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Project description:Major depressive disorder (MDD) is a leading cause of disability and reduced life expectancy, with a two-fold increase in prevalence in women compared to men. Over the last few years, identifying reliable molecular biomarkers of MDD has proved challenging, likely reflecting the fact that, in addition to sex-differences, a variety of environmental and genetic risk factors are implicated. Recently, epigenetic processes have been proposed as mediators of the impact of life experiences on functional regulation of the genome, with the potential to contribute to MDD biomarker development. In this context, here we characterized and integrated transcriptomic gene expression data with two upstream mechanisms for epigenomic regulation, DNA methylation and micro-RNAs. The 3 molecular layers were analyzed in peripheral blood samples from a well-characterized cohort of individuals with MDD (n=80) and healthy controls (n=89), and processed using 3 complementary bioinformatic strategies. First, we conducted case-control comparisons for each single omic layer, and contrasted sex-specific adaptations. Second, we leveraged network theory to define gene co-expression modules, followed by step-by-step annotations across omic layers. Finally, we implemented a genome-wide and multiomic integration strategy that included cross-validation and bootstrapping. The approach was used to systematically compare the accuracy of MDD prediction across 6 methods for dimensionality reduction and, importantly, for every combination of 1, 2 or 3 types of molecular data. Results showed that accuracy was higher when female and male cohorts were analyzed separately, rather than combined, and also progressively increased with the number of molecular datasets considered. While multiomic informational gain has already been illustrated in other medical fields, our results pave the way towards similar advances in molecular psychiatry, and have practical implications towards developing clinically useful biomarkers of MDD.
2024-12-31 | GSE251780 | GEO
Project description:Major depressive disorder (MDD) is a leading cause of disability and reduced life expectancy, with a two-fold increase in prevalence in women compared to men. Over the last few years, identifying reliable molecular biomarkers of MDD has proved challenging, likely reflecting the fact that, in addition to sex-differences, a variety of environmental and genetic risk factors are implicated. Recently, epigenetic processes have been proposed as mediators of the impact of life experiences on functional regulation of the genome, with the potential to contribute to MDD biomarker development. In this context, here we characterized and integrated transcriptomic gene expression data with two upstream mechanisms for epigenomic regulation, DNA methylation and micro-RNAs. The 3 molecular layers were analyzed in peripheral blood samples from a well-characterized cohort of individuals with MDD (n=80) and healthy controls (n=89), and processed using 3 complementary bioinformatic strategies. First, we conducted case-control comparisons for each single omic layer, and contrasted sex-specific adaptations. Second, we leveraged network theory to define gene co-expression modules, followed by step-by-step annotations across omic layers. Finally, we implemented a genome-wide and multiomic integration strategy that included cross-validation and bootstrapping. The approach was used to systematically compare the accuracy of MDD prediction across 6 methods for dimensionality reduction and, importantly, for every combination of 1, 2 or 3 types of molecular data. Results showed that accuracy was higher when female and male cohorts were analyzed separately, rather than combined, and also progressively increased with the number of molecular datasets considered. While multiomic informational gain has already been illustrated in other medical fields, our results pave the way towards similar advances in molecular psychiatry, and have practical implications towards developing clinically useful biomarkers of MDD.
2024-12-31 | GSE251779 | GEO
Project description:Major depressive disorder (MDD) is a leading cause of disability and reduced life expectancy, with a two-fold increase in prevalence in women compared to men. Over the last few years, identifying reliable molecular biomarkers of MDD has proved challenging, likely reflecting the fact that, in addition to sex-differences, a variety of environmental and genetic risk factors are implicated. Recently, epigenetic processes have been proposed as mediators of the impact of life experiences on functional regulation of the genome, with the potential to contribute to MDD biomarker development. In this context, here we characterized and integrated transcriptomic gene expression data with two upstream mechanisms for epigenomic regulation, DNA methylation and micro-RNAs. The 3 molecular layers were analyzed in peripheral blood samples from a well-characterized cohort of individuals with MDD (n=80) and healthy controls (n=89), and processed using 3 complementary bioinformatic strategies. First, we conducted case-control comparisons for each single omic layer, and contrasted sex-specific adaptations. Second, we leveraged network theory to define gene co-expression modules, followed by step-by-step annotations across omic layers. Finally, we implemented a genome-wide and multiomic integration strategy that included cross-validation and bootstrapping. The approach was used to systematically compare the accuracy of MDD prediction across 6 methods for dimensionality reduction and, importantly, for every combination of 1, 2 or 3 types of molecular data. Results showed that accuracy was higher when female and male cohorts were analyzed separately, rather than combined, and also progressively increased with the number of molecular datasets considered. While multiomic informational gain has already been illustrated in other medical fields, our results pave the way towards similar advances in molecular psychiatry, and have practical implications towards developing clinically useful biomarkers of MDD.
