Applying 'omics technologies in chemicals risk assessment: Report of an ECETOC workshop

A novel multi-omics data analysis of dose-dependent and temporal changes in regulatory pathways due to chemical perturbation: a case study on caffeine

Comprehensive analysis of multi-omics data can reveal alterations in regulatory pathways induced by cellular exposure to chemicals by characterizing biological processes at the molecular level. Data-driven omics analysis, conducted in a dose-dependent or dynamic manner, can facilitate comprehending toxicity mechanisms. This study introduces a novel multi-omics data analysis designed to concurrently examine dose-dependent and temporal patterns of cellular responses to chemical perturbations. This analysis, encompassing preliminary exploration, pattern deconstruction, and network reconstruction of multi-omics data, provides a comprehensive perspective on the dynamic behaviors of cells exposed to varying levels of chemical stimuli. Importantly, this analysis is adaptable to any number of omics layers, including site-specific phosphoproteomics. We implemented this analysis on multi-omics data obtained from HepG2 cells exposed to a range of caffeine doses over varying durations and identified six response patterns, along with their associated biomolecules and pathways. Our study demonstrates the effectiveness of the proposed multi-omics data analysis in capturing multidimensional patterns of cellular response to chemical perturbation, enhancing understanding of pathway regulation for chemical risk assessment.

Novel Time-dependent Multi-omics Integration in Sepsis-associated Liver Dysfunction

The recently developed technologies that allow the analysis of each single omics have provided an unbiased insight into ongoing disease processes. However, it remains challenging to specify the study design for the subsequent integration strategies that can associate sepsis pathophysiology and clinical outcomes. Here, we conducted a time-dependent multi-omics integration (TDMI) in a sepsis-associated liver dysfunction (SALD) model. We successfully deduced the relation of the Toll-like receptor 4 (TLR4) pathway with SALD. Although TLR4 is a critical factor in sepsis progression, it is not specified in single-omics analyses but only in the TDMI analysis. This finding indicates that the TDMI-based approach is more advantageous than single-omics analyses in terms of exploring the underlying pathophysiological mechanism of SALD. Furthermore, TDMI-based approach can be an ideal paradigm for insightful biological interpretations of multi-omics datasets that will potentially reveal novel insights into basic biology, health, and diseases, thus allowing the identification of promising candidates for therapeutic strategies.

Prenatal exposures to endocrine disrupting chemicals: The role of multi-omics in understanding toxicity

Endocrine disrupting chemicals (EDCs) are a diverse group of toxicants detected in populations globally. Prenatal EDC exposures impact birth and childhood outcomes. EDCs work through persistent changes at the molecular, cellular, and organ level. Molecular and biochemical signals or 'omics' can be measured at various functional levels - including the epigenome, transcriptome, proteome, metabolome, and the microbiome. In this narrative review, we introduce each omics and give examples of associations with prenatal EDC exposures. There is substantial research on epigenomic modifications in offspring exposed to EDCs during gestation, and a growing number of studies evaluating the transcriptome, proteome, metabolome, or microbiome in response to these exposures. Multi-omics, integrating data across omics layers, may improve understanding of disrupted function pathways related to early life exposures. We highlight several data integration methods to consider in multi-omics studies. Information from multi-omics can improve understanding of the biological processes and mechanisms underlying prenatal EDC toxicity.

Toxicometabolomics as a tool for next generation environmental risk assessment

Traditionally applied methodology in environmental risk assessment (ERA) has fallen out of step with technological advancements and regulatory requirements, challenging effectiveness and accuracy of the assessments. Extensive efforts have been focused towards a transition to a more data-driven and mechanistically-based next generation risk assessment. Metabolomics can produce detailed and comprehensive molecular insight into affected biochemical processes. Combining metabolomics with environmental toxicology can help to understand the mechanisms and/or modes of action underlying toxicity of environmental pollutants and inform adverse outcome pathways, as well as facilitate identification of biomarkers to quantify effects and/or exposure. This Technical Report describes the activities and work performed within the frame of the European Food Risk Assessment Fellowship Programme (EU-FORA), implemented at the section 'Environmental Chemistry and Toxicology' at the Department of Environmental Science at Aarhus University in Denmark with synergies to an ongoing H2020 RIA project 'EndocRine Guideline Optimisation' (ERGO). In accordance with the 'training by doing' principles of the EU-FORA, the fellowship project combined the exploration of the status of scientific discussion on methodology in ERA through literature study with hands-on training, using the metabolomics analysis pipeline established at Aarhus University. For the hands-on training, an amphibian metamorphosis assay (OECD test no.231) was used as a proof-of-concept toxicometabolomics study case. Both a targeted biomarker - and an untargeted metabolomics approach was applied.

Insights into the intestinal toxicity of foodborne mycotoxins through gut microbiota: A comprehensive review

Mycotoxins, which are fungal metabolites, pose a significant global food safety concern by extensively contaminating food and feed, thereby seriously threatening public health and economic development. Many foodborne mycotoxins exhibit potent intestinal toxicity. However, the mechanisms underlying mycotoxin-induced intestinal toxicity are diverse and complex, and effective prevention or treatment methods for this condition have not yet been established in clinical and animal husbandry practices. In recent years, there has been increasing attention to the role of gut microbiota in the occurrence and development of intestinal diseases. Hence, this review aims to provide a comprehensive summary of the intestinal toxicity mechanisms of six common foodborne mycotoxins. It also explores novel toxicity mechanisms through the "key gut microbiota-key metabolites-key targets" axis, utilizing multiomics and precision toxicology studies with a specific focus on gut microbiota. Additionally, we examine the potential beneficial effects of probiotic supplementation on mycotoxin-induced toxicity based on initial gut microbiota-mediated mycotoxicity. This review offers a systematic description of how mycotoxins impact gut microbiota, metabolites, and genes or proteins, providing valuable insights for subsequent toxicity studies of mycotoxins. Furthermore, it lays a theoretical foundation for preventing and treating intestinal toxicity caused by mycotoxins and advancing food safety practices.

Applying genomics in regulatory toxicology: a report of the ECETOC workshop on omics threshold on non-adversity

In a joint effort involving scientists from academia, industry and regulatory agencies, ECETOC's activities in Omics have led to conceptual proposals for: (1) A framework that assures data quality for reporting and inclusion of Omics data in regulatory assessments; and (2) an approach to robustly quantify these data, prior to interpretation for regulatory use. In continuation of these activities this workshop explored and identified areas of need to facilitate robust interpretation of such data in the context of deriving points of departure (POD) for risk assessment and determining an adverse change from normal variation. ECETOC was amongst the first to systematically explore the application of Omics methods, now incorporated into the group of methods known as New Approach Methodologies (NAMs), to regulatory toxicology. This support has been in the form of both projects (primarily with CEFIC/LRI) and workshops. Outputs have led to projects included in the workplan of the Extended Advisory Group on Molecular Screening and Toxicogenomics (EAGMST) group of the Organisation for Economic Co-operation and Development (OECD) and to the drafting of OECD Guidance Documents for Omics data reporting, with potentially more to follow on data transformation and interpretation. The current workshop was the last in a series of technical methods development workshops, with a sub-focus on the derivation of a POD from Omics data. Workshop presentations demonstrated that Omics data developed within robust frameworks for both scientific data generation and analysis can be used to derive a POD. The issue of noise in the data was discussed as an important consideration for identifying robust Omics changes and deriving a POD. Such variability or "noise" can comprise technical or biological variation within a dataset and should clearly be distinguished from homeostatic responses. Adverse outcome pathways (AOPs) were considered a useful framework on which to assemble Omics methods, and a number of case examples were presented in illustration of this point. What is apparent is that high dimension data will always be subject to varying processing pipelines and hence interpretation, depending on the context they are used in. Yet, they can provide valuable input for regulatory toxicology, with the pre-condition being robust methods for the collection and processing of data together with a comprehensive description how the data were interpreted, and conclusions reached.

