In vivo murine hepatic microRNA and mRNA expression signatures predicting the (non-)genotoxic carcinogenic potential of chemicals

Progress in toxicogenomics to protect human health

Toxicogenomics measures molecular features, such as transcripts, proteins, metabolites and epigenomic modifications, to understand and predict the toxicological effects of environmental and pharmaceutical exposures. Transcriptomics has become an integral tool in contemporary toxicology research owing to innovations in gene expression profiling that can provide mechanistic and quantitative information at scale. These data can be used to predict toxicological hazards through the use of transcriptomic biomarkers, network inference analyses, pattern-matching approaches and artificial intelligence. Furthermore, emerging approaches, such as high-throughput dose-response modelling, can leverage toxicogenomic data for human health protection even in the absence of predicting specific hazards. Finally, single-cell transcriptomics and multi-omics provide detailed insights into toxicological mechanisms. Here, we review the progress since the inception of toxicogenomics in applying transcriptomics towards toxicology testing and highlight advances that are transforming risk assessment.

Review and meta-analysis of gene expression biomarkers predictive of chemical-induced genotoxicity in vivo

There is growing recognition across broad sectors of the toxicology community that gene expression biomarkers have the potential to identify genotoxic and nongenotoxic carcinogens through a weight-of-evidence approach, providing opportunities to reduce reliance on the 2-year bioassay to identify carcinogens. In August 2022, a workshop within the International Workshops on Genotoxicity Testing (IWGT) was held to critically review current methods to identify genotoxicants using various 'omics profiling methods. Here, we describe the findings of a workshop subgroup focused on the state of the science regarding the use of biomarkers to identify chemicals that act as genotoxicants in vivo. A total of 1341 papers were screened to identify those that were most relevant. While six published biomarkers with characterized accuracy were initially examined, four of the six were not considered further, because they had not been tested for classification accuracy using additional sets of chemicals or other transcript profiling platforms. Two independently derived biomarkers used in conjunction with standard computational techniques can identify genotoxic chemicals in vivo (rat liver or both rat and mouse liver) on different gene expression profiling platforms. The biomarkers have predictive accuracies of ≥92%. These biomarkers have the potential to be used in conjunction with other biomarkers in integrated test strategies using short-term rodent exposures to identify genotoxic and nongenotoxic chemicals that cause cancer.

Consensus findings of an International Workshops on Genotoxicity Testing workshop on using transcriptomic biomarkers to predict genotoxicity

Gene expression biomarkers have the potential to identify genotoxic and non-genotoxic carcinogens, providing opportunities for integrated testing and reducing animal use. In August 2022, an International Workshops on Genotoxicity Testing (IWGT) workshop was held to critically review current methods to identify genotoxicants using transcriptomic profiling. Here, we summarize the findings of the workgroup on the state of the science regarding the use of transcriptomic biomarkers to identify genotoxic chemicals in vitro and in vivo. A total of 1341 papers were examined to identify the biomarkers that show the most promise for identifying genotoxicants. This analysis revealed two independently derived in vivo biomarkers and three in vitro biomarkers that, when used in conjunction with standard computational techniques, can identify genotoxic chemicals in vivo (rat or mouse liver) or in human cells in culture using different gene expression profiling platforms, with predictive accuracies of ≥92%. These biomarkers have been validated to differing degrees but typically show high reproducibility across transcriptomic platforms and model systems. They offer several advantages for applications in different contexts of use in genotoxicity testing including: early signal detection, moderate-to-high-throughput screening capacity, adaptability to different cell types and tissues, and insights on mechanistic information on DNA-damage response. Workshop participants agreed on consensus statements to advance the regulatory adoption of transcriptomic biomarkers for genotoxicity. The participants agreed that transcriptomic biomarkers have the potential to be used in conjunction with other biomarkers in integrated test strategies in vitro and using short-term rodent exposures to identify genotoxic and non-genotoxic chemicals that may cause cancer and heritable genetic effects. Following are the consensus statements from the workgroup. Transcriptomic biomarkers for genotoxicity can be used in Weight of Evidence (WoE) evaluation to: determine potential genotoxic mechanisms and hazards; identify misleading positives from in vitro genotoxicity assays; serve as new approach methodologies (NAMs) integrated into the standard battery of genotoxicity tests. Several transcriptomic biomarkers have been developed from sufficiently robust training data sets, validated with external test sets, and have demonstrated performance in multiple laboratories. These transcriptomic biomarkers can be used following established study designs and models designated through existing validation exercises in WoE evaluation. Bridging studies using a selection of training and test chemicals are needed to deviate from the established protocols to confirm performance when a transcriptomic biomarker is being applied in other: tissues, cell models, or gene expression platforms. Top dose selection and time of gene expression analysis are critical and should be established during transcriptomic biomarker development. These conditions are the only ones suited for transcriptomic biomarker use unless additional bridging or pharmacokinetic studies are conducted. Temporal effects for genotoxicants that operate via distinct mechanisms should be considered in data interpretation. Fixed transcriptomic biomarker gene sets and analytical processes do not need to be independently rederived in biomarker validation. Validation should focus on the performance of the gene set in external test sets. Robust external testing should ensure a minimum of additional chemicals spanning genotoxic and non-genotoxic modes of action. Genes in the transcriptomic biomarker do not need to be known to be mechanistically involved in genotoxicity responses. Existing frameworks described for NAMs could be applied for validation of transcriptomic biomarkers. Reproducibility of bioinformatic analysis is critical for the regulatory application of transcriptomic biomarkers. A bioinformatics expert should be involved with creating reproducible methods for the qualification and application of each transcriptomic biomarker.

