Predisposition to treatment response in major depressive episode: A peripheral blood gene coexpression network analysis

MicroRNAs as Diagnostic Biomarkers and Predictors of Antidepressant Response in Major Depressive Disorder: A Systematic Review

Despite the hardships of major depressive disorder (MDD), biomarkers for the diagnosis and pharmacological management of this condition are lacking. MicroRNAs are epigenetic mechanisms that could provide promising MDD biomarkers. Our aim was to summarize the findings and provide validation for the selection and use of specific microRNAs as biomarkers in the diagnosis and treatment of MDD. A systematic review was conducted using the PubMed/Medline, Cochrane, PsycINFO, Embase, and LILACS databases from March 2022 to November 2023, with clusters of terms based on "microRNA" and "antidepressant". Studies involving human subjects, animal models, and cell cultures were included, whereas those that evaluated herbal medicines, non-pharmacological therapies, or epigenetic mechanisms other than miRNA were excluded. The review revealed differences in the expression of various microRNAs when considering the time of assessment (before or after antidepressant treatment) and the population studied. However, due to the heterogeneity of the microRNAs investigated, the limited size of the samples, and the wide variety of antidepressants used, few conclusions could be made. Despite the observed heterogeneity, the following microRNAs were determined to be important factors in MDD and the antidepressant response: mir-1202, mir-135, mir-124, and mir-16. The findings indicate the potential for the use of microRNAs as biomarkers for the diagnosis and treatment of MDD; however, more homogeneous studies are needed.

An integrated precision medicine approach in major depressive disorder: a study protocol to create a new algorithm for the prediction of treatment response

Major depressive disorder (MDD) is the most common psychiatric disease worldwide with a huge socio-economic impact. Pharmacotherapy represents the most common option among the first-line treatment choice; however, only about one third of patients respond to the first trial and about 30% are classified as treatment-resistant depression (TRD). TRD is associated with specific clinical features and genetic/gene expression signatures. To date, single sets of markers have shown limited power in response prediction. Here we describe the methodology of the PROMPT project that aims at the development of a precision medicine algorithm that would help early detection of non-responder patients, who might be more prone to later develop TRD. To address this, the project will be organized in 2 phases. Phase 1 will involve 300 patients with MDD already recruited, comprising 150 TRD and 150 responders, considered as extremes phenotypes of response. A deep clinical stratification will be performed for all patients; moreover, a genomic, transcriptomic and miRNomic profiling will be conducted. The data generated will be exploited to develop an innovative algorithm integrating clinical, omics and sex-related data, in order to predict treatment response and TRD development. In phase 2, a new naturalistic cohort of 300 MDD patients will be recruited to assess, under real-world conditions, the capability of the algorithm to correctly predict the treatment outcomes. Moreover, in this phase we will investigate shared decision making (SDM) in the context of pharmacogenetic testing and evaluate various needs and perspectives of different stakeholders toward the use of predictive tools for MDD treatment to foster active participation and patients' empowerment. This project represents a proof-of-concept study. The obtained results will provide information about the feasibility and usefulness of the proposed approach, with the perspective of designing future clinical trials in which algorithms could be tested as a predictive tool to drive decision making by clinicians, enabling a better prevention and management of MDD resistance.

The emergence, implementation, and future growth of pharmacogenomics in psychiatry: a narrative review

