Gene expression biomarkers of response to citalopram treatment in major depressive disorder

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.

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.

ZBTB7A regulates MDD-specific chromatin signatures and astrocyte-mediated stress vulnerability in orbitofrontal cortex

Hyperexcitability in the orbitofrontal cortex (OFC) is a key clinical feature of anhedonic domains of Major Depressive Disorder (MDD). However, the cellular and molecular substrates underlying this dysfunction remain unknown. Here, cell-population-specific chromatin accessibility profiling in human OFC unexpectedly mapped genetic risk for MDD exclusively to non-neuronal cells, and transcriptomic analyses revealed significant glial dysregulation in this region. Characterization of MDD-specific cis-regulatory elements identified ZBTB7A - a transcriptional regulator of astrocyte reactivity - as an important mediator of MDD-specific chromatin accessibility and gene expression. Genetic manipulations in mouse OFC demonstrated that astrocytic Zbtb7a is both necessary and sufficient to promote behavioral deficits, cell-type-specific transcriptional and chromatin profiles, and OFC neuronal hyperexcitability induced by chronic stress - a major risk factor for MDD. These data thus highlight a critical role for OFC astrocytes in stress vulnerability and pinpoint ZBTB7A as a key dysregulated factor in MDD that mediates maladaptive astrocytic functions driving OFC hyperexcitability.

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.

Transcriptomic signatures of psychomotor slowing in peripheral blood of depressed patients: evidence for immunometabolic reprogramming

Inflammation impacts basal ganglia motor circuitry in association with psychomotor retardation, a key symptom of major depression (MD). We previously reported associations between circulating protein inflammatory biomarkers and psychomotor slowing as measured by neuropsychological tests probing psychomotor speed in patients with MD. To discover novel transcriptional signatures in peripheral blood immune cells related to psychomotor slowing, microarray data were analyzed in a primary cohort of 88 medically-stable, unmedicated, ambulatory MD patients. Results were confirmed and extended in a second cohort of 57 patients with treatment resistant depression (TRD) before and after anti-inflammatory challenge with the tumor necrosis factor antagonist infliximab versus placebo. Composite scores reflecting pure motor and cognitive-motor processing speed were linearly associated with 403 and 266 gene transcripts in each cohort, respectively (|R| > 0.30, p < 0.01), that were enriched for cytokine signaling and glycolysis-related pathways (p < 0.05). Unsupervised clustering in the primary cohort revealed two psychomotor slowing-associated gene co-expression modules that were enriched for interferon, interleukin-6, aerobic glycolysis, and oxidative phosphorylation pathways (p < 0.05, q < 0.1). Transcripts were predominantly derived from monocytes, plasmacytoid dendritic cells, and natural killer cells (p's < 0.05). In infliximab-treated TRD patients with high plasma C-reactive protein concentrations (>5 mg/L), two differential co-expression modules enriched for oxidative stress and mitochondrial degradation were associated with improvements in psychomotor reaction time (p < 0.05). These results indicate that inflammatory signaling and associated metabolic reprogramming in peripheral blood immune cells are associated with systemic inflammation in depression and may affect relevant brain circuits to promote psychomotor slowing.

Pharmacogenetics and Forensic Toxicology: A New Step towards a Multidisciplinary Approach

Pharmacogenetics analyzes the individual behavior of DNA genes after the administration of a drug. Pharmacogenetic research has been implemented in recent years thanks to the improvement in genome sequencing techniques and molecular genetics. In addition to medical purposes, pharmacogenetics can constitute an important tool for clarifying the interpretation of toxicological data in post-mortem examinations, sometimes crucial for determining the cause and modality of death. The purpose of this systematic literature review is not only to raise awareness among the forensic community concerning pharmacogenetics, but also to provide a workflow for forensic toxicologists to follow in cases of unknown causes of death related to drug use/abuse. The scientific community is called on to work hard in order to supply evidence in forensic practice, demonstrating that this investigation could become an essential tool both in civil and forensic contexts. The following keywords were used for the search engine: (pharmacogenetics) AND (forensic toxicology); (pharmacogenetics) AND (post-mortem); (pharmacogenetics) AND (forensic science); and (pharmacogenetics) AND (autopsy). A total of 125 articles were collected. Of these, 29 articles were included in this systematic review. A total of 75% of the included studies were original articles (n = 21) and 25% were case reports (n = 7). A total of 78% (n = 22) of the studies involved deceased people for whom a complete autopsy was performed, while 22% (n = 6) involved people in good health who were given a drug with a subsequent pharmacogenetic study. The most studied drugs were opioids (codeine, morphine, and methadone), followed by antidepressants (tricyclic antidepressants and venlafaxine). Furthermore, all studies highlighted the importance of a pharmacogenetics study in drug-related deaths, especially in cases of non-overdose of drugs of abuse. This study highlights the importance of forensic pharmacogenetics, a field of toxicology still not fully understood, which is of great help in cases of sudden death, deaths from overdose, deaths after the administration of a drug, and also in cases of complaint of medical malpractice.

Changes in RNA expression levels during antidepressant treatment: a systematic review

More than a third of patients treated with antidepressants experience treatment resistance. Furthermore, molecular pathways involved in antidepressant effect have yet to be fully understood. Therefore, we performed a systematic review of clinical studies that examined changes in RNA expression levels produced by antidepressant treatment. Literature search was performed through April 2021 for peer-reviewed studies measuring changes in mRNA or non-coding RNA levels before and after antidepressant treatment in human participants following PRISMA guidelines. Thirty-one studies were included in qualitative synthesis. We identified a large amount of heterogeneity between the studies for genes/RNAs measured, antidepressants used, and treatment duration. Of the six RNAs examined by more than one study, expression of the brain-derived neurotrophic factor (BDNF) gene and genes in the inflammation pathway, particularly IL-1β, were consistently reported to be altered by antidepressant treatment. Limitations of this review include heterogeneity of the studies, possibility of positive publication bias, and risk of false-negative findings secondary to small sample sizes. In conclusion, our systematic review provides an updated synthesis of RNA expression changes produced by antidepressant treatment in human participants, where genes in the BDNF and inflammatory pathways were identified as potential targets of antidepressant effect. Importantly, these findings also highlight the need for replication of the included studies in multiple strong, placebo-controlled studies for the identification of evidence-based markers that can be targeted to improve treatment outcomes.

