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

Systems biology dissection of PTSD and MDD across brain regions, cell types, and blood

The molecular pathology of stress-related disorders remains elusive. Our brain multiregion, multiomic study of posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) included the central nucleus of the amygdala, hippocampal dentate gyrus, and medial prefrontal cortex (mPFC). Genes and exons within the mPFC carried most disease signals replicated across two independent cohorts. Pathways pointed to immune function, neuronal and synaptic regulation, and stress hormones. Multiomic factor and gene network analyses provided the underlying genomic structure. Single nucleus RNA sequencing in dorsolateral PFC revealed dysregulated (stress-related) signals in neuronal and non-neuronal cell types. Analyses of brain-blood intersections in >50,000 UK Biobank participants were conducted along with fine-mapping of the results of PTSD and MDD genome-wide association studies to distinguish risk from disease processes. Our data suggest shared and distinct molecular pathology in both disorders and propose potential therapeutic targets and biomarkers.

A multi-omics study to monitor senescence-associated secretory phenotypes of Alzheimer's disease

OBJECTIVE

Alzheimer's disease (AD) is characterized by the progressive degeneration and damage of neurons in the brain. However, developing an accurate diagnostic assay using blood samples remains a challenge in clinic practice. The aim of this study was to explore senescence-associated secretory phenotypes (SASPs) in peripheral blood using mass spectrometry based multi-omics approach and to establish diagnostic assays for AD.

METHODS

This retrospective study included 88 participants, consisting of 29 AD patients and 59 cognitively normal (CN) individuals. Plasma and serum samples were examined using high-resolution mass spectrometry to identify proteomic and metabolomic profiles. Receiver operating characteristic (ROC) analysis was employed to screen biomarkers with diagnostic potential. K-nearest neighbors (KNN) algorithm was utilized to construct a multi-dimensional model for distinguishing AD from CN.

RESULTS

Proteomics analysis revealed upregulation of five plasma proteins in AD, including RNA helicase aquarius (AQR), zinc finger protein 587B (ZNF587B), C-reactive protein (CRP), fibronectin (FN1), and serum amyloid A-1 protein (SAA1), indicating their potential for AD classification. Interestingly, KNN-based three-dimensional model, comprising AQR, ZNF587B, and CRP, demonstrated its high accuracy in AD recognition, with evaluation possibilities of 0.941, 1.000, and 1.000 for the training, testing, and validation datasets, respectively. Besides, metabolomics analysis suggested elevated levels of serum phenylacetylglutamine (PAGIn) in AD.

INTERPRETATION

The multi-omics outcomes highlighted the significance of the SASPs, specifically AQR, ZNF587B, CRP, and PAGIn, in terms of their potential for diagnosing AD and suggested neuronal aging-associated pathophysiology.

B cells and the stressed brain: emerging evidence of neuroimmune interactions in the context of psychosocial stress and major depression

The immune system has emerged as a key regulator of central nervous system (CNS) function in health and in disease. Importantly, improved understanding of immune contributions to mood disorders has provided novel opportunities for the treatment of debilitating stress-related mental health conditions such as major depressive disorder (MDD). Yet, the impact to, and involvement of, B lymphocytes in the response to stress is not well-understood, leaving a fundamental gap in our knowledge underlying the immune theory of depression. Several emerging clinical and preclinical findings highlight pronounced consequences for B cells in stress and MDD and may indicate key roles for B cells in modulating mood. This review will describe the clinical and foundational observations implicating B cell-psychological stress interactions, discuss potential mechanisms by which B cells may impact brain function in the context of stress and mood disorders, describe research tools that support the investigation of their neurobiological impacts, and highlight remaining research questions. The goal here is for this discussion to illuminate both the scope and limitations of our current understanding regarding the role of B cells, stress, mood, and depression.

Associations of differentially expressed genes with psychoneurological symptoms in patients with head and neck cancer: A longitudinal study

OBJECTIVE

Patients with head and neck cancer (HNC) experience psychoneurological symptoms (PNS, i.e., depression, fatigue, sleep disturbance, pain, and cognitive dysfunction) during intensity-modulated radiotherapy (IMRT) that negatively impact their functional status, quality of life, and overall survival. The underlying mechanisms for PNS are still not fully understood. This study aimed to examine differentially expressed genes and pathways related to PNS for patients undergoing IMRT (i.e., before, end of, 6 months, and 12 months after IMRT).

