A comparative genomic study in schizophrenic and in bipolar disorder patients, based on microarray expression profiling meta-analysis

Orphan G Protein-Coupled Receptor GPR37 as an Emerging Therapeutic Target

G protein-coupled receptors (GPCRs) are successful druggable targets, making up around 35% of all FDA-approved medications. However, a large number of receptors remain orphaned, with no known endogenous ligand, representing a challenging but untapped area to discover new therapeutic targets. Among orphan GPCRs (oGPCRs) of interest, G protein-coupled receptor 37 (GPR37) is highly expressed in the central nervous system (CNS), particularly in the spinal cord and oligodendrocytes. While its cellular signaling mechanisms and endogenous receptor ligands remain elusive, GPR37 has been implicated in several important neurological conditions, including Parkinson's disease (PD), inflammation, pain, autism, and brain tumors. GPR37 structure, signaling, emerging physiology, and pharmacology are reviewed while integrating a discussion on potential therapeutic indications and opportunities.

Systems biology approach discovers comorbidity interaction of Parkinson's disease with psychiatric disorders utilizing brain transcriptome

Several studies found that most patients with Parkinson's disorder (PD) appear to have psychiatric symptoms such as depression, anxiety, hallucination, delusion, and cognitive dysfunction. Therefore, recognizing these psychiatrically symptoms of PD patients is crucial for both symptomatic therapy and better knowledge of the pathophysiology of PD. In order to address this issue, we created a bioinformatics framework to determine the effects of PD mRNA expression on understanding its relationship with psychiatric symptoms in PD patients. We have discovered a significant overlap between the sets of differentially expressed genes from PD exposed tissue and psychiatric disordered tissues using RNA-seq datasets. We have chosen Bipolar disorder and Schizophrenia as psychiatric disorders in our study. A number of significant correlations between PD and the occurrence of psychiatric diseases were also found by gene set enrichment analysis, investigations of the protein-protein interaction network, gene regulatory network, and protein-chemical agent interaction network. We anticipate that the results of this pathogenetic study will provide crucial information for understanding the intricate relationship between PD and psychiatric diseases.

Evaluation of postmortem microarray data in bipolar disorder using traditional data comparison and artificial intelligence reveals novel gene targets

Large-scale microarray studies on post-mortem brain tissues have been utilized to investigate the complex molecular pathology of bipolar disorder. However, a major challenge in characterizing the dysregulation of gene expression in patients with bipolar disorder includes the lack of convergence between different studies, limiting comprehensive understanding from individual results. In this study, we aimed to identify genes that are both validated in published literature and are important classification features of unsupervised machine learning analysis of Stanley Brain Bank microarray database, followed by augmented intelligence method to identify distinct patient molecular subgroups. Through combining traditional literature approaches and machine learning, we identified TBL1XR1, SMARCA2, and CHMP5 to be replicated in 3 of the 4 studies included our analysis. The expression of these genes segregated unique subgroups of patients with bipolar disorder. Our study suggests the involvement of PPARγ pathway regulation in patients with bipolar disorder.

Identifying novel associations in GWAS by hierarchical Bayesian latent variable detection of differentially misclassified phenotypes

Background

Heterogeneity in the definition and measurement of complex diseases in Genome-Wide Association Studies (GWAS) may lead to misdiagnoses and misclassification errors that can significantly impact discovery of disease loci. While well appreciated, almost all analyses of GWAS data consider reported disease phenotype values as is without accounting for potential misclassification.

Results

Here, we introduce Phenotype Latent variable Extraction of disease misdiagnosis (PheLEx), a GWAS analysis framework that learns and corrects misclassified phenotypes using structured genotype associations within a dataset. PheLEx consists of a hierarchical Bayesian latent variable model, where inference of differential misclassification is accomplished using filtered genotypes while implementing a full mixed model to account for population structure and genetic relatedness in study populations. Through simulations, we show that the PheLEx framework dramatically improves recovery of the correct disease state when considering realistic allele effect sizes compared to existing methodologies designed for Bayesian recovery of disease phenotypes. We also demonstrate the potential of PheLEx for extracting new potential loci from existing GWAS data by analyzing bipolar disorder and epilepsy phenotypes available from the UK Biobank. From the PheLEx analysis of these data, we identified new candidate disease loci not previously reported for these datasets that have value for supplemental hypothesis generation.

