Ontology highlight
ABSTRACT:
PROVIDER: PRJNA507658 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR8265440.fastq.gz | Fastqsanger.gz | |||
| SRR8265441.fastq.gz | Fastqsanger.gz | |||
| SRR8265442.fastq.gz | Fastqsanger.gz | |||
| SRR8265439.fastq.gz | Fastqsanger.gz |
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Project description:Background: Modern testing paradigms seek to apply human-relevant cell culture models and integrate data from multiple test systems to accurately inform potential hazards and modes of action for chemical toxicology. In genetic toxicology, the use of metabolically competent human hepatocyte cell culture models provides clear advantages over other more commonly used cell lines that require the use of external metabolic activation systems, such as rat liver S9. HepaRG™ cells are metabolically competent cells that express Phase I and II metabolic enzymes and differentiate into mature hepatocyte-like cells, making them ideal for toxicity testing. We assessed the performance of the flow cytometry in vitro micronucleus (MN) test and the TGx-DDI transcriptomic biomarker to detect DNA damage-inducing (DDI) chemicals in human HepaRG™ cells after a 3-day repeat exposure. The biomarker, developed for use in human TK6 cells, is a panel of 64 genes that accurately classifies chemicals as DDI or non-DDI. Herein, the TGx-DDI biomarker was analyzed by Ion AmpliSeq whole transcriptome sequencing to assess its classification accuracy using this more modern gene expression technology as a secondary objective. Methods: HepaRG™ cells were exposed to increasing concentrations of 10 test chemicals (six genotoxic chemicals, including one aneugen, and four non-genotoxic chemicals). Cytotoxicity and genotoxicity were measured using the In Vitro MicroFlow® kit, which was run in parallel with the TGx-DDI biomarker. Results: A concentration-related decrease in relative survival and a concomitant increase in MN frequency were observed for genotoxic chemicals in HepaRG™ cells. All five DDI and five non-DDI agents were correctly classified (as genotoxic/non-genotoxic and DDI/non-DDI) by pairing the test methods. The aneugenic agent (colchicine) yielded the expected positive result in the MN test and negative (non-DDI) result by TGx-DDI. Conclusions: This next generation genotoxicity testing strategy is aligned with the paradigm shift occurring in the field of genetic toxicology. It provides mechanistic insight in a human-relevant cell-model, paired with measurement of a conventional endpoint, to inform the potential for adverse health effects. This work provides support for combining these assays in an integrated test strategy for accurate, higher throughput genetic toxicology testing in this metabolically competent human progenitor cell line.
2020-02-18 | GSE136009 | 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:In vitro toxicogenomics signatures to predict mode of action can facilitate chemical screening. We recently demonstrated the use of the TGx-28.65 genomic biomarker (representing: toxicogenomics (TGx), developed using a 28 chemical training set, and comprising 65 genes) in classifying agents as genotoxic (DNA damaging) and non-genotoxic in human lymphoblastoid TK6 cells. Because TK6 cells do not possess cytochrome P450 activity, we confirmed accurate classification in cells co-exposed to 1% 5,6 benzoflavone/phenobarbital-induced rat liver S9 for metabolic activation. However, chemicals may require different types of S9 for activation. Here we conducted experiments in TK6 cells exposed to chemicals in the presence of 2% or 10% Aroclor-induced, or 5% ethanol-induced rat liver S9 to expand TGx-28.65 biomarker application. Gene expression profiles were produced 3-4 hr following a 4 hr co-exposure of TK cells to test chemicals (seven genotoxic and two non-genotoxic, three concentrations, and concurrent solvent controls) and S9. Relative survival and micronucleus frequency was assessed by flow cytometry in cells 20 hr after the exposure. We found that genotoxicity/non-genotoxicity could be accurately predicted for the test compounds using the different S9s. One technical replicate of cells co-treated with dexamethasone and 10% Aroclor-induced S9 was falsely predicted as genotoxic, suggesting caution in using high concentrations of S9. TGx-28.65 correctly classified low concentrations of genotoxic chemicals (those not causing cytotoxicity or micronuclei) as genotoxic, demonstrating that it is a sensitive biomarker of genotoxicity. Overall, the work confirms that different S9s can be used in TK6 cells without impairing predictivity using the TGx-28.65 biomarker.
