Transcriptome-Wide Prediction and Measurement of Combined Effects Induced by Chemical Mixture Exposure in Zebrafish Embryos
2021; National Institute of Environmental Health Sciences; Volume: 129; Issue: 4 Linguagem: Inglês
10.1289/ehp7773
ISSN1552-9924
AutoresAndreas Schüttler, Gianina Jakobs, J.M. Fix, Martin Krauß, Janet Krüger, David Leuthold, Rolf Altenburger, Wibke Busch,
Tópico(s)Molecular Biology Techniques and Applications
ResumoVol. 129, No. 4 ResearchOpen AccessTranscriptome-Wide Prediction and Measurement of Combined Effects Induced by Chemical Mixture Exposure in Zebrafish Embryosis companion ofMoving toward the Real World: Zebrafish Transcript Map Predicts Mixture Effects Using Single-Compound Data A. Schüttler, G. Jakobs, J.M. Fix, M. Krauss, J. Krüger, D. Leuthold, R. Altenburger, and W. Busch A. Schüttler Address correspondence to A. Schüttler, Federal Institute of Hydrology, Am Mainzer Tor 1, 56068 Koblenz, Germany. Email: E-mail Address: [email protected]; or W. Busch, Department of Bioanalytical Ecotoxicology, Helmholtz Centre for Environmental Research–UFZ, Permoserstr. 15, 04318 Leipzig, Germany. Email: E-mail Address: [email protected] Department Bioanalytical Ecotoxicology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany Institute for Environmental Research, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany , G. Jakobs Department Bioanalytical Ecotoxicology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany , J.M. Fix Department Bioanalytical Ecotoxicology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany , M. Krauss Department Effect-Directed Analysis, UFZ, Leipzig, Germany , J. Krüger Department Bioanalytical Ecotoxicology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany , D. Leuthold Department Bioanalytical Ecotoxicology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany , R. Altenburger Department Bioanalytical Ecotoxicology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany Institute for Environmental Research, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany , and W. Busch Address correspondence to A. Schüttler, Federal Institute of Hydrology, Am Mainzer Tor 1, 56068 Koblenz, Germany. Email: E-mail Address: [email protected]; or W. Busch, Department of Bioanalytical Ecotoxicology, Helmholtz Centre for Environmental Research–UFZ, Permoserstr. 15, 04318 Leipzig, Germany. Email: E-mail Address: [email protected] Department Bioanalytical Ecotoxicology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany Published:7 April 2021CID: 047006https://doi.org/10.1289/EHP7773Cited by:1AboutSectionsPDF Supplemental Materials ToolsDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InRedditEmail AbstractBackground: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/EHP7773IntroductionUnderstanding the combined effects of substances that occur due to mixture exposures is of long-standing interest in the biosciences. While physiologists and pharmacologists search to optimize intended drug activity by combination therapy (Kuhn-Nentwig et al. 2019; Zimmer et al. 2016), toxicologists seek to safeguard against the unintended combined effects concerning adverse outcomes in humans (Goodson et al. 2015; Jiang et al. 2018) and environmental organisms (Cedergreen 2014) resulting from mixture exposure. Society demands the inclusion of mixtures in chemical hazard and risk assessment (EC 2019). The European Commission in its European Green Deal, for example, calls for a regulatory framework that can reflect risks posed by the combined effects of multiple chemicals (EC 2019). The consideration of combined effects can also be found on the agenda of international policy advisory (Meek et al. 2011) and regulatory institutions (Rotter et al. 2018; Wegner et al. 2016).Environmental chemists have argued that multiple substances can occur in large varieties (Escher et al. 2020) and that resulting environmental exposures may be complex in composition and dynamic over time for individuals (Jiang et al. 2018). Thus, the exposure of humans and environmental organisms to mixtures of chemicals instead of to single compounds solely is rather the rule than the exception. Universal experimental evaluation of the effects resulting from each potentially occurring mixture exposure is not feasible. Thus, reasonable predictions of the effects of mixtures should be based on models that can use knowledge of the bioactivity of the compounds to estimate their combined effects in a mixture (Altenburger et al. 2013).Most existing models for combined effects are based on simplistic toxicodynamic assumptions of either simple similar or independent action (Altenburger et al. 2013). Among those, the concept of concentration addition (CA) has been shown to be rather useful to quantitatively predict the combined effects in short-term in vivo studies and the observations of gross changes, that is, apical effects such as death, growth impairment, or developmental dysfunction (reviewed by Kortenkamp et al. 2009). The situation is less clear, however, for sublethal, long-term effects and low-dose exposures. Here, the evidence is limited regarding the predictivity for outcomes. Moreover, qualitative interaction, that is, the emergence of novel outcomes not seen for the individual compounds is suggested to occur for mixture exposures in some studies (Rodea-Palomares et al. 2016; Zimmer et al. 2016).The standard in long-term toxicity assessment, for example, for prospective risk assessment, is based on in vivo assays under chronic chemical exposure. Such assays are, however, highly time and cost demanding and are, therefore, limited in throughput. As an alternative to in vivo testing, high-content in vitro assays are being conducted, and researchers have found that molecular outcomes, such as those identified using toxicogenomic measurements, may be indicative of long-term effects, for example, carcinogenicity (Li et al. 2019). Thus, it has been anticipated that toxicogenomic methods may also help in advancing insight into questions of mixture toxicology for long-term and multivariate effect detection (Altenburger et al. 2012). Evaluating mixture effects using toxicogenomics would support the establishment of this method to obtain comprehensive information for chemical hazard assessment (EFSA et al. 2018) and environmental monitoring (Bahamonde et al. 2016) in an untargeted manner.Despite these ambitious expectations, mixture effects are still perceived as the elephant in the room in the field of toxicogenomics (Schroeder et al. 2016) and there is no consensus on terminology, approaches, and assessment. A major obstacle in present toxicogenomic mixture studies results from the use of experimental designs whereby the doses used to study the compounds individually are simply put together in a mixture (Altenburger et al. 2012). The observed effect of the mixture is then compared against the effects of the single compounds. Acknowledging that toxicogenomic responses are known to be concentration dependent (Kopec et al. 2010; Smetanová et al. 2015), this assessment strategy is of limited value because any changes in the mixture response have to be judged as interactive given that reasonable expectations for the combined effect cannot be obtained (Altenburger et al. 2012; Berenbaum 1981). The challenge for mixture studies using toxicogenomic methods, therefore, is to show the validity, limits, and usefulness of established mixture methodology (Altenburger et al. 2012), which we tackle in this study.The basis for mixture prediction, when applying a concept such as CA, therefore, requires the explicit description of single-substance effects resolved at least for concentration. Regression models for concentration-dependent toxicogenomic responses have been described before (Smetanová et al. 2015; Thomas et al. 2007) and have recently been extended for the time domain in a concentration- and time-dependent response model (CTR-model) (Schüttler et al. 2019). The CTR-model describes the logarithmic fold-change (log2FC) during chemical exposure. It follows a sigmoidal shape for the concentration dependence and a biphasic shape for the time dependence of the response. Among the model parameters, Smax represents the maximum sensitivity, S. The sensitivity is defined as the reciprocal of the minimum of EC50 over time, that is, the minimum concentration over time leading to a half-maximum effect. Thus, Smax indicates the concentration dependence of the response. The parameter tmax stands for the point in time when Smax is reached, thus indicating the time dependence of response. The CTR-model has been applied to describe the toxicogenomic responses of single substances (Schüttler et al. 2019). In the present study, it provides the basis for calculating mixture expectations and for describing the observed mixture responses.To reduce the dimensionality of toxicogenomic responses, reduce noise, and improve the robustness of the CTR-model fits, the analyses were not performed on individual genes but, rather, on groups of coexpressed genes. Based on a compiled data set of many toxicogenomic zebrafish studies, similar responding genes had been clustered in 3,600 nodes on a two-dimensional 60×60 grid (a toxicogenomic universe) in a previous study (Schüttler et al. 2019). This was achieved with the help of the self-organizing map (SOM) algorithm (Kohonen 1982). The approach is based on the assumption that genes, which are functionally related or affected by the same regulators, are coexpressed (Nikkilä et al. 2002). Thus, different areas of the toxicogenomic universe can be assigned to specific functions or anatomical regions (Schüttler et al. 2019). The mapping of toxicogenomic measurements on this toxicogenomic universe results in toxicogenomic fingerprints that depict the effects of an investigated compound on the whole transcriptome for each concentration and time point (Schüttler et al. 2019). A subsequent CTR-model fit for each of the 3,600 nodes results in model parameter values that can as well be mapped on the toxicogenomic universe. These dynamic toxicogenomic fingerprints allow direct comparisons between different compounds and simplify the formulation of hypotheses regarding the affected physiological or molecular functions.In the present study, we performed a case study and evaluated the effects of a three-component synthetic mixture on the transcriptome of the zebrafish embryo (ZFE). We calculated explicit time-resolved expectations for the toxicogenomic effects of the mixture applying the concept of CA. Subsequently, we compared these predictions with experimental observations using a fixed mixture ratio (Altenburger et al. 2012), or so-called diagonal design (Berenbaum 1981), that also allows comparisons with alternative mixture concepts.In this study, we investigated the mixture of the three compounds—diuron, diclofenac, and naproxen—for which we had earlier obtained toxicogenomic fingerprints (Schüttler et al. 2019). The compounds are known to co-occur as freshwater contaminants (Bradley et al. 2017; Busch et al. 2016). Moreover, because they have been developed for specific pharmacological or toxicological applications, their molecular targets and intended action in organisms are well characterized (as summarized by Schüttler et al. 2019). We intentionally mixed two compounds with the same known molecular target and pharmacological mode of action [cyclooxygenase (COX) inhibition: diclofenac and naproxen] and one compound with a target and mode of action assumed to be dissimilar to the above (the herbicide diuron, which has no specific mode of action in fish). In our previous investigation of the components, we indeed found similar toxicogenomic responses for the two COX inhibitors, such as an alteration of transcripts related to the arachidonic acid metabolism and the activation of cyp2k18 (Schüttler et al. 2019). However, distinct differences between the effects of the two COX inhibitors and commonalities to the third compound diuron also became apparent. For example, we found genes related to pancreas development that were affected by naproxen and diuron but not by diclofenac (Schüttler et al. 2019). The same was found for the induction of cyp1a genes. We also showed that the curves for the uptake of the individual compounds into ZFE over time are very different for the three compounds (Schüttler et al. 2019). The observed induction of genes coding for respective metabolizing enzymes, such as Cyp2k18 (a zebrafish orthologue of CYP2C9) due to diclofenac and naproxen exposure, as well as the induction of Cyp1a caused with diuron and naproxen but not with diclofenac, indicates differences between the compounds and underlines the metabolic activity of ZFE, which seems to be qualitatively similar to that of mammals.In the present study, we predicted and investigated how these observations on gene expression, made under single-substance exposure, translate to effects under mixture exposure. Our objectives were to evaluate whether we can quantitatively and qualitatively predict the effects of a mixture from the effects of its components on the whole-transcriptome scale. Moreover, we tackled the question of whether we can trace the toxicogenomic effects of the components under a mixture exposure.Materials and MethodsIn this study, we used the previously published time-resolved toxicogenomic fingerprints obtained with the ZFE after exposure to diuron [Chemical Abstracts Service Registry Number (CAS RN): 330-54-1], diclofenac sodium salt (CAS RN: 15307-79-6), and naproxen sodium salt (CAS RN: 26159-34-2) (Schüttler et al. 2019) to predict the effects and component contributions in quality and quantity for a mixture of these compounds. The CTR-model parameters for all nodes of the previously published toxicogenomic fingerprints can be found in Excel Table S1.Furthermore, we experimentally retrieved a concentration- and time-resolved toxicogenomic fingerprint of the mixture of these three compounds. All data analyses were performed in R (version 3.4.3; R Core Development Team). Functions used for analysis were compiled in the custom-built R package omixR (Schüttler 2020). All scripts and code used for the data analysis can be found in the Supplemental Material, "SupplementaryInformation.html."Zebrafish HusbandryWild-type adult zebrafish, originally received from OBI pet shop (Leipzig, Germany) were kept in 40-L fish tanks containing carbon-filtered tap water at 26°C under a 10 h:14 h dark:light cycle (30 fish per tank, male:female ratio=2:1). Eggs were collected approximately 1 h after light onset and inspected using a light microscope (Olympus SZx7-ILLT). Fertilized eggs were incubated at 26°C, whereas unfertilized eggs were discarded.Mixture PreparationThe mixture contained three substances: diuron (CAS RN: 330-54-1; purity: 99.6%; batch: #SZBB265XV; Fluka), diclofenac sodium salt (CAS RN: 15307-79-6; purity: not available; batch: #BCBP9916V; Sigma), and naproxen sodium salt (CAS RN: 26159-34-2; purity: 98-102%; batch: #MKBV4690V; Sigma). The ratios of the three substances in the mixture were diuron 11%, diclofenac 2.6%, and naproxen 86.4% (the process of mixture design is described in more detail below in section "Mixture Design"). One day before the experiment, diclofenac and naproxen stock solutions were prepared in oxygenated ISO water (ISO 7346-3: 79.99 mM calcium chloride dihydrate, 20.00 mM magnesium sulfate heptahydrate, 30.83 mM sodium bicarbonate, 3.09 mM potassium chloride; pH 7.4, oxygenized) and methanol [CAS RN: 67-56-1; purity: high-performance liquid chromatography (LC) grade, J.T. Baker] and was applied as the solubilizing agent for the diuron stock solution preparation. On the day of exposure, mixture exposure solutions were prepared by diluting the respective stock solutions of mixture constituents to the desired concentration. Therefore, a specific amount of concentrated single-substance stock solution (Excel Table S2) was transferred into a 1-L volumetric flask. Subsequently, a specific amount of methanol was added to guarantee an equal fraction of solvent (0.1%) throughout all exposure solutions and, finally, the flask was filled with oxygenated ISO water up to its benchmark. Mixture exposure solutions were stirred, pH and oxygen content checked, and stored at room temperature until usage. pH (measured with pH-Meter 765 Calimatic; Knick) was adjusted to pH 7.1±0.1 and remained at or above pH 6 throughout the test. Oxygen saturation (measured with Oxi 340 with probe CellOx 325; WTW) was maintained at a level between 80% (beginning of the test) and 60% (end of the test).Diuron, naproxen, and diclofenac were quantified in the exposure media by LC coupled to high-resolution mass spectrometry (LC-HRMS) using an LTQ Orbitrap XL instrument (Thermo) with positive-mode electrospray ionization (ESI). The separation was carried out using a Thermo Ultimate 3000 LC system consisting of degasser, ternary pump, autosampler, and column oven. We used a reversed-phase gradient separation with water (Eluent A) and methanol (Eluent B), both containing 0.1% formic acid on a Kinetex C18 column (50×3.0mm, 2.6μm particle size; Phenomenex) at a flow rate of 300μL/min. The gradient started at 20% of Eluent B, was held for 0.5 min, then increased to 100% of Eluent B in 5.5 min, and subsequently held at 100% Eluent B for 8 min before reequilibration to the initial conditions. The injection volume was 15μL and the column oven was kept at 40°C. The ESI voltage was set to 3.1 kV, the heater temperature to 250°C, the sheath gas flow rate to 20 a.u., and the auxiliary gas flow rate to 5 a.u. The LTQ Orbitrap was operated in full-scan mode (m/z 80–600) at a nominal resolving power of 30,000 (referenced to m/z 400). Calibration standards were prepared matrix-matched in ISO water (calibration range 1–1,000 ng/mL) and the samples were diluted in ISO water to match this range. Quantification was done against the internal standards isoproturon-d6 (for diuron; obtained from CDN Isotopes) and diclofenac-d4 (for diclofenac and naproxen; obtained from CDN Isotopes) using the QuanBrowser of the Xcalibur software (Thermo). The internal standards were added as a mixture in methanol:water 50:50 to the samples to obtain a final concentration of 100 ng/mL.Exposure of Zebrafish EmbryosOur earlier findings (Schüttler et al. 2019) revealed that compound-specific, mode-of-action–related effects occur together with unspecific, development-related effects. Both depend on the embryonic stage because molecular targets or respective