Alternatively Spliced Genes as Biomarkers for Schizophrenia, Bipolar Disorder and Psychosis: A Blood-Based Spliceome-Profiling Exploratory Study

BrainGENIE: The Brain Gene Expression and Network Imputation Engine

In vivo experimental analysis of human brain tissue poses substantial challenges and ethical concerns. To address this problem, we developed a computational method called the Brain Gene Expression and Network-Imputation Engine (BrainGENIE) that leverages peripheral-blood transcriptomes to predict brain tissue-specific gene-expression levels. Paired blood-brain transcriptomic data collected by the Genotype-Tissue Expression (GTEx) Project was used to train BrainGENIE models to predict gene-expression levels in ten distinct brain regions using whole-blood gene-expression profiles. The performance of BrainGENIE was compared to PrediXcan, a popular method for imputing gene expression levels from genotypes. BrainGENIE significantly predicted brain tissue-specific expression levels for 2947-11,816 genes (false-discovery rate-adjusted p < 0.05), including many transcripts that cannot be predicted significantly by a transcriptome-imputation method such as PrediXcan. BrainGENIE recapitulated measured diagnosis-related gene-expression changes in the brain for autism, bipolar disorder, and schizophrenia better than direct correlations from blood and predictions from PrediXcan. We developed a convenient software toolset for deploying BrainGENIE, and provide recommendations for how best to implement models. BrainGENIE complements and, in some ways, outperforms existing transcriptome-imputation tools, providing biologically meaningful predictions and opening new research avenues.

Integration of multidimensional splicing data and GWAS summary statistics for risk gene discovery

A common strategy for the functional interpretation of genome-wide association study (GWAS) findings has been the integrative analysis of GWAS and expression data. Using this strategy, many association methods (e.g., PrediXcan and FUSION) have been successful in identifying trait-associated genes via mediating effects on RNA expression. However, these approaches often ignore the effects of splicing, which can carry as much disease risk as expression. Compared to expression data, one challenge to detect associations using splicing data is the large multiple testing burden due to multidimensional splicing events within genes. Here, we introduce a multidimensional splicing gene (MSG) approach, which consists of two stages: 1) we use sparse canonical correlation analysis (sCCA) to construct latent canonical vectors (CVs) by identifying sparse linear combinations of genetic variants and splicing events that are maximally correlated with each other; and 2) we test for the association between the genetically regulated splicing CVs and the trait of interest using GWAS summary statistics. Simulations show that MSG has proper type I error control and substantial power gains over existing multidimensional expression analysis methods (i.e., S-MultiXcan, UTMOST, and sCCA+ACAT) under diverse scenarios. When applied to the Genotype-Tissue Expression Project data and GWAS summary statistics of 14 complex human traits, MSG identified on average 83%, 115%, and 223% more significant genes than sCCA+ACAT, S-MultiXcan, and UTMOST, respectively. We highlight MSG's applications to Alzheimer's disease, low-density lipoprotein cholesterol, and schizophrenia, and found that the majority of MSG-identified genes would have been missed from expression-based analyses. Our results demonstrate that aggregating splicing data through MSG can improve power in identifying gene-trait associations and help better understand the genetic risk of complex traits.

An Investigation of How Normalisation and Local Modelling Techniques Confound Machine Learning Performance In a Mental Health Study

Machine learning (ML) is increasingly deployed on biomedical studies for biomarker development (feature selection) and diagnostic/prognostic technologies (classification). While different ML techniques produce different feature sets and classification performances, less understood is how upstream data processing methods (e.g., normalisation) impact downstream analyses. Using a clinical mental health dataset, we investigated the impact of different normalisation techniques on classification model performance. Gene Fuzzy Scoring (GFS), an in-house developed normalisation technique, is compared against widely used normalisation methods such as global quantile normalisation, class-specific quantile normalisation and surrogate variable analysis. We report that choice of normalisation technique has strong influence on feature selection. with GFS outperforming other techniques. Although GFS parameters are tuneable, good classification model performance (ROC-AUC > 0.90) is observed regardless of the GFS parameter settings. We also contrasted our results against local modelling, which is meant to improve the resolution and meaningfulness of classification models built on heterogeneous data. Local models, when derived from non-biologically meaningful subpopulations, perform worse than global models. A deep dive however, revealed that the factors driving cluster formation has little to do with the phenotype-of-interest. This finding is critical, as local models are often seen as a superior means of clinical data modelling. We advise against such naivete. Additionally, we have developed a combinatorial reasoning approach using both global and local paradigms: This helped reveal potential data quality issues or underlying factors causing data heterogeneity that are often overlooked. It also assists to explain the model as well as provides directions for further improvement.

Peripheral Blood-Based Gene Expression Studies in Schizophrenia: A Systematic Review

Schizophrenia is a disorder that is characterized by delusions, hallucinations, disorganized speech or behavior, and socio-occupational impairment. The duration of observation and variability in symptoms can make the accurate diagnosis difficult. Identification of biomarkers for schizophrenia (SCZ) can help in early diagnosis, ascertaining the diagnosis, and development of effective treatment strategies. Here we review peripheral blood-based gene expression studies for identification of gene expression biomarkers for SCZ. A literature search was carried out in PubMed and Web of Science databases for blood-based gene expression studies in SCZ. A list of differentially expressed genes (DEGs) was compiled and analyzed for overlap with genetic markers, differences based on drug status of the participants, functional enrichment, and for effect of antipsychotics. This literature survey identified 61 gene expression studies. Seventeen out of these studies were based on expression microarrays. A comparative analysis of the DEGs (n = 227) from microarray studies revealed differences between drug-naive and drug-treated SCZ participants. We found that of the 227 DEGs, 11 genes (ACOT7, AGO2, DISC1, LDB1, RUNX3, SIGIRR, SLC18A1, NRG1, CHRNB2, PRKAB2, and ZNF74) also showed genetic and epigenetic changes associated with SCZ. Functional enrichment analysis of the DEGs revealed dysregulation of proline and 4-hydroxyproline metabolism. Also, arginine and proline metabolism was the most functionally enriched pathway for SCZ in our analysis. Follow-up studies identified effect of antipsychotic treatment on peripheral blood gene expression. Of the 27 genes compiled from the follow-up studies AKT1, DISC1, HP, and EIF2D had no effect on their expression status as a result of antipsychotic treatment. Despite the differences in the nature of the study, ethnicity of the population, and the gene expression analysis method used, we identified several coherent observations. An overlap, though limited, of genetic, epigenetic and gene expression changes supports interplay of genetic and environmental factors in SCZ. The studies validate the use of blood as a surrogate tissue for biomarker analysis. We conclude that well-designed cohort studies across diverse populations, use of high-throughput sequencing technology, and use of artificial intelligence (AI) based computational analysis will significantly improve our understanding and diagnostic capabilities for this complex disorder.