2016-08-25 | GSE74216 | GEO
Project description:Efforts to develop alternatives which can at least partially replace some of the currently used in vivo tests are ongoing. The recently ended FP6 European project carcinoGENOMICS had the goal to use the combination of toxicogenomics and in vitro cell culture models for identification of genotoxic- and non-genotoxic carcinogen-specific gene signatures. In this study is presented a part of the outcome of the project and in particular the performance of the gene classifier derived after exposure of the HepaRG cell line to prototypical hepatocarcinogens. Upon analyzing the data at a gene and a pathway level by using diverse biostatistical approaches, a clear-cut separation of the genotoxic from the non-genotoxic hepatocarcinogens and non-carcinogens was achieved (up to 88% correct prediction). The most characteristic pathway for genotoxic exposure was DNA damage. Further to show the robustness of the HepaRG model, the interlaboratory reproducibility of 3 blindly tested compounds was assessed. The results showed between 20% and 35% reproducibility. The subsequent classification of the 3 blindly tested compounds resulted in correct prediction of the genotoxicant, whereas the other two compounds were misclassified. In conclusion, the combination of transcriptomics and HepaRG in vitro cell model provides a solid basis for the detection of the genotoxic potential of unknown chemicals.
2014-09-01 | GSE48990 | GEO
Project description:In vitro gene expression signatures to predict toxicological responses can provide mechanistic context for human health risk assessment purposes. We previously developed the TGx-28.65 genomic biomarker from a database of gene expression profiles in human TK6 cells exposed to 28 well-known compounds, and it comprises 65 genes that can classify chemicals as DNA damaging or non-DNA damaging. In this study, we applied the TGx-28.65 genomic biomarker in parallel with the in vitro micronucleus (MN) assay to determine if two chemicals of regulatory interest at Health Canada, disperse orange (DO: the orange azo dye 3-[[4-[(4-Nitrophenyl)azo]phenyl]benzylamino]propanenitrile) and 1,2,4-benzenetriol (BT: a metabolite of benzene) are genotoxic or non-genotoxic. Both chemicals caused dose-dependent declines in relative survival (RS) and increases in apoptosis. A strong significant increase in micronucleus induction was observed for all concentrations of BT; the top two concentrations of DO also caused a statistically significant increase in MN, but these increases were less than 2-fold above controls. TGx-28.65 analysis classified BT as genotoxic at all three concentrations and DO as genotoxic at the mid and high concentrations. Thus, although DO only induces a small increase in MN, this response is sufficient to induce a cellular DNA damage response that is likely relevant for risk assessment. Benchmark dose modeling revealed that BT is much more potent than DO; the BT benchmark dose for MN induction was similar to that of benzo[a]pyrene, (BaP: a genotoxic carcinogen), which was used as a positive control. The results strongly suggest that follow-up work is required to assess whether DO and BT are also genotoxic in vivo. This is particularly important for DO, which may require metabolic activation by bacterial gut flora to fully induce its genotoxic potential. Our previously published data and this proof of concept study suggest that the TGx-28.65 genomic biomarker has the potential to add significant value to existing approaches used to assess genotoxicity.
