Comparative genomic evidence for the involvement of schizophrenia risk genes in antipsychotic effects

Transplantation of gut microbiota derived from patients with schizophrenia induces schizophrenia-like behaviors and dysregulated brain transcript response in mice

Schizophrenia (SCZ), as a neurodevelopmental disorder and devastating disease, affects approximately 1% of the world population. Although numerous studies have attempted to elucidate the causes of SCZ occurrence, it is not clearly understood. Recently, the emerging roles of the gut microbiota in a range of brain disorders, including SCZ, have attracted much attention. While the molecular mechanism of gut microbiota in regulating the pathogenesis of SCZ is still lacking. Here, we first confirmed the difference of gut microbiome between SCZ patients and healthy controls, and then, we performed fecal microbiota transplantation (FMT) to clarify the roles of SCZ patients-derived microbiota in a specific pathogen free (SPF) mice model. 16 S rDNA sequencing confirmed that a significant difference of gut microbiome was present between two groups of FMT mice, which has a similar trend with the above human gut microbiome. Furthermore, we found that transplantation of fecal microbiota from SCZ patients into SPF mice was sufficient to induce schizophrenia-like (SCZ-like) symptoms, such as deficits in sociability and hyperactivity. Furthermore, the brains of mice colonized with SCZ microbiota displayed dysregulated transcript response and alternative splicing of SCZ-relevant genes. Moreover, 10 key genes were identified to be correlated with SCZ by an integrative transcriptome data analysis. Finally, 4 key genes were identified to be correlated with the 12 differential genera between two groups of FMT mice. Our results thus demonstrated that the gut microbiome might modify the transcriptomic profile in the brain, thereby modulating social behavior, and our present study can help better understand the link between gut microbiota and SCZ pathogenesis through the gut-brain axis.

Analysis of the caudate nucleus transcriptome in individuals with schizophrenia highlights effects of antipsychotics and new risk genes

Most studies of gene expression in the brains of individuals with schizophrenia have focused on cortical regions, but subcortical nuclei such as the striatum are prominently implicated in the disease, and current antipsychotic drugs target the striatum's dense dopaminergic innervation. Here, we performed a comprehensive analysis of the genetic and transcriptional landscape of schizophrenia in the postmortem caudate nucleus of the striatum of 443 individuals (245 neurotypical individuals, 154 individuals with schizophrenia and 44 individuals with bipolar disorder), 210 from African and 233 from European ancestries. Integrating expression quantitative trait loci analysis, Mendelian randomization with the latest schizophrenia genome-wide association study, transcriptome-wide association study and differential expression analysis, we identified many genes associated with schizophrenia risk, including potentially the dopamine D2 receptor short isoform. We found that antipsychotic medication has an extensive influence on caudate gene expression. We constructed caudate nucleus gene expression networks that highlight interactions involving schizophrenia risk. These analyses provide a resource for the study of schizophrenia and insights into risk mechanisms and potential therapeutic targets.

Gene expression changes following chronic antipsychotic exposure in single cells from mouse striatum

Schizophrenia is an idiopathic psychiatric disorder with a high degree of polygenicity. Evidence from genetics, single-cell transcriptomics, and pharmacological studies suggest an important, but untested, overlap between genes involved in the etiology of schizophrenia and the cellular mechanisms of action of antipsychotics. To directly compare genes with antipsychotic-induced differential expression to genes involved in schizophrenia, we applied single-cell RNA-sequencing to striatal samples from male C57BL/6 J mice chronically exposed to a typical antipsychotic (haloperidol), an atypical antipsychotic (olanzapine), or placebo. We identified differentially expressed genes in three cell populations identified from the single-cell RNA-sequencing (medium spiny neurons [MSNs], microglia, and astrocytes) and applied multiple analysis pipelines to contextualize these findings, including comparison to GWAS results for schizophrenia. In MSNs in particular, differential expression analysis showed that there was a larger share of differentially expressed genes (DEGs) from mice treated with olanzapine compared with haloperidol. DEGs were enriched in loci implicated by genetic studies of schizophrenia, and we highlighted nine genes with convergent evidence. Pathway analyses of gene expression in MSNs highlighted neuron/synapse development, alternative splicing, and mitochondrial function as particularly engaged by antipsychotics. In microglia, we identified pathways involved in microglial activation and inflammation as part of the antipsychotic response. In conclusion, single-cell RNA sequencing may provide important insights into antipsychotic mechanisms of action and links to findings from psychiatric genomic studies.