METHODS

Participants included 142 patients with HNC (mean age 58.9 ± 10.3 years, 72.5% male, 83.1% White). Total RNA extracted from blood leukocytes were used for genome-wide gene expression assays. Linear mixed effects model was used to examine the association between PNS and gene expression across time. Gene Ontology (GO) enrichment analysis was employed to identify pathways related to PNS.

RESULTS

A total of 1352 genes (162 upregulated, 1190 downregulated) were significantly associated with PNS across time (false discovery rate (FDR) < 0.05). Among these genes, 112 GO terms were identified (FDR < 0.05). The top 20 GO terms among the significant upregulated genes were related to immune and inflammatory responses, while the top 20 GO terms among the significant downregulated genes were associated with telomere maintenance.

CONCLUSION

This study is the first to identify genes and pathways linked to immune and inflammatory responses and telomere maintenance that are associated with PNS in patients with HNC receiving IMRT. Inflammation and aging markers may be candidate biomarkers for PNS. Understanding biological markers may produce targets for novel interventions.

Epigenome-wide analysis identifies methylome profiles linked to obsessive-compulsive disorder, disease severity, and treatment response

Obsessive-compulsive disorder (OCD) is a prevalent mental disorder affecting ~2-3% of the population. This disorder involves genetic and, possibly, epigenetic risk factors. The dynamic nature of epigenetics also presents a promising avenue for identifying biomarkers associated with symptom severity, clinical progression, and treatment response in OCD. We, therefore, conducted a comprehensive case-control investigation using Illumina MethylationEPIC BeadChip, encompassing 185 OCD patients and 199 controls recruited from two distinct sites in Germany. Rigorous clinical assessments were performed by trained raters employing the Structured Clinical Interview for DSM-IV (SCID-I). We performed a robust two-step epigenome-wide association study that led to the identification of 305 differentially methylated CpG positions. Next, we validated these findings by pinpointing the optimal set of CpGs that could effectively classify individuals into their respective groups. This approach identified a subset comprising 12 CpGs that overlapped with the 305 CpGs identified in our EWAS. These 12 CpGs are close to or in genes associated with the sweet-compulsive brain hypothesis which proposes that aberrant dopaminergic transmission in the striatum may impair insulin signaling sensitivity among OCD patients. We replicated three of the 12 CpGs signals from a recent independent study conducted on the Han Chinese population, underscoring also the cross-cultural relevance of our findings. In conclusion, our study further supports the involvement of epigenetic mechanisms in the pathogenesis of OCD. By elucidating the underlying molecular alterations associated with OCD, our study contributes to advancing our understanding of this complex disorder and may ultimately improve clinical outcomes for affected individuals.

Longitudinal early epigenomic signatures inform molecular paths of therapy response and remission in depressed patients

Introduction

The etiology of major depressive disorder (MDD) involves the interaction between genes and environment, including treatment. Early molecular signatures for treatment response and remission are relevant in a context of personalized medicine and stratification and reduce the time-to-decision. Therefore, we focused the analyses on patients that responded or remitted following a cognitive intervention of 8 weeks.

Methods

We used data from a randomized controlled trial (RCT) with MDD patients (N = 112) receiving a cognitive intervention. At baseline and 8 weeks, blood for DNA methylation (Illumina Infinium MethylationEPIC 850k BeadChip) was collected, as well as MADRS. First, responders (N = 24; MADRS-reduction of at least 50%) were compared with non-responders (N = 60). Then, we performed longitudinal within-individual analyses, for response (N = 21) and for remission (N = 18; MADRS smaller or equal to 9 and higher than 9 at baseline), respectively, as well as patients with no change in MADRS over time. At 8 weeks the sample comprised 84 individuals; 73 patients had DNA methylation for both time-points. The RnBeads package (R) was used for data cleaning, quality control, and differential DNA-methylation (limma). The within-individual paired longitudinal analysis was performed using Welch's t-test. Subsequently gene-ontology (GO) pathway analyses were performed.

Results

No CpG was genome-wide significant CpG (p < 5 × 10-8). The most significant CpG in the differential methylation analysis comparing response versus non-response was in the IQSEC1 gene (cg01601845; p = 1.53 × 10-6), linked to neurotransmission. The most significant GO-terms were linked to telomeres. The longitudinal response analysis returned 67 GO pathways with a p < 0.05. Two of the three most significant pathways were linked to sodium transport. The analysis for remission returned 46 GO terms with a p-value smaller than 0.05 with pathways linked to phosphatase regulation and synaptic functioning. The analysis with stable patients returned mainly GO-terms linked to basic cellular processes.

