRNA expression profile (GSE53987) of MDD was downloaded from Gene Expression Omnibus database, including 105 samples of three brain regions in post-mortem tissue suffered from M ...[more]

PMID: 26207134

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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.

Project description:Background and Objectives: Depressive symptoms are prominent in both major depressive disorder (MDD) and bipolar disorder (BD). However, comparative research on the network structure of depressive symptoms in these two diagnostic groups has been limited. This study aims to compare the network structure of depressive symptoms in MDD and BD, providing a deeper understanding of the depressive symptomatology of each disorder. Materials and Methods: The Zung Self-Rating Depressive Scale, a 20-item questionnaire, was administered to assess the depressive symptoms in individuals with MDD (n = 322) and BD (n = 516). A network analysis was conducted using exploratory graph analysis (EGA), and the network structure was analyzed using regularized partial correlation models. To validate the dimensionality of the Zung SDS, principal component analysis (PCA) was adopted. Centrality measures of the depressive symptoms within each group were assessed, followed by a network comparison test between the two groups. Results: In both diagnostic groups, the network analysis revealed four distinct categories, aligning closely with the PCA results. "Depressed affect" emerged as the most central symptom in both MDD and BD. Furthermore, non-core symptoms, "Personal devaluation" in MDD and "Confusion" in BD, displayed strong centrality. The network comparison test did not reveal significant differences in the network structure between MDD and BD. Conclusions: The absence of significant differences in the network structures between MDD and BD suggests that the underlying mechanisms of depressive symptoms may be similar across these disorders. The identified central symptoms, including "Depressed affect", in both disorders and the distinct non-core symptoms in each highlight the complexity of the depressive symptomatology. Future research should focus on validating these symptoms as therapeutic targets and incorporate various methodologies, including non-metric dimension reduction techniques or canonical analysis.

Dataset Information

Microarray Analysis of the Major Depressive Disorder mRNA Profile Data.

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Microarray Analysis of the Major Depressive Disorder mRNA Profile Data.

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