Outcome of IWGT workshop on transcriptomic biomarkers for genotoxicity: Key considerations for bioinformatics

As a part of the International Workshop on Genotoxicity Testing (IWGT) in 2022, a workgroup was formed to evaluate the level of validation and regulatory acceptance of transcriptomic biomarkers that identify genotoxic substances. Several such biomarkers have been developed using various molecular techniques and computational approaches. Within the IWGT workgroup on transcriptomic biomarkers, bioinformatics was a central topic of discussion, focusing on the current approaches used to process the underlying molecular data to distill a reliable predictive signal; that is, a gene set that is indicative of genotoxicity and can then be used in later studies to predict potential DNA damaging properties for uncharacterized chemicals. While early studies used microarray data, a technological shift occurred in the past decade to incorporate modern transcriptome measuring techniques such as high-throughput transcriptomics, which in turn is based on high-throughput sequencing. Herein, we present the workgroup's review of the current bioinformatic approaches to identify genes comprising transcriptomic biomarkers. Within the context of regulatory toxicology, the reproducibility of a given analysis is critical. Therefore, the workgroup provides consensus recommendations on how to facilitate sufficient reporting of experimental parameters for the analytical procedures used in a transcriptomic biomarker study, including the recommendation to develop a biomarker-specific reporting module within the OECD Omics Reporting Framework.

Systematic transcriptome-wide meta-analysis across endocrine disrupting chemicals reveals shared and unique liver pathways, gene networks, and disease associations

Cardiometabolic disorders (CMD) are a growing public health problem across the world. Among the known cardiometabolic risk factors are compounds that induce endocrine and metabolic dysfunctions, such as endocrine disrupting chemicals (EDCs). To date, how EDCs influence molecular programs and cardiometabolic risks has yet to be fully elucidated, especially considering the complexity contributed by species-, chemical-, and dose-specific effects. Moreover, different experimental and analytical methodologies employed by different studies pose challenges when comparing findings across studies. To explore the molecular mechanisms of EDCs in a systematic manner, we established a data-driven computational approach to meta-analyze 30 human, mouse, and rat liver transcriptomic datasets for 4 EDCs, namely bisphenol A (BPA), bis(2-ethylhexyl) phthalate (DEHP), tributyltin (TBT), and perfluorooctanoic acid (PFOA). Our computational pipeline uniformly re-analyzed pre-processed quality-controlled microarray data and raw RNAseq data, derived differentially expressed genes (DEGs) and biological pathways, modeled gene regulatory networks and regulators, and determined CMD associations based on gene overlap analysis. Our approach revealed that DEHP and PFOA shared stable transcriptomic signatures that are enriched for genes associated with CMDs, suggesting similar mechanisms of action such as perturbations of peroxisome proliferator-activated receptor gamma (PPARγ) signaling and liver gene network regulators VNN1 and ACOT2. In contrast, TBT exhibited highly divergent gene signatures, pathways, network regulators, and disease associations from the other EDCs. In addition, we found that the rat, mouse, and human BPA studies showed highly variable transcriptomic patterns, providing molecular support for the variability in BPA responses. Our work offers insights into the commonality and differences in the molecular mechanisms of various EDCs and establishes a streamlined data-driven workflow to compare molecular mechanisms of environmental substances to elucidate the underlying connections between chemical exposure and disease risks.