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.

Interleukin-1 Beta in Peripheral Blood Mononuclear Cell Lysates as a Longitudinal Biomarker of Response to Antidepressants: A Pilot Study

Interleukin-1 beta (IL1β) is primarily produced by monocytes in the periphery and the brain. Yet, IL1β protein levels have to date been investigated in major depressive disorder (MDD) and antidepressant response using either plasma or serum assays although with contradictory results, while mononuclear cell assays are lacking despite their extensive use in other contexts. In this pilot study, we comparatively assessed IL1β in mononuclear lysates and plasma in depressed MDD patients over treatment and healthy controls (HC). We recruited 31 consecutive adult MDD inpatients and 25 HC matched on age, sex, and BMI. Twenty-six patients completed an 8-week follow-up under treatment. IL1β was measured in both lysates and plasma in patients at baseline (T0) and at study end (T1) as well as in HC. We calculated ΔIL1β(%) for both lysates and plasma as IL1β percent changes from T0 to T1. Seventeen patients (65.4% of completers) were responders at T1 and had lower baseline BMI than non-responders (p = 0.029). Baseline IL1β from either plasma or lysates could not efficiently discriminate between depressed patients and HC, or between responders and non-responders. However, the two response groups displayed contrasting IL1β trajectories in lysates but not in plasma assays (response group by time interactions, p = 0.005 and 0.96, respectively). ΔIL1β(%) in lysates predicted response (p = 0.025, AUC = 0.81; accuracy = 84.6%) outperforming ΔIL1β(%) in plasma (p = 0.77, AUC=0.52) and was robust to adjusting for BMI. In conclusion, ΔIL1β(%) in mononuclear lysates may be a longitudinal biomarker of antidepressant response, potentially helpful in avoiding untimely switching of antidepressants, thereby warranting further investigation.

Construction and Analysis of a Diagnostic Model Based on Differential Expression Genes in Patients With Major Depressive Disorder

Background: Major depressive disorder (MDD) is a common and severe psychiatric disorder with a heavy burden on the individual and society. However, the prevalence varies significantly owing to the lack of auxiliary diagnostic biomarkers. To identify the shared differential expression genes (DEGs) with potential diagnostic value in both the hippocampus and whole blood, a systematic and integrated bioinformatics analysis was carried out. Methods: Two datasets from the Gene Expression Omnibus database (GSE53987 and GSE98793) were downloaded and analyzed separately. A weighted gene co-expression network analysis was performed to construct the co-expression gene network of DEGs from GSE53987, and the most disease-related module was extracted. The shared DEGs from the module and GSE98793 were identified using a Venn diagram. Functional pathway prediction was used to identify the most disease-related DEGs. Finally, several DEGs were chosen, and their potential diagnostic value was determined by receiver operating characteristic curve analysis. Results: After weighted gene co-expression network analysis, the most MDD-related module (MEgrey) was identified, and 623 DEGs were extracted from this module. The intersection between MEgrey and GSE98793 was calculated, and 163 common DEGs were identified. The co-expression network of 163 DEGs from these was then reconstructed. All hub genes were identified based on the connective degree of the reconstructed co-expression network. Based on the results of functional pathway enrichment, 17 candidate hub genes were identified. Finally, logistic regression and receiver operating characteristic curves showed that three candidate hub genes (CEP350, SMAD5, and HSPG2) had relatively high auxiliary value in the diagnosis of MDD. Conclusion: Our results showed that the combination of CEP350, SMAD5, and HSPG2 has a relatively high diagnostic value for MDD. Pathway enrichment analysis also showed that these genes may play an important role in the pathogenesis of MDD. These results suggest a potentially important role for this gene combination in clinical practice.

Response to fluoxetine in children and adolescents: a weighted gene co-expression network analysis of peripheral blood

The inconclusive and non-replicated results of pharmacogenetic studies of antidepressant response could be related to the lack of acknowledgement of its mechanism of action. In this scenario, gene expression studies provide and interesting framework to reveal new candidate genes for pharmacogenetic studies or peripheral biomarkers of fluoxetine response. We propose a system biology approach to analyse changes in gene expression induced by eight weeks of treatment with fluoxetine in peripheral blood. 21 naïve child and adolescents participated in the present study. Our analysis include the identification of gene co-expression modules, using Weighted Gene Co-expression Network Analysis (WGCNA), followed by protein-protein interaction (PPi) network construction coupled with functional annotation. Our results revealed two modules of co-expression genes related to fluoxetine treatment. The constructed networks from these modules were enriched for biological processes related to cellular and metabolic processes, cell communication, immune system processes, cell death, response to stimulus and neurogenesis. Some of these processes, such as immune system, replicated previous findings in the literature, whereas, neurogenesis, a mechanism proposed to be involved in fluoxetine response, had been identified for first time using peripheral tissues. In conclusion, our study identifies several biological processes in relation to fluoxetine treatment in peripheral blood, offer new candidate genes for pharmacogenetic studies and valuable markers for peripheral moderator biomarkers discovery.

