High-throughput concentration-response analysis for omics datasets

Strengthening Causal Inference in Exposomics Research: Application of Genetic Data and Methods

Advances in technologies to measure a broad set of exposures have led to a range of exposome research efforts. Yet, these efforts have insufficiently integrated methods that incorporate genetic data to strengthen causal inference, despite evidence that many exposome-associated phenotypes are heritable. Objective: We demonstrate how integration of methods and study designs that incorporate genetic data can strengthen causal inference in exposomics research by helping address six challenges: reverse causation and unmeasured confounding, comprehensive examination of phenotypic effects, low efficiency, replication, multilevel data integration, and characterization of tissue-specific effects. Examples are drawn from studies of biomarkers and health behaviors, exposure domains where the causal inference methods we describe are most often applied. Discussion: Technological, computational, and statistical advances in genotyping, imputation, and analysis, combined with broad data sharing and cross-study collaborations, offer multiple opportunities to strengthen causal inference in exposomics research. Full application of these opportunities will require an expanded understanding of genetic variants that predict exposome phenotypes as well as an appreciation that the utility of genetic variants for causal inference will vary by exposure and may depend on large sample sizes. However, several of these challenges can be addressed through international scientific collaborations that prioritize data sharing. Ultimately, we anticipate that efforts to better integrate methods that incorporate genetic data will extend the reach of exposomics research by helping address the challenges of comprehensively measuring the exposome and its health effects across studies, the life course, and in varied contexts and diverse populations. https://doi.org/10.1289/EHP9098.

Metabolomics insight into the influence of environmental factors in responses of freshwater biofilms to the model herbicide diuron

Freshwater biofilms have been increasingly used during the last decade in ecotoxicology due to their ecological relevance to assess the effect(s) of environmental stress at the community level. Despite growing knowledge about the effect of various stressors on the structure and the function of these microbial communities, a strong research effort is still required to better understand their response to chemical stress and the influence of environmental stressors in this response. To tackle this challenge, untargeted metabolomics is an approach of choice because of its capacity to give an integrative picture of the exposure to multiple stress and associated effect as well as identifying the molecular pathways involved in these responses. In this context, the present study aimed to explore the use of an untargeted metabolomics approach to unravel at the molecular/biochemical level the response of the whole biofilm to chemical stress and the influence of various environmental factors in this response. To this end, archived high-resolution mass spectrometry data from previous experiments at our laboratory on the effect of the model photosynthesis inhibitor diuron on freshwater biofilm were investigated by using innovative solutions for OMICs data (e.g., DRomics) and more usual chemometric approaches (multivariate and univariate statistical analyses). The results showed a faster (1 min) and more sensitive response of the metabolome to diuron than usual functional descriptors, including photosynthesis. Also, the metabolomics response to diuron resulted from metabolites following various trends (increasing, decreasing, U/bell shape) along increasing concentration and time. This metabolomics response was influenced by the temperature, photoperiod, and flow. A focus on a plant-specific omega-3 (eicosapentaenoic acid) playing a key role in the trophic chain highlighted the potential relevance of metabolomics approach to establish the link between molecular alteration and ecosystem structure/functioning impairment but also how complex is the response and the influence of all the tested factors on this response at the metabolomics level. Altogether, our results underline that more fundamental researches are needed to decipher the metabolomics response of freshwater biofilm to chemical stress and its link with physiological, structural, and functional responses toward the unraveling of adverse outcome pathways (AOP) for key ecosystem functions (e.g., primary production).

Evaluation of transcriptional biomarkers using a high-resolution regression approach: Concentration-dependence of selected transcripts in copper-exposed freshwater mussels (Anodonta anatina)

We tested concentration-dependence of selected gene transcripts (cat, gst, hsp70, hsp90, mt and sod) for evaluation as biomarkers of chemical stress. Contrary to the common approach of factorial designs and few exposure concentrations, we used regression across a high-resolution concentration series. Specifically, freshwater mussels (Anodonta anatina) were acutely (96 h) exposed to Cu (13 nominal concentrations, measuring 0.13-1 600 µg/L), and transcripts were measured by RT-qPCR. In digestive glands, cat, hsp90 and mt decreased with water Cu (p < 0.05), but response magnitudes saturated at < 2-fold decreases. In gills, gst, hsp70, hsp90 and mt increased with water Cu (p < 0.05). While hsp70, hsp90 and mt exceeded 2-fold increases within the exposure range, high Cu concentrations were required (38-160 µg/L). Although gill responses were generally more robust compared to digestive glands, overall small response magnitudes and moderate sensitivity may set limit for potential application as general biomarkers of chemical stress.

