Blood Transcriptomic Markers in Patients with Late-Onset Major Depressive Disorder

The regulatory variant rs1950834 confers the risk of depressive disorder by reducing LRFN5 expression

BACKGROUND

Genome-wide association studies have identified 14q21.1 as a robust risk locus for major depressive disorder (MDD). However, the underlying mechanism remains elusive. Here, we aim to explore the regulatory function of rs1950834 on leucine-rich repeat and fibronectin type III domain containing 5 (LRFN5) expression in MDD.

METHODS

Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9)-mediated genome knockout and single-base editing were used to determine the effects of rs1950834 on the binding of transcriptional factors and the expression of the target gene LRFN5. Meta-analysis of multiple transcriptomic datasets was performed to clarify the brain region responsible for LRFN5 downregulation in MDD patients. Adeno-associated virus (AAV)-mediated Lrfn5 overexpression or knockdown in the nucleus accumbens (NAc) was used to test their effects on depression-like behaviors and sensitivity to chronic unpredictable mild stress (CUMS) in male mice. Synaptic structure and functions were monitored by synaptic protein expression assay, Golgi staining, and electrophysiological analysis.

RESULTS

The risk allele (A) of rs1950834 reduced the binding affinity to RNA polymerase II subunit A (POLR2A) and the transcription factor RAD21 cohesin complex component (RAD21), leading to decreased expression of LRFN5. LRFN5 expression was downregulated specifically in the NAc of MDD patients as compared to healthy controls. Knockdown of Lrfn5 in NAc neurons induced depression-like behaviors and further exacerbated CUMS-induced phenotypes via synaptic damage, but overexpression of Lrfn5 in mouse NAc induced resilience to CUMS.

CONCLUSIONS

These findings reveal that the functional risk single nucleotide polymorphism rs1950834 at 14q21.1 regulates LRNN5 expression and function in NAc, providing a novel perspective for molecular diagnosis and targeted interventions of MDD.

A Transcriptomic Signature of Depressive Symptoms in Late Life

Background

Depressive symptoms in late life can impair daily function and accompany cognitive decline. However, the molecular mechanisms that underlie these changes in the brain remain poorly understood.

Methods

Differential expression analysis was performed on bulk-tissue RNA sequencing data generated from dorsolateral prefrontal cortex samples of elderly participants in ROS/MAP (Religious Orders Study and Memory and Aging Project; N = 998, mean age at death = 89.7 years). Bulk tissue RNA sequencing was analyzed against depressive symptoms measured prior to death, controlling for Alzheimer's disease neuropathology, medication status, and lifestyle factors. Sex-stratified models were also tested.

Results

Increased abundance of the Prader-Willi syndrome-associated gene PWAR1 (corrected p = 5.47 × 10-3) and CTDSPL2 (corrected p = .03) were associated with a higher burden of depressive symptoms in the combined sample. An additional 14 genes showed suggestive associations, including several with known links to neuropsychiatric illness (e.g., ACVR2B-AS1, COL19A1). Functional enrichment analysis revealed downregulation of aerobic metabolism and upregulation of both amino acid catabolism and DNA modification processes. Differential expression signatures were poorly correlated between males and females (Pearson r = 0.12; 95% CI, 0.10 to 0.13), and only the male group showed independently significant differential expression. Little overlap was found with previously published analyses of major depressive disorder.

Conclusions

Building on recently published single-nucleus profiling, we present the largest-ever study of transcriptomic correlates of depressive symptoms in late life, revealing new insights into sex-specific regulators. PWAR1 and CTDSPL2 were identified as putative markers of late-life depression in the dorsolateral prefrontal cortex and warrant further study.

Exploring the diagnostic potential of IL1R1 in depression and its association with lipid metabolism

Background

Depression is a complex mental disorder where oxidative stress and lipid metabolism disorders play crucial roles, yet their connection requires further exploration. This study aims to investigate the roles of oxidative stress and lipid metabolism disorders in depression using bioinformatics methods and Mendelian randomization analysis.

Methods

A differential gene expression analysis was performed on the GSE76826 dataset, followed by identification of the intersection with genes related to OS. Subsequently, support vector machine (SVM) and random forest algorithms were employed to determine the optimal division of feature variables. The diagnostic performance was evaluated using a ROC diagnostic model and Diagnostic Nomogram. Furthermore, Mendelian randomization (MR) analysis was conducted to explore the causal relationship between the gene and depression. The expression patterns of key genes in brain tissue were analyzed using the Human eFP Browser database, while their associations with metabolism-related genes were investigated using the STRING database. Finally, DrugnomeAI was utilized to assess the drug development potential of these genes, and small molecule compounds targeting them were identified through dgidb and ChEMBL databases; molecular docking studies were then conducted to evaluate their binding affinity.

Results

By conducting a comprehensive analysis of oxidative stress-related genes and depression-related target genes, we have successfully identified 12 overlapping genes. These 12 genes were selected using support vector machine and random forest algorithms. Upon analyzing the diagnostic model, it was revealed that EPAS1 and IL1R1 serve as key biomarkers for OS in depression, with IL1R1 exhibiting the highest diagnostic potential among them. Additionally, MRfen analysis suggests that IL1R1 may play a protective role against depression. Notably, this gene exhibits high expression levels in crucial brain regions such as the olfactory bulb, corpus callosum, and hippocampus. Furthermore, our findings indicate an association between IL1R1 and lipid-related genes PDGFB, PIK3R1, TNFRSFIAA NOD2, and LYN. DrugnomeAI analysis indicated promising medicinal value for ILIRI with BI 639667 demonstrating superior binding affinity among the selected small molecule drugs.

Conclusion

This study provides novel insights into the association between OS and dyslipidemia metabolism in depression, offering potential therapeutic targets for future drug development.

Biological mechanism and functional verification of key genes related to major depressive disorder and type 2 diabetes mellitus

Major depressive disorder (MDD) and type 2 diabetes (T2D) have been shown to be linked, but a comprehensive understanding of the underlying molecular mechanisms remains elusive. The purpose of this study was to explore the biological relationship between MDD and T2D and verify the functional roles of key genes. We used the Gene Expression Omnibus database to investigate the targets associated with MDD and T2D. Using linear models for microarray data, differentially expressed genes associated with MDD and T2D were identified in GSE76826 and GSE95849, respectively, and 126 shared genes were significantly upregulated. Weighted gene coexpression network analysis identified modules associated with MDD and T2D in the GSE38206 and GSE20966 datasets and identified 8 common genes. Functional enrichment analysis revealed that these genes were enriched in cell signaling, enzyme activity, cell structure and amino acid biosynthesis and involved in cell death pathways. Finally, combined with the CTD and GeneCards databases, lysophosphatidylglycerol acyltransferase 1 (LPGAT1) was identified as a key gene. LPGAT1 was validated in GSE201332 and GSE182117, and the subject operating characteristic curve showed good diagnostic potential for MDD and T2D. Additionally, we used an in vitro model of MDD related to T2D to verify the expression of LPGAT1. A subsequent gene knockdown assay revealed that the downregulation of LPGAT1 improved mitochondrial function and reduced apoptosis in damaged neurons. Taken together, our results highlight the role of LPGAT1 in the connection between MDD and T2D, and these findings provide new insights into potential therapeutic targets for depression associated with diabetes.

Bioinformatics Screening and Experimental Validation for Deciphering the Immune Signature of Late-Onset Depression

Background

Late-onset depression (LOD) is often associated with more severe cognitive impairment and a higher risk of disability and suicide. Emerging evidence suggests that immune system problems may be involved. This study aims to systematically characterize the genetic signature of LOD based on the immune landscape.

Methods

The expression profile of GSE76826 was obtained from the Gene Expression Omnibus (GEO) database to gather gene expression data for 10 LOD patients and 12 healthy controls (HC). Various analyses, such as Single-Sample Gene Set Enrichment Analysis (ssGSEA) and Weighted Gene Co-expression Network Analysis (WGCNA), were used to mine key genes closely related to LOD. ImmuCellAI helped us understand differences in the immune environment between LOD patients and controls, and we used an LOD animal model to validate the relevant immune characteristics.

Results

We found enriched immune pathways linked to LOD and adaptive immune responses. Using advanced bioinformatics techniques, we identified two key genes: apelin (APLN) and leptin (LEP), which have good diagnostic efficacy (AUC=0.925, 95% CI=1.00-0.83) for LOD. Neutrophil infiltration increased significantly in LOD, while CD8+ T lymphocytes (CD8_T) decreased. We finally constructed an animal model of LOD, validated two key genes and microglia marker genes in blood and hippocampus, and detected elevated pro-inflammatory factors such as interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α).

Conclusion

We identified and validated the presence of aberrant expression of APLN and LEP in LOD and described a possible immune mechanism involving increased release of IL-6 and TNF-α, leading to decreased CD8_T infiltration and increased neutrophil infiltration. Meanwhile, peripheral inflammation across the blood-brain barrier further promotes microglia activation, leading to neuronal damage.

