Stimulated gene expression profiles as a blood marker of major depressive disorder

m6A/m1A/m5C-Associated Methylation Alterations and Immune Profile in MDD

Major depressive disorder (MDD) is a prevalent psychiatric condition often accompanied by severe impairments in cognitive and functional capacities. This research was conducted to identify RNA modification-related gene signatures and associated functional pathways in MDD. Differentially expressed RNA modification-related genes in MDD were first identified. And a random forest model was developed and distinct RNA modification patterns were discerned based on signature genes. Then, comprehensive analyses of RNA modification-associated genes in MDD were performed, including functional analyses and immune cell infiltration. The study identified 29 differentially expressed RNA modification-related genes in MDD and two distinct RNA modification patterns. TRMT112, MBD3, NUDT21, and IGF2BP1 of the risk signature were detected. Functional analyses confirmed the involvement of RNA modification in pathways like phosphatidylinositol 3-kinase signaling and nucleotide oligomerization domain (NOD)-like receptor signaling in MDD. NUDT21 displayed a strong positive correlation with type 2 T helper cells, while IGF2BP1 negatively correlated with activated CD8 T cells, central memory CD4 T cells, and natural killer T cells. In summary, further research into the roles of NUDT21 and IGF2BP1 would be valuable for understanding MDD prognosis. The identified RNA modification-related gene signatures and pathways provide insights into MDD molecular etiology and potential diagnostic biomarkers.

Megalencephalic leukoencephalopathy with subcortical cysts: a variant update and review of the literature

The leukodystrophy megalencephalic leukoencephalopathy with subcortical cysts (MLC) is characterized by infantile-onset macrocephaly and chronic edema of the brain white matter. With delayed onset, patients typically experience motor problems, epilepsy and slow cognitive decline. No treatment is available. Classic MLC is caused by bi-allelic recessive pathogenic variants in MLC1 or GLIALCAM (also called HEPACAM). Heterozygous dominant pathogenic variants in GLIALCAM lead to remitting MLC, where patients show a similar phenotype in early life, followed by normalization of white matter edema and no clinical regression. Rare patients with heterozygous dominant variants in GPRC5B and classic MLC were recently described. In addition, two siblings with bi-allelic recessive variants in AQP4 and remitting MLC have been identified. The last systematic overview of variants linked to MLC dates back to 2006. We provide an updated overview of published and novel variants. We report on genetic variants from 508 patients with MLC as confirmed by MRI diagnosis (258 from our database and 250 extracted from 64 published reports). We describe 151 unique MLC1 variants, 29 GLIALCAM variants, 2 GPRC5B variants and 1 AQP4 variant observed in these MLC patients. We include experiments confirming pathogenicity for some variants, discuss particularly notable variants, and provide an overview of recent scientific and clinical insight in the pathophysiology of MLC.

Identification of a diagnostic model and molecular subtypes of major depressive disorder based on endoplasmic reticulum stress-related genes

Subject

Major depressive disorder (MDD) negatively affects patients' behaviours and daily lives. Due to the high heterogeneity and complex pathological features of MDD, its diagnosis remains challenging. Evidence suggests that endoplasmic reticulum stress (ERS) is involved in the pathogenesis of MDD; however, relevant diagnostic markers have not been well studied. This study aimed to screen for ERS genes with potential diagnostic value in MDD.

Methods

Gene expression data on MDD samples were downloaded from the GEO database, and ERS-related genes were obtained from the GeneCards and MSigDB databases. Differentially expressed genes (DEGs) in MDD patients and healthy subjects were identified and then integrated with ERS genes. ERS diagnostic model and nomogram were developed based on biomarkers screened using the LASSO method. The diagnostic performance of this model was evaluated. ERS-associated subtypes were identified. CIBERSORT and GSEA were used to explore the differences between the different subtypes. Finally, WGCNA was performed to identify hub genes related to the subtypes.

Results

A diagnostic model was developed based on seven ERS genes: KCNE1, PDIA4, STAU1, TMED4, MGST1, RCN1, and SHC1. The validation analysis showed that this model had a good diagnostic performance. KCNE1 expression was positively correlated with M0 macrophages and negatively correlated with resting CD4+ memory T cells. Two subtypes (SubA and SubB) were identified, and these two subtypes showed different ER score. The SubB group showed higher immune infiltration than the SubA group. Finally, NCF4, NCF2, CSF3R, and FPR2 were identified as hub genes associated with ERS molecular subtypes.

Conclusion

Our current study provides novel diagnostic biomarkers for MDD from an ERS perspective, and these findings further facilitate the use of precision medicine in MDD.

Gene expression profiling in whole blood stimulated ex vivo with lipopolysaccharide as a tool to predict post-stroke depressive symptoms: Proof-of-concept study

Prediction of post-stroke depressive symptoms (DSs) is challenging in patients without a history of depression. Gene expression profiling in blood cells may facilitate the search for biomarkers. The use of an ex vivo stimulus to the blood helps to reveal differences in gene profiles by reducing variation in gene expression. We conducted a proof-of-concept study to determine the usefulness of gene expression profiling in lipopolysaccharide (LPS)-stimulated blood for predicting post-stroke DS. Out of 262 enrolled patients with ischemic stroke, we included 96 patients without a pre-stroke history of depression and not taking any anti-depressive medication before or during the first 3 months after stroke. We assessed DS at 3 months after stroke using the Patient Health Questionnaire-9. We used RNA sequencing to determine the gene expression profile in LPS-stimulated blood samples taken on day 3 after stroke. We constructed a risk prediction model using a principal component analysis combined with logistic regression. We diagnosed post-stroke DS in 17.7% of patients. Expression of 510 genes differed between patients with and without DS. A model containing 6 genes (PKM, PRRC2C, NUP188, CHMP3, H2AC8, NOP10) displayed very good discriminatory properties (area under the curve: 0.95) with the sensitivity of 0.94 and specificity of 0.85. Our results suggest the potential utility of gene expression profiling in whole blood stimulated with LPS for predicting post-stroke DS. This method could be useful for searching biomarkers of post-stroke depression.

Investigation of the shared molecular mechanisms and hub genes between myocardial infarction and depression

Background

The pathogenesis of myocardial infarction complicating depression is still not fully understood. Bioinformatics is an effective method to study the shared pathogenesis of multiple diseases and has important application value in myocardial infarction complicating depression.

Methods

The differentially expressed genes (DEGs) between control group and myocardial infarction group (M-DEGs), control group and depression group (D-DEGs) were identified in the training set. M-DEGs and D-DEGs were intersected to obtain DEGs shared by the two diseases (S-DEGs). The GO, KEGG, GSEA and correlation analysis were conducted to analyze the function of DEGs. The biological function differences of myocardial infarction and depression were analyzed by GSVA and immune cell infiltration analysis. Four machine learning methods, nomogram, ROC analysis, calibration curve and decision curve were conducted to identify hub S-DEGs and predict depression risk. The unsupervised cluster analysis was constructed to identify myocardial infarction molecular subtype clusters based on hub S-DEGs. Finally, the value of these genes was verified in the validation set, and blood samples were collected for RT-qPCR experiments to further verify the changes in expression levels of these genes in myocardial infarction and depression.