Integrating Statistical and Machine-Learning Approach for Meta-Analysis of Bisphenol A-Exposure Datasets Reveals Effects on Mouse Gene Expression within Pathways of Apoptosis and Cell Survival

Bisphenols are important environmental pollutants that are extensively studied due to different detrimental effects, while the molecular mechanisms behind these effects are less well understood. Like other environmental pollutants, bisphenols are being tested in various experimental models, creating large expression datasets found in open access storage. The meta-analysis of such datasets is, however, very complicated for various reasons. Here, we developed an integrating statistical and machine-learning model approach for the meta-analysis of bisphenol A (BPA) exposure datasets from different mouse tissues. We constructed three joint datasets following three different strategies for dataset integration: in particular, using all common genes from the datasets, uncorrelated, and not co-expressed genes, respectively. By applying machine learning methods to these datasets, we identified genes whose expression was significantly affected in all of the BPA microanalysis data tested; those involved in the regulation of cell survival include: Tnfr2, Hgf-Met, Agtr1a, Bdkrb2; signaling through Mapk8 (Jnk1)); DNA repair (Hgf-Met, Mgmt); apoptosis (Tmbim6, Bcl2, Apaf1); and cellular junctions (F11r, Cldnd1, Ctnd1 and Yes1). Our results highlight the benefit of combining existing datasets for the integrated analysis of a specific topic when individual datasets are limited in size.

Relevance of In Vitro Transcriptomics for In Vivo Mode of Action Assessment

Recently, we reported an in vitro toxicogenomics comparison approach to categorize chemical substances according to similarities in their proposed toxicological modes of action. Use of such an approach for regulatory purposes requires, among others, insight into the extent of biological concordance between in vitro and in vivo findings. To that end, we applied the comparison approach to transcriptomics data from the Open TG-GATEs database for 137 substances with diverging modes of action and evaluated the outcomes obtained for rat primary hepatocytes and for rat liver. The results showed that a relatively small number of matches observed in vitro were also observed in vivo, whereas quite a large number of matches between substances were found to be relevant solely in vivo or in vitro. The latter could not be explained by physicochemical properties, leading to insufficient bioavailability or poor water solubility. Nevertheless, pathway analyses indicated that for relevant matches the mechanisms perturbed in vitro are consistent with those perturbed in vivo. These findings support the utility of the comparison approach as tool in mechanism-based risk assessment.

