Integrating toxicogenomics into human health risk assessment: lessons learned from the benzo[a]pyrene case study

Ecotoxicological risk of asphalt pavements to aquatic animals associated with pollutant leaching

Contaminants such as heavy metals and polycyclic aromatic hydrocarbons (PAHs) can be released from asphalt pavement and transported through stormwater runoff to nearby water bodies, leading to water pollution and potential harm to living aquatic animals. This study characterizes the heavy metal and PAH leaching from various asphalt paving materials and their potential ecotoxicological effects on zebrafish Danio rerio. Artificial runoffs were prepared in the laboratory concerning the effects of water, temperature, and traffic. The concentrations of heavy metals and PAHs in the leachates were quantified, while the toxicity assessment encompassed mortality, metal stress, PAH toxicity, inflammation, carcinogenicity, and oxidative damage. Gene expressions of related proteins or transcription factors were assessed, including metallothionines, aryl hydrocarbon receptors, interleukin-1β, interleukin-10, nuclear factor-κB, tumor necrosis factor-α, tumor suppressor p53, heat shock protein 70, and reactive oxygen species (ROS). The findings demonstrate that leachates from asphalt pavements containing waste bottom ash, crumb rubber, or specific chemicals could induce notable stress and inflammation responses in zebrafish. In addition, potential carcinogenic effects and the elevation of ROS were identified within certain treatment groups. This study represents the first attempt to assess the ecotoxicity of pavement leachates employing a live fish model, thereby improving the current understanding of the environmental impact of asphalt pavements.

Integrating gene expression and splicing dynamics across dose-response oxidative modulators

Toxicological risk assessment increasingly utilizes transcriptomics to derive point of departure (POD) and modes of action (MOA) for chemicals. One essential biological process that allows a single gene to generate several different RNA isoforms is called alternative splicing. To comprehensively assess the role of splicing dysregulation in toxicological evaluation and elucidate its potential as a complementary endpoint, we performed RNA-seq on A549 cells treated with five oxidative stress modulators across a wide dose range. Differential gene expression (DGE) showed limited pathway enrichment except at high concentrations. However, alternative splicing analysis revealed variable intron retention events affecting diverse pathways for all chemicals in the absence of significant expression changes. For instance, diazinon elicited negligible gene expression changes but progressive increase in the number of intron retention events, suggesting splicing alterations precede expression responses. Benchmark dose modeling of intron retention data highlighted relevant pathways overlooked by expression analysis. Systematic integration of splicing datasets should be a useful addition to the toxicogenomic toolkit. Combining both modalities paint a more complete picture of transcriptomic dose-responses. Overall, evaluating intron retention dynamics afforded by toxicogenomics may provide biomarkers that can enhance chemical risk assessment and regulatory decision making. This work highlights splicing-aware toxicogenomics as a possible additional tool for examining cellular responses.

Paradigm shift required for translational research on the brain

Biomedical research on the brain has led to many discoveries and developments, such as understanding human consciousness and the mind and overcoming brain diseases. However, historical biomedical research on the brain has unique characteristics that differ from those of conventional biomedical research. For example, there are different scientific interpretations due to the high complexity of the brain and insufficient intercommunication between researchers of different disciplines owing to the limited conceptual and technical overlap of distinct backgrounds. Therefore, the development of biomedical research on the brain has been slower than that in other areas. Brain biomedical research has recently undergone a paradigm shift, and conducting patient-centered, large-scale brain biomedical research has become possible using emerging high-throughput analysis tools. Neuroimaging, multiomics, and artificial intelligence technology are the main drivers of this new approach, foreshadowing dramatic advances in translational research. In addition, emerging interdisciplinary cooperative studies provide insights into how unresolved questions in biomedicine can be addressed. This review presents the in-depth aspects of conventional biomedical research and discusses the future of biomedical research on the brain.

Leaching characteristics of metals and Polycyclic Aromatic Hydrocarbons (PAHs) from asphalt paving materials

Asphalt pavement, a major type of road surface, may contain hazardous elements depending on its specific composition. A growing concern has developed regarding the potential leaching of these hazardous constituents from asphalt pavements, particularly when incorporating waste materials and additives. This study investigates the presence of heavy metals and polycyclic aromatic hydrocarbons (PAHs) in leachates from six commonly employed asphalt paving materials. A comprehensive laboratory leaching experiment was conducted on three key sample scales: asphalt binder, asphalt mortar, and asphalt mixture. The impact of the leachates was assessed by the heavy metal pollution index and the toxic equivalency factor based on the benzo[a]pyrene equivalent concentration. The results reveal that leaching tests at the binder and mortar scales provided fundamental insights into leaching characteristics within a relatively short timeframe, while the mixture-scale test was more capable of representing pollutant leaching in near-true scenarios. In addition, the results indicate potential adverse health implications associated with the incorporation of hazardous waste, such as bottom ash, into asphalt pavement. These findings hold significant implications for promoting environmentally responsible practices of asphalt pavement.