The regulation of DNA supercoiling across evolution

DNA supercoiling controls a variety of cellular processes, including transcription, recombination, chromosome replication, and segregation, across all domains of life. As a physical property, DNA supercoiling alters the double helix structure by under- or over-winding it. Intriguingly, the evolution of DNA supercoiling reveals both similarities and differences in its properties and regulation across the three domains of life. Whereas all organisms exhibit local, constrained DNA supercoiling, only bacteria and archaea exhibit unconstrained global supercoiling. DNA supercoiling emerges naturally from certain cellular processes and can also be changed by enzymes called topoisomerases. While structurally and mechanistically distinct, topoisomerases that dissipate excessive supercoils exist in all domains of life. By contrast, topoisomerases that introduce positive or negative supercoils exist only in bacteria and archaea. The abundance of topoisomerases is also transcriptionally and post-transcriptionally regulated in domain-specific ways. Nucleoid-associated proteins, metabolites, and physicochemical factors influence DNA supercoiling by acting on the DNA itself or by impacting the activity of topoisomerases. Overall, the unique strategies that organisms have evolved to regulate DNA supercoiling hold significant therapeutic potential, such as bactericidal agents that target bacteria-specific processes or anticancer drugs that hinder abnormal DNA replication by acting on eukaryotic topoisomerases specialized in this process. The investigation of DNA supercoiling therefore reveals general principles, conserved mechanisms, and kingdom-specific variations relevant to a wide range of biological questions.

Do Fragile X Syndrome and Other Intellectual Disorders Converge at Aberrant Pre-mRNA Splicing?

Fragile X Syndrome is a neuro-developmental disorder caused by the silencing of the FMR1 gene, resulting in the loss of its protein product, FMRP. FMRP binds mRNA and represses general translation in the brain. Transcriptome analysis of the Fmr1-deficient mouse hippocampus reveals widespread dysregulation of alternative splicing of pre-mRNAs. Many of these aberrant splicing changes coincide with those found in post-mortem brain tissue from individuals with autism spectrum disorders (ASDs) as well as in mouse models of intellectual disability such as PTEN hamartoma syndrome (PHTS) and Rett Syndrome (RTT). These splicing changes could result from chromatin modifications (e.g., in FXS, RTT) and/or splicing factor alterations (e.g., PTEN, autism). Based on the identities of the RNAs that are mis-spliced in these disorders, it may be that they are at least partly responsible for some shared pathophysiological conditions. The convergence of splicing aberrations among these autism spectrum disorders might be crucial to understanding their underlying cognitive impairments.

A logical relationship for schizophrenia, bipolar, and major depressive disorder. Part 1: Evidence from chromosome 1 high density association screen

Familial clustering of schizophrenia (SCZ), bipolar disorder (BPD), and major depressive disorder (MDD) was investigated systematically (Aukes et al., Genetics in Medicine, 2012, 14, 338-341) and any two or even three of these disorders could coexist in some families. Furthermore, evidence from symptomatology and psychopharmacology also imply the existence of intrinsic connections between these three major psychiatric disorders. A total of 71,445 SNPs on chromosome 1 were genotyped on 119 SCZ, 253 BPD (type-I), 177 MDD cases and 1000 controls and further validated in 986 SCZ patients in the population of Shandong province of China. Outstanding psychosis genes are systematically revealed( ATP1A4, ELTD1, FAM5C, HHAT, KIF26B, LMX1A, NEGR1, NFIA, NR5A2, NTNG1, PAPPA2, PDE4B, PEX14, RYR2, SYT6, TGFBR3, TTLL7, and USH2A). Unexpectedly, flanking genes for up to 97.09% of the associated SNPs were also replicated in an enlarged cohort of 986 SCZ patients. From the perspective of etiological rather than clinical psychiatry, bipolar, and major depressive disorder could be subtypes of schizophrenia. Meanwhile, the varied clinical feature and prognosis might be the result of interaction of genetics and epigenetics, for example, irreversible or reversible shut down, and over or insufficient expression of certain genes, which may gives other aspects of these severe mental disorders.