Discussion

Our result suggest that DNA methylation can be suitable to capture early signs of treatment response and remission following a cognitive intervention in depression. Despite not being genome-wide significant, the CpG locations and GO-terms returned by our analysis comparing patients with and without cognitive impairment, are in line with prior knowledge on pathways and genes relevant for depression treatment and cognition. Our analysis provides new hypotheses for the understanding of how treatment for depression can act through DNA methylation and induce response and remission.

Molecular basis and mechanism of action of Albizia julibrissin in depression treatment and clinical application of its formulae

Albizzia julibrissin is empirically used as an antidepressant in clinical practice. Preclinical studies have indicated that its total extracts or bioactive constituents exerted antidepressant-like responses in animal models, providing the molecular basis to reveal its underlying mechanism of action. While attempts have been made to understand the antidepressant effect of A. julibrissin, many fundamental questions regarding its mechanism of action remain to be addressed at the molecular and systems levels. In this review, we conclusively discussed the mechanism of action of A. julibrissin and A. julibrissin formulae by reviewing recent preclinical and clinical studies conducted by using depressive animal models and depressive patients. Several representative bioactive constituents and formulae were highlighted as examples, and their mechanisms of action were discussed. In addition, some representative A. julibrissin formulae that have been shown to be compatible with conventional antidepressants in clinical practice were also reviewed. Furthermore, we discussed the future research directions to reveal the underlying mechanism of A. julibrissin at the molecular and systems levels in depression treatment. The integrated study using both the molecular and systematic approaches is required not only for improving our understanding of its molecular basis and mechanisms of action, but also for providing a way to discover novel agents or approaches for the effective and systematic treatment of depression.

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

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

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.

m6A Regulator-Mediated RNA Methylation Modification Patterns are Involved in the Pathogenesis and Immune Microenvironment of Depression

Depression is a genetical disease characterized by neuroinflammatory symptoms and is difficult to diagnose and treat effectively. Recently, modification of N6-methyladenosine (m6A) at the gene level was shown to be closely related to immune regulation. This study was conducted to explore the effect of m6A modifications on the occurrence of depression and composition of the immune microenvironment. We downloaded gene expression profile data of healthy and depressed rats from the Gene Expression Omnibus. We described the overall expression of m6A regulators in animal models of depression and constructed risk and clinical prediction models using training and validation sets. Bioinformatics analysis was performed using gene ontology functions, gene set enrichment analysis, gene set variation analysis, weighted gene co-expression network analysis, and protein-protein interaction networks. We used CIBERSORT to identify immune-infiltrating cells in depression and perform correlation analysis. We then constructed two molecular subtypes of depression and assessed the correlation between the key genes and molecular subtypes. Through differential gene analysis of m6A regulators in depressed rats, we identified seven m6A regulators that were significantly upregulated in depressed rats and successfully constructed a clinical prediction model. Gene Ontology functional annotation showed that the m6A regulators enriched differentially expressed genes in biological processes, such as the regulation of mRNA metabolic processes. Further, 12 hub genes were selected from the protein-protein interaction network. Immune cell infiltration analysis showed that levels of inflammatory cells, such as CD4 T cells, were significantly increased in depressed rats and were significantly correlated with the depression hub genes. Depression was divided into two subtypes, and the correlation between hub genes and these two subtypes was clarified. We described the effect of m6A modification on the pathogenesis of depression, focusing on the role of inflammatory infiltration.

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.

Transcriptional biomarkers of response to pharmacological treatments in severe mental disorders: A systematic review

