Identifying Attributes That Influence In Vitro-to-In Vivo Concordance by Comparing In Vitro Tox21 Bioactivity Versus In Vivo DrugMatrix Transcriptomic Responses Across 130 Chemicals

Characterizing the extracellular vesicle proteomic landscape of the human airway using in vitro organotypic multi-cellular models

Extracellular vesicle (EV)-mediated intercellular communication significantly influences pulmonary cell health and disease, yet in vitro methods to investigate these mechanisms are limited. We hypothesize that organotypic models of the airway can be leveraged to investigate EV-mediated intercellular signaling, focusing on EV proteomic content as a case study. Two in vitro airway culture models were evaluated by mass spectrometry-based proteomics analysis: a tri-culture model consisting of alveolar epithelial, fibroblast, and lung microvascular endothelial cells and a co-culture model of alveolar epithelial and fibroblasts. EVs isolated from the tri-culture model were enriched with EV proteins regulating RNA-to-protein translation. EVs isolated from the co-culture model were enriched with EV biogenesis and extracellular matrix signaling proteins. These model-specific differences suggest that different pulmonary cell types uniquely affect EV composition and the biological pathways influenced by the EV proteome in recipient cells. These findings can inform future studies surrounding EV-related pulmonary disease pathogenesis and therapeutics.

Bayesian matrix completion for hypothesis testing

We aim to infer bioactivity of each chemical by assay endpoint combination, addressing sparsity of toxicology data. We propose a Bayesian hierarchical framework which borrows information across different chemicals and assay endpoints, facilitates out-of-sample prediction of activity for chemicals not yet assayed, quantifies uncertainty of predicted activity, and adjusts for multiplicity in hypothesis testing. Furthermore, this paper makes a novel attempt in toxicology to simultaneously model heteroscedastic errors and a nonparametric mean function, leading to a broader definition of activity whose need has been suggested by toxicologists. Real application identifies chemicals most likely active for neurodevelopmental disorders and obesity.

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.

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.

Exposure forecasting - ExpoCast - for data-poor chemicals in commerce and the environment

Estimates of exposure are critical to prioritize and assess chemicals based on risk posed to public health and the environment. The U.S. Environmental Protection Agency (EPA) is responsible for regulating thousands of chemicals in commerce and the environment for which exposure data are limited. Since 2009 the EPA's ExpoCast ("Exposure Forecasting") project has sought to develop the data, tools, and evaluation approaches required to generate rapid and scientifically defensible exposure predictions for the full universe of existing and proposed commercial chemicals. This review article aims to summarize issues in exposure science that have been addressed through initiatives affiliated with ExpoCast. ExpoCast research has generally focused on chemical exposure as a statistical systems problem intended to inform thousands of chemicals. The project exists as a companion to EPA's ToxCast ("Toxicity Forecasting") project which has used in vitro high-throughput screening technologies to characterize potential hazard posed by thousands of chemicals for which there are limited toxicity data. Rapid prediction of chemical exposures and in vitro-in vivo extrapolation (IVIVE) of ToxCast data allow for prioritization based upon risk of adverse outcomes due to environmental chemical exposure. ExpoCast has developed (1) integrated modeling approaches to reliably predict exposure and IVIVE dose, (2) highly efficient screening tools for chemical prioritization, (3) efficient and affordable tools for generating new exposure and dose data, and (4) easily accessible exposure databases. The development of new exposure models and databases along with the application of technologies like non-targeted analysis and machine learning have transformed exposure science for data-poor chemicals. By developing high-throughput tools for chemical exposure analytics and translating those tools into public health decisions ExpoCast research has served as a crucible for identifying and addressing exposure science knowledge gaps.

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.