Psychotropic medication efficacy and tolerability are critical treatment issues faced by individuals with psychiatric disorders and their healthcare providers. For some people, it can take months to years of a trial-and-error process to identify a medication with the ideal efficacy and tolerability profile. Current strategies (e.g. clinical practice guidelines, treatment algorithms) for addressing this issue can be useful at the population level, but often fall short at the individual level. This is, in part, attributed to interindividual variation in genes that are involved in pharmacokinetic (i.e. absorption, distribution, metabolism, elimination) and pharmacodynamic (e.g. receptors, signaling pathways) processes that in large part, determine whether a medication will be efficacious or tolerable. A precision prescribing strategy know as pharmacogenomics (PGx) assesses these genomic variations, and uses it to inform selection and dosing of certain psychotropic medications. In this review, we describe the path that led to the emergence of PGx in psychiatry, the current evidence base and implementation status of PGx in the psychiatric clinic, and finally, the future growth potential of precision psychiatry via the convergence of the PGx-guided strategy with emerging technologies and approaches (i.e. pharmacoepigenomics, pharmacomicrobiomics, pharmacotranscriptomics, pharmacoproteomics, pharmacometabolomics) to personalize treatment of psychiatric disorders.

A Nile rat transcriptomic landscape across 22 organs by ultra-deep sequencing and comparative RNA-seq pipeline (CRSP)

RNA sequencing (RNA-seq) has been a widely used high-throughput method to characterize transcriptomic dynamics spatiotemporally. However, RNA-seq data analysis pipelines typically depend on either a sequenced genome and/or corresponding reference transcripts. This limitation is a challenge for species lacking sequenced genomes and corresponding reference transcripts. The Nile rat (Arvicanthis niloticus) has two key features - it is daytime active, and it is prone to diet-induced diabetes, which makes it more similar to humans than regular laboratory rodents. However, at the time of this study, neither a Nile rat genome nor a reference transcript set were available, making it technically challenging to perform large-scale RNA-seq based transcriptomic studies. This genome-independent work progressed concurrently with our generation of a Nile rat genome. A well-annotated genome requires several iterations of manually reviewing curated transcripts and takes years to achieve. Here, we developed a Comparative RNA-Seq Pipeline (CRSP), integrating a comparative species strategy independent of a specific sequenced genome or species-matched reference transcripts. We performed benchmarking to validate that our CRSP tool can accurately quantify gene expression levels. In this study, we generated the first ultra-deep (2.3 billion × 2 paired-end) Nile rat RNA-seq data from 59 biopsy samples representing 22 major organs, providing a unique resource and spatial gene expression reference for Nile rat researchers. Importantly, CRSP is not limited to the Nile rat species and can be applied to any species without prior genomic knowledge. To facilitate a general use of CRSP, we also characterized the number of RNA-seq reads required for accurate estimation via simulation studies. CRSP and documents are available at: https://github.com/pjiang1105/CRSP.

Co-Expression Network Modeling Identifies Specific Inflammation and Neurological Disease-Related Genes mRNA Modules in Mood Disorder

Objectives: Mood disorders are a kind of serious mental illness, although their molecular factors involved in the pathophysiology remain unknown. One approach to examine the molecular basis of mood disorders is co-expression network analysis (WGCNA), which is expected to further divide the set of differentially expressed genes into subgroups (i.e., modules) in a more (biologically) meaningful way, fascinating the downstream enrichment analysis. The aim of our study was to identify hub genes in modules in mood disorders by using WGCNA.

Methods: Microarray data for expression values of 4,311,721 mRNA in peripheral blood mononuclear cells drawn from 21 MDD, 8 BD, and 24 HC individuals were obtained from GEO (GSE39653); data for genes with expression in the bottom third for 80% or more of the samples were removed. Then, the top 70% most variable genes/probs were selected for WGCNA: 27,884 probes representing 21,840 genes; correlation between module genes and mood disorder (MDD+BD vs. HC) was evaluated.

Results: About 52% of 27,765 genes were found to form 50 co-expression modules with sizes 42–3070. Among the 50 modules, the eigengenes of two modules were significantly correlated with mood disorder (p < 0.05). The saddlebrown module was found in one of the meta-modules in the network of the 50 eigengenes along with mood disorder, 6 (IER5, NFKBIZ, CITED2, TNF, SERTAD1, ADM) out of 12 differentially expressed genes identified in Savitz et al. were found in the saddlebrown module.

