Drug-induced liver injury (DILI) remains the main reason for drug development attritions largely due to poor mechanistic understanding. Toxicogenomic to interrogate the mechanism of DILI has been... Show moreDrug-induced liver injury (DILI) remains the main reason for drug development attritions largely due to poor mechanistic understanding. Toxicogenomic to interrogate the mechanism of DILI has been broadly performed. Gene co-regulation network-based transcriptome analysis is a bioinformatics approach that potentially contributes to improve mechanistic interpretation of toxicogenomic data. Here we performed an extensive concentration time course response-toxicogenomic study in the HepG2 cell line exposed to 20 DILI compounds, 7 reference compounds for stress response pathways, and 10 agonists for cytokines and growth factor receptors. We performed whole transcriptome targeted RNA sequencing to more than 500 conditions to and applied weighted gene co-regulated network analysis (WGCNA) to the transcriptomics data followed by identification of gene co-regulated networks (modules) that were strongly modulated upon the exposure of DILI compounds. Preservation analysis on the module responses of HepG2 and PHH demonstrated highly preserved adaptive stress response gene co-regulated networks. We correlated gene co-regulated networks with cell death onset and causal relationships of 67 critical target genes of these modules with onset of cell death was evaluated using RNA interference screening. We identified GTPBP2, HSPA1B, IRF1, SIRT1 and TSC22D3 as essential modulators of DILI compound-induced cell death. These genes were also induced by DILI compounds in PHH. Altogether, we demonstrate the application of large transcriptome datasets combined with network-based analysis and biological validation to uncover the candidate determinants of DILI. Show less
In this thesis we showed the applicability of the zebrafish embryo as an alternative model for hepatotoxicity testing using analysis of mechanisms through toxicogenomics. By applying a variety of... Show moreIn this thesis we showed the applicability of the zebrafish embryo as an alternative model for hepatotoxicity testing using analysis of mechanisms through toxicogenomics. By applying a variety of toxicogenomics techniques, we were able to characterize specific responses. NGS revealed that hepatotoxicity-associated gene expression remains detectable even in non-tissue specific analysis in whole body zebrafish embryo homogenates. Gene and protein expression profiling resulted in identification of a set of marker genes that could be linked to pathways and processes, which are associated with a general hepatotoxic response. Application of markers will increase the throughput of the system. Finally, we showed that the zebrafish embryo model shares similarities with in vivo and in vitro models for hepatotoxicity, where the model has more commonality with the mouse in vivo and in vitro models than with the other models. The model has the advantages of the in vitro models with the biological complexicity of an in vivo response, and we anticipate that the zebrafish embryo can contribute to the 3R strategy by reducing, refining and replacing animal studies. Our observations support that this model can have added value in a tiered approach as a pre-screen for the detection of hepatotoxic potential of compounds. Show less
Vitins, A.P.; Kienhuis, A.S.; Speksnijder, E.N.; Roodbergen, M.; Luijten, M.; Ven, L.T.M. van der 2014
Background: Availability of chemical response-specific lists of genes (gene sets) for pharmacological and/or toxic effect prediction for compounds is limited. We hypothesize that more gene sets can... Show moreBackground: Availability of chemical response-specific lists of genes (gene sets) for pharmacological and/or toxic effect prediction for compounds is limited. We hypothesize that more gene sets can be created by next-generation text mining (next-gen TM), and that these can be used with gene set analysis (GSA) methods for chemical treatment identification, for pharmacological mechanism elucidation, and for comparing compound toxicity profiles.Methods: We created 30,211 chemical response-specific gene sets for human and mouse by next-gen TM, and derived 1,189 (human) and 588 (mouse) gene sets from the Comparative Toxicogenomics Database (CTD). We tested for significant differential expression (SDE) (false discovery rate -corrected p-values < 0.05) of the next-gen TM-derived gene sets and the CTD-derived gene sets in gene expression (GE) data sets of five chemicals (from experimental models). We tested for SDE of gene sets for six fibrates in a peroxisome proliferator-activated receptor alpha (PPARA) knock-out GE dataset and compared to results from the Connectivity Map. We tested for SDE of 319 next-gen TM-derived gene sets for environmental toxicants in three GE data sets of triazoles, and tested for SDE of 442 gene sets associated with embryonic structures. We compared the gene sets to triazole effects seen in the Whole Embryo Culture (WEC), and used principal component analysis (PCA) to discriminate triazoles from other chemicals.Results: Next-gen TM-derived gene sets matching the chemical treatment were significantly altered in three GE data sets, and the corresponding CTD-derived gene sets were significantly altered in five GE data sets. Six next-gen TM-derived and four CTD-derived fibrate gene sets were significantly altered in the PPARA knock-out GE dataset. None of the fibrate signatures in cMap scored significant against the PPARA GE signature. 33 environmental toxicant gene sets were significantly altered in the triazole GE data sets. 21 of these toxicants had a similar toxicity pattern as the triazoles. We confirmed embryotoxic effects, and discriminated triazoles from other chemicals.Conclusions: Gene set analysis with next-gen TM-derived chemical response-specific gene sets is a scalable method for identifying similarities in gene responses to other chemicals, from which one may infer potential mode of action and/or toxic effect. Show less
Hettne, K.M.; Boorsma, A.; Dartel, D.A.M. van; Goeman, J.J.; Jong, E. de; Piersma, A.H.; ... ; Kors, J.A. 2013
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