Identification and Prioritization of Organic Pollutants in Human Milk from the Yangtze River Delta, China.

Pollutants in human milk are critical for evaluating maternal internal exposure and infant external exposure. However, most studies have focused on a limited range of pollutants. Here, 15 pooled samples (prepared from 467 individual samples) of human milk from three areas of the Yangtze River Delta (YRD) in China were analyzed by gas chromatography quadrupole time-of-flight mass spectrometry. In total, 171 compounds of nine types were preliminarily identified. Among these, 16 compounds, including 2,5-di-tert-butylhydroquinone and 2-tert-butyl-1,4-benzoquinone, were detected in human milk for the first time. Partial least-squares discriminant analysis identified ten area-specific pollutants, including 2-naphthylamine, 9-fluorenone, 2-isopropylthianthrone, and benzo[a]pyrene, among pooled human milk samples from Shanghai (n = 3), Jiangsu Province (n = 6), and Zhejiang Province (n = 6). Risk index (RI) values were calculated and indicated that legacy polycyclic aromatic hydrocarbons (PAHs) contributed only 20% of the total RIs for the identified PAHs and derivatives, indicating that more attention should be paid to PAHs with various functional groups. Nine priority pollutants in human milk from the YRD were identified. The most important were 4-tert-amylphenol, caffeine, and 2,6-di-tert-butyl-p-benzoquinone, which are associated with apoptosis, oxidative stress, and other health hazards. The results improve our ability to assess the health risks posed by pollutants in human milk.

Toxicity prediction: An application of alternative testing and computational toxicology in contaminated groundwater sites in Taiwan.

Groundwater contamination remains a global threat due to its toxic effects to humans and the environment. The remediation of contaminated groundwater sites can be costly, thus, identifying the priority areas of concern is important to reduce money spent on resources. In this study, we aimed to identify and rank the priority groundwater sites in a contaminated petrochemical district by combining alternative, non-animal approaches - chemical analysis, cell-based high throughput screening (HTS), and Toxicological Priority Index (ToxPi) computational toxicology tool. Groundwater samples collected from ten different sites in a contaminated district showed pollutant levels below the detection limit, however, hepatotoxic bioactivity was demonstrated in human hepatoma HepaRG cells. Integrating the pollutants information (i.e., pollutant characteristics and concentration data) with the bioactivity data of the groundwater samples, an evidence-based ranking of the groundwater sites for future remediation was established using ToxPi analysis. The currently presented combinatorial approach of screening groundwater sites for remediation purposes can further be refined by including relevant parameters, which can boost the utility of this approach for groundwater screening and future remediation.

Occurrence and Ecological Impact of Chemical Mixtures in a Semiclosed Sea by Suspect Screening Analysis.

Stress from mixtures of synthetic chemicals is among the key issues that have significant adverse impacts on the marine ecosystems. A robust screening workflow integrating toxicological-based ranking schemes is still deficient for comprehensive investigation on the main constituents in chemical mixtures that contribute to the ecological risks. In this study, the presence and compositions of a collection of priority pollutants were monitored by suspect screening analysis of seawater and estuarine water samples from the semiclosed Bohai Sea. In total, 108 organic pollutants in nine use categories were identified. Pesticides, intermediates, plastic additives, and per- and polyfluoroalkyl substances were the extensively detected chemical groups. Varied distribution patterns of the pollutants were illustrated intuitively in distinctive sampling areas by hierarchical cluster analysis, which were mainly influenced by run-off inputs, ocean currents, and chemical use history. Ecological risks of chemicals with quantified residue levels were first assessed by the toxicity-weighted concentration ranking scheme, and pentachlorophenol was found as the main contributor in the investigating areas. By optimization of multiple alternative variables (e.g., instrumental response and detection frequency), extended ranking of all the identified pollutants was plausible under the toxicological priority index framework. Similarity in toxicological endpoints of the prioritized pollutants could further been screened by ToxAlerts. Aromatic amine was highlighted as the most frequently detected structural alert (SA) for genotoxic carcinogenicity and mutagenicity. These findings fully demonstrate rationality of the ranking schemes integrated into the suspect screening analysis for profiling contamination characteristics, assessing ecological risk potentials, and prioritizing SAs.