Transcriptome Changes in Relation to Manic Episode

Bipolar disorder (BD) is highly heritable and well known for its recurrent manic and depressive episodes. The present study focused on manic episode in BD patients and aimed to investigate state-specific transcriptome alterations between acute episode and remission, including messenger RNAs (mRNAs), long noncoding RNAs (lncRNAs), and micro-RNAs (miRNAs), using microarray and RNA sequencing (RNA-Seq) platforms. BD patients were enrolled with clinical information, and peripheral blood samples collected at both acute and remission status spanning for at least 2 months were confirmed by follow-ups. Symptom severity was assessed by Young Mania Rating Scale. We enrolled six BD patients as the discovery samples and used the Affymetrix Human Transcriptome Array 2.0 to capture transcriptome data at the two time points. For replication, expression data from Gene Expression Omnibus that consisted of 11 BD patients were downloaded, and we performed a mega-analysis for microarray data of 17 patients. Moreover, we conducted RNA sequencing (RNA-Seq) in additional samples of 7 BD patients. To identify intraindividual differentially expressed genes (DEGs), we analyzed data using a linear model controlling for symptom severity. We found that noncoding genes were of majority among the top DEGs in microarray data. The expression fold change of coding genes among DEGs showed moderate to high correlations (∼0.5) across platforms. A number of lncRNAs and two miRNAs (MIR181B1 and MIR103A1) exhibited high levels of gene expression in the manic state. For coding genes, we reported that the taste function-related genes, including TAS2R5 and TAS2R3, may be mania state-specific markers. Additionally, four genes showed a nominal p-value of less than 0.05 in all our microarray data, mega-analysis, and RNA-Seq analysis. They were upregulated in the manic state and consisted of MS4A14, PYHIN1, UTRN, and DMXL2, and their gene expression patterns were further validated by quantitative real-time polymerase chain reaction (PCR) (qRT-PCR). We also performed weight gene coexpression network analysis to identify gene modules for manic episode. Genes in the mania-related modules were different from the susceptible loci of BD obtained from genome-wide association studies, and biological pathways in relation to these modules were mainly related to immune function, especially cytokine-cytokine receptor interaction. Results of the present study elucidated potential molecular targets and genomic networks that are involved in manic episode. Future studies are needed to further validate these biomarkers for their roles in the etiology of bipolar illness.

Using 3D epigenomic maps of primary olfactory neuronal cells from living individuals to understand gene regulation

As part of PsychENCODE, we developed a three-dimensional (3D) epigenomic map of primary cultured neuronal cells derived from olfactory neuroepithelium (CNON). We mapped topologically associating domains and high-resolution chromatin interactions using Hi-C and identified regulatory elements using chromatin immunoprecipitation and nucleosome positioning assays. Using epigenomic datasets from biopsies of 63 living individuals, we found that epigenetic marks at distal regulatory elements are more variable than marks at proximal regulatory elements. By integrating genotype and metadata, we identified enhancers that have different levels corresponding to differences in genetic variation, gender, smoking, and schizophrenia. Motif searches revealed that many CNON enhancers are bound by neuronal-related transcription factors. Last, we combined 3D epigenomic maps and gene expression profiles to predict enhancer-target gene interactions on a genome-wide scale. This study not only provides a framework for understanding individual epigenetic variation using a primary cell model system but also contributes valuable data resources for epigenomic studies of neuronal epithelium.

Exon Array Biomarkers for the Differential Diagnosis of Schizophrenia and Bipolar Disorder

This study developed potential blood-based biomarker tests for diagnosing and differentiating schizophrenia (SZ), bipolar disorder type I (BD), and normal control (NC) subjects using mRNA gene expression signatures. A total of 90 subjects (n = 30 each for the three groups of subjects) provided blood samples at two visits. The Affymetrix exon microarray was used to profile the expression of over 1.4 million probesets. We selected potential biomarker panels using the temporal stability of the probesets and also back-tested them at two different visits for each subject. The 18-gene biomarker panels, using logistic regression modeling, correctly differentiated the three groups of subjects with high accuracy across the two different clinical visits (83-88% accuracy). The results are also consistent with the actual data and the "leave-one-out" analyses, indicating that the models should be predictive when applied to independent data cohorts. Many of the SZ and BD subjects were taking antipsychotic and mood stabilizer medications at the time of blood draw, raising the possibility that these drugs could have affected some of the differential transcription signatures. Using an independent Illumina data set of gene expression data from antipsychotic medication-free SZ subjects, the 18-gene biomarker panels produced a receiver operating characteristic curve accuracy greater than 0.866 in patients that were less than 30 years of age and medication free. We confirmed select transcripts by quantitative PCR and the nCounter® System. The episodic nature of psychiatric disorders might lead to highly variable results depending on when blood is collected in relation to the severity of the disease/symptoms. We have found stable trait gene panel markers for lifelong psychiatric disorders that may have diagnostic utility in younger undiagnosed subjects where there is a critical unmet need. The study requires replication in subjects for ultimate proof of the utility of the differential diagnosis.

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.

A novel relationship for schizophrenia, bipolar and major depressive disorder Part 5: a hint from chromosome 5 high density association screen

Familial clustering of schizophrenia (SCZ), bipolar disorder (BPD), and major depressive disorder (MDD) was systematically reported (Aukes, M. F. Genet Med 2012, 14, 338-341) and any two or even three of these disorders could co-exist in some families. In addition, evidence from symptomatology and psychopharmacology also imply that there are intrinsic connections between these three major disorders. A total of 56,569 single nucleotide polymorphism (SNPs) on chromosome 5 were genotyped by Affymetrix Genome-Wide Human SNP array 6.0 on 119 SCZ, 253 BPD (type-I), 177 MDD patients and 1000 controls. Associated SNPs and flanking genes was screen out systematically, and cadherin pathway genes (CDH6, CDH9, CDH10, CDH12, and CDH18) belong to outstanding genes. Unexpectedly, nearly all flanking genes of the associated SNPs distinctive for BPD and MDD were replicated in an enlarged cohort of 986 SCZ patients (P ≤ 9.9E-8). Considering multiple bits of evidence, our chromosome 5 analyses implicated that bipolar and major depressive disorder might be subtypes of schizophrenia rather than two independent disease entities. Also, cadherin pathway genes play important roles in the pathogenesis of the three major mental disorders.

