Leveraging a Comprehensive Unbiased RNAseq Database Uncovers New Human Monocyte-Derived Macrophage Subtypes Within Commonly Employed In Vitro Polarization Methods.

Macrophages are pivotal innate immune cells which exhibit high phenotypic plasticity and can exist in different polarization states dependent on exposure to external stimuli. Numerous methods have been employed to simulate macrophage polarization states to test their function in vitro. However, limited research has explored whether these polarization methods yield comparable populations beyond key gene, cytokine, and cell surface marker expression. Here, we employ an unbiased comprehensive analysis using data organized through the all RNA-seq and ChIP-seq sample and signature search (ARCHS4) database, which compiles all RNAseq data deposited into the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA). In silico analyses were carried out demonstrating that commonly employed macrophage polarization methods generate distinct macrophage subsets that remained undescribed until now. Our analyses confirm existing knowledge on macrophage polarization, while revealing nuanced differences between M2a and M2c subpopulations, suggesting non-interchangeable stimuli for M2a polarization. Furthermore, we identify divergent gene expression patterns in M1 macrophages following standard polarization protocols, indicating significant subset distinctions. Consequently, equivalence cannot be assumed among polarization regimens for in vitro macrophage studies, particularly in simulating diverse pathogen responses.

Burn Pit Smoke Condensate-Mediated Toxicity in Human Airway Epithelial Cells.

Burn pits are a method of open-air waste management that was common during military operations in Iraq, Afghanistan, and other regions in Southwest Asia. Veterans returning from deployment have reported respiratory symptoms, potentially from exposure to burn pit smoke, yet comprehensive assessment of such exposure on pulmonary health is lacking. We have previously shown that exposure to condensates from burn pit smoke emissions causes inflammation and cytotoxicity in mice. In this study, we explored the effects of burn pit smoke condensates on human airway epithelial cells (HAECs) to understand their impact on cellular targets in the human lung. HAECs were cultured at the air-liquid interface (ALI) and exposed to burn pit waste smoke condensates (plywood, cardboard, plastic, mixed, and mixed with diesel) generated under smoldering and flaming conditions. Cytotoxicity was evaluated by measuring transepithelial electrical resistance (TEER) and lactate dehydrogenase (LDH) release; toxicity scores (TSs) were quantified for each exposure. Pro-inflammatory cytokine release and modulation of gene expression were examined for cardboard and plastic condensate exposures. Burn pit smoke condensates generated under flaming conditions affected cell viability, with flaming mixed waste and plywood exhibiting the highest toxicity scores. Cardboard and plastic smoke condensates modulated cytokine secretion, with GM-CSF and IL-1ß altered in more than one exposure group. Gene expression of detoxifying enzymes (ALDH1A3, ALDH3A1, CYP1A1, CYP1B1, NQO1, etc.), mucins (MUC5AC, MUC5B), and cytokines was affected by several smoke condensates. Particularly, expression of IL6 was elevated following exposure to all burn pit smoke condensates, and polycyclic aromatic hydrocarbon acenaphthene was positively associated with the IL-6 level in the basolateral media of HAECs. These observations demonstrate that exposure to smoke condensates of materials present in burn pits adversely affects HAECs and that aberrant cytokine secretion and altered gene expression profiles following burn pit material smoke exposure could contribute to the development of airway disease.

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.

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.

Toxic metal mixtures in private well water and increased risk for preterm birth in North Carolina.