Conclusion

PheLEx shows promise in reanalyzing GWAS datasets to provide supplemental candidate loci that are ignored by traditional GWAS analysis methodologies.

Circulating exosomal microRNAs in bipolar disorder

INTRODUCTION

Emerging evidence suggests central roles of miRNAs in the pathogenesis of bipolar disorder (BD). Exosomes are membrane-bound vesicles acting as "biological cargo carriers" of various types of molecules including microRNAs. In this study, we aimed to investigate circulating exosomal microRNAs as potential diagnostic biomarkers for BD.

METHODS

The exosomes were precipitated from plasma samples of patients with BD (n = 69; 15 depressed, 27 manic, 27 euthymic) and healthy controls (n = 41). Total RNA was extracted from the exosomes and the levels of miRNAs were assayed by qPCR. Dysregulated miRNAs were subjected to Kyoto Encyclopedia of Genes and Genomes" (KEGG) pathway analysis by DIANA-miRPath v3.0 to identify the predicted targets and the related pathways.

RESULTS

Thirteen miRNAs showed significant differences between patients with BD and healthy individuals; among these, MiR-484, -652-3p, -142-3p remained significantly downregulated and miR-185-5p remained significantly upregulated after accounting for multiple comparisons and adjustments for potential confounders. There were no significant alterations among different states of BD. The KEEG analysis of four dysregulated miRNAs highlighted several target pathways including PI3K/Akt signaling, fatty acid biosynthesis/metabolism, extracellular matrix and adhesion pathways.

CONCLUSION

Our findings suggest that dysregulation of miRNAs might be involved in the underlying pathophysiology of BD through several biological pathways; and highlight the importance of the exosomal miRNAs for biomarker research in BD. Further longitudinal studies may clarify the roles of exosomal miRNAs and their targets in the neurobiology of BD.

Horizontal and vertical integrative analysis methods for mental disorders omics data

In recent biomedical studies, omics profiling has been extensively conducted on various types of mental disorders. In most of the existing analyses, a single type of mental disorder and a single type of omics measurement are analyzed. In the study of other complex diseases, integrative analysis, both vertical and horizontal integration, has been conducted and shown to bring significantly new insights into disease etiology, progression, biomarkers, and treatment. In this article, we showcase the applicability of integrative analysis to mental disorders. In particular, the horizontal integration of bipolar disorder and schizophrenia and the vertical integration of gene expression and copy number variation data are conducted. The analysis is based on the sparse principal component analysis, penalization, and other advanced statistical techniques. In data analysis, integration leads to biologically sensible findings, including the disease-related gene expressions, copy number variations, and their associations, which differ from the “benchmark” analysis. Overall, this study suggests the potential of integrative analysis in mental disorder research.

Network meta-analysis correlates with analysis of merged independent transcriptome expression data

Background

Using meta-analysis, high-dimensional transcriptome expression data from public repositories can be merged to make group comparisons that have not been considered in the original studies. Merging of high-dimensional expression data can, however, implicate batch effects that are sometimes difficult to be removed. Removing batch effects becomes even more difficult when expression data was taken using different technologies in the individual studies (e.g. merging of microarray and RNA-seq data). Network meta-analysis has so far not been considered to make indirect comparisons in transcriptome expression data, when data merging appears to yield biased results.

Results

We demonstrate in a simulation study that the results from analyzing merged data sets and the results from network meta-analysis are highly correlated in simple study networks. In the case that an edge in the network is supported by multiple independent studies, network meta-analysis produces fold changes that are closer to the simulated ones than those obtained from analyzing merged data sets. Finally, we also demonstrate the practicability of network meta-analysis on a real-world data example from neuroinfection research.