Transcriptome-wide mega-analyses reveal joint dysregulation of immunologic genes and transcription regulators in brain and blood in schizophrenia

The application of microarray technology in schizophrenia research was heralded as paradigm-shifting, as it allowed for high-throughput assessment of cell and tissue function. This technology was widely adopted, initially in studies of postmortem brain tissue, and later in studies of peripheral blood. The collective body of schizophrenia microarray literature contains apparent inconsistencies between studies, with failures to replicate top hits, in part due to small sample sizes, cohort-specific effects, differences in array types, and other confounders. In an attempt to summarize existing studies of schizophrenia cases and non-related comparison subjects, we performed two mega-analyses of a combined set of microarray data from postmortem prefrontal cortices (n=315) and from ex-vivo blood tissues (n=578). We adjusted regression models per gene to remove non-significant covariates, providing best-estimates of transcripts dysregulated in schizophrenia. We also examined dysregulation of functionally related gene sets and gene co-expression modules, and assessed enrichment of cell types and genetic risk factors. The identities of the most significantly dysregulated genes were largely distinct for each tissue, but the findings indicated common emergent biological functions (e.g. immunity) and regulatory factors (e.g., predicted targets of transcription factors and miRNA species across tissues). Our network-based analyses converged upon similar patterns of heightened innate immune gene expression in both brain and blood in schizophrenia. We also constructed generalizable machine-learning classifiers using the blood-based microarray data. Our study provides an informative atlas for future pathophysiologic and biomarker studies of schizophrenia.

The role of DNA methylation in the pathophysiology and treatment of bipolar disorder

Bipolar disorder (BD) is a multifactorial illness thought to result from an interaction between genetic susceptibility and environmental stimuli. Epigenetic mechanisms, including DNA methylation, can modulate gene expression in response to the environment, and therefore might account for part of the heritability reported for BD. This paper aims to review evidence of the potential role of DNA methylation in the pathophysiology and treatment of BD. In summary, several studies suggest that alterations in DNA methylation may play an important role in the dysregulation of gene expression in BD, and some actually suggest their potential use as biomarkers to improve diagnosis, prognosis, and assessment of response to treatment. This is also supported by reports of alterations in the levels of DNA methyltransferases in patients and in the mechanism of action of classical mood stabilizers. In this sense, targeting specific alterations in DNA methylation represents exciting new treatment possibilities for BD, and the 'plastic' characteristic of DNA methylation accounts for a promising possibility of restoring environment-induced modifications in patients.

Development of a T7 Phage Display Library to Detect Sarcoidosis and Tuberculosis by a Panel of Novel Antigens

Sarcoidosis is a granulomatous inflammatory disease, diagnosed through tissue biopsy of involved organs in the absence of other causes such as tuberculosis (TB). No specific serologic test is available to diagnose and differentiate sarcoidosis from TB. Using a high throughput method, we developed a T7 phage display cDNA library derived from mRNA isolated from bronchoalveolar lavage (BAL) cells and leukocytes of sarcoidosis patients. This complex cDNA library was biopanned to obtain 1152 potential sarcoidosis antigens and a microarray was constructed to immunoscreen two different sets of sera from healthy controls and sarcoidosis. Meta-analysis identified 259 discriminating sarcoidosis antigens, and multivariate analysis identified 32 antigens with a sensitivity of 89% and a specificity of 83% to classify sarcoidosis from healthy controls. Additionally, interrogating the same microarray platform with sera from subjects with TB, we identified 50 clones that distinguish between TB, sarcoidosis and healthy controls. The top 10 sarcoidosis and TB specific clones were sequenced and homologies were searched in the public database revealing unique epitopes and mimotopes in each group. Here, we show for the first time that immunoscreenings of a library derived from sarcoidosis tissue differentiates between sarcoidosis and tuberculosis antigens. These novel biomarkers can improve diagnosis of sarcoidosis and TB, and may aid to develop or evaluate a TB vaccine.

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Immunity plays a major role in a vast array of human diseases.

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Sarcoidosis shares similarities with non-infectious and infectious granulomatous diseases, including tuberculosis.

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A highly sensitive and specific T7 phage library discriminates the immune signature between sarcoidosis patients and TB.

Novel integrative genomic tool for interrogating lithium response in bipolar disorder

We developed a novel integrative genomic tool called GRANITE (Genetic Regulatory Analysis of Networks Investigational Tool Environment) that can effectively analyze large complex data sets to generate interactive networks. GRANITE is an open-source tool and invaluable resource for a variety of genomic fields. Although our analysis is confined to static expression data, GRANITE has the capability of evaluating time-course data and generating interactive networks that may shed light on acute versus chronic treatment, as well as evaluating dose response and providing insight into mechanisms that underlie therapeutic versus sub-therapeutic doses or toxic doses. As a proof-of-concept study, we investigated lithium (Li) response in bipolar disorder (BD). BD is a severe mood disorder marked by cycles of mania and depression. Li is one of the most commonly prescribed and decidedly effective treatments for many patients (responders), although its mode of action is not yet fully understood, nor is it effective in every patient (non-responders). In an in vitro study, we compared vehicle versus chronic Li treatment in patient-derived lymphoblastoid cells (LCLs) (derived from either responders or non-responders) using both microRNA (miRNA) and messenger RNA gene expression profiling. We present both Li responder and non-responder network visualizations created by our GRANITE analysis in BD. We identified by network visualization that the Let-7 family is consistently downregulated by Li in both groups where this miRNA family has been implicated in neurodegeneration, cell survival and synaptic development. We discuss the potential of this analysis for investigating treatment response and even providing clinicians with a tool for predicting treatment response in their patients, as well as for providing the industry with a tool for identifying network nodes as targets for novel drug discovery.

