Extracellular Vesicles altered by a Per- and Polyfluoroalkyl Substance Mixture: In Vitro Dose-Dependent Release, Chemical Content, and MicroRNA Signatures involved in Liver Health.

Per- and polyfluoroalkyl substances (PFAS) have emerged as high priority contaminants due to their ubiquity and pervasiveness in the environment. Numerous PFAS co-occur across sources of drinking water, including areas of North Carolina (NC) with some detected concentrations above the Environmental Protection Agency's health advisory levels. While evidence demonstrates PFAS exposure induces harmful effects in the liver, the involvement of extracellular vesicles (EVs) as potential mediators of these effects has yet to be evaluated. This study set out to evaluate the hypothesis that PFAS mixtures induce dose-dependent release of EVs from liver cells, with exposures causing differential loading of microRNAs (miRNAs) and PFAS chemical signatures. To test this hypothesis, a defined PFAS mixture was prioritized utilizing data collected by the NC PFAS Testing Network. This mixture contained three substances, PFOS, PFOA, and PFHxA, selected based upon co-occurrence patterns and the inclusion of both short-chain (PFHxA) and long-chain (PFOA and PFOS) substances. HepG2 liver cells were exposed to equimolar PFAS, and secreted EVs were isolated from conditioned media and characterized for count and molecular content. Exposures induced a dose-dependent release of EVs carrying miRNAs that were differentially loaded upon exposure. These altered miRNA signatures were predicted to target mRNA pathways involved in hepatic fibrosis and cancer. Chemical concentrations of PFOS, PFOA, and PFHxA were also detected in both parent HepG2 cells and their released EVs, specifically within a 15-fold range after normalizing for protein content. This study therefore established EVs as novel biological responders and measurable endpoints for evaluating PFAS-induced toxicity.

A multi-omic approach identifies an autism spectrum disorder (ASD) regulatory complex of functional epimutations in placentas from children born preterm.

Children born preterm are at heightened risk of neurodevelopmental impairments, including Autism Spectrum Disorder (ASD). The placenta is a key regulator of neurodevelopmental processes, though the precise underlying molecular mechanisms remain unclear. Here, we employed a multi-omic approach to identify placental transcriptomic and epigenetic modifications related to ASD diagnosis at age 10, among children born preterm. Working with the extremely low gestational age (ELGAN) cohort, we hypothesized that a pro-inflammatory placental environment would be predictive of ASD diagnosis at age 10. Placental messenger RNA (mRNA) expression, CpG methylation, and microRNA (miRNA) expression were compared among 368 ELGANs (28 children diagnosed with ASD and 340 children without ASD). A total of 111 genes displayed expression levels in the placenta that were associated with ASD. Within these ASD-associated genes is an ASD regulatory complex comprising key genes that predicted ASD case status. Genes with expression that predicted ASD case status included Ewing Sarcoma Breakpoint Region 1 (EWSR1) (OR: 6.57 (95% CI: 2.34, 23.58)) and Bromodomain Adjacent To Zinc Finger Domain 2A (BAZ2A) (OR: 0.12 (95% CI: 0.03, 0.35)). Moreover, of the 111 ASD-associated genes, nine (8.1%) displayed associations with CpG methylation levels, while 14 (12.6%) displayed associations with miRNA expression levels. Among these, LRR Binding FLII Interacting Protein 1 (LRRFIP1) was identified as being under the control of both CpG methylation and miRNAs, displaying an OR of 0.42 (95% CI: 0.17, 0.95). This gene, as well as others identified as having functional epimutations, plays a critical role in immune system regulation and inflammatory response. In summary, a multi-omic approach was used to identify functional epimutations in the placenta that are associated with the development of ASD in children born preterm, highlighting future avenues for intervention.

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

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

Development of the InTelligence And Machine LEarning (TAME) Toolkit for Introductory Data Science, Chemical-Biological Analyses, Predictive Modeling, and Database Mining for Environmental Health Research.

Research in environmental health is becoming increasingly reliant upon data science and computational methods that can more efficiently extract information from complex datasets. Data science and computational methods can be leveraged to better identify relationships between exposures to stressors in the environment and human disease outcomes, representing critical information needed to protect and improve global public health. Still, there remains a critical gap surrounding the training of researchers on these in silico methods. We aimed to address this gap by developing the inTelligence And Machine lEarning (TAME) Toolkit, promoting trainee-driven data generation, management, and analysis methods to "TAME" data in environmental health studies. Training modules were developed to provide applications-driven examples of data organization and analysis methods that can be used to address environmental health questions. Target audiences for these modules include students, post-baccalaureate and post-doctorate trainees, and professionals that are interested in expanding their skillset to include recent advances in data analysis methods relevant to environmental health, toxicology, exposure science, epidemiology, and bioinformatics/cheminformatics. Modules were developed by study coauthors using annotated script and were organized into three chapters within a GitHub Bookdown site. The first chapter of modules focuses on introductory data science, which includes the following topics: setting up R/RStudio and coding in the R environment; data organization basics; finding and visualizing data trends; high-dimensional data visualizations; and Findability, Accessibility, Interoperability, and Reusability (FAIR) data management practices. The second chapter of modules incorporates chemical-biological analyses and predictive modeling, spanning the following methods: dose-response modeling; machine learning and predictive modeling; mixtures analyses; -omics analyses; toxicokinetic modeling; and read-across toxicity predictions. The last chapter of modules was organized to provide examples on environmental health database mining and integration, including chemical exposure, health outcome, and environmental justice indicators. Training modules and associated data are publicly available online (https://uncsrp.github.io/Data-Analysis-Training-Modules/). Together, this resource provides unique opportunities to obtain introductory-level training on current data analysis methods applicable to 21st century science and environmental health.

