Emerging investigator series: effect-based characterization of mixtures of environmental pollutants in diverse sediments

Deep Learning Bridged Bioactivity, Structure, and GC-HRMS-Readable Evidence to Decipher Nontarget Toxicants in Sediments

Identifying causative toxicants in mixtures is critical, but this task is challenging when mixtures contain multiple chemical classes. Effect-based methods are used to complement chemical analyses to identify toxicants, yet conventional bioassays typically rely on an apical and/or single endpoint, providing limited diagnostic potential to guide chemical prioritization. We proposed an event-driven taxonomy framework for mixture risk assessment that relied on high-throughput screening bioassays and toxicant identification integrated by deep learning. In this work, the framework was evaluated using chemical mixtures in sediments eliciting aryl-hydrocarbon receptor activation and oxidative stress response. Mixture prediction using target analysis explained <10% of observed sediment bioactivity. To identify additional contaminants, two deep learning models were developed to predict fingerprints of a pool of bioactive substances (event driver fingerprint, EDFP) and convert these candidates to MS-readable information (event driver ion, EDION) for nontarget analysis. Two libraries with 121 and 118 fingerprints were established, and 247 bioactive compounds were identified at confidence level 2 or 3 in sediment extract using GC-qToF-MS. Among them, 12 toxicants were analytically confirmed using reference standards. Collectively, we present a "bioactivity-signature-toxicant" strategy to deconvolute mixtures and to connect patchy data sets and guide nontarget analysis for diverse chemicals that elicit the same bioactivity.

Application of ARE-reporter systems in drug discovery and safety assessment

The human body is continuously exposed to xenobiotics and internal or external oxidants. The health risk assessment of exogenous chemicals remains a complex and challenging issue. Alternative toxicological test methods have become an essential strategy for health risk assessment. As a core regulator of constitutive and inducible expression of antioxidant response element (ARE)-dependent genes, nuclear factor erythroid 2-related factor 2 (Nrf2) plays a critical role in maintaining cellular redox homeostasis. Consistent with the properties of Nrf2-mediated antioxidant response, Nrf2-ARE activity is a direct indicator of oxidative stress and thus has been used to identify and characterize oxidative stressors and redox modulators. To screen and distinguish chemicals or environmental insults that affect the cellular antioxidant activity and/or induce oxidative stress, various in vitro cell models expressing distinct ARE reporters with high-throughput and high-content properties have been developed. These ARE-reporter systems are currently widely applied in drug discovery and safety assessment. In the present review, we provide an overview of the basic structures and applications of various ARE-reporter systems employed for discovering Nrf2-ARE modulators and characterizing oxidative stressors.

pH-Dependent Partitioning of Ionizable Organic Chemicals between the Silicone Polymer Polydimethylsiloxane (PDMS) and Water

The silicone polymer polydimethysiloxane (PDMS) is a popular passive sampler for in situ and ex situ sampling of hydrophobic organic chemicals. Despite its limited sorptive capacity for polar and ionizable organic chemicals (IOC), IOCs have been found in PDMS when extracting sediment and suspended particulate matter. The pH-dependent partitioning of 190 organics and IOCs covering a range of octanol-water partition constants log K _ow from -0.3 to 7.7 was evaluated with a 10-day shaking method using mixtures composed of all chemicals at varying ratios of mass of PDMS to volume of water. This method reproduced the PDMS-water partition constant K _PDMS/w of neutral chemicals from the literature and extended the dataset by 93 neutral chemicals. The existing quantitative structure-activity relationship between the log K _ow and K _PDMS/w could be extended with the measured K _PDMS/w linearly to a log K _ow of -0.3. Fully charged organics were not taken up into PDMS. Thirty-eight monoprotic organic acids and 42 bases showed negligible uptake of the charged species, and the pH dependence of the apparent D _PDMS/w(pH) could be explained by the fraction of neutral species multiplied by the K _PDMS/w of the neutral species of these IOCs. Seventeen multiprotic chemicals with up to three acidity constants pK _a also showed a pH dependence of D _PDMS/w(pH) with the tendency that the neutral and zwitterionic forms showed the highest D _PDMS/w(pH). D _PDMS/w(pH) of charged species of more hydrophobic multiprotic chemicals such as tetrabromobisphenol A and telmisartan was smaller but not negligible. Since these chemicals show high bioactivity, their contribution to mixture effects has to be considered when testing passive sampling extracts with in vitro bioassays. This work has further implications for understanding the role of microplastic as a vector for organic micropollutants.