Demonstrating a systems approach for integrating disparate data streams to inform decisions on children's environmental health.

BACKGROUND: The use of systems science methodologies to understand complex environmental and human health relationships is increasing. Requirements for advanced datasets, models, and expertise limit current application of these approaches by many environmental and public health practitioners. METHODS: A conceptual system-of-systems model was applied for children in North Carolina counties that includes example indicators of children's physical environment (home age, Brownfield sites, Superfund sites), social environment (caregiver's income, education, insurance), and health (low birthweight, asthma, blood lead levels). The web-based Toxicological Prioritization Index (ToxPi) tool was used to normalize the data, rank the resulting vulnerability index, and visualize impacts from each indicator in a county. Hierarchical clustering was used to sort the 100 North Carolina counties into groups based on similar ToxPi model results. The ToxPi charts for each county were also superimposed over a map of percentage county population under age 5 to visualize spatial distribution of vulnerability clusters across the state. RESULTS: Data driven clustering for this systems model suggests 5 groups of counties. One group includes 6 counties with the highest vulnerability scores showing strong influences from all three categories of indicators (social environment, physical environment, and health). A second group contains 15 counties with high vulnerability scores driven by strong influences from home age in the physical environment and poverty in the social environment. A third group is driven by data on Superfund sites in the physical environment. CONCLUSIONS: This analysis demonstrated how systems science principles can be used to synthesize holistic insights for decision making using publicly available data and computational tools, focusing on a children's environmental health example. Where more traditional reductionist approaches can elucidate individual relationships between environmental variables and health, the study of collective, system-wide interactions can enable insights into the factors that contribute to regional vulnerabilities and interventions that better address complex real-world conditions.

A systematic approach for prioritizing landfill pollutants based on toxicity: Applications and opportunities.

Landfills in the United States are a significant source of pollution to ground and surface water. Current environmental regulations require detection and/or monitoring assessments of landfill leachate for contaminants that have been deemed particularly harmful. However, the lists of contaminants to be monitored are not comprehensive. Further, landfill leachate composition varies over space and time, and thus the contaminants, and their corresponding toxicity, are not consistent across or within landfills. One of the main objectives of this study was to prioritize contaminants found in landfill leachate using a systematic, toxicity-based prioritization scheme. A literature review was conducted, and from it, 484 landfill leachate contaminants with available CAS numbers were identified. In vitro, in vivo, and predicted human toxicity data were collected from ToxCast, ECOTOX, and CTV Predictor, respectively. These data were integrated using the Toxicological Priority Index (ToxPi) for the 322 contaminants which had available toxicity data from at least two of the databases. Four modifications to this general prioritization scheme were developed to demonstrate the flexibility of this scheme for addressing varied research and applied objectives. The general scheme served as a basis for comparison of the results from the modified schemes, and allowed for identification of contaminants uniquely prioritized in each of the schemes. The schemes outlined here can be used to identify the most harmful contaminants in environmental media in order to design the most relevant mitigation strategies and monitoring plans. Finally, future research directions involving the combination of these prioritization schemes and non-target global metabolomic profiling are discussed.

Multi-dimensional in vitro bioactivity profiling for grouping of glycol ethers.