Molecular phenotypes associated with antipsychotic drugs in the human caudate nucleus

Antipsychotic drugs are the current first-line of treatment for schizophrenia and other psychotic conditions. However, their molecular effects on the human brain are poorly studied, due to difficulty of tissue access and confounders associated with disease status. Here we examine differences in gene expression and DNA methylation associated with positive antipsychotic drug toxicology status in the human caudate nucleus. We find no genome-wide significant differences in DNA methylation, but abundant differences in gene expression. These gene expression differences are overall quite similar to gene expression differences between schizophrenia cases and controls. Interestingly, gene expression differences based on antipsychotic toxicology are different between brain regions, potentially due to affected cell type differences. We finally assess similarities with effects in a mouse model, which finds some overlapping effects but many differences as well. As a first look at the molecular effects of antipsychotics in the human brain, the lack of epigenetic effects is unexpected, possibly because long term treatment effects may be relatively stable for extended periods.

Bioinformatics and Network-based Approaches for Determining Pathways, Signature Molecules, and Drug Substances connected to Genetic Basis of Schizophrenia etiology

Knowledge of heterogeneous etiology and pathophysiology of schizophrenia (SZP) is reasonably inadequate and non-deterministic due to its inherent complexity and underlying vast dynamics related to genetic mechanisms. The evolution of large-scale transcriptome-wide datasets and subsequent development of relevant, robust technologies for their analyses show promises toward elucidating the genetic basis of disease pathogenesis, its early risk prediction, and predicting drug molecule targets for therapeutic intervention. In this research, we have scrutinized the genetic basis of SZP through functional annotation and network-based system biology approaches. We have determined 96 overlapping differentially expressed genes (DEGs) from 2 microarray datasets and subsequently identified their interconnecting networks to reveal transcriptome signatures like hub proteins (FYN, RAD51, SOCS3, XIAP, AKAP13, PIK3C2A, CBX5, GATA3, EIF3K, and CDKN2B), transcription factors and miRNAs. In addition, we have employed gene set enrichment to highlight significant gene ontology (e.g., positive regulation of microglial cell activation) and relevant pathways (such as axon guidance and focal adhesion) interconnected to the genes associated with SZP. Finally, we have suggested candidate drug substances like Luteolin HL60 UP as a possible therapeutic target based on these key molecular signatures.

Transcriptional profile of pyramidal neurons in chronic schizophrenia reveals lamina-specific dysfunction of neuronal immunity

While the pathophysiology of schizophrenia has been extensively investigated using homogenized postmortem brain samples, few studies have examined changes in brain samples with techniques that may attribute perturbations to specific cell types. To fill this gap, we performed microarray assays on mRNA isolated from anterior cingulate cortex (ACC) superficial and deep pyramidal neurons from 12 schizophrenia and 12 control subjects using laser-capture microdissection. Among all the annotated genes, we identified 134 significantly increased and 130 decreased genes in superficial pyramidal neurons, while 93 significantly increased and 101 decreased genes were found in deep pyramidal neurons, in schizophrenia compared to control subjects. In these differentially expressed genes, we detected lamina-specific changes of 55 and 31 genes in superficial and deep neurons in schizophrenia, respectively. Gene set enrichment analysis (GSEA) was applied to the entire pre-ranked differential expression gene lists to gain a complete pathway analysis throughout all annotated genes. Our analysis revealed overrepresented groups of gene sets in schizophrenia, particularly in immunity and synapse-related pathways, suggesting the disruption of these pathways plays an important role in schizophrenia. We also detected other pathways previously demonstrated in schizophrenia pathophysiology, including cytokine and chemotaxis, postsynaptic signaling, and glutamatergic synapses. In addition, we observed several novel pathways, including ubiquitin-independent protein catabolic process. Considering the effects of antipsychotic treatment on gene expression, we applied a novel bioinformatics approach to compare our differential expression gene profiles with 51 antipsychotic treatment datasets, demonstrating that our results were not influenced by antipsychotic treatment. Taken together, we found pyramidal neuron-specific changes in neuronal immunity, synaptic dysfunction, and olfactory dysregulation in schizophrenia, providing new insights for the cell-subtype specific pathophysiology of chronic schizophrenia.