Identification of novel endoplasmic reticulum-related genes and their association with immune cell infiltration in major depressive disorder

BACKGROUND

Several lines of evidence point to an interaction between genetic predisposition and environmental factors in the onset of major depressive disorder (MDD). This study is aimed to investigate the pathogenesis of MDD by identifying key biomarkers, associated immune infiltration using bioinformatic analysis and human postmortem sample.

METHODS

The Gene Expression Omnibus (GEO) database of GSE98793 was adopted to identify hub genes linked to endoplasmic reticulum (ER) stress-related genes (ERGs) in MDD. Another GEO database of GSE76826 was employed to validate the novel target associated with ERGs and immune infiltration in MDD. Moreover, human postmortem sample from MDD patients was utilized to confirm the differential expression analysis of hub genes.

RESULTS

We discovered 12 ER stress-related differentially expressed genes (ERDEGs). A LASSO Cox regression analysis helped construct a diagnostic model for these ERDEGs, incorporating immune infiltration analysis revealed that three hub genes (ERLIN1, SEC61B, and USP13) show the significant and consistent expression differences between the two groups. Western blot analysis of postmortem brain samples indicated notably higher expression levels of ERLIN1 and SEC61B in the MDD group, with USP13 also tending to increase compared to control group.

LIMITATIONS

The utilization of the MDD gene chip in this analysis was sourced from the GEO database, which possesses a restricted number of pertinent gene chip samples.

CONCLUSIONS

These findings indicate that ERDEGs especially including ERLIN1, SEC61B, and USP13 associated the infiltration of immune cells may be potential diagnostic indicators for MDD.

Single-cell RNA-seq reveals the role of YAP1 in prefrontal cortex microglia in depression

BACKGROUND

Depression is a complex mood disorder whose pathogenesis involves multiple cell types and molecular pathways. The prefrontal cortex, as a key brain region for emotional regulation, plays a crucial role in depression. Microglia, as immune cells of the central nervous system, have been closely linked to the development and progression of depression through their dysfunctional states. This study aims to utilize single-cell RNA-seq technology to reveal the pathogenic mechanism of YAP1 in prefrontal cortex microglia in depression.

METHODS

Firstly, we performed cell type identification and differential analysis on normal and depressed prefrontal cortex tissues by mining single-cell RNA-seq datasets from public databases. Focusing on microglia, we conducted sub-clustering, differential gene KEGG enrichment analysis, intercellular interaction analysis, and pseudotime analysis. Additionally, a cross-species analysis was performed to explore the similarities and differences between human and rhesus monkey prefrontal cortex microglia. To validate our findings, we combined bulk RNA-Seq and WGCNA analysis to reveal key genes associated with depression and verified the relationship between YAP1 and depression using clinical samples.

RESULTS

Our study found significant changes in the proportion and transcriptional profiles of microglia in depressed prefrontal cortex tissues. Further analysis revealed multiple subpopulations of microglia and their associated differential genes and signaling pathways related to depression. YAP1 was identified as a key molecule contributing to the development of depression and was significantly elevated in depression patients. Moreover, the expression level of YAP1 was positively correlated with HAMD scores, suggesting its potential as a biomarker for predicting the onset of depression.

CONCLUSION

This study utilized single-cell RNA-seq technology to reveal the pathogenic mechanism of YAP1 in prefrontal cortex microglia in depression, providing a new perspective for a deeper understanding of the pathophysiology of depression and identifying potential targets for developing novel treatment strategies.

Identification of 7 mitochondria-related genes as diagnostic biomarkers of MDD and their correlation with immune infiltration: New insights from bioinformatics analysis

BACKGROUND

Major depressive disorder (MDD) is one of the most prevalent and debilitating psychiatric disorders. It becomes more recognized that mitochondrial dysfunction contributes to the pathophysiology of depression. However, little research has systematically investigated the mitochondria-related biomarkers for MDD diagnosis. This study aimed to develop a novel diagnostic gene signature in MDD based on mitochondria-related genes.

METHOD

We identified the differentially expressed mitochondrial-related genes (DeMRGs) by combing the gene expression data of the GEO database with mitochondria-related gene lists obtained from the MitoCarta3.0 database. Next, three kinds of machine-learning algorithms were used to screen characteristic DeMRGs. Then, we constructed a multivariable diagnostic model based on these characteristic genes and evaluated the diagnostic ability of this model. Subsequently, the immune landscape of infiltrated immune cells between MDD patients and controls was evaluated by CIBERSORT. Using consensus clustering analysis, we divided MDD patients into different clusters based on the characteristic DeMRGs expression patterns. Finally, the variations in immune cell infiltration between different clusters, and the correlation between characteristic DeMRGs and immune cell infiltration were analyzed.

RESULTS

Seven characteristic genes, including PMPCB, MRPS28, LYRM2, MGST1, COX20, PTPMT1, and STX17, were identified from the 31 DeMRGs. Based on the seven characteristic genes, we successfully constructed a diagnostic model which had relatively good diagnostic performance and potential application in the clinical diagnosis of MDD. In addition, our results also imply an intimate and comprehensive association between the characteristic DeMRGs and immune infiltrating cells.

CONCLUSION

A novel mitochondria-related gene signature with a good diagnostic performance and a relationship with immune microenvironment were identified in major depressive disorder.

Identification of the shared molecular mechanisms between major depressive disorder and COVID-19 from postmortem brain transcriptome analysis

OBJECTIVES

This study aims to investigate the molecular mechanisms underlying the interaction of major depressive disorder (MDD) and COVID-19, and on this basis, diagnostic biomarkers and potential therapeutic drugs are further explored.

METHODS

Differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to identify common key genes involved in the pathogenesis of COVID-19 and MDD. Correlations with clinical features were explored. Detailed mechanisms were further investigated through protein interaction networks, GSEA, and immune cell infiltration analysis. Finally, Enrichr's Drug Signature Database and Coremine Medical were used to predict the potential drugs associated with key genes.

RESULTS

The study identified 18 genes involved in both COVID-19 and MDD. Four key genes (MBP, CYP4B1, ERMN, and SLC26A7) were selected based on clinical relevance. A multi-gene prediction model showed good diagnostic efficiency for the two diseases: AUC of 0.852 for COVID-19 and 0.915 for MDD. GO and GSEA analyses identified specific biological functions and pathways associated with key genes in COVID-19 (axon guidance, metabolism, stress response) and MDD (neuron ensheathment, biosynthesis, glutamatergic neuron differentiation). The key genes also affected immune infiltration. Potential therapeutic drugs, including small molecules and traditional Chinese medicines, targeting these genes were identified.

CONCLUSION

This study provides insights into the complex biological mechanisms underlying COVID-19 and MDD, develops an effective diagnostic model, and predicts potential therapeutic drugs, which may contribute to the prevention and treatment of these two prevalent diseases.

Prediction and Validation of Hub Genes Related to Major Depressive Disorder Based on Co-expression Network Analysis

Major depressive disorder (MDD) is generally among the most prevalent psychiatric illnesses. Significant advances have occurred in comprehension of the MDD biology. However, it is still essential to recognize new biomarkers for potential targeted treatment of patients with MDD. The present work deals with in-depth comparative computational analyses to obtain new insights, such as gene ontology and pathway enrichment analyses and weighted gene co-expression network analysis (WGCNA) through gene expression dataset. The expression of selected hub-genes was validated in MDD patients using quantitative real-time PCR (RT-qPCR). We found that MDD progression includes the turquoise module genes (p-value = 1e-18, r = 0.97). According to gene enrichment analysis, the cytokine-mediated signaling pathway mostly involves genes in this module. By selection of four candidate hub-genes (IL6, NRG1, TNF, and BDNF), RT-qPCR validation was performed. A significant NRG1 downregulation was revealed by the RT-qPCR outcomes in MDD. In MDD patients, TNF and IL6 expression were considerably higher, and no considerable differences were found in the BDNF expression. Ultimately, based on ROC analyses, IL6, NRG1, and TNF had a higher MDD diagnostic performance. Therefore, our study presents information on the intricate association between MDD development and cytokine-mediated signaling, thus providing new rationales to develop new therapeutic approaches.

[Integration of basic and clinical researches to develop the biomarker of ‍depression]

Because of absence of the objective biomarker for major depressive disorder (MDD) or depressive state, psychiatrists depend on subjective examinations in order to properly diagnose their patients. We recently identified the candidates of the objective biomarker of depressive state of late-onset MDD by profiling gene expressions in white blood cells of patients and model mice. We also investigated whether gene expression profiling of white blood cells was useful to elucidate the biological alterations in the brain. Furthermore, we newly developed transgenic mice which will be useful for elucidating the neurological mechanisms of emotional abnormalities in psychiatric disorder. In this review, I introduce our recent research to help for understanding of translational approaches to develop the biomarker of depression.

Identification and Analyses of Crucial Genes Associated with Pathogenesis of Major Depressive Disorder

Background

Major depressive disorder is a debilitating mental condition that causes severe disability leading to a high fatality rate. No valid blood-based biomarkers for major depressive disorder are currently available. The purpose of this research is to investigate gene biomarkers and pathways that may be linked to major depressive disorder pathogenesis.

Methods

Two microarray databases were retrieved from Gene Expression Omnibus for screening of candidate differentially expressed genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed followed by protein-protein interaction network of differentially expressed genes.