Analyses of Transcriptomics Cell Signalling for Pre-Screening Applications in the Integrated Approach for Testing and Assessment of Non-Genotoxic Carcinogens

With recent rapid advancement of methodological tools, mechanistic understanding of biological processes leading to carcinogenesis is expanding. New approach methodologies such as transcriptomics can inform on non-genotoxic mechanisms of chemical carcinogens and can be developed for regulatory applications. The Organisation for the Economic Cooperation and Development (OECD) expert group developing an Integrated Approach to the Testing and Assessment (IATA) of Non-Genotoxic Carcinogens (NGTxC) is reviewing the possible assays to be integrated therein. In this context, we review the application of transcriptomics approaches suitable for pre-screening gene expression changes associated with phenotypic alterations that underlie the carcinogenic processes for subsequent prioritisation of downstream test methods appropriate to specific key events of non-genotoxic carcinogenesis. Using case studies, we evaluate the potential of gene expression analyses especially in relation to breast cancer, to identify the most relevant approaches that could be utilised as (pre-) screening tools, for example Gene Set Enrichment Analysis (GSEA). We also consider how to address the challenges to integrate gene panels and transcriptomic assays into the IATA, highlighting the pivotal omics markers identified for assay measurement in the IATA key events of inflammation, immune response, mitogenic signalling and cell injury.

Tox21-Based Comparative Analyses for the Identification of Potential Toxic Effects of Environmental Pollutants

Chemical pollution has become a prominent environmental problem. In recent years, quantitative high-throughput screening (qHTS) assays have been developed for the fast assessment of chemicals' toxic effects. Toxicology in the 21st Century (Tox21) is a well-known and continuously developing qHTS project. Recent reports utilizing Tox21 data have mainly focused on setting up mathematical models for in vivo toxicity predictions, with less attention to intuitive qHTS data visualization. In this study, we attempted to reveal and summarize the toxic effects of environmental pollutants by analyzing and visualizing Tox21 qHTS data. Via PubMed text mining, toxicity/structure clustering, and manual classification, we detected a total of 158 chemicals of environmental concern (COECs) from the Tox21 library that we classified into 13 COEC groups based on structure and activity similarities. By visualizing these COEC groups' bioactivities, we demonstrated that COECs frequently displayed androgen and progesterone antagonistic effects, xenobiotic receptor agonistic roles, and mitochondrial toxicity. We also revealed many other potential targets of the 13 COEC groups, which were not well illustrated yet, and that current Tox21 assays may not correctly classify known teratogens. In conclusion, we provide a feasible method to intuitively understand qHTS data.

The placenta epigenome-brain axis: placental epigenomic and transcriptomic responses that preprogram cognitive impairment

Aim: The placenta-brain axis reflects a developmental linkage where disrupted placental function is associated with impaired neurodevelopment later in life. Placental gene expression and the expression of epigenetic modifiers such as miRNAs may be tied to these impairments and are understudied. Materials & methods: The expression levels of mRNAs (n = 37,268) and their targeting miRNAs (n = 2083) were assessed within placentas collected from the ELGAN study cohort (n = 386). The ELGAN adolescents were assessed for neurocognitive function at age 10 and the association with placental mRNA/miRNAs was determined. Results: Placental mRNAs related to inflammatory and apoptotic processes are under miRNA control and associated with cognitive impairment at age 10. Conclusion: Findings highlight key placenta epigenome-brain relationships that support the developmental origins of health and disease hypothesis.

Integrative exposomic, transcriptomic, epigenomic analyses of human placental samples links understudied chemicals to preeclampsia

BACKGROUND

Environmental health research has recently undergone a dramatic shift, with ongoing technological advancements allowing for broader coverage of exposure and molecular biology signatures. Approaches to integrate such measures are still needed to increase understanding between systems-level exposure and biology.

OBJECTIVES

We address this gap by evaluating placental tissues to identify novel chemical-biological interactions associated with preeclampsia. This study tests the hypothesis that understudied chemicals are present in the human placenta and associated with preeclampsia-relevant disruptions, including overall case status (preeclamptic vs. normotensive patients) and underlying transcriptomic/epigenomic signatures.