Blood-based gene-expression biomarkers of post-traumatic stress disorder among deployed marines: A pilot study

The etiology of post-traumatic stress disorder (PTSD) likely involves the interaction of numerous genes and environmental factors. Similarly, gene-expression levels in peripheral blood are influenced by both genes and environment, and expression levels of many genes show good correspondence between peripheral blood and brain tissues. In that context, this pilot study sought to test the following hypotheses: (1) post-trauma expression levels of a gene subset in peripheral blood would differ between Marines with and without PTSD; (2) a diagnostic biomarker panel of PTSD among high-risk individuals could be developed based on gene-expression in readily assessable peripheral blood cells; and (3) a diagnostic panel based on expression of individual exons would surpass the accuracy of a model based on expression of full-length gene transcripts. Gene-expression levels in peripheral blood samples from 50 U.S. Marines (25 PTSD cases and 25 non-PTSD comparison subjects) were determined by microarray following their return from deployment to war-zones in Iraq or Afghanistan. The original sample was carved into training and test subsets for construction of support vector machine classifiers. The panel of peripheral blood biomarkers achieved 80% prediction accuracy in the test subset based on the expression of just two full-length transcripts (GSTM1 and GSTM2). A biomarker panel based on 20 exons attained an improved 90% accuracy in the test subset. Though further refinement and replication of these biomarker profiles are required, these preliminary results provide proof-of-principle for the diagnostic utility of blood-based mRNA-expression in PTSD among trauma-exposed individuals.

Multiplex immunoassay analysis of plasma shows prominent upregulation of growth factor activity pathways linked to GSK3β signaling in bipolar patients

BACKGROUND

Our understanding of bipolar disorder (BD) aetiology has advanced in recent years but our ability to translate this to improve patient care in the clinic is still limited.

METHODS

In this study, we have measured the concentrations of 190 different molecules using sensitive multiplex immunoassays in plasma of 17 BD patients compared to 46 matched control subjects.

RESULTS

The analyses led to the identification of 26 dysregulated proteins in BD patients compared to controls. These molecules were comprised mostly of growth factors, hormones, lipid transport and inflammatory proteins. Decreased apolipoprotein A1 has previously been associated with BD patients and this was confirmed in our study.

LIMITATIONS

The present pilot study was limited by its small sample size, use of multiple drug treatments and the lack of dietary restrictions at the time of sampling.

CONCLUSIONS

Future studies may increase our understanding of BD which will help to pave the way for much-needed patient stratification for better treatment outcomes.

Next-generation sequencing in schizophrenia and other neuropsychiatric disorders

Schizophrenia is a debilitating lifelong illness that lacks a cure and poses a worldwide public health burden. The disease is characterized by a heterogeneous clinical and genetic presentation that complicates research efforts to identify causative genetic variations. This review examines the potential of current findings in schizophrenia and in other related neuropsychiatric disorders for application in next-generation technologies, particularly whole-exome sequencing (WES) and whole-genome sequencing (WGS). These approaches may lead to the discovery of underlying genetic factors for schizophrenia and may thereby identify and target novel therapeutic targets for this devastating disorder.

On the outside, looking in: a review and evaluation of the comparability of blood and brain "-omes"

In this article, we review studies detailing the correspondence between peripheral blood and brain tissue across various domains of high-throughput -omic analysis in order to provide a context for evaluating blood-based biomarker studies. Specifically, we reviewed seven studies comparing patterns of DNA methylation (i.e., an aspect of the epigenome), eight articles comparing patterns of gene expression (i.e., the transcriptome), and three articles comparing patterns of protein expression (i.e., the proteome). Our review of the epigenomic literature suggests that CpG-island methylation levels are generally highly correlated (r = 0.90) between blood and brain. Our review of transcriptomic studies suggests that between 35% and 80% of known transcripts are present in both brain and blood tissue samples; estimates of cross-tissue correlation in expression levels were found to range from 0.25 to 0.64, with stronger correlations observed among particular subsets of genes. Relative to the epigenome and transcriptome, the proteome has not been as fully compared between brain and blood samples, highlighting an important area for future work as whole-proteome profiling methods mature. Beyond reviewing the relevant studies, we discuss some of the assumptions, methodological issues, and gaps in knowledge that should be addressed in order to better understand how the multiple "-omes" of the brain are reflected in the peripheral blood. A better understanding of these relationships is a critical precursor to the validation of biomarkers for brain disorders.

Acute prenatal exposure to a moderate dose of valproic acid increases social behavior and alters gene expression in rats

Prenatal exposure to moderate doses of valproic acid (VPA) produces brainstem abnormalities, while higher doses of this teratogen elicit social deficits in the rat. In this pilot study, we examined effects of prenatal exposure to a moderate dose of VPA on behavior and on transcriptomic expression in three brain regions that mediate social behavior. Pregnant Long Evans rats were injected with 350 mg/kg VPA or saline on gestational day 13. A modified social interaction test was used to assess social behavior and social preference/avoidance during early and late adolescence and in adulthood. VPA-exposed animals demonstrated more social investigation and play fighting than control animals. Social investigation, play fighting, and contact behavior also differed as a function of age; the frequency of these behaviors increased in late adolescence. Social preference and locomotor activity under social circumstances were unaffected by treatment or age. Thus, a moderate prenatal dose of VPA produces behavioral alterations that are substantially different from the outcomes that occur following exposure to a higher dose. At adulthood, VPA-exposed subjects exhibited transcriptomic abnormalities in three brain regions: anterior amygdala, cerebellar vermis, and orbitofrontal cortex. A common feature among the proteins encoded by the dysregulated genes was their ability to be modulated by acetylation. Analysis of the expression of individual exons also revealed that genes involved in post-translational modification and epigenetic regulation had particular isoforms that were ubiquitously dysregulated across brain regions. The vulnerability of these genes to the epigenetic effects of VPA may highlight potential mechanisms by which prenatal VPA exposure alters the development of social behavior.