Early microRNA indicators of PPARα pathway activation in the liver

MicroRNAs (miRNAs) are short non-coding RNA species that play key roles in post-transcriptional regulation of gene expression. MiRNAs also serve as a promising source of early biomarkers for different environmental exposures and health effects, although there is limited information linking miRNA changes to specific target pathways. In this study, we measured liver miRNAs in male B6C3F1 mice exposed to a known chemical activator of the peroxisome proliferator-activated receptor alpha (PPARα) pathway, di(2-ethylhexyl) phthalate (DEHP), for 7 and 28 days at concentrations of 0, 750, 1500, 3000, or 6000 ppm in feed. At the highest dose tested, DEHP altered 61 miRNAs after 7 days and 171 miRNAs after 28 days of exposure, with 48 overlapping miRNAs between timepoints. Analysis of these 48 common miRNAs indicated enrichment in PPARα–related targets and other pathways related to liver injury and cancer. Four of the 10 miRNAs exhibiting a clear dose trend were linked to the PPARα pathway: mmu-miRs-125a-5p, -182−5p, -20a−5p, and -378a−3p. mmu-miRs-182−5p and -378a−3p were subsequently measured using digital drop PCR across a dose range for DEHP and two related phthalates with weaker PPARα activity, di-n-octyl phthalate and n-butyl benzyl phthalate, following 7-day exposures. Analysis of mmu-miRs-182−5p and -378a−3p by transcriptional benchmark dose analysis correctly identified DEHP as having the greatest potency. However, benchmark dose estimates for DEHP based on these miRNAs (average 163; range 126−202 mg/kg-day) were higher on average than values for PPARα target genes (average 74; range 29−183 mg/kg-day). These findings identify putative miRNA biomarkers of PPARα pathway activity and suggest that early miRNA changes may be used to stratify chemical potency.

The promise of toxicogenomics for genetic toxicology: past, present and future

Toxicogenomics, the application of genomics to toxicology, was described as 'a new era' for toxicology. Standard toxicity tests typically involve a number of short-term bioassays that are costly, time consuming, require large numbers of animals and generally focus on a single end point. Toxicogenomics was heralded as a way to improve the efficiency of toxicity testing by assessing gene regulation across the genome, allowing rapid classification of compounds based on characteristic expression profiles. Gene expression microarrays could measure and characterise genome-wide gene expression changes in a single study and while transcriptomic profiles that can discriminate between genotoxic and non-genotoxic carcinogens have been identified, challenges with the approach limited its application. As such, toxicogenomics did not transform the field of genetic toxicology in the way it was predicted. More recently, next generation sequencing (NGS) technologies have revolutionised genomics owing to the fact that hundreds of billions of base pairs can be sequenced simultaneously cheaper and quicker than traditional Sanger methods. In relation to genetic toxicology, and thousands of cancer genomes have been sequenced with single-base substitution mutational signatures identified, and mutation signatures have been identified following treatment of cells with known or suspected environmental carcinogens. RNAseq has been applied to detect transcriptional changes following treatment with genotoxins; modified RNAseq protocols have been developed to identify adducts in the genome and Duplex sequencing is an example of a technique that has recently been developed to accurately detect mutation. Machine learning, including MutationSeq and SomaticSeq, has also been applied to somatic mutation detection and improvements in automation and/or the application of machine learning algorithms may allow high-throughput mutation sequencing in the future. This review will discuss the initial promise of transcriptomics for genetic toxicology, and how the development of NGS technologies and new machine learning algorithms may finally realise that promise.

Prenatal Bisphenol A Exposure in Mice Induces Multitissue Multiomics Disruptions Linking to Cardiometabolic Disorders

The health impacts of endocrine-disrupting chemicals (EDCs) remain debated, and their tissue and molecular targets are poorly understood. In this study, we leveraged systems biology approaches to assess the target tissues, molecular pathways, and gene regulatory networks associated with prenatal exposure to the model EDC bisphenol A (BPA). Prenatal BPA exposure at 5 mg/kg/d, a dose below most reported no-observed-adverse-effect levels, led to tens to thousands of transcriptomic and methylomic alterations in the adipose, hypothalamus, and liver tissues in male offspring in mice, with cross-tissue perturbations in lipid metabolism as well as tissue-specific alterations in histone subunits, glucose metabolism, and extracellular matrix. Network modeling prioritized main molecular targets of BPA, including Pparg, Hnf4a, Esr1, Srebf1, and Fasn as well as numerous less studied targets such as Cyp51 and long noncoding RNAs across tissues, Fa2h in hypothalamus, and Nfya in adipose tissue. Lastly, integrative analyses identified the association of BPA molecular signatures with cardiometabolic phenotypes in mouse and human. Our multitissue, multiomics investigation provides strong evidence that BPA perturbs diverse molecular networks in central and peripheral tissues and offers insights into the molecular targets that link BPA to human cardiometabolic disorders.

