Exposure of cells or organisms to chemicals can trigger a series of effects at the regulatory pathway level, which involve changes of levels, interactions, and feedback loops of biomolecules of ...different types. A single-omics technique, e.g., transcriptomics, will detect biomolecules of one type and thus can only capture changes in a small subset of the biological cascade. Therefore, although applying single-omics analyses can lead to the identification of biomarkers for certain exposures, they cannot provide a systemic understanding of toxicity pathways or adverse outcome pathways. Integration of multiple omics data sets promises a substantial improvement in detecting this pathway response to a toxicant, by an increase of information as such and especially by a systemic understanding. Here, we report the findings of a thorough evaluation of the prospects and challenges of multi-omics data integration in toxicological research. We review the availability of such data, discuss options for experimental design, evaluate methods for integration and analysis of multi-omics data, discuss best practices, and identify knowledge gaps. Re-analyzing published data, we demonstrate that multi-omics data integration can considerably improve the confidence in detecting a pathway response. Finally, we argue that more data need to be generated from studies with a multi-omics-focused design, to define which omics layers contribute most to the identification of a pathway response to a toxicant.
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•366 chemicals detected in 56 European WWTP effluents at <1 ng/L to >100 µg/L.•Three different risk metrics (RQ, HU, TU) provided complementary information.•293 mixture components of ...concern exceeded the risk threshold.•32 consensus mixture risk contributors of high concern identified.•Activated carbon and ozonation treatments reduced toxic risks below thresholds.
In this study, 56 effluent samples from 52 European wastewater treatment plants (WWTPs) were investigated for the occurrence of 499 emerging chemicals (ECs) and their associated potential risks to the environment. The two main objectives were (i) to extend our knowledge on chemicals occurring in treated wastewater, and (ii) to identify and prioritize compounds of concern based on three different risk assessment approaches for the identification of consensus mixture risk drivers of concern. Approaches include (i) PNEC and EQS-based regulatory risk quotients (RQs), (ii) species sensitivity distribution (SSD)-based hazard units (HUs) and (iii) toxic units (TUs) for three biological quality elements (BQEs) algae, crustacean, and fish.
For this purpose, solid-phase extracts were analysed with wide-scope chemical target screening via liquid chromatography high-resolution mass spectrometry (LC-HRMS), resulting in 366 detected compounds, with concentrations ranging from < 1 ng/L to > 100 µg/L. The detected chemicals were categorized with respect to critical information relevant for risk assessment and management prioritization including: (1) frequency of occurrence, (2) measured concentrations, (3) use groups, (4) persistence & bioaccumulation, and (5) modes of action. A comprehensive assessment using RQ, HU and TU indicated exceedance of risk thresholds for the majority of effluents with RQ being the most sensitive metric. In total, 299 out of the 366 compounds were identified as mixture risk contributors in one of the approaches, while 32 chemicals were established as consensus mixture risk contributors of high concern, including a high percentage (66%) of pesticides and biocides. For samples which have passed an advanced treatment using ozonation or activated carbon (AC), consistently much lower risks were estimated.
Aquatic environments are often contaminated with complex mixtures of chemicals that may pose a risk to ecosystems and human health. This contamination cannot be addressed with target analysis alone ...but tools are required to reduce this complexity and identify those chemicals that might cause adverse effects. Effect-directed analysis (EDA) is designed to meet this challenge and faces increasing interest in water and sediment quality monitoring. Thus, the present paper summarizes current experience with the EDA approach and the tools required, and provides practical advice on their application. The paper highlights the need for proper problem formulation and gives general advice for study design. As the EDA approach is directed by toxicity, basic principles for the selection of bioassays are given as well as a comprehensive compilation of appropriate assays, including their strengths and weaknesses. A specific focus is given to strategies for sampling, extraction and bioassay dosing since they strongly impact prioritization of toxicants in EDA. Reduction of sample complexity mainly relies on fractionation procedures, which are discussed in this paper, including quality assurance and quality control. Automated combinations of fractionation, biotesting and chemical analysis using so-called hyphenated tools can enhance the throughput and might reduce the risk of artifacts in laboratory work. The key to determining the chemical structures causing effects is analytical toxicant identification. The latest approaches, tools, software and databases for target-, suspect and non-target screening as well as unknown identification are discussed together with analytical and toxicological confirmation approaches. A better understanding of optimal use and combination of EDA tools will help to design efficient and successful toxicant identification studies in the context of quality monitoring in multiply stressed environments.
