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Background Hurricane Florence made landfall in North Carolina in September 2018 causing extensive flooding. Several potential point sources of hazardous substances and Superfund sites sustained water damage and contaminants may have been released into the environment. Objective This study conducted temporal analysis of contaminant distribution and potential human health risks from Hurricane Florence-associated flooding. Methods Soil samples were collected from 12 sites across four counties in North Carolina in September 2018, January and May 2019. Chemical analyses were performed for organics by gas chromatography-mass spectrometry. Metals were analyzed using inductively coupled plasma mass spectrometry. Hazard index and cancer risk were calculated using EPA Regional Screening Level Soil Screening Levels for residential soils. Results PAH and metals detected downstream from the coal ash storage pond that leaked were detected and were indicative of a pyrogenic source of contamination. PAH at these sites were of human health concern because cancer risk values exceeded 1 × 10-6 threshold. Other contaminants measured across sampling sites, or corresponding hazard index and cancer risk, did not exhibit spatial or temporal differences or were of concern. Significance This work shows the importance of rapid exposure assessment following natural disasters. It also establishes baseline levels of contaminants for future comparisons.

Contributions of the microenvironment to soft tissue sarcoma progression are relatively undefined, representing a major impediment to identifying essential regulatory networks in sarcomagenesis. Furthermore, genetic and molecular characteristics that distinguish precancerous versus cancerous microenvironments are not well known across human cancer types. While animal models have the potential to reveal these complex processes, significant impediments to such inquiries include (1) the difficulty in distinguishing microenvironmental cells from precancerous or cancer cells in tissue specimens; and (2) the challenge in defining a discrete tissue with known cancer predilection that represents a precancerous microenvironment. We developed a unique zebrafish model that allows segregation of microenvironmental, precancerous, and cancerous cell populations by fluorescence-activated cell sorting. This model exhibits high predilection for malignant peripheral nerve sheath tumor (MPNST), a type of soft tissue sarcoma with a particularly poor prognosis due to aggressive growth, limited response to conventional treatment, and ineffective targeted therapy options. Using RNA-seq, we profiled the transcriptomes of microenvironmental cells from our zebrafish MPNST model and determined that the precancerous and cancerous microenvironments exhibit broad activation of inflammatory and immune-associated signaling networks. Markers for both M1 and M2 macrophage polarization were upregulated in precancerous and cancerous microenvironments, suggesting the presence of a mixed macrophage population during sarcomagenesis. Patterns of ligand and receptor expression based on a previously defined human ligand-receptor network identified significant upregulation of multiple tumor-promoting ligands in both precancerous and cancerous microenvironments. We also identified specific ligand-receptor pairs that may mediate key signaling events during sarcoma initiation and progression. Together this work provide new insight into distinguishing characteristics of the cancer-prone cellular microenvironment that may promote MPNST initiation and progression in vertebrates. Citation Format: Heather R. Shive, John S. House, Jordan L. Ferguson, Dereje D. Jima, Aubrie A. Selmek, Dillon T. Lloyd. Characterization of the precancerous and cancer microenvironment in a zebrafish sarcoma model [abstract]. In: Proceedings of the AACR Special Conference: Sarcomas; 2022 May 9-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(18_Suppl):Abstract nr PR011.

Ion mobility spectrometry coupled with mass spectrometry (IMS-MS) is a post-ionization separation technique that can be used for rapid multidimensional analyses of complex samples. IMS-MS offers untargeted analysis, including ion-specific conformational data derived as collisional cross section (CCS) values. Here, we combine nitrogen gas drift tube CCS ((DT)CCS(N2)) and Kendrick mass defect (KMD) analyses based on CH2 and H functional units to enable compositional analyses of petroleum substances. First, polycyclic aromatic compound standards were analyzed by IMS-MS to demonstrate how CCS assists the identification of isomeric species in homologous series. Next, we used case studies of a gasoline standard previously characterized for paraffin, isoparaffin, aromatic, naphthene, and olefinic (PIANO) compounds, and a crude oil sample to demonstrate the application of the KMD analyses and CCS filtering. Finally, we propose a workflow that enables confident molecular formula assignment to the IMS-MS-derived features in petroleum samples. Collectively, this work demonstrates how rapid untargeted IMS-MS analysis and the proposed data processing workflow can be used to provide confident compositional characterization of hydrocarbon-containing substances.

Background Although maternal systemic inflammation is hypothesized to link maternal pre-pregnancy obesity to offspring metabolic dysfunction, patient empirical data are limited. Objectives In this study, we hypothesized that pre-pregnancy obesity alters systemic chemo/cytokines concentrations in pregnancy, and this alteration contributes to obesity in children. Methods In a multi-ethnic cohort of 361 mother-child pairs, we measured prenatal concentrations of plasma TNF-α, IL-6, IL-8, IL-1β, IL-4, IFN-γ, IL-12 p70 subunit, and IL-17A using a multiplex ELISA and examined associations of pre-pregnancy obesity on maternal chemo/cytokine levels, and associations of these cytokine levels with offspring body mass index z score (BMI-z) at age 2-6 years using linear regression. Results After adjusting for maternal smoking, ethnicity, age, and education, pre-pregnancy obesity was associated with increased concentrations of TNF-α (P = .026) and IFN-γ (P = .06). While we found no evidence for associations between TNF-α concentrations and offspring BMI-z, increased IFN-γ concentrations were associated with decreased BMI-z (P = .0002), primarily in Whites (P = .0011). In addition, increased maternal IL-17A concentrations were associated with increased BMI-z in offspring (P = .0005) with stronger associations in African Americans (P = .0042) than Whites (P = .24). Conclusions Data from this study are consistent with maternal obesity-related inflammation during pregnancy, increasing the risk of childhood obesity in an ethnic-specific manner.

