Investigating association of four gene regions (GABRB3, MAOB, PAH, and SLC6A4) with five symptoms in schizophrenia

Monoamine Oxidase B (MAO-B): A Target for Rational Drug Development in Schizophrenia Using PET Imaging as an Example

Monoamine oxidase B (MAO-B) is an important high-density enzyme involved in the generation of oxidative stress and central in the catabolism of dopamine, particularly in brain subcortical regions with putative implications in the pathophysiology of schizophrenia. In this chapter, we review postmortem studies, preclinical models, and peripheral and genetic studies implicating MAO-B in psychosis. A literature search in PubMed was conducted and 64 studies were found to be eligible for systematic review. We found that MAO-B could be identified as a potential target in schizophrenia. Evidence comes mostly from studies of peripheral markers, showing reduced platelet MAO-B activity in schizophrenia, together with preclinical results from MAO-B knock-out mice resulting in a hyperdopaminergic state and behavioral disinhibition. However, whether brain MAO-B is altered in vivo in patients with schizophrenia remains unknown. We therefore review methodological studies involving MAO-B positron emission tomography (PET) radioligands used to quantify MAO-B in vivo in the human brain. Given the limitations of currently available treatments for schizophrenia, elucidating whether MAO-B could be used as a target for risk stratification or clinical staging in schizophrenia could allow for a rational search for newer antipsychotics and the development of new treatments.

Gene expression meta-analysis reveals the down-regulation of three GABA receptor subunits in the superior temporal gyrus of patients with schizophrenia

One of the main theories accounting for the underlying pathophysiology of schizophrenia posits alterations in GABAergic neurotransmission. While previous gene expression studies of postmortem brain samples typically report the down-regulation of GABA related genes in schizophrenia, the results are often inconsistent and not uniform across studies. We performed a systematic gene expression analysis of 22 GABA related genes in postmortem superior temporal gyrus (STG) samples of 19 elderly subjects with schizophrenia (mean age: 77) and 14 matched controls from the Icahn school of Medicine at Mount Sinai (MSSM) cohort. To test the validity and robustness of the resulting differentially expressed genes, we then conducted a meta-analysis of the MSSM and an independent dataset from the Stanley Consortium of 14 STG samples of relatively young subjects with schizophrenia (mean age: 44) and 15 matched controls. For the first time, the findings showed the down-regulation of three GABA-receptor subunits of type A, GABRA1, GABRA2 and GABRB3, in the STG samples of subjects with schizophrenia, in both the elderly and the relatively young patients. These findings, as well as previous results, lend weight to the notion of a common upstream pathology that alters GABAergic neurotransmission in schizophrenia. GABRA1, GABRA2 and GABRB3 down-regulation may contribute to the pathophysiology and clinical manifestations of schizophrenia through altered oscillation synchronization in the STG.

Evaluation of the relationships of the WBP1L gene with schizophrenia and the general psychopathology scale based on a case-control study

WBP1L is a target of microRNA 137 (miR-137) and has been considered a candidate gene for schizophrenia (SCZ). To investigate the relationships between WBP1L and SCZ and its related symptom scales, a total of 5,993 Chinese Han subjects, including 2,128 SCZ patients and 3,865 controls, were enrolled. In addition, an independent sample set for replication study including 1,052 SCZ patients and 2,124 controls were also recruited. Thirty-two tag single nucleotide polymorphisms (SNPs) located within gene region of WBP1L were selected for genotyping and analyzing. The expression quantitative trait loci (eQTL) effects for the targeted SNPs were investigated with gene expression data from multiple human tissues. Rs4147157 (OR = 0.84, p = 1.51 × 10^-5 ) and rs284854 (OR = 1.14, p = 7.00 × 10^-4 ) were significantly associated with SCZ disease status and these association signals were replicated in our replication sample. A significant association was identified between rs4147157 and the general (β = -.66, p = .001) and total (β = -.8, p = .0042) scores of positive and negative syndrome scale scores in SCZ patients. Both SNPs were significant eQTL for genes around WBP1L in human brain tissues including ARL3 and AS3MT. To conclude, SNPs rs4147157 and rs284854 were associated with SCZ in the Chinese Han population. Additionally, rs4147157 was significantly associated with specific symptom features of SCZ.

