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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.

Many chemicals are out there in our environment, and all living species are exposed. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods – even if high throughput – are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data.We present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene/pathway level using a combined deep learning approach. deepFPlearn uses a deep autoencoder for feature reduction before training a deep feedforward neural network to predict the target association. We received good prediction qualities and showed that our feature compression preserves relevant chemical structural information. Using a vast chemical inventory (unlabeled data) as input for the autoencoder did not reduce our prediction quality but allowed capturing a much more comprehensive range of chemical structures. We predict meaningful - experimentally verified-associations of chemicals and effects on unseen data. deepFPlearn classifies hundreds of thousands of chemicals in seconds.We provide deepFPlearn as an open-source and flexible tool that can be easily retrained and customized to different application settings at https://github.com/yigbt/deepFPlearn.Supplementary informationSupplementary data are available at bioRxiv online.Contactjana.schor@ufz.de

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) persistencebioaccumulation, 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.

Groundwater is essential for the provision of drinking water in many areas around the world. The performance of the groundwater-bearing aquifer relies on the ecosystem services provided by groundwater-related organisms. Therefore, if remediation of contaminated groundwater is necessary, the remediation method has to be carefully selected to avoid risk-risk trade-offs that might impact these ecosystems. In the present study, the environmental risk of the in situ remediation agent Carbo-Iron was performed. Carbo-Iron® is a composite of zero valent nano-iron and active carbon. Existing ecotoxicity data were complemented by studies with Daphnia magna (Crustacea), Scenedesmus vacuolatus (Algae), Chironomus riparius (Insecta) and nitrifying soil microorganisms. The predicted no effect concentration of 0.1 mg/L was derived from acute and chronic ecotoxicity studies. It was compared to measured and modelled environmental concentrations of Carbo-Iron applied in a groundwater contaminated with chlorohydrocarbons in a field study and risk ratios were derived. A comprehensive assessment approach was developed further based on existing strategies and used to identify changes of the environmental risk due to the remediation of the contaminated site with Carbo-Iron. With the data used in the present study, the total environmental risk decreased by approximately 50% in the heavily contaminated zones after the application of Carbo-Iron. Thus, based on the results of the present study, the benefit of remediation with Carbo-Iron seems to outweigh its negative effects on the environment.

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.

Background: Humans and environmental organisms are constantly exposed to complex mixtures of chemicals. Extending our knowledge about the combined effects of chemicals is thus essential for assessing the potential consequences of these exposures. In this context, comprehensive molecular readouts as retrieved by omics techniques are advancing our understanding of the diversity of effects upon chemical exposure. This is especially true for effects induced by chemical concentrations that do not instantaneously lead to mortality, as is commonly the case for environmental exposures. However, omics profiles induced by chemical exposures have rarely been systematically considered in mixture contexts. Objectives: In this study, we aimed to investigate the predictability of chemical mixture effects on the whole-transcriptome scale. Methods: We predicted and measured the toxicogenomic effects of a synthetic mixture on zebrafish embryos. The mixture contained the compounds diuron, diclofenac, and naproxen. To predict concentration- and time-resolved whole-transcriptome responses to the mixture exposure, we adopted the mixture concept of concentration addition. Predictions were based on the transcriptome profiles obtained for the individual mixture components in a previous study. Finally, concentration- and time-resolved mixture exposures and subsequent toxicogenomic measurements were performed and the results were compared with the predictions. Results: This comparison of the predictions with the observations showed that the concept of concentration addition provided reasonable estimates for the effects induced by the mixture exposure on the whole transcriptome. Although nonadditive effects were observed only occasionally, combined, that is, multicomponent-driven, effects were found for mixture components with anticipated similar, as well as dissimilar, modes of action. Discussion: Overall, this study demonstrates that using a concentration- and time-resolved approach, the occurrence and size of combined effects of chemicals may be predicted at the whole-transcriptome scale. This allows improving effect assessment of mixture exposures on the molecular scale that might not only be of relevance in terms of risk assessment but also for pharmacological applications. https://doi.org/10.1289/EHP7773

The science of the total environment 769, 144324 (2021). doi:10.1016/j.scitotenv.2020.144324, Published by Elsevier Science, Amsterdam [u.a.]

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.

Lack of consistent findings in different experimental settings remains a major challenge in toxicogenomics. The present study investigated whether consistency between findings of different microarray experiments can be improved when the analysis is based on a common reference frame (“toxicogenomic universe”), which can be generated using the machine learning algorithm of the self-organizing map (SOM). This algorithm arranges and clusters genes on a 2-dimensional grid according to their similarity in expression across all considered data. In the present study, 19 data sets, comprising of 54 different adult fathead minnow liver exposure experiments, were retrieved from Gene Expression Omnibus and used to train a SOM. The resulting toxicogenomic universe aggregates 58 872 probes to 2500 nodes and was used to project, visualize, and compare the fingerprints of these 54 different experiments. For example, we could identify a common pattern, with 14% of significantly regulated nodes in common, in the data sets of an interlaboratory study of ethinylestradiol exposures. Consistency could be improved compared with the 5% total overlap in regulated genes reported before. Furthermore, we could determine a specific and consistent estrogen-related pattern of differentially expressed nodes and clusters in the toxicogenomic universe by applying additional clustering steps and comparing all obtained fingerprints. Our study shows that the SOM-based approach is useful for generating comparable toxicogenomic fingerprints and improving consistency between results of different experiments. Environ Toxicol Chem 2020;39:526–537. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

