Technical guide for applications of gene expression profiling in human health risk assessment of environmental chemicals

Computational Strategies for Assessing Adverse Outcome Pathways: Hepatic Steatosis as a Case Study

The evolving landscape of chemical risk assessment is increasingly focused on developing tiered, mechanistically driven approaches that avoid the use of animal experiments. In this context, adverse outcome pathways have gained importance for evaluating various types of chemical-induced toxicity. Using hepatic steatosis as a case study, this review explores the use of diverse computational techniques, such as structure-activity relationship models, quantitative structure-activity relationship models, read-across methods, omics data analysis, and structure-based approaches to fill data gaps within adverse outcome pathway networks. Emphasizing the regulatory acceptance of each technique, we examine how these methodologies can be integrated to provide a comprehensive understanding of chemical toxicity. This review highlights the transformative impact of in silico techniques in toxicology, proposing guidelines for their application in evidence gathering for developing and filling data gaps in adverse outcome pathway networks. These guidelines can be applied to other cases, advancing the field of toxicological risk assessment.

Discovery Phase Agrochemical Predictive Safety Assessment Using High Content In Vitro Data to Estimate an In Vivo Toxicity Point of Departure

Utilization of in vitro (cellular) techniques, like Cell Painting and transcriptomics, could provide powerful tools for agrochemical candidate sorting and selection in the discovery process. However, using these models generates challenges translating in vitro concentrations to the corresponding in vivo exposures. Physiologically based pharmacokinetic (PBPK) modeling provides a framework for quantitative in vitro to in vivo extrapolation (IVIVE). We tested whether in vivo (rat liver) transcriptomic and apical points of departure (PODs) could be accurately predicted from in vitro (rat hepatocyte or human HepaRG) transcriptomic PODs or HepaRG Cell Painting PODs using PBPK modeling. We compared two PBPK models, the ADMET predictor and the httk R package, and found httk to predict the in vivo PODs more accurately. Our findings suggest that a rat liver apical and transcriptomic POD can be estimated utilizing a combination of in vitro transcriptome-based PODs coupled with PBPK modeling for IVIVE. Thus, high content in vitro data can be translated with modest accuracy to in vivo models of ultimate regulatory importance to help select agrochemical analogs in early stage discovery program.

Investigating the dependency of in vitro benchmark concentrations on exposure time in transcriptomics experiments

There is increasing interest to employ in vitro transcriptomics experiments in toxicological testing, for example to determine a point-of-departure (PoD) for chemical safety assessment. However current practices to derive PoD tend to utilise a single exposure time despite the importance of exposure time on the manifestation of toxicity caused by a chemical. Therefore it is important to investigate both concentration and exposure time to determine how these factors affect biological responses, and as a consequence, the derivation of PoDs. In this study, metabolically competent HepaRG cells were exposed to five known toxicants over a range of concentrations and time points for subsequent gene expression analysis, using a targeted RNA expression assay (TempO-Seq). A non-parametric factor-modelling approach was used to model the collective response of all significant genes, which exploited the interdependence of differentially expressed gene responses. This in turn allowed the determination of an isobenchmark response (isoBMR) curve for each chemical in a reproducible manner. For 2 of the 5 chemicals tested, the PoD was observed to vary by 0.5-1 log-order within the 48-h timeframe of the experiment. The approach and findings presented here clearly demonstrate the need to take both concentration and exposure time into account when designing in vitro toxicogenomics experiments to determine PoD. Doing so also provides a means to use concentration-time-response modelling as a basis to extrapolate a PoD from shorter to longer exposure durations, and to identify chemicals of concern that can cause cumulative effects over time.

3' RNA-seq is superior to standard RNA-seq in cases of sparse data but inferior at identifying toxicity pathways in a model organism

Introduction: The application of RNA-sequencing has led to numerous breakthroughs related to investigating gene expression levels in complex biological systems. Among these are knowledge of how organisms, such as the vertebrate model organism zebrafish (Danio rerio), respond to toxicant exposure. Recently, the development of 3' RNA-seq has allowed for the determination of gene expression levels with a fraction of the required reads compared to standard RNA-seq. While 3' RNA-seq has many advantages, a comparison to standard RNA-seq has not been performed in the context of whole organism toxicity and sparse data. Methods and results: Here, we examined samples from zebrafish exposed to perfluorobutane sulfonamide (FBSA) with either 3' or standard RNA-seq to determine the advantages of each with regards to the identification of functionally enriched pathways. We found that 3' and standard RNA-seq showed specific advantages when focusing on annotated or unannotated regions of the genome. We also found that standard RNA-seq identified more differentially expressed genes (DEGs), but that this advantage disappeared under conditions of sparse data. We also found that standard RNA-seq had a significant advantage in identifying functionally enriched pathways via analysis of DEG lists but that this advantage was minimal when identifying pathways via gene set enrichment analysis of all genes. Conclusions: These results show that each approach has experimental conditions where they may be advantageous. Our observations can help guide others in the choice of 3' RNA-seq vs standard RNA sequencing to query gene expression levels in a range of biological systems.

From vision toward best practices: Evaluating in vitro transcriptomic points of departure for application in risk assessment using a uniform workflow

The growing number of chemicals in the current consumer and industrial markets presents a major challenge for regulatory programs faced with the need to assess the potential risks they pose to human and ecological health. The increasing demand for hazard and risk assessment of chemicals currently exceeds the capacity to produce the toxicity data necessary for regulatory decision making, and the applied data is commonly generated using traditional approaches with animal models that have limited context in terms of human relevance. This scenario provides the opportunity to implement novel, more efficient strategies for risk assessment purposes. This study aims to increase confidence in the implementation of new approach methods in a risk assessment context by using a parallel analysis to identify data gaps in current experimental designs, reveal the limitations of common approaches deriving transcriptomic points of departure, and demonstrate the strengths in using high-throughput transcriptomics (HTTr) to derive practical endpoints. A uniform workflow was applied across six curated gene expression datasets from concentration-response studies containing 117 diverse chemicals, three cell types, and a range of exposure durations, to determine tPODs based on gene expression profiles. After benchmark concentration modeling, a range of approaches was used to determine consistent and reliable tPODs. High-throughput toxicokinetics were employed to translate in vitro tPODs (µM) to human-relevant administered equivalent doses (AEDs, mg/kg-bw/day). The tPODs from most chemicals had AEDs that were lower (i.e., more conservative) than apical PODs in the US EPA CompTox chemical dashboard, suggesting in vitro tPODs would be protective of potential effects on human health. An assessment of multiple data points for single chemicals revealed that longer exposure duration and varied cell culture systems (e.g., 3D vs. 2D) lead to a decreased tPOD value that indicated increased chemical potency. Seven chemicals were flagged as outliers when comparing the ratio of tPOD to traditional POD, thus indicating they require further assessment to better understand their hazard potential. Our findings build confidence in the use of tPODs but also reveal data gaps that must be addressed prior to their adoption to support risk assessment applications.

Comparison between droplet digital PCR and reverse transcription-quantitative PCR methods to measure ecotoxicology biomarkers

Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) is currently the gold-standard technique for detecting and quantifying messenger RNA. However, without proper validation, the method may produce artefactual and non-reproducible cycle threshold values generating poor-quality data. The newer droplet digital PCR (ddPCR) method allows for the absolute quantification of targeted nucleic acids providing more sensitive and accurate measurements without requiring external standards. This study compared these two PCR-based methods to measure the expression of well-documented genes used in ecotoxicology studies. We exposed Mediterranean mussels (Mytilus galloprovincialis) to copper and analyzed gene expression in gills and digestive glands using RT-qPCR and ddPCR assays. A step-by-step methodology to optimize and compare the two technologies is described. After ten-fold serial complementary DNA dilution, both RT-qPCR and ddPCR exhibited comparable linearity and efficiency and produced statistically similar results. We conclude that ddPCR is a suitable method to assess gene expression in an ecotoxicological context. However, RT-qPCR has a shorter processing time and remains more cost-effective.

