Dr Peter Hendriksen
Peter Hendriksen is project leader and bioinformatician at the RIKILT Institute of Food Safety, Wageningen UR, the Netherlands. RIKILT has an on-going, active research programme on toxicogenomics. Studies using whole genome microarray analyses have been successfully applied on areas ranging from liver toxicity [1-5], reprotoxicity and/or endocrine disruption [6-8], immunotoxicity [9-18], nanotoxicology[5, 19, 20] and other food toxicants including marine toxins . Recently, next generation sequencing (NGS) of mRNAs has been applied for transcriptomics analysis demonstrating an improved sensitivity of this method particularly for genes with low expression levels above microarrays. RIKILT applies transcriptomics for hazard identification and effect-based detection of toxicants by: 1) unravelling mode of action (MoAs), and 2) identification of biomarkers that can be used for the development of targeted bioassays. Biological interpretation of the alterations of mRNA expression is based on two approaches: 1) assessing effects on biological pathways and processes, and 2) comparing mRNA expression profiles induced by the substance of interest to that of big databases of known compounds. For the first approach, big cellular pathway or processes gene set collections are being used from which a part is derived from open access databases. Examples of these gene sets include genes induced or repressed during endoplasmatic reticulum stress, DNA damage/repair and cholesterol metabolism. Other gene set collections are made by ourselves and these include genes specifically induced or repressed after activation of transcription factors for example PPARα, estrogen receptor or the arylhydrocarbon receptor in a range of cell types or tissues. This focus is important since changes in mRNA expression are mainly due to altered activity of transcription factors. The goal of this approach is therefore to find the upstream factors that are responsible for the alterations in mRNA expression. For the second approach, we take advantage of huge databases of expression profiles of toxicants that are currently becoming available. Before, we had good experiences with the Connectivity Map, which is a resource of 6100 arrays with expression profiles of 1309 compounds mainly used for exposure of MCF7 cells . We are now looking forward to use the 1000-fold expanded database ( http://lincscloud.org/). Clearly, these types of analyses provide a lot of output that have to be structured. For this, tools have been developed including heat maps of gene sets and Molecular Concepts Mapping as used for instance in [9, 14]. 1. Rijk, J.C., et al., Feasibility of a liver transcriptomics approach to assess bovine treatment with the prohormone dehydroepiandrosterone (DHEA). BMC Vet Res, 2010. 6: p. 44. 2. Szalowska, E., et al., Treatment of mouse liver slices with cholestatic hepatotoxicants results in down-regulation of Fxr and its target genes. BMC Med Genomics, 2013. 6(1): p. 39. 3. Szalowska, E., et al., Effect of oxygen concentration and selected protocol factors on viability and gene expression of mouse liver slices. Toxicol In Vitro, 2013. 27(5): p. 1513-24. 4. Szalowska, E., et al., Model steatogenic compounds (amiodarone, valproic Acid, and tetracycline) alter lipid metabolism by different mechanisms in mouse liver slices. PLoS One, 2014. 9(1): p. e86795. 5. van der Zande, M., et al., Sub-chronic toxicity study in rats orally exposed to nanostructured silica. Part Fibre Toxicol, 2014. 11(1): p. 8. 6. Peijnenburg, A., et al., AhR-agonistic, anti-androgenic, and anti-estrogenic potencies of 2-isopropylthioxanthone (ITX) as determined by in vitro bioassays and gene expression profiling. Toxicol In Vitro, 2010. 24(6): p. 1619-28. 7. Reitsma, M., et al., Endocrine-disrupting effects of thioxanthone photoinitiators. Toxicol Sci, 2013. 132(1): p. 64-74. 8. Hendriksen, P.J.K., E; Riethof-Poortman, J; Teerds, K; Groot, M.J.; Rijk, J.C.; Hoogenboom, R.L.; Peijnenburg, A.A., 2-Isopropylthioxantone (2-ITX) displays anti-androgenic and aryl hydrocarbon receptor agonistic activities in developing male Wistar rats. Submitted, 2014. 9. Katika, M.R., et al., Exposure of Jurkat cells to bis (tri-n-butyltin) oxide (TBTO) induces transcriptomics changes indicative for ER- and oxidative stress, T cell activation and apoptosis. Toxicol Appl Pharmacol, 2011. 254(3): p. 311-22. 10. van Kol, S.W., et al., The effects of deoxynivalenol on gene expression in the murine thymus. Toxicol Appl Pharmacol, 2011. 250(3): p. 299-311. 11. Katika, M.R., et al., Immunocytological and biochemical analysis of the mode of action of bis (tri-n-butyltin) tri-oxide (TBTO) in Jurkat cells. Toxicol Lett, 2012. 212(2): p. 126-36. 12. Katika, M.R., et al., Transcriptome analysis of the human T lymphocyte cell line Jurkat and human peripheral blood mononuclear cells exposed to deoxynivalenol (DON): New mechanistic insights. Toxicol Appl Pharmacol, 2012. 264(1): p. 51-64. 13. van Kol, S.W., et al., Transcriptomics analysis of primary mouse thymocytes exposed to bis(tri-n-butyltin)dioxide (TBTO). Toxicology, 2012. 296(1-3): p. 37-47. 14. Schmeits, P.C., et al., Assessment of the usefulness of the murine cytotoxic T cell line CTLL-2 for immunotoxicity screening by transcriptomics. Toxicol Lett, 2013. 217(1): p. 1-13. 15. Shao, J., et al., Transcriptome-based functional classifiers for direct immunotoxicity. Arch Toxicol, 2013. 16. Shao, J., et al., Toxicogenomics-Based Identification of Mechanisms for Direct Immunotoxicity. Toxicol Sci, 2013. 17. Schmeits, P.C., et al., DON shares a similar mode of action as the ribotoxic stress inducer anisomycin while TBTO shares ER stress patterns with the ER stress inducer thapsigargin based on comparative gene expression profiling in Jurkat T cells. Toxicol Lett, 2014. 224(3): p. 395-406. 18. Shao, J., et al., Transcriptome-based functional classifiers for direct immunotoxicity. Arch Toxicol, 2014. 88(3): p. 673-89. 19. Bouwmeester, H., et al., Characterization of translocation of silver nanoparticles and effects on whole-genome gene expression using an in vitro intestinal epithelium coculture model. ACS Nano, 2011. 5(5): p. 4091-103. 20. van der Zande, M., et al., Distribution, elimination, and toxicity of silver nanoparticles and silver ions in rats after 28-day oral exposure. ACS Nano, 2012. 6(8): p. 7427-42. 21. Bovee, T.F., et al., Tailored microarray platform for the detection of marine toxins. Environ Sci Technol, 2011. 45(20): p. 8965-73. 22. Lamb, J., et al., The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science, 2006. 313(5795): p. 1929-35. 23. Vempati, U.D., et al., Metadata Standard and Data Exchange Specifications to Describe, Model, and Integrate Complex and Diverse High-Throughput Screening Data from the Library of Integrated Network-based Cellular Signatures (LINCS). J Biomol Screen, 2014. 24. Walczak, A.P., et al., Behaviour of silver nanoparticles and silver ions in an in vitro human gastrointestinal digestion model. Nanotoxicology, 2013. 7: p. 1198-210.