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

High-throughput transcriptomic technologies are increasingly being used to screen environmental chemicals in vitro to provide mechanistic context for regulatory testing. The TGx-DDI biomarker is a 64-gene expression profile generated from testing 28 model chemicals or treatments (13 that cause DNA damage and 15 that do not) in human TK6 cells. While the biomarker is very accurate at predicting DNA damage inducing (DDI) potential using the nearest shrunken centroid method, the broad utility of the biomarker using other computational methods is not fully known. Here, we determined the accuracy of the biomarker used with the Running Fisher test, a nonparametric correlation test. In TK6 cells, the methods could readily differentiate DDI and non-DDI compounds with balanced accuracies of 87-97%, depending on the threshold for determining DDI positives. The methods identified DDI agents in the metabolically competent hepatocyte cell line HepaRG (accuracy = 90%) but not in HepG2 cells or hepatocytes derived from embryonic stem cells (60 and 80%, respectively). DDI was also accurately classified when the gene expression changes were derived using the nCounter technology (accuracy = 89%). In addition, we found: (1) not all genes contributed equally to the correlations; (2) the minimal overlap in genes between the biomarker and the individual comparisons required for significant positive correlation was 10 genes, but usually was much higher; and (3) different sets of genes in the biomarker can by themselves contribute to the significant correlations. Overall, these results demonstrate the utility of the biomarker to accurately classify DDI agents. Environ. Mol. Mutagen. 59:772-784, 2018. Published 2018. This article is a U.S. Government work and is in the public domain in the USA.

A miRNA signature for an environmental heterocyclic amine defined by a multi-organ carcinogenicity bioassay in the rat

Heterocyclic amines (HCAs) produced during high-temperature cooking have been studied extensively in terms of their genotoxic/genetic effects, but recent work has implicated epigenetic mechanisms involving non-coding RNAs. Colon tumors induced in the rat by 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP) have altered microRNA (miRNA) signatures linked to dysregulated pluripotency factors, such as c-Myc and Krüppel-like factor 4 (KLF4). We tested the hypothesis that dysregulated miRNAs from PhIP-induced colon tumors would provide a "PhIP signature" for use in other target organs obtained from a 1-year carcinogenicity bioassay in the rat. Downstream targets that were corroborated in the rat were then investigated in human cancer datasets. The results confirmed that multiple let-7 family members were downregulated in PhIP-induced skin, colon, lung, small intestine, and Zymbal's gland tumors, and were associated with c-myc and Hmga2 upregulation. PhIP signature miRNAs with the profile mir-21high/mir-126low/mir-29clow/mir-215low/mir-145low were linked to reduced Klf4 levels in rat tumors, and in human pan-cancer and colorectal cancer. It remains to be determined whether this PhIP signature has predictive value, given that more than 20 different genotoxic HCAs are present in the human diet, plus other agents that likely induce or repress many of the same miRNAs. Future studies should define more precisely the miRNA signatures of other HCAs, and their possible value for human risk assessment.

Detection of non-genotoxic hepatocarcinogens and prediction of their mechanism of action in rats using gene marker sets

Several studies have successfully detected hepatocarcinogenicity in rats based on gene expression data. However, prediction of hepatocarcinogens with certain mechanisms of action (MOAs), such as enzyme inducers and peroxisome proliferator-activated receptor α (PPARα) agonists, can prove difficult using a single model and requires a highly toxic dose. Here, we constructed a model for detecting non-genotoxic (NGTX) hepatocarcinogens and predicted their MOAs in rats. Gene expression data deposited in the Open Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System (TG-GATEs) was used to investigate gene marker sets. Principal component analysis (PCA) was applied to discriminate different MOAs, and a support vector machine algorithm was applied to construct the prediction model. This approach identified 106 probe sets as gene marker sets for PCA and enabled the prediction model to be constructed. In PCA, NGTX hepatocarcinogens were classified as follows based on their MOAs: cytotoxicants, PPARα agonists, or enzyme inducers. The prediction model detected hepatocarcinogenicity with an accuracy of more than 90% in 14- and 28-day repeated-dose studies. In addition, the doses capable of predicting NGTX hepatocarcinogenicity were close to those required in rat carcinogenicity assays. In conclusion, our PCA and prediction model using gene marker sets will help assess the risk of hepatocarcinogenicity in humans based on MOAs and reduce the number of two-year rodent bioassays.

