Blood-based gene expression profiles models for classification of subsyndromal symptomatic depression and major depressive disorder

Bioinformatics and systems biology approach to identify the pathogenetic link of neurological pain and major depressive disorder

Neurological pain (NP) is always accompanied by symptoms of depression, which seriously affects physical and mental health. In this study, we identified the common hub genes (Co-hub genes) and related immune cells of NP and major depressive disorder (MDD) to determine whether they have common pathological and molecular mechanisms. NP and MDD expression data was downloaded from the Gene Expression Omnibus (GEO) database. Common differentially expressed genes (Co-DEGs) for NP and MDD were extracted and the hub genes and hub nodes were mined. Co-DEGs, hub genes, and hub nodes were analyzed for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Finally, the hub nodes, and genes were analyzed to obtain Co-hub genes. We plotted Receiver operating characteristic (ROC) curves to evaluate the diagnostic impact of the Co-hub genes on MDD and NP. We also identified the immune-infiltrating cell component by ssGSEA and analyzed the relationship. For the GO and KEGG enrichment analyses, 93 Co-DEGs were associated with biological processes (BP), such as fibrinolysis, cell composition (CC), such as tertiary granules, and pathways, such as complement, and coagulation cascades. A differential gene expression analysis revealed significant differences between the Co-hub genes ANGPT2, MMP9, PLAU, and TIMP2. There was some accuracy in the diagnosis of NP based on the expression of ANGPT2 and MMP9. Analysis of differences in the immune cell components indicated an abundance of activated dendritic cells, effector memory CD8+ T cells, memory B cells, and regulatory T cells in both groups, which were statistically significant. In summary, we identified 6 Co-hub genes and 4 immune cell types related to NP and MDD. Further studies are needed to determine the role of these genes and immune cells as potential diagnostic markers or therapeutic targets in NP and 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.

Decoding and reconstructing disease relations between dry eye and depression: a multimodal investigation comprising meta-analysis, genetic pathways and Mendelian randomization

INTRODUCTION

The clinical presentations of dry eye disease (DED) and depression (DEP) often comanifest. However, the robustness and the mechanisms underlying this association were undetermined.

OBJECTIVES

To this end, we set up a three-segment study that employed multimodality results (meta-analysis, genome-wide association study [GWAS] and Mendelian randomization [MR]) to elucidate the association, common pathways and causality between DED and DEP.

METHODS

A meta-analysis comprising 26 case-control studies was first conducted to confirm the DED-DEP association. Next, we performed a linkage disequilibrium (LD)-adjusted GWAS and targeted phenotype association study (PheWAS) in East Asian TW Biobank (TWB) and European UK Biobank (UKB) populations. Single-nucleotide polymorphisms (SNPs) were further screened for molecular interactions and common pathways at the functional gene level. To further elucidate the activated pathways in DED and DEP, a systemic transcriptome review was conducted on RNA sequencing samples from the Gene Expression Omnibus. Finally, 48 MR experiments were implemented to examine the bidirectional causation between DED and DEP.

RESULTS

Our meta-analysis showed that DED patients are associated with an increased DEP prevalence (OR = 1.83), while DEP patients have a concurrent higher risk of DED (OR = 2.34). Notably, cross-disease GWAS analysis revealed that similar genetic architecture (rG = 0.19) and pleiotropic functional genes contributed to phenotypes in both diseases. Through protein-protein interaction and ontology convergence, we summarized the pleiotropic functional genes under the ontology of immune activation, which was further validated by a transcriptome systemic review. Importantly, the inverse variance-weighted (IVW)-MR experiments in both TWB and UKB populations (p value <0.001) supported the bidirectional exposure-outcome causation for DED-to-DEP and DEP-to-DED. Despite stringent LD-corrected instrumental variable re-selection, the bidirectional causation between DED and DEP remained.

CONCLUSION

With the multi-modal evidence combined, we consolidated the association and causation between DED and DEP.

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.

A primer on the use of machine learning to distil knowledge from data in biological psychiatry

Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers.

Genome-wide transcriptomic and biochemical profiling of major depressive disorder: Unravelling association with susceptibility, severity, and antidepressant response

Identifying biomarkers for diagnosing Major Depressive Disorder (MDD), assessing its severity, and guiding treatment is crucial. We conducted whole genome transcriptomic study in North Indian population, and analyzed biochemical parameters. Our longitudinal study investigated gene-expression profiles from 72 drug-free MDD patients and 50 healthy controls(HCs) at baseline and 24 patients after 12-weeks of treatment. Gene expression analyses identified differentially expressed genes(DEGs) associated with MDD susceptibility, symptom severity and treatment response, independently validated by qPCR. Hierarchical clustering revealed distinct expression patterns between MDD and HCs, also between mild and severe cases. Enrichment analyses of significant DEGs revealed inflammatory, apoptosis, and immune-related pathways in MDD susceptibility, severity, and treatment response. Simultaneously, we assessed thirty biochemical parameters in the same cohort, showed significant differences between MDD and HCs in 13 parameters with monocytes, eosinophils, creatinine, SGPT, and total protein remained independent predictors of MDD in a multivariate-regression model. Our study supports the role of altered immune/inflammatory signaling in MDD pathophysiology, offering clinically relevant biochemical parameters and insights into transcriptomic gene regulation in MDD pathogenesis and treatment response.

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.

Identification of mitophagy-related biomarkers and immune infiltration in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a life-threatening and debilitating mental health condition. Mitophagy, a form of selective autophagy that eliminates dysfunctional mitochondria, is associated with depression. However, studies on the relationship between mitophagy-related genes (MRGs) and MDD are scarce. This study aimed to identify potential mitophagy-related biomarkers for MDD and characterize the underlying molecular mechanisms.