Variation in the expression level and activity of genes involved in drug disposition and action in tissues of pharmacological importance have been increasingly investigated in patients treated with psychotropic drugs. Findings are promising, but reliable predictive biomarkers of response have yet to be identified. Here we conducted a PRISMA-compliant systematic search of PubMed, Scopus and PsycInfo up to 12 September 2020 for studies investigating RNA expression levels in cells or biofluids from patients with major depressive disorder, schizophrenia or bipolar disorder characterized for response to psychotropic drugs (antidepressants, antipsychotics or mood stabilizers) or adverse effects. Among 5497 retrieved studies, 123 (63 on antidepressants, 33 on antipsychotics and 27 on mood stabilizers) met inclusion criteria. Studies were either focused on mRNAs (n = 96), microRNAs (n = 19) or long non-coding RNAs (n = 1), with only a minority investigating both mRNAs and microRNAs levels (n = 7). The most replicated results include genes playing a role in inflammation (antidepressants), neurotransmission (antidepressants and antipsychotics) or mitochondrial function (mood stabilizers). Compared to those investigating response to antidepressants, studies focused on antipsychotics or mood stabilizers more often showed lower sample size and lacked replication. Strengths and limitations of available studies are presented and discussed in light of the specific designs, methodology and clinical characterization of included patients for transcriptomic compared to DNA-based studies. Finally, future directions of transcriptomics of psychopharmacological interventions in psychiatric disorders are discussed.

An Improved CRISPR/dCas9 Interference Tool for Neuronal Gene Suppression

The expression of genetic material governs brain development, differentiation, and function, and targeted manipulation of gene expression is required to understand contributions of gene function to health and disease states. Although recent improvements in CRISPR/dCas9 interference (CRISPRi) technology have enabled targeted transcriptional repression at selected genomic sites, integrating these techniques for use in non-dividing neuronal systems remains challenging. Previously, we optimized a dual lentivirus expression system to express CRISPR-based activation machinery in post-mitotic neurons. Here we used a similar strategy to adapt an improved dCas9-KRAB-MeCP2 repression system for robust transcriptional inhibition in neurons. We find that lentiviral delivery of a dCas9-KRAB-MeCP2 construct driven by the neuron-selective human synapsin promoter enabled transgene expression in primary rat neurons. Next, we demonstrate transcriptional repression using CRISPR sgRNAs targeting diverse gene promoters, and show superiority of this system in neurons compared to existing RNA interference methods for robust transcript specific manipulation at the complex Brain-derived neurotrophic factor (Bdnf) gene. Our findings advance this improved CRISPRi technology for use in neuronal systems for the first time, potentially enabling improved ability to manipulate gene expression states in the nervous system.

Elevated Epidermal Growth Factor (EGF) as Candidate Biomarker of Mood Disorders-Longitudinal Study in Adolescent and Young Adult Patients

Bipolar disorder (BD) is a chronic mental disorder that affects more than 1% of the population worldwide. Over 65% of patients experience early onset of the disease. Most cases of juvenile bipolar disorder begin with a depressed mood episode, and up to 50% of youth initially diagnosed with major depression go onto developing a BD. Our study aimed to find biomarkers of diagnosis conversion in young patients with mood disorders. We performed a two-year follow-up study on 79 adolescent patients diagnosed with MDD or BD, with a detailed clinical assessment at five visits. We monitored diagnosis change from MDD to BD. The control group consisted of 31 healthy youths. According to the neurodevelopmental and neuroimmunological hypotheses of mood disorders, we analyzed serum levels of brain-derived neurotrophic factor (BDNF), proBDNF, epidermal growth factor (EGF), migration inhibitory factor (MIF), stem cell factor (SCF), and correlations with clinical factors. We detected a significant disease-dependent increase in EGF level in MDD and BP patients at baseline exacerbation of depressive or hypomanic/manic episodes as well as in euthymic state compared to healthy controls. No potential biological predictors of disease conversion were found. Replication studies on a larger cohort of patients are needed.

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.

Bioinformatics analysis of a TF-miRNA-lncRNA regulatory network in major depressive disorder

Major depressive disorder (MDD) is a highly prevalent disease and one of the main causes of disability worldwide. Although many studies have partially revealed the occurrence and development process of MDD, the pathogeny and molecular mechanisms are not fully understood. Weighted gene coexpression network analysis (WGCNA) was used to explore the co-expression modules and hub genes in MDD. A protein-protein interaction (PPI) network of the most significant module and a TF-miRNA-lncRNA regulatory network of MDD were constructed using bioinformatics analysis tools. A KEGG pathway and gene ontology (GO) functional enrichment analysis of the genes in the significant module was performed using DAVID. Five hub genes in the PPI network and 10 genes in the TF-miRNA-lncRNA regulatory network with high degree values were identified, which may provide new insights for the investigation of key pathways, diagnostic bio-markers, and therapeutic targets of MDD. This study brings a novel perspective and provides valuable information to explore the molecular mechanism of 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.