Machine learning in schizophrenia genomics, a case-control study using 5,090 exomes

Our hypothesis is that machine learning (ML) analysis of whole exome sequencing (WES) data can be used to identify individuals at high risk for schizophrenia (SCZ). This study applies ML to WES data from 2,545 individuals with SCZ and 2,545 unaffected individuals, accessed via the database of genotypes and phenotypes (dbGaP). Single nucleotide variants and small insertions and deletions were annotated by ANNOVAR using the reference genome hg19/GRCh37. Rare (predicted functional) variants with a minor allele frequency ≤1% and genotype quality ≥90 including missense, frameshift, stop gain, stop loss, intronic, and exonic splicing variants were selected. A file containing all cases and controls, the names of genes with variants meeting our criteria, and the number of variants per gene for each individual, was used for ML analysis. The supervised machine-learning algorithm used the patterns of variants observed in the different genes to determine which subset of genes can best predict that an individual is affected. Seventy percent of the data was used to train the algorithm and the remaining 30% of data (n = 1,526) was used to evaluate its efficiency. The supervised ML algorithm, gradient boosted trees with regularization (eXtreme Gradient Boosting implementation) was the best performing algorithm yielding promising results (accuracy: 85.7%, specificity: 86.6%, sensitivity: 84.9%, area under the receiver-operator characteristic curve: 0.95). The top 50 features (genes) of the algorithm were analyzed using bioinformatics resources for new insights about the pathophysiology of SCZ. This manuscript presents a novel predictor which could potentially enable studies exploring disease-modifying intervention in the early stages of the disease.

The Sex Chromosome Hypothesis of Schizophrenia: Alive, Dead, or Forgotten? A Commentary and Review

The X chromosome has long been an intriguing site for harboring genes that have importance in brain development and function. It has received the most attention for having specific genes underlying the X-linked inherited intellectual disabilities, but has also been associated with schizophrenia in a number of early studies. An X chromosome hypothesis for a genetic predisposition for schizophrenia initially came from the X chromosome anomaly population data showing an excess of schizophrenia in Klinefelter's (XXY) males and triple X (XXX) females. Crow and colleagues later expanded the X chromosome hypothesis to include the possibility of a locus on the Y chromosome and, specifically, genes on X that escaped inactivation and are X-Y homologous loci. Some new information about possible risk loci on these chromosomes has come from the current large genetic consortia genome-wide association studies, suggesting that perhaps this hypothesis needs to be revisited for some schizophrenias. The following commentary reviews the early and more recent literature supporting or refuting this dormant hypothesis and emphasizes the possible candidate genes still of interest that could be explored in further studies.

A Deep Learning Approach for Predicting Antidepressant Response in Major Depression Using Clinical and Genetic Biomarkers