METHODS

A non-targeted analysis based on high-resolution mass spectrometry was used to analyze placental tissues from a cohort of 35 patients with preeclampsia (n = 18) and normotensive (n = 17) pregnancies. Molecular feature data were prioritized for confirmation based on association with preeclampsia case status and confidence of chemical identification. All molecular features were evaluated for relationships to mRNA, microRNA, and CpG methylation (i.e., multi-omic) signature alterations involved in preeclampsia.

RESULTS

A total of 183 molecular features were identified with significantly differentiated abundance in placental extracts of preeclamptic patients; these features clustered into distinct chemical groupings using unsupervised methods. Of these features, 53 were identified (mapping to 40 distinct chemicals) using chemical standards, fragmentation spectra, and chemical metadata. In general, human metabolites had the largest feature intensities and strongest associations with preeclampsia-relevant multi-omic changes. Exogenous drugs were second most abundant and had fewer associations with multi-omic changes. Other exogenous chemicals (non-drugs) were least abundant and had the fewest associations with multi-omic changes.

CONCLUSIONS

These global data trends suggest that human metabolites are heavily intertwined with biological processes involved in preeclampsia etiology, while exogenous chemicals may still impact select transcriptomic/epigenomic processes. This study serves as a demonstration of merging systems exposures with systems biology to better understand chemical-disease relationships.

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.

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.

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.

Comparing the Predictivity of Human Placental Gene, microRNA, and CpG Methylation Signatures in Relation to Perinatal Outcomes

Molecular signatures are being increasingly integrated into predictive biology applications. However, there are limited studies comparing the overall predictivity of transcriptomic vs. epigenomic signatures in relation to perinatal outcomes. This study set out to evaluate mRNA and microRNA (miRNA) expression and cytosine-guanine dinucleotide (CpG) methylation signatures in human placental tissues and relate these to perinatal outcomes known to influence maternal/fetal health; namely, birth weight, placenta weight, placental damage, and placental inflammation. The following hypotheses were tested: (1) different molecular signatures will demonstrate varying levels of predictivity towards perinatal outcomes, and (2) these signatures will show disruptions from an example exposure (i.e., cadmium) known to elicit perinatal toxicity. Multi-omic placental profiles from 390 infants in the Extremely Low Gestational Age Newborns cohort were used to develop molecular signatures that predict each perinatal outcome. Epigenomic signatures (i.e., miRNA and CpG methylation) consistently demonstrated the highest levels of predictivity, with model performance metrics including R^2 (predicted vs. observed) values of 0.36-0.57 for continuous outcomes and balanced accuracy values of 0.49-0.77 for categorical outcomes. Top-ranking predictors included miRNAs involved in injury and inflammation. To demonstrate the utility of these predictive signatures in screening of potentially harmful exogenous insults, top-ranking miRNA predictors were analyzed in a separate pregnancy cohort and related to cadmium. Key predictive miRNAs demonstrated altered expression in association with cadmium exposure, including miR-210, known to impact placental cell growth, blood vessel development, and fetal weight. These findings inform future predictive biology applications, where additional benefit will be gained by including epigenetic markers.

Development of the genomic inflammatory index (GII) to assess key maternal antecedents associated with placental inflammation

INTRODUCTION

Placental inflammation is associated with a variety of adverse health outcomes, including poor pregnancy outcomes as well as later in life health. The current clinical methodologies for evaluating placental histology for inflammation are limited in their sensitivity. The objective of this study was to develop a genomic inflammatory index (GII) that can be utilized as a biomarker to effectively quantify and evaluate placental inflammation.

METHODS

RNA-sequencing of n = 386 placentas from the Extremely Low Gestational Age Newborn (ELGAN) cohort was conducted. Transcriptional data for a biologically-targeted set of 14 genes, selected for their established role in pro-inflammatory signaling pathways, were aggregated to construct the GII. Multiple linear regression models were used to examine relationships between 47 perinatal factors and the GII.

RESULTS

The GII demonstrated a nine-fold difference across subjects and displayed positive trends with other indicators of placental inflammation. Significant differences in the GII were observed for race where women who self-identified as Black displayed higher levels of placental inflammation than those who self-identified as White women (p < 0.001). Additionally, married Black women showed reduced placental inflammation compared to those who were unmarried (beta value: 0.828, p-value: 0.032). Placentas from women who were treated with steroids during the delivery of the infant displayed higher GII levels than those who were not (p = 0.023).

