Characterization and interlaboratory comparison of a gene expression signature for differentiating genotoxic mechanisms.

Gene expression profiling technology is expected to advance our understanding of genotoxic mechanisms involving direct or indirect interaction with DNA. We exposed human lymphoblastoid TK6 cells to 14 anticancer drugs (vincristine, paclitaxel, etoposide, daunorubicin, camptothecin, amsacrine, cytosine arabinoside, hydroxyurea, methotrexate, 5-fluorouracil, cisplatin, 1-(2-chloroethyl)-3-cyclohexyl-1-nitrosourea (CCNU), 1,3-bis (2-chloroethyl)-1-nitrosourea (BCNU), and bleomycin) for 4-h and examined them immediately or after a 20-h recovery period. Cytotoxicity and genotoxicity, respectively, were evaluated by cell counting and by in vitro micronucleus assay at 24h. Effects on the cell cycle were determined by flow cytometry at 4 and 24h. Gene expression was profiled at both sampling times by using human Affymetrix U133A GeneChips (22K). Bioanalysis was done with Resolver/Rosetta software and an in-house annotation program. Cell cycle analysis and gene expression profiling allowed us to classify the drugs according to their mechanisms of action. The molecular signature is composed of 28 marker genes mainly involved in signal transduction and cell cycle pathways. Our results suggest that these marker genes could be used as a predictive model to classify genotoxins according to their direct or indirect interaction with DNA.

Characterization of DNA reactive and non-DNA reactive anticancer drugs by gene expression profiling

Role of RAD51AP1 in homologous recombination DNA repair and carcinogenesis.

Based on the assumption that compounds having similar toxic modes of action induce specific gene expression changes, the toxicity of unknown compounds can be predicted after comparison of their molecular fingerprints with those obtained with compounds of known toxicity. These predictive models will therefore rely on the characterization of marker genes. Toxicogenomics (TGX) also provides mechanistic insight into the mode of toxicity, and can therefore be used as an adjunct to the standard battery of genotoxicity tests. Promising results, highlighting the ability of TGX to differentiate genotoxic from non-genotoxic carcinogens, as well as DNA-reactive from non-DNA reactive genotoxins, have been reported. Additional data suggested the possibility of ranking genotoxins according to the nature of their interactions with DNA. This new approach could contribute to the improvement of risk assessment. TGX could be applied as a follow-up testing strategy in case of positive in vitro genotoxicity findings, and could contribute to improve our ability to identify the molecular mechanism of action and to possibly better assess dose-response curves. TGX has been found to be less sensitive than the standard genotoxicity end-points, probably because it measures the whole cell population response, when compared with standard tests designed to detect rare events in a small number of cells. Further validation will be needed (1) to better link the profiles obtained with TGX to the established genotoxicity end-points, (2) to improve the gene annotation tools, and (3) to standardise study design and data analysis and to better evaluate the impact of variability between platforms and bioinformatics approaches.

Application of toxicogenomics to genetic toxicology risk assessment.

Deficiencies in microarray technology cause unwanted variation in the hybridization signal, obscuring the true measurements of intracellular transcript levels. Here we describe a general method that can improve microarray analysis of toxicant-exposed cells that uses the intrinsic power of transcriptional coupling and toxicant concentration-expression response data. To illustrate this approach, we characterized changes in global gene expression induced in Salmonella typhimurium TA100 by 3-chloro-4-(dichloromethyl)-5-hydroxy-2(5H)-furanone (MX), the primary mutagen in chlorinated drinking water. We used the co-expression of genes within an operon and the monotonic increases or decreases in gene expression relative to increasing toxicant concentration to augment our identification of differentially expressed genes beyond Bayesian-t analysis. Operon analysis increased the number of altered genes by 95% from the list identified by a Bayesian t-test of control to the highest concentration of MX. Monotonic analysis added 46% more genes. A functional analysis of the resulting 448 differentially expressed genes yielded functional changes beyond what would be expected from only the mutagenic properties of MX. In addition to gene-expression changes in DNA-damage response, MX induced changes in expression of genes involved in membrane transport and porphyrin metabolism, among other biological processes. The disruption of porphyrin metabolism might be attributable to the structural similarity of MX, which is a chlorinated furanone, to ligands indigenous to the porphyrin metabolism pathway. Interestingly, our results indicate that the lexA regulon in Salmonella, which partially mediates the response to DNA damage, may contain only 60% of the genes present in this regulon in E. coli. In addition, nanH was found to be highly induced by MX and contains a putative lexA regulatory motif in its regulatory region, suggesting that it may be regulated by lexA. Operon and monotonic analyses improved the determination of differentially expressed genes beyond that of Bayesian-t analysis, showing that MX alters cellular metabolism involving pathways other than DNA damage. Because co-expression of similarly functioning genes also occurs in eukaryotes, this method has general applicability for improving analysis of toxicogenomic data.

/pdf/toxicogenomic-analysis-incorporating-operon-transcriptional-49gt9ty5vi.pdf

Toxicogenomic analysis incorporating operon-transcriptional coupling and toxicant concentration-expression response: analysis of MX-treated Salmonella

The multi-lab International Life Sciences Institute (ILSI) project on the application of genomics to risk assessment offered the unique opportunity to investigate the influence of variability within and between laboratories on identifying biologically relevant gene expression changes. We assessed the gene expression profiles of mouse lymphoma L5178Y cells treated with hydroxyurea (HU) in three independent studies from two different laboratories, sanofi aventis and Procter & Gamble. Cells were dosed for 4 hr and harvested immediately at the end of the treatment or after a 20-hr recovery period. Cytotoxicity and genotoxicity were evaluated by standard assays. Statistical analysis of these data revealed that, while gene expression responses to HU treatment were markedly different at 4 hr vs. 24 hr, there was otherwise a consistent pattern of dose–response across the three studies. Therefore, the studies were merged and each time point was evaluated separately. At 4 hr, we identified 173 (P < 0.0001) dose-responsive genes with a common trend in all three studies. These were mainly associated with the cell cycle, DNA repair and DNA metabolism, and in particular, the intra-S and G2/M phase checkpoints. At 24 hr, we identified 434 dose-responsive genes common across studies. These genes were involved in lymphocyte-specific activities and the activation of apoptosis via the caspase cascade. Our results show that despite inter-laboratory variability, combining the three studies in a single statistical analysis identifies more significantly-modulated genes than in any of the individual studies, due to improved statistical sensitivity. The genes identified in our study provide information that is relevant to HU biology. Environ. Mol. Mutagen., 2005. © 2005 Wiley-Liss, Inc.

Laboratory variability does not preclude identification of biological functions impacted by hydroxyurea.

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Laboratory variability does not preclude identification of biological functions impacted by hydroxyurea.