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      A transcriptome-based classifier to identify developmental toxicants by stem cell testing: design, validation and optimization for histone deacetylase inhibitors

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          Abstract

          Test systems to identify developmental toxicants are urgently needed. A combination of human stem cell technology and transcriptome analysis was to provide a proof of concept that toxicants with a related mode of action can be identified and grouped for read-across. We chose a test system of developmental toxicity, related to the generation of neuroectoderm from pluripotent stem cells (UKN1), and exposed cells for 6 days to the histone deacetylase inhibitors (HDACi) valproic acid, trichostatin A, vorinostat, belinostat, panobinostat and entinostat. To provide insight into their toxic action, we identified HDACi consensus genes, assigned them to superordinate biological processes and mapped them to a human transcription factor network constructed from hundreds of transcriptome data sets. We also tested a heterogeneous group of ‘mercurials’ (methylmercury, thimerosal, mercury(II)chloride, mercury(II)bromide, 4-chloromercuribenzoic acid, phenylmercuric acid). Microarray data were compared at the highest non-cytotoxic concentration for all 12 toxicants. A support vector machine (SVM)-based classifier predicted all HDACi correctly. For validation, the classifier was applied to legacy data sets of HDACi, and for each exposure situation, the SVM predictions correlated with the developmental toxicity. Finally, optimization of the classifier based on 100 probe sets showed that eight genes (F2RL2, TFAP2B, EDNRA, FOXD3, SIX3, MT1E, ETS1 and LHX2) are sufficient to separate HDACi from mercurials. Our data demonstrate how human stem cells and transcriptome analysis can be combined for mechanistic grouping and prediction of toxicants. Extension of this concept to mechanisms beyond HDACi would allow prediction of human developmental toxicity hazard of unknown compounds with the UKN1 test system.

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          The online version of this article (doi:10.1007/s00204-015-1573-y) contains supplementary material, which is available to authorized users.

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          Most cited references53

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            CellNet: network biology applied to stem cell engineering.

            Somatic cell reprogramming, directed differentiation of pluripotent stem cells, and direct conversions between differentiated cell lineages represent powerful approaches to engineer cells for research and regenerative medicine. We have developed CellNet, a network biology platform that more accurately assesses the fidelity of cellular engineering than existing methodologies and generates hypotheses for improving cell derivations. Analyzing expression data from 56 published reports, we found that cells derived via directed differentiation more closely resemble their in vivo counterparts than products of direct conversion, as reflected by the establishment of target cell-type gene regulatory networks (GRNs). Furthermore, we discovered that directly converted cells fail to adequately silence expression programs of the starting population and that the establishment of unintended GRNs is common to virtually every cellular engineering paradigm. CellNet provides a platform for quantifying how closely engineered cell populations resemble their target cell type and a rational strategy to guide enhanced cellular engineering.
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              Removing Batch Effects in Analysis of Expression Microarray Data: An Evaluation of Six Batch Adjustment Methods

              The expression microarray is a frequently used approach to study gene expression on a genome-wide scale. However, the data produced by the thousands of microarray studies published annually are confounded by “batch effects,” the systematic error introduced when samples are processed in multiple batches. Although batch effects can be reduced by careful experimental design, they cannot be eliminated unless the whole study is done in a single batch. A number of programs are now available to adjust microarray data for batch effects prior to analysis. We systematically evaluated six of these programs using multiple measures of precision, accuracy and overall performance. ComBat, an Empirical Bayes method, outperformed the other five programs by most metrics. We also showed that it is essential to standardize expression data at the probe level when testing for correlation of expression profiles, due to a sizeable probe effect in microarray data that can inflate the correlation among replicates and unrelated samples.
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                Author and article information

                Contributors
                +49-7531-885331 , Lisa.Hoelting@uni-konstanz.de
                Journal
                Arch Toxicol
                Arch. Toxicol
                Archives of Toxicology
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0340-5761
                1432-0738
                14 August 2015
                14 August 2015
                2015
                : 89
                : 9
                : 1599-1618
                Affiliations
                [ ]Department of Statistics, TU Dortmund University, 44139 Dortmund, Germany
                [ ]Centre for Organismal Studies, Heidelberg University, 69120 Heidelberg, Germany
                [ ]Doerenkamp-Zbinden Chair for In Vitro Toxicology and Biomedicine, University of Konstanz, Box: M657, 78457 Konstanz, Germany
                [ ]Konstanz Graduate School Chemical Biology KORS-CB, University of Konstanz, 78457 Konstanz, Germany
                [ ]Institute of Neurophysiology, Center for Molecular Medicine Cologne (CMMC), University of Cologne, 50931 Cologne, Germany
                [ ]Leibniz Research Centre for Working Environment and Human Factors, Technical University of Dortmund (IfADo), 44139 Dortmund, Germany
                [ ]Institute of Pathology, Charité-Universitätsmedizin, 10117 Berlin, Germany
                [ ]Integrative Research Institute for the Life Sciences, Institute for Theoretical Biology, Humboldt Universität, 10115 Berlin, Germany
                Article
                1573
                10.1007/s00204-015-1573-y
                4551554
                26272509
                e5647f37-23b1-4635-8e3a-2d93888108ab
                © The Author(s) 2015

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 18 February 2015
                : 22 July 2015
                Categories
                In Vitro Systems
                Custom metadata
                © Springer-Verlag Berlin Heidelberg 2015

                Toxicology
                hazard assessment,neuronal development,alternative testing,cytotoxicity,transcriptomics,developing central nervous system

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