Conclusions: We found a significant overlap for 6 hub genes (ADM, CITED2, IER5, NFKBIZ, SERTAD1, TNF) with similar co-expression and dysregulation patterns associated with mood disorder. Overall, our findings support other reports on molecular-level immune dysfunction in mood disorder and provide novel insights into the pathophysiology of mood disorder.

Multiple Pre-Treatment miRNAs Levels in Untreated Major Depressive Disorder Patients Predict Early Response to Antidepressants and Interact with Key Pathways

Major depressive disorder (MDD) is a life-impairing disorder, and early successful treatment is important for a favorable prognosis. However, early response to antidepressants differs widely among individuals, and is difficult to predict pre-treatment. As miRNAs have been reported to play important roles in depression, identification of miRNAs associated with antidepressant treatment responses and their interacting genes and pathways will be beneficial in understanding the predictors and molecular mechanisms of depression treatment. This randomized control trial examined miRNAs correlated with the early therapeutic effect of selective serotonin reuptake inhibitors (SSRIs; paroxetine or sertraline) and mirtazapine monotherapy. Before medication, we comprehensively analyzed the miRNA expression of 92 depressed participants and identified genes and pathways interacting with miRNAs. A total of 228 miRNAs were significantly correlated with depressive symptoms improvements after 2 weeks of SSRIs treatment, with miR-483.5p showing the most robust correlation. These miRNAs are involved in 21 pathways, including TGF-β, glutamatergic synapse, long-term depression, and the mitogen-activated protein kinase (MAPK) signaling pathways. Using these miRNAs enabled us to predict SSRI response at week 2 with a 57% difference. This study shows that pre-treatment levels of miRNAs could be used to predict early responses to antidepressant administration, a knowledge of genes, and an identification of genes and pathways associated with the antidepressant response.

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.

Predicting therapy outcome in a digital mental health intervention for depression and anxiety: A machine learning approach

Objective

Predicting the outcomes of individual participants for treatment interventions appears central to making mental healthcare more tailored and effective. However, little work has been done to investigate the performance of machine learning-based predictions within digital mental health interventions. Therefore, this study evaluates the performance of machine learning in predicting treatment response in a digital mental health intervention designed for treating depression and anxiety.

Methods

Several algorithms were trained based on the data of 970 participants to predict a significant reduction in depression and anxiety symptoms using clinical and sociodemographic variables. As a random forest classifier performed best over cross-validation, it was used to predict the outcomes of 279 new participants.

Results

The random forest achieved an accuracy of 0.71 for the test set (base rate: 0.67, area under curve (AUC): 0.60, p = 0.001, balanced accuracy: 0.60). Additionally, predicted non-responders showed less average reduction of their Patient Health Questionnaire-9 (PHQ-9) (-2.7, p = 0.004) and General Anxiety Disorder Screener-7 values (-3.7, p < 0.001) compared to responders. Besides pre-treatment Patient Health Questionnaire-9 and General Anxiety Disorder Screener-7 values, the self-reported motivation, type of referral into the programme (self vs. healthcare provider) as well as Work Productivity and Activity Impairment Questionnaire items contributed most to the predictions.

Conclusions

This study provides evidence that social-demographic and clinical variables can be used for machine learning to predict therapy outcomes within the context of a therapist-supported digital mental health intervention. Despite the overall moderate performance, this appears promising as these predictions can potentially improve the outcomes of non-responders by monitoring their progress or by offering alternative or additional treatment.

