Progress towards an OECD reporting framework for transcriptomics and metabolomics in regulatory toxicology.

Omics methodologies are widely used in toxicological research to understand modes and mechanisms of toxicity. Increasingly, these methodologies are being applied to questions of regulatory interest such as molecular point-of-departure derivation and chemical grouping/read-across. Despite its value, widespread regulatory acceptance of omics data has not yet occurred. Barriers to the routine application of omics data in regulatory decision making have been: 1) lack of transparency for data processing methods used to convert raw data into an interpretable list of observations; and 2) lack of standardization in reporting to ensure that omics data, associated metadata and the methodologies used to generate results are available for review by stakeholders, including regulators. Thus, in 2017, the Organisation for Economic Co-operation and Development (OECD) Extended Advisory Group on Molecular Screening and Toxicogenomics (EAGMST) launched a project to develop guidance for the reporting of omics data aimed at fostering further regulatory use. Here, we report on the ongoing development of the first formal reporting framework describing the processing and analysis of both transcriptomic and metabolomic data for regulatory toxicology. We introduce the modular structure, content, harmonization and strategy for trialling this reporting framework prior to its publication by the OECD.

Availability, Quality, and Relevance of Toxicogenomics Data for Human Health Risk Assessment: A Scoping Review of the Literature on Trihalomethanes.

Human health risk assessment (HHRA) must be adapted to the challenges of the 21st century, and the use of toxicogenomics data in HHRA is among the changes that regulatory agencies worldwide are trying to implement. However, the use of toxicogenomics data in HHRA is still limited. The purpose of this study was to explore the availability, quality, and relevance to HHRA of toxicogenomics publications as potential barriers to their use in HHRA. We conducted a scoping review of available toxicogenomics literature, using trihalomethanes as a case study. Four bibliographic databases (including the Comparative Toxicogenomics Database) were assessed. An evaluation table was developed to characterize quality and relevance of studies included on the basis of criteria proposed in the literature. Studies were selected and analyzed by 2 independent reviewers. Only 9 studies, published between 1997 and 2015, were included in the analysis. Based on the selected criteria, critical methodological details were often missing; in fact, only 3 out of 9 studies were considered to be of adequate quality for HHRA. No studies met >3 (out of 7) criteria of relevance to HHRA (eg, adequate number of doses and sample size). This first scoping review of toxicogenomics publications on trihalomethanes shows that low availability, quality, and relevance to HHRA of toxicogenomics publications presents potential barriers to their use in HHRA. Improved reporting of methodological details and study design is needed in the future so that toxicogenomics studies can be appropriately assessed regarding their quality and value for HHRA.

Ecotoxicogenomics: Microarray interlaboratory comparability.

Transcriptomic analysis can complement traditional ecotoxicology data by providing mechanistic insight, and by identifying sub-lethal organismal responses and contaminant classes underlying observed toxicity. Before transcriptomic information can be used in monitoring and risk assessment, it is necessary to determine its reproducibility and detect key steps impacting the reliable identification of differentially expressed genes. A custom 15K-probe microarray was used to conduct transcriptomics analyses across six laboratories with estuarine amphipods exposed to cyfluthrin-spiked or control sediments (10 days). Two sample types were generated, one consisted of total RNA extracts (Ex) from exposed and control samples (extracted by one laboratory) and the other consisted of exposed and control whole body amphipods (WB) from which each laboratory extracted RNA. Our findings indicate that gene expression microarray results are repeatable. Differentially expressed data had a higher degree of repeatability across all laboratories in samples with similar RNA quality (Ex) when compared to WB samples with more variable RNA quality. Despite such variability a subset of genes were consistently identified as differentially expressed across all laboratories and sample types. We found that the differences among the individual laboratory results can be attributed to several factors including RNA quality and technical expertise, but the overall results can be improved by following consistent protocols and with appropriate training.

Systematic Omics Analysis Review (SOAR) tool to support risk assessment.

