Gene expression of peripheral blood lymphocytes may discriminate patients with schizophrenia from controls

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.

A multimodal attempt to follow-up linkage regions using RNA expression, SNPs and CpG methylation in schizophrenia and bipolar disorder kindreds

The complexity of schizophrenia (SZ) and bipolar disorder (BD) has slowed down progress in understanding their genetic roots. Alternative genomic approaches are needed to bypass these difficulties. We attempted a multimodal approach to follow-up on reported linkage findings in SZ and BD from the Eastern Quebec kindreds in chromosomes 3q21, 4p34, 6p22, 8p21, 8p11, 13q11-q14, 15q13, 16p12, and 18q21. First, in 498 subjects, we measured RNA expression (47 K Illumina chips) in SZ and BD patients that we compared with their non-affected relatives (NARs) to identify, for each chromosomal region, genes showing the most significant differences in expression. Second, we performed SNP genotyping (700 K Illumina chips) and cis-eQTN analysis. Third, we measured DNA methylation on genes with RNA expression differences or eQTNs. We found a significant overexpression of the gene ITGB5 at 3q25 in SZ and BD after multiple testing p value adjustment. SPCS3 gene at 4q34, and FZD3 gene at 8p21, contained significant eQTNs after multiple testing corrections, while ITGB5 provided suggestive results. Methylation in associated genes did not explain the expression differences between patients and NARs. Our multimodal approach involving RNA expression, dense SNP genotyping and eQTN analyses, restricted to chromosomal regions having shown linkage, lowered the multiple testing burden and allowed for a deeper examination of candidate genes in SZ or BD.

DNA methylation fingerprint of monozygotic twins and their singleton sibling with intellectual disability carrying a novel KDM5C mutation

Mutations in KDM5C (lysine (K)-specific demethylase 5C) were causally associated with up to 3% of X-linked intellectual disability (ID) in males. By exome and Sanger sequencing, a novel frameshift KDM5C variant, predicted to eliminate the JmjC catalytic domain from the protein, was identified in two monozygotic twins and their older brother, which was inherited from their clinically normal mother, who had completely skewed X-inactivation. DNA methylation (DNAm) data were evaluated using the Illumina 450 K Methylation Beadchip arrays. Comparison of methylation levels between the three patients and male controls identified 399 differentially methylated CpG sites, which were enriched among those CpG sites modulated during brain development. Most of them were hypomethylated (72%), and located mainly in shores, whereas the hypermethylated CpGs were more represented in open sea regions. The DNAm changes did not differ between the monozygotic twins nor between them and their older sibling, all presenting a global hypomethylation, similar to other studies that associated DNA methylation changes to different KDM5C mutations. The 38 differentially methylated regions (DMRs) were enriched for H3K4me3 marks identified in developing brains. The remarkable similarity between the methylation changes in the monozygotic twins and their older brother is indicative that these epigenetic changes were mostly driven by the KDM5C mutation.

Gene expression profiling of the dorsolateral and medial orbitofrontal cortex in schizophrenia

Schizophrenia is a complex polygenic disorder of unknown etiology. Over 3,000 candidate genes associated with schizophrenia have been reported, most of which being mentioned only once. Alterations in cognitive processing - working memory, metacognition and mentalization - represent a core feature of schizophrenia, which indicates the involvement of the prefrontal cortex in the pathophysiology of this disorder. Hence we compared the gene expression in postmortem tissue from the left and right dorsolateral prefrontal cortex (DLPFC, Brodmann's area 46), and the medial part of the orbitofrontal cortex (MOFC, Brodmann's area 11/12), in six patients with schizophrenia and six control brains. Although in the past decade several studies performed transcriptome profiling in schizophrenia, this is the first study to investigate both hemispheres, providing new knowledge about possible brain asymmetry at the level of gene expression and its relation to schizophrenia. We found that in the left hemisphere, twelve genes from the DLPFC and eight genes from the MOFC were differentially expressed in patients with schizophrenia compared to controls. In the right hemisphere there was only one gene differentially expressed in the MOFC. We reproduce the involvement of previously reported genes TARDBP and HNRNPC in the pathogenesis of schizophrenia, and report seven novel genes: SART1, KAT7, C1D, NPM1, EVI2A, XGY2, and TTTY15. As the differentially expressed genes only partially overlap with previous studies that analyzed other brain regions, our findings indicate the importance of considering prefrontal cortical regions, especially those in the left hemisphere, for obtaining disease-relevant insights.