High-content screening data derived from physiologically-relevant in vitro models promise to improve confidence in data-integrative groupings for read-across in human health safety assessments. The biological data-based read-across concept is especially applicable to bioactive chemicals with defined mechanisms of toxicity; however, the challenge of data-derived groupings for chemicals that are associated with little or no bioactivity has not been explored. In this study, we apply a suite of organotypic and population-based in vitro models for comprehensive bioactivity profiling of twenty E-Series and P-Series glycol ethers, solvents with a broad variation in toxicity ranging from relatively non-toxic to reproductive and hematopoetic system toxicants. Both E-Series and P-Series glycol ethers elicited cytotoxicity only at high concentrations (mM range) in induced pluripotent stem cell-derived hepatocytes and cardiomyocytes. Population-variability assessment comprised a study of cytotoxicity in 94 human lymphoblast cell lines from 9 populations and revealed differences in inter-individual variability across glycol ethers, but did not indicate population-specific effects. Data derived from various phenotypic and transcriptomic assays revealed consistent bioactivity trends between both cardiomyocytes and hepatocytes, indicating a more universal, rather than cell-type specific mode-of-action for the tested glycol ethers in vitro. In vitro bioactivity-based similarity assessment using Toxicological Priority Index (ToxPi) showed that glycol ethers group according to their alcohol chain length, longer chains were associated with increased bioactivity. While overall in vitro bioactivity profiles did not correlate with in vivo toxicity data on glycol ethers, in vitro bioactivity of E-series glycol ethers were indicative of and correlated with in vivo irritation scores.

Adverse outcome pathway-driven identification of rat liver tumorigens in short-term assays.

Chemicals induce liver cancer in rodents through well characterized adverse outcome pathways (AOPs). We hypothesized that measurement of molecular initiating events (MIEs) and downstream key events (KEs) in liver cancer AOPs in short-term assays will allow early identification of chemicals and their associated doses that are likely to be tumorigenic in the liver in two-year bioassays. We tested this hypothesis using the rat in vivo TG-GATES study data to measure MIEs (genotoxicity, cytotoxicity, AhR, CAR, ER, PPARα) and associated KEs (oxidative stress, cell proliferation, liver to body weights) across 77 chemicals that could be linked to doses with previously established effects on rat liver tumor induction. Gene expression biomarkers for MIEs generally considered to be rodent specific and human irrelevant (CAR, PPARα) and for MIEs that would be considered of greater risk at human relevant exposures (ER, AhR) were built using microarray comparisons from the livers of rats treated with prototypical activators of the receptors. The genotoxicity biomarker, also a potentially human relevant MIE, was comprised of 7 p53-responsive genes known to be induced upon DNA damage. The ability of the biomarkers to accurately predict MIE activation ranged from 91% to 98%. The Toxicological Priority Index (ToxPi) was used to rank chemicals based on their ability to activate MIEs/KEs. Chemicals administered at tumorigenic doses clearly gave the highest ranked scores. Our AOP-directed approach could be used in short term assays to identify chemicals and their doses that would be predicted to cause liver tumors in rats.

Successful validation of genomic biomarkers for human immunotoxicity in Jurkat T cells in vitro.

Previously, we identified 25 classifier genes that were able to assess immunotoxicity using human Jurkat T cells. The present study aimed to validate these classifiers. For that purpose, Jurkat cells were exposed for 6 h to subcytotoxic doses of nine immunotoxicants, five non-immunotoxicants and four compounds for which human immunotoxicity has not yet been fully established. RNA was isolated and subjected to Fluidigm quantitative real time (qRT)-PCR analysis. The sensitivity, specificity and accuracy of the screening assay as based on the nine immunotoxicants and five non-immunotoxicants used in this study were 100%, 80% and 93%, respectively, which is better than the performance in our previous study. Only one compound was classified as false positive (benzo-e-pyrene). Of the four potential (non-)immunotoxicants, chlorantraniliprole and Hidrasec were classified immunotoxic and Sunset yellow and imidacloprid as non-immunotoxic. ToxPi analysis of the PCR data provided insight in the molecular pathways that were affected by the compounds. The immunotoxicants 2,3-dichloro-propanol and cypermethrin, although structurally different, affected protein metabolism and cholesterol biosynthesis and transport. In addition, four compounds, i.e. chlorpyrifos, aldicarb, benzo-e-pyrene and anti-CD3, affected genes in cholesterol metabolism and transport, protein metabolism and transcription regulation. qRT-PCR on eight additional genes coding for similar processes as defined in ToxPi analyzes, supported these results. In conclusion, the 25 immunotoxic classifiers performed very well in a screening with new non-immunotoxic and immunotoxic compounds. Therefore, the Jurkat screening assay has great promise to be applied within a tiered approach for animal free testing of human immunotoxicity.