Chemicals considered as neuroactive (such as certain pesticides, pharmaceuticals, and industrial chemicals) are among the largest groups of bioactive substances recently detected in European rivers. However, the determination of nervous-system-specific effects has been limited using in vitro tests or conventional end points including lethality. Thus, neurobehavioral tests using in vivo models (e.g., zebrafish embryo) have been proposed as complementary approaches. To investigate the specificity and sensitivity of a light-dark transition locomotor response (LMR) test in 4 to 5 days post fertilization zebrafish with respect to different modes of action (MoAs), we analyzed a set of 18 environmentally relevant compounds with various anticipated MoAs. We found that exposure-induced behavioral alterations were reproducible and dependent on concentration and time. Comparative and quantitative analyses of the obtained locomotor patterns revealed that behavioral effects were not restricted to compounds primarily known to target the nervous system. A clear distinction of MoAs based on locomotor patterns was not possible for most compounds. Furthermore, chemicals with an anticipated same MoA did not necessarily provoke similar behavioral phenotypes. Finally, we determined an increased sensitivity (≥10-fold) compared to observed mortality in the LMR assay for five of eight neuroactive chemicals as opposed to non-neuroactive compounds.

Not much is known about the biotransformation capability of zebrafish (Danio rerio) embryos. For understanding possible toxicity differences to adult fish, it might be crucial to understand the biotransformation of chemicals in zebrafish embryos i.e. as part of toxicokinetics. The biotransformation capabilities were analysed for two different stages of zebrafish embryos in conjunction with the internal concentrations of a xenobiotic. Zebrafish embryos of the late cleavage/early blastula period (2–26 hpf) and the early pharyngula period (26–50 hpf) were exposed for 24 h to the AhR binding compound benz[a]anthracene (BaA). Time dependent changes in cyp transcription (cyp1a, cyp1b1, cyp1c1 and cyp1c2) as well as concentration & time-dependent courses of BaA in the fish embryo and the exposure medium were analysed. Additionally, the CYP mediated formation of biotransformation products was investigated. We found correlations between transcriptional responses and the internal concentration for both exposure types. These correlations were depending on the start of the exposure i.e. the age of the exposed embryo. While no significant induction of the examined gene transcripts was observed in the first 12 h of exposure beginning in the blastula period a correlation was apparent when exposure started later i.e. in the pharyngula period. A significant induction of cyp1a was detected already after 1.5 h of BaA exposure. Gene transcripts for cyp1b1, cyp1c1 and cyp1c2 showed expressions distinctly different from cyp1a and were, in general, less inducible by BaA in both exposure windows. The toxicokinetic analysis showed that the biotransformation capability was fivefold higher in the older fish embryos. Biotransformation products of phase I reactions were found between 32 hpf and 50 hpf and were tentatively identified as benz[a]anthracene-phenol and benz[a]anthracene-dihydrodiol-epoxide. In conclusion, not only duration but also onset of exposure in relation to the developmental stage of zebrafish embryos is important in the analysis and interpretation of effects due to different biotransformation capabilities.

The exposome encompasses an individual's exposure to exogenous chemicals, as well as endogenous chemicals that are produced or altered in response to external stressors. While the exposome concept has been established for human health, its principles can be extended to include broader ecological issues. The assessment of exposure is tightly interlinked with hazard assessment. Here, we explore if mechanistic understanding of the causal links between exposure and adverse effects on human health and the environment can be improved by integrating the exposome approach with the adverse outcome pathway (AOP) concept that structures and organizes the sequence of biological events from an initial molecular interaction of a chemical with a biological target to an adverse outcome. Complementing exposome research with the AOP concept may facilitate a mechanistic understanding of stress-induced adverse effects, examine the relative contributions from various components of the exposome, determine the primary risk drivers in complex mixtures, and promote an integrative assessment of chemical risks for both human and environmental health. Keywords: Exposome, AOP, Systems toxicology, Systems biology, Systems chemistry, Risk assessment

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.

Polyaromatic hydrocarbons (PAH) are common pollutants of water ecosystems originating from incineration processes and contamination with mineral oil. Water solubility of PAHs is generally low; for toxicity tests with aquatic organisms, they are therefore usually dissolved in organic solvents. Here we examined the effects of a typical model PAH, phenanthrene, and a solvent, acetone, on amphipods as relevant aquatic invertebrate models. Two of these species, Eulimnogammarus verrucosus and Eulimnogammarus cyaneus, are common endemics of the oligotrophic and pristine Lake Baikal, while one, Gammarus lacustris, is widespread throughout the Holarctic and inhabits smaller and more eutrophic water bodies in the Baikal area. Neither solvent nor phenanthrene caused mortality at the applied concentrations, but both substances affected gene expression in all species. Differential gene expression was more profound in the species from Lake Baikal than in the Holarctic species. Moreover, in one of the Baikal species, E. cyaneus, we found that many known components of the cellular xenobiotic detoxification system reacted to the treatments. Finally, we detected a negative relationship between changes in transcript abundances in response to the solvent and phenanthrene. This mixture effect, weaker than the impact by a single mixture component, needs further exploration.