METHODS

The gene expression profiles of 144 MDD samples and 72 normal controls were retrieved from the Gene Expression Omnibus database, and the MRGs were extracted from the GeneCards database. Consensus clustering was used to determine MDD clusters. Immune cell infiltration was evaluated using CIBERSORT. Functional enrichment analyses were performed to determine the biological significance of mitophagy-related differentially expressed genes (MR-DEGs). Weighted gene co-expression network analysis, along with a network of protein-protein interactions (PPI), was used to identify key modules and hub genes. Based on the least absolute shrinkage and selection operator analysis and univariate Cox regression analysis, a diagnostic model was constructed and evaluated using receiver operating characteristic curves and validated with training data and external validation data. We reclassified MDD into two molecular subtypes according to biomarkers and evaluated their expression levels.

RESULTS

In total, 315 MDD-related MR-DEGs were identified. Functional enrichment analyses revealed that MR-DEGs were mainly enriched in mitophagy-related biological processes and multiple neurodegenerative disease pathways. Two distinct clusters with diverse immune infiltration characteristics were identified in the 144 MDD samples. MATR3, ACTL6A, FUS, BIRC2, and RIPK1 have been identified as potential biomarkers of MDD. All biomarkers showed varying degrees of correlation with immune cells. In addition, two molecular subtypes with distinct mitophagy gene signatures were identified.

CONCLUSIONS

We identified a novel five-MRG gene signature that has excellent diagnostic performance and identified an association between MRGs and the immune microenvironment in MDD.

Inflammatory Markers of Geriatric Depression Response to Tai Chi or Health Education Adjunct Interventions

BACKGROUND

Underlying inflammation is associated with an increased risk of depression in older adults. In this study, we examined the role of inflammatory biomarkers in antidepressant response in depressed older adults undergoing adjunct Tai Chi Chih (TCC) or Health education interventions.

METHODS

Older adults aged 60 years and above with a diagnosis of major depression were randomized to 12 weeks of TCC versus Health and Wellness Education (HEW) as an adjunct therapy to their stable antidepressant treatment regimen. A panel of 19 cytokine/chemokines was measured at baseline and 12 weeks. Five factors were derived using factor analysis. General linear models were estimated to examine the change in factor scores and the association of these changes on depression remission rates, controlling for age, sex, and body mass index.

RESULTS

Of the 170 randomized participants (TCC: n = 85 and HEW: n = 85), 55 TCC and 58 HEW completed the 3-month assessment. The groups did not differ at baseline in any measure. At follow-up, neither the changes in cytokine/chemokines scores nor the depression remission rate differed significantly between TCC and HEW. However, remitters and non-remitters differed significantly in changes in a factor composed of growth-regulated oncogene protein-alpha (GRO-alpha), epidermal growth factor (EGF), and soluble CD40 ligand (sCD40L). GRO-alpha and EGF levels (in both groups) were significantly increased in remitters compared to non-remitters.

CONCLUSION

Changes in certain cytokines/chemokines may accompany improvement in depressive symptoms in older adults. Future studies will need to explore the role of these molecules in remission of late-life depression.

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.

An insight into diagnosis of depression using machine learning techniques: a systematic review

BACKGROUND

In this modern era, depression is one of the most prevalent mental disorders from which millions of individuals are affected today. The symptoms of depression are heterogeneous and often coincide with other disorders such as bipolar disorder, Parkinson's, schizophrenia, etc. It is a serious mental illness that may lead to other health problems if left untreated. Currently, identifying individuals with depression is totally based on the expertise of the clinician's experience. In order to assist clinicians in identifying the characteristics and classifying depressed people, different types of data modalities and machine learning techniques have been incorporated by researchers in this field. This study aims to find the answers to some important questions related to the trend of publications, data modality, machine learning models, dataset usage, pre-processing techniques and feature extraction and selection techniques that are prevalent and guide the direction of future research on depression diagnosis.

METHODS

This systematic review was conducted using a broad range of articles from two major databases: IEEE Xplore and PubMed. Studies ranging from the years 2011 to April 2021 were retrieved from the databases resulting in a total of 590 articles (53 articles from the IEEE Xplore database and 537 articles from the PubMed database). Out of those, the articles which satisfied the defined inclusion criteria were investigated for further analysis.

RESULTS

A total of 135 articles were identified and analysed for this review. High growth in the number of publications has been observed in recent years. Furthermore, significant diversity in the use of data modalities and machine learning classifiers has also been noted in this study. fMRI data with an SVM classifier was found to be the most popular choice among researchers. In most of the studies, data scarcity and small sample size, particularly for neuroimaging data are major concerns. The use of identical data pre-processing tools for similar data modalities can be seen. This study also provides statistical analysis of the current framework with respect to the modality, machine learning classifier, sample size and accuracy by applying one-way ANOVA and the Tukey - Kramer test.

CONCLUSION

The results indicate that an effective fusion of machine learning techniques with a potential data modality has a promising future for assisting clinicians in automatic depression diagnosis.

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.

Identification of Key Modules and Genes Associated with Major Depressive Disorder in Adolescents

Major depressive disorder (MDD) is a leading cause of disability worldwide. Adolescence is a crucial period for the occurrence and development of depression. There are essential distinctions between adolescent and adult depression patients, and the etiology of depressive disorder is unclear. The interactions of multiple genes in a co-expression network are likely to be involved in the physiopathology of MDD. In the present study, RNA-Seq data of mRNA were acquired from the peripheral blood of MDD in adolescents and healthy control (HC) subjects. Co-expression modules were constructed via weighted gene co-expression network analysis (WGCNA) to investigate the relationships between the underlying modules and MDD in adolescents. In the combined MDD and HC groups, the dynamic tree cutting method was utilized to assign genes to modules through hierarchical clustering. Moreover, functional enrichment analysis was conducted on those co-expression genes from interested modules. The results showed that eight modules were constructed by WGCNA. The blue module was significantly associated with MDD after multiple comparison adjustment. Several Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways associated with stress and inflammation were identified in this module, including histone methylation, apoptosis, NF-kappa β signaling pathway, and TNF signaling pathway. Five genes related to inflammation, immunity, and the nervous system were identified as hub genes: CNTNAP3, IL1RAP, MEGF9, UBE2W, and UBE2D1. All of these findings supported that MDD was associated with stress, inflammation, and immune responses, helping us to obtain a better understanding of the internal molecular mechanism and to explore biomarkers for the diagnosis or treatment of depression in adolescents.

