The genetics of antipsychotic induced tremors: a genome-wide pathway analysis on the STEP-BD SCP sample

Influence and interaction of genetic, cognitive, neuroendocrine and personalistic markers to antidepressant response in Chinese patients with major depression

OBJECTIVE

Despite there is a wide range of antidepressants available, with various mechanisms of actions, the efficacy of current therapeutic options is yet satisfactory. Previous shreds of evidence have indicated that genetics, cognitive, neuroendocrine, as well as personality factors, are all intrinsically linked and contribute to the diversity of treatment outcomes. We, therefore, sought to investigate this hypothesis in this study.

METHOD

Based on 610 samples treated with a selection of serotonin reuptake inhibitor (SSRI), serotonin-norepinephrine reuptake inhibitors (SNRI), noradrenergic and specific serotonergic antidepressant (NaSSA) or tricyclic antidepressant (TCA), we compared the therapeutic effects of these four classes of drugs by survival analyses. Pharmacogenomic and survival analyses were carried out to explore the hereditary factors for curative effect and the accumulation of genetic factors was further discussed through pathway analysis and the global test. We built a machine learning-based prediction model that integrates genetic and non-genetic factors (including cognition, endocrinology, personality intelligence) to distinguish drug efficacy in single class drug situations. The values of the non-genetic makers after 6 weeks' treatment were collected to evaluate the efficacy of the model.

RESULTS

Our results from the 6-week antidepressant therapeutic study indicated that SSRI and SNRI are better treatments than those of TCA and NaSSA in the Chinese population. Among all possible paired single-agent survival analyses, citalopram and venlafaxine were more effective than mirtazapine. Allele C carriers at rs6354 (SLC6A4) and allele G carriers at rs12150214 (SLC6A4) were significantly prone to poorer treatment response to fluoxetine. Besides, the combination of three loci (rs929377-rs6191-rs32897) located in HPA pathway was significantly associated with the treatment outcome of fluoxetine. In female MDD patients, the minor allele of rs6323 and rs1137070 on the MAOA gene likely lead to a worse response to venlafaxine. Furthermore, genetic variants linked to drug efficacy tended to concentrate on the neurotrophin pathway in depressed patients comorbid with anxiety. From multivariate models, more severe cognitive deficits, psychopathic personality and lower levels of operational intelligence, and higher levels of cortisol predicted worse response status with SSRI or SNRI after 6-week treatment. Notably, genetic factors in the multi-dimensional prediction model for both classes of drugs include loci in HTR2A and CRHBP genes.

CONCLUSION

SSRI and SNRI are more suitable for the treatment of Chinese people with depression. SLC6A4 genetic variants, as well as HPA pathway, play an important role in the fluoxetine antidepressant therapeutic response while the polymorphism of MAOA gene involved in the pharmacological action of venlafaxine among female MDD patients. The presence of anxiety in MDD patients was related to the neurotrophin pathway. Genetic, cognitive, neuroendocrine, and personality intelligence factors combined have an ensemble impact on the medication effect of patients with major depression, leading to more precise and personalized medicine for specific groups of people.

Identifying key transcription factors for pharmacogenetic studies of antipsychotics induced extrapyramidal symptoms

INTRODUCTION

We explore the transcription factors involved in the molecular mechanism of antipsychotic (AP)-induced acute extrapyramidalsymptoms (EPS) in order to identify new candidate genes for pharmacogenetic studies.

METHODS

Protein-protein interaction (PPI) networks previously created from three pharmacogenomic models (in vitro, animal, and peripheral blood inhumans) were used to, by means of several bioinformatic tools; identify key transcription factors (TFs) that regulate each network. Once the TFs wereidentified, SNPs disrupting the binding sites (TFBS) of these TFs in the genes of each network were selected for genotyping. Finally, SNP-basedassociations with EPS were analyzed in a sample of 356 psychiatric patients receiving AP.

RESULTS

Our analysis identified 33 TFs expressed in the striatum, and 125 SNPs disrupting TFBS in 50 genes of our initial networks. Two SNPs (rs938112,rs2987902) in two genes (LSMAP and ABL1) were significantly associated with AP induced EPS (p < 0.001). These SNPs disrupt TFBS regulated byPOU2F1.

CONCLUSION

Our results highlight the possible role of the disruption of TFBS by SNPs in the pharmacological response to AP.