Background Chemicals induce compound-specific changes in the transcriptome of an organism (toxicogenomic fingerprints). This provides potential insights about the cellular or physiological responses to chemical exposure and adverse effects, which is needed in assessment of chemical-related hazards or environmental health. In this regard, comparison or connection of different experiments becomes important when interpreting toxicogenomic experiments. Owing to lack of capturing response dynamics, comparability is often limited. In this study, we aim to overcome these constraints. Results We developed an experimental design and bioinformatic analysis strategy to infer time- and concentration-resolved toxicogenomic fingerprints. We projected the fingerprints to a universal coordinate system (toxicogenomic universe) based on a self-organizing map of toxicogenomic data retrieved from public databases. Genes clustering together in regions of the map indicate functional relation due to co-expression under chemical exposure. To allow for quantitative description and extrapolation of the gene expression responses we developed a time- and concentration-dependent regression model. We applied the analysis strategy in a microarray case study exposing zebrafish embryos to 3 selected model compounds including 2 cyclooxygenase inhibitors. After identification of key responses in the transcriptome we could compare and characterize their association to developmental, toxicokinetic, and toxicodynamic processes using the parameter estimates for affected gene clusters. Furthermore, we discuss an association of toxicogenomic effects with measured internal concentrations. Conclusions The design and analysis pipeline described here could serve as a blueprint for creating comparable toxicogenomic fingerprints of chemicals. It integrates, aggregates, and models time- and concentration-resolved toxicogenomic data.

Groundwater is essential for the provision of drinking water in many areas around the world. The performance of the groundwater-bearing aquifer relies on the ecosystem services provided by groundwater-related organisms. Therefore, if remediation of contaminated groundwater is necessary, the remediation method has to be carefully selected to avoid risk-risk trade-offs that might impact these ecosystems. In the present study, the environmental risk of the in situ remediation agent Carbo-Iron was performed. Carbo-Iron® is a composite of zero valent nano-iron and active carbon. Existing ecotoxicity data were complemented by studies with Daphnia magna (Crustacea), Scenedesmus vacuolatus (Algae), Chironomus riparius (Insecta) and nitrifying soil microorganisms. The predicted no effect concentration of 0.1 mg/L was derived from acute and chronic ecotoxicity studies. It was compared to measured and modelled environmental concentrations of Carbo-Iron applied in a groundwater contaminated with chlorohydrocarbons in a field study and risk ratios were derived. A comprehensive assessment approach was developed further based on existing strategies and used to identify changes of the environmental risk due to the remediation of the contaminated site with Carbo-Iron. With the data used in the present study, the total environmental risk decreased by approximately 50% in the heavily contaminated zones after the application of Carbo-Iron. Thus, based on the results of the present study, the benefit of remediation with Carbo-Iron seems to outweigh its negative effects on the environment.

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. © 2019, The Author(s)., Funding text #1 1 UFZ‑Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318 Leipzig, Germany. 2 Institute for Environmental Research, RWTH Aachen University, 52074 Aachen, Germany. 3 Office of Research and Development, Atlantic Ecology Division, United States Environmental Protection Agency, Narragansett, RI, USA. 4 Center for Applied Geoscience, Eberhard Karls Uni‑ versity Tübingen, 72074 Tübingen, Germany. 5 Alterra, Wageningen University and Research Centre, P.O. Box 47, 6700 AA Wageningen, The Netherlands. 6 Environment and Climate Change Canada, Burlington, ON, Canada. 7 Sophus Bauditz Vej 19 B, 2800 Kgs. Lyngby, Denmark. 8 Water and Soil Quality Research Group, Institute of Environmental Assessment and Water Research (IDAEA‑ CSIC), Jordi Girona 18‑26, 08034 Barcelona, Spain. 9 Unité d’Ecotoxicologie Funding text #2 The SOLUTIONS Project is supported by the Seventh Framework Programme (FP7‑ENV‑2013) of the European Union under Grant Agreement No. 603437. G.A. Umbuzeiro thanks FAPESP Projects 2013/16956‑6 and 2015/24758‑5. We like to thank all partners for their continued efforts in making this project a success story. Funding text #3 The SOLUTIONS Project is supported by the Seventh Framework Programme (FP7‑ENV‑704 2013) of the European Union under Grant Agreement No. 603437. G.A. Umbuzeiro thanks 705 FAPESP Projects 2013/16956‑6 and 2015/24758‑5.

Environmental quality monitoring of water resources is challenged with providing the basis for safeguarding the environment against adverse biological effects from exposure to anthropogenic chemicals originating from diffuse and point sources. Although current regulatory efforts focus on monitoring and assessing a few legacy chemicals, many more anthropogenic chemicals are and will become detected in aquatic resources as a result of progress in analytical techniques. Assessing this type of exposure information based on available standard approaches from prospective risk assessment for single chemicals inevitably leads to indication of risk in most surface water bodies. As an alternative to generic assessment approaches, effect-based monitoring approaches are suggested. This offers the advantage of reducing uncertainties of effect extrapolation and additionally accounts for mixture effects. To become a credible complement to chemical monitoring information, however, a better understanding of the capabilities and gaps of available effect-based tools is needed. The authors therefore undertook to 1) compile organic contaminants detected in freshwater monitoring studies, 2) provide a synopsis of the mode of action knowledge available for the detected compounds, 3) perform a hazard ranking to identify priority mixtures, and 4) reflect on the challenges to make bioassays fit for effect-based monitoring. The present Focus article shows that chemical occurrence in European freshwaters seems to be highly variable in composition and relative abundancies. Further, although the present mode of action knowledge remains limited, the authors already see the need for batteries of effect-based tools if a more comprehensive coverage of prevailing effect qualities for mixtures is to be targeted. Finally, they suggest a list of organic compounds that could serve as a reference list for effect-based tool validation studies. Environ Toxicol Chem 2016;35:1887-1899. © 2016 SETAC.