A tiered testing strategy based on in vitro phenotypic and transcriptomic data for selecting representative petroleum UVCBs for toxicity evaluation in vivo

Hazard evaluation of substances of "unknown or variable composition, complex reaction products and biological materials" (UVCBs) remains a major challenge in regulatory science because their chemical composition is difficult to ascertain. Petroleum substances are representative UVCBs and human cell-based data have been previously used to substantiate their groupings for regulatory submissions. We hypothesized that a combination of phenotypic and transcriptomic data could be integrated to make decisions as to selection of group-representative worst-case petroleum UVCBs for subsequent toxicity evaluation in vivo. We used data obtained from 141 substances from 16 manufacturing categories previously tested in 6 human cell types (induced pluripotent stem cell [iPSC]-derived hepatocytes, cardiomyocytes, neurons, and endothelial cells, and MCF7 and A375 cell lines). Benchmark doses for gene-substance combinations were calculated, and both transcriptomic and phenotype-derived points of departure (PODs) were obtained. Correlation analysis and machine learning were used to assess associations between phenotypic and transcriptional PODs and to determine the most informative cell types and assays, thus representing a cost-effective integrated testing strategy. We found that 2 cell types-iPSC-derived-hepatocytes and -cardiomyocytes-contributed the most informative and protective PODs and may be used to inform selection of representative petroleum UVCBs for further toxicity evaluation in vivo. Overall, although the use of new approach methodologies to prioritize UVCBs has not been widely adopted, our study proposes a tiered testing strategy based on iPSC-derived hepatocytes and cardiomyocytes to inform selection of representative worst-case petroleum UVCBs from each manufacturing category for further toxicity evaluation in vivo.

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.

Multiomics Point of Departure (moPOD) Modeling Supports an Adverse Outcome Pathway Network for Ionizing Radiation

While adverse biological effects of acute high-dose ionizing radiation have been extensively investigated, knowledge on chronic low-dose effects is scarce. The aims of the present study were to identify hazards of low-dose ionizing radiation to Daphnia magna using multiomics dose-response modeling and to demonstrate the use of omics data to support an adverse outcome pathway (AOP) network development for ionizing radiation. Neonatal D. magna were exposed to γ radiation for 8 days. Transcriptomic analysis was performed after 4 and 8 days of exposure, whereas metabolomics and confirmative bioassays to support the omics analyses were conducted after 8 days of exposure. Benchmark doses (BMDs, 10% benchmark response) as points of departure (PODs) were estimated for both dose-responsive genes/metabolites and the enriched KEGG pathways. Relevant pathways derived using the BMD modeling and additional functional end points measured by the bioassays were overlaid with a previously published AOP network. The results showed that several molecular pathways were highly relevant to the known modes of action of γ radiation, including oxidative stress, DNA damage, mitochondrial dysfunction, protein degradation, and apoptosis. The functional assays showed increased oxidative stress and decreased mitochondrial membrane potential and ATP pool. Ranking of PODs at the pathway and functional levels showed that oxidative damage related functions had relatively low PODs, followed by DNA damage, energy metabolism, and apoptosis. These were supportive of causal events in the proposed AOP network. This approach yielded promising results and can potentially provide additional empirical evidence to support further AOP development for ionizing radiation.

New approach methodologies: A quantitative in vitro to in vivo extrapolation case study with PFASs

PER: and polyfluoroalkyl substances (PFASs) have been associated with increased blood lipids in humans. Perfluorooctanoic acid (PFOA) has been also linked with elevated alanine transferase (ALT) serum levels in humans, and in rodents the liver is a main target organ for many PFASs. With the focus on New Approach Methodologies, the chronic oral equivalent effect doses were calculated for PFOA, PFNA (perfluorononanoic acid), PFHxS (perfluorohexanesulfonic acid) and PFOS (perfluorooctane sulfonic acid) based on in vitro effects measured in the HepaRG cell line. Selected in vitro readouts were considered biomarkers for lipid disturbances and hepatotoxicity. Concentration-response data obtained from HepaRG cells on triglyceride (TG) accumulation and expression changes of 12 selected genes (some involved in cholesterol homeostasis) were converted into corresponding human dose-response data, using physiologically based kinetic (PBK) model-facilitated reverse dosimetry. Next to this, the biokinetics of the chemicals were studied in the cell system. The current European dietary PFASs exposure overlaps with the calculated oral equivalent effect doses, indicating that the latter may lead to interference with hepatic gene expression and lipid metabolism. These findings illustrate an in vitro-in silico methodology, which can be applied for more PFASs, to select those that should be prioritized for further hazard characterization.

Application of AOPs to assist regulatory assessment of chemical risks - Case studies, needs and recommendations

While human regulatory risk assessment (RA) still largely relies on animal studies, new approach methodologies (NAMs) based on in vitro, in silico or non-mammalian alternative models are increasingly used to evaluate chemical hazards. Moreover, human epidemiological studies with biomarkers of effect (BoE) also play an invaluable role in identifying health effects associated with chemical exposures. To move towards the next generation risk assessment (NGRA), it is therefore crucial to establish bridges between NAMs and standard approaches, and to establish processes for increasing mechanistically-based biological plausibility in human studies. The Adverse Outcome Pathway (AOP) framework constitutes an important tool to address these needs but, despite a significant increase in knowledge and awareness, the use of AOPs in chemical RA remains limited. The objective of this paper is to address issues related to using AOPs in a regulatory context from various perspectives as it was discussed in a workshop organized within the European Union partnerships HBM4EU and PARC in spring 2022. The paper presents examples where the AOP framework has been proven useful for the human RA process, particularly in hazard prioritization and characterization, in integrated approaches to testing and assessment (IATA), and in the identification and validation of BoE in epidemiological studies. Nevertheless, several limitations were identified that hinder the optimal usability and acceptance of AOPs by the regulatory community including the lack of quantitative information on response-response relationships and of efficient ways to map chemical data (exposure and toxicity) onto AOPs. The paper summarizes suggestions, ongoing initiatives and third-party tools that may help to overcome these obstacles and thus assure better implementation of AOPs in the NGRA.

