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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 references66

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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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            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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              Alterations in GABA-related transcriptome in the dorsolateral prefrontal cortex of subjects with schizophrenia.

              In subjects with schizophrenia, impairments in working memory are associated with dysfunction of the dorsolateral prefrontal cortex (DLPFC). This dysfunction appears to be due, at least in part, to abnormalities in gamma-aminobutyric acid (GABA)-mediated inhibitory circuitry. To test the hypothesis that altered GABA-mediated circuitry in the DLPFC of subjects with schizophrenia reflects expression changes of genes that encode selective presynaptic and postsynaptic components of GABA neurotransmission, we conducted a systematic expression analysis of GABA-related transcripts in the DLPFC of 14 pairs of schizophrenia and age-, sex- and post-mortem interval-matched control subjects using a customized DNA microarray with enhanced sensitivity and specificity. Subjects with schizophrenia exhibited expression deficits in GABA-related transcripts encoding (1) presynaptic regulators of GABA neurotransmission (67 kDa isoform of glutamic acid decarboxylase (GAD(67)) and GABA transporter 1), (2) neuropeptides (somatostatin (SST), neuropeptide Y (NPY) and cholecystokinin (CCK)) and (3) GABA(A) receptor subunits (alpha1, alpha4, beta3, gamma2 and delta). Real-time qPCR and/or in situ hybridization confirmed the deficits for six representative transcripts tested in the same pairs and in an extended cohort, respectively. In contrast, GAD(67), SST and alpha1 subunit mRNA levels, as assessed by in situ hybridization, were not altered in the DLPFC of monkeys chronically exposed to antipsychotic medications. These findings suggest that schizophrenia is associated with alterations in inhibitory inputs from SST/NPY-containing and CCK-containing subpopulations of GABA neurons and in the signaling via certain GABA(A) receptors that mediate synaptic (phasic) or extrasynaptic (tonic) inhibition. In concert with previous findings, these data suggest that working memory dysfunction in schizophrenia is mediated by altered GABA neurotransmission in certain DLPFC microcircuits.
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                Author and article information

                Journal
                BMC Psychiatry
                BMC Psychiatry
                BioMed Central
                1471-244X
                2008
                7 November 2008
                : 8
                : 87
                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
                Article
                1471-244X-8-87
                10.1186/1471-244X-8-87
                2585075
                18992145
                43635a9a-b6d0-4fdc-a7c1-db8202466e36
                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.

                History
                : 29 May 2008
                : 7 November 2008
                Categories
                Research Article

                Clinical Psychology & Psychiatry
                Clinical Psychology & Psychiatry

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