In the wake of recent advances in scientific research, personalized medicine using deep learning techniques represents a new paradigm. In this work, our goal was to establish deep learning models which distinguish responders from non-responders, and also to predict possible antidepressant treatment outcomes in major depressive disorder (MDD). To uncover relationships between the responsiveness of antidepressant treatment and biomarkers, we developed a deep learning prediction approach resulting from the analysis of genetic and clinical factors such as single nucleotide polymorphisms (SNPs), age, sex, baseline Hamilton Rating Scale for Depression score, depressive episodes, marital status, and suicide attempt status of MDD patients. The cohort consisted of 455 patients who were treated with selective serotonin reuptake inhibitors (treatment-response rate = 61.0%; remission rate = 33.0%). By using the SNP dataset that was original to a genome-wide association study, we selected 10 SNPs (including ABCA13 rs4917029, BNIP3 rs9419139, CACNA1E rs704329, EXOC4 rs6978272, GRIN2B rs7954376, LHFPL3 rs4352778, NELL1 rs2139423, NUAK1 rs2956406, PREX1 rs4810894, and SLIT3 rs139863958) which were associated with antidepressant treatment response. Furthermore, we pinpointed 10 SNPs (including ARNTL rs11022778, CAMK1D rs2724812, GABRB3 rs12904459, GRM8 rs35864549, NAALADL2 rs9878985, NCALD rs483986, PLA2G4A rs12046378, PROK2 rs73103153, RBFOX1 rs17134927, and ZNF536 rs77554113) in relation to remission. Then, we employed multilayer feedforward neural networks (MFNNs) containing 1-3 hidden layers and compared MFNN models with logistic regression models. Our analysis results revealed that the MFNN model with 2 hidden layers (area under the receiver operating characteristic curve (AUC) = 0.8228 ± 0.0571; sensitivity = 0.7546 ± 0.0619; specificity = 0.6922 ± 0.0765) performed maximally among predictive models to infer the complex relationship between antidepressant treatment response and biomarkers. In addition, the MFNN model with 3 hidden layers (AUC = 0.8060 ± 0.0722; sensitivity = 0.7732 ± 0.0583; specificity = 0.6623 ± 0.0853) achieved best among predictive models to predict remission. Our study indicates that the deep MFNN framework may provide a suitable method to establish a tool for distinguishing treatment responders from non-responders prior to antidepressant therapy.

Genetic Basis of Positive and Negative Symptom Domains in Schizophrenia

Schizophrenia is a highly heritable disorder, the genetic etiology of which has been well established. Yet despite significant advances in genetics research, the pathophysiological mechanisms of this disorder largely remain unknown. This gap has been attributed to the complexity of the polygenic disorder, which has a heterogeneous clinical profile. Examining the genetic basis of schizophrenia subphenotypes, such as those based on particular symptoms, is thus a useful strategy for decoding the underlying mechanisms. This review of literature examines the recent advances (from 2011) in genetic exploration of positive and negative symptoms in schizophrenia. We searched electronic databases PubMed, Web of Science, and Cumulative Index to Nursing and Allied Health Literature using key words schizophrenia, symptoms, positive symptoms, negative symptoms, cognition, genetics, genes, genetic predisposition, and genotype in various combinations. We identified 115 articles, which are included in the review. Evidence from these studies, most of which are genetic association studies, identifies shared and unique gene associations for the symptom domains. Genes associated with neurotransmitter systems and neuronal development/maintenance primarily constitute the shared associations. Needed are studies that examine the genetic basis of specific symptoms within the broader domains in addition to functional mechanisms. Such investigations are critical to developing precision treatment and care for individuals afflicted with schizophrenia.

GABAA and GABAB receptor dysregulation in superior frontal cortex of subjects with schizophrenia and bipolar disorder

Schizophrenia and bipolar disorder are complex psychiatric disorders that affect millions of people worldwide. Evidence from gene association and postmortem studies has identified abnormalities of the gamma-aminobutyric acid (GABA) signaling system in both disorders. Abnormal GABAergic signaling and transmission could contribute to the symptomatology of these disorders, potentially through impaired gamma oscillations which normally occur during cognitive processing. In the current study, we examined the protein expression of 14 GABA_A and two GABA_B receptor subunits in the superior frontal cortex of subjects with schizophrenia, bipolar disorder, and healthy controls. Analyses of Variance (ANOVAs) identified significant group effects for protein levels for the α1, α6, β1, β3, δ, ɛ, and π GABA_A receptor subunits and R1 and R2 GABA_B receptor subunits. Follow-up t tests confirmed changes for these subunits in subjects with schizophrenia, subjects with bipolar disorder, or both groups. Alterations in stoichiometry of GABA receptor subunits could result in altered ligand binding, transmission, and pharmacology of GABA receptors in superior frontal cortex. Thus, impaired GABAergic transmission may negatively contribute to symptoms such as anxiety or panic as well as impaired learning and information processing, all of which are disrupted in schizophrenia and bipolar disorder. Taken together, these results provide additional evidence of GABAergic receptor abnormalities in these disorders.