Derivation of metabolic point of departure using high-throughput in vitro metabolomics: investigating the importance of sampling time points on benchmark concentration values in the HepaRG cell line

Amongst omics technologies, metabolomics should have particular value in regulatory toxicology as the measurement of the molecular phenotype is the closest to traditional apical endpoints, whilst offering mechanistic insights into the biological perturbations. Despite this, the application of untargeted metabolomics for point-of-departure (POD) derivation via benchmark concentration (BMC) modelling is still a relatively unexplored area. In this study, a high-throughput workflow was applied to derive PODs associated with a chemical exposure by measuring the intracellular metabolome of the HepaRG cell line following treatment with one of four chemicals (aflatoxin B₁, benzo[a]pyrene, cyclosporin A, or rotenone), each at seven concentrations (aflatoxin B₁, benzo[a]pyrene, cyclosporin A: from 0.2048 μM to 50 μM; rotenone: from 0.04096 to 10 μM) and five sampling time points (2, 6, 12, 24 and 48 h). The study explored three approaches to derive PODs using benchmark concentration modelling applied to single features in the metabolomics datasets or annotated metabolites or lipids: (1) the 1st rank-ordered unannotated feature, (2) the 1st rank-ordered putatively annotated feature (using a recently developed HepaRG-specific library of polar metabolites and lipids), and (3) 25th rank-ordered feature, demonstrating that for three out of four chemical datasets all of these approaches led to relatively consistent BMC values, varying less than tenfold across the methods. In addition, using the 1st rank-ordered unannotated feature it was possible to investigate temporal trends in the datasets, which were shown to be chemical specific. Furthermore, a possible integration of metabolomics-driven POD derivation with the liver steatosis adverse outcome pathway (AOP) was demonstrated. The study highlights that advances in technologies enable application of in vitro metabolomics at scale; however, greater confidence in metabolite identification is required to ensure PODs are mechanistically anchored.

Metabolomic Study of Urine from Workers Exposed to Low Concentrations of Benzene by UHPLC-ESI-QToF-MS Reveals Potential Biomarkers Associated with Oxidative Stress and Genotoxicity

Benzene is a human carcinogen whose exposure to concentrations below 1 ppm (3.19 mg·m^-3) is associated with myelotoxic effects. The determination of biomarkers such as trans-trans muconic acid (AttM) and S-phenylmercapturic acid (SPMA) show exposure without reflecting the toxic effects of benzene. For this reason, in this study, the urinary metabolome of individuals exposed to low concentrations of benzene was investigated, with the aim of understanding the biological response to exposure to this xenobiotic and identifying metabolites correlated with the toxic effects induced by it. Ultra-efficient liquid chromatography coupled to a quadrupole-time-of-flight mass spectrometer (UHPLC-ESI-Q-ToF-MS) was used to identify metabolites in the urine of environmentally (n = 28) and occupationally exposed (n = 32) to benzene (mean of 22.1 μg·m^-3 and 31.8 μg·m^-3, respectively). Non-targeted metabolomics analysis by PLS-DA revealed nine urinary metabolites discriminating between groups and statistically correlated with oxidative damage (MDA, thiol) and genetic material (chromosomal aberrations) induced by the hydrocarbon. The analysis of metabolic pathways revealed important alterations in lipid metabolism. These results point to the involvement of alterations in lipid metabolism in the mechanisms of cytotoxic and genotoxic action of benzene. Furthermore, this study proves the potential of metabolomics to provide relevant information to understand the biological response to exposure to xenobiotics and identify early effect biomarkers.

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.

Consideration of metabolomics and transcriptomics data in the context of using avian embryos for toxicity testing

Early-life stage (ELS) avian toxicity tests have been proposed as a more ethical alternative to traditional standardized tests with adult birds. At the same time, 'omics approaches are gaining traction in the field of avian toxicology, but little has been done to characterize the metabolome and transcriptome at different life stages. The present study uses 'omics data from toxicity tests of 8 environmental chemicals in ELS and adult Japanese quail (Coturnix japonica) to address this data gap. Previous analyses of these data focused on responses to each of the individual chemicals. Here, we consider data from all studies to describe variation in the metabolome and transcriptome between life stages and across independent experiments, irrespective of chemical treatment. Of the 230 metabolites detected in liver, 163 were shared between the two life stages. However, many of the targeted bile acids that were present in the adult liver were absent from ELS samples. For the transcriptome, >90% of the 18,364 detected transcripts were common to both life stages. Based on the 213 genes solely detected in ELS liver, the neuroactive ligand-receptor interaction pathway was significantly enriched. Multivariate and hierarchical clustering analyses revealed that variability among independent experiments was higher for the adult than the ELS studies at both the metabolomic and transcriptomic levels. Our results indicate concordance of the two approaches, with less variation between independent experiments in the ELS metabolome and transcriptome than in adults, lending support for the use of ELS as an alternative toxicity testing strategy.

Towards a science-based testing strategy to identify maternal thyroid hormone imbalance and neurodevelopmental effects in the progeny-part III: how is substance-mediated thyroid hormone imbalance in pregnant/lactating rats or their progeny related to neurodevelopmental effects?

This review investigated which patterns of thyroid- and brain-related effects are seen in rats upon gestational/lactational exposure to 14 substances causing thyroid hormone imbalance by four different modes-of-action (inhibition of thyroid peroxidase, sodium-iodide symporter and deiodinase activities, enhancement of thyroid hormone clearance) or to dietary iodine deficiency. Brain-related parameters included motor activity, cognitive function, acoustic startle response, hearing function, periventricular heterotopia, electrophysiology and brain gene expression. Specific modes-of-action were not related to specific patterns of brain-related effects. Based upon the rat data reviewed, maternal serum thyroid hormone levels do not show a causal relationship with statistically significant neurodevelopmental effects. Offspring serum thyroxine together with offspring serum triiodothyronine and thyroid stimulating hormone appear relevant to predict the likelihood for neurodevelopmental effects. Based upon the collated database, thresholds of ≥60%/≥50% offspring serum thyroxine reduction and ≥20% and statistically significant offspring serum triiodothyronine reduction indicate an increased likelihood for statistically significant neurodevelopmental effects; accuracies: 83% and 67% when excluding electrophysiology (and gene expression). Measurements of brain thyroid hormone levels are likely relevant, too. The extent of substance-mediated thyroid hormone imbalance appears more important than substance mode-of-action to predict neurodevelopmental impairment in rats. Pertinent research needs were identified, e.g. to determine whether the phenomenological offspring thyroid hormone thresholds are relevant for regulatory toxicity testing. The insight from this review shall be used to suggest a tiered testing strategy to determine whether gestational/lactational substance exposure may elicit thyroid hormone imbalance and potentially also neurodevelopmental effects.

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.

'Omics in environmental epidemiological studies of chemical exposures: A systematic evidence map

BACKGROUND

Systematic evidence maps are increasingly used to develop chemical risk assessments. These maps can provide an overview of available studies and relevant study information to be used for various research objectives and applications. Environmental epidemiological studies that examine the impact of chemical exposures on various 'omic profiles in human populations provide relevant mechanistic information and can be used for benchmark dose modeling to derive potential human health reference values.

OBJECTIVES

To create a systematic evidence map of environmental epidemiological studies examining environmental contaminant exposures with 'omics in order to characterize the extent of available studies for future research needs.

METHODS

Systematic review methods were used to search and screen the literature and included the use of machine learning methods to facilitate screening studies. The Populations, Exposures, Comparators and Outcomes (PECO) criteria were developed to identify and screen relevant studies. Studies that met the PECO criteria after full-text review were summarized with information such as study population, study design, sample size, exposure measurement, and 'omics analysis.

RESULTS

Over 10,000 studies were identified from scientific databases. Screening processes were used to identify 84 studies considered PECO-relevant after full-text review. Various contaminants (e.g. phthalate, benzene, arsenic, etc.) were investigated in epidemiological studies that used one or more of the four 'omics of interest: epigenomics, transcriptomics, proteomics, and metabolomics . The epidemiological study designs that were used to explore single or integrated 'omic research questions with contaminant exposures were cohort studies, controlled trials, cross-sectional, and case-control studies. An interactive web-based systematic evidence map was created to display more study-related information.

CONCLUSIONS

This systematic evidence map is a novel tool to visually characterize the available environmental epidemiological studies investigating contaminants and biological effects using 'omics technology and serves as a resource for investigators and allows for a range of applications in chemical research and risk assessment needs.

