Integrating transcriptomics and metabonomics to unravel modes-of-action of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) in HepG2 cells

<p>Abstract</p> <p>Background</p> <p>The integration of different 'omics' technologies has already been shown in several <it>in vivo </it>studies to offer a complementary insight into cellular responses to toxic challenges. Being interested in developing <it>in vitro </it>cellular models as alternative to animal-based toxicity assays, we hypothesize that combining transcriptomics and metabonomics data improves the understanding of molecular mechanisms underlying the effects caused by a toxic compound also <it>in vitro </it>in human cells. To test this hypothesis, and with the focus on non-genotoxic carcinogenesis as an endpoint of toxicity, in the present study, the human hepatocarcinoma cell line HepG2 was exposed to the well-known environmental carcinogen 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD).</p> <p>Results</p> <p>Transcriptomics as well as metabonomics analyses demonstrated changes in TCDD-exposed HepG2 in common metabolic processes, e.g. amino acid metabolism, of which some of the changes only being confirmed if both 'omics' were integrated. In particular, this integrated analysis identified unique pathway maps involved in receptor-mediated mechanisms, such as the G-protein coupled receptor protein (GPCR) signaling pathway maps, in which the significantly up-regulated gene son of sevenless 1 (SOS1) seems to play an important role. SOS1 is an activator of several members of the RAS superfamily, a group of small GTPases known for their role in carcinogenesis.</p> <p>Conclusions</p> <p>The results presented here were not only comparable with other <it>in vitro </it>studies but also with <it>in vivo </it>studies. Moreover, new insights on the molecular responses caused by TCDD exposure were gained by the cross-omics analysis.</p

Identification of BC005512 as a DNA Damage Responsive Murine Endogenous Retrovirus of GLN Family Involved in Cell Growth Regulation

Genotoxicity assessment is of great significance in drug safety evaluation, and microarray is a useful tool widely used to identify genotoxic stress responsive genes. In the present work, by using oligonucleotide microarray in an in vivo model, we identified an unknown gene BC005512 (abbreviated as BC, official full name: cDNA sequence BC005512), whose expression in mouse liver was specifically induced by seven well-known genotoxins (GTXs), but not by non-genotoxins (NGTXs). Bioinformatics revealed that BC was a member of the GLN family of murine endogenous retrovirus (ERV). However, the relationship to genotoxicity and the cellular function of GLN are largely unknown. Using NIH/3T3 cells as an in vitro model system and quantitative real-time PCR, BC expression was specifically induced by another seven GTXs, covering diverse genotoxicity mechanisms. Additionally, dose-response and linear regression analysis showed that expression level of BC in NIH/3T3 cells strongly correlated with DNA damage, measured using the alkaline comet assay,. While in p53 deficient L5178Y cells, GTXs could not induce BC expression. Further functional studies using RNA interference revealed that down-regulation of BC expression induced G1/S phase arrest, inhibited cell proliferation and thus suppressed cell growth in NIH/3T3 cells. Together, our results provide the first evidence that BC005512, a member from GLN family of murine ERV, was responsive to DNA damage and involved in cell growth regulation. These findings could be of great value in genotoxicity predictions and contribute to a deeper understanding of GLN biological functions

Comparison of HepG2 and HepaRG by whole-genome gene expression analysis for the purpose of chemical hazard identification.

Direct comparison of the hepatoma cell lines HepG2 and HepaRG has previously been performed by only evaluating a limited set of genes or proteins. In this study, we examined the whole genome gene expression of both cell lines before and after exposure to the genotoxic (GTX) carcinogens aflatoxin B1 and benzo[a]pyrene and the non-genotoxic (NGTX) carcinogens cyclosporin A, 17beta-estradiol and 2,3,7,8-tetrachlorodibenzo-para-dioxin for 12 and 48 h. Before exposure, this analysis revealed an extensive network of genes and pathways which were regulated differentially for each cell line. The comparison of the basal gene expression between HepG2, HepaRG, primary human hepatocytes (PHH) and liver clearly showed that HepaRG resembles PHH and liver the most. After exposure to the GTX and NGTX carcinogens, for both cell lines, common pathways were found that are important in carcinogenesis, e.g. cell cycle regulation and apoptosis. However, also clear differences between exposed HepG2 and HepaRG were observed and these are related to common metabolic processes, immune response and transcription processes. Furthermore, HepG2 performs better in discriminating between GTX and NGTX carcinogens. In conclusion, these results have shown that HepaRG is a more suited in vitro liver model for biological interpretations of the effects of exposure to chemicals, whereas HepG2 is a more promising in vitro liver model for classification studies using the toxicogenomics approach. Although, it should be noted that only five carcinogens were used in this study

Predictive mechanisms in stem cells: an in vitro system based method for testing carcinogenicity

The identification of predictive patterns for toxicity from high‐throughput gene expression readouts in human in vitro assays is a fundamental goal of toxicogenomics. Reproducible and predictive assays based on stem cells have the potential to deliver such predictions in a more unbiased way than, for example, hepatocarcinoma cell lines. In this chapter, we describe the molecular characteristics of such a system built on a human embryonic stem‐cell‐derived assay for the liver. We report and extend published work that applied this assay for the first time to predict carcinogenicity induced by chemical compounds. We describe a bioinformatics approach that uses pathway‐wise expression patterns instead of gene‐wise expression patterns in order to identify predictive mechanisms for chemical carcinogenicity, and we show that such patterns are highly discriminative for the different toxicity classes of carcinogens