2024-12-31 | GSE251778 | GEO
Project description:Sex-specific and multiomic integration enhance accuracy of peripheral blood biomarkers of major depressive disorder
| PRJNA1055461 | ENA
Project description:Sex-specific and multiomic integration enhance accuracy of peripheral blood biomarkers of major depressive disorder [EPIC]
| PRJNA1055699 | ENA
Project description:Sex-specific and multiomic integration enhance accuracy of peripheral blood biomarkers of major depressive disorder [miRNA-Seq]
| PRJNA1055698 | ENA
Project description:Sex-specific and multiomic integration enhance accuracy of peripheral blood biomarkers of major depressive disorder [RNA-Seq]
| PRJNA1055463 | ENA
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.
2011-10-04 | E-GEOD-19738 | biostudies-arrayexpress
Project description:ObjectiveTo date, the current diagnosis of major depressive disorder (MDD) still depends on clinical symptomatologic criteria, misdiagnosis and ineffective treatment are common. The study aimed to explore circulating biomarkers for MDD diagnosis.MethodsA high-throughput antibody array technology was utilized to detect 440 circulating cytokines in eight MDD patients and eight age-and gender-matched healthy controls. LASSO regression was conducted for MDD-related characteristic proteins selection. Enzyme-linked immunosorbent assay (ELISA) was used to validate the characteristic proteins in 40 MDD patients and 40 healthy controls. Receiver operating characteristic (ROC) curve was employed to evaluate the diagnostic values of characteristic proteins for discriminating MDD patients from healthy controls. Correlations between the levels of characteristic proteins and depression severity (HAMD-17 scores) were evaluated using linear regression.ResultsThe levels of 59 proteins were found aberrant in MDD patients compared with healthy controls. LASSO regression found six MDD-related characteristic proteins including insulin, CD40L, CD155, Lipocalin-2, HGF and LIGHT. ROC curve analysis showed that the area under curve (AUC) values of six characteristic proteins were more than 0.85 in discriminating patients with MDD from healthy controls. Furthermore, significant relationship was found between the levels of insulin, CD155, Lipocalin-2, HGF, LIGHT and HAMD-17 scores in MDD group.ConclusionThese results suggested that six characteristic proteins screened from 59 proteins differential in MDD may hold promise as diagnostic biomarkers in discriminating patients with MDD. Among six characteristic proteins, insulin, CD155, Lipocalin-2, HGF and LIGHT might be useful to estimate the severity of depressive symptoms.
| S-EPMC10436517 | biostudies-literature
Project description:Major depressive disorder (MDD) exhibits numerous clinical, epidemiological, and molecular features that are consistent with partially inherited and partially acquired epigenetic misregulation. We performed microarray based DNA modification study of MDD, utilizing affected and unaffected samples from white blood cells from monozygotic twins discordant for MDD, post-mortem prefrontal cortex tissues, and sperm samples. We performed DNA methylome analysis on white blood cells from monozygotic twins discordant for depression (n=200), pre-frontal cortex (n=71), and germline samples (n=33) from affected individuals and controls (total n=304). DNA samples were enriched for unmodified fraction of the genome using DNA-modification sensitive restriction enzyme digestion followed by adaptor-mediated PCR. The enriched fractions were labelled with a fluorescent dye (Cy5) and hybridized onto the array with a common reference pool (Cy3) generated from individuals unrelated to this study.
2012-04-27 | E-GEOD-37579 | biostudies-arrayexpress