Kristin M. Eccles

Immunotoxicity has emerged as a key health concern for per- and polyfluoroalkyl substances (PFAS), with animal studies showing reduced T-dependent antibody responses (TDAR) and epidemiological studies reporting decreased vaccine antibody titres. Notably, immunotoxicity is considered one of the most sensitive endpoints for PFAS exposure and has been used to inform regulatory guidance and health-based values. Given that more than 14,000 PFAS exist and are highly environmentally persistent, evaluating the immunotoxicity of individual compounds is critical but impractical, highlighting the need for efficient, human-relevant test systems that provide mechanistically informative, immune-relevant endpoints. Here, we assessed immunomodulatory effects of six PFAS analogues using an in vitro human peripheral blood mononuclear cell (PBMC) model. Two immune stimuli were applied, including lipopolysaccharide (LPS) to trigger innate responses and phytohemagglutinin (PHA) to simulate T-cell-mediated adaptive immune activation. Critically, several PFAS analogues enhanced T-cell proliferation following PHA activation, while a non-inclusive but overlapping subset of analogues suppressed cytokine secretion in response to PHA and LPS. Transcriptomic analyses indicate reduced B-cell identity and immunoglobulin gene expression alongside increased expression of genes associated with T-cell activation and proliferation. These findings implicate a dysregulated coordination between T- and B-cell responses as a potential mechanism underlying PFAS-associated immunotoxicity. Overall, the human PBMC model demonstrated that it is a cost-effective and ethical method for identifying and characterizing key events (KEs) in the adverse outcome pathway (AOP) for PFAS-induced immunotoxicity and has the potential to be refined and incorporated into a robust new approach method (NAM) to assess the next generation of commercial PFAS.

Background: Methylmercury (MeHg) exposure poses health risks to Indigenous Peoples in Canada, who often rely on fish and wildlife as part of their traditional diet. Risk assessments tend to be overly conservative because most available data report total mercury (tHg) rather than MeHg, leading to uncertainty and potential overestimation of exposure, particularly for foods in which the proportion of tHg present as MeHg is lower or highly variable. Objective: To conduct a meta-analysis of MeHg and tHg levels in traditional foods, develop a comprehensive database, and generate empirically derived conversion factors to improve the estimation of dietary MeHg exposure. Methods: We critically reviewed paired MeHg and tHg data from 31 published studies and two unpublished datasets. Species- and tissue-specific MeHg:tHg ratios were estimated using random-effects meta-analysis, and conservative conversion factors were derived from upper confidence limits with sensitivity analyses. Conversion factors were evaluated using dietary exposure data from the Inuit Health Survey and the First Nations Food, Nutrition, and Environment Study. Variability in ratios was explored using meta-regression and within-species analyses. Results: MeHg:tHg ratios were quantified for 65 species–tissue combinations, revealing strong and systematic tissue- and species-specific patterns. Conversion factors were derived for 38 species–tissue combinations, with most values below 100%, particularly for liver and other internal organs, indicating that assuming tHg equals MeHg frequently overestimates dietary MeHg exposure. Application of these factors substantially reduced overestimation of MeHg intake in most regions, while providing limited benefit in regions dominated by low-concentration foods. Exploratory analyses indicated variability by tissue, species, and location, with limited evidence of additional effects of age or size. Significance: This study provides a systematic database and validated conversion factors for MeHg in traditional foods, improving dietary risk assessment while underscoring the need for continued species and site-specific monitoring. Impact statement: This study advances mercury exposure science by providing empirically derived conversion factors to estimate methylmercury (MeHg) in traditional foods when only total mercury is measured. By reducing overestimation of MeHg exposure while retaining a precautionary framework, the findings support more accurate dietary risk assessment, improve hazard characterization, and inform culturally relevant food safety guidance for Indigenous Peoples. The results also highlight biological and environmental drivers of variability that can guide future monitoring and exposure assessment efforts.