Dose-response metabolomics and pathway sensitivity to map molecular cartography of bisphenol A exposure

In the toxicological regime, the toxicological endpoint and its dose-response relationship are two of the most prominent characters in conducting a risk assessment for chemical exposure. Systems biological methods have been used to comprehensively characterize the impact of toxicants on the biochemical pathways. However, the majority of the current studies are only based on single-dose, and limited information can be extrapolated to other doses from these experiments, regardless of the sensitivity of each endpoint. This study aims to understand the dose-response metabolite dysregulation pattern and metabolite sensitivity at the system-biological level. Here, we applied bisphenol A (BPA), an endocrine-disrupting chemical (EDC), as the model chemical. We first employed the global metabolomics method to characterize the metabolome of breast cancer cells (MCF-7) upon exposure to different doses (0, 20, 50, and 100 µM) of BPA. The dysregulated features with a clear dose-response relationship were also effectively picked up with an R-package named TOXcms. Overall, most metabolites were dysregulated by showing a significant dose-dependent behaviour. The results suggested that BPA exposure greatly perturbed purine metabolism and pyrimidine metabolism. Interestingly, most metabolites within the purine metabolism were described as a biphasic dose-response relationship. With the established dose-response relationship, we were able to fully map the metabolite cartography of BPA exposure within a wide range of concentrations and observe some unique patterns. Furthermore, an effective concentration of certain fold changes (e.g., EC₊₁₀ means the dose at which metabolite is 10% upregulated) and metabolite sensitivity were defined and introduced to this dose-response omics information. The result showed that the purine metabolism pathway is the most venerable target of BPA, which can be a potential endogenous biomarker for its exposure. Overall, this study applied the dose-response metabolomics method to fully understand the biochemical pathway disruption of BPA treatment at different doses. Both dose-response omics strategy and metabolite sensitivity analysis can be further considered and emphasized in future chemical risk assessments.

Dose-dependent responses of metabolism and tissue injuries in clam Ruditapes philippinarum after subchronic exposure to cadmium

Marine cadmium (Cd) pollution has been globally occurring, which creates a pressing need to characterize toxicological effects and develop biomarkers for Cd. However, the dose-response relationships challenge toxicity characterization and biomarkers selection. Metabolic processes have been frequently targeted by Cd. In this work, we investigated the dose-dependent effects of Cd on metabolic endpoints in whole soft tissues as well as gill and hepatopancreas injuries in clam Ruditapes philippinarum, aiming to better understand the metabolic responses and develop biomarkers. Nuclear magnetic resonance (NMR)-based metabolomic analysis was conducted on clam whole soft tissues to identify metabolites. The enzymes and metabolites associated with tricarboxylic acid (TCA) cycle, glycolysis, and oxidative phosphorylation showed both monotonic and non-monotonic curves with the increase of Cd dose. In details, glutamine, glucose-1-phosphate, hexokinase (HK), and citrate synthase (CS) presented monotonic decreases with the increase of Cd dose, among which glutamine and CS were preferable biomarkers to Cd exposure based on lower benchmark dose (BMD) values. The monotonic decreases of HK and CS activities suggested Cd exposure potentially disrupted glycolysis and TCA cycle via inhibiting rate-limiting enzymes. In contrast, the non-monotonic responses of succinate dehydrogenase (SDH), alanine aminotransferase (ALT), and their substrates (succinate and alanine) were approximate to U- or J-shaped curves, suggesting the adaptive strategy of metabolic responses to different degrees of Cd stress, like induction of anaerobiosis as energy compensation. Especially, the alterations of succinate and SDH presented typical hormetic dose-response curves. What is more, clam hepatopancreas was more sensitive to Cd than gill in terms of injury occurrence. Overall, characterization of dose-dependent effect of Cd on metabolism and tissue injuries provides a new insight into understanding the metabolic adaptation in marine clams and risk assessment of Cd pollution.