Temporal changes in chemical contamination of green turtles (Chelonia mydas) foraging in a heavily industrialised seaport

Port Curtis, a major shipping port, has undergone significant expansion in the last decade, with plans for further development into the future. These activities may result in an increase of contaminant concentrations, threatening local wildlife including sea turtles. This study used a species-specific in vitro bioassay to examine spatial and temporal differences in exposure to, and effects of, organic contaminants in green sea turtles foraging in Port Curtis. Blood was collected from 134 green sea turtles (Chelonia mydas) from five locations in the port over four years. Organic contaminants were extracted from blood, and the cytotoxicity of the extracts to primary green sea turtle cells was assessed. Results indicated spatially similar chemical contamination throughout Port Curtis, at levels significant to sea turtle health, and with signs that chemical contamination may be increasing over time. These results can provide valuable information on the health of green turtles as further development occurs.

Comprehensive characterization of tire and road wear particles in highway tunnel road dust by use of size and density fractionation

Tire and road wear particles (TRWPs) are a major component of non-exhaust traffic emissions, but knowledge about their physico-chemical properties is limited. Road dust of a highway tunnel was fractionated by size and density, and fractions were analyzed for TRWPs, metals, seven tire tread indicator chemicals (benzothiazoles, 6-PPD and DPG) and effects in in-vitro bioassays. TRWP content in tunnel dust was very high (11-12%). The peak of the TRWP mass distribution was in the size fraction 20-50 μm, with 31-36% of the total TRWP mass and a content of up to 260 mg/g. The mass of organic tire constituents peaked in the smallest analyzed size fractions (<20 μm) with 35-55% of their total mass. They also peaked in the density fraction 1.3-1.7 g/cm³, indicating a lower TRWP density and a higher contribution of TP to TRWP (approx. 75%) than expected. Video-based shape analysis and SEM showed elongated particles, likely TRWPs, to be present in those size and density fractions ascribed to TRWPs by chemical analysis. But also irregular heteroagglomerates could be found. Solvent extracts of size and density fractions induced effects in bioassays indicative of the activation of the arylhydrocarbon receptor (AhR-CALUX) and the adaptive response to oxidative stress (AREc32). Similar comprehensive characterization of road dust from other sites may be needed to decide on whether TRWPs occurring in high concentrations in tunnel dust are suited as representative test materials for analytical purposes and TRWP fate studies.

Risk Characterization of Environmental Samples Using In Vitro Bioactivity and Polycyclic Aromatic Hydrocarbon Concentrations Data

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.

Bioavailable Environmental Pollutant Patterns in Sediments from Passive Equilibrium Sampling

Sediment-associated risks depend on the bioavailable fraction of organic chemicals and cannot be comprehended by their total concentrations. The present study investigated contamination patterns of bioavailable chemicals in sediments from various sites around the globe by using passive equilibrium sampling. The extracts had been characterized previously for mixture effects by in vitro reporter gene assays and were in this study analyzed using gas chromatography-high resolution mass spectrometry for 121 chemicals including both legacy and emerging contaminants. The spatial distribution of the detected chemicals revealed distinct contamination patterns among sampling sites. We identified compounds in common at the different sites but most contaminant mixtures were site-specific. The mixture effects of the detected chemicals were predicted with a mixture toxicity model from effect concentrations of bioactive single chemicals and detected concentrations, applying a joint model for concentration addition and independent action. The predicted mixture effects were dominated by polycyclic aromatic hydrocarbons, and among the chemicals with available effect data, 17% elicited oxidative stress response and 18% activated the arylhydrocarbon receptor. Except for two sites in Sweden, where 11 and 38% of the observed oxidative stress response were explained by the detected chemicals, less than 10% of effects in both biological end points were explained. These results provide a comprehensive investigation of bioavailable contamination patterns of sediments and may serve as an example of employing passive equilibrium sampling as a monitoring technique to integrate the risk of bioavailable sediment-associated chemicals in aquatic environments.

