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

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Author(s): Jennen Danyel | Ruiz-Aracama Ainhoa | Magkoufopoulou Christina | Peijnenburg Ad | Lommen Arjen | van Delft Joost | Kleinjans Jos

Journal: BMC Systems Biology
ISSN 1752-0509

Volume: 5;
Issue: 1;
Start page: 139;
Date: 2011;
Original page

ABSTRACT
Abstract Background The integration of different 'omics' technologies has already been shown in several in vivo studies to offer a complementary insight into cellular responses to toxic challenges. Being interested in developing in vitro 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 in vitro 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). Results 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. Conclusions The results presented here were not only comparable with other in vitro studies but also with in vivo studies. Moreover, new insights on the molecular responses caused by TCDD exposure were gained by the cross-omics analysis.

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