GPR56/ADGRG1 is associated with response to antidepressant treatment

It remains unclear why many patients with depression do not respond to antidepressant treatment. In three cohorts of individuals with depression and treated with serotonin-norepinephrine reuptake inhibitor (N = 424) we show that responders, but not non-responders, display an increase of GPR56 mRNA in the blood. In a small group of subjects we also show that GPR56 is downregulated in the PFC of individuals with depression that died by suicide. In mice, we show that chronic stress-induced Gpr56 downregulation in the blood and prefrontal cortex (PFC), which is accompanied by depression-like behavior, and can be reversed by antidepressant treatment. Gpr56 knockdown in mouse PFC is associated with depressive-like behaviors, executive dysfunction and poor response to antidepressant treatment. GPR56 peptide agonists have antidepressant-like effects and upregulated AKT/GSK3/EIF4 pathways. Our findings uncover a potential role of GPR56 in antidepressant response.

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.

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.

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

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

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

Progress in Elucidating Biomarkers of Antidepressant Pharmacological Treatment Response: A Systematic Review and Meta-analysis of the Last 15 Years

BACKGROUND

Antidepressant drugs are widely prescribed, but response rates after 3 months are only around one-third, explaining the importance of the search of objectively measurable markers predicting positive treatment response. These markers are being developed in different fields, with different techniques, sample sizes, costs, and efficiency. It is therefore difficult to know which ones are the most promising.

OBJECTIVE

Our purpose was to compute comparable (i.e., standardized) effect sizes, at study level but also at marker level, in order to conclude on the efficacy of each technique used and all analyzed markers.

METHODS

We conducted a systematic search on the PubMed database to gather all articles published since 2000 using objectively measurable markers to predict antidepressant response from five domains, namely cognition, electrophysiology, imaging, genetics, and transcriptomics/proteomics/epigenetics. A manual screening of the abstracts and the reference lists of these articles completed the search process.

RESULTS

Executive functioning, theta activity in the rostral Anterior Cingular Cortex (rACC), and polysomnographic sleep measures could be considered as belonging to the best objectively measured markers, with a combined d around 1 and at least four positive studies. For inter-category comparisons, the approaches that showed the highest effect sizes are, in descending order, imaging (combined d between 0.703 and 1.353), electrophysiology (0.294-1.138), cognition (0.929-1.022), proteins/nucleotides (0.520-1.18), and genetics (0.021-0.515).

CONCLUSION

Markers of antidepressant treatment outcome are numerous, but with a discrepant level of accuracy. Many biomarkers and cognitions have sufficient predictive value (d ≥ 1) to be potentially useful for clinicians to predict outcome and personalize antidepressant treatment.

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.

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.

Biomarkers for depression: recent insights, current challenges and future prospects

A plethora of research has implicated hundreds of putative biomarkers for depression, but has not yet fully elucidated their roles in depressive illness or established what is abnormal in which patients and how biologic information can be used to enhance diagnosis, treatment and prognosis. This lack of progress is partially due to the nature and heterogeneity of depression, in conjunction with methodological heterogeneity within the research literature and the large array of biomarkers with potential, the expression of which often varies according to many factors. We review the available literature, which indicates that markers involved in inflammatory, neurotrophic and metabolic processes, as well as neurotransmitter and neuroendocrine system components, represent highly promising candidates. These may be measured through genetic and epigenetic, transcriptomic and proteomic, metabolomic and neuroimaging assessments. The use of novel approaches and systematic research programs is now required to determine whether, and which, biomarkers can be used to predict response to treatment, stratify patients to specific treatments and develop targets for new interventions. We conclude that there is much promise for reducing the burden of depression through further developing and expanding these research avenues.

Genetic Contributions of Inflammation to Depression

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

Transcriptome-wide mega-analyses reveal joint dysregulation of immunologic genes and transcription regulators in brain and blood in schizophrenia

The application of microarray technology in schizophrenia research was heralded as paradigm-shifting, as it allowed for high-throughput assessment of cell and tissue function. This technology was widely adopted, initially in studies of postmortem brain tissue, and later in studies of peripheral blood. The collective body of schizophrenia microarray literature contains apparent inconsistencies between studies, with failures to replicate top hits, in part due to small sample sizes, cohort-specific effects, differences in array types, and other confounders. In an attempt to summarize existing studies of schizophrenia cases and non-related comparison subjects, we performed two mega-analyses of a combined set of microarray data from postmortem prefrontal cortices (n=315) and from ex-vivo blood tissues (n=578). We adjusted regression models per gene to remove non-significant covariates, providing best-estimates of transcripts dysregulated in schizophrenia. We also examined dysregulation of functionally related gene sets and gene co-expression modules, and assessed enrichment of cell types and genetic risk factors. The identities of the most significantly dysregulated genes were largely distinct for each tissue, but the findings indicated common emergent biological functions (e.g. immunity) and regulatory factors (e.g., predicted targets of transcription factors and miRNA species across tissues). Our network-based analyses converged upon similar patterns of heightened innate immune gene expression in both brain and blood in schizophrenia. We also constructed generalizable machine-learning classifiers using the blood-based microarray data. Our study provides an informative atlas for future pathophysiologic and biomarker studies of schizophrenia.

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

Antidepressant efficacy is insufficient, unpredictable and poorly understood in major depressive episode (MDE). Gene expression studies allow for the identification of significantly dysregulated genes but can limit the exploration of biological pathways. In the present study, we proposed a gene coexpression analysis to investigate biological pathways associated with treatment response predisposition and their regulation by microRNAs (miRNAs) in peripheral blood samples of MDE and healthy control subjects. We used a discovery cohort that included 34 MDE patients that were given 12-week treatment with citalopram and 33 healthy controls. Two replication cohorts with similar design were also analyzed. Expression-based gene network was built to define clusters of highly correlated sets of genes, called modules. Association between each module's first principal component of the expression data and clinical improvement was tested in the three cohorts. We conducted gene ontology analysis and miRNA prediction based on the module gene list. Nine of the 59 modules from the gene coexpression network were associated with clinical improvement. The association was partially replicated in other cohorts. Gene ontology analysis demonstrated that 4 modules were associated with cytokine production, acute inflammatory response or IL-8 functions. Finally, we found 414 miRNAs that may regulate one or several modules associated with clinical improvement. By contrast, only 12 miRNAs were predicted to specifically regulate modules unrelated to clinical improvement. Our gene coexpression analysis underlines the importance of inflammation-related pathways and the involvement of a large miRNA program as biological processes predisposing associated with antidepressant response.

