Pharmacogenetics of major depressive disorder: top genes and pathways toward clinical applications

E3 Ubiquitin Ligase Uhrf2 Knockout Reveals a Critical Role in Social Behavior and Synaptic Plasticity in the Hippocampus

The hippocampal formation, particularly the CA2 subregion, is critical for social memory formation and memory processing, relying on synaptic plasticity-a fundamental mechanism by which synapses strengthen. Given the role of the ubiquitin-proteasome system (UPS) in various nervous system processes, including learning and memory, we were particularly interested in exploring the involvement of RING-type ubiquitin E3 ligases, such as UHRF2 (NIRF), in social behavior and synaptic plasticity. Our results revealed altered social behavior in mice with systemic Uhrf2 knockout, including changes in nest building, tube dominance, and the three-chamber social novelty test. In Uhrf2 knockout mice, the entorhinal cortex-CA2 circuit showed significant reductions in synaptic plasticity during paired-pulse facilitation and long-term potentiation, while the inability to evoke synaptic plasticity in the Schaffer-collateral CA2 synapses remained unaffected. These changes in synaptic plasticity correlated with significant changes in gene expression including genes related to vesicle trafficking and transcriptional regulation. The effects of Uhrf2 knockout on synaptic plasticity and the observed gene expression changes highlight UHRF2 as a regulator of learning and memory processes at both the cellular and systemic levels. Targeting E3 ubiquitin ligases, such as UHRF2, may hold therapeutic potential for memory-related disorders, warranting further investigation.

miR-218-5p and miR-320a-5p as Biomarkers for Brain Disorders: Focus on the Major Depressive Disorder and Parkinson's Disease

Depression is one of the early and most persistent non-motor symptoms of Parkinson's disease (PD), which remains ignored, resulting in the underdiagnosis of PD. Unfortunately, scarce studies and the non-availability of diagnostic strategies cause countless complications, highlighting the need for appropriate diagnostic biomarkers. Recently, brain-enriched miRNAs regulating vital neurological functions have been proposed as potent biomarkers for therapeutic strategies. Therefore, the present study is aimed to identify the brain-enriched miR-218-5p and miR-320-5p in the serum of the Chinese depressed PD patients (n = 51) than healthy controls (n = 51) to identify their potency as biomarkers. For this purpose, depressive PD patients were recruited based on HAMA and HAMD scores and miR-218-5p and miR-320-5p and IL-6, and S100B levels were analyzed using real-time PCR (qRT-PCR) and ELISA assay, respectively. In silico analysis was performed to identify key biological pathways and hub genes involved in the psychopathology of depression in PD. Here, we found significantly downregulated miR-218-5p and miR-320-5p following higher levels of IL-6 and S100B in depressed PD patients than in control (p < 0.05). The correlation analysis revealed that both miRNAs were negatively correlated with HAMA and HAMD, and IL-6 scores, along with a positive correlation with PD duration and LEDD medication. ROC analysis showed AUC above 75% in both miRNAs in depressed PD patients, and in silico analysis revealed that both miRNA's targets regulate key neurological pathways such as axon guidance, dopaminergic synapse, and circadian rhythm. Additional analysis revealed PIK3R1, ATRX, BM1, PCDHA10, XRCC5, PPP1CB, MLLT3, CBL, PCDHA4, PLCG1, YWHAZ, CDH2, AGO3, PCDHA3, and PCDHA11 as hub-genes in PPI network. In summary, our findings show that miR-218-5p and miR-320-5p can be utilized as future biomarkers for depression in PD patients, which may aid in the early diagnosis and treatment of Parkinson's disease.

Functional and molecular characterization of suicidality factors using phenotypic and genome-wide data

Genome-wide association studies (GWAS) of suicidal thoughts and behaviors support the existence of genetic contributions. Continuous measures of psychiatric disorder symptom severity can sometimes model polygenic risk better than binarized definitions. We compared two severity measures of suicidal thoughts and behaviors at the molecular and functional levels using genome-wide data. We used summary association data from GWAS of four traits analyzed in 122,935 individuals of European ancestry: thought life was not worth living (TLNWL), thoughts of self-harm, actual self-harm, and attempted suicide. A new trait for suicidal thoughts and behaviors was constructed first, phenotypically, by aggregating the previous four traits (termed "suicidality") and second, genetically, by using genomic structural equation modeling (gSEM; termed S-factor). Suicidality and S-factor were compared using SNP-heritability (h2) estimates, genetic correlation (rg), partitioned h2, effect size distribution, transcriptomic correlations (ρGE) in the brain, and cross-population polygenic scoring (PGS). The S-factor had good model fit (χ2 = 0.21, AIC = 16.21, CFI = 1.00, SRMR = 0.024). Suicidality (h2 = 7.6%) had higher h2 than the S-factor (h2 = 2.54, Pdiff = 4.78 × 10-13). Although the S-factor had a larger number of non-null susceptibility loci (πc = 0.010), these loci had small effect sizes compared to those influencing suicidality (πc = 0.005, Pdiff = 0.045). The h2 of both traits was enriched for conserved biological pathways. The rg and ρGE support highly overlapping genetic and transcriptomic features between suicidality and the S-factor. PGS using European-ancestry SNP effect sizes strongly associated with TLNWL in Admixed Americans: Nagelkerke's R2 = 8.56%, P = 0.009 (PGSsuicidality) and Nagelkerke's R2 = 7.48%, P = 0.045 (PGSS-factor). An aggregate suicidality phenotype was statistically more heritable than the S-factor across all analyses and may be more informative for future genetic study designs interested in common genetic factors among different suicide related phenotypes.

The pharmacogenomics of selective serotonin reuptake inhibitors

Antidepressant medications are frequently used as the first line of treatment for depression. However, their effectiveness is highly variable and influenced by genetic factors. Recently, pharmacogenetic studies, including candidate-gene, genome-wide association studies or polygenic risk scores, have attempted to uncover the genetic architecture of antidepressant response. Genetic variants in at least 27 genes are linked to antidepressant treatment response in both coding and non-coding genomic regions, but evidence is largely inconclusive due to the high polygenicity of the trait and limited cohort sizes in published studies. Future studies should increase the number and diversity of participants to yield sufficient statistical power to characterize the genetic underpinnings and biological mechanisms of treatment response, improve results generalizability and reduce racial health-related inequities.

Biomarkers as predictors of treatment response to tricyclic antidepressants in major depressive disorder: A systematic review

Tricyclic antidepressants (TCAs) are frequently prescribed in case of non-response to first-line antidepressants in Major Depressive Disorder (MDD). Treatment of MDD often entails a trial-and-error process of finding a suitable antidepressant and its appropriate dose. Nowadays, a shift is seen towards a more personalized treatment strategy in MDD to increase treatment efficacy. One of these strategies involves the use of biomarkers for the prediction of antidepressant treatment response. We aimed to summarize biomarkers for prediction of TCA specific (i.e. per agent, not for the TCA as a drug class) treatment response in unipolar nonpsychotic MDD. We performed a systematic search in PubMed and MEDLINE. After full-text screening, 36 papers were included. Seven genetic biomarkers were identified for nortriptyline treatment response. For desipramine, we identified two biomarkers; one genetic and one nongenetic. Three nongenetic biomarkers were identified for imipramine. None of these biomarkers were replicated. Quality assessment demonstrated that biomarker studies vary in endpoint definitions and frequently lack power calculations. None of the biomarkers can be confirmed as a predictor for TCA treatment response. Despite the necessity for TCA treatment optimization, biomarker studies reporting drug-specific results for TCAs are limited and adequate replication studies are lacking. Moreover, biomarker studies generally use small sample sizes. To move forward, larger cohorts, pooled data or biomarkers combined with other clinical characteristics should be used to improve predictive power.