Results

A total of 803 M-DEGs, 214 D-DEGs, 13 S-DEGs and 6 hub S-DEGs (CD24, CSTA, EXTL3, RPS7, SLC25A5 and ZMAT3) were obtained in the training set and they were all involved in immune inflammatory response. The GSVA and immune cell infiltration analysis results also suggested that immune inflammation may be the shared pathogenesis of myocardial infarction and depression. The diagnostic models based on 6 hub S-DEGs found that these genes showed satisfactory combined diagnostic performance for depression. Then, two molecular subtypes clusters of myocardial infarction were identified, many differences in immune inflammation related-biological functions were found between them, and the hub S-DEGs had satisfactory molecular subtypes identification performance. Finally, the analysis results of the validation set further confirmed the value of these hub genes, and the RT-qPCR results of blood samples further confirmed the expression levels of these hub genes in myocardial infarction and depression.

Conclusion

Immune inflammation may be the shared pathogenesis of myocardial infarction and depression. Meanwhile, hub S-DEGs may be potential biomarkers for the diagnosis and molecular subtype identification of myocardial infarction and depression.

Heterogeneity in major depressive disorder: The need for biomarker-based personalized treatments

Major Depressive Disorder (MDD) or depression is a pathological mental condition affecting millions of people worldwide. Identification of objective biological markers of depression can provide for a better diagnostic and intervention criteria; ultimately aiding to reduce its socioeconomic health burden. This review provides a comprehensive insight into the major biomarker candidates that have been implicated in depression neurobiology. The key biomarker categories are covered across all the "omics" levels. At the epigenomic level, DNA-methylation, non-coding RNA and histone-modifications have been discussed in relation to depression. The proteomics system shows great promise with inflammatory markers as well as growth factors and neurobiological alterations within the endocannabinoid system. Characteristic lipids implicated in depression together with the endocrine system are reviewed under the metabolomics section. The chapter also examines the novel biomarkers for depression that have been proposed by studies in the microbiome. Depression affects individuals differentially and explicit biomarkers identified by robust research criteria may pave the way for better diagnosis, intervention, treatment, and prediction of treatment response.

The protective effects of curcumin on depression: Genes, transcription factors, and microRNAs involved

BACKGROUND

We aim to identify the molecular mechanisms for curcumin's anti-depressant properties, including genes, transcription factors, and miRNAs.

METHODS

The Comparative Toxicogenomics Database, GeneMania, Metascape, MIENTURNET, and Cytoscape software were used as important data approaches in this study.

RESULTS

Curcumin may have an anti-depressant effect via the relevant genes: ADORA2A, ALB, BDNF, FGF2, GLO1, GSK3B, IL6, MIF, NOS1, PTGS2, RELN, SELP, SOD1, and NR3C1. Co-expression (50.7 %) and physical interactions (28.7 %) were the primary relationships discovered by gene network analysis. The key pathways involved in curcumin's protective function against depression were "spinal cord injury", "regulation of apoptotic signaling pathway", "positive regulation of protein phosphorylation", "folate metabolism", "neuroinflammation and glutamatergic signaling", and "inflammation response". We also observed 74 miRNAs associated with depression that are targeted by curcumin, with hsa-miR-146a-5p having the greatest expression and interaction. PLSCR1, SNAI1, ZNF267, ATF3, and GTF2B were the most important transcription factors that regulated four curcumin-targeted genes. Curcumin's physicochemical characteristics and pharmacokinetics are consistent with its antidepressant effects due to its high gastrointestinal absorption, which did not remove it from the CNS, and its ability to penetrate the blood-brain barrier. Curcumin also inhibits CYP1A9 and CYP3A4.

LIMITATIONS

A toxicogenomic design in silico was applied.

CONCLUSIONS

Our findings suggest that therapy optimization and further research into curcumin's pharmacological properties are required before it may be utilized to treat depression.

Clinical improvement of DM1 patients reflected by reversal of disease-induced gene expression in blood

BACKGROUND

Myotonic dystrophy type 1 (DM1) is an incurable multisystem disease caused by a CTG-repeat expansion in the DM1 protein kinase (DMPK) gene. The OPTIMISTIC clinical trial demonstrated positive and heterogenous effects of cognitive behavioral therapy (CBT) on the capacity for activity and social participations in DM1 patients. Through a process of reverse engineering, this study aims to identify druggable molecular biomarkers associated with the clinical improvement in the OPTIMISTIC cohort.

METHODS

Based on full blood samples collected during OPTIMISTIC, we performed paired mRNA sequencing for 27 patients before and after the CBT intervention. Linear mixed effect models were used to identify biomarkers associated with the disease-causing CTG expansion and the mean clinical improvement across all clinical outcome measures.

RESULTS

We identified 608 genes for which their expression was significantly associated with the CTG-repeat expansion, as well as 1176 genes significantly associated with the average clinical response towards the intervention. Remarkably, all 97 genes associated with both returned to more normal levels in patients who benefited the most from CBT. This main finding has been replicated based on an external dataset of mRNA data of DM1 patients and controls, singling these genes out as candidate biomarkers for therapy response. Among these candidate genes were DNAJB12, HDAC5, and TRIM8, each belonging to a protein family that is being studied in the context of neurological disorders or muscular dystrophies. Across the different gene sets, gene pathway enrichment analysis revealed disease-relevant impaired signaling in, among others, insulin-, metabolism-, and immune-related pathways. Furthermore, evidence for shared dysregulations with another neuromuscular disease, Duchenne muscular dystrophy, was found, suggesting a partial overlap in blood-based gene dysregulation.

CONCLUSIONS

DM1-relevant disease signatures can be identified on a molecular level in peripheral blood, opening new avenues for drug discovery and therapy efficacy assessments.

Severe, but not moderate asthmatics share blood transcriptomic changes with post-traumatic stress disorder and depression

Asthma, an inflammatory disorder of the airways, is one of the most common chronic illnesses worldwide and is associated with significant morbidity. There is growing recognition of an association between asthma and mood disorders including post-traumatic stress disorder (PTSD) and major depressive disorder (MDD). Although there are several hypotheses regarding the relationship between asthma and mental health, there is little understanding of underlying mechanisms and causality. In the current study we utilized publicly available datasets of human blood mRNA collected from patients with severe and moderate asthma, MDD, and PTSD. We performed differential expression (DE) analysis and Gene Set Enrichment Analysis (GSEA) on diseased subjects against the healthy subjects from their respective datasets, compared the results between diseases, and validated DE genes and gene sets with 4 more independent datasets. Our analysis revealed that commonalities in blood transcriptomic changes were only found between the severe form of asthma and mood disorders. Gene expression commonly regulated in PTSD and severe asthma, included ORMDL3 a gene known to be associated with asthma risk and STX8, which is involved in TrkA signaling. We also identified several pathways commonly regulated to both MDD and severe asthma. This study reveals gene and pathway regulation that potentially drives the comorbidity between severe asthma, PTSD, and MDD and may serve as foci for future research aimed at gaining a better understanding of both the relationship between asthma and PTSD, and the pathophysiology of the individual disorders.

A machine learning model for predicting patients with major depressive disorder: A study based on transcriptomic data

Background

Identifying new biomarkers of major depressive disorder (MDD) would be of great significance for its early diagnosis and treatment. Herein, we constructed a diagnostic model of MDD using machine learning methods.