A Multiplex Gene Expression Assay for Direct Measurement of RNA Transcripts in Crude Lysates of the Nematode Caenorhabditis elegans Used as a Bioanalytical Tool

Gene expression profiling in Caenorhabditis elegans has been demonstrated to be a potential bioanalytical tool to detect the toxic potency of environmental contaminants. The RNA transcripts of genes responding to toxic exposure can be used as biomarkers for detecting these toxins. For routine application in environmental quality monitoring, an easy-to-use multiplex assay is required to reliably quantify expression levels of these biomarkers. In the present study, a bead-based assay was developed to fingerprint gene expression in C. elegans by quantitating messenger RNAs (mRNAs) of multiple target genes directly from crude nematode lysates, circumventing RNA extraction and purification steps. The assay uses signal amplification rather than target amplification for direct measurement of toxin-induced RNA transcripts. Using a 50-gene panel, the expression changes of four candidate reference genes and 46 target mRNAs for various contaminants and wastewaters were successfully measured, and the expression profiles indicated the type of toxin present. Moreover, the multiplex assay response was in line with previous results obtained with more time-consuming reverse-transcription quantitative polymerase chain reaction and microarray analyses. In addition, the transcriptomic profiles of nematodes exposed to wastewater samples and extracts prepared from tissues of swimming crabs were evaluated. The profiles indicated the presence of organic pollutants. The present study illustrates the successful development of a multiplex fluorescent bead-based approach using nematode C. elegans crude lysates for gene expression profiling of target RNAs. This method can be used to routinely fingerprint the presence of toxic contaminants in environmental samples and to identify the most biologically active fraction of the contaminant mixture in a toxicity identification and evaluation approach. Environ Toxicol Chem 2023;42:130-142. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Wildfire Variable Toxicity: Identifying Biomass Smoke Exposure Groupings through Transcriptomic Similarity Scoring

The prevalence of wildfires continues to grow globally with exposures resulting in increased disease risk. Characterizing these health risks remains difficult due to the wide landscape of exposures that can result from different burn conditions and fuel types. This study tested the hypothesis that biomass smoke exposures from variable fuels and combustion conditions group together based on similar transcriptional response profiles, informing which wildfire-relevant exposures may be considered as a group for health risk evaluations. Mice (female CD-1) were exposed via oropharyngeal aspiration to equal mass biomass smoke condensates produced from flaming or smoldering burns of eucalyptus, peat, pine, pine needles, or red oak species. Lung transcriptomic signatures were used to calculate transcriptomic similarity scores across exposures, which informed exposure groupings. Exposures from flaming peat, flaming eucalyptus, and smoldering eucalyptus induced the greatest responses, with flaming peat grouping with the pro-inflammatory agent lipopolysaccharide. Smoldering red oak and smoldering peat induced the least transcriptomic response. Groupings paralleled pulmonary toxicity markers, though they were better substantiated by higher data dimensionality and resolution provided through -omic-based evaluation. Interestingly, groupings based on smoke chemistry signatures differed from transcriptomic/toxicity-based groupings. Wildfire-relevant exposure groupings yield insights into risk assessment strategies to ultimately protect public health.

Integrative exposomic, transcriptomic, epigenomic analyses of human placental samples links understudied chemicals to preeclampsia

BACKGROUND

Environmental health research has recently undergone a dramatic shift, with ongoing technological advancements allowing for broader coverage of exposure and molecular biology signatures. Approaches to integrate such measures are still needed to increase understanding between systems-level exposure and biology.

OBJECTIVES

We address this gap by evaluating placental tissues to identify novel chemical-biological interactions associated with preeclampsia. This study tests the hypothesis that understudied chemicals are present in the human placenta and associated with preeclampsia-relevant disruptions, including overall case status (preeclamptic vs. normotensive patients) and underlying transcriptomic/epigenomic signatures.

METHODS

A non-targeted analysis based on high-resolution mass spectrometry was used to analyze placental tissues from a cohort of 35 patients with preeclampsia (n = 18) and normotensive (n = 17) pregnancies. Molecular feature data were prioritized for confirmation based on association with preeclampsia case status and confidence of chemical identification. All molecular features were evaluated for relationships to mRNA, microRNA, and CpG methylation (i.e., multi-omic) signature alterations involved in preeclampsia.

RESULTS

A total of 183 molecular features were identified with significantly differentiated abundance in placental extracts of preeclamptic patients; these features clustered into distinct chemical groupings using unsupervised methods. Of these features, 53 were identified (mapping to 40 distinct chemicals) using chemical standards, fragmentation spectra, and chemical metadata. In general, human metabolites had the largest feature intensities and strongest associations with preeclampsia-relevant multi-omic changes. Exogenous drugs were second most abundant and had fewer associations with multi-omic changes. Other exogenous chemicals (non-drugs) were least abundant and had the fewest associations with multi-omic changes.

CONCLUSIONS

These global data trends suggest that human metabolites are heavily intertwined with biological processes involved in preeclampsia etiology, while exogenous chemicals may still impact select transcriptomic/epigenomic processes. This study serves as a demonstration of merging systems exposures with systems biology to better understand chemical-disease relationships.

A bioinformatics framework for targeted gene expression assay design: Application to in vitro developmental neurotoxicity screening in a rat model

Brain development involves a series of intricately choreographed neuronal differentiation and maturation steps that are acutely vulnerable to interferences from chemical exposures. Many genes involved in neurodevelopmental processes show evolutionarily conserved expression patterns in mammals and may constitute useful indicators/biomarkers for the evaluation of potential developmental neurotoxicity. Based on these premises, this study developed a bioinformatics framework to guide the design of a gene expression-based in vitro developmental neurotoxicity assay targeting evolutionary conserved genes associated with neuronal differentiation and maturation in rat cerebellar granule cells (CGCs). Rat, mouse and human genes involved in neurodevelopment and presenting one-to-one orthology were selected and orthologous exons within these genes were identified. PCR primer sets were designed within these orthologous exons and their specificity was evaluated in silico. The performance and specificity of rat, mouse and human PCR primer sets were then confirmed experimentally. Finally, RT-qPCR analyses in CGCs exposed in vitro to well-known neurotoxicants (Chlorpyrifos and Chlorpyrifos oxon) uncovered perturbations of expression levels for most of the selected genes. This bioinformatics framework for gene and target sequence selection may facilitate the identification of transcriptional biomarkers for developmental neurotoxicity assays and the comparison of gene expression data across experimental models from different mammalian species.

Synephrine and caffeine combination promotes cytotoxicity, DNA damage and transcriptional modulation of apoptosis-related genes in human HepG2 cells

The abusive consumption of thermogenic supplements occurs worldwide and deserves special attention due to their use to stimulate weight loss and prevent obesity. Thermogenic formulations usually contain Synephrine (SN) and Caffeine (CAF), stimulating compounds extracted from natural sources, but no genetic toxicology studies have predicted this hazardous combination potential. This study examined the toxicogenomic responses induced by SN and CAF, either alone or in combination, in the human hepatic cell line HepG2 in vitro. SN (0.03-30 μM) and CAF (0.6-600 μM) alone did neither decrease cell viability nor induce DNA damage, as assessed using the MTT and comet assays, respectively. SN (3 μM) and CAF (30-600 μM) were combined at concentrations similar to those found in commercial dietary supplements. SN/CAF at 3:90 and 3:600 μM ratios significantly decreased cell viability and increased DNA damage levels in HepG2 cells. CAF (600 μM) and the SN/CAF association at 3:60, 3:90, and 3:600 μM ratios promoted cell death by apoptosis, as demonstrated by flow cytometry. Similar results were observed in gene expression (RT-qPCR): SN/CAF up-regulated the expression of apoptosis- (BCL-2 and CASP9) and DNA repair-related (XPC) genes. SN/CAF at 3:90 μM also downregulated the expression of cell cycle control (CDKN1A) genes. In conclusion, the SN/CAF combination reduces cell viability by inducing apoptosis, damages DNA, and modulates the transcriptional expression of apoptosis-, cell cycle-, and DNA repair-related genes in human hepatic (HepG2) cells in vitro. These effects can be worrisome to consumers of thermogenic supplements.