The phosphoproteome is a first responder in tiered cellular adaptation to chemical stress followed by proteomics and transcriptomics alteration

Next-generation risk assessment (NGRA) for environmental chemicals involves a weight of evidence (WoE) framework integrating a suite of new approach methodologies (NAMs) based on points of departure (PoD) obtained from in vitro assays. Among existing NAMs, the omic-based technologies are of particular importance based on the premise that any apical endpoint change indicative of impaired health must be underpinned by some alterations at the omics level, such as transcriptome, proteome, metabolome, epigenome and genome. Transcriptomic assay plays a leading role in providing relatively conservative PoDs compared with apical endpoints. However, it is unclear whether and how parameters measured with other omics techniques predict the cellular response to chemical perturbations, especially at exposure levels below the transcriptomically defined PoD. Multi-omics coverage may provide additional sensitive or confirmative biomarkers to complement and reduce the uncertainty in safety decisions made using targeted and transcriptomics assays. In the present study, we conducted multi-omics studies of transcriptomics, proteomics and phosphoproteomics on two prototype compounds, coumarin and 2,4-dichlorophenoxyacetic acid (2,4-D), with multiple chemical concentrations and time points, to understand the sensitivity of the three omics techniques in response to chemically-induced changes in HepG2. We demonstrated that, phosphoproteomics alterations occur not only earlier in time, but also more sensitive to lower concentrations than proteomics and transcriptomics when the HepG2 cells were exposed to various chemical treatments. The phosphoproteomics changes appear to approach maximum when the transcriptomics alterations begin to initiate. Therefore, it is proximal to the very early effects induced by chemical exposure. We concluded that phosphoproteomics can be utilized to provide a more complete coverage of chemical-induced cellular alteration and supplement transcriptomics-based health safety decision making.

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.

Evaluating the Influences of Confounding Variables on Benchmark Dose using a Case Study in the Field of Ionizing Radiation

Purpose A vast amount of data regarding the effects of radiation stressors on transcriptional changes has been produced over the past few decades. These data have shown remarkable consistency across platforms and experimental design, enabling increased understanding of early molecular effects of radiation exposure. However, the value of transcriptomic data in the context of risk assessment is not clear and represents a gap that is worthy of further consideration. Recently, benchmark dose (BMD) modeling has shown promise in correlating a transcriptional point of departure (POD) to that derived using phenotypic outcomes relevant to human health risk assessment. Although frequently applied in chemical toxicity evaluation, our group has recently demonstrated application within the field of radiation research. This approach allows the possibility to quantitatively compare radiation-induced gene and pathway alterations across various datasets using BMD values and derive meaningful biological effects. However, before BMD modeling can confidently be used, an understanding of the impact of confounding variables on BMD outputs is needed. Methods: To this end, BMD modeling was applied to a publicly available microarray dataset (Gene Expression Omnibus #GSE23515) that used peripheral blood ex-vivo gamma-irradiated at 0.82 Gy/min, at doses of 0, 0.1, 0.5 or 2 Gy, and assessed 6 hours post-exposure. The dataset comprised six female smokers (F-S), six female non-smokers (F-NS), six male smokers (M-S), and six male non-smokers (M-NS). Results: A combined total of 412 genes were fit to models and the BMD distribution was noted to be bi-modal across the four groups. A total of 74, 41, 62 and 62 genes were unique to the F-NS, M-NS, F-S and M-S groups. Sixty-two BMD modeled genes and nine pathways were common across all four groups. There were no differential sensitivity of responses in the robust common genes and pathways. Conclusion: For radiation-responsive genes and pathways common across the study groups, the BMD distribution of transcriptional activity was unaltered by sex and smoking status. Although further validation of the data is needed, these initial findings suggest BMD values for radiation relevant genes and pathways are robust and could be explored further in future studies.

Benchmark dose modeling of transcriptional data: a systematic approach to identify best practices for study designs used in radiation research

PURPOSE

Benchmark dose (BMD) modeling is a method commonly used in chemical toxicology to identify the point of departure (POD) from a dose-response curve linked to a health-related outcome. Recently, it is being explored on transcriptional data and in adverse outcome pathways (AOPs). As AOPs are informed by diverse data types, it is important to understand the impact of study parameters such as dose selection, number of replicates and dose range on BMD outputs for radiation induced genes and pathways.