A review of genetic alterations in the serotonin pathway and their correlation with psychotic diseases and response to atypical antipsychotics

Serotonin is a neurotransmitter that plays a predominant role in mood regulation. The importance of the serotonin pathway in controlling behavior and mental status is well recognized. All the serotonin elements - serotonin receptors, serotonin transporter, tryptophan hydroxylase and monoamine oxidase proteins - can show alterations in terms of mRNA or protein levels and protein sequence, in schizophrenia and bipolar disorder. Additionally, when examining the genes sequences of all serotonin elements, several single nucleotide polymorphisms (SNPs) have been found to be more prevalent in schizophrenic or bipolar patients than in healthy individuals. Several of these alterations have been associated either with different phenotypes between patients and healthy individuals or with the response of psychiatric patients to the treatment with atypical antipsychotics. The complex pattern of genetic diversity within the serotonin pathway hampers efforts to identify the key variations contributing to an individual's susceptibility to the disease. In this review article, we summarize all genetic alterations found across the serotonin pathway, we provide information on whether and how they affect schizophrenia or bipolar disorder phenotypes, and, on the contribution of familial relationships on their detection frequencies. Furthermore, we provide evidence on whether and how specific gene polymorphisms affect the outcome of schizophrenic or bipolar patients of different ethnic groups, in response to treatment with atypical antipsychotics. All data are discussed thoroughly, providing prospective for future studies.

From Prader-Willi syndrome to psychosis: translating parent-of-origin effects into schizophrenia research

Prader-Willi syndrome (PWS) is a relatively rare disorder that originates from paternally inherited deletions and maternal disomy (mUPD) within the 15q11-q13 region or alterations in the PWS imprinting center. Evidence is accumulating that mUPD underlies high prevalence of psychosis among PWS patients. Several genes involved in differentiation and survival of neurons as well as neurotransmission known to act in the development of PWS have been also implicated in schizophrenia. In this article, we provide an overview of genetic and epigenetic underpinnings of psychosis in PWS indicating overlapping points in the molecular background of PWS and schizophrenia. Simultaneously, we highlight the need for studies investigating genetic and epigenetic makeup of the 15q11-q13 in schizophrenia indicating promising candidate genes.

Genetic analysis of GABRB3 at 15q12 as a candidate gene of schizophrenia

OBJECTIVE

Copy number variations encompassing the chromosome 15q11-q13 region have been implicated in the pathogenesis of several neurodevelopmental disorders including schizophrenia. The study aimed to investigate whether the GABRB3 gene mapped to 15q12 was associated with schizophrenia.

MATERIALS AND METHODS

We resequenced the promoter and all the exonic regions of the GABRB3 gene in 349 patients with schizophrenia and 386 control participants from Taiwan using the Sanger sequencing method. We also used a reporter gene assay to assess the functional impact of variants identified from the promoter region.

RESULTS

We identified a total of six common single nucleotide polymorphisms and eight rare variants in this sample. No genetic association of these common single nucleotide polymorphisms with schizophrenia was detected. A missense mutation Y402H at exon 9 was detected in two patients and two controls. Polyphen-2 predicted that the impact of this variant was benign. In addition, we identified two patient-specific variants at the promoter of GABRB3 that showed significantly increased promoter activity in a reporter gene assay.

CONCLUSION

The identification of two private patient-only variants at the promoter region with enhanced promoter activity supports the rare allele hypothesis of schizophrenia and suggests that increased GABRB3 expression may confer an increased risk of schizophrenia.

