Archives of Toxicology

, Volume 88, Issue 12, pp 2261–2287

Toxicogenomics directory of chemically exposed human hepatocytes

  • Marianna Grinberg
  • Regina M. Stöber
  • Karolina Edlund
  • Eugen Rempel
  • Patricio Godoy
  • Raymond Reif
  • Agata Widera
  • Katrin Madjar
  • Wolfgang Schmidt-Heck
  • Rosemarie Marchan
  • Agapios Sachinidis
  • Dimitry Spitkovsky
  • Jürgen Hescheler
  • Helena Carmo
  • Marcelo D. Arbo
  • Bob van de Water
  • Steven Wink
  • Mathieu Vinken
  • Vera Rogiers
  • Sylvia Escher
  • Barry Hardy
  • Dragana Mitic
  • Glenn Myatt
  • Tanja Waldmann
  • Adil Mardinoglu
  • Georg Damm
  • Daniel Seehofer
  • Andreas Nüssler
  • Thomas S. Weiss
  • Axel Oberemm
  • Alfons Lampen
  • Mirjam M. Schaap
  • Mirjam Luijten
  • Harry van Steeg
  • Wolfgang E. Thasler
  • Jos C. S. Kleinjans
  • Rob H. Stierum
  • Marcel Leist
  • Jörg Rahnenführer
  • Jan G. Hengstler
In vitro systems

DOI: 10.1007/s00204-014-1400-x

Cite this article as:
Grinberg, M., Stöber, R.M., Edlund, K. et al. Arch Toxicol (2014) 88: 2261. doi:10.1007/s00204-014-1400-x

Abstract

A long-term goal of numerous research projects is to identify biomarkers for in vitro systems predicting toxicity in vivo. Often, transcriptomics data are used to identify candidates for further evaluation. However, a systematic directory summarizing key features of chemically influenced genes in human hepatocytes is not yet available. To bridge this gap, we used the Open TG-GATES database with Affymetrix files of cultivated human hepatocytes incubated with chemicals, further sets of gene array data with hepatocytes from human donors generated in this study, and publicly available genome-wide datasets of human liver tissue from patients with non-alcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular cancer (HCC). After a curation procedure, expression data of 143 chemicals were included into a comprehensive biostatistical analysis. The results are summarized in the publicly available toxicotranscriptomics directory (http://wiki.toxbank.net/toxicogenomics-map/) which provides information for all genes whether they are up- or downregulated by chemicals and, if yes, by which compounds. The directory also informs about the following key features of chemically influenced genes: (1) Stereotypical stress response. When chemicals induce strong expression alterations, this usually includes a complex but highly reproducible pattern named ‘stereotypical response.’ On the other hand, more specific expression responses exist that are induced only by individual compounds or small numbers of compounds. The directory differentiates if the gene is part of the stereotypical stress response or if it represents a more specific reaction. (2) Liver disease-associated genes. Approximately 20 % of the genes influenced by chemicals are up- or downregulated, also in liver disease. Liver disease genes deregulated in cirrhosis, HCC, and NASH that overlap with genes of the aforementioned stereotypical chemical stress response include CYP3A7, normally expressed in fetal liver; the phase II metabolizing enzyme SULT1C2; ALDH8A1, known to generate the ligand of RXR, one of the master regulators of gene expression in the liver; and several genes involved in normal liver functions: CPS1, PCK1, SLC2A2, CYP8B1, CYP4A11, ABCA8, and ADH4. (3) Unstable baseline genes. The process of isolating and the cultivation of hepatocytes was sufficient to induce some stress leading to alterations in the expression of genes, the so-called unstable baseline genes. (4) Biological function. Although more than 2,000 genes are transcriptionally influenced by chemicals, they can be assigned to a relatively small group of biological functions, including energy and lipid metabolism, inflammation and immune response, protein modification, endogenous and xenobiotic metabolism, cytoskeletal organization, stress response, and DNA repair. In conclusion, the introduced toxicotranscriptomics directory offers a basis for a rationale choice of candidate genes for biomarker evaluation studies and represents an easy to use source of background information on chemically influenced genes.

Keywords

Hepatotoxicity Toxicotranscriptomics Unsupervised clustering In vivo validation Steatosis Cirrhosis Hepatocellular cancer Biomarker identification Bioinformatics SEURAT-1 