MATERIALS AND METHODS

Data were selected from the Gene Expression Omnibus (GSE52403) that featured gene expression profiles of peripheral blood samples from C57BL/6 mice 6 hours post-exposure to ¹³⁷Cs gamma-radiation at 0, 1, 2, 3, 4.5, 6, 8 and 10.5 Gy. The dataset comprised a broad dose-range over multiple dose-points with consistent dose spacing and multiple biological replicates. This dataset was ideal for systematically transforming across three categories: (1) dose-range, (2) dose-spacing and (3) number of controls/replicates. Across these categories, 29 transformed datasets were compared to the original dataset to determine the impact of each transformation on the BMD outputs.

RESULTS

Most of the experimental changes did not impact the BMD outputs. The transformed datasets were largely consistent with the original dataset in terms of number of reproduced genes modeled and absolute BMD values for genes and pathways. Variations in dose selection identified the importance of the absolute value of the lowest and second dose. It was determined that dose selection should include at least two doses <1 Gy and two >5 Gy to achieve meaningful BMD outputs. Changes to the number of biological replicates in the control and non-zero dose groups impacted the overall accuracy and precision of the BMD outputs as well as the ability to fit dose-response models consistent with the original dataset.

CONCLUSION

Successful application of transcriptomic BMD modeling for radiation datasets requires considerations of the exposure dose and the number of biological replicates. Most important is the selection of the lowest doses and dose spacing. Reflections on these parameters in experimental design will provide meaningful BMD outputs that could correlate well to apical endpoints of relevance to radiation exposure assessment.

GADD45 in Stress Signaling, Cell Cycle Control, and Apoptosis

GADD45 is a gene family consisting of GADD45A, GADD45B, and GADD45G that is often induced by DNA damage and other stress signals associated with growth arrest and apoptosis. Many of these roles are carried out via signaling mediated by p38 mitogen-activated protein kinases (MAPKs). The GADD45 proteins can contribute to p38 activation either by activation of upstream kinase(s) or by direct interaction, as well as suppression of p38 activity in certain cases. In vivo, there are important tissue and cell type specific differences in the roles for GADD45 in MAPK signaling. In addition to being p53-regulated, GADD45A has also been found to contribute to p53 activation via p38. Like other stress and signaling proteins, GADD45 proteins show complex regulation and numerous effectors. More recently, aberrant GADD45 expression has been found in several human cancers, but the mechanisms behind these findings largely remain to be understood.

Nextcast: A software suite to analyse and model toxicogenomics data

The recent advancements in toxicogenomics have led to the availability of large omics data sets, representing the starting point for studying the exposure mechanism of action and identifying candidate biomarkers for toxicity prediction. The current lack of standard methods in data generation and analysis hampers the full exploitation of toxicogenomics-based evidence in regulatory risk assessment. Moreover, the pipelines for the preprocessing and downstream analyses of toxicogenomic data sets can be quite challenging to implement. During the years, we have developed a number of software packages to address specific questions related to multiple steps of toxicogenomics data analysis and modelling. In this review we present the Nextcast software collection and discuss how its individual tools can be combined into efficient pipelines to answer specific biological questions. Nextcast components are of great support to the scientific community for analysing and interpreting large data sets for the toxicity evaluation of compounds in an unbiased, straightforward, and reliable manner. The Nextcast software suite is available at: ( https://github.com/fhaive/nextcast).

Linking Coregulated Gene Modules with Polycyclic Aromatic Hydrocarbon-Related Cancer Risk in the 3D Human Bronchial Epithelium

Exposure to polycyclic aromatic hydrocarbons (PAHs) often occurs as complex chemical mixtures, which are linked to numerous adverse health outcomes in humans, with cancer as the greatest concern. The cancer risk associated with PAH exposures is commonly evaluated using the relative potency factor (RPF) approach, which estimates PAH mixture carcinogenic potential based on the sum of relative potency estimates of individual PAHs, compared to benzo[a]pyrene (BAP), a reference carcinogen. The present study evaluates molecular mechanisms related to PAH cancer risk through integration of transcriptomic and bioinformatic approaches in a 3D human bronchial epithelial cell model. Genes with significant differential expression from human bronchial epithelium exposed to PAHs were analyzed using a weighted gene coexpression network analysis (WGCNA) two-tiered approach: first to identify gene sets comodulated to RPF and second to link genes to a more comprehensive list of regulatory values, including inhalation-specific risk values. Over 3000 genes associated with processes of cell cycle regulation, inflammation, DNA damage, and cell adhesion processes were found to be comodulated with increasing RPF with pathways for cell cycle S phase and cytoskeleton actin identified as the most significantly enriched biological networks correlated to RPF. In addition, comodulated genes were linked to additional cancer-relevant risk values, including inhalation unit risks, oral cancer slope factors, and cancer hazard classifications from the World Health Organization's International Agency for Research on Cancer (IARC). These gene sets represent potential biomarkers that could be used to evaluate cancer risk associated with PAH mixtures. Among the values tested, RPF values and IARC categorizations shared the most similar responses in positively and negatively correlated gene modules. Together, we demonstrated a novel manner of integrating gene sets with chemical toxicity equivalence estimates through WGCNA to understand potential mechanisms.