Organophosphate esters in milk across thirteen countries from 2020 to 2023: Concentrations, sources, temporal trends and ToxPi priority to humans.

Recent increases in organophosphate ester (OPE) application have led to their widespread presence, yet little is known about their temporal trends in food. This study collected milk samples from 13 countries across three continents during 2020-2023, finding detectable OPEs in all samples (range: 2.25-19.7; median: 7.06 ng/g ww). Although no statistical temporal differences were found for the total OPEs during the 4-year sampling campaign, it was interesting to observe significant variations in the decreasing trend for Cl-OPEs and concentration variations for aryl-OPEs and alkyl-OPEs (p < 0.05), indicating changing OPE use patterns. Packaged milk exhibited significant higher OPE levels than those found in directly collected raw unpackaged milk, and milk with longer shelf-life showed higher OPE levels, revealing packaging material as a contamination source. No significant geographical differences were observed in milk across countries (p > 0.05), but Shandong Province, a major OPE production site in China, showed relatively higher OPE concentrations. The Monte Carlo simulation of estimated daily intakes indicated no exposure risk from OPEs through milk consumption. The molecular docking method was used to assess human hormone binding affinity with OPEs, amongst which aryl-OPEs had the highest binding energies. The Toxicological-Priority-Index method which integrated chemical property, detection frequency, risk quotients, hazardous quotients and endocrine-disrupting effects was employed to prioritize OPEs. Aryl-OPEs showed the highest scores, deserving attention in the future.

Challenges integrating skin sensitization data for assessment of difficult to test substances.

Difficult to test substances, including poorly soluble, mildly irritating, or UVCBs (unknown or variable composition complex reaction products or biological materials), producing weak or borderline in vivo results, face additional challenges in in vitro assays that often necessitate data integration in a weight of evidence (WOE) approach to inform skin sensitization potential. Here we present several case studies on difficult to test substances and highlight the utility of the toxicological priority index (ToxPi) as a data visualization tool to compare skin sensitization biological activity. The case study test substances represent two poorly soluble substances, tetrakis (2-ethylbutyl) orthosilicate and decyl palmitate, and two UVCB substances, alkylated anisole and hydrazinecarboximidamide, 2-[(2-hydroxyphenyl)methylene]-, reaction products with 2 undecanone. Data from key events within the skin sensitization adverse outcome pathway were gathered from publicly available sources or specifically generated. Incorporating the data for these case study test substances as well as data on chemicals of a known sensitization class (sensitizer, irritating non-sensitizer, and non-sensitizer) into ToxPi produced biological activity profiles which were grouped using unsupervised hierarchical clustering. Three of the case study test substances concluded to lack skin sensitization potential by traditional WOE produced biological activity profiles most consistent with non-sensi­tizing substances, whereas the prediction was less definitive for a substance considered positive by traditional WOE. Visualizing the data using bioactivity profiles can provide further support for WOE conclusions in certain circumstances but is unlikely to replace WOE as a stand-alone prediction due to limitations of the method including the impact of missing data points.

