Multi-Omics Characterization of Early- and Adult-Onset Major Depressive Disorder.
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ABSTRACT:
SUBMITTER: Grant CW
PROVIDER: S-EPMC8949112 | biostudies-literature | 2022 Mar
REPOSITORIES: biostudies-literature
Publications
Multi-Omics Characterization of Early- and Adult-Onset Major Depressive Disorder.
Grant Caroline W CW Barreto Erin F EF Kumar Rakesh R Kaddurah-Daouk Rima R Skime Michelle M Mayes Taryn T Carmody Thomas T Biernacka Joanna J Wang Liewei L Weinshilboum Richard R Trivedi Madhukar H MH Bobo William V WV Croarkin Paul E PE Athreya Arjun P AP
Journal of personalized medicine 20220306 3
Age at depressive onset (AAO) corresponds to unique symptomatology and clinical outcomes. Integration of genome-wide association study (GWAS) results with additional “omic” measures to evaluate AAO has not been reported and may reveal novel markers of susceptibility and/or resistance to major depressive disorder (MDD). To address this gap, we integrated genomics with metabolomics using data-driven network analysis to characterize and differentiate MDD based on AAO. This study first performed two ...[more]
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Project description:Search for the biomarkers of major depressive disorder (MDD) has facilitated from the genes expressed in the patientâs blood cells. Because the severity of depressive symptoms and characteristics in patients with MDD are differed by the age at onset of first depressive episode, the potential transcriptomic markers in blood cells may also be different by the age at onset of MDD. In this study, we searched the transcriptomic markers of late-onset (onset ages ⥠50 years) MDD (LOD) from the expressed genes in blood cells, and identified state-dependent biomarkers in the patients. We assessed the expressed genes in blood cells by the microarray and found that the expression levels of 3,066 probes were state-dependently changed in the blood cells of patients with LOD. Elderly (age â¥50 years) outpatients and inpatients with MDD corresponding to a DSM-IV diagnosis of the melancholy type of MDD episodes were studied. The depressive state was measured using the Structured Interview Guide for the Hamilton Depression (SIGH-D) rating scale. Syndromal remission was defined as a stage in which a participant did not meet the diagnosis of a MINI major depressive episode for a period of 2 consecutive months and had a SIGH-D score of less than 8. Twelve healthy individuals were also recruited. Blood was obtained from the participants and analyzed using an Agilent SurePrint G3 Human GE 8Ã60K v2 Microarray (Design ID: 039494).
2016-01-14 | E-GEOD-76826 | biostudies-arrayexpress
Project description:A genome-wide association study was carried out in 1020 case subjects with recurrent early-onset major depressive disorder (MDD) (onset before age 31) and 1636 control subjects screened to exclude lifetime MDD. Subjects were genotyped with the Affymetrix 6.0 platform. After extensive quality control procedures, 671 424 autosomal single nucleotide polymorphisms (SNPs) and 25 068 X chromosome SNPs with minor allele frequency greater than 1% were available for analysis. An additional 1 892 186 HapMap II SNPs were analyzed based on imputed genotypic data. Single-SNP logistic regression trend tests were computed, with correction for ancestry-informative principal component scores. No genome-wide significant evidence for association was observed, assuming that nominal P<5 × 10(-8) approximates a 5% genome-wide significance threshold. The strongest evidence for association was observed on chromosome 18q22.1 (rs17077540, P=1.83 × 10(-7)) in a region that has produced some evidence for linkage to bipolar-I or -II disorder in several studies, within an mRNA detected in human brain tissue (BC053410) and approximately 75 kb upstream of DSEL. Comparing these results with those of a meta-analysis of three MDD GWAS data sets reported in a companion article, we note that among the strongest signals observed in the GenRED sample, the meta-analysis provided the greatest support (although not at a genome-wide significant level) for association of MDD to SNPs within SP4, a brain-specific transcription factor. Larger samples will be required to confirm the hypothesis of association between MDD (and particularly the recurrent early-onset subtype) and common SNPs.