BACKGROUND: Prenatal exposure to metals in private well water may increase the risk of preterm birth (PTB) (delivery < 37 weeks' gestation). In this study, we estimated associations between arsenic, manganese, lead, cadmium, chromium, copper, and zinc concentrations in private well water and PTB incidence in North Carolina (NC). METHODS: Birth certificates from 2003-2015 (n = 1,329,071) were obtained and pregnancies were assigned exposure using the mean concentration and the percentage of tests above the maximum contaminant level (MCL) for the census tract of each individuals' residence at the time of delivery using the NCWELL database (117,960 well water tests from 1998-2019). We evaluated associations between single metals and PTB using adjusted logistic regression models. Metals mixtures were assessed using quantile-based g-computation. RESULTS: Compared with those in other census tracts, individuals residing in tracts where > 25% of tests exceeded the MCL for lead (aOR 1.10, 95%CI 1.02,1.18) or cadmium (aOR 1.11, 95% CI 1.00,1.23) had an increased odds of PTB. Conversely, those residing in areas with > 25% MCL for zinc (aOR 0.77 (95% CI: 0.56,1.02) and copper (aOR 0.53 (95% CI: 0.13,1.34)) had a reduced odds of PTB. A quartile increase in the concentrations of a mixture of lead, cadmium, and chromium was associated with a small increased odds for PTB (aOR 1.02, 95% CI 1.01, 1.03). This metal mixture effect was most pronounced among American Indian individuals (aOR per quartile increase in all metals: 1.19 (95% CI 1.06,1.34)). CONCLUSIONS: In a large study population of over one million births, lead and cadmium were found to increase the risk of PTB individually and in a mixture, with additional mixtures-related impacts estimated from co-exposure with chromium. This study highlights critical racial and ethnic health disparities in relation to private well water thereby emphasizing the urgent need for improved private well water quality to protect vulnerable populations.

Wrangling Whole Mixtures Risk Assessment: Recent Advances in Determining Sufficient Similarity.

Human health risk assessments for complex mixtures can address real-world exposures and protect public health. While risk assessors typically prefer whole mixture approaches over component-based approaches, data from the precise exposure of interest are often unavailable and surrogate data from a sufficiently similar mixture(s) are required. This review describes recent advances in determining sufficient similarity of whole, complex mixtures spanning the comparison of chemical features, bioactivity profiles, and statistical evaluation to determine "thresholds of similarity". Case studies, including water disinfection byproducts, botanical ingredients, and wildfire emissions, are used to highlight tools and methods. Limitations to application of sufficient similarity in risk-based decision making are reviewed and recommendations presented for developing best practice guidelines.

Navigating the bridge between wet and dry lab toxicology research to address current challenges with high-dimensional data.

Toxicology research has rapidly evolved, leveraging increasingly advanced technologies in high-throughput approaches to yield important information on toxicological mechanisms and health outcomes. Data produced through toxicology studies are consequently becoming larger, often producing high-dimensional data. These types of data hold promise for imparting new knowledge, yet inherently have complexities causing them to be a rate-limiting element for researchers, particularly those that are housed in "wet lab" settings (i.e., researchers that use liquids to analyze various chemicals and biomarkers as opposed to more computationally focused, "dry lab" researchers). These types of challenges represent topics of ongoing conversation amongst our team and researchers in the field. The aim of this perspective is to i) summarize hurdles in analyzing high-dimensional data in toxicology that require improved training and translation for wet lab researchers, ii) highlight example methods that have aided in translating data analysis techniques to wet lab researchers; and iii) describe challenges that remain to be effectively addressed, to date, in toxicology research. Specific aspects include methodologies that could be introduced to wet lab researchers, including data pre-processing, machine learning, and data reduction. Current challenges discussed include model interpretability, study biases, and data analysis training. Example efforts implemented to translate these data analysis techniques are also mentioned, including online data analysis resources and hands-on workshops. Questions are also posed to continue conversation in the toxicology community. Contents of this perspective represent timely issues broadly occurring in the fields of bioinformatics and toxicology that require ongoing dialogue between wet and dry lab researchers.

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.

Plasma sterols and vitamin D are correlates and predictors of ozone-induced inflammation in the lung: A pilot study.

BACKGROUND: Ozone (O3) exposure causes respiratory effects including lung function decrements, increased lung permeability, and airway inflammation. Additionally, baseline metabolic state can predispose individuals to adverse health effects from O3. For this reason, we conducted an exploratory study to examine the effect of O3 exposure on derivatives of cholesterol biosynthesis: sterols, oxysterols, and secosteroid (25-hydroxyvitamin D) not only in the lung, but also in circulation. METHODS: We obtained plasma and induced sputum samples from non-asthmatic (n = 12) and asthmatic (n = 12) adult volunteers 6 hours following exposure to 0.4ppm O3 for 2 hours. We quantified the concentrations of 24 cholesterol precursors and derivatives by UPLC-MS and 30 cytokines by ELISA. We use computational analyses including machine learning to determine whether baseline plasma sterols are predictive of O3 responsiveness. RESULTS: We observed an overall decrease in the concentration of cholesterol precursors and derivatives (e.g. 27-hydroxycholesterol) and an increase in concentration of autooxidation products (e.g. secosterol-B) in sputum samples. In plasma, we saw a significant increase in the concentration of secosterol-B after O3 exposure. Machine learning algorithms showed that plasma cholesterol was a top predictor of O3 responder status based on decrease in FEV1 (>5%). Further, 25-hydroxyvitamin D was positively associated with lung function in non-asthmatic subjects and with sputum uteroglobin, whereas it was inversely associated with sputum myeloperoxidase and neutrophil counts. CONCLUSION: This study highlights alterations in sterol metabolites in the airway and circulation as potential contributors to systemic health outcomes and predictors of pulmonary and inflammatory responsiveness following O3 exposure.

