TK6 toxicogenomics reference database
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Daniel Hyduke
PROVIDER: E-GEOD-58431 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
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Project description:TK6 cells were exposed to various perturbations and then the transcriptome profiles were collected at 4 hours to assemble a reference database to generate a Genotoxic / Nongenotoxic classifier using the nearest shrunken centroids method.
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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.
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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.
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2007-11-28 | E-GEOD-3492 | 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:The American Women’s Health Initiative study published in July 2002 caused considerable concern among hormone replacement therapy (HRT) users and prescribers in many countries. This study is an exploratory research comparing the genome wide expression profile in whole blood samples according to HRT use. Within the Norwegian Women and Cancer study, 100 postmenopausal women (50 HRT users and 50 non-HRT users) born between 1943 and 1949 with normal to high body mass index and no other medication use were selected. After total RNA extraction, amplification and labelling, the samples were hybridized together with a common reference (Universal human reference RNA, Stratagen) to Agilent Human 1A oligoarrays (G4110b, Agilent Technologies, Palo Alto, CA) containing 20,173 unique genes. Differentially expressed genes were used to build a classifier using the nearest shrunken centroids method (PAM). Then, we tested the significant changes in single genes by different methods like t-test, Significance Analysis of Microarrays (SAM) and Bayesian ANOVA analysis (BAM). Results did not reveal any distinct gene list which predicted accurately HRT exposure (error rate = 0.45). Classifier performance slightly improved (error rate = 0.29) including only women who were using continuous combined HRT treatment. According to the small amplitude of expression alterations observed after HRT use in whole blood, large sample sizes are needed to identify significant single genes differentially expressed. However, significant enrichments in biologic process of genes with small changes after HRT use were observed (e.g. receptor and transporter activities, immune response, frizzled signalling pathway, actin filament organization, glycogen metabolism). Keywords: gene expression profile and exposure
2005-10-25 | GSE3492 | 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 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