A microRNA or messenger RNA point of departure estimates an apical endpoint point of departure in a rat developmental toxicity model

Traditional developmental toxicity testing practice examines fetal apical endpoints to identify a point of departure (POD) for risk assessment. A potential new testing paradigm involves deriving a POD from a comprehensive analysis of molecular-level change. Here, the rat ketoconazole endocrine-mediated developmental toxicity model was used to test the hypothesis that maternal epigenomic (miRNA) and transcriptomic (mRNA) PODs are similar to fetal apical endpoint PODs. Sprague-Dawley rats were exposed from gestation day (GD) 6-21 to 0, 0.063, 0.2, 0.63, 2, 6.3, 20, or 40 mg/kg/day ketoconazole. Dam systemic, liver, and placenta PODs, along with GD 21 fetal resorption, body weight, and skeletal apical PODs were derived using BMDS software. GD 21 dam liver and placenta miRNA and mRNA PODs were obtained using three methods: a novel individual molecule POD accumulation method, a first mode method, and a gene set method. Dam apical POD values ranged from 2.0 to 38.6 mg/kg/day; the lowest value was for placenta histopathology. Fetal apical POD values were 10.9-20.3 mg/kg/day; the lowest value was for fetal resorption. Dam liver miRNA and mRNA POD values were 0.34-0.69 mg/kg/day, and placenta miRNA and mRNA POD values were 2.53-6.83 mg/kg/day. Epigenomic and transcriptomic POD values were similar across liver and placenta. Deriving a molecular POD from dam liver or placenta was protective of a fetal apical POD. These data support the conclusion that a molecular POD can be used to estimate, or be protective of, a developmental toxicity apical POD.

Innovation in regulatory approaches for endocrine disrupting chemicals: The journey to risk assessment modernization in Canada

Globally, regulatory authorities grapple with the challenge of assessing the hazards and risks to human and ecosystem health that may result from exposure to chemicals that disrupt the normal functioning of endocrine systems. Rapidly increasing number of chemicals in commerce, coupled with the reliance on traditional, costly animal experiments for hazard characterization - often with limited sensitivity to many important mechanisms of endocrine disruption -, presents ongoing challenges for chemical regulation. The consequence is a limited number of chemicals for which there is sufficient data to assess if there is endocrine toxicity and hence few chemicals with thorough hazard characterization. To address this challenge, regulatory assessment of endocrine disrupting chemicals (EDCs) is benefiting from a revolution in toxicology that focuses on New Approach Methodologies (NAMs) to more rapidly identify, prioritize, and assess the potential risks from exposure to chemicals using novel, more efficient, and more mechanistically driven methodologies and tools. Incorporated into Integrated Approaches to Testing and Assessment (IATA) and guided by conceptual frameworks such as Adverse Outcome Pathways (AOPs), emerging approaches focus initially on molecular interactions between the test chemical and potentially vulnerable biological systems instead of the need for animal toxicity data. These new toxicity testing methods can be complemented with in silico and computational toxicology approaches, including those that predict chemical kinetics. Coupled with exposure data, these will inform risk-based decision-making approaches. Canada is part of a global network collaborating on building confidence in the use of NAMs for regulatory assessment of EDCs. Herein, we review the current approaches to EDC regulation globally (mainly from the perspective of human health), and provide a perspective on how the advances for regulatory testing and assessment can be applied and discuss the promises and challenges faced in adopting these novel approaches to minimize risks due to EDC exposure in Canada, and our world.

Towards a toxic-free environment: perspectives for chemical risk assessment approaches

Regulatory frameworks to control chemical exposure in general living and occupational environments have changed exposure scenarios towards a widely spread contamination at relatively low doses in developed countries. In such evolving context, some critical aspects should be considered to update risk assessment and management strategies. Risk assessment in low-dose chemical exposure scenarios should take advantage of: toxicological investigations on emerging substances of interest, like those recognised as endocrine disruptors or increasingly employed nanoscale materials; human biological monitoring studies aimed to identify innovative biomarkers for known chemical exposure; “omic” technologies useful to identify hazards of chemicals and their modes of action. For updated risk assessment models, suitable toxicological studies, analyses of dose-responses at low-concentrations, environmental and biological monitoring of exposure, together with exposome studies, and the proper definition of susceptible populations may all provide helpful contributions. These may guide defining preventive measures to control the exposure and develop safe and sustainable chemicals by design. Occupational medicine can offer know-how and instruments to understand and manage such evolution towards a toxic-free environment to protect the safety and health of the workforce and, in turn, that of the general population.

Integrated multi-omics analysis reveals the underlying molecular mechanism for developmental neurotoxicity of perfluorooctanesulfonic acid in zebrafish

Limited studies on multi-omics have been conducted to comprehensively investigate the molecular mechanism underlying the developmental neurotoxicity of perfluorooctanesulfonic acid (PFOS). In this study, the locomotor behavior of zebrafish larvae was assessed under the exposure to 0.1-20 μM PFOS based on its reported neurobehavioral effect. After the number of zebrafish larvae was optimized for proteomics and metabolomics studies, three kinds of omics (i.e., transcriptomics, proteomics, and metabolomics) were carried out with zebrafish larvae exposed to 0.1, 1, 5, and 10 μM PFOS. More importantly, a data-driven integration of multi-omics was performed to elucidate the toxicity mechanism involved in developmental neurotoxicity. In a concentration-dependent manner, exposure to PFOS provoked hyperactivity and hypoactivity under light and dark conditions, respectively. Individual omics revealed that PFOS exposure caused perturbations in the pathways of neurological function, oxidative stress, and energy metabolism. Integrated omics implied that there were decisive pathways for axonal deformation, neuroinflammatory stimulation, and dysregulation of calcium ion signaling, which are more clearly specified for neurotoxicity. Overall, our findings broaden the molecular understanding of the developmental neurotoxicity of PFOS, for which multi-omics and integrated omics analyses are efficient for discovering the significant molecular pathways related to developmental neurotoxicity in zebrafish.

Transcriptomic and proteomic responses of silver nanoparticles in hepatocyte-like cells derived from human induced pluripotent stem cells

Silver nanoparticles (AgNPs) have been increasingly used in a variety of consumer products over the last decades. However, their potential adverse effects have not been fully understood. In a previous study, we characterized transcriptomic changes in human induced pluripotent stem cell (iPSC)-derived hepatocyte-like cells (HLCs) in response to AgNP exposure. Here, we report findings of a follow-up proteomic study that evaluated alternations at the protein level in the same cell after being exposed to 10 μg/ml AgNPs for 24 h. In total, 6287 proteins were identified across two groups of samples (n = 3). Among these proteins, 665 were found to be differentially regulated (fold change ≥1.25, p < 0.01) between the AgNP-treated group and the untreated control group, including 264 upregulated and 401 downregulated. Bioinformatics analysis of the proteomics data, in side-by-side comparison to the transcriptomics data, confirms and substantiates previous findings on AgNP-induced alterations in metabolism, oxidative stress, inflammation, and potential association with cancer. A mechanism of action was proposed based on these results. Collectively, the findings of the current proteomic study are consistent with those of the previous transcriptomic study and further demonstrate the usefulness of iPSC-derived HLCs as an in vitro model for liver nanotoxicology.