BDNF gene polymorphisms predicting treatment response to CBT-based rehabilitation of depression: to be submitted to: European Neuropsychopharmacology

Genetic factors were shown to play a major role in both variation of treatment response and incidence of adverse effects to medication in affective disorders. Nevertheless, there is still a lack of therapygenetic studies, investigating the prediction of psychological therapy outcomes from genetic markers. Neuroplasticity and one of its mediators, brain-derived neurotrophic factor (BDNF), are potential research targets in this field. We aimed to investigate Tag SNP polymorphisms of the BDNF gene in depressed patients treated with cognitive behavioral therapy (CBT) in the context of a standardized 6-weeks outpatient rehabilitation program. Treatment response was assessed calculating the mean differences in BDI-II (Beck Depression Inventory) scores from admission to discharge. Six BDNF SNPs, including the Val66Met polymorphism (rs6265), were genotyped. Both genotypic data and BDI-II-scores at admission and discharge were available for 277 patients. Three SNPs, rs10501087 (p = 0.005, FDRp=0.015), rs11030104 (p = 0.006, FDRp=0.012), and the Val66Met polymorphism (rs6265, p<0.001, FDRp=0.006), were significantly associated with treatment response in depressed patients, even after multiple testing correction using the false discovery rate method (FDRp). We conclude that BDNF might serve as promising genetic marker for treatment response to psychological treatment in depression. However, due to our limited sample size, further studies are needed to disentangle the role of BDNF as potential therapygenetic marker.

Precision medicine in mood disorders

The choice of the most appropriate psychoactive medication for each of our patients is always a challenge. We can use more than 100 psychoactive drugs in the treatment of mood disorders, which can be prescribed either alone or in combination. Response and tolerability problems are common, and much trial and error is often needed before achieving a satisfactory outcome. Precision medicine is therefore needed for tailoring treatment to optimize outcome. Pharmacological, clinical, and demographic factors are important and informative, but biological factors may further inform and refine prediction. Twenty years after the first reports of gene variants modulating antidepressant response, we are now confronted with the prospect of routine clinical pharmacogenetic applications in the treatment of depression. The scientific community is divided into two camps: those who are enthusiastic and those who are skeptical. Although it appears clear that the benefit of existing tools is still not completely defined, at least in the case of central nervous system gene variants, this is not the case for metabolic gene variants, which is generally accepted. Cumulative scores encompassing many variants across the entire genome will soon predict psychiatric disorder liability and outcome. At present, precision medicine in mood disorders may be implemented using clinical and pharmacokinetic factors. In the near future, a genome-wide composite genetic score in conjunction with clinical factors within each patient is the most promising approach for developing a more effective way to target treatment for patients suffering from mood disorders.

Effect of Pharmacogenetic-Based Decision Support Tools in Improving Depression Outcomes: A Systematic Review

OBJECTIVE

Evidence supporting the utility of pharmacogenetic (PGX) tests in depression is scarce. The main objectives of this study were to summarize, update, and assess the quality of the available evidence regarding PGX testing in depression as well as estimating the impact of using PGX testing tools in depression outcomes in the Middle East/North Africa (MENA) region.

METHODOLOGY

Scientific databases were systematically searched from inception to June 30, 2020 for systematic reviews and randomized controlled trials (RCTs) assessing the clinical utility of PGX tests in the treatment of depression. Meta-analyses only and RCTs that were included in eligible systematic reviews were excluded. The quality of the eligible studies was assessed using the Crowe Critical Appraisal Tool (CCAT).

RESULTS

Six systematic reviews and three RCTs met the inclusion criteria and were included in this study. The results of the systematic reviews provided weak evidence on the efficacy of PGX testing, especially in patients with moderate-severe depression at 8 weeks. In addition, there was a lack of evidence regarding safety outcomes. Newer RCTs with better quality showed clinical promise regarding efficacy outcomes, especially in patients with gene-drug interactions. No evidence was found regarding PGX testing impact in the MENA region.

CONCLUSION

This systematic review is an update and summary of the available literature on the clinical utility of PGX testing in depression. The findings of this study demonstrate that PGX testing prior to treatment initiation or during the course of therapy may improve efficacy outcomes. Further studies are warranted to assess the impact of PGX testing on safety outcomes.

Depression: Biological markers and treatment

Nowadays depression is considered as a systemic illness with different biological mechanisms involved in its etiology, including inflammatory response, hypothalamic-pituitary-adrenal (HPA) axis dysregulation and neurotransmitter and neurotrophic systems imbalance. Novel "omics" approaches, such as metabolomics and glycomics provide information about altered metabolic pathways and metabolites, as well as disturbances in glycosylation processes affected by or causing the development of depression. The clinical diagnosis of depression continues to be established based on the presence of the specific symptoms, but due to its heterogeneous underlying biological background, that differs according to the disease stage, there is an unmet need for treatment response biomarkers which would facilitate the process of appropriate treatment selection. This paper provides an overview of the role of major stress response system, the HPA axis, and its dysregulation in depression, possible involvement of neurotrophins, especially brain-derived neurotrophic factor, glial cell line-derived neurotrophic factor and insulin-like growth factor-1, in the development of depression. Article discusses how activated inflammation processes and increased cytokine levels, as well as disturbed neurotransmitter systems can contribute to different stages of depression and could specific metabolomic and glycomic species be considered as potential biomarkers of depression. The second part of the paper includes the most recent findings about available medical treatment of depression. The described biological factors impose an optimistic conclusion that they could represent easy obtainable biomarkers potentially predicting more personalized treatment and diagnostic options.

Rare Functional Variants Associated with Antidepressant Remission in Mexican-Americans: Short title: Antidepressant remission and pharmacogenetics in Mexican-Americans

INTRODUCTION

Rare genetic functional variants can contribute to 30-40% of functional variability in genes relevant to drug action. Therefore, we investigated the role of rare functional variants in antidepressant response.

METHOD

Mexican-American individuals meeting the Diagnostic and Statistical Manual-IV criteria for major depressive disorder (MDD) participated in a prospective randomized, double-blind study with desipramine or fluoxetine. The rare variant analysis was performed using whole-exome genotyping data. Network and pathway analyses were carried out with the list of significant genes.

RESULTS

The Kernel-Based Adaptive Cluster method identified functional rare variants in 35 genes significantly associated with treatment remission (False discovery rate, FDR <0.01). Pathway analysis of these genes supports the involvement of the following gene ontology processes: olfactory/sensory transduction, regulation of response to cytokine stimulus, and meiotic cell cycleprocess.

LIMITATIONS

Our study did not have a placebo arm. We were not able to use antidepressant blood level as a covariate. Our study is based on a small sample size of only 65 Mexican-American individuals. Further studies using larger cohorts are warranted.

CONCLUSION

Our data identified several rare functional variants in antidepressant drug response in MDD patients. These have the potential to serve as genetic markers for predicting drug response.

TRIAL REGISTRATION

ClinicalTrials.gov NCT00265291.

How Can Drug Metabolism and Transporter Genetics Inform Psychotropic Prescribing?

Many genetic variants in drug metabolizing enzymes and transporters have been shown to be relevant for treating psychiatric disorders. Associations are strong enough to feature on drug labels and for prescribing guidelines based on such data. A range of commercial tests are available; however, there is variability in included genetic variants, methodology, and interpretation. We herein provide relevant background for understanding clinical associations with specific variants, other factors that are relevant to consider when interpreting such data (such as age, gender, drug-drug interactions), and summarize the data relevant to clinical utility of pharmacogenetic testing in psychiatry and the available prescribing guidelines. We also highlight areas for future research focus in this field.

Associations between the 1438A/G, 102T/C, and rs7997012G/A polymorphisms of HTR2A and the safety and efficacy of antidepressants in depression: a meta-analysis

The correlations between hydroxytryptamine receptor 2A (HTR2A) gene polymorphisms (1438A/G, 102T/C, and rs7997012G/A) and the safety and efficacy of antidepressants in depression patients were constantly reported, but conclusions are debatable. This meta-analysis ascertained forty-two studies on the efficacy (including response and remission) and side-effect issued before February 2020. Pooled analyses indicated significant associations of 1438A/G polymorphism (16 studies, 1931 subjects) and higher response within dominant model (OR: 1.40, 95% CI: 1.12-1.76); rs7997012G/A polymorphism (nine studies, 1434 subjects) and higher remission in overall models (dominant model: OR: 1.30, 95% CI: 1.01-1.66; recessive model: OR: 2.20, 95% CI: 1.53-3.16; homozygote model: OR: 2.73, 95% CI: 1.78-4.17); 102T/C polymorphism (eight studies, 804 subjects) and reduced risk of side-effect within recessive (OR: 0.57, 95% CI: 0.4-0.83) and homozygote models (OR: 0.54, 95% CI: 0.29-0.99). For depression patients, genotyping of HTR2A polymorphisms is a promising tool for estimating the outcome and side-effect of antidepressants.