MicroRNA-326 acts as a molecular switch in the regulation of midbrain urocortin 1 expression

BACKGROUND

Altered levels of urocortin 1 (Ucn1) in the centrally projecting Edinger-Westphal nucleus (EWcp) of depressed suicide attempters or completers mediate the brain's response to stress, while the mechanism regulating Ucn1 expression is unknown. We tested the hypothesis that microRNAs (miRNAs), which are vital fine-tuners of gene expression during the brain's response to stress, have the capacity to modulate Ucn1 expression.

METHODS

Computational analysis revealed that the Ucn1 3' untranslated region contained a conserved binding site for miR-326. We examined miR-326 and Ucn1 levels in the EWcp of depressed suicide completers. In addition, we evaluated miR-326 and Ucn1 levels in the serum and the EWcp of a chronic variable mild stress (CVMS) rat model of behavioural despair and after recovery from CVMS, respectively. Gain and loss of miR-326 function experiments examined the regulation of Ucn1 by this miRNA in cultured midbrain neurons.

RESULTS

We found reduced miR-326 levels concomitant with elevated Ucn1 levels in the EWcp of depressed suicide completers as well as in the EWcp of CVMS rats. In CVMS rats fully recovered from stress, both serum and EWcp miR-326 levels rebounded to nonstressed levels. While downregulation of miR-326 levels in primary midbrain neurons enhanced Ucn1 expression levels, miR-326 overexpression selectively reduced the levels of this neuropeptide.

LIMITATIONS

This study lacked experiments showing that in vivo alteration of miR-326 levels alleviate depression-like behaviours. We show only correlative data for miR-325 and cocaine- and amphetamine-regulated transcript levels in the EWcp.

CONCLUSION

We identified miR-326 dysregulation in depressed suicide completers and characterized this miRNA as an upstream regulator of the Ucn1 neuropeptide expression in midbrain neurons.

Systematic review of blood transcriptome profiling in neuropsychiatric disorders: guidelines for biomarker discovery

INTRODUCTION

The utility of blood for genome-wide gene expression profiling and biomarker discovery has received much attention in patients diagnosed with major neuropsychiatric disorders. While numerous studies have been conducted, statistical rigor and clarity in terms of blood-based biomarker discovery, validation, and testing are needed.

METHODS

We conducted a systematic review of the literature to investigate methodological approaches and to assess the value of blood transcriptome profiling in research on mental disorders. We were particularly interested in statistical considerations related to machine learning, gene network analyses, and convergence across different disorders.

RESULTS

A total of 108 peripheral blood transcriptome studies across 15 disorders were surveyed: 25 studies used a variety of machine learning techniques to assess putative clinical viability of the candidate biomarkers; 11 leveraged a higher-order systems-level perspective to identify gene module-based biomarkers; and nine performed analyses across two or more neuropsychiatric phenotypes. Notably, ~50% of the surveyed studies included fewer than 50 samples (cases and controls), while ~75% included less than 100.

CONCLUSIONS

Detailed consideration of statistical analysis in the early stages of experimental planning is critical to ensure blood-based biomarker discovery and validation. Statistical guidelines are presented to enhance implementation and reproducibility of machine learning and gene network analyses across independent studies. Future studies capitalizing on larger sample sizes and emerging next-generation technologies set the stage for moving the field forwards. Copyright © 2016 John Wiley & Sons, Ltd.

Whole-genome expression analysis reveals genes associated with treatment response to escitalopram in major depression

The reasons for variability in treatment response in major depressive disorder (MDD) are not fully understood, but there is accumulating evidence suggesting that therapeutic outcomes of antidepressants can be influenced by genetic factors. In the present study we applied the microarray Illumina platform for whole genome expression profiling in depressive patients treated with escitalopram medication in order to identify genes underlying response to antidepressant treatment. The initial study sample consisted of 135 outpatients with major depressive disorder (mean age 31.1±11.6 years, 68% females) treated with escitalopram 10-20mg/day for 12 weeks, from which 87 patients (55 females) were included in gene expression analyzing. The gene expression profiles were measured on peripheral blood cells at baseline, at week 4 and at the end of treatment (week 12) using BeadChips Illumina. The fold change was used to demonstrate rate of changes in average gene expressions between studied groups. Statistical analyses were performed using the false discovery rate (FDR). The most interesting gene, which showed the predictive effect on treatment outcome by delineating low dose responders and treatment-resistant patients at the beginning of medication, was NLGN2, belonging to a family of neuronal cell surface proteins and involving in synapse formation. In addition, the several gene clusters, related to immune response, signal transduction and neurotrophin pathway, have distinguished responders from non-responders at the week 4 of treatment. After 4 weeks of escitalopram treatment (10mg/day), the YWHAZ gene has showed the highest transcriptional change in responders as compared with non-responders. Finally, at the end of the treatment we noticed that at least three genes (NR2C2, ZNF641, FKBP1A) have been strongly associated with resistance to escitalopram. Thus the results of this study support that exploration of peripheral gene expression is a useful tool in the further identification of novel genetic biomarkers for antidepressant treatment response.