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•Effect-directed analysis is a powerful tool to support monitoring.•Multiple bioassays on specific and reactive toxicity direct toxicant identification•Combination of extraction and bioassay dosing is key for toxicant identification•Promising tools for LC–MS based toxicant identification are now available
Surface waters can contain a range of micropollutants from point sources, such as wastewater effluent, and diffuse sources, such as agriculture. Characterizing the source of micropollutants is ...important for reducing their burden and thus mitigating adverse effects on aquatic ecosystems. In this study, chemical analysis and bioanalysis were applied to assess the micropollutant burden during low flow conditions upstream and downstream of three wastewater treatment plants (WWTPs) discharging into small streams in the Swiss Plateau. The upstream sites had no input of wastewater effluent, allowing a direct comparison of the observed effects with and without the contribution of wastewater. Four hundred and five chemicals were analyzed, while the applied bioassays included activation of the aryl hydrocarbon receptor, activation of the androgen receptor, activation of the estrogen receptor, photosystem II inhibition, acetylcholinesterase inhibition and adaptive stress responses for oxidative stress, genotoxicity and inflammation, as well as assays indicative of estrogenic activity and developmental toxicity in zebrafish embryos. Chemical analysis and bioanalysis showed higher chemical concentrations and effects for the effluent samples, with the lowest chemical concentrations and effects in most assays for the upstream sites. Mixture toxicity modeling was applied to assess the contribution of detected chemicals to the observed effect. For most bioassays, very little of the observed effects could be explained by the detected chemicals, with the exception of photosystem II inhibition, where herbicides explained the majority of the effect. This emphasizes the importance of combining bioanalysis with chemical analysis to provide a more complete picture of the micropollutant burden. While the wastewater effluents had a significant contribution to micropollutant burden downstream, both chemical analysis and bioanalysis showed a relevant contribution of diffuse sources from upstream during low flow conditions, suggesting that upgrading WWTPs will not completely reduce the micropollutant burden, but further source control measures will be required.
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•Chemical analysis and bioassays showed WWTP effluent as major micropollutant source.•Pesticides mainly upstream, with pharmaceuticals and consumer chemicals in effluent.•Chemical composition and effects downstream explained by mixing of upstream sources.•Only small fraction of effect in most bioassays was explained by detected chemicals.•Chemical and bio-analysis gave complementary information for pollutant assessment.
Chemicals in the environment occur in mixtures rather than as individual entities. Environmental quality monitoring thus faces the challenge to comprehensively assess a multitude of contaminants and ...potential adverse effects. Effect-based methods have been suggested as complements to chemical analytical characterisation of complex pollution patterns. The regularly observed discrepancy between chemical and biological assessments of adverse effects due to contaminants in the field may be either due to unidentified contaminants or result from interactions of compounds in mixtures. Here, we present an interlaboratory study where individual compounds and their mixtures were investigated by extensive concentration-effect analysis using 19 different bioassays. The assay panel consisted of 5 whole organism assays measuring apical effects and 14 cell- and organism-based bioassays with more specific effect observations. Twelve organic water pollutants of diverse structure and unique known modes of action were studied individually and as mixtures mirroring exposure scenarios in freshwaters. We compared the observed mixture effects against component-based mixture effect predictions derived from additivity expectations (assumption of non-interaction). Most of the assays detected the mixture response of the active components as predicted even against a background of other inactive contaminants. When none of the mixture components showed any activity by themselves then the mixture also was without effects. The mixture effects observed using apical endpoints fell in the middle of a prediction window defined by the additivity predictions for concentration addition and independent action, reflecting well the diversity of the anticipated modes of action. In one case, an unexpectedly reduced solubility of one of the mixture components led to mixture responses that fell short of the predictions of both additivity mixture models. The majority of the specific cell- and organism-based endpoints produced mixture responses in agreement with the additivity expectation of concentration addition. Exceptionally, expected (additive) mixture response did not occur due to masking effects such as general toxicity from other compounds. Generally, deviations from an additivity expectation could be explained due to experimental factors, specific limitations of the effect endpoint or masking side effects such as cytotoxicity in in vitro assays. The majority of bioassays were able to quantitatively detect the predicted non-interactive, additive combined effect of the specifically bioactive compounds against a background of complex mixture of other chemicals in the sample. This supports the use of a combination of chemical and bioanalytical monitoring tools for the identification of chemicals that drive a specific mixture effect. Furthermore, we demonstrated that a panel of bioassays can provide a diverse profile of effect responses to a complex contaminated sample. This could be extended towards representing mixture adverse outcome pathways. Our findings support the ongoing development of bioanalytical tools for (i) compiling comprehensive effect-based batteries for water quality assessment, (ii) designing tailored surveillance methods to safeguard specific water uses, and (iii) devising strategies for effect-based diagnosis of complex contamination.