Methods

The GSE98793 and GSE19738 datasets were obtained from the Gene Expression Omnibus database, and the limma R package was used to analyze differentially expressed genes (DEGs) in MDD patients. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to identify potential molecular functions and pathways. A protein-protein interaction network (PPI) was constructed, and hub genes were predicted. Random forest (RF) and artificial neural network (ANN) machine-learning algorithms were used to select variables and construct a robust diagnostic model.

Results

A total of 721 DEGs were identified in peripheral blood samples of patients with MDD. GO and KEGG analyses revealed that the DEGs were mainly enriched in cytokines, defense responses to viruses, responses to biotic stimuli, immune effector processes, responses to external biotic stimuli, and immune systems. A PPI network was constructed, and CytoHubba plugins were used to screen hub genes. Furthermore, a robust diagnostic model was established using a RF and ANN algorithm with an area under the curve of 0.757 for the training model and 0.685 for the test cohort.

Conclusion

We analyzed potential driver genes in patients with MDD and built a potential diagnostic model as an adjunct tool to assist psychiatrists in the clinical diagnosis and treatment of MDD.

Cellular and immunometabolic mechanisms of inflammation in depression: Preliminary findings from single cell RNA sequencing and a tribute to Bruce McEwen

Inflammation is associated with symptoms of anhedonia, a core feature of major depression (MD). We have shown that MD patients with high inflammation as measured by plasma C-reactive protein (CRP) and anhedonia display gene signatures of metabolic reprograming (e.g., shift to glycolysis) necessary to sustain cellular immune activation. To gain preliminary insight into the immune cell subsets and transcriptomic signatures that underlie increased inflammation and its relationship with behavior in MD at the single-cell (sc) level, herein we conducted scRNA-Seq on peripheral blood mononuclear cells from a subset of medically-stable, unmedicated MD outpatients. Three MD patients with high CRP (>3 mg/L) before and two weeks after anti-inflammatory challenge with the tumor necrosis factor antagonist infliximab and three patients with low CRP (≤3 mg/L) were studied. Cell clusters were identified using a Single Cell Wizard pipeline, followed by pathway analysis. CD14⁺ and CD16⁺ monocytes were more abundant in MD patients with high CRP and were reduced by 29% and 55% respectively after infliximab treatment. Within CD14⁺ and CD16⁺ monocytes, genes upregulated in high CRP patients were enriched for inflammatory (phagocytosis, complement, leukocyte migration) and immunometabolic (hypoxia-inducible factor [HIF]-1, aerobic glycolysis) pathways. Shifts in CD4⁺ T cell subsets included ∼30% and ∼10% lower abundance of CD4⁺ central memory (T_CM) and naïve cells and ∼50% increase in effector memory-like (T_EM-like) cells in high versus low CRP patients. T_CM cells of high CRP patients displayed downregulation of the oxidative phosphorylation (OXPHOS) pathway, a main energy source in this cell type. Following infliximab, changes in the number of CD14⁺ monocytes and CD4⁺ T_EM-like cells predicted improvements in anhedonia scores (r = 1.0, p < 0.001). In sum, monocytes and CD4⁺ T cells from MD patients with increased inflammation exhibited immunometabolic reprograming in association with symptoms of anhedonia. These findings are the first step toward determining the cellular and molecular immune pathways associated with inflammatory phenotypes in MD, which may lead to novel immunomodulatory treatments of psychiatric illnesses with increased inflammation.

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.

Tricyclic antidepressants target FKBP51 SUMOylation to restore glucocorticoid receptor activity

FKBP51 is an important inhibitor of the glucocorticoid receptor (GR) signaling. High FKBP51 levels are associated to stress-related disorders, which are linked to GR resistance. SUMO conjugation to FKBP51 is necessary for FKBP51's inhibitory action on GR. The GR/FKBP51 pathway is target of antidepressant action. Thus we investigated if these drugs could inhibit FKBP51 SUMOylation and therefore restore GR activity. Screening cells using Ni²⁺ affinity and in vitro SUMOylation assays revealed that tricyclic antidepressants- particularly clomipramine- inhibited FKBP51 SUMOylation. Our data show that clomipramine binds to FKBP51 inhibiting its interaction with PIAS4 and therefore hindering its SUMOylation. The inhibition of FKBP51 SUMOylation decreased its binding to Hsp90 and GR facilitating FKBP52 recruitment, and enhancing GR activity. Reduction of PIAS4 expression in rat primary astrocytes impaired FKBP51 interaction with GR, while clomipramine could no longer exert its inhibitory action. This mechanism was verified in vivo in mice treated with clomipramine. These results describe the action of antidepressants as repressors of FKBP51 SUMOylation as a molecular switch for restoring GR sensitivity, thereby providing new potential routes of antidepressant intervention.

Integrative omics analysis identifies differential biological pathways that are associated with regional grey matter volume changes in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is accompanied by alterations in grey matter volume. However, the biological processes associated with regional structural perturbations remain elusive.

METHODS

We applied integrative omics analysis to investigate specialized transcriptome signatures and translational determinants associated with regional grey matter variations in 2737 MDD patients relative to 3098 controls by summarizing the results from gene co-expression network analysis of Allen human brain transcriptome profiles in six donors, enrichment analysis of gene-sets and cellular structure from rodents and mediation analysis of BrainSpan proteome profile in six donors.

RESULTS

We found convergent alterations of grey matter volume in MDD were associated with transcriptome profiles enriched for synaptic transmission, metabolism, immune processes and transmembrane transport. Genes with abnormal expression in post-mortem tissue in MDD were also associated with transcriptome signatures. Further gene co-expression network and enrichment analysis of MDD-related genes in these signatures revealed the modules with higher neuronal expression were enriched in the medial temporal cortex and temporo-parietal junction with genes differentially associated with neuronal development and metabolism. Also, the modules with higher non-neuronal (e.g. astrocyte and oligodendrocyte) expression were concentrated in the rostral and dorsal anterior cingulate cortex and were separately associated with immune response and transmembrane transport. Moreover, proteins as the gene expression products mediated the association between transcriptome signatures and brain volume changes in the visual and dorsolateral prefrontal cortex.

CONCLUSIONS

Our multidimensional analyses offer a novel approach to detect specific biological pathways that capture regional structural variations in MDD, which suggests structural endophenotypes associated with MDD.

Beyond the neuron: Role of non-neuronal cells in stress disorders

Stress disorders are leading causes of disease burden in the U.S. and worldwide, yet available therapies are fully effective in less than half of all individuals with these disorders. Although to date, much of the focus has been on neuron-intrinsic mechanisms, emerging evidence suggests that chronic stress can affect a wide range of cell types in the brain and periphery, which are linked to maladaptive behavioral outcomes. Here, we synthesize emerging literature and discuss mechanisms of how non-neuronal cells in limbic regions of brain interface at synapses, the neurovascular unit, and other sites of intercellular communication to mediate the deleterious, or adaptive (i.e., pro-resilient), effects of chronic stress in rodent models and in human stress-related disorders. We believe that such an approach may one day allow us to adopt a holistic "whole body" approach to stress disorder research, which could lead to more precise diagnostic tests and personalized treatment strategies. Stress is a major risk factor for many psychiatric disorders. Cathomas et al. review new insight into how non-neuronal cells mediate the deleterious effects, as well as the adaptive, protective effects, of stress in rodent models and human stress-related disorders.