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Project description:BackgroundEarly-onset (EO) major depressive disorder (MDD) patients experience more depressive episodes and an increased risk of relapse. Thus, on a neurobiological level, adult EO patients might display brain structure and function different from adult-onset (AO) patients.MethodsA total of 103 patients (66 females) underwent magnetic resonance imaging. Structural measures of gray matter volume (GMV) and functional connectivity networks during resting state were compared between EO (≤19 years) and AO groups. Four residual major depression symptoms, mood, anxiety, insomnia, and somatic symptoms, were correlated with GMV between groups.ResultsWe found comparatively increased GMV in the EO group, namely the medial prefrontal and insular cortex, as well as the anterior hippocampus. Functional networks in EO patients showed a comparatively weaker synchronization of the left hippocampus with the adjacent amygdala, and a stronger integration with nodes in the contralateral prefrontal cortex and supramarginal gyrus. Volumetric analysis of depression symptoms associated the caudate nuclei with symptoms of insomnia, and persisting mood symptoms with the right amygdala, while finding no significant clusters for somatic and anxiety symptoms.ConclusionsThe study highlights the important role of the hippocampus and the prefrontal cortex in EO patients as part of emotion-regulation networks. Results in EO patients demonstrated subcortical volume changes irrespective of sleep and mood symptom recovery, which substantiates adolescence as a pivotal developmental phase for MDD. Longitudinal studies are needed to differentiate neural recovery trajectories while accounting for age of onset.
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Project description:Search for the biomarkers of major depressive disorder (MDD) has facilitated from the genes expressed in the patient’s blood cells. Because the severity of depressive symptoms and characteristics in patients with MDD are differed by the age at onset of first depressive episode, the potential transcriptomic markers in blood cells may also be different by the age at onset of MDD. In this study, we searched the transcriptomic markers of late-onset (onset ages ≥ 50 years) MDD (LOD) from the expressed genes in blood cells, and identified state-dependent biomarkers in the patients. We assessed the expressed genes in blood cells by the microarray and found that the expression levels of 3,066 probes were state-dependently changed in the blood cells of patients with LOD.
2016-01-14 | GSE76826 | GEO
Project description:The heterogeneity of Major Depressive Disorder (MDD) has been increasingly recognized, challenging traditional symptom-based diagnostics and the development of mechanism-targeted therapies. This study aims to identify neuroimaging-based MDD subtypes and dissect their predominant biological characteristics using multi-omics data. A total of 807 participants were included in this study, comprising 327 individuals with MDD and 480 healthy controls (HC). The amplitude of low-frequency fluctuations (ALFF), a functional neuroimaging feature, was extracted for each participant and used to identify MDD subtypes through machine learning clustering. Multi-omics data, including profiles of genetic, epigenetics, metabolomics, and pro-inflammatory cytokines, were obtained. Comparative analyses of multi-omics data were conducted between each MDD subtype and HC to explore the molecular underpinnings involved in each subtype. We identified three neuroimaging-based MDD subtypes, each characterized by unique ALFF pattern alterations compared to HC. Multi-omics analysis showed a strong genetic predisposition for Subtype 1, primarily enriched in neuronal development and synaptic regulation pathways. This subtype also exhibited the most severe depressive symptoms and cognitive decline compared to the other subtypes. Subtype 2 is characterized by immuno-inflammation dysregulation, supported by elevated IL-1 beta levels, altered epigenetic inflammatory measures, and differential metabolites correlated with IL-1 beta levels. No significant biological markers were identified for Subtype 3. Our results identify neuroimaging-based MDD subtypes and delineate the distinct biological features of each subtype. This provides a proof of concept for mechanism-targeted therapy in MDD, highlighting the importance of personalized treatment approaches based on neurobiological and molecular profiles.