Advantages of omics technology for evaluating cadmium toxicity in zebrafish

In the last decade, several advancements have been made in omics technologies and they have been applied extensively in diverse research areas. Especially in toxicological research, omics technology can efficiently and accurately generate relevant data on the molecular dynamics associated with adverse outcomes. Toxicomics is defined as the combination of toxicology and omics technologies and encompasses toxicogenomics, toxicoproteomics, and toxicometabolomics. This paper reviews the trend of applying omics technologies to evaluate cadmium (Cd) toxicity in zebrafish (D. rerio). Cd is a toxic heavy metal posing several environmental concerns; however, it is being used widely in everyday life. Zebrafish embryos and larvae are employed as standard models for many toxicity tests because they share 71.4% genetic homology with humans. This study summarizes the toxicity of Cd on the nerves, liver, heart, skeleton, etc. of zebrafish and introduces detailed omics techniques to understand the results of the toxicomic studies. Finally, the trend of toxicity evaluation in the zebrafish model of Cd based on omics technology is presented.

Strategies to apply 3Rs in preclinical testing

Animal experimentation has been fundamental in biological and biomedical research. To guarantee the maximum quality, efficacy and/or safety of products intended for the use in humans in vivo testing is necessary; however, for over 60 years, alternative methods have been developed in response to the necessity to reduce the number of animals used in experimentation, to guarantee their welfare; resorting to animal models only when strictly necessary. The three Rs (Replacement, Reduction, and Refinement), seek to ensure the rational and respectful use of laboratory animals and maintain an adequate projection in terms of bioethical considerations. This article describes different approaches to apply 3Rs in preclinical experimentation for either research or regulatory purposes.

Trends in the Application of "Omics" to Ecotoxicology and Stress Ecology

Our ability to predict and assess how environmental changes such as pollution and climate change affect components of the Earth's biome is of paramount importance. This need positioned the fields of ecotoxicology and stress ecology at the center of environmental monitoring efforts. Advances in these interdisciplinary fields depend not only on conceptual leaps but also on technological advances and data integration. High-throughput "omics" technologies enabled the measurement of molecular changes at virtually all levels of an organism's biological organization and thus continue to influence how the impacts of stressors are understood. This bibliometric review describes literature trends (2000-2020) that indicate that more different stressors than species are studied each year but that only a few stressors have been studied in more than two phyla. At the same time, the molecular responses of a diverse set of non-model species have been investigated, but cross-species comparisons are still rare. While transcriptomics studies dominated until 2016, a shift towards proteomics and multiomics studies is apparent. There is now a wealth of data at functional omics levels from many phylogenetically diverse species. This review, therefore, addresses the question of how to integrate omics information across species.

Ecotoxicogenomic analysis of zebrafish embryos exposed to triclosan and mixture triclosan and methyl triclosan using suppression subtractive hybridization and next-generation sequencing

Triclosan (TCS) and methyl-triclosan (MTCS), an environmental transformation product of biocide of TCS, have been detected in water, sediment, fish, and invertebrates. In this study, the key pathway perturbation in zebrafish (Danio rerio) embryos exposed to TCS (300 μg/L) and TCS/MTCS mixture (300 μg/L TCS + 30 μg/L MTCS) was assessed by integrating the metabolomic and transcriptomic dysregulation. The differential expressed genes (DEGs) were obtained from the subtracted cDNA libraries by using the suppression subtractive hybridization and next-generation sequencing approach. The dysregulation of twenty-eight GO terms and four KEGG pathways, including oxidative phosphorylation and cardiac muscle contraction, were shown in the TCS treatment group, indicating that TCS could disrupt the mitochondrial inner membrane function by downshifting the electrochemical gradient. Meanwhile, the addition of MTCS in the exposure would cause fourteen additional significant KEGG pathway changes, demonstrating the different effects between two exposure. A pathway-based analysis using the identified DEGs and the altered metabolites in zebrafish embryos treated with TCS and TCS/MTCS mixture, collectively, has been applied. This study demonstrated that the integration of SSH-NGS and metabolomics could reveal toxic effects and potential diseases associated with the exposures of TCS and MTCS in aquatic environments.

Approaches in metabolomics for regulatory toxicology applications

Innovative methodological approaches are needed to conduct human health and environmental risk assessments on a growing number of marketed chemicals. Metabolomics is progressively proving its value as an efficient strategy to perform toxicological evaluations of new and existing substances, and it will likely become a key tool to accelerate chemical risk assessments. However, additional guidance with widely accepted and harmonized procedures is needed before metabolomics can be routinely incorporated in decision-making for regulatory purposes. The aim of this review is to provide an overview of metabolomic strategies that have been successfully employed in toxicity assessment as well as the most promising workflows in a regulatory context. First, we provide a general view of the different steps of regulatory toxicology-oriented metabolomics. Emphasis is put on three key elements: robustness of experimental design, choice of analytical platform, and use of adapted data treatment tools. Then, examples in which metabolomics supported regulatory toxicology outputs in different scenarios are reviewed, including chemical grouping, elucidation of mechanisms of toxicity, and determination of points of departure. The overall intention is to provide insights into why and how to plan and conduct metabolomic studies for regulatory toxicology purposes.

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

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

Low Concentrations of Silver Nanoparticles and Silver Ions Perturb the Antioxidant Defense System and Nitrogen Metabolism in N2-Fixing Cyanobacteria

Although toxic effects of silver nanoparticles (AgNPs) on aquatic organisms have been extensively reported, responses of nitrogen-fixing cyanobacteria to AgNPs/Ag⁺ under environmentally relevant concentrations are largely unknown. Here, cyanobacteria were exposed to different concentrations of AgNPs (0.01, 0.1, and 1 mg/L) or Ag⁺ (0.1, 1, and 10 μg/L) for 96 h. The impacts of AgNPs and Ag⁺ on photosynthesis and N₂ fixation in cyanobacteria (Nostoc sphaeroides) were evaluated. In addition, gas chromatography-mass spectrometry (GC-MS)-based metabolomics was employed to give an instantaneous snapshot of the physiological status of the cells under AgNP/Ag⁺ exposure. Exposure to high doses of AgNPs (1 mg/L) or Ag⁺ (10 μg/L) caused growth inhibition, reactive oxygen species overproduction, malondialdehyde accumulation, and decreased N₂ fixation. In contrast, low doses of AgNPs (0.01 and 0.1 mg/L) and Ag⁺ (0.1 and 1 μg/L) did not induce observable responses. However, metabolomics revealed that metabolic reprogramming occurred even at low concentrations of AgNP and Ag⁺ exposure. Levels of a number of antioxidant defense-related metabolites, especially phenolic acid and polyphenols (gallic acid, resveratrol, isochlorogenic acid, chlorogenic acid, cinnamic acid, 3-hydroxybenzoic acid, epicatechin, catechin, and ferulic acid), significantly decreased in response to AgNPs or Ag⁺. This indicates that AgNPs and Ag⁺ can disrupt the antioxidant defense system and disturb nitrogen metabolism even at low-dose exposure. Metabolomics was shown to be a powerful tool to detect "invisible" changes, not observable by typical phenotypic-based endpoints.

New and Emerging Risk Factors in Occupational Health

Workplace health and safety is constantly evolving both in developed and developing countries. Under the tumultuous development of technology, working environments are changing, leading to the onset of new occupational hazards and unprecedented risk conditions deriving from the new ways of organizing work. At the same time, progress in medical science, with the knowledge in the fields of genetics, metabolomics, big data, and smart technologies, makes it possible to promptly identify and treat risk conditions that would have escaped notice in the past. Personalized occupational medicine represents the frontier of prevention in the workplace, from the perspective of total worker health and the sustainability of resources. The contributions to this Special Issue range from chemical, physical, and biological to psychosocial risks, and from the search for new ways to control long-known risks, such as mercury toxicity, to observations of the most frequent pathologies in the workplace in the last twenty years, such as repetitive trauma diseases, immunodeficiency transmitted as a result of biological injuries, and violence and psychological trauma in the workplace. New insights are needed in occupational health and safety practice to address the new challenges in this field.

