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Gene Expression has Distinct Associations with Brain Structure and Function in Major Depressive Disorder.

ABSTRACT: Major depressive disorder (MDD) is associated with structural and functional brain abnormalities. MDD as well as brain anatomy and function are influenced by genetic factors, but the role of gene expression remains unclear. Here, this work investigates how cortical gene expression contributes to structural and functional brain abnormalities in MDD. This work compares the gray matter volume and resting-state functional measures in a Chinese sample of 848 MDD patients and 749 healthy controls, and these case-control differences are then associated with cortical variation of gene expression. While whole gene expression is positively associated with structural abnormalities, it is negatively associated with functional abnormalities. This work observes the relationships of expression levels with brain abnormalities for individual genes, and found that transcriptional correlates of brain structure and function show opposite relations with gene dysregulation in postmortem cortical tissue from MDD patients. This work further identifies genes that are positively or negatively related to structural abnormalities as well as functional abnormalities. The MDD-related genes are enriched for brain tissue, cortical cells, and biological pathways. These findings suggest that distinct genetic mechanisms underlie structural and functional brain abnormalities in MDD, and highlight the importance of cortical gene expression for the development of cortical abnormalities.

SUBMITTER: Liu S

PROVIDER: S-EPMC9982587 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Gene Expression has Distinct Associations with Brain Structure and Function in Major Depressive Disorder.

Liu Shu S Abdellaoui Abdel A Verweij Karin J H KJH van Wingen Guido A GA

Advanced science (Weinheim, Baden-Wurttemberg, Germany) 20230113 7

Major depressive disorder (MDD) is associated with structural and functional brain abnormalities. MDD as well as brain anatomy and function are influenced by genetic factors, but the role of gene expression remains unclear. Here, this work investigates how cortical gene expression contributes to structural and functional brain abnormalities in MDD. This work compares the gray matter volume and resting-state functional measures in a Chinese sample of 848 MDD patients and 749 healthy controls, and ...[more]

PMID: 36638259

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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:ImportanceIdentifying neurobiological differences between patients with major depressive disorder (MDD) and healthy individuals has been a mainstay of clinical neuroscience for decades. However, recent meta-analyses have raised concerns regarding the replicability and clinical relevance of brain alterations in depression.ObjectiveTo quantify the upper bounds of univariate effect sizes, estimated predictive utility, and distributional dissimilarity of healthy individuals and those with depression across structural magnetic resonance imaging (MRI), diffusion-tensor imaging, and functional task-based as well as resting-state MRI, and to compare results with an MDD polygenic risk score (PRS) and environmental variables.Design, setting, and participantsThis was a cross-sectional, case-control clinical neuroimaging study. Data were part of the Marburg-Münster Affective Disorders Cohort Study. Patients with depression and healthy controls were recruited from primary care and the general population in Münster and Marburg, Germany. Study recruitment was performed from September 11, 2014, to September 26, 2018. The sample comprised patients with acute and chronic MDD as well as healthy controls in the age range of 18 to 65 years. Data were analyzed from October 29, 2020, to April 7, 2022.Main outcomes and measuresPrimary analyses included univariate partial effect size (η2), classification accuracy, and distributional overlapping coefficient for healthy individuals and those with depression across neuroimaging modalities, controlling for age, sex, and additional modality-specific confounding variables. Secondary analyses included patient subgroups for acute or chronic depressive status.ResultsA total of 1809 individuals (861 patients [47.6%] and 948 controls [52.4%]) were included in the analysis (mean [SD] age, 35.6 [13.2] years; 1165 female patients [64.4%]). The upper bound of the effect sizes of the single univariate measures displaying the largest group difference ranged from partial η2 of 0.004 to 0.017, and distributions overlapped between 87% and 95%, with classification accuracies ranging between 54% and 56% across neuroimaging modalities. This pattern remained virtually unchanged when considering either only patients with acute or chronic depression. Differences were comparable with those found for PRS but substantially smaller than for environmental variables.Conclusions and relevanceResults of this case-control study suggest that even for maximum univariate biological differences, deviations between patients with MDD and healthy controls were remarkably small, single-participant prediction was not possible, and similarity between study groups dominated. Biological psychiatry should facilitate meaningful outcome measures or predictive approaches to increase the potential for a personalization of the clinical practice.