Results

About 1181 differentially expressed genes were identified from the microarray databases. Gene Ontology analyses indicated that these differentially expressed genes were significantly enriched in mRNA splicing via spliceosome, neutrophil degranulation, peptide antigen assembly with MHC class II protein complex, and immunoglobulin production-mediated immune response. The most enriched Kyoto Encyclopedia of Genes and Genomes pathway terms of the 10 significant were Hematopoietic cell lineage. About 20 genes were identified as hub genes after pathway analyses, mostly involved in colorectal cancer and the composition of ribosomes and protein processing, including KRAS, CD86, RPL9, RPL3, and RPL18.

Conclusion

New candidate genes have been identified using bioinformatic approaches that suggest their involvement in the pathogenesis of major depressive disorder and serve as potential genetic diagnostic markers as well as new therapeutic targets.

Ferroptosis-related genes as diagnostic markers for major depressive disorder and their correlations with immune infiltration

Background

Major depression disorder (MDD) is a devastating neuropsychiatric disease, and one of the leading causes of suicide. Ferroptosis, an iron-dependent form of regulated cell death, plays a pivotal role in numerous diseases. The study aimed to construct and validate a gene signature for diagnosing MDD based on ferroptosis-related genes (FRGs) and further explore the biological functions of these genes in MDD.

Methods

The datasets were downloaded from the Gene Expression Omnibus (GEO) database and FRGs were obtained from the FerrDb database and other literatures. Least absolute shrinkage and selection operator (LASSO) regression and stepwise logistic regression were performed to develop a gene signature. Receiver operating characteristic (ROC) curves were utilized to assess the diagnostic power of the signature. Gene ontology (GO) enrichment analysis was used to explore the biological roles of these diagnostic genes, and single sample gene set enrichment analysis (ssGSEA) algorithm was used to evaluate immune infiltration in MDD. Animal model of depression was constructed to validate the expression of the key genes.

Results

Eleven differentially expressed FRGs were identified in MDD patients compared with healthy controls. A signature of three FRGs (ALOX15B, RPLP0, and HP) was constructed for diagnosis of MDD. Afterwards, ROC analysis confirmed the signature's discriminative capacity (AUC = 0.783, 95% CI = 0.719-0.848). GO enrichment analysis revealed that the differentially expressed genes (DEGs) related to these three FRGs were mainly involved in immune response. Furthermore, spearman correlation analysis demonstrated that these three FRGs were associated with infiltrating immune cells. ALOX15B and HP were significantly upregulated and RPLP0 was significantly downregulated in peripheral blood of the lipopolysaccharide (LPS)-induced depressive model.

Conclusion

Our results suggest that the novel FRG signature had a good diagnostic performance for MDD, and these three FRGs correlated with immune infiltration in MDD.

Social defeat stress induces genome-wide 5mC and 5hmC alterations in the mouse brain

Stress is adverse experience that require constant adaptation to reduce the emotional and physiological burden, or "allostatic load", of an individual. Despite their everyday occurrence, a subpopulation of individuals is more susceptible to stressors, while others remain resilient with unknown molecular signatures. In this study, we investigated the contribution of the DNA modifications, 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC), underlying the individual differences in stress susceptibility and resilience. Genome-wide 5mC and 5hmC profiles from 3- and 6-month adult male mice that underwent various durations of social defeat were generated. In 3-month animals, 5mC and 5hmC work in parallel and do not distinguish between stress-susceptible and resilient phenotypes, while in 6-month animals, 5mC and 5hmC show distinct enrichment patterns. Acute stress responses may epigenetically "prime" the animals to either increase or decrease their predisposition to depression susceptibility. In support of this, re-exposure studies reveal that the enduring effects of social defeat affect differential biological processes between susceptible and resilient animals. Finally, the stress-induced 5mC and 5hmC fluctuations across the acute-chronic-longitudinal time course demonstrate that the negative outcomes of chronic stress do not discriminate between susceptible and resilient animals. However, resilience is more associated with neuroprotective processes while susceptibility is linked to neurodegenerative processes. Furthermore, 5mC appears to be responsible for acute stress response, whereas 5hmC may function as a persistent and stable modification in response to stress. Our study broadens the scope of previous research offering a comprehensive analysis of the role of DNA modifications in stress-induced depression.

Identification of a miRNA-mRNA regulatory network for post-stroke depression: a machine-learning approach

Objective

The study aimed to explore the miRNA and mRNA biomarkers in post-stroke depression (PSD) and to develop a miRNA-mRNA regulatory network to reveal its potential pathogenesis.

Methods

The transcriptomic expression profile was obtained from the GEO database using the accession numbers GSE117064 (miRNAs, stroke vs. control) and GSE76826 [mRNAs, late-onset major depressive disorder (MDD) vs. control]. Differentially expressed miRNAs (DE-miRNAs) were identified in blood samples collected from stroke patients vs. control using the Linear Models for Microarray Data (LIMMA) package, while the weighted correlation network analysis (WGCNA) revealed co-expressed gene modules correlated with the subject group. The intersection between DE-miRNAs and miRNAs identified by WGCNA was defined as stroke-related miRNAs, whose target mRNAs were stroke-related genes with the prediction based on three databases (miRDB, miRTarBase, and TargetScan). Using the GSE76826 dataset, the differentially expressed genes (DEGs) were identified. Overlapped DEGs between stroke-related genes and DEGs in late-onset MDD were retrieved, and these were potential mRNA biomarkers in PSD. With the overlapped DEGs, three machine-learning methods were employed to identify gene signatures for PSD, which were established with the intersection of gene sets identified by each algorithm. Based on the gene signatures, the upstream miRNAs were predicted, and a miRNA-mRNA network was constructed.

Results

Using the GSE117064 dataset, we retrieved a total of 667 DE-miRNAs, which included 420 upregulated and 247 downregulated ones. Meanwhile, WGCNA identified two modules (blue and brown) that were significantly correlated with the subject group. A total of 117 stroke-related miRNAs were identified with the intersection of DE-miRNAs and WGCNA-related ones. Based on the miRNA-mRNA databases, we identified a list of 2,387 stroke-related genes, among which 99 DEGs in MDD were also embedded. Based on the 99 overlapped DEGs, we identified three gene signatures (SPATA2, ZNF208, and YTHDC1) using three machine-learning classifiers. Predictions of the three mRNAs highlight four miRNAs as follows: miR-6883-5p, miR-6873-3p, miR-4776-3p, and miR-6738-3p. Subsequently, a miRNA-mRNA network was developed.

Conclusion

The study highlighted gene signatures for PSD with three genes (SPATA2, ZNF208, and YTHDC1) and four upstream miRNAs (miR-6883-5p, miR-6873-3p, miR-4776-3p, and miR-6738-3p). These biomarkers could further our understanding of the pathogenesis of PSD.

Interferon signaling and hypercytokinemia-related gene expression in the blood of antidepressant non-responders

Only 50% of patients with depression respond to the first antidepressant drug administered. Thus, biomarkers for prediction of antidepressant responses are needed, as predicting which patients will not respond to antidepressants can optimize selection of alternative therapies. We aimed to identify biomarkers that could predict antidepressant responsiveness using a novel data-driven approach based on statistical pattern recognition. We retrospectively divided patients with major depressive disorder into antidepressant responder and non-responder groups. Comprehensive gene expression analysis was performed using peripheral blood without narrowing the genes. We designed a classifier according to our own discrete Bayes decision rule that can handle categorical data. Nineteen genes showed differential expression in the antidepressant non-responder group (n = 15) compared to the antidepressant responder group (n = 15). In the training sample of 30 individuals, eight candidate genes had significantly altered expression according to quantitative real-time polymerase chain reaction. The expression of these genes was examined in an independent test sample of antidepressant responders (n = 22) and non-responders (n = 12). Using the discrete Bayes classifier with the HERC5, IFI6, and IFI44 genes identified in the training set yielded 85% discrimination accuracy for antidepressant responsiveness in the 34 test samples. Pathway analysis of the RNA sequencing data for antidepressant responsiveness identified that hypercytokinemia- and interferon-related genes were increased in non-responders. Disease and biofunction analysis identified changes in genes related to inflammatory and infectious diseases, including coronavirus disease. These results strongly suggest an association between antidepressant responsiveness and inflammation, which may be useful for future treatment strategies for depression.

Potential shared gene signatures and molecular mechanisms between atherosclerosis and depression: Evidence from transcriptome data

BACKGROUND

Atherosclerosis and depression contribute to each other; however, mechanisms linking them at the genetic level remain unexplored. This study aimed to identify shared gene signatures and related pathways between these comorbidities.

METHODS

Atherosclerosis-related datasets were downloaded from the Gene Expression Omnibus database. Differential and weighted gene co-expression network analyses were employed to identify atherosclerosis-related genes. Depression-related genes were downloaded from the DisGeNET database, and the overlaps between atherosclerosis-related genes and depression-related genes were characterized as crosstalk genes. The functional enrichment analysis and protein-protein interaction network were performed in these gene sets. Subsequently, the Boruta algorithm and Recursive Feature Elimination algorithm were performed to identify feature-selection genes. A support vector machine was constructed to measure the accuracy of calculations, and two external validation sets were included to verify the results.

