Gene Expression Profiling in Schizophrenia and Related Mental Disorders
ABSTRACT The etiology and pathophysiology of schizophrenia and related mental disorders such as bipolar disorder and major depression remain largely unclear. Recent advances in mRNA profiling techniques made it possible to perform genome-wide gene expression analysis in a hypothesis-free manner. It was thought that this large-scale data mining approach would reveal unknown molecular cascades involved in mental disorders. Contrary to this initial expectation, however, DNA microarray results in psychiatric fields have been notoriously discordant. Here the authors review the findings of DNA microarray analysis, focusing on systematic gene expression changes in schizophrenia, as well as alterations in the expression of specific genes, that have been reported and replicated. The authors also address the probable causes for the discordance among studies, possible ways to solve the problem, and their preferred approach for data interpretation.
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- "In addition, demographic factors such as age, gender and medical history may also affect experimental data. The pH of brain samples, which reflects the agonal state of the subject, is a major factor that affects transcriptome analysis [41-43]. However, Ernst et al.  reported that the pH of brain samples has no significant effect on DNA methylation status. "
ABSTRACT: Schizophrenia is a severe psychiatric disease affecting about 1% of the world's population, with significant effects on patients and society. Genetic studies have identified several candidate risk genes or genomic regions for schizophrenia, and epidemiological studies have revealed several environmental risk factors. However, the etiology of schizophrenia still remains largely unknown. Epigenetic mechanisms such as DNA methylation and histone modifications can explain the interaction between genetic and environmental factors at the molecular level, and accumulating evidence suggests that such epigenetic alterations are involved in the pathophysiology of schizophrenia. However, replication studies to validate previous findings and investigations of the causality of epigenetic alterations in schizophrenia are needed. Here, we review epigenetic studies of schizophrenia patients using postmortem brains or peripheral tissues, focusing mainly on DNA methylation. We also highlight the recent progress and challenges in characterizing the potentially complex and dynamic patterns of epigenomic variations. Such studies are expected to contribute to our understanding of schizophrenia etiology and should provide novel opportunities for the development of therapeutic drugs.Genome Medicine 12/2012; 4(12):96. DOI:10.1186/gm397 · 4.94 Impact Factor
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- "A major goal of current neurobiology research is to define and characterize the cellular and molecular pathophysiology underlying nervous system dysfunction including neurodegenerative disorders and psychiatric illness. Over the past two decades, a fundamental component of this effort has involved human postmortem brain studies where gene expression profiles of matched tissue samples from healthy individuals and patients diagnosed with specific nervous system disorders have been compared (Horvath et al., 2011; Iwamoto and Kato, 2006; Mehta et al., 2010; Sequeira and Turecki, 2006). While this traditional approach can be confounded by a number of variables such as postmortem interval, medication history, secondary effects of illness, cause of death and the small number of brain samples available for analysis (Bahn et al., 2001; Mirnics et al., 2004; Mirnics and Pevsner, 2004), technical artifacts of gene expression analysis may also contribute to inconsistencies between published datasets among multiple laboratories. "
ABSTRACT: Initially identified as an RNA modification in the anticodon loop of tRNAs from animal, plant and eubacterial origin, the deamination of adenosine-to-inosine by RNA editing has become increasingly recognized as an important RNA processing event to generate diversity in both the transcriptome and proteome and is essential for modulating the activity of numerous proteins critical for nervous system function. Here, we focus on the editing of transcripts encoding the 2C-subtype of serotonin receptor (5HT(2C)) to generate multiple receptor isoforms that differ in G-protein coupling efficacy and constitutive activity. 5HT(2C) receptors have been implicated in the regulation of anxiety, components of the stress response, and are thought to play a role in compulsive behavioral disorders, depression and drug addiction. A number of studies have been conducted to assess whether 5HT(2C) editing is altered in individuals suffering from psychiatric disorders, yet the results from these studies have been inconsistent, and thus inconclusive. This review provides a discussion of the challenges involved with characterizing 5HT(2C) editing patterns in human postmortem tissue samples and how differences in quantitative methodology have contributed to the observed inconsistencies between multiple laboratories. Additionally, we discuss new high-throughput sequencing tools, which provide an opportunity to overcome previous methodological challenges, and permit reliable systematic analyses of RNA editing in control and pathologic disease states.Neurobiology of Disease 09/2011; 45(1):8-13. DOI:10.1016/j.nbd.2011.08.026 · 5.20 Impact Factor
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- "Thirteen human depression post-mortem microarray datasets of sufficient size for coexpression analysis were included (Table 1; Torrey et al., 2000; Iwamoto et al., 2004; Sibille et al., 2004; Aston et al., 2005; Iwamoto and Kato, 2006; Surget et al., 2009). Array data from mice submitted to the unpredictable chronic mild stress (UCMS) model of depression were also included (Surget et al., 2009). "
ABSTRACT: The structure of gene coexpression networks reflects the activation and interaction of multiple cellular systems. Since the pathology of neuropsychiatric disorders is influenced by diverse cellular systems and pathways, we investigated gene coexpression networks in major depression, and searched for putative unifying themes in network connectivity across neuropsychiatric disorders. Specifically, based on the prevalence of the lethality-centrality relationship in disease-related networks, we hypothesized that network changes between control and major depression-related networks would be centered around coexpression hubs, and secondly, that differentially expressed (DE) genes would have a characteristic position and connectivity level in those networks. Mathematically, the first hypothesis tests the relationship of differential coexpression to network connectivity, while the second “hybrid” expression-and-network hypothesis tests the relationship of differential expression to network connectivity. To answer these questions about the potential interaction of coexpression network structure with differential expression, we utilized all available human post-mortem depression-related datasets appropriate for coexpression analysis, which spanned different microarray platforms, cohorts, and brain regions. Similar studies were also performed in an animal model of depression and in schizophrenia and bipolar disorder microarray datasets. We now provide results which consistently support (1) that genes assemble into small-world and scale-free networks in control subjects, (2) that this efficient network topology is largely resilient to changes in depressed subjects, and (3) that DE genes are positioned on the periphery of coexpression networks. Similar results were observed in a mouse model of depression, and in selected bipolar- and schizophrenia-related networks. Finally, we show that baseline expression variability contributes to the propensity of genes to be network hubs.Frontiers in Neuroscience 08/2011; 5:95. DOI:10.3389/fnins.2011.00095 · 3.70 Impact Factor