Pharmacogenetic study of antipsychotic induced acute extrapyramidal symptoms in a first episode psychosis cohort: role of dopamine, serotonin and glutamate candidate genes

This study investigated whether the risk of presenting antipsychotic (AP)-induced extrapyramidal symptoms (EPS) could be related to single-nucleotide polymorphisms (SNPs) in a naturalistic cohort of first episode psychosis (FEP) patients. Two hundred and two SNPs in 31 candidate genes (involved in dopamine, serotonin and glutamate pathways) were analyzed in the present study. One hundred and thirteen FEP patients (43 presenting EPS and 70 non-presenting EPS) treated with high-potency AP (amisulpride, paliperidone, risperidone and ziprasidone) were included in the analysis. The statistical analysis was adjusted by age, gender, AP dosage, AP combinations and concomitant treatments as covariates. Four SNPs in different genes (DRD2, SLC18A2, HTR2A and GRIK3) contributed significantly to the risk of EPS after correction for multiple testing (P<1 × 10(-4)). These findings support the involvement of dopamine, serotonin and glutamate pathways in AP-induced EPS.

Review of Cohort Studies for Mood Disorders

This paper aimed to review currently available cohort studies of subjects with mood disorders such as major depressive disorder (MDD) and bipolar disorder (BD). Using the PubMed and KoreaMed databases, we reviewed eight major cohort studies. Most studies recruited participants with MDD and BD separately, so direct comparison of factors associated with diagnostic changes was difficult. Regular and frequent follow-up evaluations utilizing objective mood ratings and standardized evaluation methods in a naturalistic fashion are necessary to determine detailed clinical courses of mood disorders. Further, biological samples should also be collected to incorporate clinical findings in the development of new diagnostic and therapeutic approaches. An innovative cohort study that can serve as a platform for translational research for treatment and prevention of mood disorders is critical in determining clinical, psychosocial, neurobiological and genetic factors associated with long-term courses and consequences of mood disorders in Korean patients.

Genetics of long-term treatment outcome in bipolar disorder

Bipolar disorder (BD) shows one of the strongest genetic predispositions among psychiatric disorders and the identification of reliable genetic predictors of treatment response could significantly improve the prognosis of the disease. The present study investigated genetic predictors of long-term treatment-outcome in 723 patients with BD type I from the STEP-BD (Systematic Treatment Enhancement Program for Bipolar Disorder) genome-wide dataset. BD I patients with >6months of follow-up and without any treatment restriction (reflecting a natural setting scenario) were included. Phenotypes were the total and depressive episode rates and the occurrence of one or more (hypo)manic/mixed episodes during follow-up. Quality control of genome-wide data was performed according to standard criteria and linear/logistic regression models were used as appropriate under an additive hypothesis. Top genes were further analyzed through a pathway analysis. Genes previously involved in the susceptibility to BD (DFNB31, SORCS2, NRXN1, CNTNAP2, GRIN2A, GRM4, GRIN2B), antidepressant action (DEPTOR, CHRNA7, NRXN1), and mood stabilizer or antipsychotic action (NTRK2, CHRNA7, NRXN1) may affect long-term treatment outcome of BD. Promising findings without previous strong evidence were TRAF3IP2-AS1, NFYC, RNLS, KCNJ2, RASGRF1, NTF3 genes. Pathway analysis supported particularly the involvement of molecules mediating the positive regulation of MAPK cascade and learning/memory processes. Further studies focused on the outlined genes may be helpful to provide validated markers of BD treatment outcome.

The microtubule-associated molecular pathways may be genetically disrupted in patients with Bipolar Disorder. Insights from the molecular cascades

Bipolar Disorder is a severe disease characterized by pathological mood swings from major depressive episodes to manic ones and vice versa. The biological underpinnings of Bipolar Disorder have yet to be defined. As a consequence, pharmacological treatments are suboptimal. In the present paper we test the hypothesis that the molecular pathways involved with the direct targets of lithium, hold significantly more genetic variations associated with BD. A molecular pathway approach finds its rationale in the polygenic nature of the disease. The pathways were tested in a sample of ∼ 7,000 patients and controls. Data are available from the public NIMH database. The definition of the pathways was conducted according to the National Cancer Institute (http://pid.nci.nih.gov/). As a result, 3 out of the 18 tested pathways related to lithium action resisted the permutation analysis and were found to be associated with BD. These pathways were related to Reelin, Integrins and Aurora. A pool of genes selected from the ones linked with the above pathways was further investigated in order to identify the fine molecular mechanics shared by our significant pathways and also their link with lithium mechanism of action. The data obtained point out to a possible involvement of microtubule-related mechanics.