The search for personalized antidepressant treatments: what have we learned and where are we going

Over 20 years after the initial report of gene variants within the central nervous system modulating antidepressant response, we are now facing for the first time routine clinical pharmacogenetic applications. The scientific community is divided between enthusiasm and skepticism. It seems clear that the benefit of existing tools is not huge, at least for the central nervous system gene variants, while it is generally accepted for the metabolic gene variants. Findings from large international consortia suggest for the first time in psychiatric genetic research history that cumulative scores comprising many variants across the whole genome may reliably constitute liability factors for psychiatric disorders, this approach will most likely improve also present pharmacogenetic tools. A composite genetic score complemented with clinical risk factors for each patient is the most promising approach for a more effective method of targeted treatment for patients with depression.

Pharmacogenetic Implications for Antidepressant Pharmacotherapy in Late-Life Depression: A Systematic Review of the Literature for Response, Pharmacokinetics and Adverse Drug Reactions

Affecting up to 15% of older adults, late-life depression (LLD) is characterized by the occurrence of depressive symptoms after the age of 50-65 years and maybe pathophysiologically distinct from depression in younger adults. Therefore, LLD is challenging to treat, and predictive genetic testing might be essential to improve treatment in this vulnerable population. The current review aims to provide a summary of the literature exploring genetic associations with antidepressant treatment outcomes in late-life. We conducted a systematic search of three integrated electronic databases. We identified 29 articles investigating genetic associations with antidepressant treatment outcomes, pharmacokinetic parameters, and adverse drug reactions in older adults. Given the small number of investigations conducted in older adults, it is difficult to conclude the presence or absence of genetic associations with the outcomes of interest. In sum, the most substantial amount of evidence exists for the CYP2D6 metabolizer status, SLC6A4 5-HTTLPR, and BDNF rs6265. These findings are consistent in the literature when not restricting to older adults, suggesting that similar treatment recommendations may be provided for older adults regarding genetic variation, such as those outlined for CYP2D6 by the Clinical Pharmacogenetics Implementation Consortium. Nonetheless, further studies are required in well-characterized samples, including genome-wide data, to validate if similar treatment adjustments are appropriate in older adults, given that there appear to be significant effects of genetic variation on antidepressant treatment factors.

Current challenges and possible future developments in personalized psychiatry with an emphasis on psychotic disorders

A personalized medicine approach seems to be particularly applicable to psychiatry. Indeed, considering mental illness as deregulation, unique to each patient, of molecular pathways, governing the development and functioning of the brain, seems to be the most justified way to understand and treat disorders of this medical category. In order to extract correct information about the implicated molecular pathways, data can be drawn from sampling phenotypic and genetic biomarkers and then analyzed by a machine learning algorithm. This review describes current difficulties in the field of personalized psychiatry and gives several examples of possibly actionable biomarkers of psychotic and other psychiatric disorders, including several examples of genetic studies relevant to personalized psychiatry. Most of these biomarkers are not yet ready to be introduced in clinical practice. In a next step, a perspective on the path personalized psychiatry may take in the future is given, paying particular attention to machine learning algorithms that can be used with the goal of handling multidimensional datasets.

The genetics of bipolar disorder

Bipolar disorder (BD) is one of the most heritable mental illnesses, but the elucidation of its genetic basis has proven to be a very challenging endeavor. Genome-Wide Association Studies (GWAS) have transformed our understanding of BD, providing the first reproducible evidence of specific genetic markers and a highly polygenic architecture that overlaps with that of schizophrenia, major depression, and other disorders. Individual GWAS markers appear to confer little risk, but common variants together account for about 25% of the heritability of BD. A few higher-risk associations have also been identified, such as a rare copy number variant on chromosome 16p11.2. Large scale next-generation sequencing studies are actively searching for other alleles that confer substantial risk. As our understanding of the genetics of BD improves, there is growing optimism that some clear biological pathways will emerge, providing a basis for future studies aimed at molecular diagnosis and novel therapeutics.

AP-1 controls the p11-dependent antidepressant response

Selective serotonin reuptake inhibitors (SSRIs) are the most widely prescribed drugs for mood disorders. While the mechanism of SSRI action is still unknown, SSRIs are thought to exert therapeutic effects by elevating extracellular serotonin levels in the brain, and remodel the structural and functional alterations dysregulated during depression. To determine their precise mode of action, we tested whether such neuroadaptive processes are modulated by regulation of specific gene expression programs. Here we identify a transcriptional program regulated by activator protein-1 (AP-1) complex, formed by c-Fos and c-Jun that is selectively activated prior to the onset of the chronic SSRI response. The AP-1 transcriptional program modulates the expression of key neuronal remodeling genes, including S100a10 (p11), linking neuronal plasticity to the antidepressant response. We find that AP-1 function is required for the antidepressant effect in vivo. Furthermore, we demonstrate how neurochemical pathways of BDNF and FGF2, through the MAPK, PI3K, and JNK cascades, regulate AP-1 function to mediate the beneficial effects of the antidepressant response. Here we put forth a sequential molecular network to track the antidepressant response and provide a new avenue that could be used to accelerate or potentiate antidepressant responses by triggering neuroplasticity.

Investigating the potential role of BDNF and PRL genotypes on antidepressant response in depression patients: A prospective inception cohort study in treatment-free patients

BACKGROUND

Brain-derived neurotrophic factor (BDNF) is associated with response to antidepressant drugs in mood and anxiety disorders. Prolactin (PRL) is a pituitary hormone with behavioural effects, acting as a neurotrophic factor within the brain and may be involved in antidepressant response.

OBJECTIVES

To investigate the relationship between BDNF and PRL genotypes with antidepressant drug response.

METHODS

Prospective inception cohort of 186 Russian treatment-free participants (28 men and 158 women) between 18 and 70 years clinically diagnosed with depressive disorder who initiated antidepressant medication. DNA polymorphisms were genotyped for PRL rs1341239, BDNF rs6265 and rs7124442. Primary outcome was measured by differences in Hamilton Depression Rating Scale (∆HAM-D) scores between baseline/week two, week two/week four, and baseline/week four. Linear regression and independent t-test determined the significance between polymorphisms and ∆HAM-D.

RESULTS

Comparisons between genotypes did not reveal any significant differences in scores during the first two weeks of treatment. In the latter two weeks, BDNF rs7124442 homozygous C patients responded significantly worse in comparison to homozygous T patients during this period. Further analysis within women and in post-menopausal women found a similar comparison between alleles.

LIMITATIONS

Study lasted four weeks, which may be considered short to associate genuine antidepressant effects.

CONCLUSIONS

Patients taking tricylic antidepressants were noted to have a significant improvement in ∆HAM-D compared to patients taking SSRIs. Homozygous C BDNF rs712442 patients were found to respond significantly worse in the last two weeks of treatment.

Enriching Human Interactome with Functional Mutations to Detect High-Impact Network Modules Underlying Complex Diseases

Rapid progress in high-throughput -omics technologies moves us one step closer to the datacalypse in life sciences. In spite of the already generated volumes of data, our knowledge of the molecular mechanisms underlying complex genetic diseases remains limited. Increasing evidence shows that biological networks are essential, albeit not sufficient, for the better understanding of these mechanisms. The identification of disease-specific functional modules in the human interactome can provide a more focused insight into the mechanistic nature of the disease. However, carving a disease network module from the whole interactome is a difficult task. In this paper, we propose a computational framework, Discovering most IMpacted SUbnetworks in interactoMe (DIMSUM), which enables the integration of genome-wide association studies (GWAS) and functional effects of mutations into the protein-protein interaction (PPI) network to improve disease module detection. Specifically, our approach incorporates and propagates the functional impact of non-synonymous single nucleotide polymorphisms (nsSNPs) on PPIs to implicate the genes that are most likely influenced by the disruptive mutations, and to identify the module with the greatest functional impact. Comparison against state-of-the-art seed-based module detection methods shows that our approach could yield modules that are biologically more relevant and have stronger association with the studied disease. We expect for our method to become a part of the common toolbox for the disease module analysis, facilitating the discovery of new disease markers.