RESULTS

Based on two atherosclerosis-related datasets (GSE28829 and GSE43292), 165 genes were determined as atherosclerosis-related genes. Meanwhile, 1478 depression-related genes were obtained. After intersecting, 24 crosstalk genes were identified, and two pathways, "lipid and atherosclerosis" and "tryptophan metabolism," were revealed as mutual pathways according to the enrichment analysis results. Through the protein-protein interaction network, Molecular Complex Detection plugin, and cytoHubba plugin, PTPRC and MMP9 were identified as the hub gene. Moreover, SLC22A3, CASP1, AMPD3, and PIK3CG were recognized as feature-selection genes. Based on two external validation sets, CASP1 and MMP9 were finally determined as the critical crosstalk genes.

CONCLUSIONS

"Lipid and atherosclerosis" and "tryptophan metabolism" were possibly the pathways of atherosclerosis secondary to depression and depression due to atherosclerosis, respectively. CASP1 and MMP9 were revealed as the most pivotal candidates linking atherosclerosis and depression by mediating these two pathways. Further experimentation is needed to confirm these conclusions.

Identification of key genes in late-onset major depressive disorder through a co-expression network module

Late-onset major depressive disorder (LOD) increases the risk of disability and suicide in elderly patients. However, the complex pathological mechanism of LOD still remains unclear. We selected 10 LOD patients and 12 healthy control samples from the GSE76826 dataset for statistical analysis. Under the screening criteria, 811 differentially expressed genes (DEGs) were screened. We obtained a total of two most clinically significant modules through the weighted gene co-expression network analysis (WGCNA). Functional analysis of the genes in the most clinically significant modules was performed to explore the potential mechanism of LOD, followed by protein-protein interaction (PPI) analysis and hub gene identification in the core area of the PPI network. Furthermore, we identified immune infiltrating cells using the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm between healthy subjects and LOD patients with the GSE98793 dataset. Next, six hub genes (CD27, IL7R, CXCL1, CCR7, IGLL5, and CD79A) were obtained by intersecting hub genes with DEGs, followed by verifying the diagnostic accuracy with the receiver operating characteristic curve (ROC). In addition, we constructed the least absolute shrinkage and selection operator (LASSO) regression model for hub gene cross-validation. Finally, we found that CD27 and IGLL5 were good diagnostic indicators of LOD, and CD27 may be the key gene of immune function change in LOD. In conclusion, our research shows that the changes in the immune function may be an important mechanism in the development of LOD, which can provide some guidance for the related research of LOD in the future.

Brain differential gene expression and blood cross-validation of a molecular signature of patients with major depressive disorder

INTRODUCTION

The agreement between clinicians diagnosing major depressive disorder (MDD) is poor. The objective of this study was to identify a reproducible and robust gene expression marker capable of differentiating MDD from healthy control (HC) subjects.

MATERIALS AND METHODS

Brain and blood gene expression datasets were searched, which included subjects with MDD and HC. The largest database including different areas of brain samples (GSE80655) was used to identify an initial gene expression marker. Tests of robustness and reproducibility were then implemented in 13 brain and 7 blood independent datasets. Correlations between expression in brain and blood samples were also examined. Finally, an enrichment analysis to explore the marker biological meaning was completed.

RESULTS

Twenty-eight genes were differentially expressed in GSE80655, of which 23 were critical to differentiate MDD from HC. The accuracy obtained using the 23 genes was 0.77 and 0.8, before and after the forward selection model, respectively. The gene marker's robustness and reproducibility were between the range of 0.46 and 0.63 in the other brain datasets and between 0.45 and 0.78 for the blood datasets. Brain and blood expression tended to correlate in some samples. Thirteen of the 23 genes were related to stress and immune response.

CONCLUSION

A 23 gene expression marker was able to distinguish subjects with MDD from HC, with adequate reproducibility and low robustness in the independent databases investigated. This gene set was similarly expressed in the brain and blood and involved genes related to stress and immune response.

The Pyroptosis-Related Signature Predicts Diagnosis and Indicates Immune Characteristic in Major Depression Disease

Pyroptosis is recently identified as an inflammatory form of programmed cell death. However, the roles of pyroptosis-related genes (PS genes) in major depressive disorder (MDD) remain unclear. This study developed a novel diagnostic model for MDD based on PS genes and explored the pathological mechanisms associated with pyroptosis. First, we obtained 23 PS genes that were differentially expressed between healthy controls and MDD cases from GSE98793 dataset. There were obvious variation in immune cell infiltration profiles and immune-related pathway enrichment between healthy controls and MDD cases. Then, a novel diagnostic model consisting of eight PS genes (GPER1, GZMA, HMGB1, IL1RN, NLRC4, NLRP3, UTS2, and CAPN1) for MDD was constructed by random forest (RF) and least absolute shrinkage and selection operator (LASSO) analyses. ROC analysis revealed that our model has good diagnostic performance, AUC = 0.795 (95% CI 0.721–0.868). Subsequently, the consensus clustering method based on 23 differentially expressed PS genes was constructed to divide all MDD cases into two distinct pyroptosis subtypes (cluster A and B) with different immune and biological characteristics. Principal component analysis (PCA) algorithm was performed to calculate the pyroptosis scores (“PS-scores”) for each sample to quantify the pyroptosis regulation subtypes. The MDD patients in cluster B had higher “PS-scores” than those in cluster A. Furthermore, we also found that MDD patients in cluster B showed lower expression levels of 11 interferon (IFN)-α isoforms. In conclusion, pyroptosis may play an important role in MDD and can provide new insights into the diagnosis and underlying mechanisms of MDD.

A novel 4 immune-related genes as diagnostic markers and correlated with immune infiltrates in major depressive disorder

Background

Immune response is prevalently related with major depressive disorder (MDD) pathophysiology. However, the study on the relationship between immune-related genes (IRGs) and immune infiltrates of MDD remains scarce.

Methods

We extracted expression data of 148 MDD patients from 2 cohorts, and systematically characterized differentially expressed IRGs by using limma package in R software. Then, the LASSO and multivariate logistic regression analysis was used to identify the most powerful IRGs. Next, we analyzed the relationship between IRGs and immune infiltrates of MDD. Finally, GSE76826 was used to to verificate of IRGs as a diagnostic markers in MDD.

Results

203 different IRGs s in MDD has been identified (P < 0.05). GSEA revealed that the different IRGs was more likely to be enriched in immune-specific pathways. Then, a 9 IRGs was successfully established to predict MDD based on LASSO. Next, 4 IRGs was obtained by multivariate logistic regression analysis, and AUC for CD1C, SPP1, CD3D, CAMKK2, and IRGs model was 0.733, 0.767, 0.816, 0.800, and 0.861, suggesting that they have a good diagnostic performance. Furthermore, the proportion of T cells CD8, T cells γδ, macrophages M0, and NK cells resting in MDD group was lower than that in the healthy controls, suggesting that the immune system in MDD group is impaired. Simultaneously, CD3D was validated a reliable marker in MDD, and was positively correlated with T cells CD8. GSEA revealed high expression CD3D was more likely to be enriched in immune-specific pathways, and low expression CD3D was more likely to be enriched in glucose metabolism metabolism-specific pathways.

Conclusions

We applied bioinformatics approaches to suggest that a 4 IRGs could serve as diagnostic markers to provide a novel direction to explore the pathogenesis of MDD.

Signatures of 4 autophagy-related genes as diagnostic markers of MDD and their correlation with immune infiltration

BACKGROUND

Major depressive disorder (MDD) is a debilitating mental illness and one of the primary causes of suicide. This study attempted to develop and validate a multigene joint signature for diagnosing MDD based on autophagy-related genes (ARGs) and to explore their biological role in MDD.

METHODS

We downloaded data from the Gene Expression Omnibus (GEO) database and retrieved ARGs from the Human Autophagy Database. The limma package in R software was used to identify differentially expressed genes (DEGs). We used CIBERSORT to analyze differences in the immune microenvironment between MDD patients and controls. Finally, we examined the correlation between diagnostic markers and infiltrating immune cells to better understand the molecular immune mechanism.

RESULTS

In this study, we identified 20 differentially expressed ARGs in MDD compared to controls. A signature of 4 autophagy-related genes (GPR18, PDK4, NRG1 and EPHB2) was obtained. ROC analysis showed that our model has good diagnostic performance (AUC=0.779, 95% CI=0.709-0.848). Bioinformatics analysis validated that GPR18 may represent a new candidate gene for MDD. Correlation analysis revealed that GPR18 was positively correlated with regulatory T cells (Treg), CD8+ T cells, naive B cells, and memory B cells and negatively correlated with M0 macrophages and neutrophils in MDD.

LIMITATIONS

This was a second mining of previously published data sets. Independent studies are warranted to validate and improve the clinical utility of the identified signature.

CONCLUSIONS

We identified a novel four-ARG gene signature that has good diagnostic performance and identified an association between ARG genes and the immune microenvironment in MDD.