Blood-based gene-expression predictors of PTSD risk and resilience among deployed marines: a pilot study

Susceptibility to PTSD is determined by both genes and environment. Similarly, gene-expression levels in peripheral blood are influenced by both genes and environment, and expression levels of many genes show good correspondence between peripheral blood and brain. Therefore, our objectives were to test the following hypotheses: (1) pre-trauma expression levels of a gene subset (particularly immune-system genes) in peripheral blood would differ between trauma-exposed Marines who later developed PTSD and those who did not; (2) a predictive biomarker panel of the eventual emergence of PTSD among high-risk individuals could be developed based on gene expression in readily assessable peripheral blood cells; and (3) a predictive panel based on expression of individual exons would surpass the accuracy of a model based on expression of full-length gene transcripts. Gene-expression levels were assayed in peripheral blood samples from 50 U.S. Marines (25 eventual PTSD cases and 25 non-PTSD comparison subjects) prior to their deployment overseas to war-zones in Iraq or Afghanistan. The panel of biomarkers dysregulated in peripheral blood cells of eventual PTSD cases prior to deployment was significantly enriched for immune genes, achieved 70% prediction accuracy in an independent sample based on the expression of 23 full-length transcripts, and attained 80% accuracy in an independent sample based on the expression of one exon from each of five genes. If the observed profiles of pre-deployment mRNA-expression in eventual PTSD cases can be further refined and replicated, they could suggest avenues for early intervention and prevention among individuals at high risk for trauma exposure.

Biomarker Development for Brain-Based Disorders: Recent Progress in Psychiatry

Biomarkers are biological measures that are indicative of a specific disorder, its severity or response to treatment. They are widely used in many areas of medicine, but biomarker development for brain-based disorders lags behind. Using examples from the field of psychiatry, this article reviews the concepts of biomarkers, challenges to their development and the recent progress along those lines. In addition to discussing historical biomarker candidates such as cortisol or catecholamine levels, we include progress from recent genetic, epigenetic, proteomic, neuroimaging and EEG studies. Successful identification of biomarkers will advance the field of psychiatry towards the goal of biological tests for diagnosis, symptom management and treatment response.

Negative Symptoms of Psychosis Correlate with Gene Expression of the Wnt/β-Catenin Signaling Pathway in Peripheral Blood

Genes in the Wnt (wingless)/β-catenin signaling pathway have been implicated in schizophrenia pathogenesis. No study has examined this pathway in the broader context of psychosis symptom severity. We investigated the association between symptom severity scores and expression of 25 Wnt pathway genes in blood from 19 psychotic patients. Significant correlations between negative symptom scores and deshivelled 2 (DVL2) (r_adj = −0.70; P = 0.0008) and glycogen synthase kinase 3 beta (GSK3B) (r_adj = 0.48; P = 0.039) were observed. No gene expression levels were associated with positive symptoms. Our findings suggest that the Wnt signaling pathway may harbor biomarkers for severity of negative but not positive symptoms.

Systems biology, bioinformatics, and biomarkers in neuropsychiatry

Although neuropsychiatric (NP) disorders are among the top causes of disability worldwide with enormous financial costs, they can still be viewed as part of the most complex disorders that are of unknown etiology and incomprehensible pathophysiology. The complexity of NP disorders arises from their etiologic heterogeneity and the concurrent influence of environmental and genetic factors. In addition, the absence of rigid boundaries between the normal and diseased state, the remarkable overlap of symptoms among conditions, the high inter-individual and inter-population variations, and the absence of discriminative molecular and/or imaging biomarkers for these diseases makes difficult an accurate diagnosis. Along with the complexity of NP disorders, the practice of psychiatry suffers from a "top-down" method that relied on symptom checklists. Although checklist diagnoses cost less in terms of time and money, they are less accurate than a comprehensive assessment. Thus, reliable and objective diagnostic tools such as biomarkers are needed that can detect and discriminate among NP disorders. The real promise in understanding the pathophysiology of NP disorders lies in bringing back psychiatry to its biological basis in a systemic approach which is needed given the NP disorders' complexity to understand their normal functioning and response to perturbation. This approach is implemented in the systems biology discipline that enables the discovery of disease-specific NP biomarkers for diagnosis and therapeutics. Systems biology involves the use of sophisticated computer software "omics"-based discovery tools and advanced performance computational techniques in order to understand the behavior of biological systems and identify diagnostic and prognostic biomarkers specific for NP disorders together with new targets of therapeutics. In this review, we try to shed light on the need of systems biology, bioinformatics, and biomarkers in neuropsychiatry, and illustrate how the knowledge gained through these methodologies can be translated into clinical use providing clinicians with improved ability to diagnose, manage, and treat NP patients.

Transcriptomic analysis of postmortem brain identifies dysregulated splicing events in novel candidate genes for schizophrenia

The diverse spatial and temporal expression of alternatively spliced transcript isoforms shapes neurodevelopment and plays a major role in neuronal adaptability. Although alternative splicing is extremely common in the brain, its role in mental illnesses such as schizophrenia has received little attention. To examine this relationship, postmortem brain tissue was obtained from 20 individuals with schizophrenia (SZ) and 20 neuropsychiatrically normal comparison subjects. Gray matter samples were extracted from two brain regions implicated in the disorder: Brodmann Area 10 and caudate. Affymetrix Human Gene 1.0 ST arrays were used on four subjects per group to attain an initial profile of differential expression of transcribed elements within and across brain regions in SZ. Numerous genes of interest with altered mRNA transcripts were identified by microarray through the differential expression of particular exons and 3' untranslated regions (UTRs) between diagnostic groups. Select microarray results--including dysregulation of ENAH exon 11a and CPNE3 3'UTR--were verified by qRTPCR and replicated in the remaining independent sample of 16 SZ patients and 16 normal comparison subjects. These results, if further replicated, clearly illustrate the importance of Identifying transcriptomic variants in expression studies, and implicate novel candidate genes in the disorder.

Blood-based gene expression signatures of infants and toddlers with autism

OBJECTIVE

Autism spectrum disorders (ASDs) are highly heritable neurodevelopmental disorders that onset clinically during the first years of life. ASD risk biomarkers expressed early in life could significantly impact diagnosis and treatment, but no transcriptome-wide biomarker classifiers derived from fresh blood samples from children with autism have yet emerged.