A synthesis of evidence on inhibitory control and auditory hallucinations based on the Research Domain Criteria (RDoC) framework

The National Institute of Mental Health initiative called the Research Domain Criteria (RDoC) project aims to provide a new approach to understanding mental illness grounded in the fundamental domains of human behavior and psychological functioning. To this end the RDoC framework encourages researchers and clinicians to think outside the [diagnostic] box, by studying symptoms, behaviors or biomarkers that cut across traditional mental illness categories. In this article we examine and discuss how the RDoC framework can improve our understanding of psychopathology by zeroing in on hallucinations- now widely recognized as a symptom that occurs in a range of clinical and non-clinical groups. We focus on a single domain of functioning-namely cognitive [inhibitory] control-and assimilate key findings structured around the basic RDoC "units of analysis," which span the range from observable behavior to molecular genetics. Our synthesis and critique of the literature provides a deeper understanding of the mechanisms involved in the emergence of auditory hallucinations, linked to the individual dynamics of inhibitory development before and after puberty; favors separate developmental trajectories for clinical and non-clinical hallucinations; yields new insights into co-occurring emotional and behavioral problems; and suggests some novel avenues for treatment.

Gene-sex interactions in schizophrenia: focus on dopamine neurotransmission

Schizophrenia is a severe mental disorder, with a highly complex and heterogenous clinical presentation. Our current perspectives posit that the pathogenic mechanisms of this illness lie in complex arrays of gene × environment interactions. Furthermore, several findings indicate that males have a higher susceptibility for schizophrenia, with earlier age of onset and overall poorer clinical prognosis. Based on these premises, several authors have recently begun exploring the possibility that the greater schizophrenia vulnerability in males may reflect specific gene × sex (G×S) interactions. Our knowledge on such G×S interactions in schizophrenia is still rudimentary; nevertheless, the bulk of preclinical evidence suggests that the molecular mechanisms for such interactions are likely contributed by the neurobiological effects of sex steroids on dopamine (DA) neurotransmission. Accordingly, several recent studies suggest a gender-specific association of certain DAergic genes with schizophrenia. These G×S interactions have been particularly documented for catechol-O-methyltransferase (COMT) and monoamine oxidase (MAO), the main enzymes catalyzing DA metabolism. In the present review, we will outline the current evidence on the interactions of DA-related genes and sex-related factors, and discuss the potential molecular substrates that may mediate their cooperative actions in schizophrenia pathogenesis.

Autonomous vascular networks synchronize GABA neuron migration in the embryonic forebrain

GABA neurons, born in remote germinative zones in the ventral forebrain (telencephalon), migrate tangentially in two spatially distinct streams to adopt their specific positions in the developing cortex. The cell types and molecular cues that regulate this divided migratory route remains to be elucidated. Here we show that embryonic vascular networks are strategically positioned to fulfill the task of providing support as well as critical guidance cues that regulate the divided migratory routes of GABA neurons in the telencephalon. Interestingly, endothelial cells of the telencephalon are not homogeneous in their gene expression profiles. Endothelial cells of the periventricular vascular network have molecular identities distinct from those of the pial network. Our data suggest that periventricular endothelial cells have intrinsic programs that can significantly mold neuronal development and uncovers new insights into concepts and mechanisms of CNS angiogenesis from both developmental and disease perspectives.