Systematic Review of Multi-Omics Approaches to Investigate Toxicological Effects in Macrophages

Insights into the modes of action (MoAs) of xenobiotics are of utmost importance for the definition of adverse outcome pathways (AOPs), which are essential for a mechanism-based risk assessment. A well-established strategy to reveal MoAs of xenobiotics is the use of omics. However, often an even more comprehensive approach is needed, which can be achieved using multi-omics. Since the immune system plays a central role in the defense against foreign substances and pathogens, with the innate immune system building a first barrier, we systematically reviewed multi-omics studies investigating the effects of xenobiotics on macrophages. Surprisingly, only nine publications were identified, combining proteomics with transcriptomics or metabolomics. We summarized pathways and single proteins, transcripts, or metabolites, which were described to be affected upon treatment with xenobiotics in the reviewed studies, thus revealing a broad range of effects. In summary, we show that macrophages are a relevant model system to investigate the toxicological effects induced by xenobiotics. Furthermore, the multi-omics approaches led to a more comprehensive overview compared to only one omics layer with slight advantages for combinations that complement each other directly, e.g., proteome and metabolome.

Personalised Medicine: implication and perspectives in the field of occupational health

"Personalised medicine" relies on identifying and integrating individual variability in genomic, biological, and physiological parameters, as well as in environmental and lifestyle factors, to define "individually" targeted disease prevention and treatment. Although innovative "omic" technologies supported the application of personalised medicine in clinical, oncological, and pharmacological settings, its role in occupational health practice and research is still in a developing phase. Occupational personalised approaches have been currently applied in experimental settings and in conditions of unpredictable risks, e.g.. war missions and space flights, where it is essential to avoid disease manifestations and therapy failure. However, a debate is necessary as to whether personalized medicine may be even more important to support a redefinition of the risk assessment processes taking into consideration the complex interaction between occupational and individual factors. Indeed, "omic" techniques can be helpful to understand the hazardous properties of the xenobiotics, dose-response relationships through a deeper elucidation of the exposure-disease pathways and internal doses of exposure. Overall, this may guide the adoption/implementation of primary preventive measures protective for the vast majority of the population, including most susceptible subgroups. However, the application of personalised medicine into occupational health requires overcoming some practical, ethical, legal, economical, and socio-political issues, particularly concerning the protection of privacy, and the risk of discrimination that the workers may experience. In this scenario, the concerted action of academic, industry, governmental, and stakeholder representatives should be encouraged to improve research aimed to guide effective and sustainable implementation of personalised medicine in occupational health fields.

Are we forgetting the "proteomics" in multi-omics ecotoxicology?

Proteomics plays a significant role in discerning the effects of chemical exposures in animal taxa. Multi-omics applications have become more pervasive in toxicology, however questions remain about whether proteomics is being utilized by the community to its full potential - are we placing too much stock in transcriptomics and other omics approaches for developing adverse outcome pathways? Proteins are more relevant than transcripts because they are direct mediators of the resulting phenotype. There is also rarely perfect stoichiometry between transcript and protein abundance and transcript abundance may not accurately predict physiologic response. Proteins direct all levels of phenotype: structural proteins dictate physical form, enzymes catalyze biochemical reactions, and proteins act as signaling proteins, antibodies, transporters, ion pumps, and transcription factors to control gene expression. Molecular initiating events (MIEs) of AOPs predominantly occur at the level of the protein (e.g. ligand-receptor binding) and proteomics can elucidate novel MIEs and mapping KEs in AOPs. This critical review highlights the need for proteomics in multi-omics studies in environmental toxicology and outlines steps required for inclusion and wider acceptance in chemical risk assessment. We also present case studies of multi-omics approaches that utilize proteomics and discuss some of the challenges and opportunities for proteomics in comparative ecotoxicology. Our intention is not to minimize the importance of other omics technologies, as each has strengths and limitations, but rather to encourage researchers to consider proteomics-based methods in multi-omics studies and AOP development.

Early-life exposure to widespread environmental toxicants and maternal-fetal health risk: A focus on metabolomic biomarkers

Prenatal exposure to widespread environmental toxicants is detrimental to maternal health and fetal development. The effects of environmental toxicants on maternal and fetal metabolic profile changes have not yet been summarized. This systematic review aims to summarize the current studies exploring the association between prenatal exposure to environmental toxicants and metabolic profile alterations in mother and fetus. We searched the MEDLINE (PubMed) electronic database for relevant literature conducted up to September 18, 2019 with some key terms. From the initial 155 articles, 15 articles met the inclusion and exclusion criteria, and consist of highly heterogeneous research methods. Seven studies assessed the effects of multiple environmental pollutants (metals, organic pollutants, nicotine, air pollutants) on the maternal urine and blood metabolomic profile; five studies evaluated the effects of arsenic, polychlorinated biphenyls (PCBs), nicotine, and ambient fine particulate matter (PM_2.5) on the cord blood metabolomic profile; and one study assessed the effects of smoking exposure on the amniotic fluid metabolomic profile. The alteration of metabolic pathways in these studies mainly involve energy metabolism, hormone metabolism, oxidative stress and inflammation. No population study investigated the association between environmental toxicants and placental metabolomics. This systematic review provides evidence that prenatal exposure to a variety of environmental pollutants can affect maternal and fetal metabolomic characteristics. Integration of environmental toxicant exposure and metabolomics data in maternal-fetal samples is helpful to understand the interaction between toxicants and metabolites, so as to reveal the pathogenesis of fetal disease or diseases of fetal origin.

20th Pollutant Responses in Marine Organisms (PRIMO 20): Global issues and fundamental mechanisms caused by pollutant stress in marine and freshwater organisms

The 20th Pollutant Responses in Marine Organisms (PRIMO 20) conference provided a forum for scientists from around the world to communicate novel toxicological research findings specifically focused on aquatic organisms, by combining applied and basic research at the intersection of environmental and mechanistic toxicology. The work highlighted in this special issue of Aquatic Toxicology, a special issue of Marine Environmental Research, and presented through posters and presentations, encompass important and emerging topics in freshwater and marine toxicology. This includes multiple types of emerging contaminants including microplastics and UV filtering chemicals. Other studies aimed to further our understanding of the effects of endocrine disrupting chemicals, pharmaceuticals, and personal care products. Further research presented in this virtual issue examined the interactive effects of chemicals and pathogens, while the final set of manuscripts demonstrates continuing efforts to combine traditional biomonitoring, data from -omic technologies, and modeling for use in risk assessment and management. An additional goal of PRIMO meetings is to address the link between environmental and human health. Several articles in this issue of Aquatic Toxicology describe the appropriateness of using aquatic organisms as models for human health, while the keynote speakers, as described in the editorial below, presented research that highlighted bioaccumulation of contaminants such as PFOS and mercury from fish to marine mammals and coastal human populations such as the Gullah/GeeChee near Charleston, South Carolina, USA.

