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      Sex-specific pathways in early cardiac response to pressure overload in mice

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          Pressure overload (PO) first causes cardiac hypertrophy and then heart failure (HF), which are associated with sex differences in cardiac morphology and function. We aimed to identify genes that may cause HF-related sex differences. We used a transverse aortic constriction (TAC) mouse model leading to hypertrophy without sex differences in cardiac function after 2 weeks, but with sex differences in hypertrophy 6 and 9 weeks after TAC. Cardiac gene expression was analyzed 2 weeks after surgery. Deregulated genes were classified into functional gene ontology (GO) categories and used for pathway analysis. Classical marker genes of hypertrophy were similarly upregulated in both sexes (α-actin, ANP, BNP, CTGF). Thirty-five genes controlling mitochondrial function (PGC-1, cytochrome oxidase, carnitine palmitoyl transferase, acyl-CoA dehydrogenase, pyruvate dehydrogenase kinase) had lower expression in males compared to females after TAC. Genes encoding ribosomal proteins and genes associated with extracellular matrix remodeling exhibited relative higher expression in males (collagen 3, matrix metalloproteinase 2, TIMP2, and TGFβ2, all about twofold) after TAC. We confirmed 87% of the gene expression by real-time polymerase chain reaction. By GO classification, female-specific genes were related to mitochondria and metabolism and males to matrix and biosynthesis. Promoter studies confirmed the upregulation of PGC-1 by E2. Less downregulation of metabolic genes in female hearts and increased protein synthesis capacity and deregulation of matrix remodeling in male hearts characterize the sex-specific early response to PO. These differences could contribute to subsequent sex differences in cardiac function and HF.

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          Most cited references 39

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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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            Is Open Access

            In silico prediction of protein-protein interactions in human macrophages

            Background: Protein-protein interaction (PPI) network analyses are highly valuable in deciphering and understanding the intricate organisation of cellular functions. Nevertheless, the majority of available protein-protein interaction networks are context-less, i.e. without any reference to the spatial, temporal or physiological conditions in which the interactions may occur. In this work, we are proposing a protocol to infer the most likely protein-protein interaction (PPI) network in human macrophages. Results: We integrated the PPI dataset from the Agile Protein Interaction DataAnalyzer (APID) with different meta-data to infer a contextualized macrophage-specific interactome using a combination of statistical methods. The obtained interactome is enriched in experimentally verified interactions and in proteins involved in macrophage-related biological processes (i.e. immune response activation, regulation of apoptosis). As a case study, we used the contextualized interactome to highlight the cellular processes induced upon Mycobacterium tuberculosis infection. Conclusion: Our work confirms that contextualizing interactomes improves the biological significance of bioinformatic analyses. More specifically, studying such inferred network rather than focusing at the gene expression level only, is informative on the processes involved in the host response. Indeed, important immune features such as apoptosis are solely highlighted when the spotlight is on the protein interaction level.
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              Identifying biological themes within lists of genes with EASE.

              EASE is a customizable software application for rapid biological interpretation of gene lists that result from the analysis of microarray, proteomics, SAGE and other high-throughput genomic data. The biological themes returned by EASE recapitulate manually determined themes in previously published gene lists and are robust to varying methods of normalization, intensity calculation and statistical selection of genes. EASE is a powerful tool for rapidly converting the results of functional genomics studies from 'genes' to 'themes'.

                Author and article information

                +49-30-450525172 , +49-30-450525972 , vera.regitz-zagrosek@charite.de
                J Mol Med
                Journal of Molecular Medicine (Berlin, Germany)
                Springer-Verlag (Berlin/Heidelberg )
                30 July 2008
                September 2008
                : 86
                : 9
                : 1013-1024
                [1 ]Berlin Institute of Gender in Medicine (GiM), Charité-Universitaetsmedizin Berlin, Luisenstraße 65, 10117 Berlin, Germany
                [2 ]Center for Cardiovascular Research (CCR), Charité-Universitaetsmedizin Berlin, Hessische Str. 3-4, 10115 Berlin, Germany
                [3 ]Medizinische Klinik und Polyklinik II, Universitaetsklinikum Bonn, Sigmund-Freud-Str. 25, 53105 Bonn, Germany
                [4 ]Max-Planck-Institute for Molecular Genetics Berlin, Ihnestr. 63-73, 14195 Berlin, Germany
                [5 ]German Heart Institute Berlin (DHZB), Augustenburger Platz 1, 13353 Berlin, Germany
                [6 ]College of Physicians and Surgeons, Columbia University, New York, NY USA
                © The Author(s) 2008
                Original Article
                Custom metadata
                © Springer-Verlag 2008

                Molecular medicine

                heart, sex, gene expression, hypertrophy, pressure overload


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