Non-animal test methods to detect chemicals that cause skin allergies are accepted alternatives to animal testing for this purpose. However, some chemicals are difficult to test using these methods, e.g., substances that cause skin irritation, are not water soluble or are mixtures of different compo­nents. We compiled existing and new data on how four such chemicals activate key elements of the biological pathway leading to allergic skin reactions and compared the resulting patterns with respective patterns of many chemicals confirmed to cause skin allergy, skin irritation or neither. The patterns were visualized and analyzed with a computer software tool. The tool confirmed that three substances were non-sensitizers but did not confirm that the fourth substance was a skin sensitizer as predicted by the standard assessment. This approach, which incorporates all available data types into the assessment of difficult to test chemicals, may further reduce unnecessary animal testing.

Visualization strategies to aid interpretation of high-dimensional genotoxicity data.

This article describes a range of high-dimensional data visualization strategies that we have explored for their ability to complement machine learning algorithm predictions derived from MultiFlow® assay results. For this exercise, we focused on seven biomarker responses resulting from the exposure of TK6 cells to each of 126 diverse chemicals over a range of concentrations. Obviously, challenges associated with visualizing seven biomarker responses were further complicated whenever there was a desire to represent the entire 126 chemical data set as opposed to results from a single chemical. Scatter plots, spider plots, parallel coordinate plots, hierarchical clustering, principal component analysis, toxicological prioritization index, multidimensional scaling, t-distributed stochastic neighbor embedding, and uniform manifold approximation and projection are each considered in turn. Our report provides a comparative analysis of these techniques. In an era where multiplexed assays and machine learning algorithms are becoming the norm, stakeholders should find some of these visualization strategies useful for efficiently and effectively interpreting their high-dimensional data.

In Vitro Assessment and Toxicological Prioritization of Pesticide Mixtures at Concentrations Derived from Real Exposure in Occupational Scenarios.

Humans are daily exposed to multiple residues of pesticides with agricultural workers representing a subpopulation at higher risk. In this context, the cumulative risk assessment of pesticide mixtures is an urgent issue. The present study evaluated, as a case study, the toxicological profiles of thirteen pesticide mixtures used for grapevine protection, including ten active compounds (sulfur, potassium phosphonate, metrafenone, zoxamide, cyflufenamid, quinoxyfen, mancozeb, folpet, penconazole and dimethomorph), at concentrations used on field. A battery of in vitro tests for cell viability and oxidative stress endpoints (cytotoxicity, apoptosis, necrosis, ROS production, mitochondrial membrane potential, gene expression of markers for apoptosis and oxidative stress) was performed on two cellular models representative of main target organs of workers' and population exposure: pulmonary A549 and hepatic HepG2 cell lines. All the endpoints provided evidence for effects also at the lower concentrations used. The overall data were integrated into the ToxPI tool obtaining a toxicity ranking of the mixtures, allowing to prioritize effects also among similarly composed blends. The clustering of the toxicological profiles further provided evidence of common and different modes of action of the mixtures. The approach demonstrated to be suitable for the purpose and it could be applied also in other contexts.

The Effects of Organophosphate Esters Used as Flame Retardants and Plasticizers on Granulosa, Leydig, and Spermatogonial Cells Analyzed Using High-Content Imaging.

The replacement of regulated brominated flame retardants and plasticizers with organophosphate esters (OPEs) has led to their pervasive presence in the environment and in biological matrices. Further, there is evidence that exposure to some of these chemicals is associated with reproductive toxicity. Using a high-content imaging approach, we assessed the effects of exposure to 9 OPEs on cells related to reproductive function: KGN human granulosa cells, MA-10 mouse Leydig cells, and C18-4 mouse spermatogonial cells. The effects of OPEs were compared with those of 2,2',4,4'-tetrabromodiphenyl ether (BDE-47), a legacy brominated flame retardant. Alterations in several important cell features, including cell survival, mitochondrial dynamics, oxidative stress, lysosomes, and lipid droplets, were analyzed. Most of the OPEs tested displayed higher cytotoxicity than BDE-47 in all 3 cell lines. Effects on phenotypic parameters were specific for each cell type. Several OPEs increased total mitochondria, decreased lysosomes, increased the total area of lipid droplets, and induced oxidative stress in KGN cells; these endpoints were differentially affected in MA-10 and C18-4 cells. Alterations in cell phenotypes were highly correlated in the 2 steroidogenic cell lines for a few triaryl OPEs. Potency ranking using 2 complementary approaches, Toxicological Prioritization Index analyses and the lowest benchmark concentration/administered equivalent dose method, revealed that while most of the OPEs tested were more potent than BDE-47, others showed little to no effect. We propose that these approaches serve as lines of evidence in a screening strategy to identify the potential for reproductive and endocrine effects of emerging chemicals and assist in regulatory decision-making.