Repeat-dose toxicity prediction with Generalized Read-Across (GenRA) using targeted transcriptomic data: A proof-of-concept case study

Read-across is a data gap filling technique utilized to predict the toxicity of a target chemical using data from similar analogues. Recent efforts such as Generalized Read-Across (GenRA) facilitate automated read-across predictions for untested chemicals. GenRA makes predictions of toxicity outcomes based on "neighboring" chemicals characterized by chemical and bioactivity fingerprints. Here we investigated the impact of biological similarities on neighborhood formation and read-across performance in predicting hazard (based on repeat-dose testing outcomes from US EPA ToxRefDB v2.0). We used targeted transcriptomic data on 93 genes for 1060 chemicals in HepaRG^™ cells that measure nuclear receptor activation, xenobiotic metabolism, cellular stress, cell cycle progression, and apoptosis. Transcriptomic similarity between chemicals was calculated using binary hit-calls from concentration-response data for each gene. We evaluated GenRA performance in predicting ToxRefDB v2.0 hazard outcomes using the area under the Receiver Operating Characteristic (ROC) curve (AUC) for the baseline approach (chemical fingerprints) versus transcriptomic fingerprints and a combination of both (hybrid). For all endpoints, there were significant but only modest improvements in ROC AUC scores of 0.01 (2.1%) and 0.04 (7.3%) with transcriptomic and hybrid descriptors, respectively. However, for liver-specific toxicity endpoints, ROC AUC scores improved by 10% and 17% for transcriptomic and hybrid descriptors, respectively. Our findings suggest that using hybrid descriptors formed by combining chemical and targeted transcriptomic information can improve in vivo toxicity predictions in the right context.

EcoToxModules: Custom Gene Sets to Organize and Analyze Toxicogenomics Data from Ecological Species

Traditional results from toxicogenomics studies are complex lists of significantly impacted genes or gene sets, which are challenging to synthesize down to actionable results with a clear interpretation. Here, we defined two sets of 21 custom gene sets, called the functional and statistical EcoToxModules, in fathead minnow (Pimephales promelas) to (1) re-cast predefined molecular pathways into a toxicological framework and (2) provide a data-driven, unsupervised grouping of genes impacted by exposure to environmental contaminants. The functional EcoToxModules were identified by re-organizing KEGG pathways into biological processes that are more relevant to ecotoxicology based on the input from expert scientists and regulators. The statistical EcoToxModules were identified using co-expression analysis of publicly available microarray data (n = 303 profiles) measured in livers of fathead minnows after exposure to 38 different conditions. Potential applications of the EcoToxModules were demonstrated with two case studies that represent exposure to a pure chemical and to environmental wastewater samples. In comparisons to differential expression and gene set analysis, we found that EcoToxModule responses were consistent with these traditional results. Additionally, they were easier to visualize and quantitatively compare across different conditions, which facilitated drawing conclusions about the relative toxicity of the exposures within each case study.

A cross-sector call to improve carcinogenicity risk assessment through use of genomic methodologies

Robust genomic approaches are now available to realize improvements in efficiencies and translational relevance of cancer risk assessments for drugs and chemicals. Mechanistic and pathway data generated via genomics provide opportunities to advance beyond historical reliance on apical endpoints of uncertain human relevance. Published research and regulatory evaluations include many examples for which genomic data have been applied to address cancer risk assessment as a health protection endpoint. The alignment of mature, robust, reproducible, and affordable technologies with increasing demands for reduced animal testing sets the stage for this important transition. We present our shared vision for change from leading scientists from academic, government, nonprofit, and industrial sectors and chemical and pharmaceutical safety applications. This call to action builds upon a 2017 workshop on "Advances and Roadblocks for Use of Genomics in Cancer Risk Assessment." The authors propose a path for implementation of innovative cancer risk assessment including incorporating genomic signatures to assess mechanistic relevance of carcinogenicity and enhanced use of genomics in benchmark dose and point of departure evaluations. Novel opportunities for the chemical and pharmaceutical sectors to combine expertise, resources, and objectives to achieve a common goal of improved human health protection are identified.

A bioinformatics workflow for the evaluation of RT-qPCR primer specificity: Application for the assessment of gene expression data reliability in toxicological studies

The reliability of Reverse Transcription quantitative real-time PCR (RT-qPCR) gene expression data depends on proper primer design and RNA quality controls. Despite freely available genomic databases and bioinformatics tools, primer design deficiencies can be found across life science publications. In order to assess the prevalence of such deficiencies in the toxicological literature, 504 primer sets extracted from a random selection of 70 recent rat toxicological studies were evaluated. The specificity of each primer set was systematically analysed using a bioinformatics workflow developed from publicly available resources (NCBI Primer BLAST, in silico PCR in UCSC genome browser, Ensembl DNA database). Potential mismatches (9%), cross-matches (13.5%), co-amplification of multiple gene splice variants (9%) and sub-optimal amplicon sizes (25%) were identified for a significant proportion of the primer sets assessed in silico. Quality controls for gDNA contamination of RNA samples were infrequently reported in the surveyed manuscripts. Hence, the impacts of gDNA contamination on RT-qPCR data were further investigated, revealing that lowly expressed genes presented higher susceptibility to contaminating gDNA. In addition to the retrospective identification of potential primer design issues presented in this study, the described bioinformatics workflow can also be used prospectively to select candidate primer sets for experimental validation.

Next generation sequencing data for use in risk assessment

Next generation sequencing (NGS) represents several powerful platforms that have revolutionized RNA and DNA analysis. The parallel sequencing of millions of DNA molecules can provide mechanistic insights into toxicology and provide new avenues for biomarker discovery with growing relevance for risk assessment. The evolution of NGS technologies has improved over the last decade with increased sensitivity and accuracy to foster new biomarker assays from tissue, blood and other biofluids. NGS sequencing technologies can identify transcriptional changes and genomic targets with base pair precision in response to chemical exposure. Further, there are several exciting movements within the toxicology community that incorporate NGS platforms into new strategies for more rapid toxicological characterizations. These include the Tox21 in vitro high throughput transcriptomic screening program, development of organotypic spheroids, alternative animal models, mining archival tissues, liquid biopsy and epigenomics. This review will describe NGS-based technologies, demonstrate how they can be used as tools for target discovery in tissue and blood, and suggest how they might be applied for risk assessment.