Environmental health risk assessors are challenged to understand and incorporate new data streams as the field of toxicology continues to adopt new molecular and systems biology technologies. Systematic screening reviews can help risk assessors and assessment teams determine which studies to consider for inclusion in a human health assessment. A tool for systematic reviews should be standardized and transparent in order to consistently determine which studies meet minimum quality criteria prior to performing in-depth analyses of the data. The Systematic Omics Analysis Review (SOAR) tool is focused on assisting risk assessment support teams in performing systematic reviews of transcriptomic studies. SOAR is a spreadsheet tool of 35 objective questions developed by domain experts, focused on transcriptomic microarray studies, and including four main topics: test system, test substance, experimental design, and microarray data. The tool will be used as a guide to identify studies that meet basic published quality criteria, such as those defined by the Minimum Information About a Microarray Experiment standard and the Toxicological Data Reliability Assessment Tool. Seven scientists were recruited to test the tool by using it to independently rate 15 published manuscripts that study chemical exposures with microarrays. Using their feedback, questions were weighted based on importance of the information and a suitability cutoff was set for each of the four topic sections. The final validation resulted in 100% agreement between the users on four separate manuscripts, showing that the SOAR tool may be used to facilitate the standardized and transparent screening of microarray literature for environmental human health risk assessment.

Where will genetic toxicology testing be in 30 years' time? Summary report of the 25th Industrial Genotoxicity Group Meeting, Royal Society of Medicine, London, November 9, 2011.

A number of influences including legislation, industry and academia have encouraged advances in computational toxicology and high-throughput testing to probe more broadly putative toxicity pathways. The aim of the 25th United Kingdom Mutagen Society (UKEMS) Industrial Genotoxicity Group Annual Meeting 2011 was to explore current and upcoming research tools that may provide new cancer risk estimation approaches and discuss the genotoxicity testing paradigm of the future. The meeting considered whether computer modelling, molecular biology systems and/or adverse outcome pathway approaches can provide more accurate toxicity predictions and whether high-content study data, pluripotent stem cells or new scientific disciplines, such as epigenetics and adductomics, could be integrated into the risk assessment process. With close collaboration between industry, academia and regulators next generation predictive models and high-content tools have the potential to transform genetic toxicology testing in the 21st century.

A global toxicogenomic analysis investigating the mechanistic differences between tobacco and marijuana smoke condensates in vitro.

Like tobacco smoking, habitual marijuana smoking causes numerous adverse pulmonary effects. However, the mechanisms of action involved, especially as compared to tobacco smoke, are still unclear. To uncover putative modes of action, this study employed a toxicogenomics approach to compare the toxicological pathways perturbed following exposure to marijuana and tobacco smoke condensate in vitro. Condensates of mainstream smoke from hand-rolled tobacco and marijuana cigarettes were similarly prepared using identical smoking conditions. Murine lung epithelial cells were exposed to low, medium and high concentrations of the smoke condensates for 6h. RNA was extracted immediately or after a 4h recovery period and hybridized to mouse whole genome microarrays. Tobacco smoke condensate (TSC) exposure was associated with changes in xenobiotic metabolism, oxidative stress, inflammation, and DNA damage response. These same pathways were also significantly affected following marijuana smoke condensate (MSC) exposure. Although the effects of the condensates were largely similar, dose-response analysis indicates that the MSC is substantially more potent than TSC. In addition, steroid biosynthesis, apoptosis, and inflammation pathways were more significantly affected following MSC exposure, whereas M phase cell cycle pathways were more significantly affected following TSC exposure. MSC exposure also appeared to elicit more severe oxidative stress than TSC exposure, which may account for the greater cytotoxicity of MSC. This study shows that in general MSC impacts many of the same molecular processes as TSC. However, subtle pathway differences can provide insight into the differential toxicities of the two complex mixtures.

Inter- and intra-laboratory study to determine the reproducibility of toxicogenomics datasets.

The application of toxicogenomics as a predictive tool for chemical risk assessment has been under evaluation by the toxicology community for more than a decade. However, it predominately remains a tool for investigative research rather than for regulatory risk assessment. In this study, we assessed whether the current generation of microarray technology in combination with an in vitro experimental design was capable of generating robust, reproducible data of sufficient quality to show promise as a tool for regulatory risk assessment. To this end, we designed a prospective collaborative study to determine the level of inter- and intra-laboratory reproducibility between three independent laboratories. All test centres (TCs) adopted the same protocols for all aspects of the toxicogenomic experiment including cell culture, chemical exposure, RNA extraction, microarray data generation and analysis. As a case study, the genotoxic carcinogen benzo[a]pyrene (B[a]P) and the human hepatoma cell line HepG2 were used to generate three comparable toxicogenomic data sets. High levels of technical reproducibility were demonstrated using a widely employed gene expression microarray platform. While differences at the global transcriptome level were observed between the TCs, a common subset of B[a]P responsive genes (n=400 gene probes) was identified at all TCs which included many genes previously reported in the literature as B[a]P responsive. These data show promise that the current generation of microarray technology, in combination with a standard in vitro experimental design, can produce robust data that can be generated reproducibly in independent laboratories. Future work will need to determine whether such reproducible in vitro model(s) can be predictive for a range of toxic chemicals with different mechanisms of action and thus be considered as part of future testing regimes for regulatory risk assessment.