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Project description:BackgroundThere is a growing body of evidence suggesting that disturbance of the gut-brain axis may be one of the potential causes of major depressive disorder (MDD). However, the effects of antidepressants on the gut microbiota, and the role of gut microbiota in influencing antidepressant efficacy are still not fully understood.ResultsTo address this knowledge gap, a multi-omics study was undertaken involving 110 MDD patients treated with escitalopram (ESC) for a period of 12 weeks. This study was conducted within a cohort and compared to a reference group of 166 healthy individuals. It was found that ESC ameliorated abnormal blood metabolism by upregulating MDD-depleted amino acids and downregulating MDD-enriched fatty acids. On the other hand, the use of ESC showed a relatively weak inhibitory effect on the gut microbiota, leading to a reduction in microbial richness and functions. Machine learning-based multi-omics integrative analysis revealed that gut microbiota contributed to the changes in plasma metabolites and was associated with several amino acids such as tryptophan and its gut microbiota-derived metabolite, indole-3-propionic acid (I3PA). Notably, a significant correlation was observed between the baseline microbial richness and clinical remission at week 12. Compared to non-remitters, individuals who achieved remission had a higher baseline microbial richness, a lower dysbiosis score, and a more complex and well-organized community structure and bacterial networks within their microbiota. These findings indicate a more resilient microbiota community in remitters. Furthermore, we also demonstrated that it was not the composition of the gut microbiota itself, but rather the presence of sporulation genes at baseline that could predict the likelihood of clinical remission following ESC treatment. The predictive model based on these genes revealed an area under the curve (AUC) performance metric of 0.71.ConclusionThis study provides valuable insights into the role of the gut microbiota in the mechanism of ESC treatment efficacy for patients with MDD. The findings represent a significant advancement in understanding the intricate relationship among antidepressants, gut microbiota, and the blood metabolome. Additionally, this study offers a microbiota-centered perspective that can potentially improve antidepressant efficacy in clinical practice. By shedding light on the interplay between these factors, this research contributes to our broader understanding of the complex mechanisms underlying the treatment of MDD and opens new avenues for optimizing therapeutic approaches. Video Abstract.
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Project description:A genome scan was performed on the first phase sample of the Genetics of Recurrent Early-Onset Depression (GenRED) project. The sample consisted of 297 informative families containing 415 independent affected sibling pairs (ASPs), or, counting all possible pairs, 685 informative affected relative pairs (555 ASPs and 130 other pair types). Affected cases had recurrent major depressive disorder (MDD) with onset before age 31 years for probands or age 41 years for other affected relatives; the mean age at onset was 18.5 years, and the mean number of depressive episodes was 7.3. The Center for Inherited Disease Research genotyped 389 microsatellite markers (mean spacing of 9.3 cM). The primary linkage analysis considered allele sharing in all possible affected relative pairs with the use of the Z(lr) statistic computed by the ALLEGRO program. A secondary logistic regression analysis considered the effect of the sex of the pair as a covariate. Genomewide significant linkage was observed on chromosome 15q25.3-26.2 (Zlr=4.14, equivalent LOD = 3.73, empirical genomewide P=.023). The linkage was not sex specific. No other suggestive or significant results were observed in the primary analysis. The secondary analysis produced three regions of suggestive linkage, but these results should be interpreted cautiously because they depended primarily on the small subsample of 42 male-male pairs. Chromosome 15q25.3-26.2 deserves further study as a candidate region for susceptibility to MDD.
| S-EPMC1182079 | biostudies-literature
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: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
Project description:Oxidative stress (OS) is strongly implicated in the pathophysiology of major depressive disorder (MDD) but the molecular mechanisms remain largely unknown. The purpose of this study is to identify genes related to both OS and MDD, and further to evaluate the utility of these genes as diagnostic markers and potential treatment targets. We searched datasets related to MDD from the Gene Expression Omnibus (GEO) database for differentially expressed genes (DEGs) also related to OS according to GeneCards. Bioinformatics analyses and machine learning algorithms were used to identify hub genes mediating OS-MDD interactions. A summary data-based Mendelian randomization (SMR) approach was employed to identify possible causal genes for MDD from blood tissue eQLT data. These investigations identified 32 genes mediating OS-MDD interactions, while SMR analysis identified KCNE1 (OR = 1.057, 95%CI = 1.013-1.102, P value = 0.010), MAPK3 (OR = 1.023, 95%CI = 1.004-1.043, P value = 0.020), and STIP1 (OR = 0.792, 95%CI = 0.641-0.979, P value = 0.031) as OS-related causal genes for MDD. These genes may thus serve as useful diagnostic markers and potential therapeutic targets.
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