Screening of SERT and p11 mRNA Levels in Airline Pilots: A Translational Approach

Airline pilots are frequently exposed to numerous flights per week, changes in their circadian rhythms, and extended periods away from home. All these stressors make pilots susceptible to developing psychiatric disorders. Recently, emphasis has been placed on the need for molecular tests that help in the diagnosis of depression. The genes SLC6A4 and S100A10 encode serotonin transporter (SERT) and p11 protein, respectively. Their expression has been frequently associated with stress and depression. In this work, we quantified, by quantitative PCR, the expression of SERT and p11 in peripheral mononuclear cells of airline pilots compared to patients with depression and healthy volunteers. Moreover, by mass spectrometry, we quantified the serum serotonin levels in the same three groups. We found that SERT and p11 were overexpressed in the mononuclear cells of airline pilots and depressed patients compared to healthy volunteers. Although serum serotonin was not different between healthy volunteers and airline pilots, a decreasing trend was observed in the latter. As expected, serum serotonin in the patients was significantly lower. Alterations in SERT and p11 in airline pilots could be related to professional stress, a condition that could potentially affect their long-term mental health.

Immune targets for therapeutic development in depression: towards precision medicine

Over the past two decades, compelling evidence has emerged indicating that immune mechanisms can contribute to the pathogenesis of major depressive disorder (MDD) and that drugs with primary immune targets can improve depressive symptoms. Patients with MDD are heterogeneous with respect to symptoms, treatment responses and biological correlates. Defining a narrower patient group based on biology could increase the treatment response rates in certain subgroups: a major advance in clinical psychiatry. For example, patients with MDD and elevated pro-inflammatory biomarkers are less likely to respond to conventional antidepressant drugs, but novel immune-based therapeutics could potentially address their unmet clinical needs. This article outlines a framework for developing drugs targeting a novel patient subtype within MDD and reviews the current state of neuroimmune drug development for mood disorders. We discuss evidence for a causal role of immune mechanisms in the pathogenesis of depression, together with targets under investigation in randomized controlled trials, biomarker evidence elucidating the link to neural mechanisms, biological and phenotypic patient selection strategies, and the unmet clinical need among patients with MDD.

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.

Can (immune and other) gene expression help us to treat depression?

It is becoming clearer that it might be a combination of different biological processes such as genetic, environmental, and psychological factors, together with immune system, stress response, brain neuroplasticity and the regulation of neurotransmitters, that leads to the development of major depressive disorder (MDD). A growing number of studies have tried to investigate the underlying mechanisms of MDD by analysing the expression levels of genes (mRNA) involved in such biological processes. In this review, I have highlighted a possible key role that gene expression might play in the treatment of MDD. This is critical because many patients do not respond to antidepressant treatment or can experience side effects, causing treatment to be interrupted. Unfortunately, selecting the best antidepressant for each individual is still largely a matter of making an informed guess.

Genome-wide DNA methylation patterns associated with general psychopathology in children

Psychiatric symptoms are interrelated and found to be largely captured by a general psychopathology factor (GPF). Although epigenetic mechanisms, such as DNA methylation (DNAm), have been linked to individual psychiatric outcomes, associations with GPF remain unclear. Using data from 440 children aged 10 years participating in the Generation R Study, we examined the associations of DNAm with both general and specific (internalizing, externalizing) factors of psychopathology. Genome-wide DNAm levels, measured in peripheral blood using the Illumina 450K array, were clustered into wider co-methylation networks ('modules') using a weighted gene co-expression network analysis. One co-methylated module associated with GPF after multiple testing correction, while none associated with the specific factors. This module comprised of 218 CpG probes, of which 198 mapped onto different genes. The CpG most strongly driving the association with GPF was annotated to FZD1, a gene that has been implicated in schizophrenia and wider neurological processes. Associations between the probes contained in the co-methylated module and GPF were supported in an independent sample of children from the Avon Longitudinal Study of Parents and Children (ALSPAC), as evidenced by significant correlations in effect sizes. These findings might contribute to improving our understanding of dynamic molecular processes underlying complex psychiatric phenotypes.

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.

Gene expression studies in Depression development and treatment: an overview of the underlying molecular mechanisms and biological processes to identify biomarkers

A combination of different risk factors, such as genetic, environmental and psychological factors, together with immune system, stress response, brain neuroplasticity and the regulation of neurotransmitters, is thought to lead to the development of major depressive disorder (MDD). A growing number of studies have tried to investigate the underlying mechanisms of MDD by analysing the expression levels of genes involved in such biological processes. These studies have shown that MDD is not just a brain disorder, but also a body disorder, and this is mainly due to the interplay between the periphery and the Central Nervous System (CNS). To this purpose, most of the studies conducted so far have mainly dedicated to the analysis of the gene expression levels using postmortem brain tissue as well as peripheral blood samples of MDD patients. In this paper, we reviewed the current literature on candidate gene expression alterations and the few existing transcriptomics studies in MDD focusing on inflammation, neuroplasticity, neurotransmitters and stress-related genes. Moreover, we focused our attention on studies, which have investigated mRNA levels as biomarkers to predict therapy outcomes. This is important as many patients do not respond to antidepressant medication or could experience adverse side effects, leading to the interruption of treatment. Unfortunately, the right choice of antidepressant for each individual still remains largely a matter of taking an educated guess.

A large-scale genome-wide gene expression analysis in peripheral blood identifies very few differentially expressed genes related to antidepressant treatment and response in patients with major depressive disorder

A better understanding of the biological factors underlying antidepressant treatment in patients with major depressive disorder (MDD) is needed. We perform gene expression analyses and explore sources of variability in peripheral blood related to antidepressant treatment and treatment response in patients suffering from recurrent MDD at baseline and after 8 weeks of treatment. The study includes 281 patients, which were randomized to 8 weeks of treatment with vortioxetine (N = 184) or placebo (N = 97). To our knowledge, this is the largest dataset including both gene expression in blood and placebo-controlled treatment response measured by a clinical scale in a randomized clinical trial. We identified three novel genes whose RNA expression levels at baseline and week 8 are significantly (FDR < 0.05) associated with treatment response after 8 weeks of treatment. Among these genes were SOCS3 (FDR = 0.0039) and PROK2 (FDR = 0.0028), which have previously both been linked to depression. Downregulation of these genes was associated with poorer treatment response. We did not identify any genes that were differentially expressed between placebo and vortioxetine groups at week 8 or between baseline and week 8 of treatment. Nor did we replicate any genes identified in previous peripheral blood gene expression studies examining treatment response. Analysis of genome-wide expression variability showed that type of treatment and treatment response explains very little of the variance, a median of <0.0001% and 0.05% in gene expression across all genes, respectively. Given the relatively large size of the study, the limited findings suggest that peripheral blood gene expression might not be the best approach to explore the biological factors underlying antidepressant treatment.

