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The genotoxicity testing battery is highly sensitive for detection of chemical carcinogens. However, it features a low specificity and provides only limited mechanistic information required for risk assessment of positive findings. This is especially important in case of positive findings in the in vitro chromosome damage assays, because chromosome damage may be also induced secondarily to cell death. An increasing body of evidence indicates that toxicogenomic analysis of cellular stress responses provides an insight into mechanisms of action of genotoxicants. To evaluate the utility of such a toxicogenomic analysis we evaluated gene expression profiles of TK6 cells treated with four model genotoxic agents using a targeted high density real-time PCR approach in a multilaboratory project coordinated by the Health and Environmental Sciences Institute Committee on the Application of Genomics in Mechanism-based Risk Assessment. We show that this gene profiling technology produced reproducible data across laboratories allowing us to conclude that expression analysis of a relevant gene set is capable of distinguishing compounds that cause DNA adducts or double strand breaks from those that interfere with mitotic spindle function or that cause chromosome damage as a consequence of cytotoxicity. Furthermore, our data suggest that the gene expression profiles at early time points are most likely to provide information relevant to mechanisms of genotoxic damage and that larger gene expression arrays will likely provide richer information for differentiating molecular mechanisms of action of genotoxicants. Although more compounds need to be tested to identify a robust molecular signature, this study confirms the potential of toxicogenomic analysis for investigation of 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.

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.

Following the recent discovery that the methyl-CpG binding protein 2 (MECP2) gene located on Xq28 is involved in Rett syndrome (RTT), a wild spectrum of phenotypes, including mental handicap, has been shown to be associated with mutations in MECP2. These findings, with the compelling genetic evidence suggesting the presence in Xq28 of additional genes besides RabGDI1 and FMR2 involved in non-specific X-linked mental retardation (MRX), prompted us to investigate MECP2 in MRX families. Two novel mutations, not found in RTT, were identified. The first mutation, an E137G, was identified in the MRX16 family, and the second, R167W, was identified in a new mental retardation (MR) family shown to be linked to Xq28. In view of these data, we screened MECP2 in a cohort of 185 patients found negative for the expansions across the FRAXA CGG repeat and reported the identification of mutations in four sporadic cases of MR. One of the mutations, A140V, which we found in two patients, has been described previously, whereas the two others, P399L and R453Q, are novel mutations. In addition to the results demonstrating the involvement of MECP2 in MRX, this study shows that the frequency of mutations in MECP2 in the mentally retarded population screened for the fragile X syndrome is comparable to the frequency of the CGG expansions in FMR1. Therefore, implementation of systematic screening of MECP2 in MR patients should result in significant progress in the field of molecular diagnosis and genetic counseling of mental handicap.