Molecular Mechanisms and Therapeutic Potential of α- and β-Asarone in the Treatment of Neurological Disorders

Neurological disorders are important causes of morbidity and mortality around the world. The increasing prevalence of neurological disorders, associated with an aging population, has intensified the societal burden associated with these diseases, for which no effective treatment strategies currently exist. Therefore, the identification and development of novel therapeutic approaches, able to halt or reverse neuronal loss by targeting the underlying causal factors that lead to neurodegeneration and neuronal cell death, are urgently necessary. Plants and other natural products have been explored as sources of safe, naturally occurring secondary metabolites with potential neuroprotective properties. The secondary metabolites α- and β-asarone can be found in high levels in the rhizomes of the medicinal plant Acorus calamus (L.). α- and β-asarone exhibit multiple pharmacological properties including antioxidant, anti-inflammatory, antiapoptotic, anticancer, and neuroprotective effects. This paper aims to provide an overview of the current research on the therapeutic potential of α- and β-asarone in the treatment of neurological disorders, particularly neurodegenerative diseases such as Alzheimer’s disease (AD), Parkinson’s disease (PD), as well as cerebral ischemic disease, and epilepsy. Current research indicates that α- and β-asarone exert neuroprotective effects by mitigating oxidative stress, abnormal protein accumulation, neuroinflammation, neurotrophic factor deficit, and promoting neuronal cell survival, as well as activating various neuroprotective signalling pathways. Although the beneficial effects exerted by α- and β-asarone have been demonstrated through in vitro and in vivo animal studies, additional research is required to translate laboratory results into safe and effective therapies for patients with AD, PD, and other neurological and neurodegenerative diseases.

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.

Beneficial effects of Dendrobium nobile Lindl. Alkaloids (DNLA) on anxiety and depression induced by chronic unpredictable stress in rats

Dendrobium nobile Lindl. alkaloid (DNLA) is effective against animal models of Alzheimer's disease. This study further examined its effect on anxiety and depression produced by chronic unpredictable stress (CUS). Rats were subjected to CUS for 42 days, followed by DNLA treatment (20 mg/kg/day, po) for 28 days. The behavioral tests, histopathology, neurotransmitters and RNA-Seq were examined. DNLA attenuated body weight loss and CUS-induced anxiety/depressive-like behaviors, as evidenced by the elevated-plus-maze test, open-field test and sucrose preference. DNLA alleviated neuronal damage and loss and increased Nissl bodies in the hippocampus CA2 region and cortex. DNLA decreased CUS-elevated 5-hydroxytryptamine, dopamine and monoamine oxidase and catechol-O-methyltransferase activities in the brain. DNLA attenuated HPA activation by decreasing adrenocorticotropic hormones and the expression of corticotropin-releasing hormone receptor-1, and increased the expression of glucocorticoid receptor in the brain. RNA-Seq revealed distinct gene expression patterns among groups. Gene ontology revealed the cell projection assembly, postsynapse and centrosome as top biological processes, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment showed the cAMP, cGMP-PKG, glutamatergic synapse and circadian as major pathways for DNLA effects. Using DESeq2, CUS modulated 1700 differentially expressed genes (DEGs), which were prevented or attenuated by DNLA. CUS-induced DEGs were highly correlated with the Gene Expression Omnibus (GEO) database for anxiety and depression and were ameliorated by DNLA. Taken together, DNLA attenuated anxiety/depression-like behavior and neuronal damage induced by CUS in rats. The mechanisms could be related to regulation of the monoamine neurotransmitters and the HPA axis, and modulation of gene expression in the hippocampus.

Identification of key genes and crucial pathways for major depressive disorder using peripheral blood samples and chronic unpredictable mild stress rat models

BACKGROUND

Accurate diagnosis of major depressive disorder (MDD) remains difficult, and one of the key challenges in diagnosing MDD is the lack of reliable diagnostic biomarkers. The objective of this study was to explore gene networks and identify potential biomarkers for MDD.

METHODS

In the present study, we performed a comprehensive analysis of the mRNA expression profiles using blood samples of four patients with MDD and four controls by RNA sequencing. Differentially expressed genes (DEGs) were screened, and functional and pathway enrichment analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery. All DEGs were inputted to the STRING database to build a PPI network, and the top 10 hub genes were screened using the cytoHubba plugin of the Cytoscape software. The relative expression of 10 key genes was identified by quantitative real-time polymerase chain reaction (qRT-PCR) of blood samples from 50 MDD patients and 50 controls. Plasma levels of SQSTM1 and TNFα were measured using an enzyme-linked immunosorbent assay in blood samples of 44 MDD patients and 44 controls. A sucrose preference test was used to evaluate depression-like behavior in chronic unpredictable mild stress (CUMS) model rats. Immunofluorescence assay and western blotting were performed to study the expression of proteins in the brain samples of CUMS model rats.

