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      Prenatal Bisphenol A Exposure in Mice Induces Multitissue Multiomics Disruptions Linking to Cardiometabolic Disorders

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          Abstract

          <p id="d599642e242">The health impacts of endocrine-disrupting chemicals (EDCs) remain debated, and their tissue and molecular targets are poorly understood. In this study, we leveraged systems biology approaches to assess the target tissues, molecular pathways, and gene regulatory networks associated with prenatal exposure to the model EDC bisphenol A (BPA). Prenatal BPA exposure at 5 mg/kg/d, a dose below most reported no-observed-adverse-effect levels, led to tens to thousands of transcriptomic and methylomic alterations in the adipose, hypothalamus, and liver tissues in male offspring in mice, with cross-tissue perturbations in lipid metabolism as well as tissue-specific alterations in histone subunits, glucose metabolism, and extracellular matrix. Network modeling prioritized main molecular targets of BPA, including <i>Pparg</i>, <i>Hnf4a</i>, <i>Esr1</i>, <i>Srebf1</i>, and <i>Fasn</i> as well as numerous less studied targets such as <i>Cyp51</i> and long noncoding RNAs across tissues, <i>Fa2h</i> in hypothalamus, and <i>Nfya</i> in adipose tissue. Lastly, integrative analyses identified the association of BPA molecular signatures with cardiometabolic phenotypes in mouse and human. Our multitissue, multiomics investigation provides strong evidence that BPA perturbs diverse molecular networks in central and peripheral tissues and offers insights into the molecular targets that link BPA to human cardiometabolic disorders. </p>

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          Most cited references113

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          Is Open Access

          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Cytoscape: a software environment for integrated models of biomolecular interaction networks.

            Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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              KEGG: kyoto encyclopedia of genes and genomes.

              M Kanehisa (2000)
              KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).
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                Author and article information

                Contributors
                Journal
                Endocrinology
                The Endocrine Society
                1945-7170
                February 2019
                February 01 2019
                December 18 2018
                February 2019
                February 01 2019
                December 18 2018
                : 160
                : 2
                : 409-429
                Affiliations
                [1 ]Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, California
                [2 ]Molecular, Cellular, and Integrative Physiology Interdepartmental Program, University of California, Los Angeles, Los Angeles, California
                [3 ]Molecular Toxicology Interdepartmental Program, University of California, Los Angeles, Los Angeles, California
                [4 ]Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, California
                [5 ]Institute for Society and Genetics, University of California, Los Angeles, Los Angeles, California
                [6 ]Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, Los Angeles, California
                Article
                10.1210/en.2018-00817
                6349005
                30566610
                a15588b9-7c90-448b-b1bb-3a034ef14651
                © 2018
                History

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