Effects of Antidepressant Treatment on Neurotrophic Factors (BDNF and IGF-1) in Patients with Major Depressive Disorder (MDD)

Major depressive disorder (MDD) remains the subject of ongoing research as a multifactorial disease and a serious public health problem. There is a growing body of literature focusing on the role of neurotrophic factors in pathophysiology of MDD. A neurotrophic hypothesis of depression proposes that abnormalities of neurotrophins serum levels lead to neuronal atrophy and decreased neurogenesis, resulting in mood disorders. Consequently, in accordance with recent findings, antidepressant treatment modifies the serum levels of neurotrophins and thus leads to a clinical improvement of MDD. The purpose of this review is to summarize the available data on the effects of various antidepressants on serum levels of neurotrophins such as brain-derived neurotrophic factor (BDNF) and insulin-like growth factor (IGF-1). In addition, the authors discuss their role as prognostic factors for treatment response in MDD. A literature search was performed using the PubMed database. Following the inclusion and exclusion criteria, nine original articles and three meta-analyses were selected. The vast majority of studies have confirmed the effect of antidepressants on BDNF levels. Research on IGF-1 is limited and insufficient to describe the correlation between different antidepressant drugs and factor serum levels; however, four studies indicated a decrease in IGF-1 after treatment. Preliminary data suggest BDNF as a promising predictor of treatment response in MDD patients. The role of IGF-1 needs further investigation.

Predicting treatment effects in unipolar depression: A meta-review

There is increasing interest in clinical prediction models in psychiatry, which focus on developing multivariate algorithms to guide personalized diagnostic or management decisions. The main target of these models is the prediction of treatment response to different antidepressant therapies. This is because the ability to predict response based on patients' personal data may allow clinicians to make improved treatment decisions, and to provide more efficacious or more tolerable medications to the right patient. We searched the literature for systematic reviews about treatment prediction in the context of existing treatment modalities for adult unipolar depression, until July 2019. Treatment effect is defined broadly to include efficacy, safety, tolerability and acceptability outcomes. We first focused on the identification of individual predictor variables that might predict treatment response, and second, we considered multivariate clinical prediction models. Our meta-review included a total of 10 systematic reviews; seven (from 2014 to 2018) focusing on individual predictor variables and three focusing on clinical prediction models. These identified a number of sociodemographic, phenomenological, clinical, neuroimaging, remote monitoring, genetic and serum marker variables as possible predictor variables for treatment response, alongside statistical and machine-learning approaches to clinical prediction model development. Effect sizes for individual predictor variables were generally small and clinical prediction models had generally not been validated in external populations. There is a need for rigorous model validation in large external data-sets to prove the clinical utility of models. We also discuss potential future avenues in the field of personalized psychiatry, particularly the combination of multiple sources of data and the emerging field of artificial intelligence and digital mental health to identify new individual predictor variables.

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.

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

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

miRNAs in depression vulnerability and resilience: novel targets for preventive strategies

The exposure to stressful experiences during the prenatal period and through the first years of life is known to affect the brain developmental trajectories, leading to an enhanced vulnerability for the development of several psychiatric disorders later in life. However, not all the subjects exposed to the same stressful experience develop a pathologic condition, as some of them, activating coping strategies, become more resilient. The disclosure of mechanisms associated with stress vulnerability or resilience may allow the identification of novel biological processes and potential molecules that, if properly targeted, may prevent susceptibility or potentiate resilience. Over the last years, miRNAs have been proposed as one of the epigenetic mechanisms mediating the long-lasting effects of stress. Accordingly, they are associated with the development of stress vulnerability or resilience-related strategies. Moreover, miRNAs have been proposed as possible biomarkers able to identify subjects at high risk to develop depression and to predict the response to pharmacological treatments. In this review, we aimed to provide an overview of findings from studies in rodents and humans focused on the involvement of miRNAs in the mechanisms of stress response with the final goal to identify distinct sets of miRNAs involved in stress vulnerability or resilience. In addition, we reviewed studies on alterations of miRNAs in the context of depression, showing data on the involvement of miRNAs in the pathogenesis of the disease and in the efficacy of pharmacological treatments, discussing the potential utility of miRNAs as peripheral biomarkers able to predict the treatment response.