Assessing the efficiency of constructed wetlands in removing PPCPs from treated wastewater and mitigating the ecotoxicological impacts.

The prevalence of pharmaceuticals and personal care products (PPCPs) in municipal wastewater has led to increased concerns about their impact on both human health and ecosystem. The constructed wetlands have been recognized as one of the cost-effective and green mitigation approaches to remove the PPCPs in the municipal wastewater. In this study, the effectiveness of a full scale constructed wetlands treatment system (CCWTs) in removing the 36 PPCPs was investigated. The load mass of PPCPs discharged by the wastewater treatment plant into the CCWTs was calculated. Removal efficiencies of PPCPs were evaluated based on physico-chemical properties such as octanol-water partition coefficient (Log kow), molecular weight (MW, g mol-1) and the acid dissociation constant (pKa). The CCWTs are especially efficient in removing azithromycin, sertraline, tolfenamic acid, and diphenhydramine with removing efficiency >88%. However, the removal efficiencies of PPCPs in CCWTs exhibit a large variability, depending on physical and chemical properties of the molecules, with 4.7-96.7% for antibiotics, 5-86% for antidepressant and antiseizure drugs, 3.5-88% for NSAIDs, 29-77% for ß-blockers and statins and 5.5-94% for other types of PPCPs. In addition, the environmental risk assessment showed that majority of the PPCPs (excluding sulfamethoxazole) in the effluent yielded low aquatic risk (risk quotient, RQ ≤ 0.1) due to the efficiency of CCWTs. The toxicity index scores were calculated by integration of the predicted and available toxicological hazard data into the prioritization ranking algorithm through Toxicological Prioritization Index (ToxPi).

Utilizing High-Throughput Screening Data, Integrative Toxicological Prioritization Index Score, and Exposure-Activity Ratios for Chemical Prioritization: A Case Study of Endocrine-Active Pesticides in Food Crops.

Endocrine-active chemicals can directly act on nuclear receptors and trigger the disturbances of metabolism and a homeostatic system, which are important risk factors for complicating chronic diseases in humans. The endocrine-active potentials of pesticides acting on estrogen, androgen, and thyroid hormone receptors have been extensively evaluated for pesticides; however, the effects on other receptors are less understood. This study aims to comprehensively characterize and prioritize the endocrine-active pesticides using an exposure-activity ratio (EAR) method and toxicological prioritization index (ToxPi). The aggregate exposure assessment of pesticides was performed using a computational exposure model [stochastic human exposure and dose simulation high-throughput model (SHEDS-HT)]. Minimum in vitro point of departure values were converted to human oral equivalent doses via in vitro-to-in vivo extrapolation. The overall endocrine-disrupting potentials of pesticides were evaluated via 76 assays, representing 11 nuclear receptors. EARs and ToxPi scores were then derived to prioritize 79 pesticides in food. This case study demonstrates that EAR profiling can inform the regulatory agencies for a relevant chemical prioritization, which would direct in-depth health risk assessments in the future.

An integrative method for identification and prioritization of constituents of concern in produced water from onshore oil and gas extraction.