RESULTS

We identified 247 DEGs that were closely associated with MDD. Gene ontology analyses suggested that the DEGs were mainly enriched in negative regulation of transcription by RNA polymerase II promoter, cytoplasm, and protein binding. Moreover, Kyoto Encyclopedia of Genes and Genomes pathway analysis suggested that the DEGs were significantly enriched in the MAPK signaling pathway. Ten hub genes were screened through the PPI network, and qRT-PCR assay revealed that one and six genes were downregulated and upregulated, respectively; however, SMARCA2, PPP3CB, and RAB5C were not detected. Pathway enrichment analysis for the 10 genes showed that the mTOR signaling pathway was also enriched. A strong positive correlation was observed between SQSTM1 and TNFα protein levels in patients with MDD. LC3 II and SQSTM1 protein levels were increased in the CUMS rat model; however, p-mTOR protein levels were decreased. The sucrose preference values decreased in the CUMS rat model.

CONCLUSIONS

We identified 247 DEGs and constructed an MDD-specific network; thereafter, 10 hub genes were selected for further analysis. Our results provide novel insights into the pathogenesis of MDD. Moreover, SQSTM1, which is related to autophagy and inflammatory reactions, may play a key role in MDD. SQSTM1 may be used as a promising therapeutic target in MDD; additionally, more molecular mechanisms have been suggested that should be focused on in future in vivo and in vitro studies.

Aiding and Abetting Anhedonia: Impact of Inflammation on the Brain and Pharmacological Implications

Exogenous administration of inflammatory stimuli to humans and laboratory animals and chronic endogenous inflammatory states lead to motivational deficits and ultimately anhedonia, a core and disabling symptom of depression present in multiple other psychiatric disorders. Inflammation impacts neurotransmitter systems and neurocircuits in subcortical brain regions including the ventral striatum, which serves as an integration point for reward processing and motivational decision-making. Many mechanisms contribute to these effects of inflammation, including decreased synthesis, release and reuptake of dopamine, increased synaptic and extrasynaptic glutamate, and activation of kynurenine pathway metabolites including quinolinic acid. Neuroimaging data indicate that these inflammation-induced neurotransmitter effects manifest as decreased activation of ventral striatum and decreased functional connectivity in reward circuitry involving ventral striatum and ventromedial prefrontal cortex. Neurocircuitry changes in turn mediate nuanced effects on motivation that include decreased willingness to expend effort for reward while maintaining the ability to experience reward. Taken together, the data reveal an inflammation-induced pathophysiologic phenotype that is agnostic to diagnosis. Given the many mechanisms involved, this phenotype represents an opportunity for development of novel and/or repurposed pharmacological strategies that target inflammation and associated cellular and systemic immunometabolic changes and their downstream effects on the brain. To date, clinical trials have failed to capitalize on the unique nature of this transdiagnostic phenotype, leaving the field bereft of interpretable data for meaningful clinical application. However, novel trial designs incorporating established targets in the brain and/or periphery using relevant outcome variables (e.g., anhedonia) are the future of targeted therapy in psychiatry. SIGNIFICANCE STATEMENT: Emerging understanding of mechanisms by which peripheral inflammation can affect the brain and behavior has created unprecedented opportunities for development of pharmacological strategies to treat deficits in motivation including anhedonia, a core and disabling symptom of depression well represented in multiple psychiatric disorders. Mechanisms include inflammation and cellular and systemic immunometabolism and alterations in dopamine, glutamate, and kynurenine metabolites, revealing a target-rich environment that nevertheless has yet to be fully exploited by current clinical trial designs and drugs employed.

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 signatures differentiating major depressive disorder from subsyndromal symptomatic depression

Subsyndromal symptomatic depression (SSD) and major depressive disorder (MDD) have been classified as distinct diseases, due to their dissimilar gene expression profiles and responses to venlafaxine. To identify specific biomarkers of these two diseases, we conducted a secondary analysis of the gene expression signatures of SSD patients, MDD patients and healthy controls (n=8/group) from the study of Yi et al. Global, individual, specific, enrichment and co-expression analyses were used to compare the transcriptomic profiles of peripheral blood lymphocytes from the three groups. The global and individual analyses revealed that different genes were up- and downregulated in the SSD and MDD groups. Through our specific analysis, we identified 1719 and 3278 differentially expressed genes specifically associated with MDD and SSD, respectively. Enrichment and co-expression analyses demonstrated that the genes specific to MDD were enriched in pathways associated with hormone levels and immune responses, while those specific to SSD were associated with immune function. The specific hub gene for the MDD co-expression network was transmembrane protein 132B (TMEM132B), while the hub genes for SSD were actin-related protein 2/3 complex (ARPC2) and solute carrier family 5 member 5 (SLC5A5). This bioinformatic analysis has provided potential biomarkers that can distinguish SSD from MDD.

Machine learning and bioinformatic analysis of brain and blood mRNA profiles in major depressive disorder: A case-control study

This study analyzed gene expression messenger RNA data, from cases with major depressive disorder (MDD) and controls, using supervised machine learning (ML). We built on the methodology of prior studies to obtain more generalizable/reproducible results. First, we obtained a classifier trained on gene expression data from the dorsolateral prefrontal cortex of post-mortem MDD cases (n = 126) and controls (n = 103). An average area-under-the-receiver-operating-characteristics-curve (AUC) from 10-fold cross-validation of 0.72 was noted, compared to an average AUC of 0.55 for a baseline classifier (p = .0048). The classifier achieved an AUC of 0.76 on a previously unused testing-set. We also performed external validation using DLPFC gene expression values from an independent cohort of matched MDD cases (n = 29) and controls (n = 29), obtained from Affymetrix microarray (vs. Illumina microarray for the original cohort) (AUC: 0.62). We highlighted gene sets differentially expressed in MDD that were enriched for genes identified by the ML algorithm. Next, we assessed the ML classification performance in blood-based microarray gene expression data from MDD cases (n = 1,581) and controls (n = 369). We observed a mean AUC of 0.64 on 10-fold cross-validation, which was significantly above baseline (p = .0020). Similar performance was observed on the testing-set (AUC: 0.61). Finally, we analyzed the classification performance in covariates subgroups. We identified an interesting interaction between smoking and recall performance in MDD case prediction (58% accurate predictions in cases who are smokers vs. 43% accurate predictions in cases who are non-smokers). Overall, our results suggest that ML in combination with gene expression data and covariates could further our understanding of the pathophysiology in 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.