Methods to assess environmental exposure to hazardous chemicals have primarily focused on quantification of individual chemicals, although chemicals often occur in mixtures, presenting challenges to the traditional risk characterization framework. Sampling sites in a defined geographic region provide an opportunity to characterize chemical contaminants, with spatial interpolation as a tool to provide estimates for non-sampled sites. At the same time, the use of in vitro bioactivity measurements has been shown to be informative for rapid risk-based decisions. In this study, we measured in vitro bioactivity in 39 surface soil samples collected immediately after flooding associated with Hurricane Harvey in Texas in a residential area known to be inundated with polycyclic aromatic hydrocarbon (PAH) contaminants. Bioactivity data were from a number of functional and toxicity assays in 5 human cell types, such as induced pluripotent stem cell-derived hepatocytes, cardiomyocytes, neurons, and endothelial cells, as well as human umbilical vein endothelial cells. Data on concentrations of PAH in these samples were also available and the combination of data sources offered a unique opportunity to assess the joint spatial variation of PAH components and bioactivity. We found significant evidence of spatial correlation of a subset of PAH contaminants and of cell-based phenotypes. In addition, we show that the cell-based bioactivity data can be used to predict environmental concentrations for several PAH contaminants, as well as overall PAH summaries and cancer risk. This study’s impact lies in its demonstration that cell-based profiling can be used for rapid hazard screening of environmental samples by anchoring the bioassays to concentrations of PAH. This work sets the stage for identification of the areas of concern and direct quantitative risk characterization based on bioactivity data, thereby providing an important supplement to traditional individual chemical analyses by shedding light on constituents that may be missed from targeted chemical monitoring.

Environmental toxicants affect human health in various ways. Of the thousands of chemicals present in the environment, those with adverse effects on the endocrine system are referred to as endocrine-disrupting chemicals (EDCs). Here, we focused on a subclass of EDCs that impacts the estrogen receptor (ER), a pivotal transcriptional regulator in health and disease. Estrogenic activity of compounds can be measured by many in vitro or cell-based high throughput assays that record various endpoints from large pools of cells, and increasingly at the single-cell level. To simultaneously capture multiple mechanistic ER endpoints in individual cells that are affected by EDCs, we previously developed a sensitive high throughput/high content imaging assay that is based upon a stable cell line harboring a visible multicopy ER responsive transcription unit and expressing a green fluorescent protein (GFP) fusion of ER. High content analysis generates voluminous multiplex data comprised of minable features that describe numerous mechanistic endpoints. In this study, we present a machine learning pipeline for rapid, accurate, and sensitive assessment of the endocrine-disrupting potential of benchmark chemicals based on data generated from high content analysis. The multidimensional imaging data was used to train a classification model to ultimately predict the impact of unknown compounds on the ER, either as agonists or antagonists. To this end, both linear logistic regression and nonlinear Random Forest classifiers were benchmarked and evaluated for predicting the estrogenic activity of unknown compounds. Furthermore, through feature selection, data visualization, and model discrimination, the most informative features were identified for the classification of ER agonists/antagonists. The results of this data-driven study showed that highly accurate and generalized classification models with a minimum number of features can be constructed without loss of generality, where these machine learning models serve as a means for rapid mechanistic/phenotypic evaluation of the estrogenic potential of many chemicals., Author summary Chemical contaminants or toxicants pose environmental and health-related risks for exposure. The ability to rapidly understand their biological impact, specifically on a key modulator of important physiological and pathological states in the human body is essential for diagnosing and avoiding undesirable health outcomes during environmental emergencies. In this study, we use advanced data analytics for creating statistical models that can accurately predict the endocrinological activity of toxic chemicals based on high throughput/high content image analysis data. We focus on a subclass of chemicals that affect the estrogen receptor (ER), which is a pivotal transcriptional regulator in health and disease. The multidimensional imaging data of these benchmark chemicals are used to train a classification model to ultimately predict the impact of unknown compounds on the ER, either as agonists or antagonists. To this end, we evaluate linear and nonlinear classifiers for predicting the estrogenic activity of unknown compounds and use feature selection, data visualization, and model discrimination methodologies to identify the most informative features for the classification of ER agonists/antagonists.

Silver nanoparticles (AgNPs) are widely incorporated into consumer and biomedical products for their antimicrobial and plasmonic properties with limited risk-assessment of low-dose cumulative exposure in humans. To evaluate cellular responses to low-dose AgNP exposures across time, human liver cells (HepG2) were exposed to AgNPs with three different surface charges (1.2 μg mL(−1)) and complete gene expression was monitored across a 24-hour period. Time and AgNP surface chemistry mediate gene expression. In addition, time since cells were fed had marked effects on gene expression that should be considered. Surface chemistry of AgNPs alter gene transcription in a time-dependent manner, with the most dramatic effects in cationic AgNPs. Universal to all surface-coatings, AgNP-treated cells responded by inactivating proliferation and enabling cell cycle checkpoints. Further analysis of these universal features of AgNP cellular response, as well as more detailed analysis of specific AgNP treatments, time points, or specific genes is facilitated with an accompanying application. Taken together, these results provide a foundation for understanding hepatic response to low-dose AgNPs for future risk assessment