Dataset Information

Prioritization and comprehensive analysis of genes related to major depressive disorder.

ABSTRACT: BACKGROUND:Major depressive disorder (MDD) is a serious mental health problem in modern society, which is difficult to identify and diagnose in the early stages. Despite strong evidence supporting the heritability of MDD, progresses in large-scale and individual genetic studies remain preliminary. METHODS:In this study, a multi-data source-based prioritization (MDSP) method was proposed, and an appropriate threshold was determined for the optimization of depression-related genes (DEPgenes). Analyses on Gene Ontology biological processes, KEGG pathway and the specific pathway crosstalk network were further proposed. RESULTS:A total of 143 DEPgenes were identified and the MDD-specific network was constructed for the pathogenesis investigation and therapeutic methods development of MDD. Comparing with existing research strategies, the genetic optimization and analysis results were confirmed to be reliable. Finally, the pathway enrichment and crosstalk analyses revealed two unique pathway interaction modules that were significantly enriched with MDD genes. The related core pathways of neuroactive ligand-receptor interaction and dopaminergic synapse supported the neuropathology hypothesis of MDD. And the pathways of serotonergic synapse and morphine addiction indicated the mechanism of drug addiction caused by serotonin used in the treatment. CONCLUSIONS:This work provided a reference for the study of MDD, although future validation by extensive experimentation is still required.

SUBMITTER: Liu Y

PROVIDER: S-EPMC6565567 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Prioritization and comprehensive analysis of genes related to major depressive disorder.

Liu Yi Y Fan Pengfei P Zhang Shiyuan S Wang Yidan Y Liu Dan D

Molecular genetics & genomic medicine 20190409 6

<h4>Background</h4>Major depressive disorder (MDD) is a serious mental health problem in modern society, which is difficult to identify and diagnose in the early stages. Despite strong evidence supporting the heritability of MDD, progresses in large-scale and individual genetic studies remain preliminary.<h4>Methods</h4>In this study, a multi-data source-based prioritization (MDSP) method was proposed, and an appropriate threshold was determined for the optimization of depression-related genes ( ...[more]

PMID: 30968596

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Project description:BACKGROUND: Numerous genetic and genomic datasets related to complex diseases have been made available during the last decade. It is now a great challenge to assess such heterogeneous datasets to prioritize disease genes and perform follow up functional analysis and validation. Among complex disease studies, psychiatric disorders such as major depressive disorder (MDD) are especially in need of robust integrative analysis because these diseases are more complex than others, with weak genetic factors at various levels, including genetic markers, transcription (gene expression), epigenetics (methylation), protein, pathways and networks. RESULTS: In this study, we proposed a comprehensive analysis framework at the systems level and demonstrated it in MDD using a set of candidate genes that have recently been prioritized based on multiple lines of evidence including association, linkage, gene expression (both human and animal studies), regulatory pathway, and literature search. In the network analysis, we explored the topological characteristics of these genes in the context of the human interactome and compared them with two other complex diseases. The network topological features indicated that MDD is similar to schizophrenia compared to cancer. In the functional analysis, we performed the gene set enrichment analysis for both Gene Ontology categories and canonical pathways. Moreover, we proposed a unique pathway crosstalk approach to examine the dynamic interactions among biological pathways. Our pathway enrichment and crosstalk analyses revealed two unique pathway interaction modules that were significantly enriched with MDD genes. These two modules are neuro-transmission and immune system related, supporting the neuropathology hypothesis of MDD. Finally, we constructed a MDD-specific subnetwork, which recruited novel candidate genes with association signals from a major MDD GWAS dataset. CONCLUSIONS: This study is the first systematic network and pathway analysis of candidate genes in MDD, providing abundant important information about gene interaction and regulation in a major psychiatric disease. The results suggest potential functional components underlying the molecular mechanisms of MDD and, thus, facilitate generation of novel hypotheses in this disease. The systems biology based strategy in this study can be applied to many other complex diseases.

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

Prioritization and comprehensive analysis of genes related to major depressive disorder.

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Prioritization and comprehensive analysis of genes related to major depressive disorder.

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