Generation of the Chemical and Social Stressors Integration Technique (CASS-IT) to identify areas of holistic public health concern: An application to North Carolina.

Due to structural racism and income inequality, exposure to environmental chemicals is tightly linked to socioeconomic factors. In addition, exposure to psychosocial stressors, such as racial discrimination, as well as having limited resources, can increase susceptibility to environmentally induced disease. Yet, studies are often conducted separately in fields of social science and environmental science, reducing the potential for holistic risk estimates. To tackle this gap, we developed the Chemical and Social Stressors Integration Technique (CASS-IT) to integrate environmental chemical and social stressor datasets. The CASS-IT provides a framework to identify distinct geographic areas based on combinations of environmental chemical exposure, social vulnerability, and access to resources. It incorporates two data dimension reduction tools: k-means clustering and latent profile analysis. Here, the CASS-IT was applied to North Carolina (NC) as a case study. Environmental chemical data included toxic metals - arsenic, manganese, and lead - in private drinking well water. Social stressor data were captured by the CDC's social vulnerability index's four domains: socioeconomic status, household composition and disability, minority status and language, and housing type and transportation. Data on resources were derived from Federal Emergency Management Agency (FEMA's) Resilience and Analysis Planning Tool, which generated measures of health resources, social resources, and information resources. The results highlighted 31 NC counties where exposure to both toxic metals and social stressors are elevated, and health resources are minimal; these are counties in which environmental justice is of utmost concern. A census-tract level analysis was also conducted to demonstrate the utility of CASS-IT at different geographical scales. The tract-level analysis highlighted specific tracts within counties of concern that are particularly high priority. In future research, the CASS-IT can be used to analyze United States-wide environmental datasets providing guidance for targeted public health interventions and reducing environmental disparities.

The impact of environmental contaminants on extracellular vesicles and their key molecular regulators: A literature and database-driven review.

Exposure to environmental chemicals is now well recognized as a significant factor contributing to the global burden of disease; however, there remain critical gaps in understanding the types of biological mechanisms that link environmental chemicals to adverse health outcomes. One type of mechanism that remains understudied involves extracellular vesicles (EVs), representing small cell-derived particles capable of carrying molecular signals such as RNAs, miRNAs, proteins, lipids, and chemicals through biological fluids and imparting beneficial, neutral, or negative effects on target cells. In fact, evidence is just now starting to grow that supports the role of EVs in various disease etiologies. This review aims to (1) Provide a landscape of the current understanding of the functional relationship between EVs and environmental chemicals; (2) Summarize current knowledge of EV regulatory processes including production, packaging, and release; and (3) Conduct a database-driven analysis of known chemical-gene interactions to predict and prioritize environmentally relevant chemicals that may impact EV regulatory genes and thus EV regulatory processes. This approach to predicting environmentally relevant chemicals that may alter EVs provides a novel method for evidence-based hypothesis generation for future studies evaluating the link between environmental exposures and EVs.

Metal mixtures modeling identifies birth weight-associated gene networks in the placentas of children born extremely preterm.