Evaluation of the relationship between the gene expression level of orexin-1 receptor in the rat blood and prefrontal cortex, novelty-seeking, and proneness to methamphetamine dependence: A candidate biomarker

BACKGROUND

previous studies have suggested that methamphetamine (METH) abuse may affect orexin regulation. However, the data regarding the relationship between the current level of orexin and the vulnerability to METH abuse are minimal. Here, we have investigated the correlation between the gene expression level of the orexin-1 receptor (OX1R) in the rat prefrontal cortex (PFC) and blood lymphocytes and susceptibility to METH dependence and its impact on novelty-seeking behavior.

METHODS

male Wistar rats were first examined for novelty-seeking behavior by the novel object recognition test, and the expression level of OX1R in their blood lymphocytes was evaluated by real-time PCR. Then, the susceptibility to METH abuse was investigated by voluntary METH oral consumption test. According to the amounts of METH consumption, the animals were divided into two groups of METH preferring and non-preferring. Half of the rats in each group were sacrificed, and the level of OX1R in their blood lymphocytes and PFC tissue was measured. The other half were sacrificed for the same reason after two weeks of drug abstinence.

RESULTS

The indexes of novelty-seeking behavior were significantly higher in the METH- preferring group compared to the non-preferring animals. Furthermore, the expression level of OX1R in the blood lymphocytes and PFC in the preferring group was considerably higher than the non-preferring group.

CONCLUSION

Up-regulation of the mRNA expression level of OX1R in the lymphocytes and PFC may predict vulnerability to the METH consumption and novelty-seeking, which may serve as a potential biomarker for METH abuse.

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.

The Bidirectional Relationship of Depression and Inflammation: Double Trouble

Depression represents the number one cause of disability worldwide and is often fatal. Inflammatory processes have been implicated in the pathophysiology of depression. It is now well established that dysregulation of both the innate and adaptive immune systems occur in depressed patients and hinder favorable prognosis, including antidepressant responses. In this review, we describe how the immune system regulates mood and the potential causes of the dysregulated inflammatory responses in depressed patients. However, the proportion of never-treated major depressive disorder (MDD) patients who exhibit inflammation remains to be clarified, as the heterogeneity in inflammation findings may stem in part from examining MDD patients with varied interventions. Inflammation is likely a critical disease modifier, promoting susceptibility to depression. Controlling inflammation might provide an overall therapeutic benefit, regardless of whether it is secondary to early life trauma, a more acute stress response, microbiome alterations, a genetic diathesis, or a combination of these and other factors.

Weighted gene co-expression network analysis identifies specific modules and hub genes related to subsyndromal symptomatic depression

Objectives: The identification of the potential molecule targets for subsyndromal symptomatic depression (SSD) is critical for improving the effective clinical treatment on the mental illness. In the current study, we mined the genome-wide expression profiling and investigated the novel biological pathways associated with SSD.Methods: Expression of differentially expressed genes (DEGs) were analysed with microarrays of blood tissue cohort of eight SSD patients and eight healthy subjects. The gene co-expression is calculated by WGCNA, an R package software. The function of the genes was annotated by gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis.Results: We identified 11 modules from the 9,427 DEGs. Three co-expression modules (blue, cyan and red) showed striking correlation with the phenotypic trait between SSD and healthy controls. Gene ontology and KEGG pathway analysis demonstrated that the function of these three modules was enriched with the pathway of inflammatory response and type II diabetes mellitus. Finally, three hub genes, NT5DC1, SGSM2 and MYCBP, were identified from the blue module as significant genes.Conclusions: This first blood gene expression study in SSD observed distinct patterns between cases and controls which may provide novel insight into understanding the molecular mechanisms of SSD.

Reactive Oxygen Species Are Involved in the Development of Gastric Cancer and Gastric Cancer-Related Depression through ABL1-Mediated Inflammation Signaling Pathway

Background

The mechanisms of crosstalk between depression and gastric cancer (GC) remain ill defined. Given that reactive oxygen species (ROS) is involved in the pathophysiology of both GC and depression, we try to explore the activities of ROS in the development of GC and GC-related depression.

Methods

110 patients with newly diagnosed GC were recruited in our study. The clinical characteristics of these patients were recorded. Inflammation and oxidative stress markers were detected by ELISA. The depression status of patients with GC was assessed during follow-up. The association between ROS, ABL1, and inflammation factors was evaluated in H₂O₂-treated GC cell lines and The Cancer Genome Atlas (TCGA) database. The effect of ABL1 on inflammation was detected with Imatinib/Nilotinib-treated GC cell lines. A chronic mild stress- (CMS-) induced patient-derived xenograft (PDX) mice model was established to assess the crosstalk between depression and GC.

Results

Depression was correlated with poor prognosis of patients with GC. GC patients with depression were under a high level of oxidative status as well as dysregulated inflammation. In the CMS-induced GC PDX mice model, CMS could facilitate the development of GC. Additionally, tumor bearing could induce depressive-like behaviors of mice. With the treatment of ROS, the activities of ABL1 and inflammatory signaling were enhanced both in vitro and in vivo, and blocking the activities of ABL1 inhibited inflammatory signaling.

Conclusions

ROS-activated ABL1 mediates inflammation through regulating NF-κB1 and STAT3, which subsequently leads to the development of GC and GC-related depression.