A comparative study of 3 alternative avian toxicity testing methods: Effects on hepatic gene expression in the chicken embryo

There is growing interest in developing alternative methods to screen and prioritize chemical hazards, although few studies have compared responses across different methods. The objective of the present study was to compare 3 alternative liver methods derived from white Leghorn chicken (Gallus gallus domesticus): primary hepatocyte culture, liver slices, and liver from in ovo injected embryos. We examined hepatic gene expression changes after exposure to 3 chemicals (17β-trenbolone [17βT], 17β-estradiol [E2], and 2,3,7,8-tetrachlorodibenzo-p-dioxin [TCDD]) using a custom quantitative polymerase chain reaction (qPCR) array with 7 genes (vitellogenin [VTG], apolipoprotein [Apo], cytochrome P450 1A4 [CYP1A4], liver basic fatty acid binding protein [LBFABP], 3β hydroxysteroid dehydrogenase [HSD3β1], stearoyl coenzyme A desaturase [SCD], and estrogen sulfotransferase [SULT1E1]). Gene expression across the 3 methods was examined using hierarchical clustering. Up-regulation of CYP1A4 in response to TCDD was consistent across all methods, and the magnitude was higher in hepatocytes (>150-fold) compared with slices (>31-fold) and in ovo liver (>27-fold). In hepatocytes, SCD and VTG up-regulation in response to 17βT and E2 was >4-fold and 16-fold, respectively. The rank order of cases with significant changes in gene expression among the 3 methods was: hepatocytes (22) > in ovo liver (11) > liver slices (6). Hierarchical clustering grouped liver slices and in ovo liver as more similar, whereas hepatocytes were grouped separately from in ovo liver. More introspective comparisons are needed to understand how and why alternative methods differ and to aid in their integration into toxicity testing. Environ Toxicol Chem 2019;38:2546-2555. © 2019 SETAC.

Key characteristics of 86 agents known to cause cancer in humans

Since the inception of the International Agency for Research on Cancer (IARC) in the early 1970s, the IARC Monographs Programme has evaluated more than 1000 agents with respect to carcinogenic hazard; of these, up to and including Volume 119 of the IARC Monographs, 120 agents met the criteria for classification as carcinogenic to humans (Group 1). Volume 100 of the IARC Monographs provided a review and update of Group 1 carcinogens. These agents were divided into six broad categories: (I) pharmaceuticals; (II) biological agents; (III) arsenic, metals, fibers, and dusts; (IV) radiation; (V) personal habits and indoor combustions; and (VI) chemical agents and related occupations. Data on biological mechanisms of action (MOA) were extracted from the Monographs to assemble a database on the basis of ten key characteristics attributed to human carcinogens. After some grouping of similar agents, the characteristic profiles were examined for 86 Group 1 agents for which mechanistic information was available in the IARC Monographs up to and including Volume 106, based upon data derived from human in vivo, human in vitro, animal in vivo, and animal in vitro studies. The most prevalent key characteristic was "is genotoxic", followed by "alters cell proliferation, cell death, or nutrient supply" and "induces oxidative stress". Most agents exhibited several of the ten key characteristics, with an average of four characteristics per agent, a finding consistent with the notion that cancer development in humans involves multiple pathways. Information on the key characteristics was often available from multiple sources, with many agents demonstrating concordance between human and animal sources, particularly with respect to genotoxicity. Although a detailed comparison of the characteristics of different types of agents was not attempted here, the overall characteristic profiles for pharmaceutical agents and for chemical agents and related occupations appeared similar. Further in-depth analyses of this rich database of characteristics of human carcinogens are expected to provide additional insights into the MOA of human cancer development.

Methods for the analysis of transcriptome dynamics

The transcriptome is the complete set of transcripts in a cell or tissue and includes ribosomal RNA (rRNA), messenger RNA (mRNA), transfer RNA (tRNA), and regulatory noncoding RNA. At steady-state, the transcriptome results from a compensatory variation of the transcription and decay rate to maintain the RNA concentration constant. RNA transcription constitutes the first stage in gene expression, and thus is a major and primary mode of gene expression control. Nevertheless, regulation of RNA decay is also a key factor in gene expression control, involving either selective RNA stabilization or enhanced degradation. Transcriptome analysis allows the identification of gene expression alterations, providing new insights regarding the pathways and mechanisms involved in physiological and pathological processes. Upon perturbation of cell homeostasis, rapid changes in gene expression are required to adapt to new conditions. Thus, to better understand the regulatory mechanisms associated with gene expression alterations, it is vital to acknowledge the relative contribution of RNA synthesis and decay to the transcriptome. To the toxicology field, the study of gene expression regulation mechanisms can help identify the early and mechanistic relevant cellular events associated with a particular response. This review aims to provide a critical comparison of the available methods used to analyze the contribution of RNA transcription and decay to gene expression dynamics. Notwithstanding, an integration of the data obtained is necessary to understand the entire repercussions of gene transcription changes at a system-level. Thus, a brief overview of the methods available for the integration and analysis of the data obtained from transcriptome analysis will also be provided.

Evaluating Chemicals for Thyroid Disruption: Opportunities and Challenges with in Vitro Testing and Adverse Outcome Pathway Approaches

BACKGROUND

Extensive clinical and experimental research documents the potential for chemical disruption of thyroid hormone (TH) signaling through multiple molecular targets. Perturbation of TH signaling can lead to abnormal brain development, cognitive impairments, and other adverse outcomes in humans and wildlife. To increase chemical safety screening efficiency and reduce vertebrate animal testing, in vitro assays that identify chemical interactions with molecular targets of the thyroid system have been developed and implemented.

OBJECTIVES

We present an adverse outcome pathway (AOP) network to link data derived from in vitro assays that measure chemical interactions with thyroid molecular targets to downstream events and adverse outcomes traditionally derived from in vivo testing. We examine the role of new in vitro technologies, in the context of the AOP network, in facilitating consideration of several important regulatory and biological challenges in characterizing chemicals that exert effects through a thyroid mechanism.

DISCUSSION

There is a substantial body of knowledge describing chemical effects on molecular and physiological regulation of TH signaling and associated adverse outcomes. Until recently, few alternative nonanimal assays were available to interrogate chemical effects on TH signaling. With the development of these new tools, screening large libraries of chemicals for interactions with molecular targets of the thyroid is now possible. Measuring early chemical interactions with targets in the thyroid pathway provides a means of linking adverse outcomes, which may be influenced by many biological processes, to a thyroid mechanism. However, the use of in vitro assays beyond chemical screening is complicated by continuing limits in our knowledge of TH signaling in important life stages and tissues, such as during fetal brain development. Nonetheless, the thyroid AOP network provides an ideal tool for defining causal linkages of a chemical exerting thyroid-dependent effects and identifying research needs to quantify these effects in support of regulatory decision making. https://doi.org/10.1289/EHP5297.

Transcriptomic points-of-departure from short-term exposure studies are protective of chronic effects for fish exposed to estrogenic chemicals

Resource limitations often require risk assessors to extrapolate chronic toxicity from acute tests using assessment factors. Transcriptomic dose-response analysis following short-term exposures may provide a more reliable and biologically-based alternative for estimating chronic toxicity. Here, we demonstrate that transcriptomic dose-response analysis in fish following short-term exposure to endocrine disrupting chemicals (EDCs) provides estimates of chronic toxicity that may be used as protective points-of-departure (POD) for risk assessment. The benchmark dose (BMD) method was used on publicly available datasets (n = 5) to determine transcriptomic PODs in fish exposed to three EDCs (bisphenol A, ethinylestradiol, and diethylstilbestrol). To test for potential bias related to data processing, our analysis compared the effect of different normalization, filtering, and BMD-grouping methods on the transcriptomic PODs. The resulting PODs were then compared to the empirically-derived chronic LOEC of each substance. Normalization and filtering methods had limited impact on the final PODs. However, we found that PODs derived from ontology- or pathway-based gene grouping methods were highly variable, whereas PODs from grouping methods that focused on the most responsive genes were more stable and provided POD estimates that were most similar to the chronic LOEC. Overall, 72% of transcriptomic PODs were within 1 order of magnitude of the chronic LOEC, regardless of data analysis method. When our recommended analysis approach was applied, the concordance improved to 100%. These results suggest that toxicogenomic dose-response analysis has the potential to be a protective decision-support tool for compounds with chronic toxicity, such as EDCs.

Toxicogenomics - What added Value Do These Approaches Provide for Carcinogen Risk Assessment?