Conclusions

Network meta-analysis is a useful means to make new inferences when combining multiple independent studies of molecular, high-throughput expression data. This method is especially advantageous when batch effects between studies are hard to get removed.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2705-9) contains supplementary material, which is available to authorized users.

Enhanced Molecular Appreciation of Psychiatric Disorders Through High-Dimensionality Data Acquisition and Analytics

The initial diagnosis, molecular investigation, treatment, and posttreatment care of major psychiatric disorders (schizophrenia and bipolar depression) are all still significantly hindered by the current inability to define these disorders in an explicit molecular signaling manner. High-dimensionality data analytics, using large datastreams from transcriptomic, proteomic, or metabolomic investigations, will likely advance both the appreciation of the molecular nature of major psychiatric disorders and simultaneously enhance our ability to more efficiently diagnose and treat these debilitating conditions. High-dimensionality data analysis in psychiatric research has been heterogeneous in aims and methods and limited by insufficient sample sizes, poorly defined case definitions, methodological inhomogeneity, and confounding results. All of these issues combine to constrain the conclusions that can be extracted from them. Here, we discuss possibilities for overcoming methodological challenges through the implementation of transcriptomic, proteomic, or metabolomics signatures in psychiatric diagnosis and offer an outlook for future investigations. To fulfill the promise of intelligent high-dimensionality data-based differential diagnosis in mental disease diagnosis and treatment, future research will need large, well-defined cohorts in combination with state-of-the-art technologies.

A novel relationship for schizophrenia, bipolar and major depressive disorder Part 3: Evidence from chromosome 3 high density association screen

Familial clustering of schizophrenia (SCZ), bipolar disorder (BPD), and major depressive disorder (MDD) was systematically reported (Aukes et al, Genet Med 2012, 14, 338-341) and convergent evidence from genetics, symptomatology, and psychopharmacology imply that there are intrinsic connections between these three major psychiatric disorders, for example, any two or even three of these disorders could co-exist in some families. A total of 60, 838 single-nucleotide polymorphisms (SNPs) on chromosome 3 were genotyped by Affymetrix Genome-Wide Human SNP array 6.0 on 119 SCZ, 253 BPD (type-I), 177 MDD patients and 1,000 controls. The population of Shandong province was formed in 14 century and believed that it belongs to homogenous population. Associated SNPs were systematically revealed and outstanding susceptibility genes (CADPS, GRM7,KALRN, LSAMP, NLGN1, PRICKLE2, ROBO2) were identified. Unexpectedly, flanking genes for the associated SNPs distinctive for BPD and/or MDD were replicated in an enlarged cohort of 986 SCZ patients. The evidence from this chromosome 3 analysis supports the notion that both of bipolar and MDD might be subtypes of schizophrenia rather than independent disease entity. Also, a similar finding was detected on chromosome 5, 6, 7, and 8 (Chen et al. Am J Transl Res 2017;9 (5):2473-2491; Curr Mol Med 2016;16(9):840-854; Behav Brain Res 2015;293:241-251; Mol Neurobiol 2016. doi: 10.1007/s12035-016-0102-1). Furthermore, PRICKLE2 play an important role in the pathogenesis of three major psychoses in this population.

Transcriptome sequencing of the choroid plexus in schizophrenia

The choroid plexus (CP) has a key role in maintaining brain homeostasis by producing cerebrospinal fluid (CSF), by mediating transport of nutrients and removing metabolic products from the central nervous system and by responding to peripheral inflammatory signals. Although abnormal markers of immune response and inflammation are apparent in individuals with schizophrenia, the CP of these individuals has not been characterized. We therefore sequenced mRNA from the CP from two independent collections of individuals with schizophrenia and unaffected controls. Genes related to immune function and inflammation were upregulated in both collections. In addition, a co-expression module related to immune/inflammation response that was generated by combining mRNA-Seq data from both collections was significantly associated with disease status. The immune/inflammation-related co-expression module was positively correlated with levels of C-reactive protein (CRP), cortisol and several immune modulator proteins in the serum of the same individuals and was also positively correlated with CRP, cortisol and pro-inflammatory cytokines in the frontal cortex of the same individuals. In addition, we found a substantial number of nodes (genes) that were common to our schizophrenia-associated immune/inflammation module from the pooled data and a module we generated from lippopolysaccharides-treated mouse model data. These results suggest that the CP of individuals with schizophrenia are responding to signals from the periphery by upregulating immune/inflammation-related genes to protect the brain and maintain the homeostasis but nevertheless fails to completely prevent immune/inflammation related changes in the brain.

