A framework for the use of single-chemical transcriptomics data in predicting the hazards associated with complex mixtures of polycyclic aromatic hydrocarbons

Modeling PAH Mixture Interactions in a Human In Vitro Organotypic Respiratory Model

One of the most significant challenges in human health risk assessment is to evaluate hazards from exposure to environmental chemical mixtures. Polycyclic aromatic hydrocarbons (PAHs) are a class of ubiquitous contaminants typically found as mixtures in gaseous and particulate phases in ambient air pollution associated with petrochemicals from Superfund sites and the burning of fossil fuels. However, little is understood about how PAHs in mixtures contribute to toxicity in lung cells. To investigate mixture interactions and component additivity from environmentally relevant PAHs, two synthetic mixtures were created from PAHs identified in passive air samplers at a legacy creosote site impacted by wildfires. The primary human bronchial epithelial cells differentiated at the air-liquid interface were treated with PAH mixtures at environmentally relevant proportions and evaluated for the differential expression of transcriptional biomarkers related to xenobiotic metabolism, oxidative stress response, barrier integrity, and DNA damage response. Component additivity was evaluated across all endpoints using two independent action (IA) models with and without the scaling of components by toxic equivalence factors. Both IA models exhibited trends that were unlike the observed mixture response and generally underestimated the toxicity across dose suggesting the potential for non-additive interactions of components. Overall, this study provides an example of the usefulness of mixture toxicity assessment with the currently available methods while demonstrating the need for more complex yet interpretable mixture response evaluation methods for environmental samples.

Single-Walled vs. Multi-Walled Carbon Nanotubes: Influence of Physico-Chemical Properties on Toxicogenomics Responses in Mouse Lungs

Single-walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs) are nanomaterials with one or multiple layers of carbon sheets. While it is suggested that various properties influence their toxicity, the specific mechanisms are not completely known. This study was aimed to determine if single or multi-walled structures and surface functionalization influence pulmonary toxicity and to identify the underlying mechanisms of toxicity. Female C57BL/6J BomTac mice were exposed to a single dose of 6, 18, or 54 μg/mouse of twelve SWCNTs or MWCNTs of different properties. Neutrophil influx and DNA damage were assessed on days 1 and 28 post-exposure. Genome microarrays and various bioinformatics and statistical methods were used to identify the biological processes, pathways and functions altered post-exposure to CNTs. All CNTs were ranked for their potency to induce transcriptional perturbation using benchmark dose modelling. All CNTs induced tissue inflammation. MWCNTs were more genotoxic than SWCNTs. Transcriptomics analysis showed similar responses across CNTs at the pathway level at the high dose, which included the perturbation of inflammatory, cellular stress, metabolism, and DNA damage responses. Of all CNTs, one pristine SWCNT was found to be the most potent and potentially fibrogenic, so it should be prioritized for further toxicity testing.

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.

Transcriptome-Wide Prediction and Measurement of Combined Effects Induced by Chemical Mixture Exposure in Zebrafish Embryos

BACKGROUND

Humans and environmental organisms are constantly exposed to complex mixtures of chemicals. Extending our knowledge about the combined effects of chemicals is thus essential for assessing the potential consequences of these exposures. In this context, comprehensive molecular readouts as retrieved by omics techniques are advancing our understanding of the diversity of effects upon chemical exposure. This is especially true for effects induced by chemical concentrations that do not instantaneously lead to mortality, as is commonly the case for environmental exposures. However, omics profiles induced by chemical exposures have rarely been systematically considered in mixture contexts.

OBJECTIVES

In this study, we aimed to investigate the predictability of chemical mixture effects on the whole-transcriptome scale.

METHODS

We predicted and measured the toxicogenomic effects of a synthetic mixture on zebrafish embryos. The mixture contained the compounds diuron, diclofenac, and naproxen. To predict concentration- and time-resolved whole-transcriptome responses to the mixture exposure, we adopted the mixture concept of concentration addition. Predictions were based on the transcriptome profiles obtained for the individual mixture components in a previous study. Finally, concentration- and time-resolved mixture exposures and subsequent toxicogenomic measurements were performed and the results were compared with the predictions.

RESULTS

This comparison of the predictions with the observations showed that the concept of concentration addition provided reasonable estimates for the effects induced by the mixture exposure on the whole transcriptome. Although nonadditive effects were observed only occasionally, combined, that is, multicomponent-driven, effects were found for mixture components with anticipated similar, as well as dissimilar, modes of action.