Toll-like receptor 4-mediated cytokine synthesis and post-stroke depressive symptoms

Altered cytokine synthesis thought to contribute to the pathophysiology of post-stroke depression (PSD). Toll-like receptor 4 (TLR4) is a master regulator of innate immunity. The aim of this study was to explore the putative association between TLR4-mediated cytokine synthesis and subsequent symptoms of PSD. In total, 262 patients with ischemic stroke and without a history of PSD were included. Depressive symptoms were assessed using the Patient Health Questionnaire-9 in 170 patients on Day 8 and in 146 at 3 months after stroke. Blood samples taken on Day 3 after stroke were stimulated ex vivo with lipopolysaccharide (LPS). Ex vivo synthesized cytokines (TNFα, IP-10, IL-1β, IL-6, IL-8, IL-10, and IL-12p70) and circulating cytokines (TNFα, IL-6, sIL-6R, and IL-1ra) were measured using the enzyme-linked immunoassay or cytometric method. RNA sequencing was used to determine the gene expression profile of LPS-induced cytokines and chemokines. LPS-induced cytokine synthesis and the gene expression of TLR4-dependent cytokines and chemokines did not differ between patients with and without greater depressive symptoms. The plasma level of IL-6, but not TNFα, sIL-6R, and IL-1ra, was higher in patients who developed depressive symptoms at 3 months after stroke (median: 4.7 vs 3.4 pg/mL, P = 0.06). Plasma IL-6 predicted the severity of depressive symptoms at 3 months after stroke (β = 0.42, P = 0.03). In conclusion, TLR4-dependent cytokine synthesis was not associated with greater post-stroke depressive symptoms in this study. Circulating IL-6 might be associated with depressive symptoms occurring at 3 months after stroke.

Potential Biomarkers for Diagnosing Major Depressive Disorder Patients with Suicidal Ideation

Background

Major depressive disorder (MDD) and suicide are two major health problems, but there are still no objective methods to diagnose MDD or suicidal ideation (SI). This study was conducted to identify potential biomarkers for diagnosing MDD patients with SI.

Methods

First-episode drug-naïve MDD patients with SI and demographics-matched healthy controls (HCs) were recruited. First-episode drug-naïve MDD patients without SI were also included. The serum lipids, C-reactive protein (CRP), transferring (TRSF), homocysteine (HCY) and alpha 1-antitrypsin (AAT) in serum were detected. The univariate and multivariate statistical analyses were used to identify and validate the potential biomarkers.

Results

The 86 HCs, 53 MDD patients with SI and 20 MDD patients without SI were included in this study. Four potential biomarkers were identified: AAT, TRSF, high-density lipoprotein cholesterol (HDLC), and apolipoprotein A1 (APOA1). After one month treatment, the levels of AAT and APOA1 were significantly improved. The panel consisting of these potential biomarkers had an excellent diagnostic performance, yielding an area under the ROC curve (AUC) of 0.994 and 0.990 in the training and testing set, respectively. Moreover, this panel could effectively distinguish MDD patients with SI from MDD patients without SI (AUC=0.928).

Conclusion

These results showed that these potential biomarkers could facilitate the development of an objective method for diagnosing MDD patients with SI, and the decreased AAT levels in MDD patients might lead to the appearance of SI by resulting in the elevated inflammation.

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.

Major Depressive Disorder Is Associated With Differential Expression of Innate Immune and Neutrophil-Related Gene Networks in Peripheral Blood: A Quantitative Review of Whole-Genome Transcriptional Data From Case-Control Studies

BACKGROUND

Whole-genome transcription has been measured in peripheral blood samples as a candidate biomarker of inflammation associated with major depressive disorder.

METHODS

We searched for all case-control studies on major depressive disorder that reported microarray or RNA sequencing measurements on whole blood or peripheral blood mononuclear cells. Primary datasets were reanalyzed, when openly accessible, to estimate case-control differences and to evaluate the functional roles of differentially expressed gene lists by technically harmonized methods.

RESULTS

We found 10 eligible studies (N = 1754 depressed cases and N = 1145 healthy controls). Fifty-two genes were called significant by 2 of the primary studies (published overlap list). After harmonization of analysis across 8 accessible datasets (n = 1706 cases, n = 1098 controls), 272 genes were coincidentally listed in the top 3% most differentially expressed genes in 2 or more studies of whole blood or peripheral blood mononuclear cells with concordant direction of effect (harmonized overlap list). By meta-analysis of standardized mean difference across 4 studies of whole-blood samples (n = 1567 cases, n = 954 controls), 343 genes were found with false discovery rate <5% (standardized mean difference meta-analysis list). These 3 lists intersected significantly. Genes abnormally expressed in major depressive disorder were enriched for innate immune-related functions, coded for nonrandom protein-protein interaction networks, and coexpressed in the normative transcriptome module specialized for innate immune and neutrophil functions.

CONCLUSIONS

Quantitative review of existing case-control data provided robust evidence for abnormal expression of gene networks important for the regulation and implementation of innate immune response. Further development of white blood cell transcriptional biomarkers for inflamed depression seems warranted.

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.

Depression and suicide risk prediction models using blood-derived multi-omics data

More than 300 million people worldwide experience depression; annually, ~800,000 people die by suicide. Unfortunately, conventional interview-based diagnosis is insufficient to accurately predict a psychiatric status. We developed machine learning models to predict depression and suicide risk using blood methylome and transcriptome data from 56 suicide attempters (SAs), 39 patients with major depressive disorder (MDD), and 87 healthy controls. Our random forest classifiers showed accuracies of 92.6% in distinguishing SAs from MDD patients, 87.3% in distinguishing MDD patients from controls, and 86.7% in distinguishing SAs from controls. We also developed regression models for predicting psychiatric scales with R² values of 0.961 and 0.943 for Hamilton Rating Scale for Depression-17 and Scale for Suicide Ideation, respectively. Multi-omics data were used to construct psychiatric status prediction models for improved mental health treatment.

Differential transcriptional response following glucocorticoid activation in cultured blood immune cells: a novel approach to PTSD biomarker development

Post-traumatic stress disorder (PTSD) is a condition of stress reactivity, whose clinical manifestations are evident when patients are triggered following exposure to a traumatic event. While baseline differences in gene expression of glucocorticoid signaling and inflammatory cytokines in peripheral blood mononuclear cells (PBMCs) have been associated with PTSD, these alterations do not fully recapitulate the molecular response to physiological triggers, such as stress hormones. Therefore, it is critical to develop new techniques that will capture the dynamic transcriptional response associated with stress-activated conditions relative to baseline conditions. To achieve this goal, cultured PBMCs from combat-exposed veterans with PTSD(+) (n = 10) and without PTSD(-) (n = 10) were incubated with increasing concentrations (vehicle, 2.5 nM, 5 nM, 50 nM) of dexamethasone (DEX). Across diagnosis and dosage, several genes and gene networks were reliable markers of glucocorticoid stimulation (FDR < 5%), including enhanced expression of FKPB5, VIPR1, NR1I3, and apoptosis-related pathways, and reduced expression of NR3C1, STAT1, IRF1, and related inflammatory and cellular stress-responsive pathways. Dose-dependent differential transcriptional changes in several genes were also identified between PTSD+ and PTSD-. Robust changes in expression were observed at 2.5 nM DEX in PTSD- but not PTSD+ participants; whereas, with increasing concentrations (5 nM and 50 nM), several genes were identified to be uniquely up-regulated in PTSD+ but not PTSD- participants. Collectively, these preliminary findings suggest that genome-wide gene expression profiling of DEX-stimulated PBMCs is a promising method for the exploration of the dynamic differential molecular responses to stress hormones in PTSD, and may identify novel markers of altered glucocorticoid signaling and responsivity in PTSD.