MiR-574-5P, miR-1827, and miR-4429 as Potential Biomarkers for Schizophrenia

Schizophrenia is a severe chronic debilitating disorder with millions of affected individuals. Diagnosis is based on clinical presentations, which are made when the progressive disease has appeared. Early diagnosis may help improve the clinical outcomes and response to treatments. Lack of a reliable molecular diagnostic invokes the identification of novel biomarkers. To elucidate the molecular basis of the disease, in this study we used two mRNA expression arrays, including GSE93987 and GSE38485, and one miRNA array, GSE54914, and meta-analysis was conducted for evaluation of mRNA expression arrays via metaDE package. Using WGCNA package, we performed network analysis for both mRNA expression arrays separately. Then, we constructed protein-protein interaction network for significant modules. Limma package was employed to analyze the miRNA array for identification of dysregulated miRNAs (DEMs). Using genes of significant modules and DEMs, a mRNA-miRNA network was constructed and hub genes and miRNAs were identified. To confirm the dysregulated genes, expression values were evaluated through available datasets including GSE62333, GSE93987, and GSE38485. The ability of the detected hub miRNAs to discriminate schizophrenia from healthy controls was evaluated by assessing the receiver-operating curve. Finally, the expression levels of genes and miRNAs were evaluated in 40 schizophrenia patients compared with healthy controls via Real-Time PCR. The results confirmed dysregulation of hsa-miR-574-5P, hsa-miR-1827, hsa-miR-4429, CREBRF, ARPP19, TGFBR2, and YWHAZ in blood samples of schizophrenia patients. In conclusion, three miRNAs including hsa-miR-574-5P, hsa-miR-1827, and hsa-miR-4429 are suggested as potential biomarkers for diagnosis of schizophrenia.

Antipsychotic Behavioral Phenotypes in the Mouse Collaborative Cross Recombinant Inbred Inter-Crosses (RIX)

Schizophrenia is an idiopathic disorder that affects approximately 1% of the human population, and presents with persistent delusions, hallucinations, and disorganized behaviors. Antipsychotics are the standard pharmacological treatment for schizophrenia, but are frequently discontinued by patients due to inefficacy and/or side effects. Chronic treatment with the typical antipsychotic haloperidol causes tardive dyskinesia (TD), which manifests as involuntary and often irreversible orofacial movements in around 30% of patients. Mice treated with haloperidol develop many of the features of TD, including jaw tremors, tongue protrusions, and vacuous chewing movements (VCMs). In this study, we used genetically diverse Collaborative Cross (CC) recombinant inbred inter-cross (RIX) mice to elucidate the genetic basis of antipsychotic-induced adverse drug reactions (ADRs). We performed a battery of behavioral tests in 840 mice from 73 RIX lines (derived from 62 CC strains) treated with haloperidol or placebo in order to monitor the development of ADRs. We used linear mixed models to test for strain and treatment effects. We observed highly significant strain effects for almost all behavioral measurements investigated (P < 0.001). Further, we observed strong strain-by-treatment interactions for most phenotypes, particularly for changes in distance traveled, vertical activity, and extrapyramidal symptoms (EPS). Estimates of overall heritability ranged from 0.21 (change in body weight) to 0.4 (VCMs and change in distance traveled) while the portion attributable to the interactions of treatment and strain ranged from 0.01 (for change in body weight) to 0.15 (for change in EPS). Interestingly, close to 30% of RIX mice exhibited VCMs, a sensitivity to haloperidol exposure, approximately similar to the rate of TD in humans chronically exposed to haloperidol. Understanding the genetic basis for the susceptibility to antipsychotic ADRs may be possible in mouse, and extrapolation to humans could lead to safer therapeutic approaches for schizophrenia.

Preclinical validation of the micropipette-guided drug administration (MDA) method in the maternal immune activation model of neurodevelopmental disorders

Pharmacological treatments in laboratory rodents remain a cornerstone of preclinical psychopharmacological research and drug development. There are numerous ways in which acute or chronic pharmacological treatments can be implemented, with each method having certain advantages and drawbacks. Here, we describe and validate a novel treatment method in mice, which we refer to as the micropipette-guided drug administration (MDA) procedure. This administration method is based on a sweetened condensed milk solution as a vehicle for pharmacological substances, which motivates the animals to consume vehicle and/or drug solutions voluntarily in the presence of the experimenter. In a proof-of-concept study, we show that the pharmacokinetic profiles of the atypical antipsychotic drug, risperidone, were similar whether administered via the MDA procedure or via the conventional oral gavage method. Unlike the latter, however, MDA did not induce the stress hormone, corticosterone. Furthermore, we assessed the suitability and validity of the MDA method in a mouse model of maternal immune activation, which is frequently used as a model of immune-mediated neurodevelopmental disorders. Using this model, we found that chronic treatment (>4 weeks, once per day) with risperidone via MDA led to a dose-dependent mitigation of MIA-induced social interaction deficits and amphetamine hypersensitivity. Taken together, the MDA procedure described herein represents a novel pharmacological administration method for per os treatments in mice that is easy to implement, cost effective, non-invasive, and less stressful for the animals than conventional oral gavage methods.