Class-based cumulative risk assessment approaches have been applied to high-priority environmental contaminants such as polycyclic aromatic compounds (PACs), yet uncertainties remain in their application. In this study, we evaluated the influence of inactive chemicals on mixture modeling outcomes and explored strategies for predicting the aryl hydrocarbon receptor (AhR)-mediated toxicity of PAC mixtures. Using an in vitro AhR reporter gene assay, we tested seven defined mixtures composed of six active and seven inactive PACs. Observed concentration-response curves were compared to predictions from three established mixture models, concentration addition (CA), independent action (IA), and generalized concentration addition (GCA), using both effective concentration eliciting 10% response (EC10) and benchmark concentration (BMC₁₀) approaches. Including inactive chemicals without scaling led to consistent overestimation of potency, especially in models assuming equal efficacy. Predictive accuracy improved across all models when mixtures were limited to active chemicals and contributions were scaled to 100%, excluding inactives. Among approaches, GCA consistently produced the best agreement with measured responses, particularly when paired with BMC modeling. BMC10 values better accommodate partial agonists. Our findings support a pragmatic, mechanism-based framework for modeling environmental mixtures, one that prioritizes active components, scales their contributions, and adopts BMC-based methods to estimate potency.

Liquid crystal monomers (LCMs) are ubiquitous environmental contaminants released from electronic devices’ liquid crystal display (LCD) panels, which have led to the contamination of food, breast milk, and serum. As the toxicity of individual LCMs, not to mention their myriad mixtures, is currently very poorly characterized, there is a crucial need for investigations into the health hazards posed by exposure. In this study, 10 nonfluorinated (NF) and fluorinated (F) LCMs and 3 fluorination-based LCM mixtures were screened for metabolism and endocrine-disrupting potential in vitro at exposure-relevant concentrations using adipogenesis assays and luciferase reporter gene assays. Both NF-LCMs, F-LCMs, and their mixtures were found to alter the transcriptional activity of one or more nuclear receptors. Notably, 6 LCMs and all LCM mixtures were able to antagonize the progesterone receptor, with several displaying non-monotonic concentration-response curves. Multiple LCMs and their mixtures also increased triglyceride accumulation in murine preadipocytes and human mesenchymal stem cells in a concentration-dependent manner. The concentration addition principle underestimated the adipogenic potencies of LCM mixtures when compared with those derived from benchmark concentration analyses of empirical adipogenesis assay results, suggesting synergistic interactions. While no mechanistic pattern emerged between the bioactivities, results confirmed the metabolism and endocrine-disrupting potential of both NF-LCMs, F-LCMs, and their mixtures. This emphasizes the need to further investigate the metabolic and reproductive health impacts of LCM exposure in vivo, as well as the necessity of exploring alternative models to predict the toxicity of LCM mixtures.

Cell Painting visualizes toxicity-induced morphological changes by staining cellular structures with fluorescent dyes. Coupled with high-content imaging and analysis software, Cell Painting allows high-throughput phenotypic profiling (HTPP) to quantify phenotypic changes and estimate points of departure for toxicity assessments. Regulatory agencies have applied HTPP in 384-well plates for chemical hazard screening. In this study, established protocols for 384-well plates were adapted for use in 96-well plates to increase accessibility for laboratories with lower throughput. U-2 OS human osteosarcoma cells in 96-well plates were exposed to 12 phenotypic reference compounds for 24 h before fixation and staining with fluorescent dyes (golgi apparatus, endoplasmic reticulum, nucleic acids, cytoskeleton, mitochondria). Four independent chemical exposures across eight concentrations generated four biological replicates. Stained cells were imaged on an Opera Phenix, a high-content imaging system, and the Columbus analysis software extracted numerical values for 1300 morphological features. Features were normalized to control cells, followed by principal component analysis and a calculation of Mahalanobis for each treatment concentration. Mahalanobis distances were modeled to calculate benchmark concentrations (BMC) for chemicals. Most BMCs differed by less than one order of magnitude across experiments, demonstrating intra-laboratory consistency. Compared to published BMCs, ten compounds had comparable BMCs in both plate formats. In addition, we observed a significant inverse relationship between seeding density and Mahalanobis distances, suggesting that experimental factors like cell density may influence BMCs. Overall, we demonstrate that Cell Painting is adaptable across formats and laboratories, supporting efforts to develop and validate it as a complementary new approach methodology to existing toxicity tests.