Inhibition effects of some antidepressant drugs on pentose phosphate pathway enzymes

The glucose metabolism in the pentose cycle is essential to the source of NADPH. Deficiency of these enzymes have been linked to depression and psychotic disorders. Depression is an increasingly prevalent mental disorder which may cause loss of labor. Antidepressant drugs are commonly employed in treatments of mood disorders and anxiety treatment. The purpose of this study is to investigate the effects of aripiprazole, mirtazapine, risperidone, escitalopram and haloperidol on the activity of 6-phosphogluconate dehydrogenase (6PGD) and glucose-6-phosphate dehydrogenase (G6PD) enzymes purified from human erythrocytes. It was found that aripiprazole, mirtazapine, risperidone, escitalopram and haloperidol show effective inhibitor properties on purified G6PD and 6PGD enzymes. The IC₅₀ values of these drugs were found in the range of 26.34 μM-5.78 mM for 6PGD and 16.26 μM-3.85 mM for G6PD. The K_i values of the drugs were found in the range of 30.21 ± 4.31 μM-4.51 ± 1.83 mM for 6PGD and 14.12 ± 3.48 μM-4.98 ± 1.14 mM for G6PD. Usage of drugs with significant biological effects may be a hazard in some conditions.

Preliminary Clinical Investigation of Combinatorial Pharmacogenomic Testing for the Optimized Treatment of Depression: A Randomized Single-Blind Study

This study aims to explore the potential benefits of antidepressant drugs related to metabolic enzyme and drug-targeted genes, identify the optimal treatment of major depression, and provide a reference for individualized medication selection. A prospective randomized single-blind investigation was conducted for 8 weeks. A pharmacogenomic-based interpretive report was provided to the treating physician in the guided group. Patients in this group were informed that their medication selection was directed by DNA testing. In the unguided group, treatment was provided based on the clinical experience of the physician without the guidance of pharmacogenomic testing. Pharmacogenomic-based interpretive report was not provided to these patients until treatment completion. The 17-item Hamilton depression scale (HAMD-17), Hamilton anxiety scale, and treatment emergent symptom scale were used to assess the clinical efficacy and side effects at baseline and after 2, 4, and 8 weeks of treatment. Among the 80 initially enrolled patients with depression, 71 participated in the full data analysis sets and were designated into guided (31) and unguided (40) groups, respectively. No significant difference (P > 0.05) in HAMD-17 total scores, response and remission rates was found between the guided and unguided groups at the end of the treatment. The incidence rate of adverse reaction was 55.56% in guided group and 57.89% in the unguided group. Our study suggested that pharmacogenomic testing might not considerably improve the clinical efficiency and safety for the guided group.

Convergent neurobiological predictors of mood and anxiety symptoms and treatment response

Introduction: Mood and anxiety disorders are leading contributors to the global burden of diseases. Comorbid mood and anxiety disorders have a lifetime prevalence of ~20% globally and increases the risk for suicide, a leading cause of death. Areas covered: In this review, authors highlight recent advances in the understanding of multilevel-neurobiological mechanisms for normal/pathological human affective-functioning. The authors then address the complex interplay between environmental-adversity and molecular-genetic mediators of brain correlates of affective-symptoms. The molecular focus is strategically limited to GTF2i, BDNF, and FKBP5 genes that are, respectively, involved in transcriptional-, neurodevelopmental- and neuroendocrine-pathway mediation of affective-functions. The importance of these genes is illustrated with studies of copy-number-variants, genome-wide association (GWAS), and candidate gene-sequence variant associations with disease etiology. Authors concluded by highlighting the predictive values of integrative neurobiological processing of gene-environment interactions for affective disorder symptom management. Expert opinion: Given the transcriptional, neurodevelopmental and neuroimmune relevance of GTF2i, BDNF, and FKBP5 genes, respectively, authors reviewed the putative roles of these genes in neurobiological mediation of adaptive affective-responses. Authors discussed the importance of studying gene-dosage effects in understanding affective disorder risk biology, and how such targeted neurogenetic studies could guide precision identification of novel pharmacotherapeutic targets and aid in prediction of treatment response.

Temperament and character influence on depression treatment outcome

BACKGROUND

personality features have been repeatedly associated with depression treatment outcome in Major Depressive Disorder (MDD), however conclusive results are still lacking. Moreover, as for Bipolar Disorder (BD), results are only few and preliminary.

AIM

the aim of the present study was to perform an exploratory investigation of the influence of personality traits as assessed by the Temperament and Character Inventory (TCI), on principal depression treatment outcomes (non remission, non response and resistance).

METHODS

743 mood disorders patients (455 MDD (61.24%) and 288 BD (38.76%)) were recruited in the context of 6 European studies. Generalized logit models were performed to test the effects of TCI dimensions on treatment outcomes, considering possible confounders such as age, gender and education. Positive results were controlled for comorbidities (anxiety and substance use disorders) as well.

RESULTS

MDD Non-Remitters showed high Harm Avoidance (HA) and Self Transcendence (ST) (p = 0.0004, d = 0.40; p = 0.007, d = 0.36 respectively) and low Persistence (P) and Self Directedness (SD) (p = 0.05; d = 0.18; p = 0.002, d = 0.40, respectively); MDD Non-Responders showed a slightly different profile with high HA and low Reward Dependence (RD) and SD; finally, MDD Resistants showed low RD, P and Cooperativeness (C). In BD patients, only higher HA in non response was observed.

LIMITATIONS

the retrospective cross-sectional design, the TCI assessment regardless of the mood state and the small number of bipolar patients represent the main limitations.

CONCLUSION

specific TCI personality traits are associated with depression treatment outcome in MDD patients. The inclusion of such personality traits, together with other socio-demographic and clinical predictors, could ameliorate the accuracy of the prediction models available to date.

[Pharmacogenetics in psychiatry: state of the art]

In this article, the current literature on pharmacogenetics of antidepressants, antipsychotics and lithium are summarized by the section of Neurobiology and Genetics of the German Society of Psychiatry, Psychotherapy and Neurology (DGPPN). The publications of international expert groups and regulatory authorities are reviewed and discussed. In Germany, a statement on pharmacogenetics was also made by the gene diagnostics committee of the Ministry of Health. The DGPPN supports two recommendations: 1) to perform CYP2D6 genetic testing prior to prescription of tricyclic antidepressants and 2) to determine the HLA-B*1502 genotype in patients of Asian origin before using carbamazepine. The main obstacle for a broad application of pharmacogenetic tests in psychiatry remains the lack of large prospective studies, for both single gene-drug pair and cobinatorial pharmacogenetic tests, to evaluate the benefits of genetic testing. Psychiatrists, geneticists and funding agencies are encouraged to increase their efforts for the future benefit of psychiatric patients.

Reduced CXCL1/GRO chemokine plasma levels are a possible biomarker of elderly depression

BACKGROUND

Depression is the single largest contributor to non-fatal health loss worldwide. A role of inflammation in major depressive disorder (MDD) was suggested, and we sought to determine if cytokine levels predict the severity of depressive symptomatology or distinguish MDD patients from healthy controls (HCs).

METHODS

The severity of depressive symptoms and cognitive impairment were assessed by the Korean version of the Geriatric Depression Scale (GDS-K) and Mini-Mental State Examination (MMSE-KC) in 152 elderly subjects (76 with MDD). Plasma levels of 28 cytokines were measured and analysed as continuous predictors or dichotomized using the median value. The association between individual cytokines, MDD risk and depressive symptoms severity was investigated using multiple logistic and linear regressions that included the relevant covariates. A Cytokine Weighted Score (CWS) was calculated by weighting cytokines according to previously reported effect sizes on MDD risk. Sensitivity analyses were performed excluding subjects with significant cognitive impairment.

