Assessment of the performance of the TGx‐DDI biomarker to detect DNA damage‐inducing agents using quantitative RT‐PCR in TK6 cells

Abstract

Gene expression biomarkers are now available for application in the identification of genotoxic hazards. The TGx‐DDI transcriptomic biomarker can accurately distinguish DNA damage‐inducing (DDI) from non‐DDI exposures based on changes in the expression of 64 biomarker genes. The 64 genes were previously derived from whole transcriptome DNA microarray profiles of 28 reference agents (14 DDI and 14 non‐DDI) after 4 h treatments of TK6 human lymphoblastoid cells. To broaden the applicability of TGx‐DDI, we tested the biomarker using quantitative RT‐PCR (qPCR), which is accessible to most molecular biology laboratories. First, we selectively profiled the expression of the 64 biomarker genes using TaqMan qPCR assays in 96‐well arrays after exposing TK6 cells to the 28 reference agents for 4 h. To evaluate the classification capability of the qPCR profiles, we used the reference qPCR signature to classify 24 external validation chemicals using two different methods—a combination of three statistical analyses and an alternative, the Running Fisher test. The qPCR results for the reference set were comparable to the original microarray biomarker; 27 of the 28 reference agents (96%) were accurately classified. Moreover, the two classification approaches supported the conservation of TGx‐DDI classification capability using qPCR; the combination of the two approaches accurately classified 21 of the 24 external validation chemicals, demonstrating 100% sensitivity, 81% specificity, and 91% balanced accuracy. This study demonstrates that qPCR can be used when applying the TGx‐DDI biomarker and will improve the accessibility of TGx‐DDI for genotoxicity screening. Environ. Mol. Mutagen. 60: 122–133, 2019. © 2018 Her Majesty the Queen in Right of Canada Environmental and Molecular Mutagenesis.

Mining a human transcriptome database for chemical modulators of NRF2

“…Another major challenge since the beginning of toxicogenomics is linking changes in gene expression to specific molecular events and toxicological outcomes. Our work here in the case of NRF2 and by extrapolation increases in oxidative stress, and in other publications examining endpoints important in endocrine disruption [22,23] and DNA damage [24,57], directly addresses this issue. Moving forward, biomarkers for activation of transcription factors can, to some extent, be developed using existing microarray data.…”

EURL ECVAM Genotoxicity and Carcinogenicity Database of Substances Eliciting Negative Results in the Ames Test: Construction of the Database

“…Lastly, answers to the aforementioned questions will very likely facilitate determination of those circumstances (e.g., specific classes of compounds and/or specific chemical properties) where new targets of genomic damage might be identified and eventually be used to provide additional mechanistic evidence for assessment of genotoxic and/or carcinogenic hazard [ [42] , [43] , [44] ]. In-depth analysis of the database is on-going; the results of these analyses will be presented in a forthcoming manuscript.…”

A comparison of classical and 21st century genotoxicity tools: A proof of concept study of 18 chemicals comparing in vitro micronucleus, ToxTracker and genomics‐based methods (TGx‐DDI, whole genome clustering and connectivity mapping)

“…Employing either the ToxTracker, TGx‐DDI or the whole genome approach (selected dose only) in combination with the MN assay all have benefits as well as drawbacks in terms of cost—either associated with having compounds analyzed as a service (ToxTracker) or requiring expensive specialty instrumentation for generating genomic data. The TGx‐DDI biomarker is becoming more accessible as it has now been successfully extended to multiple more cost effective/accessible approaches such as qRT‐PCR and NanoString nCounter technology (Cho et al, 2019; Li et al, 2017), however some methods still require computational biology expertise. Further analysis of the resulting genomic data can present a challenge depending on the route taken; for example a web‐based platform has been built for TGx‐DDI analysis (https://manticore.niehs.nih.gov/tgxddi) and this platform does thus not require the user to be proficient in computation biology expertise.…”

“…A three‐pronged method is used in which a positive call is based on the agent being classified as positive for DDI in any one of three analytical interpretations of the data: (a) a probability analysis based on the nearest shrunken centroid, (b) a principal components analysis, and (c) 2‐dimensional clustering (Li et al, 2017). The two aneugenic compounds examined (VB and COL) were included in the analysis, although the TGx‐DDI biomarker is reported to not detect this mode of action (Cho et al, 2019).…”