Cross-Model Comparison of Transcriptomic Dose-Response of Short-Chain Chlorinated Paraffins

Short-chain chlorinated paraffins (SCCPs) have attracted attention because of their toxicological potential in humans and wildlife at environmentally relevant doses. However, limited information is available regarding mechanistic differences across species in terms of the biological pathways that are impacted by SCCP exposure. Here, a concentration-dependent reduced human transcriptome (RHT) approach was conducted to evaluate 15 SCCPs in HepG2 cells and compared with our previous results using a reduced zebrafish transcriptome (RZT) approach in zebrafish embryos (ZFEs). Generally, SCCPs induced a broader suite of biological pathways in ZFEs than HepG2 cells, and all of the 15 SCCPs were more potent in HepG2 cells compared to ZFEs. Despite these general differences, the transcriptional potency of SCCPs in both model systems showed a significant linear relationship (p = 0.0017, r² = 0.57), and the average ratios of transcriptional potency for each SCCP in RZT to that in RHT were ∼100,000. C₁₀H₁₄Cl₈ was the most potent SCCP, while C₁₀H₁₇Cl₅ was the least potent in both ZFEs and HepG2 cells. An adverse outcome pathway network-based analysis demonstrated model-specific responses, such as xenobiotic metabolism that may be mediated by different nuclear receptor-mediated pathways between HepG2 cells (e.g., CAR and AhR activation) and ZFEs (e.g., PXR activation). Moreover, induced transcriptional changes in ZFEs associated with pathways and molecular initiating events (e.g., activation of nicotinic acetylcholine receptor) suggest that SCCPs may disrupt neural development processes. The cross-model comparison of concentration-dependent transcriptomics represents a promising approach to assess and prioritize SCCPs.

Integrated Proteomic and Metabolomic Analysis of the Testes Characterizes BDE-47-Induced Reproductive Toxicity in Mice

A representative congener of polybrominated diphenyl ethers in the environment, 2,2',4,4'-tetrabromodiphenyl ether (BDE-47), is associated with male reproductive toxicity, yet the underlying mechanisms remain largely unclear. In this study, mice were administered environmentally relevant concentrations of BDE-47 for six weeks. Histopathological observations showed that BDE-47 induced inflammatory reactions and damaged the testes. By conducting an integrated proteomic and metabolomic analysis coupled with a bioinformatic analysis using ingenuity pathway analysis (IPA) methods, we found that BDE-47 mainly affected the molecules involved in free radical scavenging, cell death and survival, neurological disease, and inflammatory response. IPA canonical pathways showed inflammatory and apoptosis pathways, including hepatic fibrosis/hepatic stellate cell activation, the GP6 signaling pathway, tight junction signaling, acute phase response signaling, LXR/RXR activation, unfolded protein response, and FXR/RXR activation, which are related to male reproductive toxicity. Key transcriptional regulator networks were activated via a focus on upstream regulator analysis. The expression of MYC and Clu as the core transcriptional factor and targeted protein, respectively, was verified. It is further proposed that MYC may contribute to the etiology of male reproductive toxicity. These findings will improve our understanding of the mechanisms responsible for BDE-47-induced male reproductive toxicity, which may promote the discovery of useful biomarkers indicative of BDE-47 exposure.

Transcriptome-Wide Prediction and Measurement of Combined Effects Induced by Chemical Mixture Exposure in Zebrafish Embryos

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.

Global characterization of dose-dependent effects of cadmium in clam Ruditapes philippinarum