Chinese Herbal Medicine for the Treatment of Depression: Effects on the Neuroendocrine-Immune Network

The neuroimmune and neuroendocrine systems are two critical biological systems in the pathogenesis of depression. Clinical and preclinical studies have demonstrated that the activation of the neuroinflammatory response of the immune system and hyperactivity of the hypothalamus–pituitary–adrenal (HPA) axis of the neuroendocrine system commonly coexist in patients with depression and that these two systems bidirectionally regulate one another through neural, immunological, and humoral intersystem interactions. The neuroendocrine-immune network poses difficulties associated with the development of antidepressant agents directed toward these biological systems for the effective treatment of depression. On the other hand, multidrug and multitarget Chinese Herbal Medicine (CHM) has great potential to assist in the development of novel medications for the systematic pharmacotherapy of depression. In this narrative essay, we conclusively analyze the mechanisms of action of CHM antidepressant constituents and formulas, specifically through the modulation of the neuroendocrine-immune network, by reviewing recent preclinical studies conducted using depressive animal models. Some CHM herbal constituents and formulas are highlighted as examples, and their mechanisms of action at both the molecular and systems levels are discussed. Furthermore, we discuss the crosstalk of these two biological systems and the systems pharmacology approach for understanding the system-wide mechanism of action of CHM on the neuroendocrine-immune network in depression treatment. The holistic, multidrug, and multitarget nature of CHM represents an excellent example of systems medicine in the effective treatment of depression.

An Exploratory Pilot Study with Plasma Protein Signatures Associated with Response of Patients with Depression to Antidepressant Treatment for 10 Weeks

Major depressive disorder (MDD) is a leading cause of global disability with a chronic and recurrent course. Recognition of biological markers that could predict and monitor response to drug treatment could personalize clinical decision-making, minimize unnecessary drug exposure, and achieve better outcomes. Four longitudinal plasma samples were collected from each of ten patients with MDD treated with antidepressants for 10 weeks. Plasma proteins were analyzed qualitatively and quantitatively with a nanoflow LC−MS/MS technique. Of 1153 proteins identified in the 40 longitudinal plasma samples, 37 proteins were significantly associated with response/time and clustered into six according to time and response by the linear mixed model. Among them, three early-drug response markers (PHOX2B, SH3BGRL3, and YWHAE) detectable within one week were verified by liquid chromatography-multiple reaction monitoring/mass spectrometry (LC-MRM/MS) in the well-controlled 24 patients. In addition, 11 proteins correlated significantly with two or more psychiatric measurement indices. This pilot study might be useful in finding protein marker candidates that can monitor response to antidepressant treatment during follow-up visits within 10 weeks after the baseline visit.

Genomics, transcriptomics, proteomics and big data analysis in the discovery of new diagnostic markers and targets for therapy development

Highly complex endophenotypes and underlying molecular mechanisms have prevented effective diagnosis and treatment of autism spectrum disorder. Despite extensive studies to identify relevant biosignatures, no biomarker and therapeutic targets are available in the current clinical practice. While our current knowledge is still largely incomplete, -omics technology and machine learning-based big data analysis have provided novel insights on the etiology of autism spectrum disorders, elucidating systemic impairments that can be translated into biomarker and therapy target candidates. However, more integrated and sophisticated approaches are vital to realize molecular stratification and individualized treatment strategy. Ultimately, systemic approaches based on -omics and big data analysis will significantly contribute to more effective biomarker and therapy development for autism spectrum disorder.

A selective D2 dopamine receptor agonist alleviates depression through up-regulation of tyrosine hydroxylase and increased neurogenesis in hippocampus of the prenatally stressed rats

Prenatal stress (PNS) has its negative impact on both the infant hippocampal neurogenesis and pregnancy outcomes in the neonates that serves as a risk factor for postnatal depression in adult offsprings. Therefore, main objectives of the present study were to evaluate the effect of maternal chronic unpredictable mild stress (CUMS) on behavioural changes, levels of oxidative stress, changes in selective developmental signaling genes and neurogenesis in the adult brain of Wistar rats and its reversal through a selective non-ergoline D2 type dopamine receptor (D2R) agonist Ropinirole (ROPI). Effects of ROPI treatment on CUMS induced adult rats offspring were measured by assessment of behavioural tests (sucrose preference test and forced swim test), biomarkers of oxidative stress, protein expression of tyrosine hydroxylase (TH), mRNA expression of SHH, GSK-3β, β-catenin, Notch, brain-derived neurotrophic factor (BDNF), Dopamine receptor 2 (Drd2) and bromodeoxyuridine (BrdU) cell proliferation assay. The oxidative stress, protein and mRNA expression were determined in the hippocampus and prefrontal cortex while the BrdU cell proliferation was observed in the hippocampus of rat brain. PNS induced changes resulted in depression validated by the depression-like behaviours, increased oxidative stress, decreased TH expression, altered expression of selective developmental genes, along with the reduced hippocampal neurogenesis and BDNF expression in the brain of adult offsprings. Chronic ROPI treatment reversed those effects and was equally effective like Imipramine (IMI) treatment. So, the present study suggested that ROPI can be used as an antidepressant drug for the treatment of depressive disorders.