METHOD

Using a community-based, prospective, longitudinal method, we identified 60 infants and toddlers at risk for ASDs (autistic disorder and pervasive developmental disorder), 34 at-risk for language delay, 17 at-risk for global developmental delay, and 68 typically developing comparison children. Diagnoses were confirmed via longitudinal follow-up. Each child's mRNA expression profile in peripheral blood mononuclear cells was determined by microarray.

RESULTS

Potential ASD biomarkers were discovered in one-half of the sample and used to build a classifier, with high diagnostic accuracy in the remaining half of the sample.

CONCLUSIONS

The mRNA expression abnormalities reliably observed in peripheral blood mononuclear cells, which are safely and easily assayed in infants, offer the first potential peripheral blood-based, early biomarker panel of risk for autism in infants and toddlers. Future work should verify these biomarkers and evaluate whether they may also serve as indirect indices of deviant molecular neural mechanisms in autism.

Comparison of brain and blood gene expression in an animal model of negative symptoms in schizophrenia

OBJECTIVES

To investigate the potential of white blood cells as probes for central processes we have measured gene expression in both the anterior cingulate cortex and white blood cells using a putative animal model of negative symptoms in schizophrenia.

METHODS

The model is based on the capability of ketamine to induce psychotic symptoms in healthy volunteers and to worsen such symptoms in schizophrenic patients. Classical fear conditioning is used to assess emotional processing and cognitive function in animals exposed to sub-chronic ketamine vs. controls. Gene expression was measured using a commercially sourced whole genome rat gene array. Data analyses were performed using ANOVA (Systat 11).

RESULTS

In both anterior cingulate cortex and white blood cells a significant interaction between ketamine and fear conditioning could be observed. The outcome is largely supported by our subsequent metagene analysis. Moreover, the correlation between gene expression in brain and blood is about constant when no ketamine is present (r~0.4). With ketamine, however, the correlation becomes very low (r~0.2) when there is no fear, but it increases to ~0.6 when fear and ketamine are both present. Our results show that under normal conditions ketamine lowers gene expression in the brain, but this effect is completely reversed in combination with fear conditioning, indicating a stimulatory action.

CONCLUSION

This paradoxical outcome indicates that extreme care must be taken when using gene expression data from white blood cells as marker for psychiatric disorders, especially when pharmacological and environmental interactions are at play.

Finding the needle in the haystack: a review of microarray gene expression research into schizophrenia

BACKGROUND

With an estimated 80% heritability, molecular genetic research into schizophrenia has remained inconclusive. Recent large-scale, genome-wide association studies only identified a small number of susceptibility genes with individually very small effect sizes. However, the variable expression of the phenotype is not well captured in diagnosis-based research as well as when assuming a 'heterogenic risk model' (as apposed to a monogenic or polygenic model). Hence, the expression of susceptibility genes in response to environmental factors in concert with other disease-promoting or protecting genes has increasingly attracted attention.

METHOD

The current review summarises findings of microarray gene expression research with relevance to schizophrenia as they emerged over the past decade.

RESULTS

Most findings from post mortem, peripheral tissues and animal models to date have linked altered gene expression in schizophrenia to presynaptic function, signalling, myelination, neural migration, cellular immune mechanisms, and response to oxidative stress consistent with multiple small effects of many individual genes. However, the majority of results are difficult to interpret due to small sample sizes (i.e. potential type-2 errors), confounding factors (i.e. medication effects) or lack of plausible neurobiological theory.

CONCLUSION

Nevertheless, microarray gene expression research is likely to play an important role in the future when investigating gene/gene and gene/environment interactions by adopting a neurobiologically sound theoretical framework.

Construction and analysis of the protein-protein interaction networks for schizophrenia, bipolar disorder, and major depression

BACKGROUND

Schizophrenia, bipolar disorder, and major depression are devastating mental diseases, each with distinctive yet overlapping epidemiologic characteristics. Microarray and proteomics data have revealed genes which expressed abnormally in patients. Several single nucleotide polymorphisms (SNPs) and mutations are associated with one or more of the three diseases. Nevertheless, there are few studies on the interactions among the disease-associated genes and proteins.

RESULTS

This study, for the first time, incorporated microarray and protein-protein interaction (PPI) databases to construct the PPI network of abnormally expressed genes in postmortem brain samples of schizophrenia, bipolar disorder, and major depression patients. The samples were collected from Brodmann area (BA) 10 of the prefrontal cortex. Abnormally expressed disease genes were selected by t-tests comparing the disease and control samples. These genes were involved in housekeeping functions (e.g. translation, transcription, energy conversion, and metabolism), in brain specific functions (e.g. signal transduction, neuron cell differentiation, and cytoskeleton), or in stress responses (e.g. heat shocks and biotic stress).The diseases were interconnected through several "switchboard"-like nodes in the PPI network or shared abnormally expressed genes. A "core" functional module which consisted of a tightly knitted sub-network of clique-5 and -4s was also observed. These cliques were formed by 12 genes highly expressed in both disease and control samples.

CONCLUSIONS

Several previously unidentified disease marker genes and drug targets, such as SBNO2 (schizophrenia), SEC24C (bipolar disorder), and SRRT (major depression), were identified based on statistical and topological analyses of the PPI network. The shared or interconnecting marker genes may explain the shared symptoms of the studied diseases. Furthermore, the "switchboard" genes, such as APP, UBC, and YWHAZ, are proposed as potential targets for developing new treatments due to their functional and topological significance.

