Toxicogenomics directory of chemically exposed human hepatocytes
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- 作者：Marianna Grinberg (1)
Regina M. St?ber (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)
J?rg Rahnenführer (1)
Jan G. Hengstler (2) - 关键词：Hepatotoxicity ; Toxicotranscriptomics ; Unsupervised clustering ; In vivo validation ; Steatosis ; Cirrhosis ; Hepatocellular cancer ; Biomarker identification ; Bioinformatics ; SEURAT ; 1
- 刊名：Archives of Toxicology
- 出版年：2014
- 出版时间：December 2014
- 年：2014
- 卷：88
- 期：12
- 页码：2261-2287
- 全文大小：4,289 KB
- 参考文献：1. Alexa A, Rahnenführer J (2010) topGO: topGO: enrichment analysis for gene ontology. R package version 2.12.0
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5. Bauer A, Schumann A, Gilbert M, Wilhelm C, Hengstler JG, Schiller J, Fuchs B (2009) Evaluation of carbon tetrachloride-induced stress on rat hepatocytes by 31P NMR and MALDI-TOF mass spectrometry: lysophosphatidylcholine generation from unsaturated phosphatidylcholines. Chem Phys Lipids 159(1):21-9 CrossRef
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9. Ellinger-Ziegelbauer H, Gmuender H, Bandenburg A, Ahr HJ (2008) Prediction of a carcinogenic potential of rat hepatocarcinogens using toxicogenomics analysis of short-term in vivo studies. Mutat Res 637(1-):23-9 CrossRef
10. Fijten RR, Jennen DG, van Delft JH (2013) Pathways for ligand activated nuclear receptors to unravel the genomic responses induced by hepatotoxicants. Curr Drug Metab 14(10):1022-028 CrossRef
11. Froguel P, Zouali H, Sun F, Velho G, Fukumoto H, Passa P, Cohen D (1991) CA repeat polymorphism in the glucose transporter GLUT 2 gene. Nucleic Acids Res 19(13):3754 CrossRef
12. G?fvels M, Olin M, Chowdhary BP, Raudsepp T, Andersson U, Persson B, Jansson M, Bj?rkhem I, Eggertsen G (1999) Structure and chromosomal assignment of the sterol 12alpha-hydroxylase gene (CYP8B1) in human and mouse: eukaryotic cytochrome P-450 gene devoid of introns. Genomics 56(2):184-96 CrossRef
13. Godoy P (2013) Recent advances in 2D and 3D in vitro systems using primary hepatocytes, alternative hepatocyte sources and non-parenchymal liver cells and their use in investigating mechanisms of hepatotoxicity, cell signaling and ADME. Arch Toxicol 87:1315-530 CrossRef
14. Godoy P, Hengstler JG, Ilkavets I, Meyer C, Bachmann A, Müller A, Tuschl G, Mueller SO, Dooley S (2009) Extracellular matrix modulates sensitivity of hepatocytes to fibroblastoid dedifferentiation and transforming growth factor beta-induced apoptosis. Hepatology 49(6):2031-043 CrossRef
15. Godoy P, Lakkapamu S, Schug M, Bauer A, Stewart JD, Bedawi E, Hammad S, Amin J, Marchan R, Schorma - 作者单位：Marianna Grinberg (1)
Regina M. St?ber (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)
J?rg Rahnenführer (1)
Jan G. Hengstler (2)
1. Department of Statistics, TU Dortmund University, Dortmund, Germany
2. Leibniz Research Centre for Working Environment and Human Factors at the Technical University of Dortmund (IfADo), Ardeystrasse 67, 44139, Dortmund, Germany
3. Leibniz Institute for Natural Product Research and Infection Biology, Hans Knoell Institute, Jena, Germany
4. Center of Physiology and Pathophysiology, Institute of Neurophysiology, University of Cologne (UKK), Cologne, Germany
5. Laboratório de Toxicologia, Departamento de Ciências Biológicas, Universidade do Porto, Porto, Portugal
6. Division of Toxicology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
7. Department of Toxicology, Dermato-Cosmetology and Pharmacognosy (FAFY), Center for Pharmaceutical Research (CePhaR), Vrije Universiteit Brussel (VUB), Brussels, Belgium
8. Fraunhofer Institute for Toxicology and Experimental Medicine (ITEM), Hannover, Germany
9. Douglas Connect and OpenTox, Zeiningen, Switzerland
10. Cambridge Cell Networks, Cambridge, UK
11. Leadscope, Columbus, OH, USA
12. Department of Biology, University of Konstanz, Konstanz, Germany
13. Department of Chemical and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
14. Department of General-, Visceral- and Transplantation Surgery, Charité University Medicine Berlin, Berlin, Germany
15. Department of Traumatology, Eberhard Karls Universit?t Tübingen, Tübingen, Germany
16. Department of Pediatrics and Juvenile Medicine, University of Regensburg Hospital, Regensburg, Germany
17. Federal Institute for Risk Assessment, Berlin, Germany
18. Center for Health Protection, National Institute for Public Health and the Environment, Bilthoven, The Netherlands
19. Department of Surgery, Grosshadern Hospital, Munich, Germany
20. Department of Health Risk Analysis and Toxicology, Maastricht University, Maastricht, The Netherlands
21. TNO Quality of Life, Zeist, The Netherlands - ISSN：1432-0738
文摘
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.