DISCUSSION

Overall, this study demonstrates that using a concentration- and time-resolved approach, the occurrence and size of combined effects of chemicals may be predicted at the whole-transcriptome scale. This allows improving effect assessment of mixture exposures on the molecular scale that might not only be of relevance in terms of risk assessment but also for pharmacological applications. https://doi.org/10.1289/EHP7773.

Non-Animal Strategies for Toxicity Assessment of Nanoscale Materials: Role of Adverse Outcome Pathways in the Selection of Endpoints

Faster, cheaper, sensitive, and mechanisms-based animal alternatives are needed to address the safety assessment needs of the growing number of nanomaterials (NM) and their sophisticated property variants. Specifically, strategies that help identify and prioritize alternative schemes involving individual test models, toxicity endpoints, and assays for the assessment of adverse outcomes, as well as strategies that enable validation and refinement of these schemes for the regulatory acceptance are needed. In this review, two strategies 1) the current nanotoxicology literature review and 2) the adverse outcome pathways (AOPs) framework, a systematic process that allows the assembly of available mechanistic information concerning a toxicological response in a simple modular format, are presented. The review highlights 1) the most frequently assessed and reported ad hoc in vivo and in vitro toxicity measurements in the literature, 2) various AOPs of relevance to inhalation toxicity of NM that are presently under development, and 3) their applicability in identifying key events of toxicity for targeted in vitro assay development. Finally, using an existing AOP for lung fibrosis, the specific combinations of cell types, exposure and test systems, and assays that are experimentally supported and thus, can be used for assessing NM-induced lung fibrosis, are proposed.

Classifying polycyclic aromatic hydrocarbons by carcinogenic potency using in vitro biosignatures

One of the most difficult challenges for risk assessment is evaluation of chemicals that predominately co-occur in mixtures like polycyclic aromatic hydrocarbons (PAHs). We previously developed a classification model in which systems biology data collected from mice short-term after chemical exposure accurately predict tumor outcome. The present study demonstrates translation of this approach into a human in vitro model in which chemical-specific bioactivity profiles from 3D human bronchial epithelial cells (HBEC) classify PAHs by carcinogenic potency. Gene expression profiles were analyzed from HBEC exposed to carcinogenic and non-carcinogenic PAHs and classification accuracies were identified for individual pathway-based gene sets. Posterior probabilities of best performing gene sets were combined via Bayesian integration resulting in a classifier with four gene sets, including aryl hydrocarbon receptor signaling, regulation of epithelial mesenchymal transition, regulation of angiogenesis, and cell cycle G2-M. In addition, transcriptional benchmark dose modeling of benzo[a]pyrene (BAP) showed that the most sensitive gene sets to BAP regulation were largely dissimilar from those that best classified PAH carcinogenicity challenging current assumptions that BAP carcinogenicity (and subsequent mode of action) is reflective of overall PAH carcinogenicity. These results illustrate utility of using systems toxicology approaches to analyze global gene expression towards carcinogenic hazard assessment.

The APOE gene cluster responds to air pollution factors in mice with coordinated expression of genes that differs by age in humans

Little is known of gene-environment interactions for Alzheimer's disease (AD) risk factors. Apolipoprotein E (APOE) and neighbors on chromosome 19q13.3 have variants associated with risks of AD, but with unknown mechanism. This study describes novel links among the APOE network, air pollution, and age-related diseases. Mice exposed to air pollution nano-sized particulate matter (nPM) had coordinate responses of Apoe-Apoc1-Tomm40 in the cerebral cortex. In humans, the AD vulnerable hippocampus and amygdala had stronger age decline in APOE cluster expression than the AD-resistant cerebellum and hypothalamus. Using consensus weighted gene co-expression network, we showed that APOE has a conserved co-expressed network in rodent and primate brains. SOX1, which has AD-associated single nucleotide polymorphisms, was among the co-expressed genes in the human hippocampus. Humans and mice shared 87% of potential binding sites for transcription factors in APOE cluster promoter, suggesting similar inducibility and a novel link among environment, APOE cluster, and risk of AD.

