Mus musculus
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA292899 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Exploiting microRNA and mRNA profiles generated in vitro from carcinogen-exposed primary mouse hepatocytes for predicting in vivo genotoxicity and carcinogenicity
| PRJNA292907 | ENA
Project description:Exploiting microRNA and mRNA profiles generated in vitro from carcinogen-exposed primary mouse hepatocytes for predicting in vivo genotoxicity and carcinogenicity (mRNA)
| PRJNA292906 | ENA
Project description:Under REACH, the European Community Regulation on chemicals, the testing strategy for carcinogenicity is generally based on in vitro and in vivo genotoxicity assays. Given that non-genotoxic carcinogens are negative for genotoxicity, this class of carcinogens will not be detected. Therefore, alternative test are urgently needed. Non-genotoxic carcinogens, however, act through different modes of action, which complicates the development of such an assay. The aim of this study was to investigate whether gene expression profiling in primary mouse hepatocytes can be used to distinguish different modes of action of non-genotoxic carcinogens. Primary mouse hepatocytes were exposed to 16 non-genotoxic carcinogens with diverse modes of action. Upon profiling, pathway analysis was performed to obtain insight into the biological relevance of the observed changes in gene expression. To recognize similarities in mode of action at the transcriptomic level, both a supervised and an unsupervised comparison approach was applied.
2012-06-25 | E-GEOD-35058 | biostudies-arrayexpress
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.
2016-06-28 | GSE72081 | GEO
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.
2016-06-28 | GSE72014 | GEO
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.
2016-06-28 | E-GEOD-72014 | biostudies-arrayexpress
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.
2016-06-28 | E-GEOD-72081 | biostudies-arrayexpress
Project description:Under REACH, the European Community Regulation on chemicals, the testing strategy for carcinogenicity is generally based on in vitro and in vivo genotoxicity assays. Given that non-genotoxic carcinogens are negative for genotoxicity, this class of carcinogens will not be detected. Therefore, alternative test are urgently needed. Non-genotoxic carcinogens, however, act through different modes of action, which complicates the development of such an assay. The aim of this study was to investigate whether gene expression profiling in primary mouse hepatocytes can be used to distinguish different modes of action of non-genotoxic carcinogens.
2012-06-26 | GSE35058 | GEO
Project description:The current test strategy for carcinogenicity is generally based on in vitro and in vivo genotoxicity assays. Non-genotoxic carcinogens (NGTXC) are negative for genotoxicity and go undetected. Therefore, alternative tests to detect these chemicals are urgently needed. NGTXC act through different modes of action, which complicates the development of such assays. We have demonstrated recently in primary mouse hepatocytes that some, but certainly not all, NGTXC can be categorized according to their mode of action based on feature detection at a gene expression level (Schaap et al. 2012 (PMID 22710402)). Identification of a wider range of chemicals probably requires multiple in vitro systems. In the current study, we describe the added value of using mouse embryonic stem cells. In this study, the focus is on NGTXC, but we also included genotoxic carcinogens and non-carcinogens. This approach enables us to assess the robustness of this method and to evaluate the system for recognizing features of chemicals in general, which is important for application in future risk assessment. Primary mouse hepatocytes and mouse embryonic stem cells were exposed to 26 chemicals (non-genotoxic carcinogens and non-carcinogens) representing diverse modes of action. Upon profiling, an unsupervised comparison approach was applied to recognize similar features at the transcriptomic level. This Series consists of the gene expression data of the primary mouse hepatocytes. The expression data of the mouse embryonic stem cells is submitted separately under another accession number. In this study we tested 26 chemicals of which 16 non-genotoxic carcinogens, 4 genotoxic carcinogens, 2 genotoxic non-carcinogens and 4 non-carcinogens. Specification of the chemicals can be found in the readme file.
2014-10-23 | E-GEOD-44088 | biostudies-arrayexpress
Project description:The current test strategy for carcinogenicity is generally based on in vitro and in vivo genotoxicity assays. Non-genotoxic carcinogens (NGTXC) are negative for genotoxicity and go undetected. Therefore, alternative tests to detect these chemicals are urgently needed. NGTXC act through different modes of action, which complicates the development of such assays. We have demonstrated recently in primary mouse hepatocytes that some, but certainly not all, NGTXC can be categorized according to their mode of action based on feature detection at a gene expression level (Schaap et al. 2012, PMID22710402). Identification of a wider range of chemicals probably requires multiple in vitro systems. In the current study we describe the added value of using mouse embryonic stem cells. In this study the focus is on NGTXC, but we also included genotoxic carcinogens and non-carcinogens. This approach enables us to assess the robustness of this method and to evaluate the system for recognizing features of chemicals in general, which is important for application in future risk assessment. This serie consists of the gene expression data of the mouse embryonic stem cells. The expression data of the primary mouse hepatocytes cells is submitted separately under accession number GSE44088.
2014-10-27 | GSE47345 | GEO