DISCUSSION

Overall, the GII demonstrated an association between various perinatal factors and placental inflammation. It is anticipated that the GII will provide a novel genomics tool for quantifying placental inflammation, allowing for further investigation of causes, and ultimately the prevention, of inflammation in the placenta.

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.

Two distinct trophectoderm lineage stem cells from human pluripotent stem cells

The trophectoderm layer of the blastocyst-stage embryo is the precursor for all trophoblast cells in the placenta. Human trophoblast stem (TS) cells have emerged as an attractive tool for studies on early trophoblast development. However, the use of TS cell models is constrained by the limited genetic diversity of existing TS cell lines and restrictions on using human fetal tissue or embryos needed to generate additional lines. Here we report the derivation of two distinct stem cell types of the trophectoderm lineage from human pluripotent stem cells. Analogous to villous cytotrophoblasts in vivo, the first is a CDX2^- stem cell comparable with placenta-derived TS cells—they both exhibit identical expression of key markers, are maintained in culture and differentiate under similar conditions, and share high transcriptome similarity. The second is a CDX2⁺ stem cell with distinct cell culture requirements, and differences in gene expression and differentiation, relative to CDX2^- stem cells. Derivation of TS cells from pluripotent stem cells will significantly enable construction of in vitro models for normal and pathological placental development.

Exploring in vitro to in vivo extrapolation for exposure and health impacts of e-cigarette flavor mixtures

In vitro to in vivo extrapolation (IVIVE) leverages in vitro biological activities to predict corresponding in vivo exposures, therefore potentially reducing the need for animal safety testing that are traditionally performed to support the hazard and risk assessment. Interpretation of IVIVE predictions are affected by various factors including the model type, exposure route and kinetic assumptions for the test article, and choice of in vitro assay(s) that are relevant to clinical outcomes. Exposure scenarios are further complicated for mixtures where the in vitro activity may stem from one or more components in the mixture. In this study, we used electronic cigarette (EC) aerosols, a complex mixture, to explore impacts of these factors on the use of IVIVE in hazard identification, using open-source pharmacokinetic models of varying complexity and publicly available data. Results suggest in vitro assay selection has a greater impact on exposure estimates than modeling approaches. Using cytotoxicity assays, high exposure estimates (>1000 EC cartridges (pods) or > 700 mL EC liquid per day) would be needed to obtain the in vivo plasma levels that are corresponding to in vitro assay data, suggesting acute toxicity would be unlikely in typical usage scenarios. When mechanistic (Tox21) assays were used, the exposure estimates were much lower for the low end, but the range of exposure estimate became wider across modeling approaches. These proof-of-concept results highlight challenges and complexities in IVIVE for mixtures.

A role for microRNAs in the epigenetic control of sexually dimorphic gene expression in the human placenta

Aim: The contribution of miRNAs as epigenetic regulators of sexually dimorphic gene expression in the placenta is unknown. Materials & methods: 382 placentas from the extremely low gestational age newborns (ELGAN) cohort were evaluated for expression levels of 37,268 mRNAs and 2,102 miRNAs using genome-wide RNA-sequencing. Differential expression analysis was used to identify differences in the expression based on the sex of the fetus. Results: Sexually dimorphic expression was observed for 128 mRNAs and 59 miRNAs. A set of 25 miRNA master regulators was identified that likely contribute to the sexual dimorphic mRNA expression. Conclusion: These data highlight sex-dependent miRNA and mRNA patterning in the placenta and provide insight into a potential mechanism for observed sex differences in outcomes.

