Comparison of base-line and chemical-induced transcriptomic responses in HepaRG and RPTEC/TERT1 cells using TempO-Seq

Abstract

The utilisation of genome-wide transcriptomics has played a pivotal role in advancing the field of toxicology, allowing the mapping of transcriptional signatures to chemical exposures. These activities have uncovered several transcriptionally regulated pathways that can be utilised for assessing the perturbation impact of a chemical and also the identification of toxic mode of action. However, current transcriptomic platforms are not very amenable to high-throughput workflows due to, high cost, complexities in sample preparation and relatively complex bioinformatic analysis. Thus, transcriptomic investigations are usually limited in dose and time dimensions and are, therefore, not optimal for implementation in risk assessment workflows. In this study, we investigated a new cost-effective, transcriptomic assay, TempO-Seq, which alleviates the aforementioned limitations. This technique was evaluated in a 6-compound screen, utilising differentiated kidney (RPTEC/TERT1) and liver (HepaRG) cells and compared to non-transcriptomic label-free sensitive endpoints of chemical-induced disturbances, namely phase contrast morphology, xCELLigence and glycolysis. Non-proliferating cell monolayers were exposed to six sub-lethal concentrations of each compound for 24 h. The results show that utilising a 2839 gene panel, it is possible to discriminate basal tissue-specific signatures, generate dose–response relationships and to discriminate compound-specific and cell type-specific responses. This study also reiterates previous findings that chemical-induced transcriptomic alterations occur prior to cytotoxicity and that transcriptomics provides in depth mechanistic information of the effects of chemicals on cellular transcriptional responses. TempO-Seq is a robust transcriptomic platform that is well suited for in vitro toxicity experiments.Electronic supplementary materialThe online version of this article (10.1007/s00204-018-2256-2) contains supplementary material, which is available to authorized users.

Investigation of Nrf2, AhR and ATF4 Activation in Toxicogenomic Databases

“…Fluphenazine has not been established as a ligand for the AhR, its structure—a halogenated aromatic ring system—closely matches the motif involved in binding to this receptor (Donohoe et al, 2008 ). In a recent study we have demonstrated that isoniazid induced CYP1A1 in HepaRG cells, which is a potential indicator of AhR activation (Limonciel et al, 2018 ). Only Sulindac from the 160 was ranked as active using the CAC selection criteria, which may seem surprising given the frequency of oxidative injury in liver toxicities.…”

“…Potassium bromate is an oxidizing agent causing ROS injury and oxidative stress-induced DNA damage (Ballmaier and Epe, 1995 ; Limonciel et al, 2012 ). In a recent study we showed that potassium bromate activated the Nrf2 and p53 response without activation of the ATF4 response (Limonciel et al, 2018 ). Phorone can similarly activate Nrf2 due to glutathione depletion (Younes et al, 1986 ; Iannone et al, 1990 ; Oguro et al, 1996 ).…”

A Next-Generation Risk Assessment Case Study for Coumarin in Cosmetic Products

“…Transcriptomics was applied as a broad nontargeted biological screen of in vitro cellular perturbation following coumarin treatment, to complement the targeted assays (eg, Eurofins SafetyScreen44, and BioMap). Use of concentration-response modeling of transcriptomics data has been utilized to derive PoDs for chemical risk assessment ( Farmahin et al , 2017 ; Thomas et al , 2013 , 2019 ) and to identify potential MoAs for target organ toxicity ( Limonciel et al , 2018 ; Ramaiahgari et al , 2019 ). This study used 3 different cell lines (HepG2, MCF7, and 2D HepaRG) to extend biological coverage and address potential cellular variation in response to coumarin including any consequences of metabolism.…”

Comparison of Normalization Methods for Analysis of TempO-Seq Targeted RNA Sequencing Data

“…In the case of newer platforms such as TempO-Seq targeted RNA sequencing, there has not been an evaluation of the performance of several normalization methods. Recent publications using the TempO-Seq platform used DESeq2 or CPM for normalization (Grimm et al, 2016;House et al, 2017;Yeakley et al, 2017;Batai et al, 2018;Bushel et al, 2018;Hanke et al, 2018;Limonciel et al, 2018;Chappell et al, 2019;Ramaiahgari et al, 2019;Simon et al, 2019). In this study, we compared seven normalization methods using simulated data from human HepaRG control cells to determine which methods maintained genes at seven assigned FC levels ( Table 1).…”

“…In addition, by nature of the technology, it lacks 3 end bias. Recently, several studies utilized the TempO-Seq platform for whole transcriptome profiling, primarily for toxicogenomics (Grimm et al, 2016;House et al, 2017;Yeakley et al, 2017;Bushel et al, 2018;Limonciel et al, 2018;Chappell et al, 2019;Ramaiahgari et al, 2019;Simon et al, 2019), but also carcinogenomics (Batai et al, 2018;Hanke et al, 2018), and to profile formalin-fixed paraffin-embedded (FFPE) tissue (Trejo et al, 2019). However, there has not been a comprehensive comparison of normalization methods applied to TempO-Seq data.…”