Transcriptomic analyses of livers from mice exposed to 1,4-dioxane for up to 90 days to assess potential mode(s) of action underlying liver tumor development

1,4-Dioxane is a volatile organic compound with industrial and commercial applications as a solvent and in the manufacture of other chemicals. 1,4-Dioxane has been demonstrated to induce liver tumors in chronic rodent bioassays conducted at very high doses. The available evidence for 1,4-dioxane-induced liver tumors in rodents aligns with a threshold-dependent mode of action (MOA), with the underlying mechanism being less clear in the mouse than in rats. To gain a better understanding of the underlying molecular mechanisms related to liver tumor development in mice orally exposed to 1,4-dioxane, transcriptomics analysis was conducted on liver tissue collected from a 90-day drinking water study in female B6D2F₁/Crl mice (Lafranconi et al., 2020). Using tissue samples from female mice exposed to 1,4-dioxane in the drinking water at concentrations of 0, 40, 200, 600, 2,000 or 6,000 ppm for 7, 28, and 90 days, transcriptomic analyses demonstrate minimal treatment effects on global gene expression at concentrations below 600 ppm. At higher concentrations, genes involved in phase II metabolism and mitotic cell cycle checkpoints were significantly upregulated. There was an overall lack of enrichment of genes related to DNA damage response. The increase in mitotic signaling is most prevalent in the livers of mice exposed to 1,4-dioxane at the highest concentrations for 90 days. This finding aligns with phenotypic changes reported by Lafranconi et al. (2020) after 90-days of exposure to 6,000 ppm 1,4-dioxane in the same tissues. The transcriptomics analysis further supports overarching study findings demonstrating a non-mutagenic, threshold-based, mitogenic MOA for 1,4-dioxane-induced liver tumors.

Meta-analysis of transcriptomic datasets using benchmark dose modeling shows value in supporting radiation risk assessment

PURPOSE

Benchmark dose (BMD) modeling is used to determine the dose of a stressor at which a predefined increase in any biological effect above background occurs (e.g. 10% increase from control values). BMD analytical tools have the capacity to model transcriptional dose-response data to derive BMDs for genes, pathways and gene ontologies. We recently demonstrated the value of this approach to support various areas of radiation research using predominately 'in-house' generated datasets.

MATERIALS AND METHODS

As a continuation of this work, transcriptomic studies of relevance to ionizing radiation were retrieved through the Gene Expression Omnibus (GEO). The datasets were compiled and filtered, then analyzed using BMDExpress. The objective was to determine the reproducibility of BMD values in relation to pathways and genes across different exposure scenarios and compare to those derived using cytogenetic endpoints. A number of graphic visualization approaches were used to determine if BMD outputs could be correlated to parameters such as dose-rate, radiation quality and cell type.

RESULTS

Curated studies were diverse and derived from experiments with varied design and intent. Despite this, common genes and pathways were identified with low and high dose thresholds. The higher BMD values were associated with immune response and cell death, while transcripts with lower BMD values were generally related to the classic DNA damage response/repair processes, centered on TP53 signaling. Analysis of datasets with relatively similar dose-ranges under comparable experimental conditions showed a bi-modal distribution with a high degree of consistency in BMD values across shared genes and pathways, particularly for those below the 25th percentile of total distribution by dose. The median BMD values were noted to be approximately 0.5 Gy for genes/pathways that comprised mode 1. Furthermore, transcriptional BMD values derived from a subset of genes using in vivo and in vitro datasets were in accord to those using cytogenetic endpoints.

CONCLUSION

Overall, the results from this work highlight the value of the BMD methodology to derive meaningful outputs that are consistent across different models, provided the studies are conducted using a similar dose-range.

A Rat Liver Transcriptomic Point of Departure Predicts a Prospective Liver or Non-liver Apical Point of Departure