Neurodevelopmental origins of bipolar disorder: iPSC models

Bipolar disorder (BP) is a chronic neuropsychiatric condition characterized by pathological fluctuations in mood from mania to depression. Adoption, twin and family studies have consistently identified a significant hereditary component to BP, yet there is no clear genetic event or consistent neuropathology. BP has been suggested to have a developmental origin, although this hypothesis has been difficult to test since there are no viable neurons or glial cells to analyze, and research has relied largely on postmortem brain, behavioral and imaging studies, or has examined proxy tissues including saliva, olfactory epithelium and blood cells. Neurodevelopmental factors, particularly pathways related to nervous system development, cell migration, extracellular matrix, H3K4 methylation, and calcium signaling have been identified in large gene expression and GWAS studies as altered in BP. Recent advances in stem cell biology, particularly the ability to reprogram adult somatic tissues to a pluripotent state, now make it possible to interrogate these pathways in viable cell models. A number of induced pluripotent stem cell (iPSC) lines from BP patient and healthy control (C) individuals have been derived in several laboratories, and their ability to form cortical neurons examined. Early studies suggest differences in activity, calcium signaling, blocks to neuronal differentiation, and changes in neuronal, and possibly glial, lineage specification. Initial observations suggest that differentiation of BP patient-derived neurons to dorsal telencephalic derivatives may be impaired, possibly due to alterations in WNT, Hedgehog or Nodal pathway signaling. These investigations strongly support a developmental contribution to BP and identify novel pathways, mechanisms and opportunities for improved treatments.

CX3CR1 is dysregulated in blood and brain from schizophrenia patients

The molecular mechanisms underlying schizophrenia remain largely unknown. Although schizophrenia is a mental disorder, there is increasing evidence to indicate that inflammatory processes driven by diverse environmental factors play a significant role in its development. With gene expression studies having been conducted across a variety of sample types, e.g., blood and postmortem brain, it is possible to investigate convergent signatures that may reveal interactions between the immune and nervous systems in schizophrenia pathophysiology. We conducted two meta-analyses of schizophrenia microarray gene expression data (N=474) and non-psychiatric control (N=485) data from postmortem brain and blood. Then, we assessed whether significantly dysregulated genes in schizophrenia could be shared between blood and brain. To validate our findings, we selected a top gene candidate and analyzed its expression by RT-qPCR in a cohort of schizophrenia subjects stabilized by atypical antipsychotic monotherapy (N=29) and matched controls (N=31). Meta-analyses highlighted inflammation as the major biological process associated with schizophrenia and that the chemokine receptor CX3CR1 was significantly down-regulated in schizophrenia. This differential expression was also confirmed in our validation cohort. Given both the recent data demonstrating selective CX3CR1 expression in subsets of neuroimmune cells, as well as behavioral and neuropathological observations of CX3CR1 deficiency in mouse models, our results of reduced CX3CR1 expression adds further support for a role played by monocyte/microglia in the neurodevelopment of schizophrenia.

