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Putative psychosis genes in the prefrontal cortex: combined analysis of gene expression microarrays

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      Abstract

      Background

      Recent studies have shown similarities between schizophrenia and bipolar disorder in phenotypes and in genotypes, and those studies have contributed to an ongoing re-evaluation of the traditional dichotomy between schizophrenia and bipolar disorder. Bipolar disorder with psychotic features may be closely related to schizophrenia and therefore, psychosis may be an alternative phenotype compared to the traditional diagnosis categories.

      Methods

      We performed a cross-study analysis of 7 gene expression microarrays that include both psychosis and non-psychosis subjects. These studies include over 400 microarray samples (163 individual subjects) on 3 different Affymetrix microarray platforms.

      Results

      We found that 110 transcripts are differentially regulated (p < 0.001) in psychosis after adjusting for confounding variables with a multiple regression model. Using a quantitative PCR, we validated a set of genes such as up-regulated metallothioneins (MT1E, MT1F, MT1H, MT1K, MT1X, MT2A and MT3) and down-regulated neuropeptides (SST, TAC1 and NPY) in the dorsolateral prefrontal cortex of psychosis patients.

      Conclusion

      This study demonstrates the advantages of cross-study analysis in detecting consensus changes in gene expression across multiple microarray studies. Differential gene expression between individuals with and without psychosis suggests that psychosis may be a useful phenotypic variable to complement the traditional diagnosis categories.

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      Most cited references 71

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      DAVID: Database for Annotation, Visualization, and Integrated Discovery.

      Functional annotation of differentially expressed genes is a necessary and critical step in the analysis of microarray data. The distributed nature of biological knowledge frequently requires researchers to navigate through numerous web-accessible databases gathering information one gene at a time. A more judicious approach is to provide query-based access to an integrated database that disseminates biologically rich information across large datasets and displays graphic summaries of functional information. Database for Annotation, Visualization, and Integrated Discovery (DAVID; http://www.david.niaid.nih.gov) addresses this need via four web-based analysis modules: 1) Annotation Tool - rapidly appends descriptive data from several public databases to lists of genes; 2) GoCharts - assigns genes to Gene Ontology functional categories based on user selected classifications and term specificity level; 3) KeggCharts - assigns genes to KEGG metabolic processes and enables users to view genes in the context of biochemical pathway maps; and 4) DomainCharts - groups genes according to PFAM conserved protein domains. Analysis results and graphical displays remain dynamically linked to primary data and external data repositories, thereby furnishing in-depth as well as broad-based data coverage. The functionality provided by DAVID accelerates the analysis of genome-scale datasets by facilitating the transition from data collection to biological meaning.
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        The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements.

        Over the last decade, the introduction of microarray technology has had a profound impact on gene expression research. The publication of studies with dissimilar or altogether contradictory results, obtained using different microarray platforms to analyze identical RNA samples, has raised concerns about the reliability of this technology. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and data analysis issues. Expression data on four titration pools from two distinct reference RNA samples were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Here we describe the experimental design and probe mapping efforts behind the MAQC project. We show intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed. This study provides a resource that represents an important first step toward establishing a framework for the use of microarrays in clinical and regulatory settings.
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          Microarray validation: factors influencing correlation between oligonucleotide microarrays and real-time PCR

          Quantitative real-time PCR (qPCR) is a commonly used validation tool for confirming gene expression results obtained from microarray analysis; however, microarray and qPCR data often result in disagreement. The current study assesses factors contributing to the correlation between these methods in five separate experiments employing two-color 60-mer oligonucleotide microarrays and qPCR using SYBR green. Overall, significant correlation was observed between microarray and qPCR results (ρ=0.708, p 0.80 consistently observed when quality scores are applied.
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            Author and article information

            Affiliations
            [1]Stanley Laboratory of Brain Research, 9800 Medical Center Dr. Bldg 2C, Rockville, MD 20850, USA
            [2]Elashoff Consulting, Redwood City, CA 94065, USA
            [3]Stanley Laboratory of Developmental Neurovirology, Johns Hopkins University, School of Medicine, 600 North Wolfe Street, Blalock 1105, Baltimore, MD 21287, USA
            [4]Stanley Medical Research Institute, 8401 Connecticut Ave, Suite 200, Chevy Chase, MD 20815, USA
            Contributors
            Journal
            BMC Psychiatry
            BMC Psychiatry
            BioMed Central
            1471-244X
            2008
            7 November 2008
            : 8
            : 87
            2585075
            1471-244X-8-87
            18992145
            10.1186/1471-244X-8-87
            Copyright © 2008 Choi et al; licensee BioMed Central Ltd.

            This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

            Categories
            Research Article

            Clinical Psychology & Psychiatry

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