Sex-specific hippocampal 5-hydroxymethylcytosine is disrupted in response to acute stress

Environmental stress is among the most important contributors to increased susceptibility to develop psychiatric disorders. While it is well known that acute environmental stress alters gene expression, the molecular mechanisms underlying these changes remain largely unknown. 5-hydroxymethylcytosine (5hmC) is a novel environmentally sensitive epigenetic modification that is highly enriched in neurons and is associated with active neuronal transcription. Recently, we reported a genome-wide disruption of hippocampal 5hmC in male mice following acute stress that was correlated to altered transcript levels of genes in known stress related pathways. Since sex-specific endocrine mechanisms respond to environmental stimulus by altering the neuronal epigenome, we examined the genome-wide profile of hippocampal 5hmC in female mice following exposure to acute stress and identified 363 differentially hydroxymethylated regions (DhMRs) linked to known (e.g., Nr3c1 and Ntrk2) and potentially novel genes associated with stress response and psychiatric disorders. Integration of hippocampal expression data from the same female mice found stress-related hydroxymethylation correlated to altered transcript levels. Finally, characterization of stress-induced sex-specific 5hmC profiles in the hippocampus revealed 778 sex-specific acute stress-induced DhMRs some of which were correlated to altered transcript levels that produce sex-specific isoforms in response to stress. Together, the alterations in 5hmC presented here provide a possible molecular mechanism for the adaptive sex-specific response to stress that may augment the design of novel therapeutic agents that will have optimal effectiveness in each sex.

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

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

Modulatory Effects of Antidepressant Classes on the Innate and Adaptive Immune System in Depression

Current reviews exploring for unique immune-modulatory profiles of antidepressant classes are limited by focusing mainly on cytokine modulation only and neglecting other aspects of the innate and adaptive immune system. These reviews also do not include recent comparative clinical trials, immune-genetic studies and therapeutics with unique neurotransmitter profiles (e. g., agomelatine). This systematic review extends the established literature by comprehensively reviewing the effects of antidepressants classes on both the innate and adaptive immune system. Antidepressants appear, in general, to reduce pro-inflammatory factor levels, particularly C-reactive protein (CRP), tumour necrosis factor (TNF)-α, interleukin (IL)-1β and IL-6. We caution against conclusions as to which antidepressant possesses the greater anti-inflammatory effect, given the methodological heterogeneity among studies and the small number of comparative studies. The effects of antidepressant classes on adaptive immune factors are complex and poorly understood, and few studies have been conducted. Methodological heterogeneity is high among these studies (e. g., length of study, cohort characteristics, dosage used and immune marker analysis). We recommend larger, comparative studies - in clinical and pre-clinical populations.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Genome-wide microarray analysis of gene expression profiling in major depression and antidepressant therapy

Major depressive disorder (MDD) is a serious health concern worldwide. Currently there are no predictive tests for the effectiveness of any particular antidepressant in an individual patient. Thus, doctors must prescribe antidepressants based on educated guesses. With the recent advent of scientific research, genome-wide gene expression microarray studies are widely utilized to analyze hundreds of thousands of biomarkers by high-throughput technologies. In addition to the candidate-gene approach, the genome-wide approach has recently been employed to investigate the determinants of MDD as well as antidepressant response to therapy. In this review, we mainly focused on gene expression studies with genome-wide approaches using RNA derived from peripheral blood cells. Furthermore, we reviewed their limitations and future directions with respect to the genome-wide gene expression profiling in MDD pathogenesis as well as in antidepressant therapy.

Transcriptomics and the mechanisms of antidepressant efficacy

The mechanisms by which antidepressants have their effects are not clear and the reasons for variability in treatment outcomes are also unknown. However, there is evidence from candidate gene research that indicates gene expression changes may be involved in antidepressant action. In this study, we examined antidepressant-induced alterations in gene expression on a transcriptome-wide scale, exploring associations with treatment response. Blood samples were taken from a subset of depressed patients from the GENDEP study (n=136) before and after eight weeks of treatment with either escitalopram or nortriptyline. Transcriptomic data were obtained from these samples using Illumina HumanHT-12 v4 Expression BeadChip microarrays. When analysing individual genes, we observed that changes in the expression of two genes (MMP28 and KXD1) were associated with better response to nortriptyline. Considering connectivity between genes, we identified modules of genes that were highly coexpressed. In the whole sample, changes in one of the ten identified coexpression modules showed significant correlation with treatment response (cor=0.27, p=0.0029). Using transcriptomic approaches, we have identified gene expression correlates of the therapeutic effects of antidepressants, highlighting possible molecular pathways involved in efficacious antidepressant treatment.

Comparison of plasma MicroRNA levels in drug naive, first episode depressed patients and healthy controls

Major depression is the most common psychiatric disorder. The diagnosis of depression depends on a patient's subjective complaints, and the nature of the heterogeneous disorder. Thus, there is no known biomarker for depression to date. Previous research has indicated that microRNAs are dysregulated in bipolar disorder and schizophrenia. We aimed to investigate microRNA dysregulation in plasma samples of patients with major depression. Venous blood samples of 50 depressed patients and 41 healthy controls were collected and the quantification of microRNAs was established using qRT-PCR. We found miR-320a significantly downregulated and miR-451a significantly upregulated in depressed patients. We also found miR-17-5p and miR-223-3p upregulated, but not as significantly as miR-451a. Merging our results with previous published data shows that the blood miR-320 family may be a potential microRNA family dysregulated in major depression. Research should be performed on miR-320-related pathways and their relationship to depression. Additionally, miR-451a could serve as a candidate biomarker for depression based on the acting mechanism of ketamine. Studies targeting miR-451a levels before and after treatment could be helpful.

Converging evidence implicates the abnormal microRNA system in schizophrenia

BACKGROUND

Previous findings are inconsistent; yet, converging evidence suggests an association between schizophrenia (SZ) and the impairment of posttranscriptional regulation of brain development through microRNA (miRNA) systems.