Psychiatric Prescribing Patterns for Depression Treatment in an Outpatient Depression Clinic

OBJECTIVE

To elucidate psychiatric prescribing patterns for depression treatment in patients being seen by an outpatient depression clinic as of 2018.

EXPERIMENTAL DESIGN

Single-center, observational analysis.

PRINCIPLE OBSERVATION

Selective serotonin receptor inhibitors are most commonly used by patients, and the majority of trials have adequate duration (2 months or longer).

CONCLUSION

Healthcare providers observed in this study follow depression treatment guidelines and ensure medications are given an adequate trial.

Phosphodiesterase-5 inhibitors: Shedding new light on the darkness of depression?

BACKGROUND

Phosphodiesterase-5 inhibitors (PDE5Is) are used to treat erectile dysfunction (ED). Recently, the antidepressant-like effect of PDE5Is was demonstrated in animal models of depression. In clinical settings, PDE5Is were studied only for ED associated depression. Hence, there are no studies evaluating the effects of PDE5Is for the treatment of major depressive disorder (MDD) without ED. In this review article, we aimed to discuss the use of PDE5Is in the context of MDD, highlighting the roles of PDE genes in the development of MDD, the potential mechanisms by which PDE5Is can be beneficial for MDD and the potentials and limitations of PDE5Is repurposing to treat MDD.

METHODS

We used PubMed (MEDLINE) database to collect the studies cited in this review. Papers written in English language regardless the year of publication were selected.

RESULTS

A few preclinical studies support the antidepressant-like activity of PDE5Is. Clinical studies in men with ED and depression suggest that PDE5Is improve depressive symptoms. No clinical studies were conducted in subjects suffering from depression without ED. Antidepressant effect of PDE5Is may be explained by multiple mechanisms including inhibition of brain inflammation and modulation of neuroplasticity.

LIMITATIONS

The low number of preclinical and absence of clinical studies to support the antidepressant effect of PDE5Is.

CONCLUSIONS

No clinical trial was conducted to date evaluating PDE5Is in depressed patients without ED. PDE5Is' anti-inflammatory and neuroplasticity mechanisms may justify the potential antidepressant effect of these drugs. Despite this, clinical trials evaluating their efficacy in depressed patients need to be conducted.

Downregulation of peripheral PTGS2/COX-2 in response to valproate treatment in patients with epilepsy

Antiepileptic drug therapy has significant inter-patient variability in response towards it. The current study aims to understand this variability at the molecular level using microarray-based analysis of peripheral blood gene expression profiles of patients receiving valproate (VA) monotherapy. Only 10 unique genes were found to be differentially expressed in VA responders (n = 15) and 6 genes in the non-responders (n = 8) (fold-change >2, p < 0.05). PTGS2 which encodes cyclooxygenase-2, COX-2, showed downregulation in the responders compared to the non-responders. PTGS2/COX-2 mRNA profiles in the two groups corresponded to their plasma profiles of the COX-2 product, prostaglandin E₂ (PGE₂). Since COX-2 is believed to regulate P-glycoprotein (P-gp), a multidrug efflux transporter over-expressed at the blood-brain barrier (BBB) in drug-resistant epilepsy, the pathway connecting COX-2 and P-gp was further explored in vitro. Investigation of the effect of VA upon the brain endothelial cells (hCMEC/D3) in hyperexcitatory conditions confirmed suppression of COX-2-dependent P-gp upregulation by VA. Our findings suggest that COX-2 downregulation by VA may suppress seizure-mediated P-gp upregulation at the BBB leading to enhanced drug delivery to the brain in the responders. Our work provides insight into the association of peripheral PTGS2/COX-2 expression with VA efficacy and the role of COX-2 as a potential therapeutic target for developing efficacious antiepileptic treatment.

