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 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:Many innovative techniques and scientific improvements are available to tackle societal concerns, like public health safety and confining the risk of cancerous exposure to chemicals, but have not been thoroughly tested and implicated yet. We investigated if microRNA and mRNA transcription profiles can be implemented in a short-term carcinogen classifier assay. Our study is additionally focusing on the drawbacks of present-day carcinogen screening strategies and also aims to contribute to a more ethical approach towards animal use and welfare within risk assessment. Since current in vitro and in silico assays are still not able to mimic the in vivo situation accurately we set out to develop an alternative short-term in vivo assay. Five genotoxic, seven non-genotoxic and five non-carcinogen exposure studies were used to investigate if murine hepatic microRNA and mNA profiles after 7-day exposure are suitable tools to classify carcinogens. Classification analyses showed that a small transcript set, consisting of both microRNA and mRNA, is able to classify the genotoxic, non-genotoxic and non-carcinogens tested with 100% accuracy. The results indicate that microRNAs have the potential to be used as transcriptional classifiers and that a short-term transcriptional classifier assay in mice can be a powerful tool in carcinogenicity risk assessment. Since current in vitro and in silico assays are still not able to mimic the in vivo situation accurately we set out to develop an alternative short-term in vivo assay. Five genotoxic, seven non-genotoxic and five non-carcinogen exposure studies were used to investigate if murine hepatic microRNA and mNA profiles after 7-day exposure are suitable tools to classify carcinogens. Classification analyses showed that a small transcript set, consisting of both microRNA and mRNA, is able to classify the genotoxic, non-genotoxic and non-carcinogens tested with 100% accuracy. The results indicate that microRNAs have the potential to be used as transcriptional classifiers and that a short-term transcriptional classifier assay in mice can be a powerful tool in carcinogenicity risk assessment.

Dataset Information

Exploiting microRNA and mRNA profiles generated in vitro from carcinogen-exposed primary mouse hepatocytes for predicting in vivo genotoxicity and carcinogenicity

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