RESULTS

CXCL10/IP-10 levels were higher in subjects with MDD vs. HCs while the opposite was observed for CXCL1/GRO. Only the second association survived after adjusting for possible confounders and excluding subjects with severe cognitive impairment. Using dichotomized cytokine levels, CXCL1/GRO and TNF-α were negatively associated with MDD. The CWS was also negatively associated with MDD. Cytokine levels did not predict the severity of depressive symptoms.

LIMITATIONS

Our cross-sectional approach was not able to longitudinally evaluate any temporal fluctuations in the considered cytokine levels.

CONCLUSIONS

This study found significantly lower CXCL1/GRO chemokine plasma levels in elderly subjects with MDD compared to HCs.

Association Study Between Serotonin Transporter Gene and Fluoxetine Response in Mexican Patients With Major Depressive Disorder

OBJECTIVE

Pharmacogenetic studies have identified genetic variants associated with fluoxetine response in patients with major depression disorder (MDD). The serotonin transporter gene is the principal site of action of selective serotonin reuptake inhibitors. Previous studies analyzing SLC6A4 gene variants are inconsistent and differ among populations. The aim of the present study was to analyze the association between 5-HTTLPR/rs24531 triallelic polymorphism and fluoxetine response in Mexican patients with MDD.

METHODS

We analyzed a sample of 150 patients with MDD. Fluoxetine response was assessed according to a reduction in the Hamilton Depression Rating Scale and Montgomery Depression Rating Scale scores of 50% or more at 8 weeks from baseline. In addition, we analyzed the genotype and allele distribution between responder and nonresponder patients in a subgroup of very severe depression patients.

RESULTS

We did not find association between fluoxetine responders and 5-HTTLPR/rs25531 variants (P = 0.0637). However, in the analysis of severe depression at baseline (Hamilton Depression Rating Scale ≥ 25), we observed a high frequency of low activity alleles (S/LG) in nonresponders patients (P = 0.0102).

CONCLUSIONS

Our findings showed an association between low activity alleles of SLC6A4 gene and fluoxetine nonresponse in patients with severe depression.

Further Support for the Involvement of Genetic Variants Related to the Serotonergic Pathway in the Antidepressant Response in Children and Adolescents After a 12-Month Follow-Up: Impact of the HTR2A rs7997012 Polymorphism

Objective: Fluoxetine is an effective and well-tolerated pharmacological treatment for children and adolescents with major depressive disorder (MDD). However, a high percentage of patients do not respond. There is a substantial genetic contribution to this variable clinical outcome. Based on previous genetic results of our group and given the lack of pharmacogenetics studies of antidepressant response with a long follow-up period, we evaluated the influence of single nucleotide polymorphisms (SNPs) in genes related to the serotonergic pathway on remission and recovery in children and adolescents diagnosed with MDD after 12 months of initiating fluoxetine treatment. Methods: The assessment was performed in 46 patients. All of them were visited at least once a month during the 12-month follow-up. Psychiatrists interviewed patients and their parents to explore clinical improvement. A total of 75 genotyped SNPs in 10 candidate genes were included in the genetic association analysis with remission and recovery. Bonferroni correction for multiple testing was applied to avoid false positive results. Results: The HTR2A rs7997012 SNP was significantly associated after Bonferroni correction with clinical improvement. Particularly, the homozygotes for the major allele (GG) showed the highest percentage of remitters and the highest score reductions on the Clinical Global Impressions-Severity (CGI-S) scale. Moreover, although the results were on the border of statistical significance, the GG homozygotes also tended to experience fewer readmissions during the follow-up period Conclusions: These results provide more evidence of the involvement of genetic variants related to the serotonergic pathway in the antidepressant response. Studies with larger cohorts are needed to integrate all relevant variants into clinical predictors of antidepressant response.

Clinical Implementation of Pharmacogenetic Decision Support Tools for Antidepressant Drug Prescribing

The accrual and analysis of genomic sequencing data have identified specific genetic variants that are associated with major depressive disorder. Moreover, substantial investigations have been devoted to identifying gene-drug interactions that affect the response to antidepressant medications by modulating their pharmacokinetic or pharmacodynamic properties. Despite these advances, individual responses to antidepressants, as well as the unpredictability of adverse side effects, leave clinicians with an imprecise prescribing strategy that often relies on trial and error. These limitations have spawned several combinatorial pharmacogenetic testing products that are marketed to physicians. Typically, combinatorial pharmacogenetic decision support tools use algorithms to integrate multiple genetic variants and assemble the results into an easily interpretable report to guide prescribing of antidepressants and other psychotropic medications. The authors review the evidence base for several combinatorial pharmacogenetic decision support tools whose potential utility has been evaluated in clinical settings. They find that, at present, there are insufficient data to support the widespread use of combinatorial pharmacogenetic testing in clinical practice, although there are clinical situations in which the technology may be informative, particularly in predicting side effects.

Effect of cytochrome CYP2C19 metabolizing activity on antidepressant response and side effects: Meta-analysis of data from genome-wide association studies

Cytochrome (CYP) P450 enzymes have a primary role in antidepressant metabolism and variants in these polymorphic genes are targets for pharmacogenetic investigation. This is the first meta-analysis to investigate how CYP2C19 polymorphisms predict citalopram/escitalopram efficacy and side effects. CYP2C19 metabolic phenotypes comprise poor metabolizers (PM), intermediate and intermediate+ metabolizers (IM; IM+), extensive and extensive+ metabolizers (EM [wild type]; EM+) and ultra-rapid metabolizers (UM) defined by the two most common CYP2C19 functional polymorphisms (rs4244285 and rs12248560) in Caucasians. These polymorphisms were genotyped or imputed from genome-wide data in four samples treated with citalopram or escitalopram (GENDEP, STAR*D, GenPod, PGRN-AMPS). Treatment efficacy was assessed by standardized percentage symptom improvement and by remission. Side effect data were available at weeks 2-4, 6 and 9 in three samples. A fixed-effects meta-analysis was performed using EM as the reference group. Analysis of 2558 patients for efficacy and 2037 patients for side effects showed that PMs had higher symptom improvement (SMD = 0.43, CI = 0.19-0.66) and higher remission rates (OR = 1.55, CI = 1.23-1.96) compared to EMs. At weeks 2-4, PMs showed higher risk of gastro-intestinal (OR = 1.26, CI = 1.08-1.47), neurological (OR = 1.28, CI = 1.07-1.53) and sexual side effects (OR = 1.52, CI = 1.23-1.87; week 6 values were similar). No difference was seen at week 9 or in total side effect burden. PMs did not have higher risk of dropout at week 4 compared to EMs. Antidepressant dose was not different among CYP2C19 groups. CYP2C19 polymorphisms may provide helpful information for guiding citalopram/escitalopram treatment, despite PMs being relatively rare among Caucasians (∼2%).

The functional variant rs334558 of GSK3B is associated with remission in patients with depressive disorders

Purpose

GSK3B and AKT1 genes have been implicated in the pathogenesis of a number of psychiatric and neurological disorders. Furthermore, their genetic variants are associated with response to antidepressant pharmacotherapy. As the evidence is still incomplete and inconsistent, continuing efforts to investigate the role of these two genes in the pathogenesis and treatment of brain disorders is necessary. The aim of our study was thus to evaluate the association of variants of these two genes with depressive disorders and drug treatment response.

Patients and methods

In the present study, 222 patients with a depressive disorder who underwent pharmacological antidepressant treatment were divided into remitters and non-remitters following a 28-day course of pharmacotherapy. The association of a depressive disorder and remission rates with polymorphisms rs334558 in the GSK3B gene and rs1130214 and rs3730358 in the AKT1 gene was evaluated with a chi-square test.

Results

Neither of the studied genetic variants was associated with a depressive disorder. Furthermore, frequencies of alleles and genotypes for rs1130214 and rs3730358 were not different in the groups of remitters and non-remitters. However, the activating allele T of the functional polymorphism rs334558 was significantly associated with remission, when all types of antidepressant drugs were included. This association continued as a trend when only patients taking selective serotonin reuptake inhibitors were considered.