Identifying a toxicity point of departure (POD) is a required step in human health risk characterization of crop protection molecules, and this POD has historically been derived from apical endpoints across a battery of animal-based toxicology studies. Using rat transcriptome and apical data for 79 molecules obtained from Open TG-GATES (Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System) (632 datasets), the hypothesis was tested that a short-term exposure, transcriptome-based liver biological effect POD (BEPOD) could estimate a longer-term exposure "systemic" apical endpoint POD. Apical endpoints considered were body weight, clinical observation, kidney weight and histopathology and liver weight and histopathology. A BMDExpress algorithm using Gene Ontology Biological Process gene sets was optimized to derive a liver BEPOD most predictive of a systemic apical POD. Liver BEPODs were stable from 3 h to 29 days of exposure; the median fold difference of the 29-day BEPOD to BEPODs from earlier time points was approximately 1 (range: 0.7-1.1). Strong positive correlation (Pearson R = 0.86) and predictive accuracy (root mean square difference = 0.41) were observed between a concurrent (29 days) liver BEPOD and the systemic apical POD. Similar Pearson R and root mean square difference values were observed for comparisons between a 29-day systemic apical POD and liver BEPODs derived from 3 h to 15 days of exposure. These data across 79 molecules suggest that a longer-term exposure study apical POD from liver and non-liver compartments can be estimated using a liver BEPOD derived from an acute or subacute exposure study.

Short-term toxicogenomics as an alternative approach to chronic in vivo studies for derivation of points of departure: A case study in the rat with a triazole fungicide

The derivation of an apical endpoint point of departure (POD) from animal-intensive testing programs has been the traditional cornerstone of human health risk assessment. Replacement of in vivo chronic studies with novel approaches, such as toxicogenomics, holds promise for future alternative testing paradigms that significantly reduce animal testing. We hypothesized that a toxicogenomic POD following a 14 day exposure in the rat would approximate the most sensitive apical endpoint POD derived from a battery of chronic, carcinogenicity, reproduction and endocrine guideline toxicity studies. To test this hypothesis, we utilized myclobutanil, a triazole fungicide, as a model compound. In the 14 day study, male rats were administered 0 (vehicle), 30, 150, or 400 mg/kg/day myclobutanil via oral gavage. Endpoints evaluated included traditional apical, hormone, and liver and testis transcriptomic (whole genome RNA sequencing) data. From the transcriptomic data, liver and testis biological effect POD (BEPOD) values were derived. Myclobutanil exposure for 14 days resulted in increased liver weight, altered serum hormones, liver histopathology, and differential gene expression in liver and testis. The liver and testis BEPODs from the short-term study were 22.2 and 25.4 mg/kg/day, respectively. These BEPODs were approximately an order of magnitude higher than the most sensitive apical POD identified from the two year cancer bioassay based on testis atrophy (1.4 mg/kg/day). This study demonstrates the promise of using a short-term study BEPOD to derive a POD for human health risk assessment while substantially reducing animal testing.

A toxicogenomic approach for the risk assessment of the food contaminant acetamide

Acetamide (CAS 60-35-5) is detected in common foods. Chronic rodent bioassays led to its classification as a group 2B possible human carcinogen due to the induction of liver tumors in rats. We used a toxicogenomics approach in Wistar rats gavaged daily for 7 or 28 days at doses of 300 to 1500 mg/kg/day (mkd) to determine a point of departure (POD) and investigate its mode of action (MoA). Ki67 labeling was increased at doses ≥750 mkd up to 3.3-fold representing the most sensitive apical endpoint. Differential gene expression analysis by RNA-Seq identified 1110 and 1814 differentially expressed genes in male and female rats, respectively, following 28 days of treatment. Down-regulated genes were associated with lipid metabolism while up-regulated genes included cell signaling, immune response, and cell cycle functions. Benchmark dose (BMD) modeling of the Ki67 labeling index determined the BMD10 lower confidence limit (BMDL10) as 190 mkd. Transcriptional BMD modeling revealed excellent concordance between transcriptional POD and apical endpoints. Collectively, these results indicate that acetamide is most likely acting through a mitogenic MoA, though specific key initiating molecular events could not be elucidated. A POD value of 190 mkd determined for cell proliferation is suggested for risk assessment purposes.

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.

An integrative computational approach for a prioritization of key transcription regulators associated with nanomaterial-induced toxicity

A rapid increase of new nanomaterial products poses new challenges for their risk assessment. Current traditional methods for estimating potential adverse health effect of nanomaterials (NMs) are complex, time consuming and expensive. In order to develop new prediction tests for nanotoxicity evaluation, a systems biology approach and data from high-throughput omics experiments can be used. We present a computational approach that combines reverse engineering techniques, network analysis and pathway enrichment analysis for inferring the transcriptional regulation landscape and its functional interpretation. To illustrate this approach, we used published transcriptomic data derived from mice lung tissue exposed to carbon nanotubes (NM-401 and NRCWE-26). Because fibrosis is the most common adverse effect of these NMs, we included in our analysis the data for bleomycin (BLM) treatment, which is a well-known fibrosis inducer. We inferred gene regulatory networks for each NM and BLM to capture functional hierarchical regulatory structures between genes and their regulators. Despite the different nature of the lung injury caused by nanoparticles and BLM, we identified several conserved core regulators for all agents. We reason that these regulators can be considered as early predictors of toxic responses after NMs exposure. This integrative approach, which refines traditional methods of transcriptomic analysis, can be useful for prioritization of potential core regulators and generation of new hypothesis about mechanisms of nanoparticles toxicity.