Transcriptomic profiling as biological markers of depression - A pilot study in unipolar and bipolar women

OBJECTIVES

A significant challenge in psychiatry is the differential diagnosis of depressive episodes in the course of mood disorders. Gene expression profiling may provide an opportunity for such distinguishment.

METHODS

We studied differentially expressed genes in women with a depressive episode in the course of unipolar depression (UD) (n = 24) and bipolar disorder types I (BDI) (n = 13) and II (BDII) (n = 19), and healthy controls (n = 15).

RESULTS

Different types of depression varied in the number and type of up or down-regulated genes. The pathway analysis showed: in UD, up-regulated rheumatoid arthritis pathway (including ITGB2, CXCL8, TEK, TLR4 genes), and down-regulated taste transduction pathway (TAS2R10, TAS2R46, TAS2R14, TAS2R43, TAS2R45, TAS2R19, TAS2R13, TAS2R20, GNG13); in BDI, eight down-regulated pathways: glutamatergic synapse, retrograde endocannabinoid signalling, axon guidance, calcium signalling, nicotine addiction, PI3K-Akt signalling, drug metabolism - cytochrome P450, and morphine addiction; in BDII, up-regulated osteoclast differentiation and Notch signalling pathway, and down-regulated type I diabetes mellitus pathway. Distinct expression markers analysis uncovered the unique for UD, up-regulated bladder cancer pathway (HBEGF and CXCL8 genes).

CONCLUSIONS

This pilot study suggests a probability of differentiating depression in the course of UD, BDI, and II, based on transcriptomic profiling.

Intracellular calcium and inflammatory markers, mediated by purinergic stimulation, are differentially regulated in monocytes of patients with major depressive disorder

The P2X7 receptor (P2X7R) is a ligand-gated ion channel that is being recognized as a major player in neuropsychiatric disorders such as Major Depressive Disorder (MDD). P2X7R activation is triggered by high extracellular ATP concentrations, leading to channel opening and inducing an increase in cytosolic calcium concentration ([Ca²⁺]_c), that activates the inflammatory pathway. Those receptors are expressed not only in CNS cells but also in peripheral blood cells, where they are activated in response to inflammatory molecules such as bacterial lipopolysaccharide (LPS). LPS induced-tissue damage promotes an elevation of extracellular ATP, triggering the NRLP3-inflammasome assembly and activation that, sequentially, induces caspase-1 cleavage and IL-1β processing and secretion. In this context, we attempt to understand the role of P2X7R in [Ca²⁺]_c homeostasis regulation, inflammasome expression and its pharmacological modulation in MDD. For this purpose, monocytes were isolated from peripheral blood of MDD patients and [Ca²⁺]_c was monitored with the intracellular probe Fura-2. Our results point out to P2X7R as the responsible of the Ca²⁺ imbalance, as well as TNF-α-dependent activation of caspase-1 in MDD patients. In addition, P2X7R blockade with its specific antagonist, JNJ-47965567, reduces the Ca²⁺ entry upon Bz-ATP exposure. Altogether, our results point that MDD patients have both, Ca²⁺ homeostasis alteration and an inflammatory status, which promote an independent-inflammasome activation of caspase-1. Therefore, we propose the pharmacological modulation of P2X7R as a therapeutic approach against MDD symptoms.

Gene expression profiling in peripheral blood lymphocytes for major depression: preliminary cues from Chinese discordant sib-pair study

The etiology of major depressive disorder (MDD) involves many factors such as heredity and environment. There are very few MDD-related studies in Chinese population using twin or sib-pairs for depression-control samples. Here we used the microarray approach and compared gene expression profiling of peripheral blood lymphocytes from 6 sib-pairs discordant on lifetime history of MDD. Within sib-pair differentially expressed genes are obvious fewer in the 1st, 2nd, and 5th compared with those in the 3rd, 4th, and 6th sib-pairs. Gene expression pattern of these DEGs distinguished MDD individuals from the normal one in 3rd, 4th, and 6th sib-pair but not in the 1st, 2nd, and 5th pair, suggesting heterogeneity of different sib-pairs and somewhat commonalities among the 3rd, 4th, and 6th sib-pairs. Comprehensive protein interaction network analysis revealed two key genes PTH and FGF2 in a dominant network where the majority of the genes were significantly down-regulated. PTH was significantly down-regulated in all the sib-pairs while FGF2 was in the 3rd, 4th, and 6th sib-pairs. KEGG enrichment analysis of all the DEGs in networks showed that PTH and related genes were significantly enriched in the pathway of parathyroid hormone secretion, synthesis, and action while FGF2 and related genes were significantly enriched in the pathways of cancer and specifically breast cancer. Generally reduced expression of these genes in peripheral blood lymphocytes of MDD individuals implied their functional repression associated with MDD. Pending validation in more samples, the findings in this study provided valuable cues for understanding the potential mechanism of MDD, as well as potential markers for the diagnosis and treatment of depression in the Chinese population.

Optimized protocol for the extraction of RNA and DNA from frozen whole blood sample stored in a single EDTA tube

Cryopreservation of whole blood is useful for DNA collection, and clinical and basic research. Blood samples in ethylenediaminetetraacetic acid disodium salt (EDTA) tubes stored at − 80 °C are suitable for DNA extraction, but not for high-quality RNA extraction. Herein, a new methodology for high-quality RNA extraction from human blood samples is described. Quickly thawing frozen whole blood on aluminum blocks at room temperature could minimize RNA degradation, and improve RNA yield and quality compared with thawing the samples in a 37 °C water bath. Furthermore, the use of the NucleoSpin RNA kit increased RNA yield by fivefold compared with the PAXgene Blood RNA Kit. Thawing blood samples on aluminum blocks significantly increased the DNA yield by ~ 20% compared with thawing in a 37 °C water bath or on ice. Moreover, by thawing on aluminum blocks and using the NucleoSpin RNA and QIAamp DNA Blood kits, the extraction of RNA and DNA of sufficient quality and quantity was achieved from frozen EDTA whole blood samples that were stored for up to 8.5 years. Thus, extracting RNA from frozen whole blood in EDTA tubes after long-term storage is feasible. These findings may help advance gene expression analysis, as well as biomarker research for various diseases.

Identification of Diagnostic Markers for Major Depressive Disorder Using Machine Learning Methods

Background

Major depressive disorder (MDD) is a global health challenge that impacts the quality of patients’ lives severely. The disorder can manifest in many forms with different combinations of symptoms, which makes its clinical diagnosis difficult. Robust biomarkers are greatly needed to improve diagnosis and to understand the etiology of the disease. The main purpose of this study was to create a predictive model for MDD diagnosis based on peripheral blood transcriptomes.

Materials and Methods

We collected nine RNA expression datasets for MDD patients and healthy samples from the Gene Expression Omnibus database. After a series of quality control and heterogeneity tests, 302 samples from six studies were deemed suitable for the study. R package “MetaOmics” was applied for systematic meta-analysis of genome-wide expression data. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic effectiveness of individual genes. To obtain a better diagnostic model, we also adopted the support vector machine (SVM), random forest (RF), k-nearest neighbors (kNN), and naive Bayesian (NB) tools for modeling, with the RF method being used for feature selection.

Results

Our analysis revealed six differentially expressed genes (AKR1C3, ARG1, KLRB1, MAFG, TPST1, and WWC3) with a false discovery rate (FDR) < 0.05 between MDD patients and control subjects. We then evaluated the diagnostic ability of these genes individually. With single gene prediction, we achieved a corresponding area under the curve (AUC) value of 0.63 ± 0.04, 0.67 ± 0.07, 0.70 ± 0.11, 0.64 ± 0.08, 0.68 ± 0.07, and 0.62 ± 0.09, respectively, for these genes. Next, we constructed the classifiers of SVM, RF, kNN, and NB with an AUC of 0.84 ± 0.09, 0.81 ± 0.10, 0.73 ± 0.11, and 0.83 ± 0.09, respectively, in validation datasets, suggesting that the SVM classifier might be superior for constructing an MDD diagnostic model. The final SVM classifier including 70 feature genes was capable of distinguishing MDD samples from healthy controls and yielded an AUC of 0.78 in an independent dataset.

Conclusion

This study provides new insights into potential biomarkers through meta-analysis of GEO data. Constructing different machine learning models based on these biomarkers could be a valuable approach for diagnosing MDD in clinical practice.

Distinct epigenetic signatures between adult-onset and late-onset depression

The heterogeneity of major depressive disorder (MDD) is attributed to the fact that diagnostic criteria (e.g., DSM-5) are only based on clinical symptoms. The discovery of blood biomarkers has the potential to change the diagnosis of MDD. The purpose of this study was to identify blood biomarkers of DNA methylation by strategically subtyping patients with MDD by onset age. We analyzed genome-wide DNA methylation of patients with adult-onset depression (AOD; age ≥ 50 years, age at depression onset < 50 years; N = 10) and late-onset depression (LOD; age ≥ 50 years, age at depression onset ≥ 50 years; N = 25) in comparison to that of 30 healthy subjects. The methylation profile of the AOD group was not only different from that of the LOD group but also more homogenous. Six identified methylation CpG sites were validated by pyrosequencing and amplicon bisulfite sequencing as potential markers for AOD in a second set of independent patients with AOD and healthy control subjects (N = 11). The combination of three specific methylation markers achieved the highest accuracy (sensitivity, 64%; specificity, 91%; accuracy, 77%). Taken together, our findings suggest that DNA methylation markers are more suitable for AOD than for LOD patients.