METHODS

This study aims to (1) compare the overall frequency of 121 rare variants (RVs) in 59 genes associated with the miRNA system in genome-wide association studies (GWAS)-derived data including 768 SZ cases and 1348 healthy controls and validated in an independent GWAS data including 1802 SZ cases and 1447 controls; (2) profile genome-wide miRNA expression in blood collected from 15 early-onset SZ (EOS) cases and 15 healthy controls; and (3) construct a miRNA-messenger RNA (mRNA) regulatory network using our previous genome-wide mRNA expression data generated from a separate sample of 18 EOS cases and 12 healthy controls.

RESULTS

Our findings indicate that: (1) In genes associated with the control of miRNAs, there are approximately 50% more RVs in SZ cases than in controls (P ≤ 2.62E-10); (2) The observed lower miRNA activity in EOS patients compared with the healthy controls suggests that miRNAs are abnormally downregulated; (3) There exists a predicted regulatory network among some downregulated miRNAs and some upregulated mRNAs.

CONCLUSIONS

Collectively, results from all 3 lines of evidence, suggest that the genetically based dysregulation of miRNA systems undermines miRNAs' inhibitory effects, resulting in the abnormal upregulation of genome transcription in the development of SZ.

Biomarkers for Psychiatry: The Journey from Fantasy to Fact, a Report of the 2013 CINP Think Tank

BACKGROUND

A think tank sponsored by the Collegium Internationale Neuropsychopharmacologium (CINP) debated the status and prospects of biological markers for psychiatric disorders, focusing on schizophrenia and major depressive disorder.

METHODS

Discussions covered markers defining and predicting specific disorders or domains of dysfunction, as well as predicting and monitoring medication efficacy. Deliberations included clinically useful and viable biomarkers, why suitable markers are not available, and the need for tightly-controlled sample collection.

RESULTS

Different types of biomarkers, appropriate sensitivity, specificity, and broad-based exploitability were discussed. Whilst a number of candidates are in the discovery phases, all will require replication in larger, real-life cohorts. Clinical cost-effectiveness also needs to be established.

CONCLUSIONS

Since a single measure is unlikely to suffice, multi-modal strategies look more promising, although they bring greater technical and implementation complexities. Identifying reproducible, robust biomarkers will probably require pre-competitive consortia to provide the resources needed to identify, validate, and develop the relevant clinical tests.

RNA expression profiling in depressed patients suggests retinoid-related orphan receptor alpha as a biomarker for antidepressant response

Response to antidepressant treatment is highly variable with some patients responding within a few weeks, whereas others have to wait for months until the onset of clinical effects. Gene expression profiling may be a tool to identify markers of antidepressant treatment response and new potential drug targets. In a first step, we selected 12 male, age- and severity-matched pairs of remitters and nonresponders, and analyzed expression profiles in peripheral blood at admission and after 2 and 5 weeks of treatment using Illumina expression arrays. We identified 127 transcripts significantly associated with treatment response with a minimal P-value of 9.41 × 10(-)(4) (false discovery rate-corrected). Analysis of selected transcripts in an independent replication sample of 142 depressed inpatients confirmed that lower expression of retinoid-related orphan receptor alpha (RORa, P=6.23 × 10(-4)), germinal center expressed transcript 2 (GCET2, P=2.08 × 10(-2)) and chitinase 3-like protein 2 (CHI3L2, P=4.45 × 10(-2)) on admission were associated with beneficial treatment response. In addition, leukocyte-specific protein 1 (LSP1) significantly decreased after 5 weeks of treatment in responders (P=2.91 × 10(-2)). Additional genetic, in vivo stress responsitivity data and murine gene expression findings corroborate our finding of RORa as a transcriptional marker of antidepressant response. In summary, using a genome-wide transcriptomics approach and subsequent validation studies, we identified several transcripts including the circadian gene transcript RORa that may serve as biomarkers indicating antidepressant treatment response.

Testing the predictive value of peripheral gene expression for nonremission following citalopram treatment for major depression

Major depressive disorder (MDD) in general, and anxious-depression in particular, are characterized by poor rates of remission with first-line treatments, contributing to the chronic illness burden suffered by many patients. Prospective research is needed to identify the biomarkers predicting nonremission prior to treatment initiation. We collected blood samples from a discovery cohort of 34 adult MDD patients with co-occurring anxiety and 33 matched, nondepressed controls at baseline and after 12 weeks (of citalopram plus psychotherapy treatment for the depressed cohort). Samples were processed on gene arrays and group differences in gene expression were investigated. Exploratory analyses suggest that at pretreatment baseline, nonremitting patients differ from controls with gene function and transcription factor analyses potentially related to elevated inflammation and immune activation. In a second phase, we applied an unbiased machine learning prediction model and corrected for model-selection bias. Results show that baseline gene expression predicted nonremission with 79.4% corrected accuracy with a 13-gene model. The same gene-only model predicted nonremission after 8 weeks of citalopram treatment with 76% corrected accuracy in an independent validation cohort of 63 MDD patients treated with citalopram at another institution. Together, these results demonstrate the potential, but also the limitations, of baseline peripheral blood-based gene expression to predict nonremission after citalopram treatment. These results not only support their use in future prediction tools but also suggest that increased accuracy may be obtained with the inclusion of additional predictors (eg, genetics and clinical scales).

