Project description:The well-defined battery of in vitro systems applied within chemical cancer risk assessment is often characterised by a high false-positive rate, thus repeatedly failing to correctly predict the in vivo genotoxic and carcinogenic properties of test compounds. Toxicogenomics, i.e. mRNA-profiling, has been proven successful in improving the prediction of genotoxicity in vivo and the understanding of underlying mechanisms. Recently, microRNAs have been discovered as post-transcriptional regulators of mRNAs. It is thus hypothesised that using microRNA response-patterns may further improve current prediction methods. This study aimed at predicting genotoxicity and non-genotoxic carcinogenicity in vivo, by comparing microRNA- and mRNA-based profiles, using a frequently applied in vitro liver model and exposing this to a range of well-chosen prototypical carcinogens. Primary mouse hepatocytes (PMH) were treated for 24 and 48h with 21 chemical compounds [genotoxins (GTX) vs. non-genotoxins (NGTX) and non-genotoxic carcinogens (NGTX-C) versus non-carcinogens (NC)]. MicroRNA and mRNA expression changes were analysed by means of Exiqon and Affymetrix microarray-platforms, respectively. Classification was performed by using Prediction Analysis for Microarrays (PAM). Compounds were randomly assigned to training and validation sets (repeated 10 times). Before prediction analysis, pre-selection of microRNAs and mRNAs was performed by using a leave-one-out t-test. No microRNAs could be identified that accurately predicted genotoxicity or non-genotoxic carcinogenicity in vivo. However, mRNAs could be detected which appeared reliable in predicting genotoxicity in vivo after 24h (7 genes) and 48h (2 genes) of exposure (accuracy: 90% and 93%, sensitivity: 65% and 75%, specificity: 100% and 100%). Tributylinoxide and para-Cresidine were misclassified. Also, mRNAs were identified capable of classifying NGTX-C after 24h (5 genes) as well as after 48h (3 genes) of treatment (accuracy: 78% and 88%, sensitivity: 83% and 83%, specificity: 75% and 93%). Wy-14,643, phenobarbital and ampicillin trihydrate were misclassified. We conclude that genotoxicity and non-genotoxic carcinogenicity probably cannot be accurately predicted based on microRNA profiles. Overall, transcript-based prediction analyses appeared to clearly outperform microRNA-based analyses. The study investigated differential gene expression in primary mouse hepatocyte mRNA following 24 and 48 hours of exposure to di(2-ethylhexyl)phthalate(DEHP), 4-Acetylaminofluorene(4AAF), Curcumin(Cur), Phenobarbital(PhB), Reserpine(Res), 7,12-Dimethylbenzanthracene (DMBA), Resorcinol(Resorcinol), Para-cresidine(pCres), Phenacetin(Phen), Diclofenac(diclo), Wy 14643(Wy), Tributyltinoxide(TBTO), Benzo[a]pyrene(BaP), 8-Hydroxyquinoline [AKA 8-quinolinol](8HQ), 17-b-estradiol(E2), ampicillin(AmpC), cisplatin(CisPl), Aflatoxin B1(AFB1), Cyclosporin A(CsA), 2,3,7,8-Tetrachlorodibenzo-p-dioxin(TCDD), Quercetin(Que) or their responsive solvent (dimethylsulfoxide(DMSO), Ethanol(ETOH), phosphate buffered saline(PBS)). Three biological replicates per compound/solvent. In total 184 arrays.