Global knockdown of glutamate decarboxylase 67 elicits emotional abnormality in mice

Reduced expression of glutamate decarboxylase 67 (GAD67), encoded by the Gad1 gene, is a consistent finding in postmortem brains of patients with several psychiatric disorders, including schizophrenia, bipolar disorder and major depressive disorder. The dysfunction of GAD67 in the brain is implicated in the pathophysiology of these psychiatric disorders; however, the neurobiological consequences of GAD67 dysfunction in mature brains are not fully understood because the homozygous Gad1 knockout is lethal in newborn mice. We hypothesized that the tetracycline-controlled gene expression/suppression system could be applied to develop global GAD67 knockdown mice that would survive into adulthood. In addition, GAD67 knockdown mice would provide new insights into the neurobiological impact of GAD67 dysfunction. Here, we developed Gad1^{tTA/STOP−tetO} biallelic knock-in mice using Gad1^STOP−tetO and Gad1^tTA knock-in mice, and compared them with Gad1^+/+ mice. The expression level of GAD67 protein in brains of Gad1^{tTA/STOP−tetO} mice treated with doxycycline (Dox) was decreased by approximately 90%. The GABA content was also decreased in the brains of Dox-treated Gad1^{tTA/STOP−tetO} mice. In the open-field test, Dox-treated Gad1^{tTA/STOP−tetO} mice exhibited hyper-locomotor activity and decreased duration spent in the center region. In addition, acoustic startle responses were impaired in Dox-treated Gad1^{tTA/STOP−tetO} mice. These results suggest that global reduction in GAD67 elicits emotional abnormalities in mice. These GAD67 knockdown mice will be useful for elucidating the neurobiological mechanisms of emotional abnormalities, such as anxiety symptoms associated with psychiatric disorders.

Identification of Crucial Genes and Diagnostic Value Analysis in Major Depressive Disorder Using Bioinformatics Analysis

AIM AND OBJECTIVE

Despite the prevalence and burden of major depressive disorder (MDD), our current understanding of the pathophysiology is still incomplete. Therefore, this paper aims to explore genes and evaluate their diagnostic ability in the pathogenesis of MDD.

METHODS

Firstly, the expression profiles of mRNA and microRNA were downloaded from the gene expression database and analyzed by the GEO2R online tool to identify differentially expressed genes (DEGs) and differentially expressed microRNAs (DEMs). Then, the DAVID tool was used for functional enrichment analysis. Secondly, the comprehensive protein- protein interaction (PPI) network was analyzed using Cytoscape, and the network MCODE was applied to explore hub genes. Thirdly, the receiver operating characteristic (ROC) curve of the core gene was drawn to evaluate clinical diagnostic ability. Finally, mirecords was used to predict the target genes of DEMs.

RESULTS

A total of 154 genes were identified as DEGs, and 14 microRNAs were identified as DEMs. Pathway enrichment analysis showed that DEGs were mainly involved in hematopoietic cell lineage, PI3K-Akt signaling pathway, cytokinecytokine receptor interaction, chemokine signaling pathway, and JAK-STAT signaling pathway. Three important modules are identified and selected by the MCODE clustering algorithm. The top 12 hub genes including CXCL16, CXCL1, GNB5, GNB4, OPRL1, SSTR2, IL7R, MYB, CSF1R, GSTM1, GSTM2, and GSTP1 were identified as important genes for subsequent analysis. Among these important hub genes, GSTM2, GNB4, GSTP1 and CXCL1 have good diagnostic ability. Finally, by combining these four genes, the diagnostic ability of MDD can be improved to 0.905, which is of great significance for the clinical diagnosis of MDD.

CONCLUSION

Our results indicate that GSTM2, GNB4, GSTP1 and CXCL1 have potential diagnostic markers and are of great significance in clinical research and diagnostic application of MDD. This result needs a large sample study to further confirm the pathogenesis of MDD.

Characterization of the signature of peripheral innate immunity in women with later-life major depressive disorder

The prevalence of depression in later life is higher in women than in men. However, the sex difference in the pathophysiology of depression in elderly patients is not fully understood. Here, we performed gene expression profiling in leukocytes of middle-aged and elderly patients with major depressive disorder, termed later-life depression (LLD) in this context, and we characterized the sex-dependent pathophysiology of LLD. A microarray dataset obtained from leukocytes of patients (aged ≥50 years) with LLD (32 males and 39 females) and age-matched healthy individuals (20 males and 24 females) was used. Differentially expressed probes were determined by comparing the expression levels between patients and healthy individuals, and then functional annotation analyses (Ingenuity Pathway Analysis, Reactome pathway analysis, and cell-type enrichment analysis) were performed. A total of 1656 probes were differentially expressed in LLD females, but only 3 genes were differentially expressed in LLD males. The differentially expressed genes in LLD females were relevant to leukocyte extravasation signaling, Tec kinase signaling and the innate immune response. The upregulated genes were relevant to myeloid lineage cells such as CD14⁺ monocytes. In contrast, the downregulated genes were relevant to CD4⁺ and CD8⁺ T cells. Remarkable innate immune signatures are present in the leukocytes of LLD females but not males. Because inflammation is involved in the pathophysiology of depression, the altered inflammatory activity may be involved in the pathophysiology of LLD in women. In contrast, abnormal inflammation may be an uncommon feature in LLD males.

EpistasisRank and EpistasisKatz: interaction network centrality methods that integrate prior knowledge networks

MOTIVATION

An important challenge in gene expression analysis is to improve hub gene selection to enrich for biological relevance or improve classification accuracy for a given phenotype. In order to incorporate phenotypic context into co-expression, we recently developed an epistasis-expression network centrality method that blends the importance of gene-gene interactions (epistasis) and main effects of genes. Further blending of prior knowledge from functional interactions has the potential to enrich for relevant genes and stabilize classification.

RESULTS

We develop two new expression-epistasis centrality methods that incorporate interaction prior knowledge. The first extends our SNPrank (EpistasisRank) method by incorporating a gene-wise prior knowledge vector. This prior knowledge vector informs the centrality algorithm of the inclination of a gene to be involved in interactions by incorporating functional interaction information from the Integrative Multi-species Prediction database. The second method extends Katz centrality to expression-epistasis networks (EpistasisKatz), extends the Katz bias to be a gene-wise vector of main effects and extends the Katz attenuation constant prefactor to be a prior-knowledge vector for interactions. Using independent microarray studies of major depressive disorder, we find that including prior knowledge in network centrality feature selection stabilizes the training classification and reduces over-fitting.

AVAILABILITY AND IMPLEMENTATION

Methods and examples provided at https://github.com/insilico/Rinbix and https://github.com/insilico/PriorKnowledgeEpistasisRank.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A methylation study of long-term depression risk

Recurrent and chronic major depressive disorder (MDD) accounts for a substantial part of the disease burden because this course is most prevalent and typically requires long-term treatment. We associated blood DNA methylation profiles from 581 MDD patients at baseline with MDD status 6 years later. A resampling approach showed a highly significant association between methylation profiles in blood at baseline and future disease status (P = 2.0 × 10^-16). Top MWAS results were enriched specific pathways, overlapped with genes found in GWAS of MDD disease status, autoimmune disease and inflammation, and co-localized with eQTLS and (genic enhancers of) of transcription sites in brain and blood. Many of these findings remained significant after correction for multiple testing. The major themes emerging were cellular responses to stress and signaling mechanisms linked to immune cell migration and inflammation. This suggests that an immune signature of treatment-resistant depression is already present at baseline. We also created a methylation risk score (MRS) to predict MDD status 6 years later. The AUC of our MRS was 0.724 and higher than risk scores created using a set of five putative MDD biomarkers, genome-wide SNP data, and 27 clinical, demographic and lifestyle variables. Although further studies are needed to examine the generalizability to different patient populations, these results suggest that methylation profiles in blood may present a promising avenue to support clinical decision making by providing empirical information about the likelihood MDD is chronic or will recur in the future.

Seeking for potential pathogenic genes of major depressive disorder in the Gene Expression Omnibus database

INTRODUCTION

Major depressive disorder (MDD) is one of the most common mental disorders worldwide. The aim of this study was to identify potential pathological genes in MDD.

METHODS

We searched and downloaded gene expression data from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs) in MDD. Then, Kyoto Encyclopedia of Genes and Genomes pathway, Gene Ontology analysis, and protein-protein interaction (PPI) network were applied to investigate the biological function of identified DEGs. The quantitative real-time polymerase chain reaction and a published dataset were used to validate the result of bioinformatics analysis.