International audience; The numbers of potential neurotoxicants in the environment are raising and pose a great risk for humans and the environment. Currently neurotoxicity assessment is mostly performed to predict and prevent harm to human populations. Despite all the efforts invested in the last years in developing novel in vitro or in silico test systems, in vivo tests with rodents are still the only accepted test for neurotoxicity risk assessment in Europe. Despite an increasing number of reports of species showing altered behaviour, neurotoxicity assessment for species in the environment is not required and therefore mostly not performed. Considering the increasing numbers of environmental contaminants with potential neurotoxic potential, eco-neurotoxicity should be also considered in risk assessment. In order to do so novel test systems are needed that can cope with species differences within ecosystems. In the field, online-biomonitoring systems using behavioural information could be used to detect neurotoxic effects and effect-directed analyses could be applied to identify the neurotoxicants causing the effect. Additionally, toxic pressure calculations in combination with mixture modelling could use environmental chemical monitoring data to predict adverse effects and prioritize pollutants for laboratory testing. Cheminformatics based on computational toxicological data from in vitro and in vivo studies could help to identify potential neurotoxicants. An array of in vitro assays covering different modes of action could be applied to screen compounds for neurotoxicity. The selection of in vitro assays could be guided by AOPs relevant for eco-neurotoxicity. In order to be able to perform risk assessment for eco-neurotoxicity, methods need to focus on the most sensitive species in an ecosystem. A test battery using species from different trophic levels might be the best approach. To implement eco-neurotoxicity assessment into European risk assessment, cheminformatics and in vitro screening tests could be used as first approach to identify eco-neurotoxic pollutants. In a second step, a small species test battery could be applied to assess the risks of ecosystems.

Pesticides and biocides (PaB) are ubiquitously present in aquatic ecosystems due to their widespread application and have been detected in rivers at concentrations that may cause distress to aquatic life. Many of these compounds accumulate in sediments acting as long-term source for aquatic ecosystems. However, data on sediment contamination with current-use PaB in Europe are scarce. Thus, in this study, we elucidated PaB patterns and associated risks in sediments of seven major European rivers focusing on their last stretch as an integrative sink of particles transported by these rivers. Sediments were extracted with pressurized liquid extraction (PLE) using a broad-spectrum method recovering many compound classes with a wide range of physicochemical properties. Altogether 126 compounds were analyzed and 81 of them were detected with LC-HRMS and GC-NCI-MS/MS at least in one of the sediments. The highest number of compounds was detected (59) in River Elbe sediments close to Cuxhaven with outstanding concentrations ranging from 0.8 to 1691 mg/g organic carbon. Multivariate analysis identified a cluster with 3 ubiquitous compounds (cyhalothrin, carbendazim, fenpropimorph) and three clusters of chemicals with higher variability within and between rivers. Risk assessment indicates an acute toxic risk to benthic crustaceans at all investigated sites with the pyrethroids tefluthrin and cyfluthrin together with the fungicide carbendazim as the main drivers. Risks to algae were driven at most sites almost exclusively by photosynthesis inhibitors with estuary-specific herbicide mixtures, while in the rivers Po and Gironde cell division inhibitors played an important role at some sites. Mixtures of specific concern have been defined and suggested for integration in future monitoring programs.

In vitro bioassays are increasingly used for water quality monitoring. Surface water samples often need to be enriched to observe an effect and solid-phase extraction (SPE) is commonly applied for this purpose. The applied methods are typically optimised for the recovery of target chemicals and not for effect recovery for bioassays. A review of the few studies that have evaluated SPE recovery for bioassays showed a lack of experimentally determined recoveries. Therefore, we systematically measured effect recovery of a mixture of 579 organic chemicals covering a wide range of physicochemical properties that were spiked into a pristine water sample and extracted using large volume solid-phase extraction (LVSPE). Assays indicative of activation of xenobiotic metabolism, hormone receptor-mediated effects and adaptive stress responses were applied, with non-specific effects determined through cytotoxicity measurements. Overall, effect recovery was found to be similar to chemical recovery for the majority of bioassays and LVSPE blanks had no effect. Multi-layer SPE exhibited greater recovery of spiked chemicals compared to LVSPE, but the blanks triggered cytotoxicity at high enrichment. Chemical recovery data together with single chemical effect data were used to retrospectively estimate with reverse recovery modelling that there was typically less than 30% effect loss expected due to reduced SPE recovery in published surface water monitoring studies. The combination of targeted experiments and mixture modelling clearly shows the utility of SPE as a sample preparation method for surface water samples, but also emphasizes the need for adequate controls when extraction methods are adapted from chemical analysis workflows.

Environment international 114, 95-106 (2018). doi:10.1016/j.envint.2018.02.013, Published by Elsevier Science, Amsterdam [u.a.]