Transcriptomic predictors of inflammation-induced depressed mood

Inflammation plays a significant role in the pathophysiology of depression. However, not all individuals exposed to inflammatory challenge develop depression, and identifying those at risk is necessary to develop targeted monitoring, prevention, and treatment strategies. Within a randomized double-blind placebo-controlled study (n = 115), we examined whether leukocyte transcriptome profiles predicted inflammation-induced depressed mood in volunteers who received low-dose intravenous endotoxin (n = 58; aged 18-50). At baseline, transcription factor (TF) activities were assessed using genome-wide transcriptional profiling of peripheral blood mononuclear cells and promoter-based bioinformatic analyses. Then, participants were administered endotoxin. Self-reported depressed mood was assessed using the Profile of Mood States. Based on extant studies linking transcriptional profiles to depressive disorder, we examined whether post-endotoxin depressed mood is predicted by baseline activity of TFs related to immune activation, sympathetic activation, and glucocorticoid insensitivity: respectively, nuclear factor kappa B (NF-kB), cAMP response element-binding protein (CREB), and glucocorticoid receptor (GR). Twenty-one participants (36%) experienced an increase in depressed mood from baseline to 2 h post endotoxin, when depressive response peaks. Bioinformatics analyses controlling for age, sex, ethnicity, body mass index, and physical sickness response revealed that post-endotoxin depressed mood was predicted by increased baseline activity of TFs related to inflammation (NF-kB) and beta-adrenergic signaling (CREB) and by decreased activity of GR-related TFs (P's < 0.001). Inflammation-induced depressed mood is predicted by peripheral transcriptome profiles related to immune activation, sympathetic activation, and glucocorticoid insensitivity. With further replication, these stress-related molecular profiles could be used for a novel genomic approach for identifying individuals at high-risk for the inflammatory subtype of depression.

Genetic biomarkers for differential diagnosis of major depressive disorder and bipolar disorder: A systematic and critical review

Depressive symptoms are present in the depressive mood state of bipolar disorder (BPD) and major depression disorder (MDD). Often, in clinical practice, BPD patients are misdiagnosed with MDD. Therefore, genetic biomarkers could contribute to the improvement of differential diagnosis between BPD and MDD. This systematic and critical review aimed to find in literature reliable genetic biomarkers that may show differences between BPD and MDD. This systematic review followed the PRISMA-P method. The terms used to search PubMed, Scopus, PsycINFO, and Web of Science were depress*, bipolar, diagnos*, genetic*, biomark*. After applying the selection criteria, N = 27 studies were selected, being n = 9 about biomarkers for BPD; n = 15, about MDD; and n = 3 for distinguishing MDD from BPD. A total of N = 3086 subjects were assessed in the selected studies (n = 486 in BPD group; n = 1212 in MDD group; and n = 1388, healthy control group). The articles were dated up to June 2017. Of the N = 27 studies, n = 16 assessed gene, n = 1 miRNA, n = 2 lcnRNA and n = 3 protein expressions, n = 4 methylation, and n = 4 polymorphisms. Some studies applied more than one of these genetic analyses. To find reliable genetic biomarkers we have taken into account the methodological care during the studies development and their validity. The genetic biomarkers selected are related to genes that play a fundamental role in synaptic plasticity, neurogenesis, mood control, brain ageing, immune-inflammatory processes and mitochondrial respiratory chain. BDNF gene expression was one of the genetic biomarkers that highlighted because of its capacity of distinguishing BPD and MDD groups, and being adequately reproduced by more than one selected study.

Association between increased serum interleukin-6 levels and sustained attention deficits in patients with major depressive disorder

BACKGROUND

The pathophysiology of cognitive impairment in patients with the major depressive disorder (MDD) may involve neuroinflammation mediated by cytokines.

OBJECTIVE

The aim of this study was to examine the serum interleukin-6 (IL-6) levels, sustained attention, and their association in patients with MDD.

METHODS

Thirty patients with MDD and 30 healthy controls were enrolled in this case-control study. Sustained attention was measured using the Rapid Visual Information Processing (RVP) task in the Cambridge Neuropsychological Tests Automated Battery. The serum IL-6 levels of all subjects were assessed by sandwich enzyme-linked immunosorbent assays.

RESULTS

There were significant differences in the log10RVP total hits, log10RVP total misses, and log10RVP mean latency between patients with MDD and healthy controls (F = 6.04, p = 0.017; F = 19.77, p < 0.0001; F = 14.42, p < 0.0001, respectively). The serum levels of Log10IL-6 were significantly higher in patients with MDD than in healthy controls (F = 192.27, p < 0.0001). The log10IL-6 levels were also positively correlated with the log10RVP mean latency in patients with MDD (r = 0.45, p = 0.013). A further stepwise multivariate regression analysis indicated that the log10IL-6 levels were significantly associated with the log10RVP mean latency in patients with MDD (β = 0.31, t = 2.41, p = 0.025).

CONCLUSIONS

Our data suggested that increased IL-6 levels were associated with the psychopathology of MDD, and that abnormal IL-6 levels were implicated in the impairment of sustained attention in patients with MDD.

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.