Co-expression network modeling identifies key long non-coding RNA and mRNA modules in altering molecular phenotype to develop stress-induced depression in rats

Long non-coding RNAs (lncRNAs) have recently emerged as one of the critical epigenetic controllers, which participate in several biological functions by regulating gene transcription, mRNA splicing, protein interaction, etc. In a previous study, we reported that lncRNAs may play a role in developing depression pathophysiology. In the present study, we have examined how lncRNAs are co-expressed with gene transcripts and whether specific lncRNA/mRNA modules are associated with stress vulnerability or resiliency to develop depression. Differential regulation of lncRNAs and coding RNAs were determined in hippocampi of three group of rats comprising learned helplessness (LH, depression vulnerable), non-learned helplessness (NLH, depression resilient), and tested controls (TC) using a single-microarray-based platform. Weighted gene co-expression network analysis (WGCNA) was conducted to correlate the expression status of protein-coding transcripts with lncRNAs. The associated co-expression modules, hub genes, and biological functions were analyzed. We found signature co-expression networks as well as modules that underlie normal as well as aberrant response to stress. We also identified specific hub and driver genes associated with vulnerability and resilience to develop depression. Altogether, our study provides evidence that lncRNA associated complex trait-specific networks may play a crucial role in developing depression.

MicroRNAs in Major Depressive Disorder

Major depressive disorder (MDD) is a severe and chronic psychiatric disorder with a high prevalence in the population. Although our understanding of its pathophysiological mechanisms has significantly increased over the years, available treatments still present several limitations and are not effective to all MDD patients. Epigenetic mechanisms have recently been suggested to play key roles in MDD pathogenesis and treatment, including the effects of small noncoding RNAs known as microRNAs (miRNAs). miRNAs can modulate gene expression posttranscriptionally by interfering with the stability and translation of messenger RNA molecules and are also known to cross-talk with other epigenetic mechanisms. In this review, we will summarize and discuss recent findings of alterations in miRNAs in tissues of patients with MDD and evidence of treatment-induced effects in these molecules.

Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review

BACKGROUND

No previous study has comprehensively reviewed the application of machine learning algorithms in mood disorders populations. Herein, we qualitatively and quantitatively evaluate previous studies of machine learning-devised models that predict therapeutic outcomes in mood disorders populations.

METHODS

We searched Ovid MEDLINE/PubMed from inception to February 8, 2018 for relevant studies that included adults with bipolar or unipolar depression; assessed therapeutic outcomes with a pharmacological, neuromodulatory, or manual-based psychotherapeutic intervention for depression; applied a machine learning algorithm; and reported predictors of therapeutic response. A random-effects meta-analysis of proportions and meta-regression analyses were conducted.

RESULTS

We identified 639 records: 75 full-text publications were assessed for eligibility; 26 studies (n=17,499) and 20 studies (n=6325) were included in qualitative and quantitative review, respectively. Classification algorithms were able to predict therapeutic outcomes with an overall accuracy of 0.82 (95% confidence interval [CI] of [0.77, 0.87]). Pooled estimates of classification accuracy were significantly greater (p < 0.01) in models informed by multiple data types (e.g., composite of phenomenological patient features and neuroimaging or peripheral gene expression data; pooled proportion [95% CI] = 0.93[0.86, 0.97]) when compared to models with lower-dimension data types (pooledproportion=0.68[0.62,0.74]to0.85[0.81,0.88]).

LIMITATIONS

Most studies were retrospective; differences in machine learning algorithms and their implementation (e.g., cross-validation, hyperparameter tuning); cannot infer importance of individual variables fed into learning algorithm.

CONCLUSIONS

Machine learning algorithms provide a powerful conceptual and analytic framework capable of integrating multiple data types and sources. An integrative approach may more effectively model neurobiological components as functional modules of pathophysiology embedded within the complex, social dynamics that influence the phenomenology of mental disorders.