Evidence-based assessment on environmental mixture using a concentration-dependent transcriptomics approach

Development of new approach methodologies is urgently needed to characterize the likelihood that complex mixtures of chemicals affect water quality. Omics advances in ecotoxicology allow assessment on a broadest coverage of disrupted biological pathway by mixtures. Here the usefulness of transcriptomic analyses for evaluation of combined effects and identification of main effect components are explored. Two artificial mixtures (Mix 1 and Mix 2) were tested by a concentration-dependent reduced zebrafish transcriptome (CRZT) approach and toxicity bioassays using zebrafish embryos. Then, the toxicities and transcriptomic effects of 12 component chemicals on embryos were incorporated into additivity models to characterize the combined effects of chemicals in mixtures and to identify the main bioactive compounds. Mix 1 and Mix 2 displayed similar embryo toxicities (LD₅₀: 6.6 μM and 8.7 μM, respectively), however, Mix 2 elicited broader biological process perturbations and 5-fold higher transcriptome potency (point of departure eliciting a 20% pathway response, POD_path20) than Mix 1. The predicted mixture toxicities derived from additivity expectations deviated by 2-fold or less from the measured embryo toxicities except for the Jaw defect endpoint; most biological processes deviated by 3-fold or less. Finally, diclofenac (DFC) and propiconazole (PCZ) were identified as the main contributing components (≥80% explanation) to the embryo toxicity and biological process perturbations by Mix 1. While DFC and chlorophene (CLP) explained up to 80% of the embryo toxicities and biological effects of Mix 2 associated with development and Metabolism processes. The CRZT approach provides a powerful tool for assessment of biological pathways perturbed by chemicals in mixtures and for identification of main bioactive compounds.

Soil exposed to silver nanoparticles reveals significant changes in community structure and altered microbial transcriptional profiles

Anthropogenic activities can disrupt soil ecosystems, normally resulting in reduced soil microbial health. Regulatory agencies need to determine the effects of uncharacterized substances on soil microbial health to establish the safety of these chemicals if they end up in the environment. Previous work has focused on measuring traditional ecotoxicologial endpoints within the categories of microbial biomass, activity, and community structure/diversity. Because these tests can be labor intensive, lengthy to conduct, and cannot measure changes in individual gene functions, we wanted to establish whether metatranscriptomics could be used as a more sensitive endpoint and provide a perspective on community function that is more informative than taxonomic identification of microbes alone. We spiked a freshly collected sandy loam soil (Vulcan, Alberta, Canada) with 0, 60, 145, 347, 833, and 2000 mg kg^-1 of silver nanoparticles (AgNPs), a known antagonist of microorganisms due to its propensity for dissolution of toxic silver ions. Assessments performed in our previous work using traditional tests demonstrated the toxicity of AgNPs on soil microbial processes. We expanded this analysis with genomics-based tests by measuring changes in community taxonomic structure and function using 16S rDNA profiling and metatranscriptomics. In addition to identifying bacterial taxa affected by AgNPs, we found that genes involved in heavy metal resistance (e.g., the CzcA efflux pump) and other toxicity response pathways were highly upregulated in the presence of silver. Dose-response analysis using BMDExpress2 software successfully modeled many physiologically relevant genes responding to low concentrations of AgNPs. We found that the transcriptomic point of departure (BMD₅₀) was lower than the IC50s calculated using the traditional tests in our previous work. These results suggest that dose-response modeling of metatranscriptomic gene expression is a useful tool in soil microbial health assessment. SUMMARY: Genomics-based endpoints for the assessment of soil microbial health can be used to perform quantitative dose-response modeling, and soil-based RNAseq adds functional insights.

Pathway-based assessment of single chemicals and mixtures by a high-throughput transcriptomics approach