A Novel Animal Model for Studying Depression Featuring the Induction of the Unfolded Protein Response in Hippocampus

Depression is the leading cause of disability worldwide with global distribution of 322 million people suffering from the disease. While much is understood about depression, the underlying pathophysiology is yet to be fully characterized. Recently, the unfolded protein response (UPR) has been shown to be involved in regulating key aspects like inflammation, cell death, and behavioral depression. The UPR is an evolutionarily conserved ancient response system that reacts to the stressful environmental impact on a cell; the net effect of stress to a cell is that the quality of protein folding is diminished. The UPR responds by repairing and removing misfolded proteins and, if necessary, initiates apoptosis. Here, we demonstrate that the UPR is not only involved in depression, but that its activation causes a depressive phenotype. The hippocampi of rats were directly infused with 500 ng of tunicamycin (TM), an agent that initiates the UPR by blocking N-terminal glycosylation. Three to 8 days post-surgery, the rats showed depressive behavior in escape latency, forced swim despair, sucrose preference anhedonia, and also physiological signs of depression like decreased weight. Further, these behavioral changes were associated with enhanced expression of key UPR genes and proteins in the hippocampus. We propose that this model will make an excellent tool for studying depression and for understanding pathways that are affected by the UPR which directly causes depressive behavior.

Identification of diagnostic markers for major depressive disorder by cross-validation of data from whole blood samples

Background

Major depressive disorder (MDD) is a severe disease characterized by multiple pathological changes. However, there are no reliable diagnostic biomarkers for MDD. The aim of the current study was to investigate the gene network and biomarkers underlying the pathophysiology of MDD.

Methods

In this study, we conducted a comprehensive analysis of the mRNA expression profile of MDD using data from Gene Expression Omnibus (GEO). The MDD dataset (GSE98793) with 128 MDD and 64 control whole blood samples was divided randomly into two non-overlapping groups for cross-validated differential gene expression analysis. The gene ontology (GO) enrichment and gene set enrichment analysis (GSEA) were performed for annotation, visualization, and integrated discovery. Protein–protein interaction (PPI) network was constructed by STRING database and hub genes were identified by the CytoHubba plugin. The gene expression difference and the functional similarity of hub genes were investigated for further gene expression and function exploration. Moreover, the receiver operating characteristic curve was performed to verify the diagnostic value of the hub genes.

Results

We identified 761 differentially expressed genes closely related to MDD. The Venn diagram and GO analyses indicated that changes in MDD are mainly enriched in ribonucleoprotein complex biogenesis, antigen receptor-mediated signaling pathway, catalytic activity (acting on RNA), structural constituent of ribosome, mitochondrial matrix, and mitochondrial protein complex. The GSEA suggested that tumor necrosis factor signaling pathway, Toll-like receptor signaling pathway, apoptosis pathway, and NF-kappa B signaling pathway are all crucial in the development of MDD. A total of 20 hub genes were selected via the PPI network. Additionally, the identified hub genes were downregulated and show high functional similarity and diagnostic value in MDD.

Conclusions

Our findings may provide novel insight into the functional characteristics of MDD through integrative analysis of GEO data, and suggest potential biomarkers and therapeutic targets for MDD.

Mirtazapine attenuates anxiety- and depression-like behaviors in rats during cocaine withdrawal

BACKGROUND

Anxiety and depression, key symptoms of the cocaine withdrawal syndrome in human addicts, are considered the main factors that precipitate relapse in chronic cocaine addiction. Preclinical studies have found that rodents exposed to different withdrawal periods show an increase in anxiety and depressive-like behavior. Mirtazapine - a tetracyclic medication - is used primarily to treat depression and, sometimes, anxiety. It has also successfully improved withdrawal symptoms in drug-dependent patients.

AIM

This study sought to determine whether chronic dosing of mirtazapine during cocaine withdrawal reduced depression- and anxiety-like behaviors that characterize cocaine withdrawal in animals.

METHODS

Cocaine pre-treated Wistar rats were subjected to a 60-day cocaine withdrawal period during which depression- and anxiety-like behaviors were evaluated in open field tests (OFT), the elevated plus-maze (EPM), the light-dark box test (LDT), the forced swimming test (FST) and spontaneous locomotor activity (SLA).