2017-10-16 | GSE95188 | GEO
Project description:In vitro gene expression signatures to predict toxicological responses can provide mechanistic context for human health risk assessment purposes. We previously developed the TGx-28.65 genomic biomarker from a database of gene expression profiles in human TK6 cells exposed to 28 well-known compounds, and it comprises 65 genes that can classify chemicals as DNA damaging or non-DNA damaging. In this study, we applied the TGx-28.65 genomic biomarker in parallel with the in vitro micronucleus (MN) assay to determine if two chemicals of regulatory interest at Health Canada, disperse orange (DO: the orange azo dye 3-[[4-[(4-Nitrophenyl)azo]phenyl]benzylamino]propanenitrile) and 1,2,4-benzenetriol (BT: a metabolite of benzene) are genotoxic or non-genotoxic. Both chemicals caused dose-dependent declines in relative survival (RS) and increases in apoptosis. A strong significant increase in micronucleus induction was observed for all concentrations of BT; the top two concentrations of DO also caused a statistically significant increase in MN, but these increases were less than 2-fold above controls. TGx-28.65 analysis classified BT as genotoxic at all three concentrations and DO as genotoxic at the mid and high concentrations. Thus, although DO only induces a small increase in MN, this response is sufficient to induce a cellular DNA damage response that is likely relevant for risk assessment. Benchmark dose modeling revealed that BT is much more potent than DO; the BT benchmark dose for MN induction was similar to that of benzo[a]pyrene, (BaP: a genotoxic carcinogen), which was used as a positive control. The results strongly suggest that follow-up work is required to assess whether DO and BT are also genotoxic in vivo. This is particularly important for DO, which may require metabolic activation by bacterial gut flora to fully induce its genotoxic potential. Our previously published data and this proof of concept study suggest that the TGx-28.65 genomic biomarker has the potential to add significant value to existing approaches used to assess genotoxicity.
2017-10-16 | GSE95187 | GEO
Project description:The use of integrated approaches in genetic toxicology, including the incorporation of gene expression data to determine the molecular pathways involved in the response, is becoming more common. In a companion paper, a genomic biomarker was developed in human TK6 cells to classify chemicals as genotoxic or non-genotoxic. Because TK6 cells are not metabolically competent, we set out to broaden the utility of the biomarker for use with chemicals requiring metabolic activation. Specifically, chemical exposures were conducted in the presence of rat liver S9. The ability of the biomarker to classify genotoxic (benzo[a]pyrene, BaP; aflatoxin B1, AFB1) and non-genotoxic (dexamethasone, DEX; phenobarbital, PB) agents correctly was evaluated. Cells were exposed to increasing chemical concentrations for 4h and collected 0h, 4h and 20h post-exposure. Relative survival, apoptosis, and micronucleus frequency were measured at 24h. Transcriptome profiles were measured with Agilent microarrays. Statistical modeling and bioinformatics tools were applied to classify each chemical using the genomic biomarker. BaP and AFB1 were correctly classified as genotoxic at the mid- and high concentrations at all three time points, whereas DEX was correctly classified as non-genotoxic at all concentrations and time points. The high concentration of PB was misclassified at 24h, suggesting that cytotoxicity at later time points may cause misclassification. The data suggest that the use of S9 does not impair the ability of the biomarker to classify genotoxicity in TK6 cells. Finally, we demonstrate that the biomarker is also able to accurately classify genotoxicity using a publicly available dataset derived from human HepaRG cells.
2015-04-15 | GSE51174 | GEO
Project description:The use of integrated approaches in genetic toxicology, including the incorporation of gene expression data to determine the molecular pathways involved in the response, is becoming more common. In a companion paper, a genomic biomarker was developed in human TK6 cells to classify chemicals as genotoxic or non-genotoxic. Because TK6 cells are not metabolically competent, we set out to broaden the utility of the biomarker for use with chemicals requiring metabolic activation. Specifically, chemical exposures were conducted in the presence of rat liver S9. The ability of the biomarker to classify genotoxic (benzo[a]pyrene, BaP; aflatoxin B1, AFB1) and non-genotoxic (dexamethasone, DEX; phenobarbital, PB) agents correctly was evaluated. Cells were exposed to increasing chemical concentrations for 4h and collected 0h, 4h and 20h post-exposure. Relative survival, apoptosis, and micronucleus frequency were measured at 24h. Transcriptome profiles were measured with Agilent microarrays. Statistical modeling and bioinformatics tools were applied to classify each chemical using the genomic biomarker. BaP and AFB1 were correctly classified as genotoxic at the mid- and high concentrations at all three time points, whereas DEX was correctly classified as non-genotoxic at all concentrations and time points. The high concentration of PB was misclassified at 24h, suggesting that cytotoxicity at later time points may cause misclassification. The data suggest that the use of S9 does not impair the ability of the biomarker to classify genotoxicity in TK6 cells. Finally, we demonstrate that the biomarker is also able to accurately classify genotoxicity using a publicly available dataset derived from human HepaRG cells.