Global long non-coding RNA expression in the rostral anterior cingulate cortex of depressed suicides

Long non-coding RNAs (lncRNAs) are an emerging class of regulatory RNA that may be implicated in psychiatric disorders. Here we performed RNA-sequencing in the rostral anterior cingulate cortex of 26 depressed suicides and 24 matched controls. We first performed differential lncRNA expression analysis, and then conducted Weighted Gene Co-expression Network Analysis (WGCNA) to identify co-expression modules associating with depression and suicide. We identified 23 differentially expressed lncRNAs (FDR < 0.1) as well as their differentially expressed overlapping and antisense protein-coding genes. Several of these overlapping or antisense genes were associated with interferon signaling, which is a component of the innate immune response. Using WGCNA, we identified modules of highly co-expressed genes associated with depression and suicide and found protein-coding genes highly connected to differentially expressed lncRNAs within these modules. These protein-coding genes were located distal to their associated lncRNAs and were found to be part of several GO terms enriched in the significant modules, which include: cytoskeleton organization, plasma membrane, cell adhesion, nucleus, DNA-binding, and regulation of dendrite development and morphology. Altogether, we report that lncRNAs are differentially expressed in the brains of depressed individuals who died by suicide and may represent regulators of important molecular functions and biological processes.

Transcriptomic and epigenomic biomarkers of antidepressant response

BACKGROUND

Antidepressant treatment is associated with a high rate of poor response, and thus, biomarker development is warranted.

METHODS

We aimed to synthesize studies investigating gene expression, small RNAs, and epigenomic biomarkers of antidepressant response. We conducted a narrative review of the literature.

RESULTS

Firstly, we detailed the challenges involved, in terms of biological tissues, relevant study time frames, and mandatory statistical tools. Secondly we synthesized results obtained in gene expression studies, focusing mainly on genome-wide studies, particularly small non-coding RNA, including micro-RNA and other small RNA species. In addition, we reviewed the potential biomarkers of antidepressant response arising from studies investigating DNA methylation variation and histone modifications.

LIMITATIONS

We did not conduct a meta-analysis due to the heterogeneity of the study.

CONCLUSION

Although promising, the field of gene expression and epigenomic biomarkers of antidepressant response is still in its infancy, and needs further development to define useful biomarkers in clinical practice.

Study of the in vitro modulation exerted by the antidepressant drug escitalopram on the expression of candidate microRNAs and their target genes

Recent studies indicated a role of microRNAs (miRNAs, small non-coding RNAs which regulate the expression of target genes by acting on mRNAs) in several neural processes, in the pathogenetic mechanisms of neuropsychiatric diseases and in the action of psychotropic drugs. A modulation induced by the antidepressant drug escitalopram on the expression levels of 30 miRNAs was highlighted in the blood of patients suffering from major depressive disorder. With the aim to investigate the effects of escitalopram in an in vitro model, we performed an analysis of the effects produced by escitalopram on the profiles of the 6 miRNAs found to be more significantly modulated in the above-mentioned study (miR-130b, miR-26a and -26b, let-7f, miR-770-5p, miR-34c-5p) in human U87 glioblastoma cells. Cells were treated with the drug for 24, 48 and 72h. The obtained results confirmed a significant increase of let-7f, both after 48 (p=0.031) and 72h (p=0.022), and of miR-26a after 48h (p=0.032). On the same experimental model, a transcriptome analysis was conducted after 72h, highlighting a drug-induced modulation of 1184 protein-coding genes, 207 of which represent let-7f targets. Particularly interesting was the downregulation of BCOR, CCND1 and ATR, validated let-7f targets, which play a key role in the mechanisms of neurogenesis, neuroplasticity and protection from oxidative stress in the brain, indicating that escitalopram could exert downstream effects on gene expression through the regulation of specific miRNAs.

