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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ABSTRACT:
ORGANISM(S): Rattus norvegicus Mus musculus Homo sapiens
PROVIDER: GSE72088 | GEO | 2016/06/28
SECONDARY ACCESSION(S): PRJNA292907
REPOSITORIES: GEO
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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:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2016-06-28 | E-GEOD-72088 | 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-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.
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Project description: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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Project description:BackgroundMulti-walled carbon nanotubes (MWCNT) have received attention due to extraordinary properties, resulting in concerns for occupational health and safety. Costs and ethical concerns of animal testing drive a need for in vitro models with predictive power in respiratory toxicity. The aim of this study was to assess pro-inflammatory response (Interleukin-8 expression, IL-8) and genotoxicity (DNA strand breaks) caused by MWCNT with different physicochemical properties in different pulmonary cell models and correlate these to previously published in vivo data. Seven MWCNT were selected; two long/thick (NRCWE-006/Mitsui-7 and NM-401), two short/thin (NM-400 and NM-403), a pristine (NRCWE-040) and two surface modified; hydroxylated (NRCWE-041) and carboxylated (NRCWE-042). Carbon black Printex90 (CB) was included as benchmark material. Human alveolar epithelial cells (A549) and monocyte-derived macrophages (THP-1a) were exposed to nanomaterials (NM) in submerged conditions, and two materials (NM-400 and NM-401) in co-cultures of A549/THP-1a and lung fibroblasts (WI-38) in an air-liquid interface (ALI) system. Effective doses were quantified by thermo-gravimetric-mass spectrometry analysis (TGA-MS). To compare genotoxicity in vitro and in vivo, we developed a scoring system based on a categorization of effects into standard deviation (SD) units (< 1, 1, 2, 3 or 4 standard deviation increases) for the increasing genotoxicity.ResultsEffective doses were shown to be 25 to 53%, and 21 to 57% of the doses administered to A549 and THP-1a, respectively. In submerged conditions (A549 and THP-1a cells), all NM induced dose-dependent IL-8 expression. NM-401 and NRCWE-006 caused the strongest pro-inflammatory response. In the ALI-exposed co-culture, only NM-401 caused increased IL-8 expression, and no DNA strand breaks were observed. Strong correlations were found between in vitro and in vivo inflammation when doses were normalized by surface area (also proxy for diameter and length). Significantly increased DNA damage was found for all MWCNT in THP-1a cells, and for short MWCNT in A549 cells. A concordance in genotoxicity of 83% was obtained between THP-1a cells and broncho-alveolar lavaged (BAL) cells.ConclusionThis study shows correlations of pro-inflammatory potential in A549 and THP-1a cells with neutrophil influx in mice, and concordance in genotoxic response between THP-1a cells and BAL cells, for seven MWCNT.
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