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: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.

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Shared peripheral blood biomarkers for Alzheimer's disease, major depressive disorder, and type 2 diabetes and cognitive risk factor analysis.

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Shared peripheral blood biomarkers for Alzheimer's disease, major depressive disorder, and type 2 diabetes and cognitive risk factor analysis.

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