In the United States, onshore oil and gas extraction operations generate an estimated 900 billion gallons of produced water annually, making it the largest waste stream associated with upstream development of petroleum hydrocarbons. Management and disposal practices of produced water vary from deep well injection to reuse of produced water in agricultural settings. However, there is relatively little information with regard to the chemical or toxicological characteristics of produced water. A comprehensive literature review was performed, screening nearly 16,000 published articles, and identifying 129 papers that included data on chemicals detected in produced water. Searches for information on the potential ecotoxicological or mammalian toxicity of these chemicals revealed that the majority (56%) of these compounds have not been a subject of safety evaluation or mechanistic toxicology studies and 86% lack data to be used to complete a risk assessment, which underscores the lack of toxicological information for the majority of chemical constituents in produced water. The objective of this study was to develop a framework to identify potential constituents of concern in produced water, based on available and predicted toxicological hazard data, to prioritize these chemicals for monitoring, treatment, and research. In order to integrate available evidence to address gaps in toxicological hazard on the chemicals in produced water, we have catalogued available information from ecological toxicity studies, toxicity screening databases, and predicted toxicity values. A Toxicological Priority Index (ToxPi) approach was applied to integrate these various data sources. This research will inform stakeholders and decision-makers on the potential hazards in produced water. In addition, this work presents a method to prioritize compounds that, based on hazard and potential exposure, may be considered during various produced water reuse strategies to reduce possible human health risks and environmental impacts.

Identifying adipogenic chemicals: Disparate effects in 3T3-L1, OP9 and primary mesenchymal multipotent cell models.

3T3-L1 pre-adipocytes are used commonly to identify new adipogens, but this cell line has been shown to produce variable results. Here, potential adipogenic chemicals (identified in the ToxCast dataset using the Toxicological Priority Index) were tested for their ability to induce adipocyte differentiation in 3T3-L1 cells, OP9 cells and primary mouse bone marrow multipotent stromal cells (BM-MSC). Ten of the 36 potential adipogens stimulated lipid accumulation in at least one model (novel: fenthion, quinoxyfen, prallethrin, allethrin, pyrimethanil, tebuconzaole, 2,4,6-tris (tert-butyl)phenol; known: fentin, pioglitazone, 3,3',5,5'-tetrabromobisphenol A). Only prallethrin and pioglitazone enhanced lipid accumulation in all models. OP9 cells were significantly more sensitive to chemicals known to activate PPARγ through RXR than the other models. Coordinate effects on adipocyte and osteoblast differentiation were investigated further in BM-MSCs. Lipid accumulation was correlated with the ability to stimulate expression of the PPARγ target gene, Plin1. Induction of lipid accumulation also was associated with reduction in alkaline phosphatase activity. Allethrin, prallethrin, and quinoxyfen strongly suppressed osteogenic gene expression. BM-MSCs were useful in coordinately investigating pro-adipogenic and anti-osteogenic effects. Overall, the results show that additional models should be used in conjunction with 3T3-L1 cells to identify a broader spectrum of adipogens and their coordinate effects on osteogenesis.

A set of six Gene expression biomarkers and their thresholds identify rat liver tumorigens in short-term assays.

Traditional methods for cancer risk assessment are retrospective, resource-intensive, and not feasible for the vast majority of environmental chemicals. In earlier studies, we used a set of six biomarkers to accurately identify liver tumorigens in transcript profiles derived from chemically-treated rats using either a Toxicological Priority Index (ToxPi) approach or using derived biomarker thresholds for cancer. The biomarkers consisting of 7-113 genes are used to predict the most common liver cancer molecular initiating events: genotoxicity, cytotoxicity and activation of the xenobiotic receptors AhR, CAR, ER, and PPARα. In the present study, we apply and evaluate the performance of these methods for cancer prediction in an independent rat liver study of 44 chemicals (6 h-7d exposures) examined by Affymetrix arrays. In the first approach, ToxPi ranking of biomarker scores consistently gave the highest scores to tumorigenic chemical-dose pairs; balanced accuracies for identification of liver tumorigenic chemicals were up to 89 %. The second approach used tumorigenic thresholds derived in the present study or from our earlier study that were set at the maximum value for chemical-dose exposures without detectable liver tumor outcomes. Using these thresholds, balanced accuracies were up to 90 %. Both approaches identified all tumorigenic chemicals. Almost all of the tumorigenic chemicals activated more than one MIE. We also compared biomarker responses between two types of profiling platforms (Affymetrix full-genome array, TempO-Seq 1500+ array containing ∼2600 genes) and found that the lack of the full set of biomarker genes on the 1500+ array resulted in decreased ability to identify chemicals that activate the MIEs. Overall, these results demonstrate that predictive approaches based on the 6 biomarkers could be used in short-term assays to identify chemicals and their doses that induce liver tumors, the most common endpoint in rodent bioassays.