RESULTS

A total of 514 DEGs were identified in MDD. In the PPI network, some hub genes with high degrees were identified, such as EEF2, RPL26L1, RPLP0, PRPF8, LSM3, DHX9, RSRC1, and AP2B1. The result of in vitro validation of RPL26L1, RSRC1, TOMM20L, RPLPO, PRPF8, AP2B1, STIP1, and C5orf45 was consistent with the bioinformatics analysis. Electronic validation of C5orf45, STIP1, PRPF8, AP2B1, and SLC35E1 was consistent with the bioinformatics analysis.

DISCUSSION

The deregulated genes could be used as potential pathological factors of MDD. In addition, EEF2, RPL26L1, RPLP0, PRPF8, LSM3, DHX9, RSRC1, and AP2B1 might be therapeutic targets for MDD.

Identification of Key Genes and the Pathophysiology Associated With Major Depressive Disorder Patients Based on Integrated Bioinformatics Analysis

Background: At present, laboratory blood tests to support major depressive disorder (MDD) diagnosis are not available. This study aimed to screen potential mRNAs for peripheral blood biomarkers and novel pathophysiology of MDD. Methods: The present study utilized public data from two mRNA microarray datasets to analyze the hub genes changes related to MDD. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of differentially expressed genes (DEGs) were performed. Finally, some potential mRNA quality biomarkers for hub gene expression in blood were identified. Results: A total of 25 significantly co-upregulated DEGs and 98 co-downregulated DEGs were obtained from two datasets. The pathway enrichment analyses showed that co-upregulated genes were significantly enriched in the regulation of cell-matrix adhesion and mitochondrial membrane permeability which were involved in the apoptotic process. Co-downregulated genes were mainly involved in the neutrophil activation which in turn was involved in the immune response, degranulation and cell-mediated immunity, positive regulation of immune response, the Toll-like receptor signaling pathway, and the NOD-like receptor signaling pathway. From the PPI network, 14 hub genes were obtained. Among them, the subnetworks of PLCG1, BCL2A1, TLR8, FADD, and TLR4 screened out from our study have been shown to play a role in immune and inflammation responses. Discussion: The potential molecular mechanisms that have been identified simultaneously include innate immunity, neuroinflammation, and neurotrophic factors for synapse function and development.

Altered expression of long noncoding RNAs in patients with major depressive disorder

Although major depressive disorder (MDD) is a leading cause of disability worldwide, its pathophysiology is poorly understood. Increasing evidence suggests that aberrant regulation of transcription plays a key role in the pathophysiology of MDD. Recently, long noncoding RNAs (lncRNAs) have been recognized for their important functions in chromatin structure, gene expression, and the subsequent manifestation of various biological processes in the central nervous system. However, it is unclear whether the aberrant expression and function of lncRNAs are associated with the pathophysiology of MDD. In this study, we sought to evaluate the expression of lncRNAs in peripheral blood leukocytes as potential biomarkers for MDD. We measured the expression levels of 83 lncRNAs in the peripheral blood leukocytes of 29 MDD patients and 29 age- and gender-matched healthy controls using quantitative reverse transcription PCR (RT-qPCR) analysis. We found that MDD patients exhibited distinct expression signatures. Specifically, the expression level of one lncRNA (RMRP) was lower while the levels of four (Y5, MER11C, PCAT1, and PCAT29) were higher in MDD patients compared to healthy controls. The expression level of RMRP was correlated with depression severity as measured by the Hamilton Depression Rating Scale (HAM-D). Moreover, RMRP expression was lower in a mouse model of depression, corroborating the observation from MDD patients. Taken together, our data suggest that lower RMRP levels may serve as a potential biomarker for MDD.

Time Course of Changes in Peripheral Blood Gene Expression During Medication Treatment for Major Depressive Disorder

Changes in gene expression (GE) during antidepressant treatment may increase understanding of the action of antidepressant medications and serve as biomarkers of efficacy. GE changes in peripheral blood are desirable because they can be assessed easily on multiple occasions during treatment. We report here on GE changes in 68 individuals who were treated for 8 weeks with either escitalopram alone, or escitalopram followed by bupropion. GE changes were assessed after 1, 2, and 8 weeks of treatment, with significant changes observed in 156, 121, and 585 peripheral blood gene transcripts, respectively. Thirty-one transcript changes were shared between the 1- and 8-week time points (seven upregulated, 24 downregulated). Differences were detected between the escitalopram- and bupropion-treated subjects, although there was no significant association between GE changes and clinical outcome. A subset of 18 genes overlapped with those previously identified as differentially expressed in subjects with MDD compared with healthy control subjects. There was statistically significant overlap between genes differentially expressed in the current and previous studies, with 10 genes overlapping in at least two previous studies. There was no enrichment for genes overexpressed in nervous system cell types, but there was a trend toward enrichment for genes in the WNT/β-catenin pathway in the anterior thalamus; three genes in this pathway showed differential expression in the present and in three previous studies. Our dataset and other similar studies will provide an important source of information about potential biomarkers of recovery and for potential dysregulation of GE in MDD.

Loss of Glutamate Decarboxylase 67 in Somatostatin-Expressing Neurons Leads to Anxiety-Like Behavior and Alteration in the Akt/GSK3β Signaling Pathway

Major depressive disorder (MDD) is a highly prevalent psychiatric disorder worldwide. Several lines of evidence suggest that the dysfunction of somatostatin (SOM) neurons is associated with the pathophysiology of MDD. Importantly, most SOM neurons are γ-aminobutyric acid (GABA) interneurons. However, whether the dysfunction of GABAergic neurotransmission from SOM neurons contributes to the pathophysiology of MDD remains elusive. To address this issue, we investigated the emotional behaviors and relevant molecular mechanism in mice lacking glutamate decarboxylase 67 (GAD67), an isoform of GABA-synthesizing enzyme, specifically in SOM neurons (SOM-GAD67 mice). The SOM-GAD67 mice exhibited anxiety-like behavior in the open-field test without an effect on locomotor activity. The SOM-GAD67 mice showed depression-like behavior in neither the forced swimming test nor the sucrose preference test. In addition, the ability to form contextual fear memory was normal in the SOM-GAD67 mice. Furthermore, the plasma corticosterone level was normal in the SOM-GAD67 mice both under baseline and stress conditions. The expression ratios of p-Akt^Ser473/Akt and p-GSK3β^Ser9/GSK3β were decreased in the frontal cortex of SOM-GAD67 mice. Taken together, these data suggest that the loss of GAD67 from SOM neurons may lead to the development of anxiety-like but not depression-like states mediated by modification of Akt/GSK3β activities.

BMAL1 knockout macaque monkeys display reduced sleep and psychiatric disorders

Circadian disruption is a risk factor for metabolic, psychiatric and age-related disorders, and non-human primate models could help to develop therapeutic treatments. Here, we report the generation of BMAL1 knockout cynomolgus monkeys for circadian-related disorders by CRISPR/Cas9 editing of monkey embryos. These monkeys showed higher nocturnal locomotion and reduced sleep, which was further exacerbated by a constant light regimen. Physiological circadian disruption was reflected by the markedly dampened and arrhythmic blood hormonal levels. Furthermore, BMAL1-deficient monkeys exhibited anxiety and depression, consistent with their stably elevated blood cortisol, and defective sensory processing in auditory oddball tests found in schizophrenia patients. Ablation of BMAL1 up-regulated transcriptional programs toward inflammatory and stress responses, with transcription networks associated with human sleep deprivation, major depressive disorders, and aging. Thus, BMAL1 knockout monkeys are potentially useful for studying the physiological consequences of circadian disturbance, and for developing therapies for circadian and psychiatric disorders.

Approaching to the Essence of Major Depressive Disorder

Major Depressive Disorder (MDD) is a serious neuropsychic disease. It destroys person’s family relationship and social connections seriously. Latest WHO investigation disclosed nearly 4.4% of the population worldwide (approximately 322 million people) were being affected by MDD extensively [1]. While in China, Dong M, et al. reported the occurrence rate of suicide attempt during hospitalization and after the onset of MDD were 17.3% (95% CI: 12.4-23.7%) and 42.1% (95% CI: 26.1-60.0%) respectively [2]. Another research made by Grupta S, et al. announced MDD in urban China might be under-diagnosed and untreated [3].

Altered plasma protein glycosylation in a mouse model of depression and in patients with major depression

BACKGROUND

Glycosylation is a common posttranslational modification in protein biosynthesis that is implicated in several disease states. It has been reported that specific protein glycan structures are useful as biomarkers for cancer and some neuropsychiatric diseases; however, the relationship between plasma protein glycosylation and major depressive disorder (MDD) has not been investigated to date. The aim of this study was to determine whether plasma protein glycan structures are altered in depression using a stress-based mouse model and samples from patients with MDD.

METHODS

We used chronic ultra-mildly stressed mice that were untreated or treated with imipramine as mouse models of depression and remission, respectively. We also made comparisons between samples from depressed and remitted patients with MDD. Protein glycosylation was analyzed using a lectin microarray that included 45 lectins with binding affinities for various glycan structures.

RESULTS

Sia-alpha2-6Gal/GalNAc was a commonly altered glycan structure in both depression model mice and patients with MDD. Moreover, the expression of ST6GALNAC2 was decreased in leukocytes from patients with MDD.