Placental genomic and epigenomic signatures associated with infant birth weight highlight mechanisms involved in collagen and growth factor signaling

Birth weight (BW) represents an important clinical and toxicological measure, indicative of the overall health of the newborn as well as potential risk for later-in-life outcomes. BW can be influenced by endogenous and exogenous factors and is known to be heavily impacted in utero by the health and function of the placenta. An aspect that remains understudied is the influence of genomic and epigenomic programming within the placenta on infant BW. To address this gap, we set out to test the hypothesis that genes involved in critical placental cell signaling are associated with infant BW, and are likely regulated, in part, through epigenetic mechanisms based on microRNA (miRNA) mediation. This study leveraged a robust dataset based on 390 infants born at low gestational age (ranged 23-27 weeks) to evaluate genome-wide expression profiles of both mRNAs and miRNAs in placenta tissues and relate these to infant BW. A total of 254 mRNAs and 268 miRNAs were identified as associated with BW, the majority of which showed consistent associations across placentas derived from both males and females. BW-associated mRNAs were found to be enriched for important biological pathways, including glycoprotein VI (the major receptor for collagen), human growth, and hepatocyte growth factor signaling, a portion of which were predicted to be regulated by BW-associated miRNAs. These miRNA-regulated pathways highlight key mechanisms potentially linking endogenous/exogenous factors to changes in birth outcomes that may be deleterious to infant and later-in-life health.

Developing novel in vitro methods for the risk assessment of developmental and placental toxicants in the environment

During pregnancy, the placenta is critical for the regulation of maternal homeostasis and fetal growth and development. Exposures to environmental chemicals during pregnancy can be detrimental to the health of the placenta and therefore adversely impact maternal and fetal health. Though research on placental-derived developmental toxicity is expanding, testing is limited by the resources required for traditional test methods based on whole animal experimentation. Alternative strategies utilizing in vitro methods are well suited to contribute to more efficient screening of chemical toxicity and identification of biological mechanisms underlying toxicity outcomes. This review aims to summarize methods that can be used to evaluate toxicity resulting from exposures during the prenatal period, with a focus on newer in vitro methods centered on placental toxicity. The following key aspects are reviewed: (i) traditional test methods based on animal developmental toxicity testing, (ii) in vitro methods using monocultures and explant models, as well as more recently developed methods, including co-cultures, placenta-on-a-chip, and 3-dimensional (3D) cell models, (iii) endpoints that are commonly measured using in vitro designs, and (iv) the translation of in vitro methods into chemical evaluations and risk assessment applications. We conclude that findings from in vitro placental models can contribute to the screening of potentially hazardous chemicals, elucidation of chemical mechanism of action, incorporation into adverse outcome pathways, estimation of doses eliciting toxicity, derivation of extrapolation factors, and characterization of overall risk of adverse outcomes, representing key components of chemical regulation in the 21^st century.

Improving the Utility of the Tox21 Dataset by Deep Metadata Annotations and Constructing Reusable Benchmarked Chemical Reference Signatures

The Toxicology in the 21st Century (Tox21) project seeks to develop and test methods for high-throughput examination of the effect certain chemical compounds have on biological systems. Although primary and toxicity assay data were readily available for multiple reporter gene modified cell lines, extensive annotation and curation was required to improve these datasets with respect to how FAIR (Findable, Accessible, Interoperable, and Reusable) they are. In this study, we fully annotated the Tox21 published data with relevant and accepted controlled vocabularies. After removing unreliable data points, we aggregated the results and created three sets of signatures reflecting activity in the reporter gene assays, cytotoxicity, and selective reporter gene activity, respectively. We benchmarked these signatures using the chemical structures of the tested compounds and obtained generally high receiver operating characteristic (ROC) scores, suggesting good quality and utility of these signatures and the underlying data. We analyzed the results to identify promiscuous individual compounds and chemotypes for the three signature categories and interpreted the results to illustrate the utility and re-usability of the datasets. With this study, we aimed to demonstrate the importance of data standards in reporting screening results and high-quality annotations to enable re-use and interpretation of these data. To improve the data with respect to all FAIR criteria, all assay annotations, cleaned and aggregate datasets, and signatures were made available as standardized dataset packages (Aggregated Tox21 bioactivity data, 2019).