The role of tumour necrosis factor alpha and soluble tumour necrosis factor alpha receptors in the symptomatology of schizophrenia

Background Immunological mechanisms may be responsible for the development and maintenance of schizophrenia symptoms. Aim The aim of this study is to measure tumour necrosis factor-alpha (TNF-α), soluble tumour necrosis factor-alpha receptor I (sTNF-αRI), and soluble tumour necrosis factor-alpha receptor II (sTNF-αRII) levels in patients with schizophrenia and healthy individuals, and to determine their relationship with the symptoms of schizophrenia. Methods Serum TNF-α, sTNF-αRI and sTNF-αRII levels were measured. The Positive and Negative Syndrome Scale (PANSS) was administered for patients with schizophrenia (n = 35), and the results were compared with healthy controls (n = 30). Hierarchical regression analyses were undertaken to predict the levels of TNF-α, sTNF-αRI and sTNF-αRII. Results No significant difference was observed in TNF-α levels, but sTNF-αRI and sTNF-αRII levels were lower in patients with schizophrenia. Serum sTNF-αRI and sTNF-αRII levels were found to be negatively correlated with the negative subscale score of the PANSS, and sTNF-αRI levels were also negatively correlated with the total score of the PANSS. Smoking, gender, body mass index were not correlated with TNF-α and sTNF-α receptor levels. Conclusions These results suggest that there may be a change in anti-inflammatory response in patients with schizophrenia due to sTNF-αRI and sTNF-αRII levels. The study also supports low levels of TNF activity in schizophrenia patients with negative symptoms.

Omics-Based Biomarkers: Application of Metabolomics in Neuropsychiatric Disorders

One of the major concerns of modern society is to identify putative biomarkers that serve as a valuable early diagnostic tool to identify a subset of patients with increased risk to develop neuropsychiatric disorders. Biomarker identification in neuropsychiatric disorders is proposed to offer a number of important benefits to patient well-being, including prediction of forthcoming disease, diagnostic precision, and a level of disease description that would guide treatment choice. Nowadays, the metabolomics approach has unlocked new possibilities in diagnostics of devastating disorders like neuropsychiatric disorders. Metabolomics-based technologies have the potential to map early biochemical changes in disease and hence provide an opportunity to develop predictive biomarkers that can be used as indicators of pathological abnormalities prior to development of clinical symptoms of neuropsychiatric disorders. This review highlights different -omics strategies for biomarker discovery in neuropsychiatric disorders. We also highlight initial outcomes from metabolomics studies in psychiatric disorders such as schizophrenia, bipolar disorder, and addictive disorders. This review will also present issues and challenges regarding the implementation of the metabolomics approach as a routine diagnostic tool in the clinical laboratory in context with neuropsychiatric disorders.

Co-expression network of neural-differentiation genes shows specific pattern in schizophrenia

Background

Schizophrenia is a neurodevelopmental disorder with genetic and environmental factors contributing to its pathogenesis, although the mechanism is unknown due to the difficulties in accessing diseased tissue during human neurodevelopment. The aim of this study was to find neuronal differentiation genes disrupted in schizophrenia and to evaluate those genes in post-mortem brain tissues from schizophrenia cases and controls.

Methods

We analyzed differentially expressed genes (DEG), copy number variation (CNV) and differential methylation in human induced pluripotent stem cells (hiPSC) derived from fibroblasts from one control and one schizophrenia patient and further differentiated into neuron (NPC). Expression of the DEG were analyzed with microarrays of post-mortem brain tissue (frontal cortex) cohort of 29 schizophrenia cases and 30 controls. A Weighted Gene Co-expression Network Analysis (WGCNA) using the DEG was used to detect clusters of co-expressed genes that werenon-conserved between adult cases and controls brain samples.

Results

We identified methylation alterations potentially involved with neuronal differentiation in schizophrenia, which displayed an over-representation of genes related to chromatin remodeling complex (adjP = 0.04). We found 228 DEG associated with neuronal differentiation. These genes were involved with metabolic processes, signal transduction, nervous system development, regulation of neurogenesis and neuronal differentiation. Between adult brain samples from cases and controls there were 233 DEG, with only four genes overlapping with the 228 DEG, probably because we compared single cell to tissue bulks and more importantly, the cells were at different stages of development. The comparison of the co-expressed network of the 228 genes in adult brain samples between cases and controls revealed a less conserved module enriched for genes associated with oxidative stress and negative regulation of cell differentiation.

Conclusion

This study supports the relevance of using cellular approaches to dissect molecular aspects of neurogenesis with impact in the schizophrenic brain. We showed that, although generated by different approaches, both sets of DEG associated to schizophrenia were involved with neocortical development. The results add to the hypothesis that critical metabolic changes may be occurring during early neurodevelopment influencing faulty development of the brain and potentially contributing to further vulnerability to the illness.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-015-0098-9) contains supplementary material, which is available to authorized users.

Schizophrenia biomarkers: translating the descriptive into the diagnostic

While schizophrenia and mental health are qualitatively distinct at the level of clinical presentation, the specific molecular signatures that underlie, or associate with, illness are not. Biomarker identification in schizophrenia is intended to offer a number of important benefits to patient well-being including prediction of future illness, diagnostic clarity and a level of disease description that would guide treatment choice. However, the choice of sample and form of analysis used to produce useful biomarkers is still uncertain. In this review, advances from recent studies spanning the technical spectrum are presented together with comment on their comparative strengths and weaknesses. To date, these studies have aided our understanding of the pathological processes associated with illness much more than they have provided robust biomarkers. A number of reasons for this observation are suggested, as are new strategies for the extraction of biomarkers from large '-omics' datasets.