It is still a major challenge to protect humans at workplaces and in the environment. To cope with this task, it is a prerequisite to obtain detailed information on the extent of chemical perturbations of biological pathways, in particular, adaptive vs. adverse effects and the dose-response relationships. This knowledge serves as the basis for the classification of non-carcinogens and carcinogens and for further distinguishing carcinogens in genotoxic (DNA damaging) or non-genotoxic compounds. Basing on quantitative dose-response relationships, points of departures can be derived for chemical risk assessment. In recent years, new methods have shown their capability to support the established rodent models of carcinogenicity testing. In vitro high throughput screening assays assess more comprehensively cell response. In addition, omics technologies were applied to study the mode of action of chemicals whereby the term "toxicogenomics" comprises various technologies such as transcriptomics, epigenomics, or metabolomics. This review aims to summarize the current state of toxicogenomic approaches in risk science and to compare them with established ones. For example, measurement of global transcriptional changes generates meaningful information for toxicological risk assessment such as accurate classification of genotoxic/non-genotoxic carcinogens. Alteration in mRNA expression offers previously unknown insights in the mode of action and enables the definition of key events. Based on these, benchmark doses can be calculated for the transition from an adaptive to an adverse state. In short, this review assesses the potential and challenges of transcriptomics and addresses the impact of other omics technologies on risk assessment in terms of hazard identification and dose-response assessment.

Gene networking in colistin-induced nephrotoxicity reveals an adverse outcome pathway triggered by proteotoxic stress

Colistin has been widely used for the treatment of infections of multidrug-resistant Gram-negative bacteria, despite the fact that it induces serious kidney injury as a side effect. To investigate the mechanism underlying its nephrotoxicity, colistin methanesulfonate sodium (CMS; 25 or 50 mg/kg) was administered via intraperitoneal injection to Sprague-Dawley rats daily over 7 days. Serum biochemistry and histopathology indicated that nephrotoxicity occurred in the rats administered with CMS. Whole-genome microarrays indicated 894 differentially expressed genes in the group treated with CMS (analysis of variance, false discovery rate <0.05, fold-change ≥1.3). Gene pathway and networking analyses revealed that genes associated with proteotoxic stress, including ribosome synthesis, protein translation, and protein folding, were significantly associated with the nephrotoxicity induced by CMS. It was found that colistin inhibited the expression of the target genes heat shock factor 1 and nuclear factor erythroid-2-related factor-2, which are associated with proteostasis, and that nephrotoxicity of CMS may be initiated by proteotoxic stress due to heat shock response inhibition, leading to oxidative stress, endoplasmic reticulum stress, cell cycle arrest and apoptosis, eventually leading to cell death. A putative adverse outcome pathway was constructed based on the integrated gene networking data, which may clarify the mode of action of colistin-induced nephrotoxicity.

Hepatic transcriptional dose-response analysis of male and female Fischer rats exposed to hexabromocyclododecane

Hexabromocyclododecane (HBCD) is a brominated flame retardant found in the environment and human tissues. The toxicological effects of HBCD exposure are not clearly understood. We employed whole-genome RNA-sequencing on liver samples from male and female Fischer rats exposed to 0, 250, 1250, and 5000 mg technical mixture of HBCD/kg diet for 28 days to gain further insight into HBCD toxicity. HBCD altered 428 and 250 gene transcripts in males and females, respectively, which were involved in metabolism of xenobiotics, oxidative stress, immune response, metabolism of glucose and lipids, circadian regulation, cell cycle, fibrotic activity, and hormonal balance. Signature analysis supported that HBCD operates through the constitutive androstane and pregnane X receptors. The median transcriptomic benchmark dose (BMD) for the lowest statistically significant pathway was within 1.5-fold of the BMD for increased liver weight, while the BMD for the lowest pathway with at least three modeled genes (minimum 5% of pathway) was similar to the lowest apical endpoint BMD. The results show how transcriptional analyses can inform mechanisms underlying chemical toxicity and the doses at which potentially adverse effects occur. This experiment is part of a larger study exploring the use of toxicogenomics and high-throughput screening for human health risk assessment.

Developments in toxicogenomics: understanding and predicting compound-induced toxicity from gene expression data

The toxicogenomics field aims to understand and predict toxicity by using 'omics' data in order to study systems-level responses to compound treatments. In recent years there has been a rapid increase in publicly available toxicological and 'omics' data, particularly gene expression data, and a corresponding development of methods for its analysis. In this review, we summarize recent progress relating to the analysis of RNA-Seq and microarray data, review relevant databases, and highlight recent applications of toxicogenomics data for understanding and predicting compound toxicity. These include the analysis of differentially expressed genes and their enrichment, signature matching, methods based on interaction networks, and the analysis of co-expression networks. In the future, these state-of-the-art methods will likely be combined with new technologies, such as whole human body models, to produce a comprehensive systems-level understanding of toxicity that reduces the necessity of in vivo toxicity assessment in animal models.

Emerging technologies for food and drug safety

Emerging technologies are playing a major role in the generation of new approaches to assess the safety of both foods and drugs. However, the integration of emerging technologies in the regulatory decision-making process requires rigorous assessment and consensus amongst international partners and research communities. To that end, the Global Coalition for Regulatory Science Research (GCRSR) in partnership with the Brazilian Health Surveillance Agency (ANVISA) hosted the seventh Global Summit on Regulatory Science (GSRS17) in Brasilia, Brazil on September 18-20, 2017 to discuss the role of new approaches in regulatory science with a specific emphasis on applications in food and medical product safety. The global regulatory landscape concerning the application of new technologies was assessed in several countries worldwide. Challenges and issues were discussed in the context of developing an international consensus for objective criteria in the development, application and review of emerging technologies. The need for advanced approaches to allow for faster, less expensive and more predictive methodologies was elaborated. In addition, the strengths and weaknesses of each new approach was discussed. And finally, the need for standards and reproducible approaches was reviewed to enhance the application of the emerging technologies to improve food and drug safety. The overarching goal of GSRS17 was to provide a venue where regulators and researchers meet to develop collaborations addressing the most pressing scientific challenges and facilitate the adoption of novel technical innovations to advance the field of regulatory science.

Editor's Highlight: Application of Gene Set Enrichment Analysis for Identification of Chemically Induced, Biologically Relevant Transcriptomic Networks and Potential Utilization in Human Health Risk Assessment

The rate of new chemical development in commerce combined with a paucity of toxicity data for legacy chemicals presents a unique challenge for human health risk assessment. There is a clear need to develop new technologies and incorporate novel data streams to more efficiently inform derivation of toxicity values. One avenue of exploitation lies in the field of transcriptomics and the application of gene expression analysis to characterize biological responses to chemical exposures. In this context, gene set enrichment analysis (GSEA) was employed to evaluate tissue-specific, dose-response gene expression data generated following exposure to multiple chemicals for various durations. Patterns of transcriptional enrichment were evident across time and with increasing dose, and coordinated enrichment plausibly linked to the etiology of the biological responses was observed. GSEA was able to capture both transient and sustained transcriptional enrichment events facilitating differentiation between adaptive versus longer term molecular responses. When combined with benchmark dose (BMD) modeling of gene expression data from key drivers of biological enrichment, GSEA facilitated characterization of dose ranges required for enrichment of biologically relevant molecular signaling pathways, and promoted comparison of the activation dose ranges required for individual pathways. Median transcriptional BMD values were calculated for the most sensitive enriched pathway as well as the overall median BMD value for key gene members of significantly enriched pathways, and both were observed to be good estimates of the most sensitive apical endpoint BMD value. Together, these efforts support the application of GSEA to qualitative and quantitative human health risk assessment.

Omics tools: New challenges in aquatic nanotoxicology?