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•Multiple mixtures of water pollutants demonstrated combined effects across bioassays.•Bioassays detected joint responses from active components against a background.•Mixture effects were in agreement with an additivity assumption.•Jointly, apical and receptor-based assays retrieved mixture components bioactivities.
Environmental water quality monitoring aims to provide the data required for safeguarding the environment against adverse biological effects from multiple chemical contamination arising from ...anthropogenic diffuse emissions and point sources. Here, we integrate the experience of the international EU-funded project SOLUTIONS to shift the focus of water monitoring from a few legacy chemicals to complex chemical mixtures, and to identify relevant drivers of toxic effects. Monitoring serves a range of purposes, from control of chemical and ecological status compliance to safeguarding specific water uses, such as drinking water abstraction. Various water sampling techniques, chemical target, suspect and non-target analyses as well as an array of in vitro, in vivo and in situ bioanalytical methods were advanced to improve monitoring of water contamination. Major improvements for broader applicability include tailored sampling techniques, screening and identification techniques for a broader and more diverse set of chemicals, higher detection sensitivity, standardized protocols for chemical, toxicological, and ecological assessments combined with systematic evidence evaluation techniques. No single method or combination of methods is able to meet all divergent monitoring purposes. Current monitoring approaches tend to emphasize either targeted exposure or effect detection. Here, we argue that, irrespective of the specific purpose, assessment of monitoring results would benefit substantially from obtaining and linking information on the occurrence of both chemicals and potentially adverse biological effects. In this paper, we specify the information required to: (1) identify relevant contaminants, (2) assess the impact of contamination in aquatic ecosystems, or (3) quantify cause–effect relationships between contaminants and adverse effects. Specific strategies to link chemical and bioanalytical information are outlined for each of these distinct goals. These strategies have been developed and explored using case studies in the Danube and Rhine river basins as well as for rivers of the Iberian Peninsula. Current water quality assessment suffers from biases resulting from differences in approaches and associated uncertainty analyses. While exposure approaches tend to ignore data gaps (i.e., missing contaminants), effect-based approaches penalize data gaps with increased uncertainty factors. This integrated work suggests systematic ways to deal with mixture exposures and combined effects in a more balanced way, and thus provides guidance for future tailored environmental monitoring.
Some recent studies showed that in vitro bioassays based on fish or human estrogen receptor (ER) activation may have distinct responses to environmental samples, highlighting the need to better ...understand bioassay-specific ER response to environmental mixtures. For this purpose, we investigated a 12-compound mixture in two mixture ratios (M1 and M2) on zebrafish (zf) liver cells stably expressing zfERα (ZELHα cells) or zfERβ2 (ZELHβ2 cells) and on human ER-reporter gene (MELN) cells. The mixture included the well-known ER ligands bisphenol A (BPA) and genistein (GEN), and other compounds representatives of a freshwater background contamination. In this context, the study aimed at assessing the robustness of concentration addition (CA) model and the potential confounding influence of other chemicals by testing subgroups of ER activators, ER inhibitors or ER activators and inhibitors combined. Individual chemical testing showed a higher prevalence of ER inhibitors in zebrafish than human cells (e.g. propiconazole), and some chemicals inhibited zfER but activated hER response (e.g. benzo(a)pyrene, triphenylphosphate). The estrogenic activity of M1 and M2 was well predicted by CA in MELN cells, whereas it was significantly lower than predicted in ZELHβ2 cells, contrasting with the additive effects observed for BPA and GEN binary mixtures. When testing the subgroups of ER activators and inhibitors combined, the deviation from additivity in ZELHβ2 cells was caused by zebrafish-specific inhibiting chemicals. This study provides novel information on the ability of environmental pollutants to interfere with zfER signalling and shows that non-estrogenic chemicals can influence the response to a mixture of xeno-estrogens in a bioassay-specific manner.
•12-chemical mixtures including xenoestrogens were tested in ER-reporter gene assays.•Human and zebrafish cells had distinct estrogenic response to the mixtures.•Several ER inhibitors were identified but in zebrafish cells only.•Inhibitors decreased the ER response in zebrafish cells compared with expected additivity.•Non-estrogenic chemicals influenced ER mixture response in a cell-specific manner.