Omics-based methods are increasingly used in current ecotoxicology. Therefore, a large number of observations for various toxic substances and organisms are available and may be used for identifying modes of action, adverse outcome pathways, or novel biomarkers. For these purposes, good statistical analysis of toxicogenomic data is vital. In contrast to established ecotoxicological techniques, concentration-response modeling is rarely used for large datasets. Instead, statistical hypothesis testing is prevalent, which provides only a limited scope for inference. The present study therefore applied automated concentration-response modeling for 3 different ecotoxicotranscriptomic and ecotoxicometabolomic datasets. The modeling process was performed by simultaneously applying 9 different regression models, representing distinct mechanistic, toxicological, and statistical ideas that result in different curve shapes. The best-fitting models were selected by using Akaike's information criterion. The linear and exponential models represented the best data description for more than 50% of responses. Models generating U-shaped curves were frequently selected for transcriptomic signals (30%), and sigmoid models were identified as best fit for many metabolomic signals (21%). Thus, selecting the models from an array of different types seems appropriate, because concentration-response functions may vary because of the observed response type, and they also depend on the compound, the organism, and the investigated concentration and exposure duration range. The application of concentration-response models can help to further tap the potential of omics data and is a necessary step for quantitative mixture effect assessment at the molecular response level.

Sites of wastewater discharge are hotspots for pollution of freshwaters with organic micropollutants and are often associated with adverse effects to aquatic organisms. The assessment, monitoring and managment of these hotspots is challenged by variations in the pollutant mixture composition due to season, weather conditions and random spills. In this study, we unraveled temporal exposure patterns in organic micropollutant mixtures from wastewater discharge and analyzed respective acute and sublethal risks for aquatic organisms. Samples were taken from two components of a separate sewer system i) a wastewater treatment plant (WWTP) and ii) a rain sewer of a medium size town as well as from the receiving river in different seasons. Rain sewer samples were separately collected for rain and dry - weather conditions. We analyzed 149 compounds by liquid chromatography-tandem mass spectrometry (LC-MS/MS). By considering the pollution dynamics in the point sources, we reduced the complexity of pollutant mixtures by k-means clustering to a few emission groups representing temporal and weather-related pollution patterns. From these groups, we derived biological quality element (BQE) - specific risk patterns. In most cases, one main risk driving emission group and a few individual risk driving compounds were identified for each BQE. While acute risk for fish was quite low, algae were exposed to seasonally emitted herbicides (terbuthylazine, spiroxamine) and crustaceans to randomly spilled insecticides (diazinon, dimethoate). Sublethal risks for all BQE were strongly influenced by constantly emitted pollutants, above all, pharmaceuticals. Variability of risks in the river was mainly driven by water discharge of the river rather than by season or peak events. Overall, the studied WWTP represented the major pollution source with a specific emission of agricultural compounds. However, the investigated rain sewer showed to be a constant pollution source due to illicit connections and was an important entry route for high loads of insecticides and biocides due to spills or incorrect disposal. By considering these pollution and risk dynamics, monitoring strategies may be optimized with a special focus on times of low flow conditions in the river, rain events and seasonally emitted risk drivers.

Numerous studies have been published in the past years investigating the transcriptome of the zebrafish embryo (ZFE) upon being subjected to chemical stress. Aiming at a more mechanistic understanding of the results of such studies, knowledge about commonalities of transcript regulation in response to chemical stress is needed. Thus, our goal in this study was to identify and interpret genes and gene sets constituting a general response to chemical exposure. Therefore, we aggregated and reanalyzed published toxicogenomics data obtained with the ZFE. We found that overlap of differentially transcribed genes in response to chemical stress across independent studies is generally low and the most commonly differentially transcribed genes appear in less than 50% of all treatments across studies. However, effect size analysis revealed several genes showing a common trend of differential expression, among which genes related to calcium homeostasis emerged as key, especially in exposure settings up to 24 h post-fertilization. Additionally, we found that these and other downregulated genes are often linked to anatomical regions developing during the respective exposure period. Genes showing a trend of increased expression were, among others, linked to signaling pathways (e.g., Wnt, Fgf) as well as lysosomal structures and apoptosis. The findings of this study help to increase the understanding of chemical stress responses in the developing zebrafish embryo and provide a starting point to improve experimental designs for this model system. In future, improved time- and concentration-resolved experiments should offer better understanding of stress response patterns and access to mechanistic information.

Surface waters can contain a diverse range of organic pollutants, including pesticides, pharmaceuticals and industrial compounds. While bioassays have been used for water quality monitoring, there is limited knowledge regarding the effects of individual micropollutants and their relationship to the overall mixture effect in water samples. In this study, a battery of in vitro bioassays based on human and fish cell lines and whole organism assays using bacteria, algae, daphnids and fish embryos was assembled for use in water quality monitoring. The selection of bioassays was guided by the principles of adverse outcome pathways in order to cover relevant steps in toxicity pathways known to be triggered by environmental water samples. The effects of 34 water pollutants, which were selected based on hazard quotients, available environmental quality standards and mode of action information, were fingerprinted in the bioassay test battery. There was a relatively good agreement between the experimental results and available literature effect data. The majority of the chemicals were active in the assays indicative of apical effects, while fewer chemicals had a response in the specific reporter gene assays, but these effects were typically triggered at lower concentrations. The single chemical effect data were used to improve published mixture toxicity modeling of water samples from the Danube River. While there was a slight increase in the fraction of the bioanalytical equivalents explained for the Danube River samples, for some endpoints less than 1% of the observed effect could be explained by the studied chemicals. The new mixture models essentially confirmed previous findings from many studies monitoring water quality using both chemical analysis and bioanalytical tools. In short, our results indicate that many more chemicals contribute to the biological effect than those that are typically quantified by chemical monitoring programs or those regulated by environmental quality standards. This study not only demonstrates the utility of fingerprinting single chemicals for an improved understanding of the biological effect of pollutants, but also highlights the need to apply bioassays for water quality monitoring in order to prevent underestimation of the overall biological effect.

