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Genomic Profiling Uncovers a Molecular Pattern for Toxicological Characterization of Mutagens and Promutagens In Vitro
Author(s) -
Kathleen Boehme,
Yasmin Dietz,
Philip Hewitt,
Stefan O. Mueller
Publication year - 2011
Publication title -
toxicological sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.352
H-Index - 183
eISSN - 1096-6080
pISSN - 1096-0929
DOI - 10.1093/toxsci/kfr090
Subject(s) - genotoxicity , transcriptome , computational biology , gene expression profiling , biology , gene , in vitro , gene expression , dna damage , toxicogenomics , in vitro toxicology , genetics , dna , chemistry , toxicity , organic chemistry
The aim of this study was to evaluate the suitability of global gene expression profiling for the characterization and identification of mutagens and promutagens in vitro. To enable detection of both mutagenic and promutagenic compounds, we cotreated HepG2 cells with a rat liver S9 fraction as metabolic activation system (MAS), supplementing the limited drug metabolic capability of HepG2 cells. Illumina BeadChip arrays were used to quantify gene expression changes after treatment with three well-known mutagenic, three promutagenic, as well as two non-genotoxic reference compounds for a period of 24 or 48 h. Statistical data analysis revealed 91 genes being most representative for the (pro-)genotoxic response. Several processes such as cellular differentiation and the complex interactive regulation of the stress and DNA damage response via the transcriptional modulators STAT1, SP1, and P53 were differentially regulated. The gene set evaluated was further used to predict the genotoxic characteristics of N-nitrosodiethylamine (DEN) after its metabolic activation. Although no clear response could be established in P53 activation experiments, DEN was classified correctly as nongenotoxic without S9 and genotoxic in the presence of the MAS by means of its transcriptomic pattern. Our data support that mechanistic profiling in vitro is a useful tool compared with single endpoint detections to predict genotoxicity.

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