Mixture Risk Drivers in Freshwater Sediments and Their Bioavailability Determined Using Passive Equilibrium Sampling

The identification of mixture risk drivers is a great challenge for sediment assessment, especially when taking bioavailability into consideration. The bioavailable portion, which comprises the organic contaminants in pore water and the ones bound to organic carbon, was accessed by equilibrium partitioning to polydimethylsiloxane (PDMS). The exhaustive solvent and PDMS extracts were toxicologically characterized with a battery of in vitro reporter gene assays and chemically analyzed with liquid and gas chromatography coupled to high-resolution mass spectrometry. The bioavailable fractions of mixture effects and individual chemicals were mostly lower than 0.1, indicating that more than 90% of the substances are strongly bound and would not pose an immediate risk but could potentially be remobilized in the long term. Despite 655 organic chemicals analyzed, only 0.1-28% of the observed biological effects was explained by the detected compounds in whole sediments, while 0.009-3.3% was explained by bioavailable chemicals. The mixture effects were not only dominated by legacy pollutants (e.g., polycyclic aromatic hydrocarbons (PAHs) in the bioassay for activation of the aryl-hydrocarbon receptor (AhR) and oxidative stress response (AREc32)) but also by present-use chemicals (e.g., plastic additives for binding to the peroxisome proliferator-activated receptor γ (PPARγ)), with different fingerprints between whole sediments and bioavailable extracts. Our results highlight the necessity to involve different bioassays with diverse effect profiles and broader selection of contaminants along with bioavailability for the risk assessment of chemical mixtures in sediments.

A first assessment of microplastics and other anthropogenic particles in Hudson Bay and the surrounding eastern Canadian Arctic waters of Nunavut

Microplastics are a globally ubiquitous contaminant, invading the most remote regions, including the Arctic. To date, our understanding of the distribution and sources of microplastics in the Arctic is limited but growing. This study aims to advance our understanding of microplastics in the Arctic. Surface water, zooplankton, sediment, and snow samples were collected from Hudson Bay to north Baffin Bay onboard the CCGS Amundsen from July to August 2017. Samples were examined for microplastics, which were chemically identified via Raman spectroscopy for surface water and zooplankton and Fourier transform infrared spectroscopy for sediment. We found that 90% of surface water and zooplankton samples, and 85% of sediment samples, contained microplastics or other anthropogenic particles. Mean anthropogenic particle concentrations, which includes microplastics, were 0.22 ± 0.23 (per litre) for surface water, 3.51 ± 4.00 (per gram) for zooplankton, and 1.94 ± 4.12 (per gram) for sediment. These concentrations were not related to the human populations upstream, suggesting that microplastic contamination in the Arctic is from long-range transport. Overall, this study highlights the presence of microplastics across the eastern Canadian Arctic, in multiple media, and offers evidence of long-range transport via ocean and atmospheric currents. Further research is needed to better understand sources, distribution, and effects to Arctic ecosystems.

Using the HPTLC-bioluminescence bacteria assay for the determination of acute toxicities in marine sediments and its eligibility as a monitoring assessment tool

For an integrated ecological risk assessment of marine sediment contamination, the determination of target-compound concentrations by e.g. mass spectrometric methods is not sufficient to explain sediment toxicity. Due to the presence of a multitude of environmental contaminants in this complex matrix causing a mixed toxicity, the identification and assessment of main toxicants is a challenge. One approach in identifying main toxicants is the application of effect-directed analysis (EDA). In this study, an EDA approach was developed using high performance thin layer chromatography (HPTLC) coupled to bioluminescence bacteria detection with Aliivibrio fischeri for the determination of marine sediments acute toxicity. In a first attempt, the HPTLC separation was optimized with a fast, two-step gradient to separate main hydrophobic organic contaminant (HOC) classes found in marine sediments. An easy-to-use evaluation script for the resulting bioluminescence inhibition images was programed using R. The developed method was applied to sediment extracts of two different sample sets: (i) Fourteen marine sediment samples from the German Bight representing a wide range of contaminant loads and sediment properties and (ii) sediment samples from a core representing temporal trends of contamination. Results from the HPTLC-bioluminescence bacteria assay were compared to HOC concentrations determined by GC-MS/MS. A correlation of the determined inhibition Γ-values for the PAH inhibition zone to PAH concentrations showed a very good agreement (R² = 0.91). The results of this study were used to evaluate the suitability of the EDA approach to be used as an assessment tool for marine sediments.