Drivers of and Obstacles to the Adoption of Toxicogenomics for Chemical Risk Assessment: Insights from Social Science Perspectives

BACKGROUND

Some 20 y ago, scientific and regulatory communities identified the potential of omics sciences (genomics, transcriptomics, proteomics, metabolomics) to improve chemical risk assessment through development of toxicogenomics. Recognizing that regulators adopt new scientific methods cautiously given accountability to diverse stakeholders, the scope and pace of adoption of toxicogenomics tools and data have nonetheless not met the ambitious, early expectations of omics proponents.

OBJECTIVE

Our objective was, therefore, to inventory, investigate, and derive insights into drivers of and obstacles to adoption of toxicogenomics in chemical risk assessment. By invoking established social science frameworks conceptualizing innovation adoption, we also aimed to develop recommendations for proponents of toxicogenomics and other new approach methodologies (NAMs).

METHODS

We report findings from an analysis of 56 scientific and regulatory publications from 1998 through 2017 that address the adoption of toxicogenomics for chemical risk assessment. From this purposeful sample of toxicogenomics discourse, we identified major categories of drivers of and obstacles to adoption of toxicogenomics tools and data sets. We then mapped these categories onto social science frameworks for conceptualizing innovation adoption to generate actionable insights for proponents of toxicogenomics.

DISCUSSION

We identify the most salient drivers and obstacles. From 1998 through 2017, adoption of toxicogenomics was understood to be helped by drivers such as those we labeled Superior scientific understanding, New applications, and Reduced cost & increased efficiency but hindered by obstacles such as those we labeled Insufficient validation, Complexity of interpretation, and Lack of standardization. Leveraging social science frameworks, we find that arguments for adoption that draw on the most salient drivers, which emphasize superior and novel functionality of omics as rationales, overlook potential adopters' key concerns: simplicity of use and compatibility with existing practices. We also identify two perspectives-innovation-centric and adopter-centric-on omics adoption and explain how overreliance on the former may be undermining efforts to promote toxicogenomics. https://doi.org/10.1289/EHP6500.

Comparison of Approaches for Determining Bioactivity Hits from High-Dimensional Profiling Data

Phenotypic profiling assays are untargeted screening assays that measure a large number (hundreds to thousands) of cellular features in response to a stimulus and often yield diverse and unanticipated profiles of phenotypic effects, leading to challenges in distinguishing active from inactive treatments. Here, we compare a variety of different strategies for hit identification in imaging-based phenotypic profiling assays using a previously published Cell Painting data set. Hit identification strategies based on multiconcentration analysis involve curve fitting at several levels of data aggregation (e.g., individual feature level, aggregation of similarly derived features into categories, and global modeling of all features) and on computed metrics (e.g., Euclidean and Mahalanobis distance metrics and eigenfeatures). Hit identification strategies based on single-concentration analysis included measurement of signal strength (e.g., total effect magnitude) and correlation of profiles among biological replicates. Modeling parameters for each approach were optimized to retain the ability to detect a reference chemical with subtle phenotypic effects while limiting the false-positive rate to 10%. The percentage of test chemicals identified as hits was highest for feature-level and category-based approaches, followed by global fitting, whereas signal strength and profile correlation approaches detected the fewest number of active hits at the fixed false-positive rate. Approaches involving fitting of distance metrics had the lowest likelihood for identifying high-potency false-positive hits that may be associated with assay noise. Most of the methods achieved a 100% hit rate for the reference chemical and high concordance for 82% of test chemicals, indicating that hit calls are robust across different analysis approaches.

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.

Mode of action-based risk assessment of genotoxic carcinogens

The risk assessment of chemical carcinogens is one major task in toxicology. Even though exposure has been mitigated effectively during the last decades, low levels of carcinogenic substances in food and at the workplace are still present and often not completely avoidable. The distinction between genotoxic and non-genotoxic carcinogens has traditionally been regarded as particularly relevant for risk assessment, with the assumption of the existence of no-effect concentrations (threshold levels) in case of the latter group. In contrast, genotoxic carcinogens, their metabolic precursors and DNA reactive metabolites are considered to represent risk factors at all concentrations since even one or a few DNA lesions may in principle result in mutations and, thus, increase tumour risk. Within the current document, an updated risk evaluation for genotoxic carcinogens is proposed, based on mechanistic knowledge regarding the substance (group) under investigation, and taking into account recent improvements in analytical techniques used to quantify DNA lesions and mutations as well as "omics" approaches. Furthermore, wherever possible and appropriate, special attention is given to the integration of background levels of the same or comparable DNA lesions. Within part A, fundamental considerations highlight the terms hazard and risk with respect to DNA reactivity of genotoxic agents, as compared to non-genotoxic agents. Also, current methodologies used in genetic toxicology as well as in dosimetry of exposure are described. Special focus is given on the elucidation of modes of action (MOA) and on the relation between DNA damage and cancer risk. Part B addresses specific examples of genotoxic carcinogens, including those humans are exposed to exogenously and endogenously, such as formaldehyde, acetaldehyde and the corresponding alcohols as well as some alkylating agents, ethylene oxide, and acrylamide, but also examples resulting from exogenous sources like aflatoxin B1, allylalkoxybenzenes, 2-amino-3,8-dimethylimidazo[4,5-f] quinoxaline (MeIQx), benzo[a]pyrene and pyrrolizidine alkaloids. Additionally, special attention is given to some carcinogenic metal compounds, which are considered indirect genotoxins, by accelerating mutagenicity via interactions with the cellular response to DNA damage even at low exposure conditions. Part C finally encompasses conclusions and perspectives, suggesting a refined strategy for the assessment of the carcinogenic risk associated with an exposure to genotoxic compounds and addressing research needs.

Transcriptomic and Proteomic Tools in the Study of Hg Toxicity: What Is Missing?

Mercury is a hazardous substance that has unique neurodevelopmental toxic effects in humans. However, the precise sequence of molecular events that culminate in Hg-induced neuropathology is still unknown. Though the omics studies have been generating an enormous amount of new data about Hg toxicity, our ability to interpret such a large quantity of information is still limited. In this opinion article, we will reinforce the necessity of new high throughput and accurate analytical proteomic methodologies, especially, thiol and selenol-proteome. Overall, we posit that improvements in thiol- and selenol-proteomic analyses will be pivotal in identifying the primary cellular targets of Hg. However, a better understanding of the complex cascades and molecular pathways involved in its toxicity will require extensive complementary studies in more complex systems.

In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways

In silico toxicology is an emerging field. It gains increasing importance as research is aiming to decrease the use of animal experiments as suggested in the 3R principles by Russell and Burch. In silico toxicology is a means to identify hazards of compounds before synthesis, and thus in very early stages of drug development. For chemical industries, as well as regulatory agencies it can aid in gap‐filling and guide risk minimization strategies. Techniques such as structural alerts, read‐across, quantitative structure–activity relationship, machine learning, and deep learning allow to use in silico toxicology in many cases, some even when data is scarce. Especially the concept of adverse outcome pathways puts all techniques into a broader context and can elucidate predictions by mechanistic insights.

This article is categorized under:

Structure and Mechanism > Computational Biochemistry and Biophysics

Data Science > Chemoinformatics

Using FFPE RNA-Seq with 12 marker genes to evaluate genotoxic and non-genotoxic rat hepatocarcinogens

Introduction

Various challenges have been overcome with regard to applying 'omics technologies for chemical risk assessments. Previously we published results detailing targeted mRNA sequencing (RNA-Seq) on a next generation sequencer using intact RNA derived from freshly frozen rat liver tissues. We successfully discriminated genotoxic hepatocarcinogens (GTHCs) from non-genotoxic hepatocarcinogens (NGTHCs) using 11 selected marker genes. Based on this, we next attempted to use formalin-fixed paraffin-embedded (FFPE) pathology specimens for RNA-Seq analyses.