Wildfires and extracellular vesicles: Exosomal MicroRNAs as mediators of cross-tissue cardiopulmonary responses to biomass smoke.

INTRODUCTION: Wildfires are a threat to public health world-wide that are growing in intensity and prevalence. The biological mechanisms that elicit wildfire-associated toxicity remain largely unknown. The potential involvement of cross-tissue communication via extracellular vesicles (EVs) is a new mechanism that has yet to be evaluated. METHODS: Female CD-1 mice were exposed to smoke condensate samples collected from the following biomass burn scenarios: flaming peat; smoldering peat; flaming red oak; and smoldering red oak, representing lab-based simulations of wildfire scenarios. Lung tissue, bronchoalveolar lavage fluid (BALF) samples, peripheral blood, and heart tissues were collected 4 and 24 h post-exposure. Exosome-enriched EVs were isolated from plasma, physically characterized, and profiled for microRNA (miRNA) expression. Pathway-level responses in the lung and heart were evaluated through RNA sequencing and pathway analyses. RESULTS: Markers of cardiopulmonary tissue injury and inflammation from BALF samples were significantly altered in response to exposures, with the greatest changes occurring from flaming biomass conditions. Plasma EV miRNAs relevant to cardiovascular disease showed exposure-induced expression alterations, including miR-150, miR-183, miR-223-3p, miR-30b, and miR-378a. Lung and heart mRNAs were identified with differential expression enriched for hypoxia and cell stress-related pathways. Flaming red oak exposure induced the greatest transcriptional response in the heart, a large portion of which were predicted as regulated by plasma EV miRNAs, including miRNAs known to regulate hypoxia-induced cardiovascular injury. Many of these miRNAs had published evidence supporting their transfer across tissues. A follow-up analysis of miR-30b showed that it was increased in expression in the heart of exposed mice in the absence of changes to its precursor molecular, pri-miR-30b, suggesting potential transfer from external sources (e.g., plasma). DISCUSSION: This study posits a potential mechanism through which wildfire exposures induce cardiopulmonary responses, highlighting the role of circulating plasma EVs in intercellular and systems-level communication between tissues.

Environmental mixtures and breast cancer: identifying co-exposure patterns between understudied vs breast cancer-associated chemicals using chemical inventory informatics.

BACKGROUND: Although evidence linking environmental chemicals to breast cancer is growing, mixtures-based exposure evaluations are lacking. OBJECTIVE: This study aimed to identify environmental chemicals in use inventories that co-occur and share properties with chemicals that have association with breast cancer, highlighting exposure combinations that may alter disease risk. METHODS: The occurrence of chemicals within chemical use categories was characterized using the Chemical and Products Database. Co-exposure patterns were evaluated for chemicals that have an association with breast cancer (BC), no known association (NBC), and understudied chemicals (UC) identified through query of the Silent Spring Institute's Mammary Carcinogens Review Database and the U.S. Environmental Protection Agency's Toxicity Reference Database. UCs were ranked based on structure and physicochemical similarities and co-occurrence patterns with BCs within environmentally relevant exposure sources. RESULTS: A total of 6793 chemicals had data available for exposure source occurrence analyses. 50 top-ranking UCs spanning five clusters of co-occurring chemicals were prioritized, based on shared properties with co-occuring BCs, including chemicals used in food production and consumer/personal care products, as well as potential endocrine system modulators. SIGNIFICANCE: Results highlight important co-exposure conditions that are likely prevalent within our everyday environments that warrant further evaluation for possible breast cancer risk. IMPACT STATEMENT: Most environmental studies on breast cancer have focused on evaluating relationships between individual, well-known chemicals and breast cancer risk. This study set out to expand this research field by identifying understudied chemicals and mixtures that may occur in everyday environments due to their patterns of commercial use. Analyses focused on those that co-occur alongside chemicals associated with breast cancer, based upon in silico chemical database querying and analysis. Particularly in instances when understudied chemicals share physicochemical properties and structural features with carcinogens, these chemical mixtures represent conditions that should be studied in future clinical, epidemiological, and toxicological studies.