Evaluation and identification of reliable reference genes for pharmacogenomics, toxicogenomics, and small RNA expression analysis.

Pharmacogenomics, toxicogenomics, and small RNA expression analysis are three of the most active research topics in the biological, biomedical, pharmaceutical, and toxicological fields. All of these studies are based on gene expression analysis, which requires reference genes to reduce the variations derived from different amounts of starting materials and different efficiencies of RNA extraction and cDNA synthesis. Thus, accurate normalization to one or several constitutively expressed reference genes is a prerequisite to valid gene expression studies. Although selection of reliable reference genes has been conducted in previous studies in several animals and plants, no research has been focused on pharmacological targets, and very few studies have had a toxicological context. More interestingly, no studies have been performed to identify reference genes for small RNA analysis although small RNA, particularly microRNA (miRNA)-related research is currently one of the fastest-moving topics. In this study, using MCF-7 breast cancer cells as a model, we employed quantitative real-time PCR (qRT-PCR), one of the most reliable methods for gene expression analysis in many research fields, to evaluate and to determine the most reliable reference genes for pharmacogenomics and toxicogenomics studies as well as for small RNA expression analysis. We tested the transcriptional expression of five protein-coding genes as well as five non-coding genes in MCF-7 cells treated with five different pharmaceuticals or toxicants [paclitaxel (PTX), gossypol (GOS), methyl jasmonate (JAS), L-nicotine (NIC), and melamine (mela)] and analyzed the stability of the selected reference genes by four different methods: geNorm, NormFinder, BestKeeper, and the comparative ΔCt method. According to our analysis, a protein-coding gene, hTBCA and four non-coding genes, hRNU44, hRNU48, hU6, and hRNU47, appear to be the most reliable reference genes for the five chemical treatments. Similar results were also obtained in dose-response and time-course assays with gossypol (GOS) treatment. Our results demonstrated that traditionally used reference genes, such as 18s RNA, ß-actin, and GAPDH, are not reliable reference genes for pharmacogenomics and toxicogenomics studies. In contrast, hTBCA and small RNAs are more stable during drug treatment, and they are better reference genes for pharmacogenomics and toxicogenomics studies. To widely use these genes as reference genes, these results should be corroborated by studies with other human cell lines and additional drugs classes and hormonal treatments.

Population-based discovery of toxicogenomics biomarkers for hepatotoxicity using a laboratory strain diversity panel.

Toxicogenomic studies are increasingly used to uncover potential biomarkers of adverse health events, enrich chemical risk assessment, and to facilitate proper identification and treatment of persons susceptible to toxicity. Current approaches to biomarker discovery through gene expression profiling usually utilize a single or few strains of rodents, limiting the ability to detect biomarkers that may represent the wide range of toxicity responses typically observed in genetically heterogeneous human populations. To enhance the utility of animal models to detect response biomarkers for genetically diverse populations, we used a laboratory mouse strain diversity panel. Specifically, mice from 36 inbred strains derived from Mus mus musculus, Mus mus castaneous, and Mus mus domesticus origins were treated with a model hepatotoxic agent, acetaminophen (300 mg/kg, ig). Gene expression profiling was performed on liver tissue collected at 24 h after dosing. We identified 26 population-wide biomarkers of response to acetaminophen hepatotoxicity in which the changes in gene expression were significant across treatment and liver necrosis score but not significant for individual mouse strains. Importantly, most of these biomarker genes are part of the intracellular signaling involved in hepatocyte death and include genes previously associated with acetaminophen-induced hepatotoxicity, such as cyclin-dependent kinase inhibitor 1A (p21) and interleukin 6 signal transducer (Il6st), and genes not previously associated with acetaminophen, such as oncostatin M receptor (Osmr) and MLX interacting protein like (Mlxipl). Our data demonstrate that a multistrain approach may provide utility for understanding genotype-independent toxicity responses and facilitate identification of novel targets of therapeutic intervention.