Modulation of intestinal microbiota by the probiotic VSL#3 resets brain gene expression and ameliorates the age-related deficit in LTP

The intestinal microbiota is increasingly recognized as a complex signaling network that impacts on many systems beyond the enteric system modulating, among others, cognitive functions including learning, memory and decision-making processes. This has led to the concept of a microbiota-driven gut–brain axis, reflecting a bidirectional interaction between the central nervous system and the intestine. A deficit in synaptic plasticity is one of the many changes that occurs with age. Specifically, the archetypal model of plasticity, long-term potentiation (LTP), is reduced in hippocampus of middle-aged and aged rats. Because the intestinal microbiota might change with age, we have investigated whether the age-related deficit in LTP might be attenuated by changing the composition of intestinal microbiota with VSL#3, a probiotic mixture comprising 8 Gram-positive bacterial strains. Here, we report that treatment of aged rats with VSL#3 induced a robust change in the composition of intestinal microbiota with an increase in the abundance of Actinobacteria and Bacterioidetes, which was reduced in control-treated aged rats. VSL#3 administration modulated the expression of a large group of genes in brain tissue as assessed by whole gene expression, with evidence of a change in genes that impact on inflammatory and neuronal plasticity processes. The age-related deficit in LTP was attenuated in VSL#3-treated aged rats and this was accompanied by a modest decrease in markers of microglial activation and an increase in expression of BDNF and synapsin. The data support the notion that intestinal microbiota can be manipulated to positively impact on neuronal function.

Monitoring candidate gene expression variations before, during and after a first major depressive episode in a 51-year-old man

BACKGROUND

Although psychiatric disorders are frequently characterized by clinical heterogeneity, high recurrence, and unpredictable prognosis, studies of mRNA expression variations in blood cells from psychiatric patients constitute a promising avenue to establish clinical biomarkers. We report here, to our knowledge, the first genetic monitoring of a major depressive episode (MDE).

CASE PRESENTATION

The subject is a 51-year-old male, who was healthy at baseline and whose blood mRNA was monitored over 67 weeks for expression variations of 9 candidate genes. At week 20 the subject experienced a mild to moderate unexpected MDE, and oral antidepressant treatment was initiated at week 29. At week 36, the patient recovered from his MDE. After 6 months, antidepressant treatment was discontinued and the subject remained free of depressive symptoms. Genetic monitoring revealed that mRNA expression of SLC6A4/5HTT increased with the emergence of a depressive state, which later returned to basal levels after antidepressant treatment and during MDE recovery. PDLIM5, S100A10 and TNF mRNA showed also an interesting pattern of expression with regards to MDE evolution.

CONCLUSION

This case demonstrated the applicability of peripheral mRNA expression as a way to monitor the natural history of MDE.

Pharmacogenomic predictors of citalopram treatment outcome in major depressive disorder

OBJECTIVES

A significant proportion of patients with major depressive disorder (MDD) do not improve following treatment with first-line antidepressants and, currently, there are no objective indicators of predictors of antidepressant response. The aim of this study was to investigate pre-treatment peripheral gene expression differences between future remitters and non-responders to citalopram treatment and identify potential pharmacogenomic predictors of response.

METHODS

We conducted a gene expression study using Affymetrix HG-U133 Plus2 microarrays in peripheral blood samples from untreated individuals with MDD (N = 77), ascertained at a community outpatient clinic, prior to an 8-week treatment with citalopram. Gene expression differences were assessed between remitters and non-responders to treatment. Technical validation of significant probesets was carried out by qRT-PCR.

RESULTS

A total of 434 probesets displayed significant correlation to change in score and 33 probesests were differentially expressed between eventual remitters and non-responders. Probesets for SMAD 7 (SMA- and MAD-related protein 7) and SIGLECP3 (sialic acid-binding immunoglobulin-like lectin, pseudogene 3) were the most significant differentially expressed genes following FDR correction, and both were down-regulated in individuals who responded to treatment.

CONCLUSIONS

These findings point to SMAD7 and SIGLECP3 as candidate predictive biomarkers of antidepressant response.

Gene expression: biomarker of antidepressant therapy?

While antidepressant therapy is an essential treatment of major depression, a substantial group of treated patients do not respond to therapy, or suffer from severe side effects. Moreover, the time of onset of the clinical improvement is often delayed. Antidepressants as currently available usually enhance serotonergic, noradrenergic and dopaminergic neurotransmission and may contribute to the inadequate remission rates for major depression. Therefore biomarkers enabling the identification of subgroups of patients and also finding unprecedented targets would provide the basis for personalized medication and thus improve treatment efficacy and reduce side effects. Several pharmacogenetic studies on antidepressant treatment response using single nucleotide polymorphism (SNPs) mapping have been performed but provided only modest findings. Therefore the analysis of gene expression to integrate genomic activity and environmental effects promises a new approach to cope with the complexity of factors influencing antidepressant treatment. Here gene expression studies focusing on candidate genes and genome-wide approaches using RNA derived from peripheral blood cells are reviewed. The most promising findings exist for hypothalamic-pituitary-adrenal (HPA) axis, inflammation and neuroplasticity related genes. However, straightforward translation into tailored treatment is still unlikely. Contradictory results limit the clinical use of the findings. Future studies are necessary, which could include functional analysis and consider gene-environment interactions.

On the outside, looking in: a review and evaluation of the comparability of blood and brain "-omes"

In this article, we review studies detailing the correspondence between peripheral blood and brain tissue across various domains of high-throughput -omic analysis in order to provide a context for evaluating blood-based biomarker studies. Specifically, we reviewed seven studies comparing patterns of DNA methylation (i.e., an aspect of the epigenome), eight articles comparing patterns of gene expression (i.e., the transcriptome), and three articles comparing patterns of protein expression (i.e., the proteome). Our review of the epigenomic literature suggests that CpG-island methylation levels are generally highly correlated (r = 0.90) between blood and brain. Our review of transcriptomic studies suggests that between 35% and 80% of known transcripts are present in both brain and blood tissue samples; estimates of cross-tissue correlation in expression levels were found to range from 0.25 to 0.64, with stronger correlations observed among particular subsets of genes. Relative to the epigenome and transcriptome, the proteome has not been as fully compared between brain and blood samples, highlighting an important area for future work as whole-proteome profiling methods mature. Beyond reviewing the relevant studies, we discuss some of the assumptions, methodological issues, and gaps in knowledge that should be addressed in order to better understand how the multiple "-omes" of the brain are reflected in the peripheral blood. A better understanding of these relationships is a critical precursor to the validation of biomarkers for brain disorders.