CYP2D6 genotype and smoking influence fluvoxamine steady-state concentration in Japanese psychiatric patients: lessons for genotype-phenotype association study design in translational pharmacogenetics

The CYP2D6 enzyme is a capacity-limited high-affinity drug elimination pathway that metabolizes numerous psychiatric medicines. The capacity-limited nature of this enzyme suggests that drug dose may serve as an important factor that influence genotype-phenotype associations. However, dose dependency of CYP2D6 genotype contributions to drug elimination, and its interaction with environmental factors (e.g., smoking) did not receive adequate attention in translational study designs. Fluvoxamine is a selective serotonin reuptake inhibitor antidepressant. Fluvoxamine concentration is one of the factors previously linked to clinical remission in moderate to severe depression. We investigated the joint effect of smoking (an inducer of CYP1A2) and CYP2D6 genotype on interindividual variability in fluvoxamine steady-state concentration. Fluvoxamine concentration was measured in 87 patients treated with 50, 100, 150 or 200 mg/d. While CYP2D6 genotype significantly influenced fluvoxamine concentration in all four dose groups (p < 0.05), the percentage variance explained (R²) by CYP2D6 decreased as the dose of fluvoxamine increased. Smoking status (nonsmokers vs. smoking 20 or more cigarettes/d) significantly affected fluvoxamine concentration in the 50 mg/d group only (p = 0.005). Together, CYP2D6 genotype and smoking status explained 23% of the variance in fluvoxamine concentration but only at the low 50 mg/d dose group. These findings contribute to evidence-based and personalized choice of fluvoxamine dose using smoking status and CYP2D6 genetic variation. Additionally, these data lend evidence for drug dose as an important variable in translational pharmacogenetic study design and pharmaceutical phenotype associations with capacity-limited drug metabolism pathways such as CYP2D6.

Dysfunctional gene splicing as a potential contributor to neuropsychiatric disorders

Alternative pre-mRNA splicing is a major mechanism by which the proteomic diversity of eukaryotic genomes is amplified. Much akin to neuropsychiatric disorders themselves, alternative splicing events can be influenced by genetic, developmental, and environmental factors. Here, we review the evidence that abnormalities of splicing may contribute to the liability toward these disorders. First, we introduce the phenomenon of alternative splicing and describe the processes involved in its regulation. We then review the evidence for specific splicing abnormalities in a wide range of neuropsychiatric disorders, including psychotic disorders (schizophrenia), affective disorders (bipolar disorder and major depressive disorder), suicide, substance abuse disorders (cocaine abuse and alcoholism), and neurodevelopmental disorders (autism). Next, we provide a theoretical reworking of the concept of "gene-focused" epidemiologic and neurobiologic investigations. Lastly, we suggest potentially fruitful lines for future research that should illuminate the nature, extent, causes, and consequences of alternative splicing abnormalities in neuropsychiatric disorders.

Schizophrenia risk genes: Implications for future drug development and discovery

Present-day development of improved treatments for schizophrenia is hindered by uncertain models of disease, inter-individual response variability in clinical trials and a paucity of sensitive measures of treatment effects. Findings from genetic research emphasize the potential for schizophrenia risk genes to help develop focused treatments, discover new drug targets and provide markers of clinical subtypes. Advances in genetic technologies also provide novel modes of drug discovery in schizophrenia such as transcriptomics, epigenetics and transgenic animal models. In this review, we discuss proven and proposed ways risk genes can be used to enhance the development and discovery of treatments for schizophrenia and highlight key studies in these approaches.

Therapeutic implications of down-regulation of cyclophilin D in bipolar disorder

We previously reported that neuron-specific mutant Polg1 (mitochondrial DNA polymerase) transgenic (Tg) mice exhibited bipolar disorder (BD)-like phenotypes such as periodic activity change and altered circadian rhythm. In this study, we re-evaluated two datasets resulting from DNA microarray analysis to estimate a biological pathway associated with the disorder. The gene lists were derived from the comparison between post-mortem brains of BD patients and control subjects, and from the comparison between the brains of Tg and wild-type mice. Gene ontology analysis showed that 16 categories overlapped in the altered gene expression profiles of BD patients and the mouse model. In the brains of Tg mice, 33 genes showed similar changes in the frontal cortex and hippocampus compared to wild-type mice. Among the 33 genes, SFPQ and PPIF were differentially expressed in post-mortem brains of BD patients compared to control subjects. The only gene consistently down-regulated in both patients and the mouse model was PPIF, which encodes cyclophilin D (CypD), a component of the mitochondrial permeability transition pore. A blood-brain barrier-permeable CypD inhibitor significantly improved the abnormal behaviour of Tg mice at 40 mg/kg.d. These findings collectively suggest that CypD is a promising target for a new drug for BD.

Positive symptoms of psychosis correlate with expression of ubiquitin proteasome genes in peripheral blood

Several brain- and blood-based gene expression studies in patients with psychotic disorders (e.g., schizophrenia) have identified genes in the ubiquitin proteasome system (UPS) pathway as putative biomarkers. However, to date an examination of the UPS pathway in the broader context of symptom severity in psychosis has not been conducted. The purpose of this study was to investigate the correlation between clinical scores on the Scales for the Assessment of Positive and Negative Symptoms (SAPS-SANS) and expression of 43 highly annotated genes within the UPS pathway in blood from patients with psychosis. A sample of 19 psychotic patients diagnosed with schizophrenia (n = 13) or bipolar disorder (n = 6) were recruited. Pearson's partial correlations, adjusting for gender, ethnicity, age, education, medication, smoking, and past 6-month substance use, were performed between each of the selected UPS genes and both scales. Significant Bonferroni-adjusted positive associations were observed between SAPS scores and two ubiquitin conjugation genes (i.e., UBE2K, SIAH2), while a negative association was observed with one deubiquitination gene (i.e., USP2). No gene expression levels were significantly associated with scores on the SANS after correction for multiple testing. Our findings suggest that dysregulation of the UPS, specifically ubiquitin conjugation and deubiquitination, may point to a possible underlying biological mechanism for severity of positive but not negative symptoms.