In vivo Signatures of Genotoxic and Non-genotoxic Chemicals

This chapter reviews the findings from a broad array of in vivo genomic studies with the goal of identifying a general signature of genotoxicity (GSG) that is indicative of exposure to genotoxic agents (i.e. agents that are active in either the bacterial mutagenesis and/or the in vivo micronucleus test). While the GSG has largely emerged from systematic studies of rat and mouse liver, its response is evident across a broad collection of genotoxic treatments that cover a variety of tissues and species. Pathway-based characterization of the GSG indicates that it is enriched with genes that are regulated by p53. In addition to the GSG, another pan-tissue signature related to bone marrow suppression (a common effect of genotoxic agent exposure) is reviewed. Overall, these signatures are quite effective in identifying genotoxic agents; however, there are situations where false positive findings can occur, for example when necrotizing doses of non-genotoxic soft electrophiles (e.g. thioacetamide) are used. For this reason specific suggestions for best practices for generating for use in the creation and application of in vivo genomic signatures are reviewed.

Exploiting microRNA and mRNA profiles generated in vitro from carcinogen-exposed primary mouse hepatocytes for predicting in vivo genotoxicity and carcinogenicity

The well-defined battery of in vitro systems applied within chemical cancer risk assessment is often characterised by a high false-positive rate, thus repeatedly failing to correctly predict the in vivo genotoxic and carcinogenic properties of test compounds. Toxicogenomics, i.e. mRNA-profiling, has been proven successful in improving the prediction of genotoxicity in vivo and the understanding of underlying mechanisms. Recently, microRNAs have been discovered as post-transcriptional regulators of mRNAs. It is thus hypothesised that using microRNA response-patterns may further improve current prediction methods. This study aimed at predicting genotoxicity and non-genotoxic carcinogenicity in vivo, by comparing microRNA- and mRNA-based profiles, using a frequently applied in vitro liver model and exposing this to a range of well-chosen prototypical carcinogens. Primary mouse hepatocytes (PMH) were treated for 24 and 48h with 21 chemical compounds [genotoxins (GTX) vs. non-genotoxins (NGTX) and non-genotoxic carcinogens (NGTX-C) versus non-carcinogens (NC)]. MicroRNA and mRNA expression changes were analysed by means of Exiqon and Affymetrix microarray-platforms, respectively. Classification was performed by using Prediction Analysis for Microarrays (PAM). Compounds were randomly assigned to training and validation sets (repeated 10 times). Before prediction analysis, pre-selection of microRNAs and mRNAs was performed by using a leave-one-out t-test. No microRNAs could be identified that accurately predicted genotoxicity or non-genotoxic carcinogenicity in vivo. However, mRNAs could be detected which appeared reliable in predicting genotoxicity in vivo after 24h (7 genes) and 48h (2 genes) of exposure (accuracy: 90% and 93%, sensitivity: 65% and 75%, specificity: 100% and 100%). Tributylinoxide and para-Cresidine were misclassified. Also, mRNAs were identified capable of classifying NGTX-C after 24h (5 genes) as well as after 48h (3 genes) of treatment (accuracy: 78% and 88%, sensitivity: 83% and 83%, specificity: 75% and 93%). Wy-14,643, phenobarbital and ampicillin trihydrate were misclassified. We conclude that genotoxicity and non-genotoxic carcinogenicity probably cannot be accurately predicted based on microRNA profiles. Overall, transcript-based prediction analyses appeared to clearly outperform microRNA-based analyses.