Three water contaminants were selected to be tested in the zebrafish embryo toxicity test (DarT) in order to investigate the sensitivity of the zebrafish embryo toxicity test with respect to mixture effect detection. The concentration-response curves for the observed effects lethality and hypo-pigmentation were calculated after an exposure of the embryos for 96 h with a fungicide (carbendazim), a plasticizer or propellent precursor (2,4-DNT: 2,4- dinitrotoluene) and an aromatic compound (AαC: 2-amino-9H-pyrido[2,3-b]indol), respectively. Follow-up mixture tests were based on the calculated LC50 or EC50 of the single compounds and combined effects were predicted according to the mixture concepts of concentration addition (CA) and independent action (IA). The order of toxicity for the single substances was carbendazim (LC50 = 1.25 μM) < AαC (LC50 = 8.16 μM) < 2,4-DNT (LC50 = 177.05 μM). For AαC and 2,4 DNT hypo-pigmentation was observed in addition (AαC EC50 = 1.81 μM; 2,4-DNT EC50 = 8.81 μM). Two binary and one ternary mixture were studied on lethality and one on hypo-pigmentation: 2,4-DNT/AαC (LC50 = 119.21 μM, EC50 = 5.37 μM), carbendazim/AαC (LC50 = 4.49 μM) and AαC/Carbendazim/2,4 DNT (LC50 = 108.62 μM). Results showed that the effects were in agreement with the CA model when substances were tested in mixtures. Therefore, in a reasonable worst case scenario substance combination effects in fish embryos were at maximum only prone to overestimation when using CA as the mixture concept.

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.

Tungsten carbide (WC) and cobalt (Co) are constituents of hard metals and are used for the production of extremely hard tools. Previous studies have identified greater cytotoxic potential of WC-based nanopartides if particles contained Co. The aim of this study was to investigate whether the formation of reactive oxygen species (ROS) and micronuclei would help explain the impact on cultured mammalian cells by three different tungsten-based nanoparticles (WCS, WCL, WCL-Co (S: small; L: large)). The selection of particles allowed us to study the influence of particle properties, e.g. surface area, and the presence of Co on the toxicological results. WCS and WCL/WCL-Co differed in their crystalline structure and surface area, whereas WCS/WCL and WCL-Co differed in their cobalt content. WCL and WCL-Co showed neither a genotoxic potential nor ROS induction. Contrary to that. WCS nanoparticles induced the formation of both ROS and micronuclei. CoCl2 was tested in relevant concentrations and induced no ROS formation, but increased the rate of micronuclei at concentrations exceeding those present in WCL-Co. In conclusion, ROS and micronuclei formation could not be associated with the presence of Co in the WC-based particles. The contrasting responses elicited by WCS vs. WCL appear to be due to large differences in crystalline structure. (C) 2012 Elsevier B.V. All rights reserved.

Cellular internalisation of industrial engineered nanoparticles is undesired and a reason for concern. Here we investigated and compared the ability of seven different mammalian cell cultures in vitro to incorporate six kinds of engineered nanoparticles, focussing on the role of cell type and particle properties in particle uptake. Uptake was examined using light and electron microscopy coupled with energy dispersive X-ray spectroscopy (EDX) for particle element identification. Flow cytometry was applied for semi-quantitative analyses of particle uptake and for exploring the influence on uptake by the phagocytosis inhibitor Cytochalasin D (CytoD). All particles studied were found to enter each kind of cultured cells. Yet, particles were never found within cell nuclei. The presence of the respective particles within the cells was confirmed by EDX. Live-cell imaging revealed the time-dependent process of internalisation of technical nanoparticles, which was exemplified by tungsten carbide particle uptake into the human skin cells, HaCaT. Particles were found to co-localise with lysosomal structures within the cells. The incorporated nanoparticles changed the cellular granularity, as measured by flow cytometry, already after 3 h of exposure in a particle specific manner. By correlating particle properties with flow cytometry data, only the primary particle size was found to be a weakly influential property for particle uptake. CytoD, an inhibitor of actin filaments and therewith of phagocytosis, significantly inhibited the internalisation of particle uptake in only two of the seven investigated cell cultures. Our study, therefore, supports the notion that nanoparticles can enter mammalian cells quickly and easily, irrespective of the phagocytic ability of the cells.

