Dataset Information

Application of the TGx-28.65 toxicogenomics biomarker to classify genotoxic and non-genotoxic chemicals in human TK6 cells in the presence of rat liver S9

ABSTRACT: 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.

ORGANISM(S): Homo sapiens

PROVIDER: GSE74216 | GEO | 2016/08/25

SECONDARY ACCESSION(S): PRJNA299569

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

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. This experiment examined the whole genome transcriptional response of TK6 cells exposed to DMSO (+S9) and 10 μg/ml BaP (-S9) (both compared to DMSO (-S9)) for 4 hours followed by a 20h in fresh media (24h time point), in addition to 7.5 mM dexamethasone (+S9) (compared to DMSO (+S9)). Each condition had 3 biological replicates, which were pooled for gene expression analysis. There were a total 3 samples (6 arrays) included in the final analysis using a two-colour dye swap design.

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 classifier is also able to accurately classify genotoxicity using a publicly available dataset derived from human HepaRG cells. This experiment examined the whole genome transcriptional response of TK6 cells exposed to 0.1 µM aflatoxin B1 (-S9), 10 µg/ml benzo[a]pyrene (-S9), 7.5 mM dexamethasone (-S9) and 24 µg/ml cisplatin (+S9) for 4 hours followed by a 0h, 4h and 20h recovery in fresh media (4h, 8h, and 24h time points, respectively), in addition to negative controls (±S9), vehicle controls (±S9), a direct-acting positive control, cisplatin (-S9) at 24 μg/ml and a positive control requiring metabolic activation, BaP (+S9) at 10 μg/ml. Each concentration and time point had 3 biological replicates, which were pooled for gene expression analysis. There were a total of 14 samples (28 arrays) included in the final analysis using a two-colour dye swap design.

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.

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. This experiment examined the whole genome transcriptional response of TK6 cells exposed to Dexamethasone for 4 hours followed by a 0h, 4h and 20h recovery in fresh media (4h, 8h, and 24h time points, respectively) at 2 different concentrations, including 0.63 mM and 1mM, in addition to negative controls (±S9), vehicle controls (±S9) and a direct-acting positive control, cisplatin (-S9) at 24 ?g/ml (PC-24). Each concentration and time point had 3 biological replicates. There were a total 22 samples (44 arrays) included in the final analysis using a two-colour dye swap design. Please note that each sample data table contains normalized data combined from two replicates and the sample-dye assignment information for each of the raw data files is included in the 'readme.txt' (available as Series supplementary file).

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. This experiment examined the whole genome transcriptional response of TK6 cells exposed to BaP for 4 hours followed by a 4h in fresh media (8h time point) at 3 different concentrations, including a low (0.45 ?g/ml), a medium (1.4 ?g/ml), and a high (10 ?g/ml) concentration, in addition to negative controls (±S9), vehicle controls (±S9) and a direct-acting positive control, cisplatin (-S9) at 24 ?g/ml (PC-24). Each concentration and time point had 3 biological replicates. There were a total 12 samples (24 arrays) included in the final analysis using a two-colour dye swap design. Please note that each sample data table contains normalized data combined from two replicates and the sample-dye assignment information for each of the raw data files is included in the 'readme.txt' (available as Series supplementary file).

Dataset Information

Application of the TGx-28.65 toxicogenomics biomarker to classify genotoxic and non-genotoxic chemicals in human TK6 cells in the presence of rat liver S9

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets