Curated mode-of-action data and effect concentrations for chemicals relevant for the aquatic environment.

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.

Analysis of vascular disruption in zebrafish embryos as an endpoint to predict developmental toxicity.

Inhibition of angiogenesis is an important mode of action for the teratogenic effect of chemicals and drugs. There is a gap in the availability of simple, experimental screening models for the detection of angiogenesis inhibition. The zebrafish embryo represents an alternative test system which offers the complexity of developmental differentiation of an entire organism while allowing for small-scale and high-throughput screening. Here we present a novel automated imaging-based method to detect the inhibition of angiogenesis in early life stage zebrafish. Video subtraction was used to identify the location and number of functional intersegmental vessels according to the detection of moving blood cells. By exposing embryos to multiple tyrosine kinase inhibitors including SU4312, SU5416, Sorafenib, or PTK787, we confirmed that this method can detect concentration-dependent inhibition of angiogenesis. Parallel assessment of arterial and venal aorta ruled out a potential bias by impaired heart or blood cell development. In contrast, the histone deacetylase inhibitor valproic acid did not affect ISV formation supporting the specificity of the angiogenic effects. The new test method showed higher sensitivity, i.e. lower effect concentrations, relative to a fluorescent reporter gene strain (Tg(KDR:EGFP)) exposed to the same tyrosine kinase inhibitors indicating that functional effects due to altered tubulogenesis or blood transport can be detected before structural changes of the endothelium are visible by fluorescence imaging. Comparison of exposure windows indicated higher specificity for angiogenesis when exposure started at later embryonic stages (24 h post-fertilization). One of the test compounds was showing particularly high specificity for angiogenesis effects (SU4312) and was, therefore, suggested as a model compound for the identification of molecular markers of angiogenic disruption. Our findings establish video imaging in wild-type strains as viable, non-invasive, high-throughput method for the detection of chemical-induced angiogenic disruption in zebrafish embryos.

A data-derived reference mixture representative of European wastewater treatment plant effluents to complement mixture assessment.

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.

New approach methodologies in human regulatory toxicology - Not if, but how and when!

The predominantly animal-centric approach of chemical safety assessment has increasingly come under pressure. Society is questioning overall performance, sustainability, continued relevance for human health risk assessment and ethics of this system, demanding a change of paradigm. At the same time, the scientific toolbox used for risk assessment is continuously enriched by the development of "New Approach Methodologies" (NAMs). While this term does not define the age or the state of readiness of the innovation, it covers a wide range of methods, including quantitative structure-activity relationship (QSAR) predictions, high-throughput screening (HTS) bioassays, omics applications, cell cultures, organoids, microphysiological systems (MPS), machine learning models and artificial intelligence (AI). In addition to promising faster and more efficient toxicity testing, NAMs have the potential to fundamentally transform today's regulatory work by allowing more human-relevant decision-making in terms of both hazard and exposure assessment. Yet, several obstacles hamper a broader application of NAMs in current regulatory risk assessment. Constraints in addressing repeated-dose toxicity, with particular reference to the chronic toxicity, and hesitance from relevant stakeholders, are major challenges for the implementation of NAMs in a broader context. Moreover, issues regarding predictivity, reproducibility and quantification need to be addressed and regulatory and legislative frameworks need to be adapted to NAMs. The conceptual perspective presented here has its focus on hazard assessment and is grounded on the main findings and conclusions from a symposium and workshop held in Berlin in November 2021. It intends to provide further insights into how NAMs can be gradually integrated into chemical risk assessment aimed at protection of human health, until eventually the current paradigm is replaced by an animal-free "Next Generation Risk Assessment" (NGRA).

A metabolomics approach to reveal the mechanism of developmental toxicity in zebrafish embryos exposed to 6-propyl-2-thiouracil.

A crucial component of a substance registration and regulation is the evaluation of human prenatal developmental toxicity. Current toxicological tests are based on mammalian models, but these are costly, time consuming and may pose ethical concerns. The zebrafish embryo has evolved as a promising alternative model to study developmental toxicity. However, the implementation of the zebrafish embryotoxicity test is challenged by lacking information on the relevance of observed morphological alterations in fish for human developmental toxicity. Elucidating the mechanism of toxicity could help to overcome this limitation. Through LC-MS/MS and GC-MS metabolomics, we investigated whether changes to the endogenous metabolites can indicate pathways associated with developmental toxicity. To this aim, zebrafish embryos were exposed to different concentrations of 6-propyl-2-thiouracil (PTU), a compound known to induce developmental toxicity. The reproducibility and the concentration-dependence of the metabolome response and its association with morphological alterations were studied. Major morphological findings were reduced eye size, and other craniofacial anomalies; major metabolic changes included increased tyrosine, pipecolic acid and lysophosphatidylcholine levels, decreased methionine levels, and disturbance of the 'Phenylalanine, tyrosine and tryptophan biosynthesis' pathway. This pathway, and the changes in tyrosine and pipecolic acid levels could be linked to the mode of action of PTU, i.e., inhibition of thyroid peroxidase (TPO). The other findings suggested neurodevelopmental impairments. This proof-of-concept study demonstrated that metabolite changes in zebrafish embryos are robust and provide mechanistic information associated with the mode of action of PTU.

AI for predicting chemical-effect associations at the chemical universe level-deepFPlearn.

Many chemicals are present in our environment, and all living species are exposed to them. 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 feed-forward 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.

A risk based assessment approach for chemical mixtures from wastewater treatment plant effluents.

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.

Disentangling multiple chemical and non-chemical stressors in a lotic ecosystem using a longitudinal approach.

Meeting ecological and water quality standards in lotic ecosystems is often failed due to multiple stressors. However, disentangling stressor effects and identifying relevant stressor-effect-relationships in complex environmental settings remain major challenges. By combining state-of-the-art methods from ecotoxicology and aquatic ecosystem analysis, we aimed here to disentangle the effects of multiple chemical and non-chemical stressors along a longitudinal land use gradient in a third-order river in Germany. We distinguished and evaluated four dominant stressor categories along this gradient: (1) Hydromorphological alterations: Flow diversity and substrate diversity correlated with the EU-Water Framework Directive based indicators for the quality element macroinvertebrates, which deteriorated at the transition from near-natural reference sites to urban sites. (2) Elevated nutrient levels and eutrophication: Low to moderate nutrient concentrations together with complete canopy cover at the reference sites correlated with low densities of benthic algae (biofilms). We found no more systematic relation of algal density with nutrient concentrations at the downstream sites, suggesting that limiting concentrations are exceeded already at moderate nutrient concentrations and reduced shading by riparian vegetation. (3) Elevated organic matter levels: Wastewater treatment plants (WWTP) and stormwater drainage systems were the primary sources of bioavailable dissolved organic carbon. Consequently, planktonic bacterial production and especially extracellular enzyme activity increased downstream of those effluents showing local peaks. (4) Micropollutants and toxicity-related stress: WWTPs were the predominant source of toxic stress, resulting in a rapid increase of the toxicity for invertebrates and algae with only one order of magnitude below the acute toxic levels. This toxicity correlates negatively with the contribution of invertebrate species being sensitive towards pesticides (SPEARpesticides index), probably contributing to the loss of biodiversity recorded in response to WWTP effluents. Our longitudinal approach highlights the potential of coordinated community efforts in supplementing established monitoring methods to tackle the complex phenomenon of multiple stress.

Prospects and challenges of multi-omics data integration in toxicology.

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.

A Self-Organizing Map of the Fathead Minnow Liver Transcriptome to Identify Consistent Toxicogenomic Patterns across Chemical Fingerprints.

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.

Transcriptome-level effects of the model organic pollutant phenanthrene and its solvent acetone in three amphipod species.

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.

Comparison between transcriptomic responses to short-term stress exposures of a common Holarctic and endemic Lake Baikal amphipods.

BACKGROUND: Lake Baikal is one of the oldest freshwater lakes and has constituted a stable environment for millions of years, in stark contrast to small, transient bodies of water in its immediate vicinity. A highly diverse endemic endemic amphipod fauna is found in one, but not the other habitat. We ask here whether differences in stress response can explain the immiscibility barrier between Lake Baikal and non-Baikal faunas. To this end, we conducted exposure experiments to increased temperature and the toxic heavy metal cadmium as stressors. RESULTS: Here we obtained high-quality de novo transcriptome assemblies, covering mutiple conditions, of three amphipod species, and compared their transcriptomic stress responses. Two of these species, Eulimnogammarus verrucosus and E. cyaneus, are endemic to Lake Baikal, while the Holarctic Gammarus lacustris is a potential invader. CONCLUSIONS: Both Baikal species possess intact stress response systems and respond to elevated temperature with relatively similar changes in their expression profiles. G. lacustris reacts less strongly to the same stressors, possibly because its transcriptome is already perturbed by acclimation conditions.

Environmental risk or benefit? Comprehensive risk assessment of groundwater treated with nano Fe0-based Carbo-Iron®.

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.

Map and model-moving from observation to prediction in toxicogenomics.

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.

Combined effects of environmental xeno-estrogens within multi-component mixtures: Comparison of in vitro human- and zebrafish-based estrogenicity bioassays.

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 zfERß2 (ZELHß2 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 ZELHß2 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 ZELHß2 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.

Can Environmentally Relevant Neuroactive Chemicals Specifically Be Detected with the Locomotor Response Test in Zebrafish Embryos?

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.

Solid-phase extraction as sample preparation of water samples for cell-based and other in vitro bioassays.

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.

Mixture effects in samples of multiple contaminants - An inter-laboratory study with manifold bioassays.

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.

Characterization and risk assessment of seasonal and weather dynamics in organic pollutant mixtures from discharge of a separate sewer system.

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.

Screening of Pesticide and Biocide Patterns As Risk Drivers in Sediments of Major European River Mouths: Ubiquitous or River Basin-Specific Contamination?

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.