Cadmium (Cd) is being frequently detected in marine organisms. However, dose-dependent effects of Cd challenged unraveling the toxicological mechanisms of Cd to marine organisms and developing biomarkers. Here, the dose-dependent effects of Cd on clams Ruditapes philippinarum following exposure to 5 doses of Cd (3, 9, 27, 81, 243 μg/L) were investigated using benchmark dose (BMD) method. By model fitting, calculation of BMD values was performed on transcriptomic profiles, metals concentrations, and antioxidant indices. Cd exposure induced not only significant Cd accumulation in clams, but also marked alterations of essential metals such as Ca, Cu, Zn, Mn, and Fe. Gene regulation posed little influence on essential metal homeostasis, indicated by poor enrichment of differentially expressed genes (DEGs) associated with metal binding and metal transport in lower concentrations of Cd-treated groups. BMD analysis on biological processes and pathways showed that peptide cross-linking was the most sensitive biological process to Cd exposure, followed by focal adhesion, ubiquitin mediated proteolysis, and apoptosis. Occurrence of apoptosis was also confirmed by TUENL-positive staining in gills and hepatopancreas of clams treated with Cd. Furthermore, many DEGs, such as transglutaminases (TGs), metallothionein (MT), STEAP2-like and laccase, which presented linear or monotonic curves and relatively low BMD values, were potentially preferable biomarkers in clams to Cd. Overall, BMD analysis on transcriptomic profiles, metals concentrations and biochemical endpoints unraveled the sensitiveness of key events in response to Cd treatments, which provided new insights in exploring the toxicological mechanisms of Cd in clams as well as biomarker selection.

Toxicological Effects of Inorganic Nanoparticle Mixtures in Freshwater Mussels

The toxicological effects of nanoparticles mixtures in aquatic organisms are poorly understood. The purpose of this study was to examine the tissue metal loadings and sublethal effects of silver (nAg), cerium oxide (nCeO), copper oxide (nCuO) and zinc oxide (nZnO) nanoparticles individually at 50 µg/L and in two mixtures to freshwater mussels Dreissena bugensis. The mixtures consisted of 12.5 µg/L of each nanoparticle (Mix50) and 50 µg/L of each nanoparticles (Mix200). After a 96-h exposure period, mussels were analyzed for morphological changes, air time survival, bioaccumulation, inflammation (cyclooxygenase or COX activity), lipid peroxidation (LPO), DNA strand breaks, labile Zn, acetylcholinesterase (AChE) and protein–ubiquitin levels. The data revealed that mussels accumulated the nanoparticles with nCeO and nAg were the least and most bioavailable, respectively. Increased tissue metal loadings were observed for nCeO and nCuO in mixtures, while no mixture effects were observed for nAg and nZnO. The weight loss during air emersion was lower in mussels exposed to nCuO alone but not by the mixture. On the one hand, labile Zn levels was increased with nZnO but returned to control values with the Mix50 and Mix200, suggesting antagonism. On the other hand, DNA strand breaks were reduced for both mixtures compared to controls or to the nanoparticles individually, suggesting potentiation of effects. The same was found for protein–ubiquitin levels, which were decreased by nCeO and nCuO alone but not when in mixtures, which increased their levels. In conclusion, the data revealed that the behavior and effects of nanoparticles were influenced by other nanoparticles where antagonist and potentiation interactions were identified.

Prioritizing phthalate esters (PAEs) using experimental in vitro/vivo toxicity assays and computational in silico approaches

Phthalate esters (PAEs) pose prominent ecological risks owing to their multiplex toxicity potentials and ubiquitous detection in the environment. Therefore, this study aims to prioritize the individual and mixtures of six PAEs based on their toxicological implications using in vitro and vivo models exposed at environmentally relevant concentrations. Results were further confirmed using in silico Combination index (CI) and Independent action (IA), and molecular docking models. Among PAEs, DEHP revealed prominent in vitro/vivo toxicity followed by DEP, DBP, and DMP. Importantly, binary mixtures particularly C2-C6 and C11-C15 exhibited greater developmental toxicity, apoptosis, and perturbed the HPG pathway. The CI and IA models forecasted antagonistic and additive effects at Fa = 0.5 and Fa = 0.9 using in vitro Acinetobacter sp. Tox2. Conversely, in zebrafish, the IA model predicted mixture effects in the following order: additive > synergistic > antagonistic on the regulation of the HPG pathway, which was consistent with experimental results from Acridine Orange (AO) staining and apoptosis gene expression. Molecular docking for estrogen receptors (ERα, ERβ) revealed the highest binding energy scores for DEHP, compared to other PAEs. In short, our findings confirm that individual and mixtures of PAEs behave as xenoestrogens in the freshwater ecosystem with DEHP as a priority compound.

