We have developed a new exchangeable gene trap vector, pU-17, carrying the intron-lox71-splicing acceptor (SA)-βgeo-loxP-pA-lox2272-pSP73-lox511. The SA contains three stop codons in-frame with the ATG of βgalactosidase/neomycin-resistance fusion gene (βgeo) that can function in promoter trapping. We found that the trap vector was highly selective for integrations in the introns adjacent to the exon containing the start codon. Furthermore, by using the Cre-mutant lox system, we successfully replaced the βgeo gene with the enhanced green fluorescent protein (EGFP) gene, established mouse lines with the replaced clones, removed the selection marker gene by mating with Flp-deleter mice, and confirmed that the replaced EGFP gene was expressed in the same pattern as the βgeo gene. Thus, using this pU-17 trap vector, we can initially carry out random mutagenesis, and then convert it to a gain-of-function mutation by replacing the βgeo gene with any gene of interest to be expressed under the control of the trapped promoter through Cre-mediated recombination.

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-Carcinogenicity is one of the most serious toxic effects of chemicals, and highly accurate methods for predicting carcinogens are strongly desired for human health. Here, we developed a new prediction system named "CARCINOscreen®" for evaluating the carcinogenic potentials of chemicals using the gene expression profiles of liver tissues from rats after a 28-day repeated dose toxicity study. The prediction formula was generated using a support vector machine with predictive genes selected from 68 training chemical datasets; a predictive score was then calculated to predict the carcinogenic potentials of the tested chemicals. To ensure the accuracy of the prediction system, the chemicals were divided into three groups (Groups 1 to 3) according to the resulting hepatic gene expression profiles, and a prediction formula was generated for each group. The prediction system was capable of predicting the carcinogenicity of training carcinogens and non-carcinogens with an accuracy of 92.9% to 100%. The final prediction result was determined based on the maximum prediction value obtained with three independent prediction formulas to build up the CARCINOscreen®. The system was able to predict carcinogenicity accurately in 94.1% of the 68 training chemicals. An external validation trial was performed with 16 chemicals, consisting of various carcinogens targeting rat liver or other organs and non-carcinogens. The system identified 68.8% of all the chemicals and 100% of the rat liver carcinogens as carcinogens. Thus, the CARCINOscreen®, a novel system for predicting hepatocarcinogenicity, is a promising tool for the prediction of rat liver carcinogens.

This study aimed at discriminating carcinogens on the basis of hepatic transcript profiling in the rats administrated with a variety of carcinogens and non-carcinogens. We conducted 28-day toxicity tests in male F344 rats with 47 carcinogens and 26 non-carcinogens, and then investigated periodically the hepatic gene expression profiles using custom microarrays. By hierarchical cluster analysis based on significantly altered genes, carcinogens were clustered into three major groups (Group 1 to 3). The formation of these groups was not affected by the gene sets used as well as the administration period, indicating that the grouping of carcinogens was universal independent of the conditions of both statistical analysis and toxicity testing. Seventeen carcinogens belonging to Group 1 were composed of mainly rat hepatocarcinogens, most of them being mutagenic ones. Group 2 was formed by three subgroups, which were composed of 23 carcinogens exhibiting distinct properties in terms of genotoxicity and target tissues, namely nonmutagenic hepatocarcinogens, and mutagenic and nonmutagenic carcinogens both of which are targeted to other tissues. Group 3 contained 6 carcinogens including 4 estrogenic substances, implying the group of estrogenic carcinogens. Gene network analyses revealed that the significantly altered genes in Group 1 included Bax, Tnfrsf6, Btg2, Mgmt and Abcb1b, suggesting that p53-mediated signaling pathway involved in early pathologic alterations associated with preceding mutagenic carcinogenesis. Thus, the common transcriptional signatures for each group might reflect the early molecular events of carcinogenesis and hence would enable us to identify the biomarker genes, and then to develop a new assay for carcinogenesis prediction.

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