In recent years, the implication of genomics into ecotoxicological studies has been studied closely to allow a better understanding of organism's responses to environmental contaminants including engineering nanomaterials (ENMs). ENMs are increasingly produced for various applications including cosmetics, electronics, sports equipment, biomedicine and agriculture. Because of their small size, ENMs possess chemical or physical characteristics improved compared to the corresponding macro-sized material. As their application expend, the release of manufactured ENMs into the environment is likely to increase and concern over impacts for the aquatic ecosystem is growing. Several studies reported deleterious effect of ENMs to aquatic organisms, but there is little information about the molecular mechanisms of toxicity. The development of ecotoxicogenomic approaches will improve the characterization of cellular and molecular modes of action of ENMs to aquatic organisms and allow a better prediction of contaminants toxicity. This paper presents an overview of transciptomic/proteomic studies in freshwater and marine organisms exposed to ENMs. Overall, induction of gene expression in relations to defense mechanisms, immune responses, growth and reproduction were measured after ENMs exposures of organisms, but with different patterns depending on exposure duration and concentrations used. In addition, some studies reported a positive correlation between gene expression and cellular modifications, but not at the individual level.

Benchmark Dose Modeling Estimates of the Concentrations of Inorganic Arsenic That Induce Changes to the Neonatal Transcriptome, Proteome, and Epigenome in a Pregnancy Cohort

Prenatal inorganic arsenic (iAs) exposure influences the expression of critical genes and proteins associated with adverse outcomes in newborns, in part through epigenetic mediators. The doses at which these genomic and epigenomic changes occur have yet to be evaluated in the context of dose-response modeling. The goal of the present study was to estimate iAs doses that correspond to changes in transcriptomic, proteomic, epigenomic, and integrated multi-omic signatures in human cord blood through benchmark dose (BMD) modeling. Genome-wide DNA methylation, microRNA expression, mRNA expression, and protein expression levels in cord blood were modeled against total urinary arsenic (U-tAs) levels from pregnant women exposed to varying levels of iAs. Dose-response relationships were modeled in BMDExpress, and BMDs representing 10% response levels were estimated. Overall, DNA methylation changes were estimated to occur at lower exposure concentrations in comparison to other molecular endpoints. Multi-omic module eigengenes were derived through weighted gene co-expression network analysis, representing co-modulated signatures across transcriptomic, proteomic, and epigenomic profiles. One module eigengene was associated with decreased gestational age occurring alongside increased iAs exposure. Genes/proteins within this module eigengene showed enrichment for organismal development, including potassium voltage-gated channel subfamily Q member 1 (KCNQ1), an imprinted gene showing differential methylation and expression in response to iAs. Modeling of this prioritized multi-omic module eigengene resulted in a BMD(BMDL) of 58(45) μg/L U-tAs, which was estimated to correspond to drinking water arsenic concentrations of 51(40) μg/L. Results are in line with epidemiological evidence supporting effects of prenatal iAs occurring at levels <100 μg As/L urine. Together, findings present a variety of BMD measures to estimate doses at which prenatal iAs exposure influences neonatal outcome-relevant transcriptomic, proteomic, and epigenomic profiles.

Integration of the TGx-28.65 genomic biomarker with the flow cytometry micronucleus test to assess the genotoxicity of disperse orange and 1,2,4-benzenetriol in human TK6 cells

In vitro gene expression signatures to predict toxicological responses can provide mechanistic context for regulatory testing. We previously developed the TGx-28.65 genomic biomarker from a database of gene expression profiles derived from human TK6 cells exposed to 28 well-known compounds. The biomarker comprises 65 genes that can classify chemicals as DNA damaging or non-DNA damaging. In this study, we applied the TGx-28.65 genomic biomarker in parallel with the in vitro micronucleus (MN) assay to determine if two chemicals of regulatory interest at Health Canada, disperse orange (DO: the orange azo dye 3-[[4-[(4-Nitrophenyl)azo]phenyl] benzylamino]propanenitrile) and 1,2,4-benzenetriol (BT: a metabolite of benzene) are genotoxic or non-genotoxic. Both chemicals caused dose-dependent declines in relative survival and increases in apoptosis. A strong significant increase in MN induction was observed for all concentrations of BT; the top two concentrations of DO also caused a statistically significant increase in MN, but these increases were <2-fold above controls. TGx-28.65 analysis classified BT as genotoxic at all three concentrations and DO as genotoxic at the mid and high concentrations. Thus, although DO only caused a small increase in MN, this response was sufficient to induce a cellular DNA damage response. Benchmark dose modeling confirmed that BT is much more potent than DO. The results strongly suggest that follow-up work is required to assess whether DO and BT are also genotoxic in vivo. This is particularly important for DO, which may require metabolic activation by bacterial gut flora to fully induce its genotoxic potential. Our previously published data and this proof of concept study suggest that the TGx-28.65 genomic biomarker has the potential to add significant value to existing approaches used to assess genotoxicity.

The Role of Omics in the Application of Adverse Outcome Pathways for Chemical Risk Assessment

In conjunction with the second International Environmental Omics Symposium (iEOS) conference, held at the University of Liverpool (United Kingdom) in September 2014, a workshop was held to bring together experts in toxicology and regulatory science from academia, government and industry. The purpose of the workshop was to review the specific roles that high-content omics datasets (eg, transcriptomics, metabolomics, lipidomics, and proteomics) can hold within the adverse outcome pathway (AOP) framework for supporting ecological and human health risk assessments. In light of the growing number of examples of the application of omics data in the context of ecological risk assessment, we considered how omics datasets might continue to support the AOP framework. In particular, the role of omics in identifying potential AOP molecular initiating events and providing supportive evidence of key events at different levels of biological organization and across taxonomic groups was discussed. Areas with potential for short and medium-term breakthroughs were also discussed, such as providing mechanistic evidence to support chemical read-across, providing weight of evidence information for mode of action assignment, understanding biological networks, and developing robust extrapolations of species-sensitivity. Key challenges that need to be addressed were considered, including the need for a cohesive approach towards experimental design, the lack of a mutually agreed framework to quantitatively link genes and pathways to key events, and the need for better interpretation of chemically induced changes at the molecular level. This article was developed to provide an overview of ecological risk assessment process and a perspective on how high content molecular-level datasets can support the future of assessment procedures through the AOP framework.

High-Throughput Screening Data Interpretation in the Context of In Vivo Transcriptomic Responses to Oral Cr(VI) Exposure

The toxicity of hexavalent chromium [Cr(VI)] in drinking water has been studied extensively, and available in vivo and in vitro studies provide a robust dataset for application of advanced toxicological tools to inform the mode of action (MOA). This study aimed to contribute to the understanding of Cr(VI) MOA by evaluating high-throughput screening (HTS) data and other in vitro data relevant to Cr(VI), and comparing these findings to robust in vivo data, including transcriptomic profiles in target tissues. Evaluation of Tox21 HTS data for Cr(VI) identified 11 active assay endpoints relevant to the Ten Key Characteristics of Carcinogens (TKCCs) that have been proposed by other investigators. Four of these endpoints were related to TP53 (tumor protein 53) activation mapping to genotoxicity (KCC#2), and four were related to cell death/proliferation (KCC#10). HTS results were consistent with other in vitro data from the Comparative Toxicogenomics Database. In vitro responses were compared to in vivo transcriptomic responses in the most sensitive target tissue, the duodenum, of mice exposed to ≤ 180 ppm Cr(VI) for 7 and 90 days. Pathways that were altered both in vitro and in vivo included those relevant to cell death/proliferation. In contrast, pathways relevant to p53/DNA damage were identified in vitro but not in vivo. Benchmark dose modeling and phenotypic anchoring of in vivo transcriptomic responses strengthened the finding that Cr(VI) causes cell stress/injury followed by proliferation in the mouse duodenum at high doses. These findings contribute to the body of evidence supporting a non-mutagenic MOA for Cr(VI)-induced intestinal cancer.