cell types need to be present to be affected. Particularly, in a developing system, such as the ZFE, toxicity is dependent not only on exposure duration but also on the exposure start point, age, and developmental stage of the embryo [addressed in more detail by Jakobs et al. (2020)]. Therefore, we started exposures of ZFEs to the above-described mixture solution on purpose at 24 h postfertilization (hpf) to avoid many unspecific effects that can be expected when disturbing the first hours of development (Kimmel et al. 1995). The test used in our experiments can be considered to be a ZFE toxicity test because the zebrafish larvae we used were never older than 96 hpf. This test is considered to be a nonanimal test alternative to the adult acute fish toxicity test (Scholz et al. 2008) and the fish early life stage test (Scholz et al. 2018).Determination of LethalityExposure concentrations for the transcriptome analyses were anchored to lethal effects obtained after 72 h postexposure (hpe). To determine lethal effects, six technical replicates were used for controls and three for mixture treatments. Each replicate contained three embryos that were exposed to 6mL of control or diluted mixture exposure solution, respectively, and incubated for the desired exposure time in 7.5-mL gas chromatography (GC) vials (VWR International), closed with an aluminum lid and an aluminum-coated septum (Supelco Analytical). Effects were observed with a light microscope (Olympus SZx7-ILLT). Three independent experiments were performed with a broad range of dilutions to get complete dose–response curves with 0–100% lethality.Lethal effects were modeled similar to those proposed by Scholze et al. (2001) with two different regression models (logit and weibull; Excel Table S3) using a maximum-likelihood approach [R package bbme (Bolker and R Development Core Team 2017)]. The best-fitting model was selected based on the Akaike information criterion (AIC). From these curves, the LC25 and LC05 were determined, and a dilution factor was calculated to determine the five concentrations applied for the microarray analysis according to Equations 1 and 2. More details on this can be found in the paper by Schüttler et al. (2019). Dilution factor (df)=LC25LC0.56; [1] Exposure concentrations=LC25dfx; x=0,1,2,4,6. [2] Transcriptome ExperimentFor the transcriptome experiment, 20 embryos per replicate were transferred to two 20-mL GC vials at 24 hpf. A volume of 18mL of exposure medium was added to each vial, and the vials were sealed and incubated in a shaking climate chamber (climate chamber: Vötsch 1514, Vötsch Industrietechnik GmbH (Balingen-Frommern); Edmund Bühler SM-30, setting: 26°C, 75 rpm, 12 h:12 h light:dark) until sampling. For our experiment, we chose a dense sampling design probing increasing concentrations and consecutive sampling times. Such a design allows decreasing the number of replicates (Sefer et al. 2016). Embryos were exposed to a range of five different concentrations of the mixture (C1=43.1μmol/L, C2=61.06μmol/L, C3=86.49μmol/L, C4=102.93μmol/L, and C5=122.52μmol/L), and RNA was extracted at 3, 6, 12, 24, 48, and 72 hpe.RNA Extraction and Microarray AnalysisTwo vials of ZFE (20 ZFE in total) were harvested per replicate. We took two replicates for treatments and three for controls at each observation time point. ZFE were transferred into Eppendorf tubes and stored at −80°C after the addition of Trizol and homogenization using a T10 basic Ultra-Turrax (IKA, Werke GmbH & Co.) for 20 s at maximum speed. RNA isolation was performed using a pipetting robot (Microlab Star, Hamilton Life Science Robotics) following the manual provided for Total RNA Extraction Kit MagMAX 96 for microarrays and conducted in a 96-well plate. The quality of isolated RNA was assessed using a Bioanalyzer (Agilent 2100 Technologies) and the Agilent RNA 6000 Nano Kit. RNA samples were used for further processing if RIN values derived from ribosomal RNA absorption adopted values >7 and calculated concentrations exceeded 25 ng/μL. We selected two replicates for all controls, and for the highest exposure concentration (C5=122.52μmol/L) at all time points, as well as for the longest exposure duration (72 h) and all concentrations, respectively. For all other time points and concentrations, the microarray was measured for one replicate. All RNA samples were diluted to a concentration level of 25 ng/μL (10μL in total) by the addition of RNAse free water. 