The ever-increasing number of chemicals and complex mixtures demands a high-throughput and cost-effective approach for chemical safety assessment. High-throughput transcriptomics (HTT) is promising in investigating genome-scale perturbation of chemical exposure in concentration-dependent manner. However, the application of HTT has been limited due to lack of methodology for single chemicals and mixture assessment. This study aimed to evaluate the ability of a newly-developed human reduced transcriptomics (RHT) approach to assess pathway-based profiles of single chemicals, and to develop a biological pathway-based approach for benchmarking mixture potency using single chemical-based prediction model. First, concentration-dependent RHT were used to qualitatively and quantitatively differentiate pathway-based patterns of different chemicals, using three model toxicants, 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), triclosan (TCS) and 5-Chloro-6-hydroxy-2,2',4,4'-tetrabromodiphenyl ether (5-Cl-6-OH-BDE-47). AHR-regulated genes and pathways were most sensitively induced by TCDD, while TCS and 5-Cl-6-OH-BDE-47 were much less potent in AHR-associated activation, which was concordant with known MoA of each single chemical. Second, two artificial mixtures and their components of twelve individual chemicals were performed with concentration-dependent RHT. Concentration addition (CA) and independent action (IA) models were used to predict transcriptional potency of mixtures from transcriptomics of individual chemicals. For overall bioactivity, CA and IA models can both predict potency of observed responses within 95% confidence interval. For specific biological processes, multiple biological processes such as hormone signaling and DNA damage can be predicted using CA models for mixtures. The concentration-dependent RHT can provide a powerful approach for qualitative and quantitative assessment of biological pathway perturbated by environment chemical and mixtures.

Whole Genome Sequencing of the Mutamouse Model Reveals Strain- and Colony-Level Variation, and Genomic Features of the Transgene Integration Site

The MutaMouse transgenic rodent model is widely used for assessing in vivo mutagenicity. Here, we report the characterization of MutaMouse's whole genome sequence and its genetic variants compared to the C57BL/6 reference genome. High coverage (>50X) next-generation sequencing (NGS) of whole genomes from multiple MutaMouse animals from the Health Canada (HC) colony showed ~5 million SNVs per genome, ~20% of which are putatively novel. Sequencing of two animals from a geographically separated colony at Covance indicated that, over the course of 23 years, each colony accumulated 47,847 (HC) and 17,677 (Covance) non-parental homozygous single nucleotide variants. We found no novel nonsense or missense mutations that impair the MutaMouse response to genotoxic agents. Pairing sequencing data with array comparative genomic hybridization (aCGH) improved the accuracy and resolution of copy number variants (CNVs) calls and identified 300 genomic regions with CNVs. We also used long-read sequence technology (PacBio) to show that the transgene integration site involved a large deletion event with multiple inversions and rearrangements near a retrotransposon. The MutaMouse genome gives important genetic context to studies using this model, offers insight on the mechanisms of structural variant formation, and contributes a framework to analyze aCGH results alongside NGS data.

Systematic developmental neurotoxicity assessment of a representative PAH Superfund mixture using zebrafish

Superfund sites often consist of complex mixtures of polycyclic aromatic hydrocarbons (PAHs). It is widely recognized that PAHs pose risks to human and environmental health, but the risks posed by exposure to PAH mixtures are unclear. We constructed an environmentally relevant PAH mixture with the top 10 most prevalent PAHs (SM10) from a Superfund site derived from environmental passive sampling data. Using the zebrafish model, we measured body burden at 48 hours post fertilization (hpf) and evaluated the developmental and neurotoxicity of SM10 and the 10 individual constituents at 24 hours post fertilization (hpf) and 5 days post fertilization (dpf). Zebrafish embryos were exposed from 6 to 120 hpf to (1) the SM10 mixture, (2) a variety of individual PAHs: pyrene, fluoranthene, retene, benzo[a]anthracene, chrysene, naphthalene, acenaphthene, phenanthrene, fluorene, and 2-methylnaphthalene. We demonstrated that SM10 and only 3 of the individual PAHs were developmentally toxic. Subsequently, we constructed and exposed developing zebrafish to two sub-mixtures: SM3 (comprised of 3 of the developmentally toxicity PAHs) and SM7 (7 non-developmentally toxic PAHs). We found that the SM3 toxicity profile was similar to SM10, and SM7 unexpectedly elicited developmental toxicity unlike that seen with its individual components. The results demonstrated that the overall developmental toxicity in the mixtures could be explained using the general concentration addition model. To determine if exposures activated the AHR pathway, spatial expression of CYP1A was evaluated in the 10 individual PAHs and the 3 mixtures at 5 dpf. Results showed activation of AHR in the liver and vasculature for the mixtures and some individual PAHs. Embryos exposed to SM10 during development and raised in chemical-free water into adulthood exhibited decreased learning and responses to startle stimulus indicating that developmental SM10 exposures affect neurobehavior. Collectively, these results exemplify the utility of zebrafish to investigate the developmental and neurotoxicity of complex mixtures.