Prenatal exposure to toxic metals is linked to numerous adverse birth and later-in-life outcomes. These outcomes are tied to disrupted biological processes in fetal-derived tissues including the placenta and umbilical cord yet the precise pathways are understudied in these target tissues. We set out to examine the relationship between metal concentrations in umbilical cord and altered gene expression networks in placental tissue. These novel relationships were investigated in a subset of the Extremely Low Gestational Age Newborn (ELGAN) cohort (n = 226). Prenatal exposure to 11 metals/metalloids was measured using inductively coupled plasma tandem-mass spectrometry (ICP-MS/MS) in cord tissue, ensuring passage through the placental barrier. RNA-sequencing was used to quantify >37,000 mRNA transcripts. Differentially expressed genes (DEGs) were identified with respect to each metal. Weighted gene co-expression analysis identified gene networks modulated by metals. Two innovative mixtures modeling techniques, namely principal components analysis and quantile-based g-computation, were employed to identify genes/gene networks associated with multi-metal exposure. Individually, lead was associated with the strongest genomic response of 191 DEGs. Joint lead and cadmium exposure was related to 657 DEGs, including DNA Methyl Transferase 1 (DNMT1). These genes were enriched for the Eukaryotic Initiation Factor 2 (EIF2) pathway. Four gene networks, each containing genes within a Nuclear Factor kappa-light-chain-enhancer of Activated B Cells (NF-kB)-mediated network, were significantly increased in average expression level in relation to increases in all metal concentrations. All four of these metal mixture-associated gene networks were negatively correlated with important predictors of neonatal health including birth weight, placenta weight, and fetal growth. Bringing together novel methodologies from epidemiological mixtures analyses and toxicogenomics, applied to a unique cohort of extremely preterm children, the present study highlighted critical genes and pathways in the placenta dysregulated by prenatal metal mixtures. These represent potential mechanisms underlying the developmental origins of metal-induced disease.

Computational Approach to Link Chemicals in Anthropogenic Smoke Particulate Matter with Toxicity.

A weighted chemical coexpression network analysis (WCCNA) was utilized to identify chemicals co-modulated to variable burning of anthropogenic materials and to link chemicals to biological responses (lung toxicity and mutagenicity). Polyaromatic hydrocarbons (PAHs) were co-modulated with increased concentrations in flaming smoke particulate matter (PM) from the burning of plastic-containing materials and showed significant association with increased neutrophil influx, cytokine levels, and mutagenicity. Inorganic elements were co-modulated with increased concentrations in flaming plywood and cardboard smoke PM and showed significant association with increased protein and albumin levels. This study shows the potential for using a computational network analysis to identify and prioritize hazardous chemical components within complex environmental mixtures and provides guidance on key chemical tracers required for intervention research to protect public health from the exposure.

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.

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.

A Transformative Vision for an Omics-Based Regulatory Chemical Testing Paradigm.

Use of molecular data in human and ecological health risk assessments of industrial chemicals and agrochemicals has been anticipated by the scientific community for many years; however, these data are rarely used for risk assessment. Here, a logic framework is proposed to explore the feasibility and future development of transcriptomic methods to refine and replace the current apical endpoint-based regulatory toxicity testing paradigm. Four foundational principles are outlined and discussed that would need to be accepted by stakeholders prior to this transformative vision being realized. Well-supported by current knowledge, the first principle is that transcriptomics is a reliable tool for detecting alterations in gene expression that result from endogenous or exogenous influences on the test organism. The second principle states that alterations in gene expression are indicators of adverse or adaptive biological responses to stressors in an organism. Principle 3 is that transcriptomics can be employed to establish a benchmark dose-based point of departure (POD) from short-term, in vivo studies at a dose level below which a concerted molecular change (CMC) is not expected. Finally, Principle 4 states that the use of a transcriptomic POD (set at the CMC dose level) will support a human health-protective risk assessment. If all four principles are substantiated, this vision is expected to transform aspects of the industrial chemical and agrochemical risk assessment process that are focused on establishing safe exposure levels for mammals across numerous toxicological contexts resulting in a significant reduction in animal use while providing equal or greater protection of human health. Importantly, these principles and approaches are also generally applicable for ecological safety assessment.

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.

Children born extremely preterm are at increased risk for neurodevelopmental impairments such as cerebral palsy, intellectual disability and autism. The biological processes that lead to these impairments likely begin before birth and involve altered placental function. In this study, the authors analyzed placental genomic and epigenomic data from children who were born extremely preterm in relation to cognitive assessments at 10 years of age. They examined the differences between the expression of placental genes and molecules that influence the expression of placental genes, comparing children who had impaired cognition at 10 years with children who did not. The results demonstrated elevated expression levels of genes involved in inflammatory processes and molecules that control the expression of these genes within the placentas of children who had impaired cognition at age 10.

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.