Project description:The well-defined battery of in vitro systems applied within chemical cancer risk assessment is often characterised by a high false-positive rate, thus repeatedly failing to correctly predict the in vivo genotoxic and carcinogenic properties of test compounds. Toxicogenomics, i.e. mRNA-profiling, has been proven successful in improving the prediction of genotoxicity in vivo and the understanding of underlying mechanisms. Recently, microRNAs have been discovered as post-transcriptional regulators of mRNAs. It is thus hypothesised that using microRNA response-patterns may further improve current prediction methods. This study aimed at predicting genotoxicity and non-genotoxic carcinogenicity in vivo, by comparing microRNA- and mRNA-based profiles, using a frequently applied in vitro liver model and exposing this to a range of well-chosen prototypical carcinogens. Primary mouse hepatocytes (PMH) were treated for 24 and 48h with 21 chemical compounds [genotoxins (GTX) vs. non-genotoxins (NGTX) and non-genotoxic carcinogens (NGTX-C) versus non-carcinogens (NC)]. MicroRNA and mRNA expression changes were analysed by means of Exiqon and Affymetrix microarray-platforms, respectively. Classification was performed by using Prediction Analysis for Microarrays (PAM). Compounds were randomly assigned to training and validation sets (repeated 10 times). Before prediction analysis, pre-selection of microRNAs and mRNAs was performed by using a leave-one-out t-test. No microRNAs could be identified that accurately predicted genotoxicity or non-genotoxic carcinogenicity in vivo. However, mRNAs could be detected which appeared reliable in predicting genotoxicity in vivo after 24h (7 genes) and 48h (2 genes) of exposure (accuracy: 90% and 93%, sensitivity: 65% and 75%, specificity: 100% and 100%). Tributylinoxide and para-Cresidine were misclassified. Also, mRNAs were identified capable of classifying NGTX-C after 24h (5 genes) as well as after 48h (3 genes) of treatment (accuracy: 78% and 88%, sensitivity: 83% and 83%, specificity: 75% and 93%). Wy-14,643, phenobarbital and ampicillin trihydrate were misclassified. We conclude that genotoxicity and non-genotoxic carcinogenicity probably cannot be accurately predicted based on microRNA profiles. Overall, transcript-based prediction analyses appeared to clearly outperform microRNA-based analyses. The study investigated differential gene expression in primary mouse hepatocyte mRNA following 24 and 48 hours of exposure to di(2-ethylhexyl)phthalate(DEHP), 4-Acetylaminofluorene(4AAF), Curcumin(Cur), Phenobarbital(PhB),Reserpine(Res), 7,12-Dimethylbenzanthracene (DMBA), Resorcinol(Resorcinol), Para-cresidine(pCres), Phenacetin(Phen), Diclofenac(diclo),Wy 14643(Wy), Tributyltinoxide(TBTO), Benzo[a]pyrene(BaP), 8-Hydroxyquinoline [AKA 8-quinolinol](8HQ), 17-b-estradiol(E2), ampicillin(AmpC), cisplatin(CisPl), Aflatoxin B1(AFB1), Cyclosporin A(CsA), 2,3,7,8-Tetrachlorodibenzo-p-dioxin(TCDD), Quercetin(Que) or their responsive solvent (dimethylsulfoxide(DMSO), Ethanol(ETOH), phosphate buffered saline(PBS)). Three biological replicates per compound/solvent. In total 184 arrays.

Project description:For assessing the cancer-causing potential for humans of a chemical compound, the conventional approach is the use of the 2-year rodent carcinogenicity bioassay, thus alternatives such as in vitro toxicogenomics are highly desired. In the present study, the transcriptomics responses following exposure to genotoxic (GTX) and non-genotoxic (NGTX) hepatocarcinogens and non-carcinogens (NC) in five liver-based in vitro models, namely conventional and epigenetically-stabilized cultures of primary rat hepatocytes, the human hepatoma-derived HepaRG and HepG2 cell lines and the human embryonic stem cell-derived hepatocyte-like cells hES-Heps are examined and compared. The global gene expression profiles of five commonly used liver-based in vitro systems are investigated, namely conventional cultures of primary rat hepatocytes (HepsC), Trichostatin A (TSA)-stabilized cultures of primary rat hepatocytes (HepsT), human embryonic stem cell-derived hepatocyte-like cells (hES-Hep), HepG2 and HepaRG cells. These models are exposed to 15 prototypical compounds which have been carefully chosen and belong to 3 toxic classes i.e. (i) genotoxic (GTX) (aflatoxin B1, AFB1; 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone, NNK; 2-nitrofluorene, 2NF; benzo(a)pyrene, BaP; cyclophosphamide, CYCLO), (ii) non-genotoxic (NGTX) (methapyrilene hydrochloride, MPH; piperonylbutoxide , PIPB; Wy-14643, WYE; phenobarbital sodium, SPB; 12-O-tetradecanoylphorbol-13-acetate, TPA) carcinogens and (iii) non-carcinogens (NC) (nifedipine, NIF; clonidine, CND; D-mannitol, MAN; tolbutamide, TOL; diclofenac sodium, SDF). For the gene- and pathway- based analysis, the raw microarray data was re-annotated to Ensembl version 61 genome and Gene Chip Robust Multi-array Average (GC-RMA) normalized (Dai et al. 2005). This resulted in 11,187 and 18,919 probe sets for the rat and the human models, respectively. For the classification analysis, the raw microarray data was annotated using the chip description files from Affymetrix and then GC-RMA normalized. In order to eliminate batch effects, data from the different studies were half-z normalized. Half-z-normalization adjusts the logarithmic expression values of transcripts within a group such that each transcript has zero mean.