Validation of Transcriptomics-Based In Vitro Methods

The field of transcriptomics has expanded rapidly during the last decades. This methodology provides an exceptional framework to study not only molecular changes underlying the adverse effects of a given compound, but also to understand its Mode of Action (MoA). However, the implementation of transcriptomics-based tests within the regulatory arena is not a straightforward process. One of the major obstacles in their regulatory implementation is still the interpretation of this new class of data and the judgment of the level of confidence of these tests. A key element in the regulatory acceptance of transcriptomics-based tests is validation, which still represents a major challenge. Although important advances have been made in the development and standardisation of such tests, to date there is limited experience with their validation. Taking into account the experience acquired so far, this chapter describes those aspects that were identified as important in the validation process of transcriptomics-based tests, including the assessment of standardisation, reliability and relevance. It also critically discusses the challenges posed to validation in relation to the specific characteristics of these approaches and their application in the wider context of testing strategies.

Barriers to the use of toxicogenomics data in human health risk assessment: A survey of Canadian risk assessors

Regulatory agencies worldwide need to modernize human health risk assessment (HHRA) to meet challenges of the 21st century. Toxicogenomics is at the core of this improvement. Today, however, the use of toxicogenomics data in HHRA is very limited. The purpose of this survey was to identify barriers to the application of toxicogenomics data in HHRA by human health risk assessors. An online survey targeting Canadian risk assessors gathered information on their knowledge and perception of toxicogenomics, their current and future inclusion of toxicogenomics data in HHRA, and barriers to the use of such data. Twenty-nine (29) participants completed a questionnaire after 2 months of solicitation. The results show that the application of toxicogenomics data in Canada is marginal, with 85% of respondents reporting that they never or rarely used such data. Knowledge of toxicogenomics by Canadian risk assessors is also limited: about two-thirds of respondents (68%) were not at all or only slightly familiar with the concept. Lack of guidelines for toxicogenomics data interpretation, data quality assessment and on their use in HHRA, were found to be major barriers. In conclusion, there is a need for interventions aimed at facilitating the use of toxicogenomics data in HHRA, when available.

Recommended approaches in the application of toxicogenomics to derive points of departure for chemical risk assessment

There is increasing interest in the use of quantitative transcriptomic data to determine benchmark dose (BMD) and estimate a point of departure (POD) for human health risk assessment. Although studies have shown that transcriptional PODs correlate with those derived from apical endpoint changes, there is no consensus on the process used to derive a transcriptional POD. Specifically, the subsets of informative genes that produce BMDs that best approximate the doses at which adverse apical effects occur have not been defined. To determine the best way to select predictive groups of genes, we used published microarray data from dose–response studies on six chemicals in rats exposed orally for 5, 14, 28, and 90 days. We evaluated eight approaches for selecting genes for POD derivation and three previously proposed approaches (the lowest pathway BMD, and the mean and median BMD of all genes). The relationship between transcriptional BMDs derived using these 11 approaches and PODs derived from apical data that might be used in chemical risk assessment was examined. Transcriptional BMD values for all 11 approaches were remarkably aligned with corresponding apical PODs, with the vast majority of toxicogenomics PODs being within tenfold of those derived from apical endpoints. We identified at least four approaches that produce BMDs that are effective estimates of apical PODs across multiple sampling time points. Our results support that a variety of approaches can be used to derive reproducible transcriptional PODs that are consistent with PODs produced from traditional methods for chemical risk assessment.

Transcriptomic responses in the oral cavity of F344 rats and B6C3F1 mice following exposure to Cr(VI): Implications for risk assessment

Exposure to hexavalent chromium [Cr(VI)] in drinking water was previously reported to increase oral tumor incidence in F344 rats. To investigate the mode of action for these tumors, transcriptomic profiles in oral mucosa samples of F344 rats and B6C3F1 mice were analyzed following exposure to 0.1-180 ppm Cr(VI) for 7 or 90 days. In rats, genome-wide microarray analyses identified no significantly differentially expressed genes (DEGs) at either time point. In mice, 14 and 1 DEGs were respectively identified after 7 and 90 days of exposure. Therefore, relaxed statistical criteria were employed to identify potential DEGs (pDEGs), followed by high-throughput benchmark dose modeling to identify responsive pDEGs for pathway enrichment analysis. This identified 288 and 168 pDEGs in the rat oral mucosa, of which only 20 and 7 showed evidence of dose-response. No significant pathway enrichment was obtained with either pDEG or dose-responsive pDEG lists. Similar results were obtained in mice. These analyses indicate a negligible transcriptional response in the oral mucosa of both species. Comparison of the total number of gene changes in the oral mucosa of rats and mice with responses in the duodenum of animals from the same study demonstrated remarkable dose-response concordance across tissues and species as a function of tissue chromium concentration. The low chromium levels in the oral mucosa and negligible transcript response are consistent with an absence of tissue lesions. These findings are used to compare the merits of linear and nonlinear approaches for deriving toxicity criteria based on the oral tumors in rats. Environ. Mol. Mutagen. 57:706-716, 2016. © 2016 The Authors. Environmental and Molecular Mutagenesis Published by Wiley Periodicals, Inc.

The Next Generation of Risk Assessment Multi-Year Study-Highlights of Findings, Applications to Risk Assessment, and Future Directions

BACKGROUND

The Next Generation (NexGen) of Risk Assessment effort is a multi-year collaboration among several organizations evaluating new, potentially more efficient molecular, computational, and systems biology approaches to risk assessment. This article summarizes our findings, suggests applications to risk assessment, and identifies strategic research directions.

OBJECTIVE

Our specific objectives were to test whether advanced biological data and methods could better inform our understanding of public health risks posed by environmental exposures.

METHODS

New data and methods were applied and evaluated for use in hazard identification and dose-response assessment. Biomarkers of exposure and effect, and risk characterization were also examined. Consideration was given to various decision contexts with increasing regulatory and public health impacts. Data types included transcriptomics, genomics, and proteomics. Methods included molecular epidemiology and clinical studies, bioinformatic knowledge mining, pathway and network analyses, short-duration in vivo and in vitro bioassays, and quantitative structure activity relationship modeling.

DISCUSSION

NexGen has advanced our ability to apply new science by more rapidly identifying chemicals and exposures of potential concern, helping characterize mechanisms of action that influence conclusions about causality, exposure-response relationships, susceptibility and cumulative risk, and by elucidating new biomarkers of exposure and effects. Additionally, NexGen has fostered extensive discussion among risk scientists and managers and improved confidence in interpreting and applying new data streams.

CONCLUSIONS

While considerable uncertainties remain, thoughtful application of new knowledge to risk assessment appears reasonable for augmenting major scope assessments, forming the basis for or augmenting limited scope assessments, and for prioritization and screening of very data limited chemicals. Citation: Cote I, Andersen ME, Ankley GT, Barone S, Birnbaum LS, Boekelheide K, Bois FY, Burgoon LD, Chiu WA, Crawford-Brown D, Crofton KM, DeVito M, Devlin RB, Edwards SW, Guyton KZ, Hattis D, Judson RS, Knight D, Krewski D, Lambert J, Maull EA, Mendrick D, Paoli GM, Patel CJ, Perkins EJ, Poje G, Portier CJ, Rusyn I, Schulte PA, Simeonov A, Smith MT, Thayer KA, Thomas RS, Thomas R, Tice RR, Vandenberg JJ, Villeneuve DL, Wesselkamper S, Whelan M, Whittaker C, White R, Xia M, Yauk C, Zeise L, Zhao J, DeWoskin RS. 2016. The Next Generation of Risk Assessment multiyear study-highlights of findings, applications to risk assessment, and future directions. Environ Health Perspect 124:1671-1682; http://dx.doi.org/10.1289/EHP233.