Applicability of gene expression and systems biology to develop pharmacogenetic predictors; antipsychotic-induced extrapyramidal symptoms as an example

Pharmacogenetics has been driven by a candidate gene approach. The disadvantage of this approach is that is limited by our current understanding of the mechanisms by which drugs act. Gene expression could help to elucidate the molecular signatures of antipsychotic treatments searching for dysregulated molecular pathways and the relationships between gene products, especially protein-protein interactions. To embrace the complexity of drug response, machine learning methods could help to identify gene-gene interactions and develop pharmacogenetic predictors of drug response. The present review summarizes the applicability of the topics presented here (gene expression, network analysis and gene-gene interactions) in pharmacogenetics. In order to achieve this, we present an example of identifying genetic predictors of extrapyramidal symptoms induced by antipsychotic.

Detecting key genes regulated by miRNAs in dysfunctional crosstalk pathway of myasthenia gravis

Myasthenia gravis (MG) is a neuromuscular autoimmune disorder resulting from autoantibodies attacking components of the neuromuscular junction. Recent studies have implicated the aberrant expression of microRNAs (miRNAs) in the pathogenesis of MG; however, the underlying mechanisms remain largely unknown. This study aimed to identify key genes regulated by miRNAs in MG. Six dysregulated pathways were identified through differentially expressed miRNAs and mRNAs in MG, and significant crosstalk was detected between five of these. Notably, crosstalk between the "synaptic long-term potentiation" pathway and four others was mediated by five genes involved in the MAPK signaling pathway. Furthermore, 14 key genes regulated by miRNAs were detected, of which six-MAPK1, RAF1, PGF, PDGFRA, EP300, and PPP1CC-mediated interactions between the dysregulated pathways. MAPK1 and RAF1 were responsible for most of this crosstalk (80%), likely reflecting their central roles in MG pathogenesis. In addition, most key genes were enriched in immune-related local areas that were strongly disordered in MG. These results provide new insight into the pathogenesis of MG and offer new potential targets for therapeutic intervention.

Genetics of psychotropic medication induced side effects in two independent samples of bipolar patients

The treatment of bipolar disorder (BD) usually requires combination therapies, with the critical issue of the emergence of adverse drug reactions (ADRs) and the possibility of low treatment adherence. Genetic polymorphisms are hypothesized to modulate the pharmacodynamics of psychotropic drugs, representing potential biological markers of ADRs. This study investigated genes involved in the regulation of neuroplasticity (BDNF, ST8SIA2), second messenger cascades (GSK3B, MAPK1, and CREB1), circadian rhythms (RORA), transcription (SP4, ZNF804A), and monoaminergic system (HTR2A and COMT) in the risk of neurological, psychic, autonomic, and other ADRs. Two independent samples of BD patients naturalistically treated were included (COPE-BD n = 147; STEP-BD n = 659). In the COPE-BD 34 SNPs were genotyped, while in the STEP-BD polymorphisms in the selected genes were extracted from the genome-wide dataset. Each ADRs group was categorized as absent-mild or moderate-severe and logistic regression with appropriate covariates was applied to identify possible risk genotypes/alleles. 58.5 and 93.5 % of patients were treated with mood stabilizers, 44.2 and 50.7 % were treated with antipsychotics, and 69.4 and 46.1 % were treated with antidepressants in the COPE-BD and STEP-BD, respectively. Our findings suggested that ST8SIA2 may be associated with psychic ADRs, as shown in the COPE-BD (rs4777989 p = 0.0017) and STEP-BD (rs56027313, rs13379489 and rs10852173). A cluster of RORA SNPs around rs2083074 showed an effect on psychic ADRs in the STEP-BD. Trends supporting the association between HTR2A and autonomic ADRs were found in both samples. Confirmations are needed particularly for ST8SIA2 and RORA since the few available data regarding their role in relation to psychotropic ADRs.

From pharmacogenetics to pharmacogenomics: the way toward the personalization of antidepressant treatment

OBJECTIVE

Major depressive disorder is the most common psychiatric disorder, worldwide, yet response and remission rates are still unsatisfactory. The identification of genetic predictors of antidepressant (AD) response could provide a promising opportunity to improve current AD efficacy through the personalization of treatment. The major steps and findings along this path are reviewed together with their clinical implications and limitations.

METHOD

We systematically reviewed the literature through MEDLINE and Embase database searches, using any word combination of "antidepressant," "gene," "polymorphism," "pharmacogenetics," "genome-wide association study," "GWAS," "response," and "adverse drug reactions." Experimental works and reviews published until March 2012 were collected and compared.