Neurobehavioral changes arising from early life dopamine signaling perturbations

Dopamine (DA) signaling is critical to the modulation of multiple brain functions including locomotion, reinforcement, attention and cognition. The literature provides strong evidence that altered DA availability and actions can impact normal neurodevelopment, with both early and enduring consequences on anatomy, physiology and behavior. An appreciation for the developmental contributions of DA signaling to brain development is needed to guide efforts to preclude and remedy neurobehavioral disorders, such as attention-deficit/hyperactivity disorder, addiction, bipolar disorder, schizophrenia and autism spectrum disorder, each of which exhibits links to DA via genetic, cellular and/or pharmacological findings. In this review, we highlight research pursued in preclinical models that use genetic and pharmacological approaches to manipulate DA signaling at sensitive developmental stages, leading to changes at molecular, circuit and/or behavioral levels. We discuss how these alterations can be aligned with traits displayed by neuropsychiatric diseases. Lastly, we review human studies that evaluate contributions of developmental perturbations of DA systems to increased risk for neuropsychiatric disorders.

Investigation of Schizophrenia with Human Induced Pluripotent Stem Cells

Schizophrenia is a chronic and severe neuropsychiatric condition manifested by cognitive, emotional, affective, perceptual, and behavioral abnormalities. Despite decades of research, the biological substrates driving the signs and symptoms of the disorder remain elusive, thus hampering progress in the development of treatments aimed at disease etiologies. The recent emergence of human induced pluripotent stem cell (hiPSC)-based models has provided the field with a highly innovative approach to generate, study, and manipulate living neural tissue derived from patients, making possible the exploration of fundamental roles of genes and early-life stressors in disease-relevant cell types. Here, we begin with a brief overview of the clinical, epidemiological, and genetic aspects of the condition, with a focus on schizophrenia as a neurodevelopmental disorder. We then highlight relevant technical advancements in hiPSC models and assess novel findings attained using hiPSC-based approaches and their implications for disease biology and treatment innovation. We close with a critical appraisal of the developments necessary for both further expanding knowledge of schizophrenia and the translation of new insights into therapeutic innovations.

Consistent gene signature of schizophrenia identified by a novel feature selection strategy from comprehensive sets of transcriptomic data

The etiology of schizophrenia (SCZ) is regarded as one of the most fundamental puzzles in current medical research, and its diagnosis is limited by the lack of objective molecular criteria. Although plenty of studies were conducted, SCZ gene signatures identified by these independent studies are found highly inconsistent. As one of the most important factors contributing to this inconsistency, the feature selection methods used currently do not fully consider the reproducibility among the signatures discovered from different datasets. Therefore, it is crucial to develop new bioinformatics tools of novel strategy for ensuring a stable discovery of gene signature for SCZ. In this study, a novel feature selection strategy (1) integrating repeated random sampling with consensus scoring and (2) evaluating the consistency of gene rank among different datasets was constructed. By systematically assessing the identified SCZ signature comprising 135 differentially expressed genes, this newly constructed strategy demonstrated significantly enhanced stability and better differentiating ability compared with the feature selection methods popular in current SCZ research. Based on a first-ever assessment on methods' reproducibility cross-validated by independent datasets from three representative studies, the new strategy stood out among the popular methods by showing superior stability and differentiating ability. Finally, 2 novel and 17 previously reported transcription factors were identified and showed great potential in revealing the etiology of SCZ. In sum, the SCZ signature identified in this study would provide valuable clues for discovering diagnostic molecules and potential targets for SCZ.