Application of Bayesian networks in determining nanoparticle-induced cellular outcomes using transcriptomics

Inroads have been made in our understanding of the risks posed to human health and the environment by nanoparticles (NPs) but this area requires continuous research and monitoring. Machine learning techniques have been applied to nanotoxicology with very encouraging results. This study deals with bridging physicochemical properties of NPs, experimental exposure conditions and in vitro characteristics with biological effects of NPs on a molecular cellular level from transcriptomics studies. The bridging is done by developing and implementing Bayesian Networks (BNs) with or without data preprocessing. The BN structures are derived either automatically or methodologically and compared. Early stage nanotoxicity measurements represent a challenge, not least when attempting to predict adverse outcomes and modeling is critical to understanding the biological effects of exposure to NPs. The preprocessed data-driven BN showed improved performance over automatically structured BN and the BN with unprocessed datasets. The prestructured BN captures inter relationships between NP properties, exposure condition and in vitro characteristics and links those with cellular effects based on statistic correlation findings. Information gain analysis showed that exposure dose, NP and cell line variables were the most influential attributes in predicting the biological effects. The BN methodology proposed in this study successfully predicts a number of toxicologically relevant cellular disrupted biological processes such as cell cycle and proliferation pathways, cell adhesion and extracellular matrix responses, DNA damage and repair mechanisms etc., with a success rate >80%. The model validation from independent data shows a robust and promising methodology for incorporating transcriptomics outcomes in a hazard and, by extension, risk assessment modeling framework by predicting affected cellular functions from experimental conditions.

Bridging the Data Gap From in vitro Toxicity Testing to Chemical Safety Assessment Through Computational Modeling

Chemical toxicity testing is moving steadily toward a human cell and organoid-based in vitro approach for reasons including scientific relevancy, efficiency, cost, and ethical rightfulness. Inferring human health risk from chemical exposure based on in vitro testing data is a challenging task, facing various data gaps along the way. This review identifies these gaps and makes a case for the in silico approach of computational dose-response and extrapolation modeling to address many of the challenges. Mathematical models that can mechanistically describe chemical toxicokinetics (TK) and toxicodynamics (TD), for both in vitro and in vivo conditions, are the founding pieces in this regard. Identifying toxicity pathways and in vitro point of departure (PoD) associated with adverse health outcomes requires an understanding of the molecular key events in the interacting transcriptome, proteome, and metabolome. Such an understanding will in turn help determine the sets of sensitive biomarkers to be measured in vitro and the scope of toxicity pathways to be modeled in silico. In vitro data reporting both pathway perturbation and chemical biokinetics in the culture medium serve to calibrate the toxicity pathway and virtual tissue models, which can then help predict PoDs in response to chemical dosimetry experienced by cells in vivo. Two types of in vitro to in vivo extrapolation (IVIVE) are needed. (1) For toxic effects involving systemic regulations, such as endocrine disruption, organism-level adverse outcome pathway (AOP) models are needed to extrapolate in vitro toxicity pathway perturbation to in vivo PoD. (2) Physiologically-based toxicokinetic (PBTK) modeling is needed to extrapolate in vitro PoD dose metrics into external doses for expected exposure scenarios. Linked PBTK and TD models can explore the parameter space to recapitulate human population variability in response to chemical insults. While challenges remain for applying these modeling tools to support in vitro toxicity testing, they open the door toward population-stratified and personalized risk assessment.

Cyclodextrin-Promoted Fluorescence Detection of Aromatic Toxicants and Toxicant Metabolites in Commercial Milk Products

The detection of polycyclic aromatic hydrocarbons (PAHs) and their metabolites in food and in agricultural sources is an important research objective due to the PAHs' known persistence, carcinogenicity, and toxicity. PAHs have been found in the milk of lactating cows, and in the leaves and stems of plants grown in PAH-contaminated areas, thereby making their way into both cow milk and plant milk alternatives. Reported herein is the rapid, sensitive, and selective detection of 10 PAHs and PAH metabolites in a variety of cow milks and plant milk alternatives using fluorescence energy transfer from the PAH to a high quantum yield fluorophore, combined with subsequent array-based statistical analyses of the fluorescence emission signals. This system operates with high sensitivity (low micromolar detection limits), selectivity (100% differentiation even between structurally similar analytes), and general applicability (for both unmodified lipophilic PAHs and highly polar oxidized PAH metabolites, as well as for different cow and plant milk samples). These promising results show significant potential to be translated into solid-state devices for the rapid, sensitive, and selective detection of PAHs and their metabolites in complex, commercial food products.

