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Chem Res Toxicol. 2010 Mar 15;23(3):578-90. doi: 10.1021/tx900325g.

Impact of environmental chemicals on key transcription regulators and correlation to toxicity end points within EPA's ToxCast program.

Author information

1
National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, North Carolina, USA. martin.matt@epa.gov

Abstract

Exposure to environmental chemicals adds to the burden of disease in humans and wildlife to a degree that is difficult to estimate and, thus, mitigate. The ability to assess the impact of existing chemicals for which little to no toxicity data are available or to foresee such effects during early stages of chemical development and use, and before potential exposure occurs, is a pressing need. However, the capacity of the current toxicity evaluation approaches to meet this demand is limited by low throughput and high costs. In the context of EPA's ToxCast project, we have evaluated a novel cellular biosensor system (Factorial (1) ) that enables rapid, high-content assessment of a compound's impact on gene regulatory networks. The Factorial biosensors combined libraries of cis- and trans-regulated transcription factor reporter constructs with a highly homogeneous method of detection enabling simultaneous evaluation of multiplexed transcription factor activities. Here, we demonstrate the application of the technology toward determining bioactivity profiles by quantitatively evaluating the effects of 309 environmental chemicals on 25 nuclear receptors and 48 transcription factor response elements. We demonstrate coherent transcription factor activity across nuclear receptors and their response elements and that Nrf2 activity, a marker of oxidative stress, is highly correlated to the overall promiscuity of a chemical. Additionally, as part of the ToxCast program, we identify molecular targets that associate with in vivo end points and represent modes of action that can serve as potential toxicity pathway biomarkers and inputs for predictive modeling of in vivo toxicity.

PMID:
20143881
DOI:
10.1021/tx900325g
[Indexed for MEDLINE]

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