Identification and prioritization of gene sets associated with schizophrenia risk by co-expression network analysis in human brain

Schizophrenia polygenic risk is plausibly manifested by complex transcriptional dysregulation in the brain, involving networks of co-expressed and functionally related genes. The main purpose of this study was to identify and prioritize co-expressed gene sets in a hierarchical manner, based on the strength of the relationships with clinical diagnosis and with polygenic risk for schizophrenia. Weighted Gene Co-expression Network Analysis (WGCNA) was applied to RNA-quality-adjusted DLPFC RNA-Seq data from the LIBD Postmortem Human Brain Repository (90 controls, 74 schizophrenia cases; all Caucasians) to construct co-expression networks and detect "modules" of co-expressed genes. After multiple internal and external validation procedures, modules of selected interest were tested for enrichment in biological ontologies, for association with schizophrenia polygenic risk scores (PRSs) and with diagnosis, and also for enrichment in genes within the significant GWAS loci reported by the Psychiatric Genomic Consortium (PGC2). The association between schizophrenia genetic signals and modules of co-expression converged on one module showing not only a significant association with both diagnosis and PRS but also significant overlap with 36 PGC2 loci genes, deemed the strongest candidates for drug targets. This module contained many genes involved in synaptic signaling and neuroplasticity. Fifty-three PGC2 genes were in modules associated only with diagnosis and 59 in modules unrelated to diagnosis or PRS. Our study highlights complex relationships between gene co-expression networks in the brain and clinical state and polygenic risk for SCZ and provides a strategy for using this information in selecting and prioritizing potentially targetable gene sets for therapeutic drug development.

Prefrontal Coexpression of Schizophrenia Risk Genes Is Associated With Treatment Response in Patients

BACKGROUND

Gene coexpression networks are relevant to functional and clinical translation of schizophrenia risk genes. We hypothesized that schizophrenia risk genes converge into coexpression pathways that may be associated with gene regulation mechanisms and with response to treatment in patients with schizophrenia.

METHODS

We identified gene coexpression networks in two prefrontal cortex postmortem RNA sequencing datasets (n = 688) and replicated them in four more datasets (n = 1295). We identified and replicated (p values < .001) a single module enriched for schizophrenia risk loci (13 risk genes in 10 loci). In silico screening of potential regulators of the schizophrenia risk module via bioinformatic analyses identified two transcription factors and three microRNAs associated with the risk module. To translate postmortem information into clinical phenotypes, we identified polymorphisms predicting coexpression and combined them to obtain an index approximating module coexpression (Polygenic Coexpression Index [PCI]).

RESULTS

The PCI-coexpression association was successfully replicated in two independent brain transcriptome datasets (n = 131; p values < .05). Finally, we tested the association between the PCI and short-term treatment response in two independent samples of patients with schizophrenia treated with olanzapine (n = 167). The PCI was associated with treatment response in the positive symptom domain in both clinical cohorts (p values < .05).

CONCLUSIONS

In summary, our findings in 1983 samples of human postmortem prefrontal cortex show that coexpression of a set of genes enriched for schizophrenia risk genes is relevant to treatment response. This coexpression pathway may be coregulated by transcription factors and microRNA associated with it.

Reduced cortical thickness related to single nucleotide polymorphisms in the major histocompatibility complex region in antipsychotic-naive schizophrenia

The aim of this study was to explore the relationships between changes in cortical thickness and single nucleotide polymorphisms (SNPs) in the major histocompatibility complex (MHC) region in a group of antipsychotic-naive schizophrenia (AN-SCZ) patients. Methods Twenty-five AN-SCZ patients and 51 healthy controls (HCs) participated in this study. General linear models were used to identify associations between the average cortical thicknesses of each brain region (N = 68) and each of the 11 SNPs in the MHC region in the AN-SCZ patients and HCs. Next, we performed independent-sample t tests to investigate whether cortical thickness was significantly lower in the AN-SCZ patients than in HCs in the brain regions that were significantly associated with the SNPs. Finally, we examined the correlations between clinical symptoms and cortical thickness in the above brain areas in the whole AN-SCZ group using Pearson correlation tests. Results Seven of the 11 SNPs within the MHC region were significantly associated with cortical thickness only in the AN-SCZ patients; these included rs1635, rs1736913, rs2021722, rs204999, rs2523722, rs3131296, and rs9272105. The AN-SCZ patients had significantly thinner cortical thicknesses in the above brain regions, especially the prefrontal cortex. Furthermore, the left entorhinal region was negatively correlated with Positive and Negative Symptom Scale (PANSS) activation scores in the AN-SCZ group (r = -0.601, p = 0.03). Conclusions This study provides evidence demonstrating the potential effects of MHC risk variants in cortical thickness deficits in AN-SCZ. These data also support the notion that the immune system plays critical roles in the pathology of schizophrenia, which is mediated via the modulation of the development of cerebral cortical structures.