Supplementary material

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Fig. S1: Corresponding data to Fig. 1 summarizing all further incubation conditions besides the high concentration and 24h exposure already shown in Fig. 1. A. Low concentration, 2h, 8h and 24h incubation; B. middle concentration, 2h, 8h and 24h incubation; C. high concentration, 2h, 8h and 24h incubation. (PPTX 1204 kb)
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Fig. S2: Numbers of significantly downregulated genes. The x-axis lists all compounds that were tested at the indicated concentration for the corresponding period. The y-axis gives the number of - downregulated genes with at least 1.5-, 2.0- and 3.0-fold change. The result shows that the number of deregulated genes differs strongly between the chemicals. The figure corresponds to Fig. 2 where the upregulated genes are shown. Dark green: more than 1.5-fold downregulated; light green: more than 2-fold downregulated; black: more than 3-fold downregulated. (PPTX 714 kb)
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Fig. S3: ‘Exclusivity analysis’ of the downregulated genes. This analysis first determines the 100 strongest downregulated genes across all compounds. Next, these genes are assigned to the compound with the most extreme fold change. The analysis corresponds to Fig. 4 where the upregulated genes are shown. (PPTX 150 kb)
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Fig. S4: Selection values for the downregulated genes. A selection value of e.g. five means that at least five compounds downregulate (3-fold) the indicated number of genes. The figure corresponds to Fig. 8A where the selection values for the upregulated genes are shown. (PPTX 116 kb)

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Marianna Grinberg
    • 1
  • Regina M. Stöber
    • 2
  • Karolina Edlund
    • 2
  • Eugen Rempel
    • 1
  • Patricio Godoy
    • 2
  • Raymond Reif
    • 2
  • Agata Widera
    • 2
  • Katrin Madjar
    • 1
  • Wolfgang Schmidt-Heck
    • 3
  • Rosemarie Marchan
    • 2
  • Agapios Sachinidis
    • 4
  • Dimitry Spitkovsky
    • 4
  • Jürgen Hescheler
    • 4
  • Helena Carmo
    • 5
  • Marcelo D. Arbo
    • 5
  • Bob van de Water
    • 6
  • Steven Wink
    • 6
  • Mathieu Vinken
    • 7
  • Vera Rogiers
    • 7
  • Sylvia Escher
    • 8
  • Barry Hardy
    • 9
  • Dragana Mitic
    • 10
  • Glenn Myatt
    • 11
  • Tanja Waldmann
    • 12
  • Adil Mardinoglu
    • 13
  • Georg Damm
    • 14
  • Daniel Seehofer
    • 14
  • Andreas Nüssler
    • 15
  • Thomas S. Weiss
    • 16
  • Axel Oberemm
    • 17
  • Alfons Lampen
    • 17
  • Mirjam M. Schaap
    • 18
  • Mirjam Luijten
    • 18
  • Harry van Steeg
    • 18
  • Wolfgang E. Thasler
    • 19
  • Jos C. S. Kleinjans
    • 20
  • Rob H. Stierum
    • 21
  • Marcel Leist
    • 12
  • Jörg Rahnenführer
    • 1
  • Jan G. Hengstler
    • 2
  1. 1.Department of StatisticsTU Dortmund UniversityDortmundGermany
  2. 2.Leibniz Research Centre for Working Environment and Human Factors at the Technical University of Dortmund (IfADo)DortmundGermany
  3. 3.Leibniz Institute for Natural Product Research and Infection Biology, Hans Knoell InstituteJenaGermany
  4. 4.Center of Physiology and Pathophysiology, Institute of NeurophysiologyUniversity of Cologne (UKK)CologneGermany
  5. 5.Laboratório de Toxicologia, Departamento de Ciências BiológicasUniversidade do PortoPortoPortugal
  6. 6.Division of Toxicology, Leiden Academic Centre for Drug ResearchLeiden UniversityLeidenThe Netherlands
  7. 7.Department of Toxicology, Dermato-Cosmetology and Pharmacognosy (FAFY), Center for Pharmaceutical Research (CePhaR)Vrije Universiteit Brussel (VUB)BrusselsBelgium
  8. 8.Fraunhofer Institute for Toxicology and Experimental Medicine (ITEM)HannoverGermany
  9. 9.Douglas Connect and OpenToxZeiningenSwitzerland
  10. 10.Cambridge Cell NetworksCambridgeUK
  11. 11.LeadscopeColumbusUSA
  12. 12.Department of BiologyUniversity of KonstanzKonstanzGermany
  13. 13.Department of Chemical and Biological EngineeringChalmers University of TechnologyGothenburgSweden
  14. 14.Department of General-, Visceral- and Transplantation SurgeryCharité University Medicine BerlinBerlinGermany
  15. 15.Department of TraumatologyEberhard Karls Universität TübingenTübingenGermany
  16. 16.Department of Pediatrics and Juvenile MedicineUniversity of Regensburg HospitalRegensburgGermany
  17. 17.Federal Institute for Risk AssessmentBerlinGermany
  18. 18.Center for Health ProtectionNational Institute for Public Health and the EnvironmentBilthovenThe Netherlands
  19. 19.Department of SurgeryGrosshadern HospitalMunichGermany
  20. 20.Department of Health Risk Analysis and ToxicologyMaastricht UniversityMaastrichtThe Netherlands
  21. 21.TNO Quality of LifeZeistThe Netherlands