Reduced ENA78 levels as novel biomarker for major depressive disorder and venlafaxine efficiency: Result from a prospective longitudinal study

Although lines of evidence demonstrated that cytokines play an important role in the pathogenesis of major depressive disorder (MDD), none of the them have been established as reliable biomarkers. We use our previous whole-genome cRNA microarray data to identify epithelial cell-derived neutrophil-activating peptide 78 (ENA78), the most differentially expressed cytokine in peripheral blood between MDD patients and healthy controls; and then we confirmed the result by the quantitative reverse transcription-polymerase chain reaction (RT-qPCR) and enzyme-linked immunosorbent assay (ELISA) for mRNA and protein level, respectively, in an independent drug-naïve first-episode sample set. In addition, to replicate the role of plasma ENA 78 in MDD, and determine the role of ENA78 on the venlafaxine efficiency, we further detected the plasma ENA78 in another independent 8- week follow-up sample set. We found that both of mRNA and plasma of ENA78 decreased in MDD patients, and displayed much lower after venlafaxine treatment. We also found that venlafaxine non-responders had lower level of peripheral plasma ENA78 prior to treatment than responders. Our findings for the first time provided strong evidence that the ENA78 may play a key role of mediator in pathogenesis of MDD and in the mechanism of vinlafaxine effects on MDD indicating that reduced ENA78 may be a potential biomarker for diagnosing of MDD and predicting of response to venlafaxine.

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.

MicroRNAs 146a/b-5 and 425-3p and 24-3p are markers of antidepressant response and regulate MAPK/Wnt-system genes

Antidepressants (ADs) are the most common treatment for major depressive disorder (MDD). However, only ∼30% of patients experience adequate response after a single AD trial, and this variability remains poorly understood. Here, we investigated microRNAs (miRNAs) as biomarkers of AD response using small RNA-sequencing in paired samples from MDD patients enrolled in a large, randomized placebo-controlled trial of duloxetine collected before and 8 weeks after treatment. Our results revealed differential expression of miR-146a-5p, miR-146b-5p, miR-425-3p and miR-24-3p according to treatment response. These results were replicated in two independent clinical trials of MDD, a well-characterized animal model of depression, and post-mortem human brains. Furthermore, using a combination of bioinformatics, mRNA studies and functional in vitro experiments, we showed significant dysregulation of genes involved in MAPK/Wnt signalling pathways. Together, our results indicate that these miRNAs are consistent markers of treatment response and regulators of the MAPK/Wnt systems.

Antidepressant drugs are the most common treatment for depressive episodes but only a fraction of patients experience adequate response. Here the authors find dysregulation of miRNAs in peripheral blood samples from depressed patients after antidepressant treatment, and show that the miRNAs are regulators of psychiatrically relevant signalling pathways.

A novel relationship for schizophrenia, bipolar and major depressive disorder Part 5: a hint from chromosome 5 high density association screen

Familial clustering of schizophrenia (SCZ), bipolar disorder (BPD), and major depressive disorder (MDD) was systematically reported (Aukes, M. F. Genet Med 2012, 14, 338-341) and any two or even three of these disorders could co-exist in some families. In addition, evidence from symptomatology and psychopharmacology also imply that there are intrinsic connections between these three major disorders. A total of 56,569 single nucleotide polymorphism (SNPs) on chromosome 5 were genotyped by Affymetrix Genome-Wide Human SNP array 6.0 on 119 SCZ, 253 BPD (type-I), 177 MDD patients and 1000 controls. Associated SNPs and flanking genes was screen out systematically, and cadherin pathway genes (CDH6, CDH9, CDH10, CDH12, and CDH18) belong to outstanding genes. Unexpectedly, nearly all flanking genes of the associated SNPs distinctive for BPD and MDD were replicated in an enlarged cohort of 986 SCZ patients (P ≤ 9.9E-8). Considering multiple bits of evidence, our chromosome 5 analyses implicated that bipolar and major depressive disorder might be subtypes of schizophrenia rather than two independent disease entities. Also, cadherin pathway genes play important roles in the pathogenesis of the three major mental disorders.

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.

Correlative study of peripheral ATP1A1 gene expression level to anxiety severity score on major depressive disorder patients

BACKGROUND

Major depressive disorder (MDD) frequently co-occurs with other psychiatric problems. Our previous study showed that ATP1A1 gene expression level was significantly decreased in MDD patients. This research explores the potential correlations between the ATP1A1 expression level reduction and MDD patients' clinical manifestation.

METHODS

All participant patients were diagnosed by Diagnostic and Statistical Manual of Mental Disorders - 4th edition (DSM-IV). Hamilton rating scale for depression (HAM-D) and anxiety (HAM-A) were applied to group patients into different categories. ATP1A1 expression level was measured by reverse transcript real-time polymerase chain reaction.

RESULTS

ATP1A1 expression levels of all MDD subgroups showed significant reduction compared to the control group (p<0.01). Further, the trend of ATP1A1 expression level reduction is significantly related to MDD patients' HAM-A scores (p<0.01). However, there was no significance between ATP1A1 level and HAM-D scores (p>0.05).

CONCLUSIONS

ATP1A1 expression level reduction is related to MDD anxiety score, which may be an explanation for the clinical manifestations and the underlining physiological mechanisms.

Depression accelerates the development of gastric cancer through reactive oxygen species‑activated ABL1 (Review)

Depression is a common symptom among gastric cancer (GC) patients and serves as a potential indication of poor prognosis and advanced cancer clinical stage. However, the molecular mechanism of depression‑associated poor prognoses of GC patients remains unclear. Recent studies have revealed that GC patients with depression are under high levels of oxidative stress (OS) status that is accompanied by the dysfunction of numerous proto‑oncogenes, including the ABL proto‑oncogene 1 (ABL1), which is a non‑receptor tyrosine kinase. Recent evidence indicates that ABL1 was dysregulated in both major depressive disorder (MDD) and cancer patients with depression, and high levels of reactive oxygen species (ROS) can lead to the activation of ABL1 in response to OS and that activated ABL1 subsequently contributes to development of GC via interactions with the downstream targets and corresponding signaling pathways. In this review, we examine the evidence to illuminate the molecular mechanism of ABL1 in the progression of GC patients with depression and identify out new and effective methods for the initial and long‑term treatment of GC.