Background
Humans and wildlife are continuously exposed to chemical mixtures. These mixtures vary in composition but typically contain hundreds of micropollutants at low concentrations. As it is not ...feasible to measure the toxicity of all possibly occurring mixtures, there is a need to predict mixture toxicity. Two models, Concentration Addition (CA) and Independent Action (IA), have been applied to estimate mixture toxicity. Here, we compared measured with predicted toxicity of nine mixtures designed from 15 environmentally relevant substances in zebrafish embryos to investigate the usability of these models for predicting phenotypic effects in a whole organism short term acute assay.
Results
In total, we compared 177 toxicity values derived from 31 exposure scenarios with their predicted counterparts. Our results show that mixture toxicity was either correctly estimated (86%) by the
prediction window
, the concentration-effect space that is spanned between both models, or was underestimated with both models (14%). The CA model correctly predicted the measured mixture toxicity in 100% of cases when a prediction deviation factor of 2.5 was allowed. However, prediction accuracy of mixture toxicity prediction was dependent on exposure duration and mixture potency. The CA model showed highest prediction quality for long-term exposure with highly potent mixtures, respectively, whereas IA proved to be more accurate for short-term exposure with less potent mixtures. Obtained mixture concentration–response curves were steep and indicated the occurrence of remarkable combined effects as mixture constituents were applied at concentrations below their respective individual effect threshold in 90% of all investigated cases.
Conclusions
Experimental factors, such as exposure duration or mixture potency, influence the prediction accuracy of both inspected models. The CA model showed highest prediction accuracy even for a set of diverse mixtures and various exposure conditions. However, the
prediction window
served as the most robust predicator to estimate mixture toxicity. Overall, our results demonstrate the importance of considering mixture toxicity in risk assessment schemes and give guidance for future experiment design regarding mixture toxicity investigations.
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•Design of reference mixture of organic micropollutants from European WWTP effluents.•Mixture toxicity testing based on eight organism and cell-based assays.•Evaluation of mixture ...risk based on measured and predicted effect data.•Reference mixture (EWERBmix) represents baseline risk for European WWTP effluents.•EWERBmix is a proxy for monitoring and regulation purposes addressing mixture risks.
Aquatic environments are polluted with a multitude of organic micropollutants, which challenges risk assessment due the complexity and diversity of pollutant mixtures. The recognition that certain source-specific background pollution occurs ubiquitously in the aquatic environment might be one way forward to approach mixture risk assessment. To investigate this hypothesis, we prepared one typical and representative WWTP effluent mixture of organic micropollutants (EWERBmix) comprised of 81 compounds selected according to their high frequency of occurrence and toxic potential. Toxicological relevant effects of this reference mixture were measured in eight organism- and cell-based bioassays and compared with predicted mixture effects, which were calculated based on effect data of single chemicals retrieved from literature or different databases, and via quantitative structure-activity relationships (QSARs). The results show that the EWERBmix supports the identification of substances which should be considered in future monitoring efforts. It provides measures to estimate wastewater background concentrations in rivers under consideration of respective dilution factors, and to assess the extent of mixture risks to be expected from European WWTP effluents. The EWERBmix presents a reasonable proxy for regulatory authorities to develop and implement assessment approaches and regulatory measures to address mixture risks. The highlighted data gaps should be considered for prioritization of effect testing of most prevalent and relevant individual organic micropollutants of WWTP effluent background pollution. The here provided approach and EWERBmix are available for authorities and scientists for further investigations. The approach presented can furthermore serve as a roadmap guiding the development of archetypic background mixtures for other sources, geographical settings and chemical compounds, e.g. inorganic pollutants.
Chemicals in the aquatic environment can be harmful to organisms and ecosystems. Knowledge on effect concentrations as well as on mechanisms and modes of interaction with biological molecules and ...signaling pathways is necessary to perform chemical risk assessment and identify toxic compounds. To this end, we developed criteria and a pipeline for harvesting and summarizing effect concentrations from the US ECOTOX database for the three aquatic species groups algae, crustaceans, and fish and researched the modes of action of more than 3,300 environmentally relevant chemicals in literature and databases. We provide a curated dataset ready to be used for risk assessment based on monitoring data and the first comprehensive collection and categorization of modes of action of environmental chemicals. Authorities, regulators, and scientists can use this data for the grouping of chemicals, the establishment of meaningful assessment groups, and the development of in vitro and in silico approaches for chemical testing and assessment.
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