Transcription profiling by array of the effect of chemical mutagens and carcinogens on gene expression profiles in human TK6 cells
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER:
PROVIDER: E-TABM-1223 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
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.
2015-04-15 | E-GEOD-58782 | biostudies-arrayexpress
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.
2015-05-14 | E-GEOD-68855 | biostudies-arrayexpress
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).
2015-04-15 | E-GEOD-51173 | biostudies-arrayexpress
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).
2015-04-15 | E-GEOD-51174 | biostudies-arrayexpress
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 Aflatoxin B1 for 4 hours followed by a 0h, 4h and 20h recovery in fresh media (4h, 8h, and 24h time points, respectively) at 3 different concentrations, including a low (0.025 μM), a medium (0.075 μM), and a high (0.1 μM) concentration, in addition to negative controls (±S9), vehicle controls (±S9) and a direct-acting positive control, cisplatin (-S9) at 1 μg/ml (PC-1) and 24 μg/ml (PC-24). Each concentration and time point had 3 biological replicates. There were a total 30 samples (60 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).
2015-04-15 | E-GEOD-51171 | 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 (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 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 0h and 20h recovery in fresh media (4h and 24h time points, respectively) at 3 different concentrations, including a low (0.45 μg/ml), a medium (1.4 μg/ml), and a high (10 μg/ml) concentration, in addition to negative vehicle controls (±S9) and a direct-acting positive control, cisplatin (-S9) at 24 μg/ml (PC-1). Each concentration and time point had 3 biological replicates. There were a total 23 samples (46 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).
2015-04-15 | E-GEOD-51172 | 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. 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 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 phenobarbital for 4 hours followed by recovery times of 4h and 20h in fresh media (8h and 24h time points, respectively) at 3 different concentrations, including a low (1 mM), a medium (3.2 mM), and a high (10 mM) concentration, in addition to a vehicle control (+S9). Each concentration and time point had 3 biological replicates, which were pooled for gene expression analysis. There were a total of 6 samples (12 arrays) included in the final analysis using a two-colour dye swap design.
2015-05-12 | E-GEOD-68774 | biostudies-arrayexpress
Project description:We are investigating the response of human lymphoblastoid cells to low-dose exposure of environmental metals We used microarrays to detail the global programme of gene expression upon response to low-dose metals Keywords: dose TK6 cells were grown to mid log-phase and exposed to low-doses of arsenic and cadmium. RNA was collected 24 hrs after exposure.
2010-04-14 | E-GEOD-20320 | biostudies-arrayexpress