Conclusion

The present study provides support that the functional polymorphism rs334558 of GSK3B may play a role as a useful genetic and pharmacogenetic biomarker in the framework of personalized medicine approach.

Pharmacogenetic evaluation of a DISP1 gene variant in antidepressant treatment of obsessive-compulsive disorder

OBJECTIVES

A recent genome-wide association study (GWAS) in obsessive-compulsive disorder (OCD) reported a significant marker in the dispatched homolog 1 (Drosophila) gene (DISP1 gene) associated with serotonin reuptake inhibitor (SRI) antidepressant response (Qin et al., ). DISP1 has never been examined before in terms of association with SRI response until this GWAS. We attempt to replicate the GWAS finding by investigating the association of the DISP1 rs17162912 polymorphism with SRI response in our sample of 112 European Caucasian OCD patients.

METHODS

Patients were previously treated naturalistically with up to 6 different SRIs sequentially, including 5 selective SRIs (fluoxetine, fluvoxamine, sertraline, paroxetine, and citalopram) and 1 SRI (clomipramine). Each medication trial was evaluated retrospectively for response and was rated categorically as either responder or nonresponder using the Clinical Global Impression-Improvement scale. Fisher's exact test was used to investigate the relationship between the DISP1 rs17162912 genotype distribution and SRI response.

RESULTS

We did not observe a significant association between rs17162912 and SRI response (p = .32).

CONCLUSION

This replication study did not support the role of DISP1 in predicting SRI response in OCD; however, methodological differences between the original GWAS and our study, as well as limited power and low minor allele frequency, may have hindered replication.

Gene-based analysis of genes related to neurotrophic pathway suggests association of BDNF and VEGFA with antidepressant treatment-response in depressed patients

It is well established that brain-derived neurotrophic factor (BDNF) signaling pathway plays a key role in the pathophysiology of major depressive disorder (MDD) and in therapeutic mechanisms of antidepressants. We aim to identify genetic vairiants related to MDD susceptibility and antidepressant therapeutic response by using gene-based association analysis with genes related to the neurotrophic pathway. The present study investigated the role of genetic variants in the 10 neurotrophic-related genes (BDNF, NGFR, NTRK2, MTOR, VEGFA, S100A10, SERPINE1, ARHGAP33, GSK3B, CREB1) in MDD susceptibility through a case-control (455 MDD patients and 2,998 healthy controls) study and in antidepressant efficacy (n = 455). Measures of antidepressant therapeutic efficacy were evaluated using the 21-item Hamilton Rating Scale for Depression. Our single-marker and gene-based analyses with ten genes related to the neurotrophic pathway identified 6 polymorphisms that reached a significant level (p-value < 5.0 × 10⁻³) in both meta- and mega-analyses in antidepressant therapeutic response. One polymorphism was mapped to BDNF and 5 other polymorphisms were mapped to VEGFA. For case-control association study, we found that all of these reported polymorphisms and genes did not reach a suggestive level. The present study supported a role of BDNF and VEGFA variants in MDD therapeutic response.

Epigenetic mechanisms of major depression: Targeting neuronal plasticity

Major depressive disorder is one of the most common mental illnesses as it affects more than 350 million people globally. Major depressive disorder is etiologically complex and disabling. Genetic factors play a role in the etiology of major depression. However, identical twin studies have shown high rates of discordance, indicating non-genetic mechanisms as well. For instance, stressful life events increase the risk of depression. Environmental stressors also induce stable changes in gene expression within the brain that may lead to maladaptive neuronal plasticity in regions implicated in disease pathogenesis. Epigenetic events alter the chromatin structure and thus modulate expression of genes that play a role in neuronal plasticity, behavioral response to stress, depressive behaviors, and response to antidepressants. Here, we review new information regarding current understanding of epigenetic events that may impact depression. In particular, we discuss the roles of histone acetylation, DNA methylation, and non-coding RNA. These novel mechanisms of action may lead to new therapeutic strategies for treating major depression.

Neuroplasticity and second messenger pathways in antidepressant efficacy: pharmacogenetic results from a prospective trial investigating treatment resistance

Genes belonging to neuroplasticity, monoamine, circadian rhythm, and transcription factor pathways were investigated as modulators of antidepressant efficacy. The present study aimed (1) to replicate previous findings in an independent sample with treatment-resistant depression (TRD), and (2) to perform a pathway analysis to investigate the possible molecular mechanisms involved. 220 patients with major depressive disorder who were non-responders to a previous antidepressant were treated with venlafaxine for 4-6 weeks and in case of non-response with escitalopram for 4-6 weeks. Symptoms were assessed using the Montgomery Asberg Depression Rating Scale. The phenotypes were response and remission to venlafaxine, non-response (TRDA) and non-remission (TRDB) to neither venlafaxine nor escitalopram. 50 tag SNPs in 14 genes belonging to the pathways of interest were tested for association with phenotypes. Molecular pathways (KEGG database) that included one or more of the genes associated with the phenotypes were investigated also in the STAR*D sample. The associations between ZNF804A rs7603001 and response, CREB1 rs2254137 and remission were replicated, as well as CHL1 rs2133402 and lower risk of TRD. Other CHL1 SNPs were potential predictors of TRD (rs1516340, rs2272522, rs1516338, rs2133402). The MAPK1 rs6928 SNP was consistently associated with all the phenotypes. The protein processing in endoplasmic reticulum pathway (hsa04141) was the best pathway that may explain the mechanisms of MAPK1 involvement in antidepressant response. Signals in genes previously associated with antidepressant efficacy were confirmed for CREB1, ZNF804A and CHL1. These genes play pivotal roles in synaptic plasticity, neural activity and connectivity.

Serotonergic 5HTTLPR/rs25531 s-allele homozygosity associates with violent suicides in male citalopram users

Depressive disorders are involved as a background factor in over 50% of suicide cases. The most widely used antidepressants today are serotonin selective reuptake inhibitors (SSRIs). However, not all users benefit from SSRI medication. Although the overall number of suicides in Finland have decreased notably during the last decade, the annual rate is still relatively high, particularly in male population. In this study, we tested the hypothesis that the genetic variants associated with decreased citalopram efficiency, 5HTTLPR/rs25531, and increased impulsive behavior, MAOA-uVNTR and HTR2B Q20*, are more frequent among citalopram users committing suicide than among the citalopram users in general. Also the effect of alcohol was evaluated. The study population comprised 349 suicide victims (184 males and 165 females). Based on the suicide method used, cases were divided into two groups; violent (88 males and 49 females) and non-violent (96 males and 116 females). The control group (284; 159 males and 125 females) consisted of citalopram users who died of causes other than suicide. We found that male citalopram users with low functioning s/s genotype of 5HTTLPR/rs25531 were in increased risk to commit violent suicide (OR 2.50, 95%CI 1.15-5.42, p = 0.020). Surprisingly, high blood alcohol concentration was observed to be a risk factor only in non-violent suicides (both males and females), but not in violent ones. No association between suicides and MAOA-uVNTR and HTR2B Q20*, which have been previously connected to violent and impulsive behavior, was detected.

Pharmacogenomics in the treatment of mood disorders: Strategies and Opportunities for personalized psychiatry

Personalized medicine (personalized psychiatry in a specific setting) is a new model towards individualized care, in which knowledge from genomics and other omic pillars (microbiome, epigenomes, proteome, and metabolome) will be combined with clinical data to guide efforts to new drug development and targeted prescription of the existing treatment options. In this review, we summarize pharmacogenomic studies in mood disorders that may lay the foundation towards personalized psychiatry. In addition, we have discussed the possible strategies to integrate data from omic pillars as a future path to personalized psychiatry. So far, the progress of uncovering single nucleotide polymorphisms (SNPs) underpinning treatment efficacy in mood disorders (e.g., SNPs associated with selective serotonin re-uptake inhibitors or lithium treatment response in patients with bipolar disorder and major depressive disorder) are encouraging, but not adequate. Genetic studies have pointed to a number of SNPs located at candidate genes that possibly influence response to; (a) antidepressants COMT, HTR2A, HTR1A, CNR1, SLC6A4, NPY, MAOA, IL1B, GRIK4, BDNF, GNB3, FKBP5, CYP2D6, CYP2C19, and ABCB1 and (b) mood stabilizers (lithium) 5-HTT, TPH, DRD1, FYN, INPP1, CREB1, BDNF, GSK3β, ARNTL, TIM, DPB, NR3C1, BCR, XBP1, and CACNG2. We suggest three alternative and complementary strategies to implement knowledge gained from pharmacogenomic studies. The first strategy can be to implement diagnostic, therapeutic, or prognostic genetic testing based on candidate genes or gene products. The second alternative is an integrative analysis (systems genomics approach) to combine omics data obtained from the different pillars of omics investigation, including genomics, epigenomes, proteomics, metabolomics and microbiomes. The main goal of system genomics is an identification and understanding of biological pathways, networks, and modules underlying drug-response. The third strategy aims to the development of multivariable diagnostic or prognostic algorithms (tools) combining individual's genomic information (polygenic score) with other predictors (e.g., omics pillars, neuroimaging, and clinical characteristics) to finally predict therapeutic outcomes. An integration of molecular science with that of traditional clinical practice is the way forward to drug discoveries and novel therapeutic approaches and to characterize psychiatric disorders leading to a better predictive, preventive, and personalized medicine (PPPM) in psychiatry. With future advances in the omics technology and methodological developments for data integration, the goal of PPPM in psychiatry is promising.

Efficacy of prospective pharmacogenetic testing in the treatment of major depressive disorder: results of a randomized, double-blind clinical trial

BACKGROUND

A 12-week, double-blind, parallel, multi-center randomized controlled trial in 316 adult patients with major depressive disorder (MDD) was conducted to evaluate the effectiveness of pharmacogenetic (PGx) testing for drug therapy guidance.

METHODS

Patients with a CGI-S ≥ 4 and requiring antidepressant medication de novo or changes in their medication regime were recruited at 18 Spanish public hospitals, genotyped with a commercial PGx panel (Neuropharmagen®), and randomized to PGx-guided treatment (n = 155) or treatment as usual (TAU, control group, n = 161), using a computer-generated random list that locked or unlocked psychiatrist access to the results of the PGx panel depending on group allocation. The primary endpoint was the proportion of patients achieving a sustained response (Patient Global Impression of Improvement, PGI-I ≤ 2) within the 12-week follow-up. Patients and interviewers collecting the PGI-I ratings were blinded to group allocation. Between-group differences were evaluated using χ2-test or t-test, as per data type.

RESULTS

Two hundred eighty patients were available for analysis at the end of the 12-week follow-up (PGx n = 136, TAU n = 144). A difference in sustained response within the study period (primary outcome) was not observed (38.5% vs 34.4%, p = 0.4735; OR = 1.19 [95%CI 0.74-1.92]), but the PGx-guided treatment group had a higher responder rate compared to TAU at 12 weeks (47.8% vs 36.1%, p = 0.0476; OR = 1.62 [95%CI 1.00-2.61]), and this difference increased after removing subjects in the PGx-guided group when clinicians explicitly reported not to follow the test recommendations (51.3% vs 36.1%, p = 0.0135; OR = 1.86 [95%CI 1.13-3.05]). Effects were more consistent in patients with 1-3 failed drug trials. In subjects reporting side effects burden at baseline, odds of achieving a better tolerability (Frequency, Intensity and Burden of Side Effects Rating Burden subscore ≤2) were higher in the PGx-guided group than in controls at 6 weeks and maintained at 12 weeks (68.5% vs 51.4%, p = 0.0260; OR = 2.06 [95%CI 1.09-3.89]).

CONCLUSIONS

PGx-guided treatment resulted in significant improvement of MDD patient's response at 12 weeks, dependent on the number of previously failed medication trials, but not on sustained response during the study period. Burden of side effects was also significantly reduced.

TRIAL REGISTRATION

European Clinical Trials Database 2013-002228-18 , registration date September 16, 2013; ClinicalTrials.gov NCT02529462 , retrospectively registered: August 19, 2015.

Polymorphisms of 5-HTT LPR and GNβ3 825C>T and Response to Antidepressant Treatment in Functional Dyspepsia: A Study from The Functional Dyspepsia Treatment Trial

OBJECTIVES

The Functional Dyspepsia Treatment Trial reported that amitriptyline (AMI) was associated with adequate relief of functional dyspepsia (FD) symptoms, but the pharmacogenetics of antidepressant response in FD are not known. GNβ3 825C>T CC genotype has been previously linked to FD and TT genotype to antidepressant response in depression. The ss genotype of the 5-HTT LPR variant of the serotonin transporter gene (SLC6A4) has been linked to selective serotonin reuptake inhibitor (SSRI) response. We aimed to examine whether GNβ3 825C>T and 5-HTT LPR polymorphisms result in differential treatment effects in FD patients receiving antidepressant therapy.

METHODS

Participants were randomized to receive placebo, 50 mg AMI, or 10 mg escitalopram (ESC). The primary end point was adequate relief for ≥5 weeks of the last 10 weeks. Genotyping of GNβ3 825C>T and 5-HTT LPR was performed utilizing PCR-based methods.

RESULTS

GNβ3 825C>T and 5-HTT LPR genotype data were available for 256 (88%) and 246 (84%) patients, respectively. Both polymorphisms were in Hardy-Weinberg equilibrium. In tests for differential treatment, neither 5-HTT LPR nor GNβ3 825C>T genotype influenced response to therapy (P=0.89 and P=0.54, respectively). Although there was a tendency for a more favorable response to ESC in the SS/LS genotype compared to the LL genotype groups (40% vs. 31% reporting adequate relief of FD symptoms) among those in the ESC treatment arm, this was not significant (P=0.43).

CONCLUSIONS

GNβ3 825C>T and 5-HTT LPR genetic variants do not alter treatment response to tricyclic and SSRI antidepressants in FD.

Consensus paper of the WFSBP Task Force on Genetics: Genetics, epigenetics and gene expression markers of major depressive disorder and antidepressant response

Major depressive disorder (MDD) is a heritable disease with a heavy personal and socio-economic burden. Antidepressants of different classes are prescribed to treat MDD, but reliable and reproducible markers of efficacy are not available for clinical use. Further complicating treatment, the diagnosis of MDD is not guided by objective criteria, resulting in the risk of under- or overtreatment. A number of markers of MDD and antidepressant response have been investigated at the genetic, epigenetic, gene expression and protein levels. Polymorphisms in genes involved in antidepressant metabolism (cytochrome P450 isoenzymes), antidepressant transport (ABCB1), glucocorticoid signalling (FKBP5) and serotonin neurotransmission (SLC6A4 and HTR2A) were among those included in the first pharmacogenetic assays that have been tested for clinical applicability. The results of these investigations were encouraging when examining patient-outcome improvement. Furthermore, a nine-serum biomarker panel (including BDNF, cortisol and soluble TNF-α receptor type II) showed good sensitivity and specificity in differentiating between MDD and healthy controls. These first diagnostic and response-predictive tests for MDD provided a source of optimism for future clinical applications. However, such findings should be considered very carefully because their benefit/cost ratio and clinical indications were not clearly demonstrated. Future tests may include combinations of different types of biomarkers and be specific for MDD subtypes or pathological dimensions.

Pharmacogenetic studies: a tool to improve antidepressant therapy

The World Health Organization (WHO) predicts that major depressive disorder (MDD) will be the second leading cause of death and disability by 2020. Nowadays, approximately 60-70% of patients with this disorder have shown the lack of effectiveness and tolerability of the therapy with antidepressants. The US Food and Drug Administration (FDA) and the European Medicine Agency (EMA) are including pharmacogenetic information in the labeling of several antidepressants. The presence of this information represents the relevance of genetic polymorphisms in drug response. These pharmacogenetic studies have been based on the knowledge of genes involved in pharmacokinetic (CYP2D6, CYP2C19 and ABCB1) and pharmacodynamic (SLC6A4, HTR2A, BDNF, GNB3 and FKBP5) processes of antidepressant medications. The knowledge of the genotype of patients with MDD is an important tool for personalized therapy that can improve their clinical response to treatment. In this review, we highlight the most relevant genes involved in the metabolism of antidepressants (ADs) or the genes related to the presence of adverse reactions.