A multi-omics concentration-response framework uncovers novel understanding of triclosan effects in the chlorophyte Scenedesmus vacuolatus

In aquatic ecosystems, the biocide triclosan represents a hazard for the non-target microalgae. So far, algal responses were mainly investigated at apical levels hampering the acquisition of a holistic view on primary, adaptive, and compensatory stress responses. We assessed responses of the chlorophyte Scenedesmus vacuolatus to triclosan at apical (growth, photosynthesis) and molecular (transcriptome, metabolome) levels for comparative pathway sensitivity analysis. For each responsive signal (contigs, metabolites), a concentration-response curve was modeled and effect concentrations were calculated leading to the setting of cumulative sensitivity distributions. Molecular responses showed higher sensitivity than apical observations. The functional annotation of contigs and metabolites revealed 118 metabolic pathways putatively impaired by triclosan, highlighting a wide repercussion on the algal metabolism. Metabolites involved in the lipid metabolism showed decreasing trends along the concentration gradient and a globally highest sensitivity, pointing to the primary target of triclosan. The pathways involved in xenobiotic degradation and membrane transporters were mainly regulated in the transcriptome with increasing response trends comprising compensatory responses. The suggested novel approach, combining apical and multi-omics analyses in a concentration-response framework improves mechanistic understanding and mode of action analysis on non-targeted organisms and is suggested to better implement high-throughput multi-omics data in environmental risk assessment.

Concentration-dependent transcriptome of zebrafish embryo for environmental chemical assessment

Mechanistic information is essential to screen and predict the adverse effects of a large number of chemicals during early-life exposure. Concentration-dependent omics can capture the extent of perturbations of biological pathways or processes and provide information on the mechanism of toxicity. However, the application of concentration-dependent transcriptome to assess the developmental toxicity of environmental chemicals is still limited. Here, twelve chemicals representing five different modes of action (MOAs) were tested by the concentration-dependent reduced zebrafish transcriptome approach (CRZT) in combination with a phenotype-based high content screen (PHCS). The responsiveness, sensitivity and mechanistic differentiation of CRZT were validated in comparison with PHCS. First, PHCS identified 10 chemicals with obvious embryotoxicity (LD₅₀ range: 2.11-70.68 μM), while the potencies of the biological pathways perturbed by 12 chemicals (POD_path20 range: 0.002-2.1 μM) were demonstrated by CRZT. Second, although the potency of the transcriptome perturbations was positively correlated with lethality (LD₅₀) (R² = 0.64, P-value < 0.05) for most tested chemicals, BbF was non-embryotoxic but was the most potent on the perturbance of biological pathways. Finally, the profiles of the perturbed biological processes and the transcriptome potency (POD_path20) captured by CRZT could effectively classify most chemicals corresponding to their known MOAs. In summary, CRZT could significantly improve testing the developmental toxicity of environmental chemicals.

Individual and combined mechanistic toxicity of sulfonamides and their implications for ecological risk assessment in the Three Gorges Reservoir Area (TGRA), China

Sulfonamides (SAs) are conventional veterinary antibiotics that pose ecological risks in the aquatic environment. This study aims to evaluate the environmental concerns of SAs in the Three Gorges Reservoir Area (TGRA) and their toxicogenetic implications. Here, we employed various in vitro and in vivo bioassays to determine the combine toxicogenetic effects of SAs, which were further confirmed through applying Combination Index (CI) and Independent Action (IA) models. Among the investigated SAs, sulfamethoxazole (SMX) appeared as the individual chemical with relatively high environmental effects and elevated in vitro and in vivo toxicity. Importantly, exposure to the binary mixtures of SAs induced higher developmental toxicity and significantly perturbed the detoxification pathway in zebrafish, compared to that of individual compound exposure. Moreover, the CI and IA models indicated greater synergistic effects of SAs binary mixtures as SMX-SMR, SMX-ST, and SPY-ST on the Acinetobacter sp. Tox2 at Fa = 0.5. Contrarily, IA model predicted the additive, antagonistic and synergistic effects of SAs mixtures on the transcriptional responses of detoxification pathways in zebrafish, implying the different mode of actions (MoAs) for SAs to induce mixture toxicity in vivo. Thus, the nature of toxicological interactions of SAs should be considered while performing their ecological risk assessment.