Transcriptional study after Beauvericin and Enniatin B combined exposure in Jurkat T cells.

Simultaneous mycotoxins toxicity is complex and non-predictable based on their individual toxicities. Beauvericin and Enniatins are emerging mycotoxins highly co-occurrent in food and feed, and their cytotoxicity has been reported in several human cell lines. RNA-seq studies of individual exposure in Jurkat cells demonstrated human genome perturbation mainly affecting mitochondrial pathways, however, both mycotoxins showed differences between their toxic responses. This study investigates the transcriptional effects of combined exposure to Beauvericin and Enniatin B (1:1) (0.1, 0.5, 1.5 µM; 24 h) in Jurkat cells by qPCR on 30 selected target genes (10 mitochondrial, 20 nuclear). Gene expression after combined and individual exposures were compared and functional data analysis (ToxPi) on the most relevant biological processes (cycle and apoptosis regulation; cholesterol metabolism and transport; cellular signaling transduction; cellular stress responses; immune regulation; protein metabolism; retinoic acid metabolism; transcription regulation) was applied to RNA-seq data from individual exposure (1.5, 3, 5 µM; 24 h; Jurkat cells). Transcriptional changes, especially at mitochondrial level, were observed after Beauvericin-Enniatin B co-exposure including down-regulation of antioxidant activity related genes. Different expression patterns between combined and individual exposures were identified. ToxPi analysis confirmed different dose-dependent relationship profiles between these two mycotoxins after individual exposure.

An Integrated Approach Using Publicly Available Resources for Identifying and Characterizing Chemicals of Potential Toxicity Concern: Proof-of-Concept With Chemicals That Affect Cancer Pathways.

We developed an integrated, modular approach to predicting chemical toxicity relying on in vitro assay data, linkage of molecular targets to disease categories, and software for ranking chemical activity and examining structural features (chemotypes). We evaluate our approach in a proof-of-concept exercise to identify and prioritize chemicals of potential carcinogenicity concern. We identified 137 cancer pathway-related assays from a subset of U.S. EPA's ToxCast platforms. We mapped these assays to key characteristics of carcinogens and found they collectively assess 5 of 10 characteristics. We ranked all 1061 chemicals screened in Phases I and II of ToxCast by their activity in the selected cancer pathway-related assays using Toxicological Prioritization Index software. More chemicals used as biologically active agents (eg, pharmaceuticals) ranked in the upper 50% versus lower 50%. Twenty-three chemotypes are enriched in the top 5% (n = 54) of chemicals; these features may be important for their activity in cancer pathway-related assays. The biological coverage of the ToxCast assays related to cancer pathways is limited and short-term assays may not capture the biology of some key characteristics. Metabolism is also minimal in the assays. The ability of our approach to identify chemicals with cancer hazard is limited with the current input data, but we expect that our approach can be applied with future iterations of ToxCast and other data for improved chemical prioritization and characterization. The novel approach and proof-of-concept exercise described here for ranking chemicals for potential carcinogenicity concern is modular, adaptable, and amenable to evolving data streams.