RESULTS

We found that chronic dosing with different doses of mirtazapine (30 and 60 mg/kg) decreased depression- and anxiety-like behaviors induced by different doses of cocaine (10, 20 and 40 mg/kg) during the 60-day cocaine withdrawal.

INTERPRETATION

Our results suggest that the pharmacological effect of mirtazapine on its target sites of action (α₂-adrenergic and 5-HT_2A and 5-HT₃ receptors) within the brain may improve depression- and anxiety-like behaviors for long periods.

CONCLUSION

Therefore, the findings support the use of mirtazapine as a potentially effective therapy to reduce anxiety and depressive-like behavior during cocaine withdrawal.

Do Noncoding RNAs Mediate the Efficacy of Energy Psychology?

BACKGROUND

There are over 100 published studies of a therapy called Emotional Freedom Techniques (EFT). This popular form of energy psychology combines elements of established methods like cognitive therapy with acupressure. Our group reported the first evidence of its mechanisms of action at the molecular level, showing that it can influence levels of the stress hormone cortisol.

OBJECTIVES

Given recent advances in molecular genomics that have identified noncoding ribonucleic acid (RNA) molecules as important regulators of gene expression, the aim of this study is to explore the possibility that microRNAs play a role in mediating the effects of EFT.

METHODS

We measured microRNA levels in stored blood samples from our previous study in which veterans were randomized into an EFT group receiving EFT and treatment as usual throughout a 10-week intervention period, and a control group receiving only treatment as usual during the intervention period and then receiving EFT. A broad panel of 800 microRNAs was probed using a multiplexed, direct hybridization, and detection system.

RESULTS

All of the microRNA targets were expressed at low levels and most were below thresholds established by negative control probes. Baseline variability was determined using samples collected from the control group at the start and end of the intervention period, and used to filter out targets that were too noisy under control conditions to be able to distinguish a response to treatment. Analysis of the remaining viable targets found a general trend of reduced expression following EFT, compared to expression levels in samples from the control group during the intervention period. The most notable decreases in expression levels were found for 2 microRNAs: let-7b and let-7c, although no significance was found after adjusting for multiple comparisons.

CONCLUSIONS

These preliminary data support the feasibility of measuring microRNA expression level changes that correlate with effective EFT therapy.

REM sleep's unique associations with corticosterone regulation, apoptotic pathways, and behavior in chronic stress in mice

Sleep disturbances are common in stress-related disorders but the nature of these sleep disturbances and how they relate to changes in the stress hormone corticosterone and changes in gene expression remained unknown. Here we demonstrate that in response to chronic mild stress, rapid–eye-movement sleep (REMS), a sleep state involved in emotion regulation and fear conditioning, changed first and more so than any other measured sleep characteristic. Transcriptomic profiles related to REMS continuity and theta oscillations overlapped with those for corticosterone, as well as with predictors for anhedonia, and were enriched for apoptotic pathways. These data highlight the central role of REMS in response to stress and warrant further investigation into REMS’s involvement in stress-related mental health disorders.

One of sleep’s putative functions is mediation of adaptation to waking experiences. Chronic stress is a common waking experience; however, which specific aspect of sleep is most responsive, and how sleep changes relate to behavioral disturbances and molecular correlates remain unknown. We quantified sleep, physical, endocrine, and behavioral variables, as well as the brain and blood transcriptome in mice exposed to 9 weeks of unpredictable chronic mild stress (UCMS). Comparing 46 phenotypic variables revealed that rapid–eye-movement sleep (REMS), corticosterone regulation, and coat state were most responsive to UCMS. REMS theta oscillations were enhanced, whereas delta oscillations in non-REMS were unaffected. Transcripts affected by UCMS in the prefrontal cortex, hippocampus, hypothalamus, and blood were associated with inflammatory and immune responses. A machine-learning approach controlling for unspecific UCMS effects identified transcriptomic predictor sets for REMS parameters that were enriched in 193 pathways, including some involved in stem cells, immune response, and apoptosis and survival. Only three pathways were enriched in predictor sets for non-REMS. Transcriptomic predictor sets for variation in REMS continuity and theta activity shared many pathways with corticosterone regulation, in particular pathways implicated in apoptosis and survival, including mitochondrial apoptotic machinery. Predictor sets for REMS and anhedonia shared pathways involved in oxidative stress, cell proliferation, and apoptosis. These data identify REMS as a core and early element of the response to chronic stress, and identify apoptosis and survival pathways as a putative mechanism by which REMS may mediate the response to stressful waking experiences.