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.
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.
- MeSH
- biotest metody MeSH
- buněčné linie MeSH
- chemické látky znečišťující vodu * MeSH
- kvalita vody * MeSH
- lidé MeSH
- monitorování životního prostředí metody MeSH
- řeky MeSH
- ryby MeSH
- voda MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Responsive genes for fish embryos have been identified so far for some endocrine pathways but not for androgens. Using transcriptome analysis and multiple concentration-response modeling, we identified putative androgen-responsive genes in zebrafish embryos exposed to 0.05-5000 nM 11-ketotestosterone for 24 h. Four selected genes with sigmoidal concentration-dependent expression profiles (EC50 = 6.5-30.0 nM) were characterized in detail. The expression of cyp2k22 and slco1f4 was demonstrated in the pronephros; lipca was detected in the liver, and sult2st3 was found in the olfactory organs and choroid plexus. Their expression domains, the function of human orthologs, and a pathway analysis suggested a role of these genes in the metabolism of hormones. Hence, it was hypothesized that they were induced to compensate for elevated hormone levels. The induction of sult2st3 and cyp2k22 by 11-ketotestosterone was repressed by co-exposure to the androgen receptor antagonist nilutamide supporting a potential androgen receptor mediated regulation. Sensitivity (expressed as EC50 values) of sult2st3 and cyp2k22 gene expression induction after exposure to other steroidal hormones (11-ketotestosterone ∼ testosterone > progesterone > cortisol > ethinylestradiol) correlated with their known binding affinities to zebrafish androgen receptor. Hence, these genes might represent potential markers for screening of androgenic compounds in the zebrafish embryo.
- MeSH
- androgenní receptory genetika MeSH
- androgeny genetika metabolismus MeSH
- antagonisté androgenů farmakologie MeSH
- dánio pruhované embryologie genetika MeSH
- embryo nesavčí účinky léků MeSH
- ethinylestradiol farmakologie MeSH
- exprese genu účinky léků MeSH
- imidazolidiny farmakologie MeSH
- lidé MeSH
- stanovení celkové genové exprese MeSH
- testosteron analogy a deriváty farmakologie MeSH
- vývojová regulace genové exprese účinky léků MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- ženské pohlaví MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
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.
- MeSH
- dánio pruhované růst a vývoj metabolismus MeSH
- ekosystém * MeSH
- embryo nesavčí účinky léků metabolismus MeSH
- genomika * MeSH
- látky znečišťující životní prostředí toxicita MeSH
- lineární modely MeSH
- metabolomika * MeSH
- rychlé screeningové testy MeSH
- sekvenční analýza hybridizací s uspořádaným souborem oligonukleotidů MeSH
- tetrachlorethylen toxicita MeSH
- transkriptom účinky léků MeSH
- zvířata MeSH
- Check Tag
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