Changes in gene expression and methylation in the blood of patients with first-episode psychosis

Schizophrenia is a severe mental health disorder with high heritability. The investigation of individuals during their first-episode psychosis (FEP), before the progression of psychotic disorders and especially before treatment with antipsychotic medications, is particularly helpful for understanding this complex disease and for the identification of potential biomarkers. In this study, we compared the expression of genes that are involved in neurotransmission and neurodevelopment of antipsychotic-naive FEP in the peripheral blood of patients (n=51) and healthy controls (n=51). In addition, we investigated the differentially expressed genes with respect to a) DNA methylation, b) the correlation between gene expression and clinical variables (PANSS), and c) gene expression changes after risperidone treatment. Expression levels of 11 genes were quantified with SYBR Green. For methylation analysis, bisulfite sequencing was performed. A significant decrease in GCH1 mRNA levels was observed in FEP patients relative to controls. Also, when we compare the FEP patients after risperidone treatment with controls, this difference remains significant, and no significant differences were observed in GCH1 mRNA levels when comparing patients before and after risperidone treatment. Additionally, although the differences were non-significant after Bonferroni correction, the expression of GCH1 seemed to be correlated with PANSS scores, and the GCH1 promoter region was more methylated in FEP than in controls, thus corroborating the results obtained at the mRNA level. Few studies have been conducted on GCH1, and future studies are needed to clarify its potential role in the progression of schizophrenia.

Evaluation of gene expression classification studies: factors associated with classification performance

Classification methods used in microarray studies for gene expression are diverse in the way they deal with the underlying complexity of the data, as well as in the technique used to build the classification model. The MAQC II study on cancer classification problems has found that performance was affected by factors such as the classification algorithm, cross validation method, number of genes, and gene selection method. In this paper, we study the hypothesis that the disease under study significantly determines which method is optimal, and that additionally sample size, class imbalance, type of medical question (diagnostic, prognostic or treatment response), and microarray platform are potentially influential. A systematic literature review was used to extract the information from 48 published articles on non-cancer microarray classification studies. The impact of the various factors on the reported classification accuracy was analyzed through random-intercept logistic regression. The type of medical question and method of cross validation dominated the explained variation in accuracy among studies, followed by disease category and microarray platform. In total, 42% of the between study variation was explained by all the study specific and problem specific factors that we studied together.

Coding and noncoding gene expression biomarkers in mood disorders and schizophrenia

Mood disorders and schizophrenia are common and complex disorders with consistent evidence of genetic and environmental influences on predisposition. It is generally believed that the consequences of disease, gene expression, and allelic heterogeneity may be partly the explanation for the variability observed in treatment response. Correspondingly, while effective treatments are available for some patients, approximately half of the patients fail to respond to current neuropsychiatric treatments. A number of peripheral gene expression studies have been conducted to understand these brain-based disorders and mechanisms of treatment response with the aim of identifying suitable biomarkers and perhaps subgroups of patients based upon molecular fingerprint. In this review, we summarize the results from blood-derived gene expression studies implemented with the aim of discovering biomarkers for treatment response and classification of disorders. We include data from a biomarker study conducted in first-episode subjects with schizophrenia, where the results provide insight into possible individual biological differences that predict antipsychotic response. It is concluded that, while peripheral studies of expression are generating valuable results in pathways involving immune regulation and response, larger studies are required which hopefully will lead to robust biomarkers for treatment response and perhaps underlying variations relevant to these complex disorders.

Gene expression analysis reveals schizophrenia-associated dysregulation of immune pathways in peripheral blood mononuclear cells

Peripheral blood mononuclear cells (PBMCs) represent an accessible tissue source for gene expression profiling in schizophrenia that could provide insight into the molecular basis of the disorder. This study used the Illumina HT_12 microarray platform and quantitative real time PCR (QPCR) to perform mRNA expression profiling on 114 patients with schizophrenia or schizoaffective disorder and 80 non-psychiatric controls from the Australian Schizophrenia Research Bank (ASRB). Differential expression analysis revealed altered expression of 164 genes (59 up-regulated and 105 down-regulated) in the PBMCs from patients with schizophrenia compared to controls. Bioinformatic analysis indicated significant enrichment of differentially expressed genes known to be involved or associated with immune function and regulating the immune response. The differential expression of 6 genes, EIF2C2 (Ago 2), MEF2D, EVL, PI3, S100A12 and DEFA4 was confirmed by QPCR. Genome-wide expression analysis of PBMCs from individuals with schizophrenia was characterized by the alteration of genes with immune system function, supporting the hypothesis that the disorder has a significant immunological component in its etiology.