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.

Mechanism-based risk assessment strategy for drug-induced cholestasis using the transcriptional benchmark dose derived by toxicogenomics

Cholestasis is one of the major causes of drug-induced liver injury (DILI), which can result in withdrawal of approved drugs from the market. Early identification of cholestatic drugs is difficult due to the complex mechanisms involved. In order to develop a strategy for mechanism-based risk assessment of cholestatic drugs, we analyzed gene expression data obtained from the livers of rats that had been orally administered with 12 known cholestatic compounds repeatedly for 28 days at three dose levels. Qualitative analyses were performed using two statistical approaches (hierarchical clustering and principle component analysis), in addition to pathway analysis. The transcriptional benchmark dose (tBMD) and tBMD 95% lower limit (tBMDL) were used for quantitative analyses, which revealed three compound sub-groups that produced different types of differential gene expression; these groups of genes were mainly involved in inflammation, cholesterol biosynthesis, and oxidative stress. Furthermore, the tBMDL values for each test compound were in good agreement with the relevant no observed adverse effect level. These results indicate that our novel strategy for drug safety evaluation using mechanism-based classification and tBMDL would facilitate the application of toxicogenomics for risk assessment of cholestatic DILI.

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.

Inferring transcription factor activity from microarray data reveals novel targets for toxicological investigations

Transcription factors (TFs) are important modulators of the inducible portion of the transcriptome, and therefore relevant in the context of exposure to exogenous compounds. Current approaches to predict the activity of TFs in biological systems are usually restricted to a few entities at a time due to low-throughput techniques targeting a limited fraction of annotated human TFs. Therefore, high-throughput alternatives may help to identify new targets of mechanistic and predictive value in toxicological investigations. In this study, we inferred the activity multiple TFs using publicly available microarray data from primary human hepatocytes exposed to hundreds of chemicals and evaluated these molecular profiles using multiple correspondence analysis. Our results demonstrate that the lowest dose and latest exposure time (24h) in a subset of chemicals generates a signature indicative of carcinogenicity possibly due to DNA-damaging properties. Furthermore, profiles from the earliest exposure time (2h) and highest dose creates clusters of chemicals implicated in the development of diverse forms of drug-induced liver injury (DILI). Both approaches yielded a number of TFs with similar activity across groups of chemicals, including TFs known in toxicological responses such as AhR, NFE2L2 (Nrf2), NF-κB and PPARG. FOXM1, IRF1 and E2F4 were some of the TFs identified that may be relevant in genotoxic carcinogenesis. SMADs (SMAD1, SMAD2, SMAD5) and KLF5 were identified as some of potentially new TFs whose inferred activities were linked to acute and progressive outcomes in DILI. In conclusion this study offers a novel mechanistic approach targeting TF activity during chemical exposure.

Impact of Acrylamide on Calcium Signaling and Cytoskeletal Filaments in Testes From F344 Rat

Acrylamide (AA) at high exposure levels is neurotoxic, induces testicular toxicity, and increases dominant lethal mutations in rats. RNA-sequencing in testes was used to identify differentially expressed genes (DEG), explore AA-induced pathway perturbations that could contribute to AA-induced testicular toxicity and then used to derive a benchmark dose (BMD). Male F344/DuCrl rats were administered 0.0, 0.5, 1.5, 3.0, 6.0, or 12.0 mg AA/kg bw/d in drinking water for 5, 15, or 31 days. The experimental design used exposure levels that spanned and exceeded the exposure levels used in the rat dominant lethal, 2-generation reproductive toxicology, and cancer bioassays. The time of sample collection was based on previous studies that developed gene expression-based BMD. At 12.0 mg/kg, there were 38, 33, and 65 DEG ( P value <.005; fold change >1.5) in the testes after 5, 15, or 31 days of exposure, respectively. At 31 days, there was a dose-dependent increase in the number of DEG, and at 12.0 mg/kg/d the top three functional clusters affected by AA exposure were actin filament organization, response to calcium ion, and regulation of cell proliferation. The BMD lower 95% confidence limit using DEG ranged from 1.8 to 6.8 mg/kg compared to a no-observed-adverse-effect-level of 2.0 mg/kg/d for male reproductive toxicity. These results are consistent with the known effects of AA on calcium signaling and cytoskeletal actin filaments leading to neurotoxicity and suggest that AA can cause rat dominant lethal mutations by these same mechanisms leading to impaired chromosome segregation during cell division.

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.

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.

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.