Sex-specific transcriptional signatures in human depression

Major depressive disorder (MDD) is a leading cause of disease burden worldwide. While the incidence, symptoms and treatment of MDD all point toward major sex differences, the molecular mechanisms underlying this sexual dimorphism remain largely unknown. Here, combining differential expression and gene coexpression network analyses, we provide a comprehensive characterization of male and female transcriptional profiles associated with MDD across six brain regions. We overlap our human profiles with those from a mouse model, chronic variable stress, and capitalize on converging pathways to define molecular and physiological mechanisms underlying the expression of stress susceptibility in males and females. Our results show a major rearrangement of transcriptional patterns in MDD, with limited overlap between males and females, an effect seen in both depressed humans and stressed mice. We identify key regulators of sex-specific gene networks underlying MDD and confirm their sex-specific impact as mediators of stress susceptibility. For example, downregulation of the female-specific hub gene Dusp6 in mouse prefrontal cortex mimicked stress susceptibility in females, but not males, by increasing ERK signaling and pyramidal neuron excitability. Such Dusp6 downregulation also recapitulated the transcriptional remodeling that occurs in prefrontal cortex of depressed females. Together our findings reveal marked sexual dimorphism at the transcriptional level in MDD and highlight the importance of studying sex-specific treatments for this disorder.

Differential Peripheral Proteomic Biosignature of Fluoxetine Response in a Mouse Model of Anxiety/Depression

The incorporation of peripheral biomarkers in the treatment of major depressive disorders (MDD) could improve the efficiency of treatments and increase remission rate. Peripheral blood mononuclear cells (PBMCs) represent an attractive biological substrate allowing the identification of a drug response signature. Using a proteomic approach with high-resolution mass spectrometry, the present study aimed to identify a biosignature of antidepressant response (fluoxetine, a Selective Serotonin Reuptake Inhibitor) in PBMCs in a mouse model of anxiety/depression. Following determination of an emotionality score, using complementary behavioral analysis of anxiety/depression across three different tests (Elevated Plus Maze, Novelty Suppressed Feeding, Splash Test), we showed that a 4-week corticosterone treatment (35 μg/ml, CORT model) in C57BL/6NTac male mice induced an anxiety/depressive-like behavior. Then, chronic fluoxetine treatment (18 mg/kg/day for 28 days in the drinking water) reduced corticosterone-induced increase in emotional behavior. However, among 46 fluoxetine-treated mice, only 30 of them presented a 50% decrease in emotionality score, defining fluoxetine responders (CORT/Flx-R). To determine a peripheral biological signature of fluoxetine response, proteomic analysis was performed from PBMCs isolated from the “most” affected corticosterone/vehicle (CORT/V), corticosterone/fluoxetine responders and non-responders (CORT/Flx-NR) animals. In comparison to CORT/V, a total of 263 proteins were differently expressed after fluoxetine exposure. Expression profile of these proteins showed a strong similarity between CORT/Flx-R and CORT/Flx-NR (R = 0.827, p < 1e^-7). Direct comparison of CORT/Flx-R and CORT/Flx-NR groups revealed 100 differently expressed proteins, representing a combination of markers associated either with the maintenance of animals in a refractory state, or associated with behavioral improvement. Finally, 19 proteins showed a differential direction of expression between CORT/Flx-R and CORT/Flx-NR that drove them away from the CORT-treated profile. Among them, eight upregulated proteins (RPN2, HSPA9, NPTN, AP2B1, UQCRC2, RACK-1, TOLLIP) and one downregulated protein, TLN2, were previously associated with MDD or antidepressant drug response in the literature. Future preclinical studies will be required to validate whether proteomic changes observed in PBMCs from CORT/Flx-R mice mirror biological changes in brain tissues.

Investigation of miR-1202, miR-135a, and miR-16 in Major Depressive Disorder and Antidepressant Response

BACKGROUND

Major depressive disorder is a debilitating illness, which is most commonly treated with antidepressant drugs. As the majority of patients do not respond on their first trial, there is great interest in identifying biological factors that indicate the most appropriate treatment for each patient. Studies suggest that microRNA represent excellent biomarkers to predict antidepressant response.