Application of toxicogenomics to genetic toxicology risk assessment.

Based on the assumption that compounds having similar toxic modes of action induce specific gene expression changes, the toxicity of unknown compounds can be predicted after comparison of their molecular fingerprints with those obtained with compounds of known toxicity. These predictive models will therefore rely on the characterization of marker genes. Toxicogenomics (TGX) also provides mechanistic insight into the mode of toxicity, and can therefore be used as an adjunct to the standard battery of genotoxicity tests. Promising results, highlighting the ability of TGX to differentiate genotoxic from non-genotoxic carcinogens, as well as DNA-reactive from non-DNA reactive genotoxins, have been reported. Additional data suggested the possibility of ranking genotoxins according to the nature of their interactions with DNA. This new approach could contribute to the improvement of risk assessment. TGX could be applied as a follow-up testing strategy in case of positive in vitro genotoxicity findings, and could contribute to improve our ability to identify the molecular mechanism of action and to possibly better assess dose-response curves. TGX has been found to be less sensitive than the standard genotoxicity end-points, probably because it measures the whole cell population response, when compared with standard tests designed to detect rare events in a small number of cells. Further validation will be needed (1) to better link the profiles obtained with TGX to the established genotoxicity end-points, (2) to improve the gene annotation tools, and (3) to standardise study design and data analysis and to better evaluate the impact of variability between platforms and bioinformatics approaches.

Clearing the standards landscape: the semantics of terminology and their impact on toxicogenomics.

The emergence of the microarray data standards, especially the Minimum Information About a Microarray Experiment (MIAME), has spurred several organizations to develop their own standards for a myriad of technologies, including proteomics and metabolomics. These efforts have facilitated the creation of several large-scale gene expression repositories, including the toxicology-focused Chemical Effects in Biological Systems Knowledgebase at the National Institute of Environmental Health Sciences. Recently, efforts have been moved toward developing toxicogenomic data standards (e.g., MIAME-Tox), and the U.S. Food and Drug Administration and the U.S. Environmental Protection Agency either have developed or are developing regulatory guidance with respect to pharmaco- and toxicogenomics. However, for the toxicology community to be engaged in the process of standards development and approval, there needs to be a more thorough understanding of the terms associated with electronic data sharing and communication, especially with respect to defining the terms "standard," "controlled vocabulary," "object model," "markup language," and "ontology." This review will discuss these terms, especially as they pertain to toxicogenomics, how they relate to one-another, and what current efforts exist that may impact toxicology.

A strategy capitalizing on synergies: the Reporting Structure for Biological Investigation (RSBI) working group.

In this article we present the Reporting Structure for Biological Investigation (RSBI), a working group under the Microarray Gene Expression Data (MGED) Society umbrella. RSBI brings together several communities to tackle the challenges associated with integrating data and representing complex biological investigations, employing multiple OMICS technologies. Currently, RSBI includes environmental genomics, nutrigenomics and toxicogenomics communities, where independent activities are underway to develop databases and establish data communication standards within their respective domains. The RSBI working group has been conceived as a "single point of focus" for these communities, conforming to general accepted view that duplication and incompatibility should be avoided where possible. This endeavour has aimed to synergize insular solutions into one common terminology between biologically driven standardisation efforts and has also resulted in strong collaborations and shared understanding between those in the technological domain. Through extensive liaisons with many standards efforts, several threads have been woven with the hope that ultimately technology-centered standards and their specific extensions into biological domains of interest will not only stand alone, but will also be able to function together, as interchangeable modules.

Toxicogenomics data: the road to acceptance.

Regulatory agencies believe microarray data could be extremely useful in testing prospective chemical products and investigating safety problems after chemicals have been marketed. However, it has not yet been clearly established how this information will be used by agencies in the approval of pharmaceuticals and other chemicals--or even whether companies will be required to submit microarray data. Although some private companies are already voluntarily submitting microarray data along with their drug and pesticide applications, others are hesitant to do so. Most of the stakeholders involved agree that standardization of microarray experiment procedures and of genomic signatures are key to the broad acceptance and use of these data.