Umbilical cord blood-based gene signatures related to prenatal major depressive disorder

BACKGROUND

Prenatal exposure to depression has been considered as a risk factor for adverse childhood, while it is accompanied by unknown molecular mechanisms. The aim of this study was to identify differentially expressed genes (DEGs) and associated biological processes between cord blood samples from neonates born to mothers who exposed to major depressive disorder (MDD) and healthy mothers.

METHODS

The microarray data GSE114852 were downloaded to analyze the mRNA expression profiles of umbilical cord blood with 31 samples exposed to prenatal MDD and 62 samples with healthy mothers. Kyoto Encyclopedia of Genes and Genomes pathway and Gene ontology enrichment analyses were conducted to identify associated biochemical pathways and functional categories of the DEGs. The protein-protein interaction network was constructed and the top 10 hub genes in the network were predicted.

RESULTS

The results showed several immunity related processes, such as "phagosome", "Epstein-Barr virus infection", "proteasome", "positive regulation of I-kappaB kinase/NF-kappaB signaling", "interferon-gamma-mediated signaling pathway", and "tumor necrosis factor" presented significant differences between two groups. Most of the hub genes (for example PSMD2, PSMD6, PSMB8, PSMB9) were also associated with immune pathways.

CONCLUSION

This bioinformatic analysis demonstrated immune-mediated mechanisms might play a fatal role in abnormalities in fetal gene expression profiles caused by prenatal MDD.

Altered mRNA expressions for N-methyl-D-aspartate receptor-related genes in WBC of patients with major depressive disorder

OBJECTIVE

Major depressive disorder (MDD) is a complex mental disorder. The lack of well-established biomarkers hinders its diagnosis, treatment, and new-drug development. N-methyl-D-aspartate receptor (NMDAR) dysfunction has been implicated in the pathogenesis of MDD. This study examined whether expressions of the NMDAR-related genes are characteristic of MDD.

METHODS

Expressions of NMDAR-related genes including SRR, SHMT2, PSAT1, GCAT, GAD1, SLC1A4, NRG1 and COMT in peripheral WBCs of 110 patients with MDD (25 drug-naïve, 21 drug-free, and 64 medicated patients) and 125 healthy individuals were measured using quantitative PCR.

RESULTS

The mRNA expression levels of SRR, PSAT1, GCAT, GAD1, NRG1 and COMT were significantly different among the four groups (all p < 0.05). For drug-naïve patients, the ΔΔCT values of SRR, PSAT1, GCAT, GAD1, and NRG1 mRNA expressions were significantly different from those in healthy individuals (all p < 0.05). The ROC analysis of the ΔΔCT values of the target genes for differentiating drug-naïve patients from healthy controls showed an excellent sensitivity (0.960) and modest specificity (0.640) (AUC = 0.889). Drug-free and medicated patients obtained less favorable AUC values while compared to healthy controls. The results for the age- and sex-matched cohort were similar to those of the unmatched cohort.

CONCLUSIONS

This is the first study demonstrating that the peripheral mRNA expression levels of NMDAR-related genes may be altered in patients with MDD, especially drug-naïve individuals. The finding supports the NMDAR hypothesis of depression. Whether mRNA expresssion of NMDAR-related genes could serve as a potential biomarker of MDD deserves further investigations.

Differentially expressed genes related to major depressive disorder and antidepressant response: genome-wide gene expression analysis

Treatment response to antidepressants is limited and varies among patients with major depressive disorder (MDD). To discover genes and mechanisms related to the pathophysiology of MDD and antidepressant treatment response, we performed gene expression analyses using peripheral blood specimens from 38 MDD patients and 14 healthy individuals at baseline and at 6 weeks after the initiation of either selective serotonin reuptake inhibitor (SSRI) or mirtazapine treatment. The results were compared with results from public microarray data. Seven differentially expressed genes (DEGs) between MDD patients and controls were identified in our study and in the public microarray data: CD58, CXCL8, EGF, TARP, TNFSF4, ZNF583, and ZNF587. CXCL8 was among the top 10 downregulated genes in both studies. Eight genes related to SSRI responsiveness, including BTNL8, showed alterations in gene expression in MDD. The expression of the FCRL6 gene differed between SSRI responders and nonresponders and changed after SSRI treatment compared to baseline. In evaluating the response to mirtazapine, 21 DEGs were identified when comparing MDD patients and controls and responders and nonresponders. These findings suggest that the pathophysiology of MDD and treatment response to antidepressants are associated with a number of processes, including DNA damage and apoptosis, that can be induced by immune activation and inflammation.

Differences in the expression of several genes before and after different antidepressant treatments were found in patients with major depressive disorder (MDD), and may help identify patients most likely to benefit from specific drugs. Researchers in South Korea led by Doh Kwan Kim and Soo-Youn Lee at Samsung Medical Center, Seoul, examined gene expression across the 28,869 genes in 38 patients with MDD and 14 healthy individuals. They also validated their findings using existing databases of gene expression in patients with MDD and healthy controls. The research suggests that genes involved in the immune response and inflammation are significantly alternated in MDD and are predictable in which patients respond well to antidepressants. These findings may help develop new approaches to antidepressant therapies, and assist tailoring of treatment to the specific needs of different patients.

How to measure glucocorticoid receptor's sensitivity in patients with stress-related psychiatric disorders

Stress is a state of derailed homeostasis and a main environmental risk factor for psychiatric diseases. Chronic or uncontrollable stress may lead to a dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis, which is a common feature of stress-related psychiatric disorders. One of the key mechanisms underlying a disturbed HPA axis is an impaired function of the glucocorticoid receptor (GR) with an enhanced or reduced feedback sensitivity for glucocorticoids and subsequently altered concentrations of peripheral cortisol. GR function is regulated by a multiprotein complex including the different expression of the hsp90 co-chaperone FK 506 binding protein 51 (FKBP5) that may be genetically determined or acquired in response to stressful stimuli. Specific patterns of a dysregulation of the HPA axis and GR function are found in different stress-related psychiatric entities e.g. major depression, job-related exhaustion or posttraumatic stress disorder. GR challenge tests like the dexamethasone-suppression test (DST), the dexamethasone-corticotropin-releasing hormone (dex-CRH) test or most recently the analysis of the dexamethasone-induced gene expression are employed to sensitively measure HPA axis activity in these disorders. They provide information for a stratification of phenotypic similar but neurobiological diverse psychiatric disorders. In this review we present a synopsis of GR challenge tests with a focus on the application of the DST, the CRH test and the dex-CRH test as well as the dexamethasone-induced gene expression in stress-related psychiatric entities.