Transcriptional benchmark dose modeling: Exploring how advances in chemical risk assessment may be applied to the radiation field

Recent advances in "-omics" technologies have simplified capacity to concurrently assess expression profiles of thousands of targets in a cellular system. However, compilation and analysis of "omics" data in support of human health protection remains a challenge. Benchmark dose (BMD) modeling is currently being employed in chemical risk assessment to estimate acceptable levels of exposure. Although typically applied to conventional endpoints, newer software has enabled this application to be extended to transcriptomic datasets. BMD analytical tools now have the capacity to model transcriptional dose-response data to derive meaningful BMD values for genes, pathways and gene ontologies. In this report, radiation data obtained from the Gene Expression Omnibus (GEO) were analyzed to generate BMD values for transcriptional responses. The datasets comprised microarray analyses of human blood gamma-irradiated ex vivo (0-20 Gy) and human-derived cell lines exposed to alpha particle radiation (0.5-1.5 Gy). The distributions of BMDs for statistically significant genes and pathways in response to radiation exposure were examined and compared across studies. BMD modeling could identify pathway/gene sensitivities across wide radiation dose ranges, experimental conditions (time-points, cell types) and radiation qualities. BMD analysis offered a new approach to examine transcriptional data. The results were shown to provide information on transcriptional thresholds of effects to support refined risk assessments for low dose ionizing radiation exposures, derive gene-based values for relative biological effectiveness and identify pathways involved in radiation sensitivities across cell types which may extend to applications a clinical setting. Environ. Mol. Mutagen. 57:589-604, 2016. © 2016 Wiley Periodicals, Inc.

Toxicogenomics Case Study: Furan

Development of pragmatic methodologies for human health risk assessment is required to address current regulatory challenges. We applied three toxicogenomic approaches—quantitative, predictive, and mechanistic—to a case study in mice exposed for 3 weeks to the hepatocarcinogen furan. We modeled the dose response of a variety of transcriptional endpoints and found that they produced benchmark doses similar to the furan-dependent cancer benchmark doses. Meta-analyses showed strong similarity between furan-dependent gene expression changes and those associated with several hepatic pathologies. Molecular pathways facilitated the development of a molecular mode of action for furan-induced hepatocellular carcinogenicity. Finally, we compared transcriptomic profiles derived from formalin-fixed and paraffin-embedded (FFPE) samples with those from high-quality frozen samples to evaluate whether archival samples are a viable option for toxicogenomic studies. The advantage of using FFPE tissues is that they are very well characterized (phenotypically); the disadvantage is that formalin degrades biomacromolecules, including RNA. We found that FFPE samples can be used for toxicogenomics using a ribo-depletion RNA-seq protocol. Our case study demonstrates the utility of toxicogenomics data to human health risk assessment, the potential of archival FFPE tissue samples, and identifies viable strategies toward the reduction of animal usage in chemical testing.

A risk assessment-driven quantitative comparison of gene expression profiles in PBMCs and white adipose tissue of humans and rats after isoflavone supplementation

Quantitative insight into species differences in risk assessment is expected to reduce uncertainty and variability related to extrapolation from animals to humans. This paper explores quantification and comparison of gene expression data between tissues and species from intervention studies with isoflavones. Gene expression data from peripheral blood mononuclear cells (PBMCs) and white adipose tissue (WAT) after 8wk isoflavone interventions in postmenopausal women and ovariectomized F344 rats were used. A multivariate model was applied to quantify gene expression effects, which showed 3-5-fold larger effect sizes in rats compared to humans. For estrogen responsive genes, a 5-fold greater effect size was found in rats than in humans. For these genes, intertissue correlations (r = 0.23 in humans, r = 0.22 in rats) and interspecies correlation in WAT (r = 0.31) were statistically significant. Effect sizes, intertissue and interspecies correlations for some groups of genes within energy metabolism, inflammation and cell cycle processes were significant, but weak. Quantification of gene expression data reveals differences between rats and women in effect magnitude after isoflavone supplementation. For risk assessment, quantification of gene expression data and subsequent calculation of intertissue and interspecies correlations within biological pathways will further strengthen knowledge on comparability between tissues and species.

Analysis of gene expression in mouse brain regions after exposure to 1.9 GHz radiofrequency fields

PURPOSE

To assess 1.9 GHz radiofrequency (RF) field exposure on gene expression within a variety of discrete mouse brain regions using whole genome microarray analysis.

MATERIALS AND METHODS

Adult male C57BL/6 mice were exposed to 1.9 GHz pulse-modulated or continuous-wave RF fields for 4 h/day for 5 consecutive days at whole body average (WBA) specific absorption rates of 0 (sham), ∼0.2 W/kg and ∼1.4 W/kg. Total RNA was isolated from the auditory cortex, amygdala, caudate, cerebellum, hippocampus, hypothalamus, and medial prefrontal cortex and differential gene expression was assessed using Illumina MouseWG-6 (v2) BeadChip arrays. Validation of potentially responding genes was conducted by RT-PCR.

RESULTS

When analysis of gene expression was conducted within individual brain regions when controlling the false discovery rate (FDR), no differentially expressed genes were identified relative to the sham control. However, it must be noted that most fold changes among groups were observed to be less than 1.5-fold and this study had limited ability to detect such small changes. While some genes were differentially expressed without correction for multiple-comparisons testing, no consistent pattern of response was observed among different RF-exposure levels or among different RF-modulations.

CONCLUSIONS

The current study provides the most comprehensive analysis of potential gene expression changes in the rodent brain in response to RF field exposure conducted to date. Within the exposure conditions and limitations of this study, no convincing evidence of consistent changes in gene expression was found in response to 1.9 GHz RF field exposure.

Nano-risk Science: application of toxicogenomics in an adverse outcome pathway framework for risk assessment of multi-walled carbon nanotubes

BACKGROUND

A diverse class of engineered nanomaterials (ENMs) exhibiting a wide array of physical-chemical properties that are associated with toxicological effects in experimental animals is in commercial use. However, an integrated framework for human health risk assessment (HHRA) of ENMs has yet to be established. Rodent 2-year cancer bioassays, clinical chemistry, and histopathological endpoints are still considered the 'gold standard' for detecting substance-induced toxicity in animal models. However, the use of data derived from alternative toxicological tools, such as genome-wide expression profiling and in vitro high-throughput assays, are gaining acceptance by the regulatory community for hazard identification and for understanding the underlying mode-of-action. Here, we conducted a case study to evaluate the application of global gene expression data in deriving pathway-based points of departure (PODs) for multi-walled carbon nanotube (MWCNT)-induced lung fibrosis, a non-cancer endpoint of regulatory importance.

METHODS

Gene expression profiles from the lungs of mice exposed to three individual MWCNTs with different physical-chemical properties were used within the framework of an adverse outcome pathway (AOP) for lung fibrosis to identify key biological events linking MWCNT exposure to lung fibrosis. Significantly perturbed pathways were categorized along the key events described in the AOP. Benchmark doses (BMDs) were calculated for each perturbed pathway and were used to derive transcriptional BMDs for each MWCNT.

RESULTS

Similar biological pathways were perturbed by the different MWCNT types across the doses and post-exposure time points studied. The pathway BMD values showed a time-dependent trend, with lower BMDs for pathways perturbed at the earlier post-exposure time points (24 h, 3d). The transcriptional BMDs were compared to the apical BMDs derived by the National Institute for Occupational Safety and Health (NIOSH) using alveolar septal thickness and fibrotic lesions endpoints. We found that regardless of the type of MWCNT, the BMD values for pathways associated with fibrosis were 14.0-30.4 μg/mouse, which are comparable to the BMDs derived by NIOSH for MWCNT-induced lung fibrotic lesions (21.0-27.1 μg/mouse).

CONCLUSIONS

The results demonstrate that transcriptomic data can be used to as an effective mechanism-based method to derive acceptable levels of exposure to nanomaterials in product development when epidemiological data are unavailable.