Transcriptional Profiling of Dibenzo[def,p]chrysene-induced Spleen Atrophy Provides Mechanistic Insights into its Immunotoxicity in MutaMouse

Dibenzo[def,p]chrysene (DBC) is the most carcinogenic polycyclic aromatic hydrocarbon (PAH) examined to date. We investigated the immunotoxicity of DBC, manifested as spleen atrophy, following acute exposure of adult MutaMouse males by oral gavage. Mice were exposed to 0, 2.0, 6.2, or 20.0 mg DBC /kg-bw per day, for 3 days. Genotoxic endpoints (DBC-DNA adducts and lacZ mutant frequency in spleen and bone marrow, and red blood cell micronucleus frequency) and global gene expression changes were measured. All of the genotoxicity measures increased in a dose-dependent manner in spleen and bone marrow. Gene expression analysis showed that DBC activates p53 signaling pathways related to cellular growth and proliferation, which was evident even at the low dose. Strikingly, the expression profiles of DBC exposed mouse spleens were highly inversely correlated with the expression profiles of the only published toxicogenomics dataset of enlarged mouse spleen. This analysis suggested a central role for Bnip3l, a pro-apoptotic protein involved in negative regulation of erythroid maturation. RT-PCR confirmed expression changes in several genes related to apoptosis, iron metabolism, and aryl hydrocarbon receptor signaling that are regulated in the opposite direction during spleen atrophy versus benzo[a]pyrene-mediated splenomegaly. In addition, benchmark dose modeling of toxicogenomics data yielded toxicity estimates that are very close to traditional toxicity endpoints. This work illustrates the power of toxicogenomics to reveal rich mechanistic information for immunotoxic compounds and its ability to provide information that is quantitatively similar to that derived from standard toxicity methods in health risk assessment.

BMDExpress Data Viewer - a visualization tool to analyze BMDExpress datasets

Regulatory agencies increasingly apply benchmark dose (BMD) modeling to determine points of departure for risk assessment. BMDExpress applies BMD modeling to transcriptomic datasets to identify transcriptional BMDs. However, graphing and analytical capabilities within BMDExpress are limited, and the analysis of output files is challenging. We developed a web‐based application, BMDExpress Data Viewer (http://apps.sciome.com:8082/BMDX_Viewer/), for visualizing and graphing BMDExpress output files. The application consists of “Summary Visualization” and “Dataset Exploratory” tools. Through analysis of transcriptomic datasets of the toxicants furan and 4,4′‐methylenebis(N,N‐dimethyl)benzenamine, we demonstrate that the “Summary Visualization Tools” can be used to examine distributions of gene and pathway BMD values, and to derive a potential point of departure value based on summary statistics. By applying filters on enrichment P‐values and minimum number of significant genes, the “Functional Enrichment Analysis” tool enables the user to select biological processes or pathways that are selectively perturbed by chemical exposure and identify the related BMD. The “Multiple Dataset Comparison” tool enables comparison of gene and pathway BMD values across multiple experiments (e.g., across timepoints or tissues). The “BMDL‐BMD Range Plotter” tool facilitates the observation of BMD trends across biological processes or pathways. Through our case studies, we demonstrate that BMDExpress Data Viewer is a useful tool to visualize, explore and analyze BMDExpress output files. Visualizing the data in this manner enables rapid assessment of data quality, model fit, doses of peak activity, most sensitive pathway perturbations and other metrics that will be useful in applying toxicogenomics in risk assessment. © 2015 Her Majesty the Queen in Right of Canada. Journal of Applied Toxicology published by John Wiley & Sons, Ltd.

We developed BMDExpress Data Viewer, which contains two collections of tools, “Summary Visualization Tools” and “Dataset Exploratory Tools,” to visualize and analyze BMDExpress output files. Through two case studies, we demonstrate the capabilities of graphically examining transcriptomic dose–response datasets in a risk assessment context by comparing and observing trends in transcriptomic benchmark doses (BMDs) for biological processes and pathways. Our results illustrate that BMDExpress Data Viewer is a useful tool to visualize, explore and analyze BMDExpress output files.

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

Toxicogenomics promises to be an important part of future human health risk assessment of environmental chemicals. The application of gene expression profiles (e.g., for hazard identification, chemical prioritization, chemical grouping, mode of action discovery, and quantitative analysis of response) is growing in the literature, but their use in formal risk assessment by regulatory agencies is relatively infrequent. Although additional validations for specific applications are required, gene expression data can be of immediate use for increasing confidence in chemical evaluations. We believe that a primary reason for the current lack of integration is the limited practical guidance available for risk assessment specialists with limited experience in genomics. The present manuscript provides basic information on gene expression profiling, along with guidance on evaluating the quality of genomic experiments and data, and interpretation of results presented in the form of heat maps, pathway analyses and other common approaches. Moreover, potential ways to integrate information from gene expression experiments into current risk assessment are presented using published studies as examples. The primary objective of this work is to facilitate integration of gene expression data into human health risk assessments of environmental chemicals.