A Deep Learning Approach for Predicting Antidepressant Response in Major Depression Using Clinical and Genetic Biomarkers

In the wake of recent advances in scientific research, personalized medicine using deep learning techniques represents a new paradigm. In this work, our goal was to establish deep learning models which distinguish responders from non-responders, and also to predict possible antidepressant treatment outcomes in major depressive disorder (MDD). To uncover relationships between the responsiveness of antidepressant treatment and biomarkers, we developed a deep learning prediction approach resulting from the analysis of genetic and clinical factors such as single nucleotide polymorphisms (SNPs), age, sex, baseline Hamilton Rating Scale for Depression score, depressive episodes, marital status, and suicide attempt status of MDD patients. The cohort consisted of 455 patients who were treated with selective serotonin reuptake inhibitors (treatment-response rate = 61.0%; remission rate = 33.0%). By using the SNP dataset that was original to a genome-wide association study, we selected 10 SNPs (including ABCA13 rs4917029, BNIP3 rs9419139, CACNA1E rs704329, EXOC4 rs6978272, GRIN2B rs7954376, LHFPL3 rs4352778, NELL1 rs2139423, NUAK1 rs2956406, PREX1 rs4810894, and SLIT3 rs139863958) which were associated with antidepressant treatment response. Furthermore, we pinpointed 10 SNPs (including ARNTL rs11022778, CAMK1D rs2724812, GABRB3 rs12904459, GRM8 rs35864549, NAALADL2 rs9878985, NCALD rs483986, PLA2G4A rs12046378, PROK2 rs73103153, RBFOX1 rs17134927, and ZNF536 rs77554113) in relation to remission. Then, we employed multilayer feedforward neural networks (MFNNs) containing 1-3 hidden layers and compared MFNN models with logistic regression models. Our analysis results revealed that the MFNN model with 2 hidden layers (area under the receiver operating characteristic curve (AUC) = 0.8228 ± 0.0571; sensitivity = 0.7546 ± 0.0619; specificity = 0.6922 ± 0.0765) performed maximally among predictive models to infer the complex relationship between antidepressant treatment response and biomarkers. In addition, the MFNN model with 3 hidden layers (AUC = 0.8060 ± 0.0722; sensitivity = 0.7732 ± 0.0583; specificity = 0.6623 ± 0.0853) achieved best among predictive models to predict remission. Our study indicates that the deep MFNN framework may provide a suitable method to establish a tool for distinguishing treatment responders from non-responders prior to antidepressant therapy.

A Genome-Wide Association Study and Complex Network Identify Four Core Hub Genes in Bipolar Disorder

Bipolar disorder is a common and severe mental illness with unsolved pathophysiology. A genome-wide association study (GWAS) has been used to find a number of risk genes, but it is difficult for a GWAS to find genes indirectly associated with a disease. To find core hub genes, we introduce a network analysis after the GWAS was conducted. Six thousand four hundred fifty eight single nucleotide polymorphisms (SNPs) with p < 0.01 were sifted out from Wellcome Trust Case Control Consortium (WTCCC) dataset and mapped to 2045 genes, which are then compared with the protein–protein network. One hundred twelve genes with a degree >17 were chosen as hub genes from which five significant modules and four core hub genes (FBXL13, WDFY2, bFGF, and MTHFD1L) were found. These core hub genes have not been reported to be directly associated with BD but may function by interacting with genes directly related to BD. Our method engenders new thoughts on finding genes indirectly associated with, but important for, complex diseases.

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.

Differential gene expression in patients with subsyndromal symptomatic depression and major depressive disorder

Background

Subsyndromal symptomatic depression (SSD) is a subtype of subthreshold depressive and can lead to significant psychosocial functional impairment. Although the pathogenesis of major depressive disorder (MDD) and SSD still remains poorly understood, a set of studies have found that many same genetic factors play important roles in the etiology of these two disorders. Nowadays, the differential gene expression between MDD and SSD is still unknown. In our previous study, we compared the expression profile and made the classification with the leukocytes by using whole-genome cRNA microarrays among drug-free first-episode subjects with SSD, MDD and matched healthy controls (8 subjects in each group), and finally determined 48 gene expression signatures. Based on these findings, we further clarify whether these genes mRNA was different expressed in peripheral blood in patients with SSD, MDD and healthy controls (60 subjects respectively)

Method

With the help of the quantitative real-time reverse transcription-polymerase chain reaction (RT-qPCR), we gained gene relative expression levels among the three groups.

Results

We found that there are three of the forty eight co-regulated genes had differential expression in peripheral blood among the three groups, which are CD84, STRN, CTNS gene (F = 3.528, p = 0.034; F = 3.382, p = 0.039; F = 3.801, p = 0.026, respectively) while there were no significant differences for other genes.

Conclusion

CD84, STRN, CTNS gene may have significant value for performing diagnostic functions and classifying SSD, MDD and healthy controls.

A Novel Urinary Metabolite Signature for Non-invasive Post-stroke Depression Diagnosis

Post-stroke depression (PSD) is the most common psychiatric complication in stroke survivors that has been associated with increased physical disability, distress, poor rehabilitation, and suicidal ideation. However, there are still no biomarkers available to support objective laboratory testing for this disorder. Here, a GC-MS-based urinary metabolomics approach was used to characterize the urinary metabolic profiling of PSD (stroke) subjects and non-PSD (health controls) subjects in order to identify and validate urinary metabolite biomarkers for PSD. Six metabolites, azelaic acid, glyceric acid, pseudouridine, 5-hydroxyhexanoic acid, tyrosine, and phenylalanine, were defined as biomarkers. A combined panel of these six urinary metabolites could effectively discriminate between PSD subjects and non-PSD subjects, achieving an area under the receiver-operating characteristic curve (AUC) of 0.961 in a training set (n = 72 PSD subjects and n = 146 non-PSD subjects). Moreover, this urinary biomarker panel was capable of discriminating blinded test samples (n = 58 PSD patients and n = 109 non-PSD subjects) with an AUC of 0.954. These findings suggest that a urine-based laboratory test using these biomarkers may be useful in the diagnosis of PSD.

Genetic Contributions of Inflammation to Depression

This paper describes the effects of immune genes genetic variants and mRNA expression on depression's risk, severity, and response to antidepressant treatment, through a systematic review on all papers published between 2000 and 2016. Our results, based largely on case-control studies, suggest that common genetic variants and gene-expression pathways are involved in both immune activation and depression. The most replicated and relevant genetic variants include polymorphisms in the genes for interleukin (IL)-1β, IL-6, IL-10, monocyte chemoattractant protein-1, tumor necrosis factor-alpha, C-reactive protein, and phospholipase A2. Moreover, increased blood cytokines mRNA expression (especially of IL-1β) identifies patients that are less likely to respond to conventional antidepressants. However, even for the most replicated findings there are inconsistent results, not only between studies, but also between the immune effects of the genetic variants and the resulting effects on depression. We find evidence that these discrepant findings may be explained, at least in part, by the heterogeneity of the depression immunophenotype, by environmental influences and gene × environment interactions, and by the complex interfacing of genetic variants with gene expression. Indeed, some of the most robust findings have been obtained in patients developing depression in the context of treatment with interferon-alpha, a widely used model to mimic depression in the context of inflammation. Further 'omics' approaches, through GWAS and transcriptomics, will finally shed light on the interaction between immune genes, their expression, and the influence of the environment, in the pathogenesis of depression.

Gene expression-based biological test for major depressive disorder: an advanced study

PURPOSE

Recently, we could distinguished patients with major depressive disorder (MDD) from nonpsychiatric controls with high accuracy using a panel of five gene expression markers (ARHGAP24, HDAC5, PDGFC, PRNP, and SLC6A4) in leukocyte. In the present study, we examined whether this biological test is able to discriminate patients with MDD from those without MDD, including those with schizophrenia and bipolar disorder.

PATIENTS AND METHODS

We measured messenger ribonucleic acid expression levels of the aforementioned five genes in peripheral leukocytes in 17 patients with schizophrenia and 36 patients with bipolar disorder using quantitative real-time polymerase chain reaction (PCR), and we combined these expression data with our previous expression data of 25 patients with MDD and 25 controls. Subsequently, a linear discriminant function was developed for use in discriminating between patients with MDD and without MDD.