Findings

In this study we performed FFPE RNA-Seq to compare a typical GTHC, 2-acetylaminofluorene (AAF) to genotoxicity equivocal p-cresidine (CRE). CRE is used as a synthetic chemical intermediate, and this compound is classified as an IARC 2B carcinogen and is mutagenic in S. typhimurium, which is non-genotoxic to rat livers as assessed by single strand DNA damage analysis. RNA-Seq was used to examine liver FFPE samples obtained from groups of five 10-week-old male F344 rats that were fed with chemicals (AAF: 0.025% and CRE: 1% in food) for 4 weeks or from controls that were fed a basal diet. We extracted RNAs from FFPE samples and RNA-Seq was performed on a MiniSeq (Illumina) using the TruSeq custom RNA panel. AAF induced remarkable differences in the expression of eight genes (Aen, Bax, Btg2, Ccng1, Gdf15, Mbd1, Phlda3 and Tubb4b) from that in the control group, while CRE only induced expression changes in Gdf15, as shown using Tukey's test. Gene expression profiles for nine genes (Aen, Bax, Btg2, Ccng1, Cdkn1a, Gdf15, Mbd1, Phlda3, and Plk2) differed.between samples treated with AAF and CRE. Finally, principal component analysis (PCA) of 12 genes (Aen, Bax, Btg2, Ccnf, Ccng1, Cdkn1a, Gdf15, Lrp1, Mbd1, Phlda3, Plk2, and Tubb4b) using our previous Open TG-GATE data plus FFPE-AAF and FFPE-CRE successfully differentiated FFPE-AAF, as GTHC, from FFPE-CRE, as NGHTC.

Conclusion

Our results suggest that FFPE RNA-Seq and PCA are useful for evaluating typical rat GTHCs and NGTHCs.

In vitro toxicological characterisation of the antifungal compound soybean toxin (SBTX)

Soybean toxin (SBTX) is a protein isolated from soybean seeds and composed of two polypeptide subunits (17 and 27 kDa). SBTX has in vitro activity against phytopathogenic fungi such as Cercospora sojina, Aspergillus niger, and Penicillium herguei, and yeasts like Candida albicans, C. parapsilosis, Kluyveromyces marxiannus, and Pichia membranifaciens. The present study aimed to analyze in vitro whether SBTX causes any side effects on non-target bacterial and mammalian cells that could impede its potential use as a novel antifungal agent. SBTX at 100 μg/mL and 200 μg/mL did not hinder the growth of the bacteria Salmonella enterica (subspecies enterica serovar choleraesuis), Bacillus subtilis (subspecies spizizenii) and Staphylococcus aureus. Moreover, SBTX at concentrations up to 500 μg/mL did not significantly affect the viability of erythrocytes, neutrophils, and human intestinal Caco-2 cells. To study whether SBTX could induce relevant alterations in gene expression, in vitro DNA microarray experiments were conducted in which differentiated Caco-2 cells were exposed for 24 h to 100 μg/mL or 200 μg/mL SBTX. SBTX up-regulated genes involved in cell cycle and immune response pathways, but down-regulated genes that play a role in cholesterol biosynthesis and platelet degranulation pathways. Thus, although SBTX did not affect bacteria, nor induced cytotoxity in mammalian cells, it affected some biological pathways in the human Caco-2 cell line that warrants further investigation.

Validation of automated pipetting systems for cell culture seeding, exposure and bio-analytical assays in sulfur mustard toxicology

In vitro cell culture experiments are highly important techniques to accelerate drug discovery, conduct safety testing and reduce the need for animal studies. Therefore, automatization may help to enhance the technical precision, reduce external (including operator's) influence on the data and thus improve reliability. Prior to application in scientific studies, validation of automated systems is absolutely necessary. In this study we present the validation of two combined automated pipetting systems to conduct toxicity studies in HaCaT cells consisting of cell seeding, noxious agent exposure and several assays to assess cell survival, apoptosis and interleukin production. After initial validation of pipetting accuracy, we compared homogeneity after automated seeding to plates seeded by expert laboratory technicians. Moreover, automated dispensing of a potentially unstable noxious agent was analyzed in terms of speed and consistency. We found a 2 % technical imprecision for the cell survival assay and 4.5-6 % for the other assays, bioluminescent and ELISA techniques. Thus, we could demonstrate the excellent technical precision of our assays. In a final step, we found that intraday variations, though acceptable, were much larger than technical variations and had to assume an intraday biological variability between different wells of the same experimental group.

Towards the development of an omics data analysis framework

The use of various omics techniques for scientific research is increasing. While toxicogenomics studies have already produced substantial data on diverse omics platforms, to date there has been little routine application in regulatory toxicology. This is despite the promises and excitement of 20 years ago when it was widely speculated that omics methods would reduce or even replace animal use and allow a much enhanced understanding of hazard and susceptibility. One of the reasons for this has been a trepidation about relying on the produced data. It has been argued that omics outputs might not be sufficiently reliable for regulatory application because the techniques, bioinformatics and interpretation can vary. For these reasons the robustness of the obtained results is questioned. This reticence to trust omics data is further magnified by the lack of internationally agreed upon guidelines and protocols for both the generation and processing of omics data. One way forward would be to reach a consensus on an omics data analysis framework (ODAF) for regulatory application (R-ODAF) based on rigorous data analysis. The authors of this article are involved in a Long-Range Research Initiative (LRI) project that will propose an R-ODAF for transcriptomics data. The R-ODAF will then be reviewed and evaluated by the main regulatory agencies and consensus forums such as the Organization for Economic Co-operation and Development (OECD). This work builds on The MicroArray Quality Control work that developed standards for the generation of data from microarrays and sequencing but not for reporting or analysis.

Comparison of the potential mechanisms for hepatotoxicity of p-dialkoxy chlorobenzenes in rat primary hepatocytes for read-across

Read-across based on only structural similarity is considered to have a risk of error in chemical risk assessment. Under these circumstances, considering biological similarity based on adverse outcome pathways using in vitro omics technologies is expected to enhance the accuracy and robustness of conclusions in read-across. However, due to a lack of practical case studies, key considerations and use of these technologies for data gap filling are not well discussed. Here we extracted and compared the potential mechanisms for hepatotoxicity for structural analogs of p-dialkoxy chlorobenzenes including 1,4-dichloro-2,5-dimethoxybenzene (DDMB), 2,5-dichloro-1,4-diethoxybenzene (DDEB), 2-chloro-1,4-dimethoxybenzene (CDMB), and 1-chloro-2,5-diethoxybenzene (CDEB) using in vitro omics technologies for read-across. To reveal the potential mechanisms for hepatotoxicity, we conducted microarray analysis with rat primary hepatocytes. The results showed that three (DDMB, DDEB, CDEB) of the four chemicals affected similar biological pathways such as peroxisome proliferation, oxidative stress, and mitochondrial dysfunction. Furthermore, these biological pathways are consistent with in vivo hepatotoxicity in the source chemical, DDMB. In contrast, CDMB did not affect a specific toxicological pathway. Taken together, these data show the potential mechanisms for hepatotoxicity for three chemicals (DDMB, DDEB, CDEB) and provide novel insights into grouping chemicals using in vitro toxicogenomics for read-across.