Integration of metabolic activation with a predictive toxicogenomics signature to classify genotoxic versus nongenotoxic chemicals in human TK6 cells

The use of integrated approaches in genetic toxicology, including the incorporation of gene expression data to determine the molecular pathways involved in the response, is becoming more common. In a companion article, a genomic biomarker was developed in human TK6 cells to classify chemicals as genotoxic or nongenotoxic. Because TK6 cells are not metabolically competent, we set out to broaden the utility of the biomarker for use with chemicals requiring metabolic activation. Specifically, chemical exposures were conducted in the presence of rat liver S9. The ability of the biomarker to classify genotoxic (benzo[a]pyrene, BaP; aflatoxin B1, AFB1) and nongenotoxic (dexamethasone, DEX; phenobarbital, PB) agents correctly was evaluated. Cells were exposed to increasing chemical concentrations for 4 hr and collected 0 hr, 4 hr, and 20 hr postexposure. Relative survival, apoptosis, and micronucleus frequency were measured at 24 hr. Transcriptome profiles were measured with Agilent microarrays. Statistical modeling and bioinformatics tools were applied to classify each chemical using the genomic biomarker. BaP and AFB1 were correctly classified as genotoxic at the mid‐ and high concentrations at all three time points, whereas DEX was correctly classified as nongenotoxic at all concentrations and time points. The high concentration of PB was misclassified at 24 hr, suggesting that cytotoxicity at later time points may cause misclassification. The data suggest that the use of S9 does not impair the ability of the biomarker to classify genotoxicity in TK6 cells. Finally, we demonstrate that the biomarker is also able to accurately classify genotoxicity using a publicly available dataset derived from human HepaRG cells. Environ. Mol. Mutagen. 56:520–534, 2015. © 2015 The Authors. Environmental and Molecular Mutagenesis Published by Wiley Periodicals, Inc.

[1]  Alan E. Hubbard,et al.  Effect of Chemical Mutagens and Carcinogens on Gene Expression Profiles in Human TK6 Cells , 2012, PloS one.

[2]  S. O. Mueller,et al.  Genomic profiling uncovers a molecular pattern for toxicological characterization of mutagens and promutagens in vitro. , 2011, Toxicological sciences : an official journal of the Society of Toxicology.

[3]  Peter Kasper,et al.  Relevance and follow-up of positive results in in vitro genetic toxicity assays: an ILSI-HESI initiative. , 2007, Mutation research.

[4]  Center for Food Safety and Applied Nutrition. , 1997, Nutrition reviews.

[5]  T Sofuni,et al.  ICH-harmonised guidances on genotoxicity testing of pharmaceuticals: evolution, reasoning and impact. , 1999, Mutation research.

[6]  J. Kleinjans,et al.  A transcriptomics-based in vitro assay for predicting chemical genotoxicity in vivo. , 2012, Carcinogenesis.

[7]  A. Hubbard,et al.  Correction: Effect of Chemical Mutagens and Carcinogens on Gene Expression Profiles in Human TK6 Cells , 2012, PLoS ONE.

[8]  Shibing Deng,et al.  Characterization and interlaboratory comparison of a gene expression signature for differentiating genotoxic mechanisms. , 2009, Toxicological sciences : an official journal of the Society of Toxicology.

[9]  Jiri Aubrecht,et al.  Comparison of toxicogenomics and traditional approaches to inform mode of action and points of departure in human health risk assessment of benzo[a]pyrene in drinking water , 2015, Critical reviews in toxicology.

[10]  Rupert G. Miller Simultaneous Statistical Inference , 1966 .

[11]  Jason E. Stewart,et al.  Minimum information about a microarray experiment (MIAME)—toward standards for microarray data , 2001, Nature Genetics.

[12]  G. L. Kedderis,et al.  Dose-response assessment of naphthalene-induced genotoxicity and glutathione detoxication in human TK6 lymphoblasts. , 2012, Toxicological sciences : an official journal of the Society of Toxicology.

[13]  G. Warnes,et al.  Differentiation of DNA reactive and non-reactive genotoxic mechanisms using gene expression profile analysis. , 2004, Mutation research.

[14]  Michael L Bittner,et al.  Stress-specific signatures: expression profiling of p53 wild-type and -null human cells , 2005, Oncogene.

[15]  Raffaella Corvi,et al.  Toxicogenomics applied to in vitro carcinogenicity testing with Balb/c 3T3 cells revealed a gene signature predictive of chemical carcinogens. , 2010, Toxicological sciences : an official journal of the Society of Toxicology.

[16]  R. Tibshirani,et al.  Diagnosis of multiple cancer types by shrunken centroids of gene expression , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[17]  G. Churchill,et al.  Statistical design and the analysis of gene expression microarray data. , 2007, Genetical research.

[18]  D. Watson,et al.  Relationships between genomic, cell cycle, and mutagenic responses of TK6 cells exposed to DNA damaging chemicals. , 2005, Mutation research.

[19]  Jiri Aubrecht,et al.  Development of a toxicogenomics signature for genotoxicity using a dose‐optimization and informatics strategy in human cells , 2015, Environmental and molecular mutagenesis.

[20]  GUIDANCE DOCUMENT,et al.  Guidance on Genotoxicity Testing and Data Interpretation for Pharmaceuticals Intended for Human Use , 2008 .

[21]  H. Yamazaki,et al.  Binding of diverse environmental chemicals with human cytochromes P450 2A13, 2A6, and 1B1 and enzyme inhibition. , 2013, Chemical research in toxicology.

[22]  Marilyn J Aardema,et al.  Identification of a gene expression profile that discriminates indirect-acting genotoxins from direct-acting genotoxins. , 2004, Mutation research.

[23]  Jiri Aubrecht,et al.  Voluntary exploratory data submissions to the US FDA and the EMA: experience and impact , 2010, Nature Reviews Drug Discovery.

[24]  J. Kleinjans,et al.  Transcriptomic responses generated by hepatocarcinogens in a battery of liver-based in vitro models. , 2013, Carcinogenesis.

[25]  M. Hayashi,et al.  Summary of major conclusions from the 5th IWGT, Basel, Switzerland, 17-19 August 2009. , 2011, Mutation research.

[26]  Thorsten Dickhaus,et al.  Simultaneous Statistical Inference , 2014, Springer Berlin Heidelberg.

[27]  Perikles Simon,et al.  Q-Gene: Processing Quantitative Real-time RT-PCR Data , 2003, Bioinform..