A Review of Recent Advances in Benchmark Dose Methodology

In this review, recent methodological developments for the benchmark dose (BMD) methodology are summarized. Specifically, we introduce the advances for the main steps in BMD derivation: selecting the procedure for defining a BMD from a predefined benchmark response (BMR), setting a BMR, selecting a dose-response model, and estimating the corresponding BMD lower limit (BMDL). Although the last decade has shown major progress in the development of BMD methodology, there is still room for improvement. Remaining challenges are the implementation of new statistical methods in user-friendly software and the lack of consensus about how to derive the BMDL.

Use cases, best practice and reporting standards for metabolomics in regulatory toxicology

Metabolomics is a widely used technology in academic research, yet its application to regulatory science has been limited. The most commonly cited barrier to its translation is lack of performance and reporting standards. The MEtabolomics standaRds Initiative in Toxicology (MERIT) project brings together international experts from multiple sectors to address this need. Here, we identify the most relevant applications for metabolomics in regulatory toxicology and develop best practice guidelines, performance and reporting standards for acquiring and analysing untargeted metabolomics and targeted metabolite data. We recommend that these guidelines are evaluated and implemented for several regulatory use cases.

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.

DRomics: A Turnkey Tool to Support the Use of the Dose-Response Framework for Omics Data in Ecological Risk Assessment

Omics approaches (e.g., transcriptomics, metabolomics) are promising for ecological risk assessment (ERA) since they provide mechanistic information and early warning signals. A crucial step in the analysis of omics data is the modeling of concentration-dependency which may have different trends including monotonic (e.g., linear, exponential) or biphasic (e.g., U shape, bell shape) forms. The diversity of responses raises challenges concerning detection and modeling of significant responses and effect concentration (EC) derivation. Furthermore, handling high-throughput data sets is time-consuming and requires effective and automated processing routines. Thus, we developed an open source tool (DRomics, available as an R-package and as a web-based service) which, after elimination of molecular responses (e.g., gene expressions from microarrays) with no concentration-dependency and/or high variability, identifies the best model for concentration-response curve description. Subsequently, an EC (e.g., a benchmark dose) is estimated from each curve, and curves are classified based on their model parameters. This tool is especially dedicated to manage data obtained from an experimental design favoring a great number of tested doses rather than a great number of replicates and also to handle properly monotonic and biphasic trends. The tool finally provides restitution for a table of results that can be directly used to perform ERA approaches.

Deciphering the Combined Effects of Environmental Stressors on Gene Transcription: A Conceptual Approach

The use of classical mixture toxicity models to predict the combined effects of environmental stressors based on toxicogenomics (OMICS) data is still in its infancy. Although several studies have made attempts to implement mixture modeling in OMICS analysis to understand the low-dose interactions of stressors, it is not clear how interactions occur at the molecular level and how results generated from such approaches can be better used to inform future studies and cumulative hazard assessment of multiple stressors. The present work was therefore conducted to propose a conceptual approach for combined effect assessment using global gene expression data, as illustrated by a case study on assessment of combined effects of gamma radiation and depleted uranium (DU) on Atlantic salmon ( Salmo salar). Implementation of the independent action (IA) model in reanalysis of a previously published microarray gene expression dataset was performed to describe gene expression patterns of combined effects and identify key gene sets and pathways that were relevant for understanding the interactive effects of these stressors. By using this approach, 3120 differentially expressed genes (DEGs) were found to display additive effects, whereas 279 (273 synergistic, 6 antagonistic) were found to deviate from additivity. Functional analysis further revealed that multiple toxicity pathways, such as oxidative stress responses, cell cycle regulation, lipid metabolism, and immune responses were enriched by DEGs showing synergistic gene expression. A key toxicity pathway of DNA damage leading to enhanced tumorigenesis signaling is highlighted and discussed in detail as an example of how to take advantage of the approach. Furthermore, a conceptual workflow describing the integration of combined effect modeling, OMICS analysis, and bioinformatics is proposed. The present study presents a conceptual framework for utilizing OMICS data in combined effect assessment and may provide novel strategies for dealing with data analysis and interpretation of molecular responses of multiple stressors.

The Transcriptome of the Zebrafish Embryo After Chemical Exposure: A Meta-Analysis

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.