Project description:OBJECTIVE:Imaging studies of major depressive disorder have reported structural and functional abnormalities in a variety of spatially diverse brain regions. Quantitative meta-analyses of this literature, however, have failed to find statistically significant between-study spatial convergence, other than transdiagnostic-only effects. In the present study, the authors applied a novel multimodal meta-analytic approach to test the hypothesis that major depression exhibits spatially convergent structural and functional brain abnormalities. METHODS:This coordinate-based meta-analysis included voxel-based morphometry (VBM) studies and resting-state voxel-based pathophysiology (VBP) studies of blood flow, glucose metabolism, regional homogeneity, and amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF). Input data were grouped into three primary meta-analytic classes: gray matter atrophy, increased function, and decreased function in patients with major depression relative to healthy control subjects. In secondary meta-analyses, the data were grouped across primary categories, and in tertiary analyses, by medication status and absence of psychiatric comorbidity. Activation likelihood estimation was used for all analyses. RESULTS:A total of 92 publications reporting 152 experiments were identified, collectively representing 2,928 patients with major depressive disorder. The primary analyses detected no convergence across studies. The secondary analyses identified portions of the subgenual cingulate cortex, hippocampus, amygdala, and putamen as demonstrating convergent abnormalities. The tertiary analyses (clinical subtypes) showed improved convergence relative to the secondary analyses. CONCLUSIONS:Coordinate-based meta-analysis identified spatially convergent structural (VBM) and functional (VBP) abnormalities in major depression. The findings suggest replicable neuroimaging features associated with major depression, beyond the transdiagnostic effects reported in previous meta-analyses, and support a continued research focus on the subgenual cingulate and other selected regions' role in depression.

Project description:A cardinal symptom of Major Depressive Disorder (MDD) is the disruption of circadian patterns. Yet, to date, there is no direct evidence of circadian clock dysregulation in the brains of MDD patients. Circadian rhythmicity of gene expression has been observed in animals and peripheral human tissues, but its presence and variability in the human brain was difficult to characterize. Here we applied time-of-death analysis to gene expression data from high-quality postmortem brains, examining 24-hour cyclic patterns in six cortical and limbic regions of 55 subjects with no history of psychiatric or neurological illnesses ('Controls') and 34 MDD patients. Our dataset covered ~12,000 transcripts in the dorsolateral prefrontal cortex (DLPFC), anterior cingulate cortex (AnCg), hippocampus (HC), amygdala (AMY), nucleus accumbens (NAcc) and cerebellum (CB). Several hundred transcripts in each region showed 24-hour cyclic patterns in Controls, and >100 transcripts exhibited consistent rhythmicity and phase-synchrony across regions. Among the top ranked rhythmic genes were the canonical clock genes BMAL1(ARNTL), PER1-2-3, NR1D1(REV-ERB), DBP, BHLHE40(DEC1), and BHLHE41(DEC2). The phasing of known circadian genes was consistent with data derived from other diurnal mammals. Cyclic patterns were much weaker in MDD brains, due to shifted peak timing and potentially disrupted phase relationships between individual circadian genes. This is the first transcriptome-wide analysis of cyclic patterns in the human brain and demonstrates a rhythmic rise and fall of gene expression in regions outside of the suprachiasmatic nucleus in control subjects. The description of its breakdown in MDD suggest novel molecular targets for treatment of mood disorders. Sample collection, including human subject recruitment and characterization, tissue dissection, and RNA extraction, was described previously (see Evans 2003 Neurobiol Dis 14:240-250, Li 2004 Biol Psychiatry 55:346-352). RNA samples for different regions came from the same set of brains from 86 control subjects. Sample size varied by region: AnCg (n=70 controls), DLPFC (n=83), CB (n=51), AMY (n=32), HC (n=63) and NAcc (n=66). Many of the samples were run on two or more chips, resulting in the following chip counts for each region: AnCg = 124, DLPFC = 161, CB = 79, AMY = 62, HC = 108 and NaCC = 136. We ran each sample on at least two microarrays using the Affymetrix U133-A or U133Plus-v2 GeneChips, however, we only used the U133A part of the U133Plus-v2 GeneChip in our analysis. We applied RMA (Robust Multi-array Analysis) (see Irizarry 2003 Biostatistics 4:249-264, Irizarry 2003 Nucleic Acids Res 31:e15) to summarize probe set expression levels, using a custom Chip Definition File, resulting in expression data for 11,911 ENTREZ transcripts. Microarray data for each region were analyzed separately. All downstream analyses were performed in R. Details of the data processing, including data cleaning and normalization, are located in the Supplementary Materials (section 1). After data filtering, 1,424 microarrays remained, corresponding to 776 unique RNA samples in six regions.

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Gene Expression has Distinct Associations with Brain Structure and Function in Major Depressive Disorder.

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Gene Expression has Distinct Associations with Brain Structure and Function in Major Depressive Disorder.

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