2015-04-15 | GSE51172 | GEO
Project description:The use of integrated approaches in genetic toxicology, including the incorporation of gene expression data to determine the molecular pathways involved in the response, is becoming more common. In a companion paper, a genomic biomarker was developed in human TK6 cells to classify chemicals as genotoxic or non-genotoxic. Because TK6 cells are not metabolically competent, we set out to broaden the utility of the biomarker for use with chemicals requiring metabolic activation. Specifically, chemical exposures were conducted in the presence of rat liver S9. The ability of the biomarker to classify genotoxic (benzo[a]pyrene, BaP; aflatoxin B1, AFB1) and non-genotoxic (dexamethasone, DEX; phenobarbital, PB) agents correctly was evaluated. Cells were exposed to increasing chemical concentrations for 4h and collected 0h, 4h and 20h post-exposure. Relative survival, apoptosis, and micronucleus frequency were measured at 24h. Transcriptome profiles were measured with Agilent microarrays. Statistical modeling and bioinformatics tools were applied to classify each chemical using the genomic biomarker. BaP and AFB1 were correctly classified as genotoxic at the mid- and high concentrations at all three time points, whereas DEX was correctly classified as non-genotoxic at all concentrations and time points. The high concentration of PB was misclassified at 24h, suggesting that cytotoxicity at later time points may cause misclassification. The data suggest that the use of S9 does not impair the ability of the biomarker to classify genotoxicity in TK6 cells. Finally, we demonstrate that the biomarker is also able to accurately classify genotoxicity using a publicly available dataset derived from human HepaRG cells.
2015-04-15 | GSE58782 | GEO
Project description:The use of integrated approaches in genetic toxicology, including the incorporation of gene expression data to determine the molecular pathways involved in the response, is becoming more common. In a companion paper, a genomic biomarker was developed in human TK6 cells to classify chemicals as genotoxic or non-genotoxic. Because TK6 cells are not metabolically competent, we set out to broaden the utility of the biomarker for use with chemicals requiring metabolic activation. Specifically, chemical exposures were conducted in the presence of rat liver S9. The ability of the biomarker to classify genotoxic (benzo[a]pyrene, BaP; aflatoxin B1, AFB1) and non-genotoxic (dexamethasone, DEX; phenobarbital, PB) agents correctly was evaluated. Cells were exposed to increasing chemical concentrations for 4h and collected 0h, 4h and 20h post-exposure. Relative survival, apoptosis, and micronucleus frequency were measured at 24h. Transcriptome profiles were measured with Agilent microarrays. Statistical modeling and bioinformatics tools were applied to classify each chemical using the genomic biomarker. BaP and AFB1 were correctly classified as genotoxic at the mid- and high concentrations at all three time points, whereas DEX was correctly classified as non-genotoxic at all concentrations and time points. The high concentration of PB was misclassified at 24h, suggesting that cytotoxicity at later time points may cause misclassification. The data suggest that the use of S9 does not impair the ability of the biomarker to classify genotoxicity in TK6 cells. Finally, we demonstrate that the biomarker is also able to accurately classify genotoxicity using a publicly available dataset derived from human HepaRG cells.
2015-04-15 | GSE51173 | GEO