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Identification of potential blood biomarkers associated with suicide in major depressive disorder.

ABSTRACT: Suicides have increased to over 48,000 deaths yearly in the United States. Major depressive disorder (MDD) is the most common diagnosis among suicides, and identifying those at the highest risk for suicide is a pressing challenge. The objective of this study is to identify changes in gene expression associated with suicide in brain and blood for the development of biomarkers for suicide. Blood and brain were available for 45 subjects (53 blood samples and 69 dorsolateral prefrontal cortex (DLPFC) samples in total). Samples were collected from MDD patients who died by suicide (MDD-S), MDDs who died by other means (MDD-NS) and non-psychiatric controls. We analyzed gene expression using RNA and the NanoString platform. In blood, we identified 14 genes which significantly differentiated MDD-S versus MDD-NS. The top six genes differentially expressed in blood were: PER3, MTPAP, SLC25A26, CD19, SOX9, and GAR1. Additionally, four genes showed significant changes in brain and blood between MDD-S and MDD-NS; SOX9 was decreased and PER3 was increased in MDD-S in both tissues, while CD19 and TERF1 were increased in blood but decreased in DLPFC. To our knowledge, this is the first study to analyze matched blood and brain samples in a well-defined population of MDDs demonstrating significant differences in gene expression associated with completed suicide. Our results strongly suggest that blood gene expression is highly informative to understand molecular changes in suicide. Developing a suicide biomarker signature in blood could help health care professionals to identify subjects at high risk for suicide.

SUBMITTER: Mamdani F

PROVIDER: S-EPMC9010430 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Identification of potential blood biomarkers associated with suicide in major depressive disorder.

Mamdani Firoza F Weber Matthieu D MD Bunney Blynn B Burke Kathleen K Cartagena Preston P Walsh David D Lee Francis S FS Barchas Jack J Schatzberg Alan F AF Myers Richard M RM Watson Stanley J SJ Akil Huda H Vawter Marquis P MP Bunney William E WE Sequeira Adolfo A

Translational psychiatry 20220414 1

Suicides have increased to over 48,000 deaths yearly in the United States. Major depressive disorder (MDD) is the most common diagnosis among suicides, and identifying those at the highest risk for suicide is a pressing challenge. The objective of this study is to identify changes in gene expression associated with suicide in brain and blood for the development of biomarkers for suicide. Blood and brain were available for 45 subjects (53 blood samples and 69 dorsolateral prefrontal cortex (DLPFC ...[more]

PMID: 35422091

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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: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.

Project description:BackgroundMajor depressive disorder (MDD) is a life-threatening and debilitating mental health condition. Mitophagy, a form of selective autophagy that eliminates dysfunctional mitochondria, is associated with depression. However, studies on the relationship between mitophagy-related genes (MRGs) and MDD are scarce. This study aimed to identify potential mitophagy-related biomarkers for MDD and characterize the underlying molecular mechanisms.MethodsThe gene expression profiles of 144 MDD samples and 72 normal controls were retrieved from the Gene Expression Omnibus database, and the MRGs were extracted from the GeneCards database. Consensus clustering was used to determine MDD clusters. Immune cell infiltration was evaluated using CIBERSORT. Functional enrichment analyses were performed to determine the biological significance of mitophagy-related differentially expressed genes (MR-DEGs). Weighted gene co-expression network analysis, along with a network of protein-protein interactions (PPI), was used to identify key modules and hub genes. Based on the least absolute shrinkage and selection operator analysis and univariate Cox regression analysis, a diagnostic model was constructed and evaluated using receiver operating characteristic curves and validated with training data and external validation data. We reclassified MDD into two molecular subtypes according to biomarkers and evaluated their expression levels.ResultsIn total, 315 MDD-related MR-DEGs were identified. Functional enrichment analyses revealed that MR-DEGs were mainly enriched in mitophagy-related biological processes and multiple neurodegenerative disease pathways. Two distinct clusters with diverse immune infiltration characteristics were identified in the 144 MDD samples. MATR3, ACTL6A, FUS, BIRC2, and RIPK1 have been identified as potential biomarkers of MDD. All biomarkers showed varying degrees of correlation with immune cells. In addition, two molecular subtypes with distinct mitophagy gene signatures were identified.ConclusionsWe identified a novel five-MRG gene signature that has excellent diagnostic performance and identified an association between MRGs and the immune microenvironment in MDD.

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Identification of potential blood biomarkers associated with suicide in major depressive disorder.

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Identification of potential blood biomarkers associated with suicide in major depressive disorder.

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