One task in risk assessment of engineered nanoparticles is toxicological studies. A suitable interpretation of these investigations demands a comprehensive physical-chemical characterization. Here, we present an approach to gain well-dispersed nanoparticles in physiological media. Therefore, a step-by-step procedure is demonstrated on two different tungsten carbide nanopowders which can be transferred to other powders. The procedure includes a comprehensive powder characterization, followed by a preparation of a non-physiologic, electrostatically stable nanoparticle suspension and finally closes with investigations of the particles' behavior in different physiological media. Our study showed that the particles agglomerate in protein-free media. In this context, dependencies of mass- and surface-based nanoparticle concentrations as well as of different physiological media were analyzed. In the presence of bovine serum albumin (BSA) or serum, the agglomeration process is decelerated or, at the appropriate protein amount, prevented.

Due to their increased production and use, engineered nanoparticles are expected to be released into the aquatic environment where particles may agglomerate. The aim of this study was to explore the role of agglomeration of nanoparticles in the uptake and expression of toxicity in the rainbow trout (Oncorhynchus mykiss) gill cell line, RTgill-W1. This cell line was chosen as model because it is known to be amenable to culture in complete as well as greatly simplified exposure media. Nano-sized tungsten carbide (WC) with or without cobalt doping (WC-Co), two materials relevant in the heavy metal industry, were applied as model particles. These particles were suspended in culture media with decreasing complexity from L15 with 10% fetal bovine serum (FBS) to L15 to L15/ex, containing only salts, galactose and pyruvate of the complete medium L15. Whereas the serum supplement in L15 retained primary nanoparticle suspensions, agglomerates were formed quickly in L15 and L15/ex. Nevertheless, scanning electron microscopy (SEM) coupled with energy dispersive X-ray (EDX) elemental analysis revealed an uptake of both WC and WC-Co nanoparticles into RTgill-W1 cells irrespective of the state of agglomeration of nanoparticles. The localisation seemed to be restricted to the cytoplasm, as no particles were observed in the nucleus of cells. Moreover, reduction in cell viability between 10 and 50% compared to controls were observed upon particle exposure in all media although the pattern of impact varied depending on the medium and exposure time. Short-term exposure of cells led to significant cytotoxicity at the highest nominal particle concentrations, irrespective of the particle type or exposure medium. In contrast, long-term exposures led to preferential toxicity in the simplest medium, L15/ex, and an enhanced toxicity by the cobalt-containing WC nanoparticles in all exposure media. The composition of the exposure media also influenced the toxicity of the cobalt ions, which may dissolve from the WC-Co nanoparticles, with cells reacting much more sensitively toward cobalt ions in the absence of FBS. However, the toxicity observed by ionic cobalt alone did not explain the toxicity of the WC-Co nanoparticles, suggesting that the combination of metallic Co and WC is the cause of the increased particle toxicity of WC-Co. Taken together, our findings indicate that minimal exposure media can lead to rapid agglomeration of nanoparticles but that agglomeration does not prevent uptake into cells and the expression of toxicity.

Background Tungsten carbide nanoparticles are being explored for their use in the manufacture of hard metals. To develop nanoparticles for broad applications, potential risks to human health and the environment should be evaluated and taken into consideration. Objective We aimed to assess the toxicity of well-characterized tungsten carbide (WC) and cobaltdoped tungsten carbide (WC-Co) nanoparticle suspensions in an array of mammalian cells. Methods We examined acute toxicity of WC and of WC-Co (10% weight content Co) nanoparticles in different human cell lines (lung, skin, and colon) as well as in rat neuronal and glial cells (i.e., primary neuronal and astroglial cultures and the oligodendro cyte precursor cell line OLN-93). Furthermore, using electron microscopy, we assessed whether nanoparticles can be taken up by living cells. We chose these in vitro systems in order to evaluate for potential toxicity of the nanoparticles in different mammalian organs (i.e., lung, skin, intestine, and brain). Results Chemical–physical characterization confirmed that WC as well as WC-Co nanoparticles with a mean particle size of 145 nm form stable suspensions in serum-containing cell culture media. WC nanoparticles were not acutely toxic to the studied cell lines. However, cytotoxicity became apparent when particles were doped with Co. The most sensitive were astrocytes and colon epithelial cells. Cytotoxicity of WC-Co nanoparticles was higher than expected based on the ionic Co content of the particles. Analysis by electron microscopy demonstrated presence of WC nanoparticles within mammalian cells. Conclusions Our findings demonstrate that doping of WC nanoparticles with Co markedly increases their cytotoxic effect and that the presence of WC-Co in particulate form is essential to elicit this combinatorial effect., Environmental Health Perspectives, 117 (4), ISSN:1552-9924, ISSN:0091-6765

In sub-Saharan Africa malaria is a leading cause of morbidity and mortality among young children. Detailed knowledge of spatial variation of malaria epidemiology and associated risk factors is important for planning and evaluating malaria-control measures. The spatial variation of malaria incidences and socioeconomic factors were assessed over 21 months from January 2003 to September 2005 in 535 children from 9 villages of a small rural area with high Plasmodium falciparum transmission in Ghana. Household positions were mapped by use of a global positioning system and the spatial effects on malaria rates were assessed by means of ecological analyses and bivariate Poisson regression controlling for possible confounding factors. Malaria incidence was surprisingly heterogeneous between villages and ecological analyses showed strong correlations with village area (R/2 = 0.74; P = .003) and population size (R/2 = 0.68; P = .006). Malaria risk was affected by a number of socioeconomic factors. Poisson regression showed an independent linear rate reduction with increasing distance between childrens households and the fringe of the forest. The exact location of households in villages is an independent and important factor for the variation of malaria incidence in children from high-transmission areas. This fact should be considered in the planning of intervention trials and in spatial targeting of malaria interventions at a local level. (authors)