LIMITATIONS

Our study samples were small and we did not identify specific alpha2-6Gal/GalNAc-sialylated proteins.

CONCLUSIONS

The glycan structure Sia-alpha2-6GalNAc in plasma protein and ST6GALNAC2 expression in peripheral leukocytes may have utility as candidate biomarkers for the clinical diagnosis and monitoring of MDD.

Exploring Potential Biomarkers Underlying Pathogenesis of Alzheimer's Disease by Differential Co-expression Analysis

BACKGROUND

Alzheimer's Disease (AD) is the most common form of dementia in the elderly. Due to the facts that biological causes of AD are complex in addition to increasing rates of AD worldwide, a deeper understanding of AD etiology is required for AD treatment and diagnosis.

METHODS

To identify molecular pathological alterations in AD brains, GSE36980 series containing microarray data samples from temporal cortex, frontal cortex and hippocampus were downloaded from Gene Expression Omnibus (GEO) database and valid gene symbols were subjected to building a gene co-expression network by a bioinformatics tool known as differential regulation from differential co-expression (DCGL) software package. Then, a network-driven integrative analysis was performed to find significant genes and underlying biological terms.

RESULTS

A total of 17088 unique genes were parsed into three independent differential co-expression networks. As a result, a small number of differentially co-regulated genes mostly in frontal and hippocampus lobs were detected as potential biomarkers related to AD brains. Ultimately differentially co-regulated genes were enriched in biological terms including response to lipid and fatty acid and pathways mainly signaling pathway such as G-protein signaling pathway and glutamate receptor groups II and III. By conducting co-expression analysis, our study identified multiple genes that may play an important role in the pathogenesis of AD.

CONCLUSION

The study aimed to provide a systematic understanding of the potential relationships among these genes and it is hoped that it could aid in AD biomarker discovery.

A comprehensive regional analysis of genome-wide expression profiles for major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a global health challenge. In recent years, a large number of genome-wide expression studies (GWES) have been carried out to identify the transcriptomic profiles for MDD. The objective of this work was to carry out a comprehensive meta-analysis of available GWES for MDD.

METHODS

GWES for MDD with available raw data were searched in NCBI GEO, Array Express and Stanley databases. Raw GWES data were preprocessed and normalized and meta-analytical procedures were carried out with the Network Analyst program. 743 samples from 24 primary studies were included in our meta-analyses for blood (Blo), amygdala (Amy), cerebellum (Cer), anterior cingulate cortex (ACC) and prefrontal cortex (PFC) regions. A functional enrichment analysis was carried out.

RESULTS

We identified 35, 793, 231, 668 and 252 differentially expressed (DE) genes for Blo, Amy, Cer, ACC and PFC regions. A region-dependent significant enrichment for several functional categories, such as gene ontologies, signaling pathways and topographic parameters, was identified. There was convergence with other available genome-wide studies, such as GWAS, DNA methylation analyses and miRNA expression studies.

LIMITATIONS

Raw data were not available for several primary studies that have been published previously.

CONCLUSIONS

This is the largest meta-analysis for GWES in MDD. The examination of convergence of genome-wide evidence and of the functional enrichment analysis provides a global overview of potential neural signaling mechanisms dysregulated in MDD. Our comprehensive analysis of several brain regions identified lists of DE genes for MDD that are interesting candidates for further studies.

Translational Identification of Transcriptional Signatures of Major Depression and Antidepressant Response

Major depressive disorder (MDD) is a highly prevalent mental illness whose therapy management remains uncertain, with more than 20% of patients who do not achieve response to antidepressants. Therefore, identification of reliable biomarkers to predict response to treatment will greatly improve MDD patient medical care. Due to the inaccessibility and lack of brain tissues from living MDD patients to study depression, researches using animal models have been useful in improving sensitivity and specificity of identifying biomarkers. In the current study, we used the unpredictable chronic mild stress (UCMS) model and correlated stress-induced depressive-like behavior (n = 8 unstressed vs. 8 stressed mice) as well as the fluoxetine-induced recovery (n = 8 stressed and fluoxetine-treated mice vs. 8 unstressed and fluoxetine-treated mice) with transcriptional signatures obtained by genome-wide microarray profiling from whole blood, dentate gyrus (DG), and the anterior cingulate cortex (ACC). Hierarchical clustering and rank-rank hypergeometric overlap (RRHO) procedures allowed us to identify gene transcripts with variations that correlate with behavioral profiles. As a translational validation, some of those transcripts were assayed by RT-qPCR with blood samples from 10 severe major depressive episode (MDE) patients and 10 healthy controls over the course of 30 weeks and four visits. Repeated-measures ANOVAs revealed candidate trait biomarkers (ARHGEF1, CMAS, IGHMBP2, PABPN1 and TBC1D10C), whereas univariate linear regression analyses uncovered candidates state biomarkers (CENPO, FUS and NUBP1), as well as prediction biomarkers predictive of antidepressant response (CENPO, NUBP1). These data suggest that such a translational approach may offer new leads for clinically valid panels of biomarkers for MDD.

Identification of commonly altered genes between in major depressive disorder and a mouse model of depression

The heterogeneity of depression (due to factors such as varying age of onset) may explain why biological markers of major depressive disorder (MDD) remain uncertain. We aimed to identify gene expression markers of MDD in leukocytes using microarray analysis. We analyzed gene expression profiles of patients with MDD (age ≥50, age of depression onset <50) (N = 10, depressed state; N = 13, remitted state). Seven-hundred and ninety-seven genes (558 upregulated, 239 downregulated when compared to those of 30 healthy subjects) were identified as potential markers for MDD. These genes were then cross-matched to microarray data obtained from a mouse model of depression (676 genes, 148 upregulated, 528 downregulated). Of the six common genes identified between patients and mice, five genes (SLC35A3, HIST1H2AL, YEATS4, ERLIN2, and PLPP5) were confirmed to be downregulated in patients with MDD by quantitative real-time polymerase chain reaction. Of these genes, HIST1H2AL was significantly decreased in a second set of independent subjects (age ≥20, age of onset <50) (N = 18, subjects with MDD in a depressed state; N = 19, healthy control participants). Taken together, our findings suggest that HIST1H2AL may be a biological marker of MDD.

Gene expression alterations in the medial prefrontal cortex and blood cells in a mouse model of depression during menopause

Aims

The prevalence of major depressive disorder (MDD) is higher in women than in men, and this may be due to the decline in estrogen levels that occurs during the menopausal transition. We studied the biological alterations in the medial prefrontal cortex (mPFC), which is a region that is highly implicated in the neurobiology of MDD, and the blood cells (BCs) of ovariectomized (OVX) mice subjected to chronic mild stress (CMS), which represents a mouse model of depression during menopause.

Main methods

The mPFC and the BCs were obtained from the same individuals. Gene expression levels were analyzed by microarray. The data were used for the Ingenuity Pathway Analysis and the Gene Ontology analysis.

Key findings

The gene expression alterations (GEAs) induced by OVX were mainly associated with ribosomal and mitochondrial functions in both the mPFC and the BCs. Rapamycin-insensitive companion of mTOR (RICTOR) was identified as a possible upstream regulator of the OVX-induced GEAs in both tissues. The CMS-induced GEAs were associated with retinoic acid receptor signaling, inflammatory cytokines and post-synaptic density in the mPFC, but not in the BCs.

Significance

OVX and CMS independently affect biological pathways in the mPFC, which is involved in the development of the depression-like phenotype. Because a subset of the OVX-induced GEAs in the mPFC also occurred in the BCs, the GEAs in the BCs might be a useful probe to predict biological pathways in the corresponding brain tissue under specific conditions such as OVX in females.

Novel One-Tube-One-Step Real-Time Methodology for Rapid Transcriptomic Biomarker Detection: Signal Amplification by Ternary Initiation Complexes

We have developed a novel RNA detection method, termed signal amplification by ternary initiation complexes (SATIC), in which an analyte sample is simply mixed with the relevant reagents and allowed to stand for a short time under isothermal conditions (37 °C). The advantage of the technique is that there is no requirement for (i) heat annealing, (ii) thermal cycling during the reaction, (iii) a reverse transcription step, or (iv) enzymatic or mechanical fragmentation of the target RNA. SATIC involves the formation of a ternary initiation complex between the target RNA, a circular DNA template, and a DNA primer, followed by rolling circle amplification (RCA) to generate multiple copies of G-quadruplex (G4) on a long DNA strand like beads on a string. The G4s can be specifically fluorescence-stained with N(3)-hydroxyethyl thioflavin T (ThT-HE), which emits weakly with single- and double-stranded RNA/DNA but strongly with parallel G4s. An improved dual SATIC system, which involves the formation of two different ternary initiation complexes in the RCA process, exhibited a wide quantitative detection range of 1-5000 pM. Furthermore, this enabled visual observation-based RNA detection, which is more rapid and convenient than conventional isothermal methods, such as reverse transcription-loop-mediated isothermal amplification, signal mediated amplification of RNA technology, and RNA-primed rolling circle amplification. Thus, SATIC methodology may serve as an on-site and real-time measurement technique for transcriptomic biomarkers for various diseases.