RESULTS

This expression panel was able to segregate patients with MDD from those without MDD with a sensitivity and specificity of 64% and 67.9%, respectively.

CONCLUSION

Further research to identify MDD-specific markers is needed to improve the performance of this biological test.

Distinctive gene expression profile in women with history of postpartum depression

Postpartum depression (PPD) is a disease which incorporates a variety of depressive states differing in nature and severity. To assist in the understanding of the pathogenesis of the disease, we aimed to ascertain a molecular mechanism underlying PPD development. We applied microarray technology to characterize gene expression of euthymic women with a history of PPD and compared the results with healthy controls. Our study demonstrated that women who considered euthymic on a clinical level, in fact, had an altered molecular profile when compared to participants with no PPD history. We identified nine genes significantly distinguished expression in post- depressive women; they may serve as a diagnostic tool for the detection of a predisposition to PPD. Our findings contribute significantly to the understanding of PPD etiology and its pathogenesis, offer a plausible explanation for the risk of the PPD recurrence, and may also contribute to clinical treatment.

Disease-specific classification using deconvoluted whole blood gene expression

Blood-based biomarker assays have an advantage in being minimally invasive. Diagnostic and prognostic models built on peripheral blood gene expression have been reported for various types of disease. However, most of these studies focused on only one disease type, and failed to address whether the identified gene expression signature is disease-specific or more widely applicable across diseases. We conducted a meta-analysis of 46 whole blood gene expression datasets covering a wide range of diseases and physiological conditions. Our analysis uncovered a striking overlap of signature genes shared by multiple diseases, driven by an underlying common pattern of cell component change, specifically an increase in myeloid cells and decrease in lymphocytes. These observations reveal the necessity of building disease-specific classifiers that can distinguish different disease types as well as normal controls, and highlight the importance of cell component change in deriving blood gene expression based models. We developed a new strategy to develop blood-based disease-specific models by leveraging both cell component changes and cell molecular state changes, and demonstrate its superiority using independent datasets.

Epigenetic Modifications of Major Depressive Disorder

Major depressive disorder (MDD) is a chronic disease whose neurological basis and pathophysiology remain poorly understood. Initially, it was proposed that genetic variations were responsible for the development of this disease. Nevertheless, several studies within the last decade have provided evidence suggesting that environmental factors play an important role in MDD pathophysiology. Alterations in epigenetics mechanism, such as DNA methylation, histone modification and microRNA expression could favor MDD advance in response to stressful experiences and environmental factors. The aim of this review is to describe genetic alterations, and particularly altered epigenetic mechanisms, that could be determinants for MDD progress, and how these alterations may arise as useful screening, diagnosis and treatment monitoring biomarkers of depressive disorders.

Seeking an objective diagnosis of depression

Major depressive disorder (MDD: unipolar depression) is widely distributed in the USA and world-wide populations and it is one of the leading causes of disability in both adolescents and adults. Traditional diagnostic approaches for MDD are based on patient interviews, which provide a subjective assessment of clinical symptoms which are frequently shared with other maladies. Reliance upon clinical assessments and patient interviews for diagnosing MDD is frequently associated with misdiagnosis and suboptimal treatment outcomes. As such, there is increasing interest in the identification of objective methods for the diagnosis of depression. Newer technologies from genomics, transcriptomics, proteomics, metabolomics and imaging are technically sophisticated and objective but their application to diagnostic tests in psychiatry is still emerging. This brief overview evaluates the technical basis for these technologies and discusses how the extension of their clinical performance can lead to an objective diagnosis of MDD.

Bisphenol A alters transcript levels of biomarker genes for Major Depressive Disorder in vascular endothelial cells and colon cancer cells

Bisphenol A (BPA) is capable of mimicking endogenous hormones with potential consequences for human health and BPA exposure has been associated with several human diseases including neuropsychiatric disorders. Here, quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) results show that BPA at low concentrations (10 ng/mL and 1 μg/mL) induces differential transcript levels of four biomarker genes for Major Depressive Disorder (MDD) in HT29 human colon adenocarcinona cell line and Human Umbilical Vein Endothelial Cells (HUVEC). These results substantiate increasing concerns of BPA exposure in levels currently detected in humans.

Alterations in leukocyte transcriptional control pathway activity associated with major depressive disorder and antidepressant treatment

Major depressive disorder (MDD) is associated with a significantly elevated risk of developing serious medical illnesses such as cardiovascular disease, immune impairments, infection, dementia and premature death. Previous work has demonstrated immune dysregulation in subjects with MDD. Using genome-wide transcriptional profiling and promoter-based bioinformatic strategies, we assessed leukocyte transcription factor (TF) activity in leukocytes from 20 unmedicated MDD subjects versus 20 age-, sex- and ethnicity-matched healthy controls, before initiation of antidepressant therapy, and in 17 of the MDD subjects after 8 weeks of sertraline treatment. In leukocytes from unmedicated MDD subjects, bioinformatic analysis of transcription control pathway activity indicated an increased transcriptional activity of cAMP response element-binding/activating TF (CREB/ATF) and increased activity of TFs associated with cellular responses to oxidative stress (nuclear factor erythroid-derived 2-like 2, NFE2l2 or NRF2). Eight weeks of antidepressant therapy was associated with significant reductions in Hamilton Depression Rating Scale scores and reduced activity of NRF2, but not in CREB/ATF activity. Several other transcriptional regulation pathways, including the glucocorticoid receptor (GR), nuclear factor kappa-B cells (NF-κB), early growth response proteins 1-4 (EGR1-4) and interferon-responsive TFs, showed either no significant differences as a function of disease or treatment, or activities that were opposite to those previously hypothesized to be involved in the etiology of MDD or effective treatment. Our results suggest that CREB/ATF and NRF2 signaling may contribute to MDD by activating immune cell transcriptome dynamics that ultimately influence central nervous system (CNS) motivational and affective processes via circulating mediators.

Longitudinal monitoring of the serotonin transporter gene expression to assess major depressive episode evolution

BACKGROUND

Mood disorders are frequently characterized by uncertain prognosis and studying mRNA expression variations in blood cells represents a promising avenue of identifying biomarkers for mood disorders. State-dependent gene expression variations have been described during a major depressive episode (MDE), in particular for SLC6A4 mRNA, but how this transcript varies in relation to MDE evolution remains unclear. In this study, we prospectively assessed time trends of SCL6A4 mRNA expression in responder and nonresponder patients.

METHODS

We examined SLC6A4 mRNA expression in blood samples from 13 patients treated for severe MDE and their matched controls by reverse transcription and quantitative PCR. All subjects were followed for 30 weeks. Patients were classified as either responders or nonresponders based on improvement of depression according to the 17-item Hamilton Depression Rating Scale. Using a longitudinal design, we ascertained mRNA expression at baseline, 2, 8, and 30 weeks and compared mRNA expression between responder and nonresponder patients, and matched controls.

RESULTS

We observed a decrease of SLC6A4 mRNA expression in responder patients across a 30-week follow-up, while nonresponder patients exhibited up-regulated SLC6A4 mRNA.

CONCLUSION

Peripheral SLC6A4 mRNA expression could serve as a biomarker for monitoring and follow-up during an MDE and may help to more appropriately select individualized treatments.