Genome-wide linkage scan of quantitative traits representing symptom dimensions in multiplex schizophrenia families

Symptom dimensions of schizophrenia are likely to be the intermediate phenotypes under the control of disease-susceptibility genes, or separate traits related to disease-modifier genes. This study aimed to identify chromosomal loci linked to symptom dimensions of schizophrenia through genome-wide quantitative trait locus (QTL) linkage analysis. The study subjects consisted of 56 families with 183 members including 123 affected individuals. Symptom evaluations were performed on lifetime basis. Through principal component factor analysis, eight quantitative phenotypes representing symptom dimensions were identified. Genotyping was done for 6008 SNP markers, and genome-wide QTL linkage analysis was performed. No symptom dimension showed a significant linkage attaining genome-wide empirical thresholds. We observed seven regions yielding linkage signals attaining genome-wide empirical thresholds for suggestive linkage (NPL Z score = 2.78-3.49); chromosome 15q26.1 for 'non-paranoid delusion factor', 2p24.3 and 7q31.1 for 'prodromal impairment factor', 1q32.1, 9p21.3, and 9q31.2 for 'negative symptom factor', and 10p13 for 'disorganization factor'. Among these loci, chromosome 2p24.3 and 1q32.1 overlap with susceptibility loci of schizophrenia identified in our previous linkage studies. This study suggests the existence of genetic loci related to various clinical features of schizophrenia. Further genetic analyses for these dimensional phenotypes are warranted.

Expression of GABAA α2-, β1- and ε-receptors are altered significantly in the lateral cerebellum of subjects with schizophrenia, major depression and bipolar disorder

There is abundant evidence that dysfunction of the γ-aminobutyric acid (GABA)ergic signaling system is implicated in the pathology of schizophrenia and mood disorders. Less is known about the alterations in protein expression of GABA receptor subunits in brains of subjects with schizophrenia and mood disorders. We have previously demonstrated reduced expression of GABA(B) receptor subunits 1 and 2 (GABBR1 and GABBR2) in the lateral cerebella of subjects with schizophrenia, bipolar disorder and major depressive disorder. In the current study, we have expanded these studies to examine the mRNA and protein expression of 12 GABA(A) subunit proteins (α1, α2, α3, α5, α6, β1, β2, β3, δ, ε, γ2 and γ3) in the lateral cerebella from the same set of subjects with schizophrenia (N=9-15), bipolar disorder (N=10-15) and major depression (N=12-15) versus healthy controls (N=10-15). We found significant group effects for protein levels of the α2-, β1- and ε-subunits across treatment groups. We also found a significant group effect for mRNA levels of the α1-subunit across treatment groups. New avenues for treatment, such as the use of neurosteroids to promote GABA modulation, could potentially ameliorate GABAergic dysfunction in these disorders.

Identification of rare recurrent copy number variants in high-risk autism families and their prevalence in a large ASD population

Structural variation is thought to play a major etiological role in the development of autism spectrum disorders (ASDs), and numerous studies documenting the relevance of copy number variants (CNVs) in ASD have been published since 2006. To determine if large ASD families harbor high-impact CNVs that may have broader impact in the general ASD population, we used the Affymetrix genome-wide human SNP array 6.0 to identify 153 putative autism-specific CNVs present in 55 individuals with ASD from 9 multiplex ASD pedigrees. To evaluate the actual prevalence of these CNVs as well as 185 CNVs reportedly associated with ASD from published studies many of which are insufficiently powered, we designed a custom Illumina array and used it to interrogate these CNVs in 3,000 ASD cases and 6,000 controls. Additional single nucleotide variants (SNVs) on the array identified 25 CNVs that we did not detect in our family studies at the standard SNP array resolution. After molecular validation, our results demonstrated that 15 CNVs identified in high-risk ASD families also were found in two or more ASD cases with odds ratios greater than 2.0, strengthening their support as ASD risk variants. In addition, of the 25 CNVs identified using SNV probes on our custom array, 9 also had odds ratios greater than 2.0, suggesting that these CNVs also are ASD risk variants. Eighteen of the validated CNVs have not been reported previously in individuals with ASD and three have only been observed once. Finally, we confirmed the association of 31 of 185 published ASD-associated CNVs in our dataset with odds ratios greater than 2.0, suggesting they may be of clinical relevance in the evaluation of children with ASDs. Taken together, these data provide strong support for the existence and application of high-impact CNVs in the clinical genetic evaluation of children with ASD.