Gene Expression Profiles Associated with Brain Aging are Altered in Schizophrenia

Existence of aging associated transcriptional differences in the schizophrenia brain was investigated in RNA sequencing data from 610 postmortem Dorso-Lateral Pre-Frontal Cortex (DLPFC) samples in the CommondMind Consortium (CMC) and the psychENCODE cohorts. This analysis discovered that the trajectory of gene expression changes that occur during brain aging differed between schizophrenia cases and unaffected controls. Mainly, the identified gene expression differences between the diagnosis groups shrank in magnitude following 60 years of age. A differential expression analysis restricted to the 40 to 60 year age group identified 556 statistically significant loci that replicated and had highly consistent gene expression fold changes in the two cohorts. An interaction between age and diagnosis in the wider psychENCODE cohort was also detected. Gene set enrichment analysis discovered disruptions in mitochondria, RNA splicing and phosphoprotein gene pathways. The identified differentially expressed genes in the two cohorts were also significantly enriched in genomic regions associated with schizophrenia although no enrichment was observed for differentially expressed genes identified in the 40 to 60 year age group. This work implicates disruptions to the normal brain aging processes in the pathology of schizophrenia and demonstrates the need for age stratification in schizophrenia postmortem brain gene expression studies.

Genetics of Eating Disorders: What the Clinician Needs to Know

Anorexia nervosa (AN), bulimia nervosa (BN), and binge-eating disorder (BED) are heritable conditions that are influenced by both genetic and environmental factors. Recent genome-wide association studies (GWAS) of AN have identified specific genetic loci implicated in AN, and genetic correlations have implicated both psychiatric and metabolic factors in its origin. No GWAS have been performed for BN or BED. Genetic counseling is an important tool and can aid families and patients in understanding risk for these illnesses.

Identifying the genetic risk factors for treatment response to lurasidone by genome-wide association study: A meta-analysis of samples from three independent clinical trials

A genome-wide association study (GWAS) of response of schizophrenia patients to the atypical antipsychotic drug, lurasidone, based on two double-blind registration trials, identified SNPs from four classes of genes as predictors of efficacy, but none were genome wide significant (GWS). After inclusion of data from a third lurasidone trial, meta-analysis identified a GWS marker and other findings consistent with our first study. The primary end-point was change in Total Positive and Negative Syndrome Scale (PANSS) between baseline and last observation carried forward. rs4736253, a genetic locus near KCNK9, encoding the K_2P9.1 potassium channel, with a role in cognition and neurodevelopment, was the top marker in patients of European ancestry (EUR) (n = 264), reaching GWS (p = 4.78 × 10^-8). rs10180106 (p = 4.92 × 10^-7), located at an intron region of CTNNA2, a SCZ risk gene important for dendritic spine stabilization, was one of other best response markers for EUR patients. SNPs at STXBP5L (rs511841, p = 2.63 × 10^-7) were the top markers for patients of African ancestry (n = 158). The association between PTPRD, NRG1, and MAGI1 previously reported to be related to response to lurasidone in the first two trials, showed a trend of significant association in the third trial. None of these genetic loci showed significant associations with clinical response in the corresponding placebo groups (n = 107 for EUR; n = 58 for AFR). This meta-analysis yielded the first GWAS-based GWS biomarker for lurasidone response and additional support for the conclusion that genes related to synaptic biology and/or risk for SCZ are the strongest predictors of response to lurasidone in schizophrenia patients.

Protein-Protein Interaction Analysis of Common Top Genes in Obsessive-Compulsive Disorder (OCD) and Schizophrenia: Towards New Drug Approach Obsessive-Compulsive disorder (OCD) and Schizophrenia Comorbidity Gene Analysis

Comorbidty is common among psychiatric disorders including obsessive-compulsive disorder and schizophrenia with a high rate. Many studies suggested that the disorders may have same etiological bases. In this regard, shared pathways of glutamate, dopaminergic, and serotonin are the known ones. Here, the common significant genes are examined to understand the possible molecular origin of the disorders in terms of sequence and functional features. Exploring the underling mechanisms of OCD and schizophrenia is important to achieve a better treatment options. Methods of Cytoscape software following R statistical software were applied for this purpose. Needleman-Wunsch global alignment algorithm was used to determine pair-wise similarities followed by clustering methods, AGNES and PAM in R statistical programming software. The results indicate that SLC1A1, DRD2, DRD4, BDNF, ESR1, CDH2, GRIN2B, TNFa, GABBR1, and OLIG2 are significantly common for the two disorders and PPI network analysis showed the important key genes in the interaction profile. ESR1 (estrogen receptor α) as a key hub-bottleneck gene regulates many underling mechanisms of the brain. Application of global alignments indicates some of the genes with sequence similarities also elucidate similar biological terms.