Identification of IL6 as a susceptibility gene for major depressive disorder

Our previous work implied that interleukin 6 (IL6) may be a biological marker for major depressive disorder (MDD). In this study, we performed a comprehensive genetic study to determine the association between the gene encoding IL6 (IL6) and MDD in Han Chinese. There were 50 drug-naïve MDD patients and 50 healthy controls undergoing an mRNA expression study. A sample of 772 patients with MDD and 759 healthy controls were used for genetic analysis. Next, we performed an eQTL analysis to identify whether risk SNP(s) is associated with IL6 expression in brain. Our results showed that patients with MDD have higher levels of IL6 than healthy controls (P = 0.008). The SNP rs1800797 has a significant association with MDD (P = 0.01) in a dominant model. The eQTL analysis showed a marginally significant association between the rs1800797 and IL6 expression in the frontal cortex (P = 0.087). Our preliminary findings are suggestive of an association between rs1800797 and the risk of MDD. Further investigations are required to evaluate this association in larger samples to increase statistical power, and to examine the correlation between rs1800797 and IL6 methylation patterns.

The Current Situation on Major Depressive Disorder in China: Research on Mechanisms and Clinical Practice

Depression is the most disabling disorder worldwide that accounts for the highest proportion of global burden attributable to mental disorders. Major depressive disorder (MDD) is characterized by deep sadness, reduced energy, vegetative nervous system dysregulation, cognitive dysfunction, and even a high suicidal tendency. Although other treatment choices are available, antidepressant medication is the front-line treatment option for MDD. Regarding clinical efficacy, only ~50% of patients respond to frontline antidepressants, and <33% obtain remission. Currently, objective indexes to guide clinical decisions are still lacking. Furthermore, knowledge about the neurobiological mechanisms underlying discrepant antidepressant outcomes is still also fragmentary. In the present review, we discuss the current research progress and clinical opinions on MDD in China.

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.

Complement factor H and susceptibility to major depressive disorder in Han Chinese

BACKGROUND

Accumulating evidence suggests that altered immunity contributes to the development of major depressive disorder (MDD).

AIMS

To examine whether complement factor H (CFH), a regulator of activation of the alternative pathway of the complement cascade, confers susceptibility to MDD.

METHOD

Expression analyses were tested in 53 unmedicated people with MDD and 55 healthy controls. A two-stage genetic association analysis was performed in 3323 Han Chinese with or without MDD. Potential associations between CFH single nucleotide polymorphisms and age at MDD onset were evaluated.

RESULTS

CFH levels were significantly lower in the MDD group at both protein and mRNA levels (P = 0.009 and P = 0.014 respectively). A regulatory variant in the CFH gene, rs1061170, showed statistically significant genotypic and allelic differences between the MDD and control groups (genotypic P = 0.0005, allelic P = 0.0001). Kaplan-Meier survival analysis showed that age at onset of MDD was significantly associated with the C allele of rs1061170 (log rank statistic χ(2) = 6.82, P = 0.009). The C-allele carriers had a younger age at onset of MDD (22.2 years, s.d. = 4.0) than those without the C allele (23.6 years, s.d. = 4.3).

CONCLUSIONS

CFH is likely to play an important role in the development of MDD. rs1061170 has an important effect on age at onset of MDD in Han Chinese and may therefore be related to early pathogenesis of MDD, although further study is needed.

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.

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

We investigated transcriptomic markers of late-onset major depressive disorder (LOD; onset age of first depressive episode ≥ 50 years) from the genes expressed in blood cells and identified state-dependent transcriptomic markers in these patients. We assessed the genes expressed in blood cells by microarray and found that the expression levels of 3,066 probes were state-dependently changed in the blood cells of patients with LOD. To select potential candidates from those probes, we assessed the genes expressed in the blood of an animal model of depression, ovariectomized female mice exposed to chronic ultra-mild stress, by microarray and cross-matched the differentially expressed genes between the patients and the model mice. We identified 14 differentially expressed genes that were similarly changed in both patients and the model mice. By assessing statistical significance using real-time quantitative PCR (RT-qPCR), the following 4 genes were selected as candidates: cell death-inducing DFFA-like effector c (CIDEC), ribonuclease 1 (RNASE1), solute carrier family 36 member-1 (SLC36A1), and serine/threonine/tyrosine interacting-like 1 (STYXL1). The discriminating ability of these 4 candidate genes was evaluated in an independent cohort that was validated. Among them, CIDEC showed the greatest discriminant validity (sensitivity 91.3% and specificity 87.5%). Thus, these 4 biomarkers should be helpful for properly diagnosing LOD.

Blood-based gene expression signatures of medication-free outpatients with major depressive disorder: integrative genome-wide and candidate gene analyses

Several microarray-based studies have investigated gene expression profiles in major depressive disorder (MDD), yet with highly variable findings. We examined blood-based genome-wide expression signatures of MDD, focusing on molecular pathways and networks underlying differentially expressed genes (DEGs) and behaviours of hypothesis-driven, evidence-based candidate genes for depression. Agilent human whole-genome arrays were used to measure gene expression in 14 medication-free outpatients with MDD who were at least moderately ill and 14 healthy controls matched pairwise for age and sex. After filtering, we compared expression of entire probes between patients and controls and identified DEGs. The DEGs were evaluated by pathway and network analyses. For the candidate gene analysis, we utilized 169 previously prioritized genes and examined their case-control separation efficiency and correlational co-expression network in patients relative to controls. The 317 screened DEGs mapped to a significantly over-represented pathway, the "synaptic transmission" pathway. The protein-protein interaction network was also significantly enriched, in which a number of key molecules for depression were included. The co-expression network of candidate genes was markedly disrupted in patients. This study provided evidence for an altered molecular network along with several key molecules in MDD and confirmed that the candidate genes are worthwhile targets for depression research.