2.3μL (57 ng RNA) were used as the starting amount of RNA for the spike mix preparation.Transcript abundance was measured with microarray analysis using Oaklabs ArrayXS Zebrafish microarray slides (XS-200,104, Oaklabs; National Center for Biotechnology Information Gene Expression Omnibus platform accession: GPL19785). Microarray experiments were performed using the Agilent Low Input Quick Amp WT Labeling Kit according to the Agilent One-Color Microarray-Based Exon Analysis Protocol (version 2.0; August 2015). This protocol included the introduction of spike-in RNA, RNA transcription, and amplification into complementary DNA (cDNA), and cDNA transcription and amplification into cRNA with simultaneous incorporation of Cy3 (fluorescently labeled cytidine nucleotide). The cRNA was fragmented and hybridized to Oaklabs ArrayXS Zebrafish microarray slides using the Agilent hybridization kit and protocol as well as Agilent hybridization oven and chambers. Subsequently, microarray slides were washed and scanned with the Agilent High-Resolution Microarray Scanner according to the Agilent protocol. Intensity values were extracted from captured images using Agilent Feature Extraction software (version 11.5.1.1).Dynamic Toxicogenomic Fingerprints of the Components and the MixtureDynamic toxicogenomic fingerprints from the compounds diuron, diclofenac, and naproxen were taken from Schüttler et al. (2019) (Excel Table S1). Measured transcriptome data from the mixture exposure experiment were mapped on the toxicogenomic universe. This included quality control, normalization using the cyclic loess method (Bolstad et al. 2003), log2-transformation, and normalization of expression level against the control of the respective time point controls. Finally, all transcripts were clustered into 3,600 nodes of a previously trained SOM. Details of this process are described by Schüttler et al. (2019). Next, the CTR-model was fitted to the obtained responses under mixture exposure to retrieve model parameter values and a dynamic toxicogenomic fingerprint of the mixture as follows: log2FC(c)=log2FCmax1+e−slope×(log(c)−log(EC50)),Sensitivity=S(t)=1EC50(t)Smax×e−0.5×(log(t)−log(tmax)Sdur)2,log2FC(c,t)=log2FCmax[1+exp(−slope×(log(c)−log(1(Smax×exp(−0.5×(log(t)−log(tmax)Sdur)2)))))]+∈,∈∼N(0,σ2), [3] where log2FCmax corresponds to the maximum fold-change of the respective node across all conditions, Smax is the maximum sensitivity (1/EC50) of the node, tmax is the point in time with maximum sensitivity, and Sdur represents a measure of the duration of the sensitivity interval. For parameter estimation, the R implementation of the algorithm shuffled complex evolution (described by Duan et al. 1993) in the R package hydromad (Andrews et al. 2011) was applied. Further details regarding parameter estimation can be found in the paper by Schüttler et al. (2019). To check for the quality of model fits the AIC-weights were calculated in comparison with a null-model and a spline (see also the "Results" section and Figure S9).Determination of Significantly Affected NodesSignificantly affected nodes were determined by comparing the 95% confidence interval (CI) for the regression model fits with the 2.5% and 97.5% quantiles of control measurements. Nodes showing a sum of differences between these confidence bands above or below zero were identified as significantly affected (Schüttler et al. 2019).Mixture Effect Prediction—Concentration AdditionMixture effects were predicted for each node in the toxicogenomic universe applying the mixture concept of CA. The maximum log2FCs per node obtained in the experiments with the individual chemicals (Excel Table S1) were summed to determine the expected direction of regulation under mixture exposure for each node. A positive sum was taken as an indication for up-regulation, a negative sum for down-regulation. The highest (for up-regulation) or lowest (for down-regulation) measured log2FC in the single-substance exposures was set as maximum log2FC for the CTR-model. Subsequently, the CTR-model parameters were retrieved for each node and compound for the expected mixture direction. If an effect induced by a single substance was <20% of the maximum effect or an AIC-weight for the fitted model was <0.7, we assumed no effect for this substance on the respective node. For mixture prediction, a bootstrap approach was employed and errors were sampled from an error distribution fitted for the single substances (n=100). For each sampled error combination, the CA was calculated based on the formula for multicomponent mixtures as described by Faust et al. (2001): ECX(mixture)=(∑i=1npiECX,i)−1, [4] where ECX(mixture) represents the effect concentration for effect X of the mixture, ECX,i the effect concentration of subst
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