An integrative test strategy for cancer hazard identification

Assessment of genotoxic and carcinogenic potential is considered one of the basic requirements when evaluating possible human health risks associated with exposure to chemicals. Test strategies currently in place focus primarily on identifying genotoxic potential due to the strong association between the accumulation of genetic damage and cancer. Using genotoxicity assays to predict carcinogenic potential has the significant drawback that risks from non-genotoxic carcinogens remain largely undetected unless carcinogenicity studies are performed. Furthermore, test systems already developed to reduce animal use are not easily accepted and implemented by either industries or regulators. This manuscript reviews the test methods for cancer hazard identification that have been adopted by the regulatory authorities, and discusses the most promising alternative methods that have been developed to date. Based on these findings, a generally applicable tiered test strategy is proposed that can be considered capable of detecting both genotoxic as well as non-genotoxic carcinogens and will improve understanding of the underlying mode of action. Finally, strengths and weaknesses of this new integrative test strategy for cancer hazard identification are presented.

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

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.

Alternative signaling network activation through different insulin receptor family members caused by pro-mitogenic antidiabetic insulin analogues in human mammary epithelial cells

Introduction

Insulin analogues are designed to have improved pharmacokinetic parameters compared to regular human insulin. This provides a sustained control of blood glucose levels in diabetic patients. All novel insulin analogues are tested for their mitogenic side effects, however these assays do not take into account the molecular mode of action of different insulin analogues. Insulin analogues can bind the insulin receptor and the insulin-like growth factor 1 receptor with different affinities and consequently will activate different downstream signaling pathways.

Methods

Here we used a panel of MCF7 human breast cancer cell lines that selectively express either one of the isoforms of the INSR or the IGF1R. We applied a transcriptomics approach to assess the differential transcriptional programs activated in these cells by either insulin, IGF1 or X10 treatment.

Results

Based on the differentially expressed genes between insulin versus IGF1 and X10 treatment, we retrieved a mitogenic classifier gene set. Validation by RT-qPCR confirmed the robustness of this gene set. The translational potential of these mitogenic classifier genes was examined in primary human mammary cells and in mammary gland tissue of mice in an in vivo model. The predictive power of the classifier genes was evaluated by testing all commercial insulin analogues in the in vitro model and defined X10 and glargine as the most potent mitogenic insulin analogues.

Conclusions

We propose that these mitogenic classifier genes can be used to test the mitogenic potential of novel insulin analogues as well as other alternative molecules with an anticipated affinity for the IGF1R.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-015-0600-5) contains supplementary material, which is available to authorized users.

Identification of specific mRNA signatures as fingerprints for carcinogenesis in mice induced by genotoxic and nongenotoxic hepatocarcinogens

Long-term rodent carcinogenicity studies for evaluation of chemicals and pharmaceuticals concerning their carcinogenic potential to humans are currently receiving critical revision. Additional data from mechanistic studies can support cancer risk assessment by clarifying the underlying mode of action. In the course of the IMI MARCAR project, a European consortium of EFPIA partners and academics, which aims to identify biomarkers for nongenotoxic carcinogenesis, a toxicogenomic mouse liver database was generated. CD-1 mice were orally treated for 3 and 14 days with 3 known genotoxic hepatocarcinogens: C.I. Direct Black 38, Dimethylnitrosamine and 4,4'-Methylenedianiline; 3 nongenotoxic hepatocarcinogens: 1,4-Dichlorobenzene, Phenobarbital sodium and Piperonyl butoxide; 4 nonhepatocarcinogens: Cefuroxime sodium, Nifedipine, Prazosin hydrochloride and Propranolol hydrochloride; and 3 compounds that show ambiguous results in genotoxicity testing: Cyproterone acetate, Thioacetamide and Wy-14643. By liver mRNA expression analysis using individual animal data, we identified 64 specific biomarker candidates for genotoxic carcinogens and 69 for nongenotoxic carcinogens for male mice at day 15. The majority of genotoxic carcinogen biomarker candidates possess functions in DNA damage response (eg, apoptosis, cell cycle progression, DNA repair). Most of the identified nongenotoxic carcinogen biomarker candidates are involved in regulation of cell cycle progression and apoptosis. The derived biomarker lists were characterized with respect to their dependency on study duration and gender and were successfully used to characterize carcinogens with ambiguous genotoxicity test results, such as Wy-14643. The identified biomarker candidates improve the mechanistic understanding of drug-induced effects on the mouse liver that result in hepatocellular adenomas and/or carcinomas in 2-year mouse carcinogenicity studies.