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

Abstract

In vitro transcriptional signatures that predict toxicities can facilitate chemical screening. We previously developed a transcriptomic biomarker (known as TGx‐28.65) for classifying agents as genotoxic (DNA damaging) and non‐genotoxic in human lymphoblastoid TK6 cells. Because TK6 cells do not express cytochrome P450s, we confirmed accurate classification by the biomarker 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 investigated the response of TK6 cells to higher percentages of Aroclor‐, benzoflavone/phenobarbital‐, or ethanol‐induced rat liver S9 to expand TGx‐28.65 biomarker applicability. Transcriptional profiles were derived 3 to 4 hr following a 4 hr co‐exposure of TK6 cells to test chemicals and S9. Preliminary studies established that 10% Aroclor‐ and 5% ethanol‐induced S9 alone did not induce the TGx‐28.65 biomarker genes. Seven genotoxic and two non‐genotoxic chemicals (and concurrent solvent and positive controls) were then tested with one of the S9s (selected based on cell survival and micronucleus induction). Relative survival and micronucleus frequency was assessed by flow cytometry in cells 20 hr post‐exposure. Genotoxic/non‐genotoxic chemicals were accurately classified using the different S9s. One technical replicate of cells co‐treated with dexamethasone and 10% Aroclor‐induced S9 was falsely classified as genotoxic, suggesting caution in using high S9 concentrations. Even low concentrations of genotoxic chemicals (those not causing cytotoxicity) were correctly classified, demonstrating that TGx‐28.65 is a sensitive biomarker of genotoxicity. A meta‐analysis of datasets from 13 chemicals supports that different S9s can be used in TK6 cells, without impairing classification using the TGx‐28.65 biomarker. Environ. Mol. Mutagen. 57:243–260, 2016. © 2016 Her Majesty the Queen in Right of Canada. Environmental and Molecular Mutagenesis © 2016 Environmental Mutagen Society

Using a gene expression biomarker to identify DNA damage‐inducing agents in microarray profiles

“…To determine if our microarray analysis approach could be used as an alternative Tier 0 screening model to identify DDI compounds, a classification analysis was performed on 59 biosets from TK6 cells that were treated with 18 chemicals known to induce DNA damage. The biosets came from four studies (Kuehner et al, ; Buick et al, ; Yauk et al, ; Buick et al, ). In the Buick et al and Yauk et al studies, the cells were treated at 2 or 3 concentrations for 4, 7, or 8 hr and all of the chemicals except cisplatin were used to treat cells in the presence of S9 extract.…”

“…The heat map indicates that in example 1, a positive −Log( P value) can be achieved if there is consistent expression of mostly negatively regulated genes. We note that there were very few examples of this in the dataset (three examples that had a %up minus %down of less than −60%) and consisted of two chemicals (2‐aminobenzanthracene and acetaminophen) from the same study (Yauk et al, ). Examples 2–4 have increasing numbers of induced vs. repressed genes.…”

“…Predictive accuracy in TK6 cells was carried out using four datasets. Biosets were derived from treatments consisting of 15 chemicals carried out as described (Buick et al, ; Yauk et al, ; Buick et al, ). Additional biosets came from Kuehner et al () (Gene Expression Omnibus Accession number, GSE41296) in which TK6 cells were treated with methyl methanesulfonate, ethyl methanesulfonate, or formaldehyde for 24 hr.…”

“…Initial studies indicated that the TGx‐DDI biomarker differentiates DDI and non‐DDI compounds with 100% accuracy (Li et al, ). In follow up studies, the efficiency of the biomarker for accurately classifying compounds that require metabolic activation was demonstrated using different metabolic activating systems and TK6 cells (Buick et al, ; Yauk et al, ). In addition, using data from a microarray platform not used in the original studies (Affymetrix) that measured gene expression in the metabolically competent human liver cell line HepaRG, the biomarker correctly classified 15 out of 15 chemicals (Buick et al, ; Li et al, ; Yauk et al, ).…”

“…In follow up studies, the efficiency of the biomarker for accurately classifying compounds that require metabolic activation was demonstrated using different metabolic activating systems and TK6 cells (Buick et al, ; Yauk et al, ). In addition, using data from a microarray platform not used in the original studies (Affymetrix) that measured gene expression in the metabolically competent human liver cell line HepaRG, the biomarker correctly classified 15 out of 15 chemicals (Buick et al, ; Li et al, ; Yauk et al, ). More recently, it was demonstrated that the biomarker can accurately classify 45 test agents across a broad set of chemical classes using the nCounter high‐throughput cell‐based testing platform and probability analysis, principal components analysis, and two‐dimensional clustering (Li et al, ).…”