In this article, we provide a proof of concept evaluating the utility of the U.S. Tox21 high-throughput screening approach to assess the hazard of chemical mixtures using 2 estrogen receptor (ER) assays. A subset of chemicals identified in Phase I of the Tox21 program as active in the ER agonist assay were used to design mixtures for testing in Phase II. Individual chemicals and mixtures were evaluated in 2 cell-based ER alpha (ERα) activation assays: One incorporating a transfected ligand-binding domain in an ERα β-lactamase reporter cell line (ER-bla) and the full-length endogenous receptor in the MCF7 cell line with a luciferase reporter gene (ER-luc). Concentration–response data from individual chemicals were used to predict the joint effect based on mixtures modeling methods and were compared with observed mixtures data to assess model fit. The models tended to overpredict mixture responses in the ER-bla assay, whereas predictions were closer to observed responses in the ER-luc assay, indicating that a full-length endogenous ER is a preferred model for high-throughput mixture analysis. Lessons learned from this research include the importance of analyzing the individual chemicals used for predictions and the mixtures in the same experimental paradigm to minimize variation, developing methods for imputing missing values from incomplete concentration–response curves, and establishing criteria to determine when inactive chemicals should be omitted from mixture predictions.

The Arctic faces increasing exposure to environmental chemicals such as metals, posing health risks to humans and wildlife. Biomonitoring of polar bears (Ursus maritimus) can be used to quantify chemicals in the environment and in traditional foods consumed by the Inuit. However, typically, these samples are collected through invasive or terminal methods. The biomonitoring of feces could be a useful alternative to the current metal monitoring method within the Arctic. Here, we aim to 1) quantify the relationship between concentrations of metals in the feces and tissues (muscle, liver, and fat) of polar bears using predictive modeling, 2) develop an easy-to-use conversion tool for use in community-based monitoring programs to non-invasively estimate contaminant concentrations in polar bears tissues and 3) demonstrate the application of these models by examining potential exposure risk for humans from consumption of polar bear muscle. Fecal, muscle, liver, and fat samples were harvested from 49 polar bears through a community-based monitoring program. The samples were analyzed for 32 metals. Exploratory analysis indicated that mean metal concentrations generally did not vary by age or sex, and many of the metals measured in feces were positively correlated with the internal tissue concentration. We developed predictive linear regression models between internal (muscle, liver, fat) and external (feces) metal concentrations and further explored the mercury and methylmercury relationships for utility risk screening. Using the cross-validated regression coefficients, we developed a conversion tool that contributes to the One Health approach by understanding the interrelated health of humans, wildlife, and the environment in the Arctic. The findings support using feces as a biomonitoring tool for assessing contaminants in polar bears. Further research is needed to validate the developed models for other regions in the Arctic and assess the impact of environmental weathering on fecal metal concentrations.

Quantifying the exposome is key to understanding how the environment impacts human health and disease. However, accurately, and cost-effectively quantifying exposure in large population health studies remains a major challenge. Geospatial technologies offer one mechanism to integrate high-dimensional environmental data into epidemiology studies, but can present several challenges. In June 2021, the National Institute of Environmental Health Sciences (NIEHS) held a workshop bringing together experts in exposure science, geospatial technologies, data science and population health to address the need for integrating multiscale geospatial environmental data into large population health studies. The primary objectives of the workshop were to highlight recent applications of geospatial technologies to examine the relationships between environmental exposures and health outcomes; identify research gaps and discuss future directions for exposure modeling, data integration and data analysis strategies; and facilitate communications and collaborations across geospatial and population health experts. This commentary provides a high-level overview of the scientific topics covered by the workshop and themes that emerged as areas for future work, including reducing measurement errors and uncertainty in exposure estimates, and improving data accessibility, data interoperability, and computational approaches for more effective multiscale and multi-source data integration, along with potential solutions.