+1 Recommend
0 collections
      • Record: found
      • Abstract: found
      • Article: not found

      Human embryonic stem cell-derived test systems for developmental neurotoxicity: a transcriptomics approach

      , , , , , , , , , , , , , , , ,   , , , , , , , , , , , , , , , , , , , , ,

      Archives of Toxicology


      Methylmercury, Valproic acid, Transcription factor, Reproductive toxicity, Alternative testing strategies

      Read this article at

          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.


          Developmental neurotoxicity (DNT) and many forms of reproductive toxicity (RT) often manifest themselves in functional deficits that are not necessarily based on cell death, but rather on minor changes relating to cell differentiation or communication. The fields of DNT/RT would greatly benefit from in vitro tests that allow the identification of toxicant-induced changes of the cellular proteostasis, or of its underlying transcriptome network. Therefore, the ‘human embryonic stem cell (hESC)-derived novel alternative test systems (ESNATS)’ European commission research project established RT tests based on defined differentiation protocols of hESC and their progeny. Valproic acid (VPA) and methylmercury (MeHg) were used as positive control compounds to address the following fundamental questions: (1) Does transcriptome analysis allow discrimination of the two compounds? (2) How does analysis of enriched transcription factor binding sites (TFBS) and of individual probe sets (PS) distinguish between test systems? (3) Can batch effects be controlled? (4) How many DNA microarrays are needed? (5) Is the highest non-cytotoxic concentration optimal and relevant for the study of transcriptome changes? VPA triggered vast transcriptional changes, whereas MeHg altered fewer transcripts. To attenuate batch effects, analysis has been focused on the 500 PS with highest variability. The test systems differed significantly in their responses (<20 % overlap). Moreover, within one test system, little overlap between the PS changed by the two compounds has been observed. However, using TFBS enrichment, a relatively large ‘common response’ to VPA and MeHg could be distinguished from ‘compound-specific’ responses. In conclusion, the ESNATS assay battery allows classification of human DNT/RT toxicants on the basis of their transcriptome profiles.

          Electronic supplementary material

          The online version of this article (doi:10.1007/s00204-012-0967-3) contains supplementary material, which is available to authorized users.

          Related collections

          Most cited references 63

          • Record: found
          • Abstract: found
          • Article: not found

          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.
            • Record: found
            • Abstract: found
            • Article: not found

            Exploration, normalization, and summaries of high density oligonucleotide array probe level data.

            In this paper we report exploratory analyses of high-density oligonucleotide array data from the Affymetrix GeneChip system with the objective of improving upon currently used measures of gene expression. Our analyses make use of three data sets: a small experimental study consisting of five MGU74A mouse GeneChip arrays, part of the data from an extensive spike-in study conducted by Gene Logic and Wyeth's Genetics Institute involving 95 HG-U95A human GeneChip arrays; and part of a dilution study conducted by Gene Logic involving 75 HG-U95A GeneChip arrays. We display some familiar features of the perfect match and mismatch probe (PM and MM) values of these data, and examine the variance-mean relationship with probe-level data from probes believed to be defective, and so delivering noise only. We explain why we need to normalize the arrays to one another using probe level intensities. We then examine the behavior of the PM and MM using spike-in data and assess three commonly used summary measures: Affymetrix's (i) average difference (AvDiff) and (ii) MAS 5.0 signal, and (iii) the Li and Wong multiplicative model-based expression index (MBEI). The exploratory data analyses of the probe level data motivate a new summary measure that is a robust multi-array average (RMA) of background-adjusted, normalized, and log-transformed PM values. We evaluate the four expression summary measures using the dilution study data, assessing their behavior in terms of bias, variance and (for MBEI and RMA) model fit. Finally, we evaluate the algorithms in terms of their ability to detect known levels of differential expression using the spike-in data. We conclude that there is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model which removes probe-specific affinities.
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Cytoscape 2.8: new features for data integration and network visualization

              Summary: Cytoscape is a popular bioinformatics package for biological network visualization and data integration. Version 2.8 introduces two powerful new features—Custom Node Graphics and Attribute Equations—which can be used jointly to greatly enhance Cytoscape's data integration and visualization capabilities. Custom Node Graphics allow an image to be projected onto a node, including images generated dynamically or at remote locations. Attribute Equations provide Cytoscape with spreadsheet-like functionality in which the value of an attribute is computed dynamically as a function of other attributes and network properties. Availability and implementation: Cytoscape is a desktop Java application released under the Library Gnu Public License (LGPL). Binary install bundles and source code for Cytoscape 2.8 are available for download from Contact:

                Author and article information

                [ ]Department of Biology, University of Konstanz (UKN), 78457 Constance, Germany
                [ ]OÜ Quretec (Qure), Limited Liability Company, 51003 Tartu, Estonia
                [ ]Institute of Computer Science, University of Tartu, 50409 Tartu, Estonia
                [ ]Center of Physiology and Pathophysiology, Institute of Neurophysiology, University of Cologne (UKK), Robert-Koch-Str. 39, 50931 Cologne, Germany
                [ ]Leibniz Research Centre for Working Environment and Human Factors (IfADo), Technical University of Dortmund, 44139 Dortmund, Germany
                [ ]Commission of the European Communities (JRC) Joint Research Centre, 1049 Brussels, Belgium
                [ ]Department of Pathology and Immunology, Geneva Medical Faculty, University of Geneva (UNIGE), 1211 Geneva 4, Switzerland
                [ ]Brunel University (Brunel), Uxbridge, UB8 3PH UK
                [ ]Nederlandse Organisatie voor Toegepast Natuurwetenschappelijk Onderzoek (TNO), 2628 VK Delft, The Netherlands
                [ ]Gottfried Wilhelm Leibniz University (LUH), Institute for Biostatistics, 30167 Hannover, Germany
                [ ]Department of Statistics, TU Dortmund University , 44221 Dortmund, Germany
                +49-221-4787373 , +49-221-4786965 ,
                Arch Toxicol
                Arch. Toxicol
                Archives of Toxicology
                Springer-Verlag (Berlin/Heidelberg )
                21 November 2012
                21 November 2012
                January 2013
                : 87
                : 1
                : 123-143
                © The Author(s) 2012
                Custom metadata
                © Springer-Verlag Berlin Heidelberg 2013


                Comment on this article