A method for biomarker measurements in peripheral blood mononuclear cells isolated from anxious and depressed mice: β-arrestin 1 protein levels in depression and treatment

A limited number of biomarkers in the central and peripheral systems which are known may be useful for diagnosing major depressive disorders and predicting the effectiveness of antidepressant (AD) treatments. Since 60% of depressed patients do not respond adequately to medication or are resistant to ADs, it is imperative to delineate more accurate biomarkers. Recent clinical studies suggest that β-arrestin 1 levels in human mononuclear leukocytes may be an efficient biomarker. If potential biomarkers such as β-arrestin 1 could be assessed from a source such as peripheral blood cells, then they could be easily monitored and used to predict therapeutic responses. However, no previous studies have measured β-arrestin 1 levels in peripheral blood mononuclear cells (PBMCs) in anxious/depressive rodents. This study aimed to develop a method to detect β-arrestin protein levels through immunoblot analyses of mouse PBMCs isolated from whole blood. In order to validate the approach, β-arrestin levels were then compared in na\"{\i}ve, anxious/depressed mice, and anxious/depressed mice treated with a selective serotonin reuptake inhibitor (fluoxetine, 18~mg/kg/day in the drinking water). The results demonstrated that mouse whole blood collected by submandibular bleeding permitted isolation of enough PBMCs to assess circulating proteins such as β-arrestin 1. β-Arrestin 1 levels were successfully measured in healthy human subject and na\"{\i}ve mouse PBMCs. Interestingly, PBMCs from anxious/depressed mice showed significantly reduced β-arrestin 1 levels. These decreased β-arrestin 1 expression levels were restored to normal levels with chronic fluoxetine treatment. The results suggest that isolation of PBMCs from mice by submandibular bleeding is a useful technique to screen putative biomarkers of the pathophysiology of mood disorders and the response to ADs. In addition, these results confirm that β-arrestin 1 is a potential biomarker for depression.

Biomarkers predicting antidepressant treatment response: how can we advance the field?

Major depression, affecting an estimated 350 million people worldwide, poses a serious social and economic threat to modern societies. There are currently two major problems calling for innovative research approaches, namely, the absence of biomarkers predicting antidepressant response and the lack of conceptually novel antidepressant compounds. Both, biomarker predicting a priori whether an individual patient will respond to the treatment of choice as well as an early distinction of responders and nonresponders during antidepressant therapy can have a significant impact on improving this situation. Biosignatures predicting antidepressant response a priori or early in treatment would enable an evidence-based decision making on available treatment options. However, research to date does not identify any biologic or genetic predictors of sufficient clinical utility to inform the selection of specific antidepressant compound for an individual patient. In this review, we propose an optimized translational research strategy to overcome some of the major limitations in biomarker discovery. We are confident that early transfer and integration of data between both species, ideally leading to mutual supportive evidence from both preclinical and clinical studies, are most suitable to address some of the obstacles of current depression research.

Coding and noncoding gene expression biomarkers in mood disorders and schizophrenia

Mood disorders and schizophrenia are common and complex disorders with consistent evidence of genetic and environmental influences on predisposition. It is generally believed that the consequences of disease, gene expression, and allelic heterogeneity may be partly the explanation for the variability observed in treatment response. Correspondingly, while effective treatments are available for some patients, approximately half of the patients fail to respond to current neuropsychiatric treatments. A number of peripheral gene expression studies have been conducted to understand these brain-based disorders and mechanisms of treatment response with the aim of identifying suitable biomarkers and perhaps subgroups of patients based upon molecular fingerprint. In this review, we summarize the results from blood-derived gene expression studies implemented with the aim of discovering biomarkers for treatment response and classification of disorders. We include data from a biomarker study conducted in first-episode subjects with schizophrenia, where the results provide insight into possible individual biological differences that predict antipsychotic response. It is concluded that, while peripheral studies of expression are generating valuable results in pathways involving immune regulation and response, larger studies are required which hopefully will lead to robust biomarkers for treatment response and perhaps underlying variations relevant to these complex disorders.

Responder and nonresponder patients exhibit different peripheral transcriptional signatures during major depressive episode

To date, it remains impossible to guarantee that short-term treatment given to a patient suffering from a major depressive episode (MDE) will improve long-term efficacy. Objective biological measurements and biomarkers that could help in predicting the clinical evolution of MDE are still warranted. To better understand the reason nearly half of MDE patients respond poorly to current antidepressive treatments, we examined the gene expression profile of peripheral blood samples collected from 16 severe MDE patients and 13 matched controls. Using a naturalistic and longitudinal design, we ascertained mRNA and microRNA (miRNA) expression at baseline, 2 and 8 weeks later. On a genome-wide scale, we detected transcripts with roles in various biological processes as significantly dysregulated between MDE patients and controls, notably those involved in nucleotide binding and chromatin assembly. We also established putative interactions between dysregulated mRNAs and miRNAs that may contribute to MDE physiopathology. We selected a set of mRNA candidates for quantitative reverse transcriptase PCR (RT-qPCR) to validate that the transcriptional signatures observed in responders is different from nonresponders. Furthermore, we identified a combination of four mRNAs (PPT1, TNF, IL1B and HIST1H1E) that could be predictive of treatment response. Altogether, these results highlight the importance of studies investigating the tight relationship between peripheral transcriptional changes and the dynamic clinical progression of MDE patients to provide biomarkers of MDE evolution and prognosis.