Identification of a group of brominated flame retardants as novel androgen receptor antagonists and potential neuronal and endocrine disrupters

Brominated flame-retardants (BFRs) are used in industrial products to reduce the risk of fire. However, their continuous release into the environment is a concern as they are often persistent, bioaccumulating and toxic. Information on the impact these compounds have on human health and wildlife is limited and only a few of them have been identified to disrupt hormone receptor functions. In the present study we used in silico modeling to determine the interactions of selected BFRs with the human androgen receptor (AR). Three compounds were found to dock into the ligand-binding domain of the human AR and these were further tested using in vitro analysis. Allyl 2,4,6-tribromophenyl ether (ATE), 2-bromoallyl 2,4,6-tribromophenyl ether (BATE) and 2,3-dibromopropyl-2,4,6-tribromophenyl ether (DPTE) were observed to act as AR antagonists. These BFRs have recently been detected in the environment, in house dust and in aquatic animals. The compounds have been detected at high concentrations in both blubber and brain of seals and we therefore also assessed their impact on the expression of L-type amino acid transporter system (LAT) genes, that are needed for amino acid uptake across the blood-brain barrier, as disruption of LAT gene function has been implicated in several brain disorders. The three BFRs down-regulated the expression of AR target genes that encode for prostate specific antigen (PSA), 5α-reductases and β-microseminoprotein. The potency of PSA inhibition was of the same magnitude as the common prostate cancer drugs, demonstrating that these compounds are strong AR antagonists. Western blot analysis of AR protein showed that ATE, BATE and DPTE decreased the 5α-dihydrotestosterone-induced AR protein levels, further confirming that these BFRs act as AR antagonists. The transcription of the LAT genes was altered by the three BFRs, indicating an effect on amino-acid uptake across cellular membranes and blood-brain barrier. This study demonstrated that ATE, BATE and DPTE are potent AR antagonists and the alterations in LAT gene transcription suggest that these compounds can affect neuronal functions and should be considered as potential neurotoxic and endocrine disrupting compounds.

Kinetic characterization of the human O-phosphoethanolamine phospho-lyase reveals unconventional features of this specialized pyridoxal phosphate-dependent lyase

Human O-phosphoethanolamine (PEA) phospho-lyase is a pyridoxal 5'-phosphate (PLP) dependent enzyme that catalyzes the degradation of PEA to acetaldehyde, phosphate and ammonia. Physiologically, the enzyme is involved in phospholipid metabolism and is expressed mainly in the brain, where its expression becomes dysregulated in the course of neuropsychiatric diseases. Mechanistically, PEA phospho-lyase shows a remarkable substrate selectivity, strongly discriminating against other amino compounds structurally similar to PEA. Herein, we studied the enzyme under steady-state and pre-steady-state conditions, analyzing its kinetic features and getting insights into the factors that contribute to its specificity. The pH dependence of the catalytic parameters and the pattern of inhibition by the product phosphate and by other anionic compounds suggest that the active site of PEA phospho-lyase is optimized to bind dianionic groups and that this is a prime determinant of the enzyme specificity towards PEA. Single- and multiple-wavelength stopped-flow studies show that upon reaction with PEA the main absorption band of PLP (λmax  = 412 nm) rapidly blue-shifts to ~ 400 nm. Further experiments suggest that the newly formed and rather stable 400-nm species most probably represents a Michaelis (noncovalent) complex of PEA with the enzyme. Accumulation of such an early intermediate during turnover is unusual for PLP-dependent enzymes and appears counterproductive for absolute catalytic performance, but it can contribute to optimize substrate specificity. PEA phospho-lyase may hence represent a case of selectivity-efficiency tradeoff. In turn, the strict specificity of the enzyme seems important to prevent inactivation by other amines, structurally resembling PEA, that occur in the brain.

Evolutionary approach for relative gene expression algorithms

A Relative Expression Analysis (RXA) uses ordering relationships in a small collection of genes and is successfully applied to classiffication using microarray data. As checking all possible subsets of genes is computationally infeasible, the RXA algorithms require feature selection and multiple restrictive assumptions. Our main contribution is a specialized evolutionary algorithm (EA) for top-scoring pairs called EvoTSP which allows finding more advanced gene relations. We managed to unify the major variants of relative expression algorithms through EA and introduce weights to the top-scoring pairs. Experimental validation of EvoTSP on public available microarray datasets showed that the proposed solution significantly outperforms in terms of accuracy other relative expression algorithms and allows exploring much larger solution space.