RESULTS

Numerous genes pertaining to several functional systems were associated with AD response. The more robust findings were found for the following genes: solute carrier family 6 (neurotransmitter transporter), member 4; serotonin receptor 1A and 2A; brain-derived neurotrophic factor; and catechol-O-methyltransferase. Genome-wide association studies (GWASs) provided many top markers, even if none of them reached genome-wide significance.

CONCLUSIONS

AD pharmacogenetics have not produced any knowledge applicable to routine clinical practice yet, as results were mainly inconsistent across studies. Despite this, the rising awareness about methodological deficits of past studies could allow for the identication of more suitable strategies, such as the integration of the GWAS approach with the candidate gene approach, and innovative methodologies, such as pathway analysis and study of depressive endophenotypes.

The implications of genomics on the nursing care of adults with neuropsychiatric conditions

PURPOSE

Neuropsychiatric disorders contribute substantially to disease burden and quality of life across the lifespan and the globe. The purpose of this article is to review the state of the science regarding genomic contributions to selected common neuropsychiatric conditions and to examine the consequent immediate and future implications for nursing practice and research.

ORGANIZING CONSTRUCT

Our work is guided by an ecological model that recognizes that common diseases are complex or multifactorial, meaning that multiple genomic and environmental factors contribute to their etiology.

METHODS

A review of the literature was conducted to determine the state of the science in relationship to the genomic contributions to selected neuropsychiatric disorders.

FINDINGS

Neuropsychiatric conditions are genomically heterogeneous, both within a single disorder and across groups of disorders. While recent genomic research yields clinically validated and useful information for a small subset of persons (e.g., predictive genetic testing for Huntington disease and early-onset Alzheimer disease), broad clinical application of genetic information is not yet available. In addition, the implications of genomics for the development and targeting of nonpharmacologic treatment strategies is largely unexplored.

CONCLUSIONS

Further research is needed to expand knowledge beyond genomic risk for the presence of disease to knowledge about the genomic risk for symptoms, symptom burden, and tailored symptom management interventions.

CLINICAL RELEVANCE

Knowledge about the genomic influences on neuropsychiatric conditions suggests important implications for practicing nurses in the identification of persons at risk, provision of follow-up support, and in the administration of medications.

Pathway analysis using information from allele-specific gene methylation in genome-wide association studies for bipolar disorder

Bipolar disorder (BPD) is a complex psychiatric trait with high heritability. Despite efforts through conducting genome-wide association (GWA) studies, the success of identifying susceptibility loci for BPD has been limited, which is partially attributed to the complex nature of its pathogenesis. Pathway-based analytic strategy is a powerful tool to explore joint effects of gene sets within specific biological pathways. Additionally, to incorporate other aspects of genomic data into pathway analysis may further enhance our understanding for the underlying mechanisms for BPD. Patterns of DNA methylation play important roles in regulating gene expression and function. A commonly observed phenomenon, allele-specific methylation (ASM) describes the associations between genetic variants and DNA methylation patterns. The present study aimed to identify biological pathways that are involve in the pathogenesis of BPD while incorporating brain specific ASM information in pathway analysis using two large-scale GWA datasets in Caucasian populations. A weighting scheme was adopted to take ASM information into consideration for each pathway. After multiple testing corrections, we identified 88 and 15 enriched pathways for their biological relevance for BPD in the Genetic Association Information Network (GAIN) and the Wellcome Trust Case Control Consortium dataset, respectively. Many of these pathways were significant only when applying the weighting scheme. Three ion channel related pathways were consistently identified in both datasets. Results in the GAIN dataset also suggest for the roles of extracellular matrix in brain for BPD. Findings from Gene Ontology (GO) analysis exhibited functional enrichment among genes of non-GO pathways in activity of gated channel, transporter, and neurotransmitter receptor. We demonstrated that integrating different data sources with pathway analysis provides an avenue to identify promising and novel biological pathways for exploring the underlying molecular mechanisms for bipolar disorder. Further basic research can be conducted to target the biological mechanisms for the identified genes and pathways.

Relating human genetic variation to variation in drug responses

Although sequencing a single human genome was a monumental effort a decade ago, more than 1000 genomes have now been sequenced. The task ahead lies in transforming this information into personalized treatment strategies that are tailored to the unique genetics of each individual. One important aspect of personalized medicine is patient-to-patient variation in drug response. Pharmacogenomics addresses this issue by seeking to identify genetic contributors to human variation in drug efficacy and toxicity. Here, we present a summary of the current status of this field, which has evolved from studies of single candidate genes to comprehensive genome-wide analyses. Additionally, we discuss the major challenges in translating this knowledge into a systems-level understanding of drug physiology, with the ultimate goal of developing more effective personalized clinical treatment strategies.