For degradation of halogenated chemicals in groundwater Carbo-Iron®, a composite of activated carbon and nano-sized Fe 0 , was developed (Mackenzie et al., 2012). Potential effects of this nanocomposite on fish were assessed. Beyond the contaminated zone Fe 0 can be expected to have oxidized and Carbo-Iron was used in its oxidized form in ecotoxicological tests. Potential effects of Carbo Iron in zebrafish ( Danio rerio ) were investigated using a 48 h embryo toxicity test under static conditions, a 96 h acute test with adult fish under semi-static conditions and a 34 d fish early life stage test (FELST) in a flow-through system. Particle diameters in test suspensions were determined via dynamic light scattering (DLS) and ranged from 266 to 497 nm. Particle concentrations were measured weekly in samples from the FELST using a method based on the count rate in DLS. Additionally, uptake of particles into test organisms was investigated using microscopic methods. Furthermore, effects of Carbo-Iron on gene expression were investigated by microarray analysis in zebrafish embryos. In all tests performed, no significant lethal effects were observed. Furthermore, Carbo-Iron had no significant influence on weight and length of fish as determined in the FELST. In the embryo test and the early life stage test, growth of fungi on the chorion was observed at Carbo-Iron concentrations between 6.3 and 25 mg/L. Fungal growth did not affect survival, hatching success and growth. In the embryo test, no passage of Carbo-Iron particles into the perivitelline space or the embryo was observed. In juvenile and adult fish, Carbo-Iron was detected in the gut at the end of exposure. In juvenile fish exposed to Carbo-Iron for 29 d and subsequently kept for 5 d in control water, Carbo-Iron was no longer detectable in the gut. Global gene expression in zebrafish embryos was not significantly influenced by Carbo-Iron.

Environmental quality monitoring of water resources is challenged with providing the basis for safeguarding the environment against adverse biological effects of anthropogenic chemical contamination from diffuse and point sources. While current regulatory efforts focus on monitoring and assessing a few legacy chemicals, many more anthropogenic chemicals can be detected simultaneously in our aquatic resources. However, exposure to chemical mixtures does not necessarily translate into adverse biological effects nor clearly shows whether mitigation measures are needed. Thus, the question which mixtures are present and which have associated combined effects becomes central for defining adequate monitoring and assessment strategies. Here we describe the vision of the international, EU-funded project SOLUTIONS, where three routes are explored to link the occurrence of chemical mixtures at specific sites to the assessment of adverse biological combination effects. First of all, multi-residue target and non-target screening techniques covering a broader range of anticipated chemicals co-occurring in the environment are being developed. By improving sensitivity and detection limits for known bioactive compounds of concern, new analytical chemistry data for multiple components can be obtained and used to characterise priority mixtures. This information on chemical occurrence will be used to predict mixture toxicity and to derive combined effect estimates suitable for advancing environmental quality standards. Secondly, bioanalytical tools will be explored to provide aggregate bioactivity measures integrating all components that produce common (adverse) outcomes even for mixtures of varying compositions. The ambition is to provide comprehensive arrays of effect-based tools and trait-based field observations that link multiple chemical exposures to various environmental protection goals more directly and to provide improved in situ observations for impact assessment of mixtures. Thirdly, effect-directed analysis (EDA) will be applied to identify major drivers of mixture toxicity. Refinements of EDA include the use of statistical approaches with monitoring information for guidance of experimental EDA studies. These three approaches will be explored using case studies at the Danube and Rhine river basins as well as rivers of the Iberian Peninsula. The synthesis of findings will be organised to provide guidance for future solution-oriented environmental monitoring and explore more systematic ways to assess mixture exposures and combination effects in future water quality monitoring. The SOLUTIONS Project is supported by the Seventh Framework Programme (FP7-ENV-2013) of the European Union under grant agreement no. 603437.

Omics-based methods are increasingly used in current ecotoxicology. Therefore, a large number of observations for various toxic substances and organisms are available and may be used for identifying modes of action, adverse outcome pathways, or novel biomarkers. For these purposes, good statistical analysis of toxicogenomic data is vital. In contrast to established ecotoxicological techniques, concentration-response modeling is rarely used for large datasets. Instead, statistical hypothesis testing is prevalent, which provides only a limited scope for inference. The present study therefore applied automated concentration-response modeling for 3 different ecotoxicotranscriptomic and ecotoxicometabolomic datasets. The modeling process was performed by simultaneously applying 9 different regression models, representing distinct mechanistic, toxicological, and statistical ideas that result in different curve shapes. The best-fitting models were selected by using Akaike's information criterion. The linear and exponential models represented the best data description for more than 50% of responses. Models generating U-shaped curves were frequently selected for transcriptomic signals (30%), and sigmoid models were identified as best fit for many metabolomic signals (21%). Thus, selecting the models from an array of different types seems appropriate, because concentration-response functions may vary because of the observed response type, and they also depend on the compound, the organism, and the investigated concentration and exposure duration range. The application of concentration-response models can help to further tap the potential of omics data and is a necessary step for quantitative mixture effect assessment at the molecular response level.