A Reduced Transcriptome Approach to Assess Environmental Toxicants Using Zebrafish Embryo Test

Omics approaches can monitor responses and alterations of biological pathways at genome-scale, which are useful to predict potential adverse effects by environmental toxicants. However, high throughput application of transcriptomics in chemical assessment is limited due to the high cost and lack of "standardized" toxicogenomic methods. Here, a reduced zebrafish transcriptome (RZT) approach was developed to represent the whole transcriptome and to profile bioactivity of chemical and environmental mixtures in zebrafish embryo. RZT gene set of 1637 zebrafish Entrez genes was designed to cover a wide range of biological processes, and to faithfully capture gene-level and pathway-level changes by toxicants compared with the whole transcriptome. Concentration-response modeling was used to calculate the effect concentrations (ECs) of DEGs and corresponding molecular pathways. To validate the RZT approach, quantitative analysis of gene expression by RNA-ampliseq technology was used to identify differentially expressed genes (DEGs) at 32 hpf following exposure to seven serial dilutions of reference chemical BPA (10-10E^-5μM) or each of four water samples ranging from wastewater to drinking water (relative enrichment factors 10-6.4 × 10^-4). The RZT-ampliseq-embryo approach was both sensitive and able to identify a wide spectrum of biological activities associated with BPA exposure. Water quality was benchmarked based on the sensitivity distribution curve of biological pathways detected using RZT-ampliseq-embryo. Finally, the most sensitive biological pathways were identified, including those linked with adverse reproductive outcomes, genotoxicity and development outcomes. RZT-ampliseq-embryo approach provides an efficient and cost-effective tool to prioritize toxicants based on responsiveness of biological pathways.

Zebrafish biosensor for toxicant induced muscle hyperactivity

Robust and sensitive detection systems are a crucial asset for risk management of chemicals, which are produced in increasing number and diversity. To establish an in vivo biosensor system with quantitative readout for potential toxicant effects on motor function, we generated a transgenic zebrafish line TgBAC(hspb11:GFP) which expresses a GFP reporter under the control of regulatory elements of the small heat shock protein hspb11. Spatiotemporal hspb11 transgene expression in the musculature and the notochord matched closely that of endogenous hspb11 expression. Exposure to substances that interfere with motor function induced a dose-dependent increase of GFP intensity beginning at sub-micromolar concentrations, while washout of the chemicals reduced the level of hspb11 transgene expression. Simultaneously, these toxicants induced muscle hyperactivity with increased calcium spike height and frequency. The hspb11 transgene up-regulation induced by either chemicals or heat shock was eliminated after co-application of the anaesthetic MS-222. TgBAC(hspb11:GFP) zebrafish embryos provide a quantitative measure of muscle hyperactivity and represent a robust whole organism system for detecting chemicals that affect motor function.

Identification and Characterization of Androgen-Responsive Genes in Zebrafish Embryos

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.

Metabolic Effect Level Index Links Multivariate Metabolic Fingerprints to Ecotoxicological Effect Assessment

A major goal of ecotoxicology is the prediction of adverse outcomes for populations from sensitive and early physiological responses. A snapshot of the physiological state of an organism can be provided by metabolic fingerprints. However, to inform chemical risk assessment, multivariate metabolic fingerprints need to be converted to readable end points suitable for effect estimation and comparison. The concentration- and time-dependent responsiveness of metabolic fingerprints to the PS-II inhibitor isoproturon was investigated by use of a Myriophyllum spicatum bioassay. Hydrophilic and lipophilic leaf extracts were analyzed with gas chromatography-mass spectrometry (GC-MS) and preprocessed with XCMS. Metabolic changes were aggregated in the quantitative metabolic effect level index (MELI), allowing effect estimation from Hill-based concentration-response models. Hereby, the most sensitive response on the concentration scale was revealed by the hydrophilic MELI, followed by photosynthetic efficiency and, 1 order of magnitude higher, by the lipophilic MELI and shoot length change. In the hydrophilic MELI, 50% change compares to 30% inhibition of photosynthetic efficiency and 10% inhibition of dry weight change, indicating effect development on different response levels. In conclusion, aggregated metabolic fingerprints provide quantitative estimates and span a broad response spectrum, potentially valuable for establishing adverse outcome pathways of chemicals in environmental risk assessment.