Pharmacogenetics and Imaging-Pharmacogenetics of Antidepressant Response: Towards Translational Strategies

Genetic variation underlies both the response to antidepressant treatment and the occurrence of side effects. Over the past two decades, a number of pharmacogenetic variants, among these the SCL6A4, BDNF, FKBP5, GNB3, GRIK4, and ABCB1 genes, have come to the forefront in this regard. However, small effects sizes, mixed results in independent samples, and conflicting meta-analyses results led to inherent difficulties in the field of pharmacogenetics translating these findings into clinical practice. Nearly all antidepressant pharmacogenetic variants have potentially pleiotropic effects in which they are associated with major depressive disorder, intermediate phenotypes involved in emotional processes, and brain areas affected by antidepressant treatment. The purpose of this article is to provide a comprehensive review of the advances made in the field of pharmacogenetics of antidepressant efficacy and side effects, imaging findings of antidepressant response, and the latest results in the expanding field of imaging-pharmacogenetics studies. We suggest there is mounting evidence that genetic factors exert their impact on treatment response by influencing brain structural and functional changes during antidepressant treatment, and combining neuroimaging and genetic methods may be a more powerful way to detect biological mechanisms of response than either method alone. The most promising imaging-pharmacogenetics findings exist for the SCL6A4 gene, with converging associations with antidepressant response, frontolimbic predictors of affective symptoms, and normalization of frontolimbic activity following antidepressant treatment. More research is required before imaging-pharmacogenetics informed personalized medicine can be applied to antidepressant treatment; nevertheless, inroads have been made towards assessing genetic and neuroanatomical liability and potential clinical application.

ABCB1 genotyping in the treatment of depression

P-glycoprotein (P-gp), the gene product of ABCB1, is a drug transporter at the blood–brain barrier and could be a limiting factor for entrance of antidepressants into the brain, the target site of antidepressant action. Animal studies showed that brain concentrations of many antidepressants depend on P-gp. In humans, ABCB1 genotyping in the treatment of depression rests on the assumption that genetic variations in ABCB1 explain individual differences in antidepressant response via their effects on P-gp expression at the blood–brain barrier. High P-gp expression is hypothesized to lead to lower and often insufficient brain concentrations of P-gp substrate antidepressants. In this review, we summarize 32 studies investigating the question of whether ABCB1 polymorphisms predict clinical efficacy and/or tolerability of antidepressants in humans and evaluate the clinical application status of ABCB1 genotyping in depression treatment.

TSPAN5, ERICH3 and selective serotonin reuptake inhibitors in major depressive disorder: pharmacometabolomics-informed pharmacogenomics

Millions of patients suffer from major depressive disorder (MDD), but many do not respond to selective serotonin reuptake inhibitor (SSRI) therapy. We used a pharmacometabolomics-informed pharmacogenomics research strategy to identify genes associated with metabolites that were related to SSRI response. Specifically, 306 MDD patients were treated with citalopram or escitalopram and blood was drawn at baseline, 4 and 8 weeks for blood drug levels, genome-wide single nucleotide polymorphism (SNP) genotyping and metabolomic analyses. SSRI treatment decreased plasma serotonin concentrations (P<0.0001). Baseline and plasma serotonin concentration changes were associated with clinical outcomes (P<0.05). Therefore, baseline and serotonin concentration changes were used as phenotypes for genome-wide association studies (GWAS). GWAS for baseline plasma serotonin concentrations revealed a genome-wide significant (P=7.84E-09) SNP cluster on chromosome four 5' of TSPAN5 and a cluster across ERICH3 on chromosome one (P=9.28E-08) that were also observed during GWAS for change in serotonin at 4 (P=5.6E-08 and P=7.54E-07, respectively) and 8 weeks (P=1.25E-06 and P=3.99E-07, respectively). The SNPs on chromosome four were expression quantitative trait loci for TSPAN5. Knockdown (KD) and overexpression (OE) of TSPAN5 in a neuroblastoma cell line significantly altered the expression of serotonin pathway genes (TPH1, TPH2, DDC and MAOA). Chromosome one SNPs included two ERICH3 nonsynonymous SNPs that resulted in accelerated proteasome-mediated degradation. In addition, ERICH3 and TSPAN5 KD and OE altered media serotonin concentrations. Application of a pharmacometabolomics-informed pharmacogenomic research strategy, followed by functional validation, indicated that TSPAN5 and ERICH3 are associated with plasma serotonin concentrations and may have a role in SSRI treatment outcomes.

Toward a Global Roadmap for Precision Medicine in Psychiatry: Challenges and Opportunities

Mental disorders represent a major public health burden worldwide. This is likely to rise in the next decade, with the highest increases predicted to occur in low- and middle-income countries. Current psychotropic medication treatment guidelines focus on uniform approaches to the treatment of heterogeneous disorders and achieve only partial therapeutic success. Developing a global precision medicine approach in psychiatry appears attractive, given the value of this approach in other fields of medicine, such as oncology and infectious diseases. In this horizon scanning analysis, we review the salient opportunities and challenges for precision medicine in psychiatry over the next decade. Variants within numerous genes involved in a range of pathways have been implicated in psychotropic drug response and might ultimately be used to guide choice of pharmacotherapy. Multipronged approaches such as multi-omics (genomics, proteomics, metabolomics) analyses and systems diagnostics together with high-throughput sequencing and genotyping technologies hold promise for identifying precise and targeted treatments in mental disorders. To date, however, the vast majority of pharmacogenomics work has been undertaken in high-income countries on a relatively small proportion of the global population, and many other challenges face the field. Opportunities and challenges for establishing a global roadmap for precision medicine in psychiatry are discussed in this article.

Circuit-wide Transcriptional Profiling Reveals Brain Region-Specific Gene Networks Regulating Depression Susceptibility

Depression is a complex, heterogeneous disorder and a leading contributor to the global burden of disease. Most previous research has focused on individual brain regions and genes contributing to depression. However, emerging evidence in humans and animal models suggests that dysregulated circuit function and gene expression across multiple brain regions drive depressive phenotypes. Here, we performed RNA sequencing on four brain regions from control animals and those susceptible or resilient to chronic social defeat stress at multiple time points. We employed an integrative network biology approach to identify transcriptional networks and key driver genes that regulate susceptibility to depressive-like symptoms. Further, we validated in vivo several key drivers and their associated transcriptional networks that regulate depression susceptibility and confirmed their functional significance at the levels of gene transcription, synaptic regulation, and behavior. Our study reveals novel transcriptional networks that control stress susceptibility and offers fundamentally new leads for antidepressant drug discovery.

The FKBP5 polymorphism rs1360780 influences the effect of an algorithm-based antidepressant treatment and is associated with remission in patients with major depression

OBJECTIVE

The FKBP5-gene influences the HPA-system by modulating the sensitivity of the glucocorticoid receptor (GR). The polymorphism rs1360780 has been associated with response in studies with heterogeneous antidepressant treatment. In contrast, several antidepressant studies with standardized antidepressant treatment could not detect this effect. We therefore compared patients with standardized vs naturalistic antidepressant treatment to (a) investigate a possible interaction between FKBP5-genotype and treatment mode and (b) replicate the effect of the FKBP5-genotype on antidepressant treatment outcome.

METHODS

A total of 298 major depressive disorder (MDD) inpatients from the multicentred German project and the Zurich Algorithm Project were genotyped for their FKBP5 status. Patients were treated as usual (n=127) or according to a standardized algorithm (n=171). Main outcome criteria was remission (Hamilton Depression Rating Scale-21<10).

RESULTS

We detected an interaction of treatment as usual (TAU) treatment and C-allele with the worst outcome for patients combining those two factors (HR=0.46; p=0.000). Even though C-allele patients did better when treated in the structured, stepwise treatment algorithm (SSTR) group, we still could confirm the influence of the FKBP5-genotype in the whole sample (HR=0.52; p=0.01).

CONCLUSIONS

This is the first study to show an interaction between a genetic polymorphism and treatment mode. Patients with the C-allele of the rs1360780 polymorphism seem to benefit from a standardized antidepressant treatment.