METHODS

We investigated the expression of miR-1202, miR-135a, and miR-16 in peripheral blood from 2 cohorts of depressed patients who received 8 weeks of antidepressant therapy. Expression was quantified at baseline and after treatment, and its relationship to treatment response and depressive symptoms was assessed.

RESULTS

In both cohorts, responders displayed lower baseline miR-1202 levels compared with nonresponders, which increased following treatment.

CONCLUSIONS

Ultimately, our results support the involvement of microRNA in antidepressant response and suggest that quantification of their levels in peripheral samples represents a valid approach to informing treatment decisions.

Recent advances in predicting responses to antidepressant treatment

Major depressive disorder is one of the leading causes of disability in the world since depression is highly frequent and causes a strong burden. In order to reduce the duration of depressive episodes, clinicians would need to choose the most effective therapy for each individual right away. A prerequisite for this would be to have biomarkers at hand that would predict which individual would benefit from which kind of therapy (for example, pharmacotherapy or psychotherapy) or even from which kind of antidepressant class. In the past, neuroimaging, electroencephalogram, genetic, proteomic, and inflammation markers have been under investigation for their utility to predict targeted therapies. The present overview demonstrates recent advances in all of these different methodological areas and concludes that these approaches are promising but also that the aim to have such a marker available has not yet been reached. For example, the integration of markers from different systems needs to be achieved. With ongoing advances in the accuracy of sensing techniques and improvement of modelling approaches, this challenge might be achievable.

Potential Use of MicroRNA for Monitoring Therapeutic Response to Antidepressants

Major depressive disorder (MDD) is a serious and common psychiatric disorder that affects millions of people worldwide. The most common treatment methods for MDD are antidepressant drugs, many of which act by regulating monoamines by inhibiting pre-synaptic reuptake and/or by modulating monoamine receptors. Despite advances in antidepressants and other treatment options, therapy is often based on subjective decisions made by the physician. Moreover, it requires time to determine treatment outcome and to define whether the prescribed treatment is effective. Biomarkers may help identify individuals with MDD who are more likely to respond to specific antidepressant treatment and may thus provide more objectivity in treatment decision making. MicroRNA as biomarkers of antidepressant response has engendered substantial enthusiasm. In this review, we give a detailed overview of biomarkers, particularly the major studies that have investigated microRNA in relationship to antidepressant treatment response.

microRNA profiling in atherosclerosis, diabetes, and migraine

microRNAs (miRNAs) are a broad group of endogenous small non-coding molecules that reduce the transcription of mRNA and play a key role in post-transcriptional gene processes. miRNAs are involved in onset and progression of several human disorders such as infectious and immune non-infectious diseases, cancers, metabolic and cardiovascular disorders. They regulate the expression of gene targets (e.g. oncogenes and tumor suppressor genes) and act as gene repressors with mRNA binding and cleavage. The increasing evidence that miRNAs play a key role in the pathogenesis of cardiovascular conditions could radically change the future management approach to these disorders. This review focuses on current knowledge about the influence of miRNAs on cardiovascular disease, with particular regard to common conditions such as atherosclerosis, diabetes and migraine. Key messages miRNAs are a group of endogenous small non-coding RNA segments measuring 19-25 nucleotides that are involved in physiologic processes and onset and progression of disorders such as infectious and immune non-infectious diseases, cancers, metabolic and cardiovascular disorders. miRNAs expression guarantees vascular integrity, by regulating apoptosis, VEGF pathway and VCAM 1 expression (-126), and is involved in atherosclerotic plaque formation process and progression. Hyperglycemia, overt diabetes, and their complications are associated with overexpression of several miRNAs. An altered expression of miRNAs has also been postulated in migraine patients, although only a few preliminary studies have so far been performed with this respect.