Chemical Mixtures in Household Environments: In Silico Predictions and In Vitro Testing of Potential Joint Action on PPARγ in Human Liver Cells.

There are thousands of chemicals that humans can be exposed to in their everyday environments, the majority of which are currently understudied and lack substantial testing for potential exposure and toxicity. This study aimed to implement in silico methods to characterize the chemicals that co-occur across chemical and product uses in our everyday household environments that also target a common molecular mediator, thus representing understudied mixtures that may exacerbate toxicity in humans. To detail, the Chemical and Products Database (CPDat) was queried to identify which chemicals co-occur across common exposure sources. Chemicals were preselected to include those that target an important mediator of cell health and toxicity, the peroxisome proliferator activated receptor gamma (PPARγ), in liver cells that were identified through query of the ToxCast/Tox21 database. These co-occurring chemicals were thus hypothesized to exert potential joint effects on PPARγ. To test this hypothesis, five commonly co-occurring chemicals (namely, benzyl cinnamate, butyl paraben, decanoic acid, eugenol, and sodium dodecyl sulfate) were tested individually and in combination for changes in the expression of PPARγ and its downstream target, insulin receptor (INSR), in human liver HepG2 cells. Results showed that these likely co-occurring chemicals in household environments increased both PPARγ and INSR expression more significantly when the exposures occurred as mixtures vs. as individual chemicals. Future studies will evaluate such chemical combinations across more doses, allowing for further quantification of the types of joint action while leveraging this method of chemical combination prioritization. This study demonstrates the utility of in silico-based methods to identify chemicals that co-occur in the environment for mixtures toxicity testing and highlights relationships between understudied chemicals and changes in PPARγ-associated signaling.

Predictive modeling of biological responses in the rat liver using in vitro Tox21 bioactivity: Benefits from high-throughput toxicokinetics.

Computational methods are needed to more efficiently leverage data from in vitro cell-based models to predict what occurs within whole body systems after chemical insults. This study set out to test the hypothesis that in vitro high-throughput screening (HTS) data can more effectively predict in vivo biological responses when chemical disposition and toxicokinetic (TK) modeling are employed. In vitro HTS data from the Tox21 consortium were analyzed in concert with chemical disposition modeling to derive nominal, aqueous, and intracellular estimates of concentrations eliciting 50% maximal activity. In vivo biological responses were captured using rat liver transcriptomic data from the DrugMatrix and TG-Gates databases and evaluated for pathway enrichment. In vivo dosing data were translated to equivalent body concentrations using HTTK modeling. Random forest models were then trained and tested to predict in vivo pathway-level activity across 221 chemicals using in vitro bioactivity data and physicochemical properties as predictor variables, incorporating methods to address imbalanced training data resulting from high instances of inactivity. Model performance was quantified using the area under the receiver operator characteristic curve (AUC-ROC) and compared across pathways for different combinations of predictor variables. All models that included toxicokinetics were found to outperform those that excluded toxicokinetics. Biological interpretation of the model features revealed that rather than a direct mapping of in vitro assays to in vivo pathways, unexpected combinations of multiple in vitro assays predicted in vivo pathway-level activities. To demonstrate the utility of these findings, the highest-performing model was leveraged to make new predictions of in vivo biological responses across all biological pathways for remaining chemicals tested in Tox21 with adequate data coverage (n = 6617). These results demonstrate that, when chemical disposition and toxicokinetics are carefully considered, in vitro HT screening data can be used to effectively predict in vivo biological responses to chemicals.

Criteria pollutant impacts of volatile chemical products informed by near-field modeling.

Consumer, industrial, and commercial product usage is a source of exposure to potentially hazardous chemicals. In addition, cleaning agents, personal care products, coatings, and other volatile chemical products (VCPs), evaporate and react in the atmosphere producing secondary pollutants. Here, we show high air emissions from VCP usage (≥ 14 kg person-1 yr-1, at least 1.7× higher than current operational estimates) are supported by multiple estimation methods and constraints imposed by ambient levels of ozone, hydroxyl radical (OH) reactivity, and the organic component of fine particulate matter (PM2.5) in Pasadena, California. A near-field model, which estimates human chemical exposure during or in the vicinity of product use, indicates these high air emissions are consistent with organic product usage up to ~75 kg person-1 yr-1, and inhalation of consumer products could be a non-negligible exposure pathway. After constraining the PM2.5 yield to 5% by mass, VCPs produce ~41% of the photochemical organic PM2.5 (1.1 ± 0.3 µg m-3) and ~17% of maximum daily 8-hr average ozone (9 ± 2 ppb) in summer Los Angeles. Therefore, both toxicity and ambient criteria pollutant formation should be considered when organic substituents are developed for VCPs in pursuit of safer and sustainable products and cleaner air.