Project description:The well-defined battery of in vitro systems applied within chemical cancer risk assessment is often characterised by a high false-positive rate, thus repeatedly failing to correctly predict the in vivo genotoxic and carcinogenic properties of test compounds. Toxicogenomics, i.e. mRNA-profiling, has been proven successful in improving the prediction of genotoxicity in vivo and the understanding of underlying mechanisms. Recently, microRNAs have been discovered as post-transcriptional regulators of mRNAs. It is thus hypothesised that using microRNA response-patterns may further improve current prediction methods. This study aimed at predicting genotoxicity and non-genotoxic carcinogenicity in vivo, by comparing microRNA- and mRNA-based profiles, using a frequently applied in vitro liver model and exposing this to a range of well-chosen prototypical carcinogens. Primary mouse hepatocytes (PMH) were treated for 24 and 48h with 21 chemical compounds [genotoxins (GTX) vs. non-genotoxins (NGTX) and non-genotoxic carcinogens (NGTX-C) versus non-carcinogens (NC)]. MicroRNA and mRNA expression changes were analysed by means of Exiqon and Affymetrix microarray-platforms, respectively. Classification was performed by using Prediction Analysis for Microarrays (PAM). Compounds were randomly assigned to training and validation sets (repeated 10 times). Before prediction analysis, pre-selection of microRNAs and mRNAs was performed by using a leave-one-out t-test. No microRNAs could be identified that accurately predicted genotoxicity or non-genotoxic carcinogenicity in vivo. However, mRNAs could be detected which appeared reliable in predicting genotoxicity in vivo after 24h (7 genes) and 48h (2 genes) of exposure (accuracy: 90% and 93%, sensitivity: 65% and 75%, specificity: 100% and 100%). Tributylinoxide and para-Cresidine were misclassified. Also, mRNAs were identified capable of classifying NGTX-C after 24h (5 genes) as well as after 48h (3 genes) of treatment (accuracy: 78% and 88%, sensitivity: 83% and 83%, specificity: 75% and 93%). Wy-14,643, phenobarbital and ampicillin trihydrate were misclassified. We conclude that genotoxicity and non-genotoxic carcinogenicity probably cannot be accurately predicted based on microRNA profiles. Overall, transcript-based prediction analyses appeared to clearly outperform microRNA-based analyses.

Project description:The need for an efficient carcinogenicity test prompted this study, in which we used a microarray-based genomics approach, with a short-term in vivo model, in combination with insights from statistical and mechanistic analyses. We performed additional experiments to support the significance of the microarray results. Carcinogens were evaluated based on differences in the mechanisms involved in the response to genotoxic (GTX) carcinogens and non-genotoxic (NGTX) carcinogens. Microarray data were analyzed for 2 time points after treatment with the following 6 carcinogens. The analysis was performed using t-tests to compare the fold changes, and we selected differentially expressed genes (DEGs) and evaluated the reasons for differential expression in terms of cellular pathways and processes. We mapped the DEG-related pathways to analyze cellular processes, and we were able to uncover significant mechanisms that involve critical cellular components, such as CDKN1A (p21) and BAX. In addition, a comparison of the data from two time points showed that the repeated administration model was more effective than a single administration for carcinogen research. The classification analysis of selected DEGs was performed by setting microarray data of 4 carcinogens as test sets; these test sets were evaluated as classifiers. Microarray results were further supported using the Comet and micronucleus assays. It was found that gene expression profiling using microarrays, followed by pathway analysis, was effective in increasing the understanding of the characteristics of different carcinogens, and the efficiency of these methods was exemplified by the short-term (3 day) nature of the animal experiments. The following 10 hepatocarcinogens were used: 2-acetylaminofluorene (53-96-3), diethylnitrosamine (55-18-5), 4-dimethylamino-3'-methyl azobenzene (55-80-1), clofibrate (637-07-0), 1,4-dioxane (123-91-1), DL-ethionine (67-21-0), 1,3-dichloro-2'-propanol (96-23-1), urethane (51-79-6), sodium nitrite (7632-00-0), and methyleugenol (93-15-2). Forty-one data were obtained for the training compounds in this study. We treated rats with one of three compounds or a control for each GTX and NGTX experimental group and measured 2 time points for each group. All experimental procedures were repeated three times. We excluded 1 file that was found to be a failure. Each ‘.CEL’ file obtained as a raw data file was standardized using the RMA (Robust Multi-array Average) algorithm.

Dataset Information

Transcription profiling of primary mouse hepatocytes reveals classification for genotoxic and non-genotoxic carcinogens by gene expression improves with exposure time

Publications

Discrimination for genotoxic and nongenotoxic carcinogens by gene expression profiling in primary mouse hepatocytes improves with exposure time.

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