Improving the Screening Analysis of Pesticide Metabolites in Human Biomonitoring by Combining High-Throughput In Vitro Incubation and Automated LC-HRMS Data Processing.

There is a current need to monitor human exposure to a large number of pesticides and other chemicals of emerging concern (CECs). This requires screening analysis with high confidence for these compounds and their metabolites in complex matrices, which is hampered by the fact that no reference standards are available for most metabolites. We address this challenge by a high-throughput workflow based on incubation of pesticides (or other CECs) with human liver S9, followed by solid-phase extraction, liquid chromatography-high-resolution mass spectrometry (LC-HRMS) analysis, and automated data processing to generate a database (retention time, precursor m/z, and MS2 spectral library) for the annotation in human samples. The metabolite prioritization consists of statistical comparisons and mass defect and m/z range filtering to obtain a subset of probable phase I metabolites, for which molecular formulas and likely metabolic transformation are retrieved. We tested the workflow on 22 pesticides, for which we could determine 91 metabolite molecular formulas which are only partly covered by the literature and/or predicted by in silico metabolization. Our workflow allows for an efficient generation of metabolite reference information, which can be used directly for annotating LC-HRMS data from human samples. A full structure elucidation of individual metabolites can be limited to those being actually present in human samples.

Synthesis of (R)-mandelic acid and (R)-mandelic acid amide by recombinant E. coli strains expressing a (R)-specific oxynitrilase and an arylacetonitrilase.

OBJECTIVES: Chiral 2-hydroxycarboxylic acids and 2-hydroxycarboxamides are valuable synthons for the chemical industry. RESULTS: The biocatalytic syntheses of (R)-mandelic acid and (R)-mandelic acid amide by recombinant Escherichia coli clones were studied. Strains were constructed which simultaneously expressed a (R)-specific oxynitrilase (hydroxynitrile lyase) from the plant Arabidopsis thaliana together with the arylacetonitrilase from the bacterium Pseudomonas fluorescens EBC191. In addition, recombinant strains were constructed which expressed a previously described acid tolerant variant of the oxynitrilase and an amide forming variant of the nitrilase. The whole cell catalysts which simultaneously expressed the (R)-specific oxynitrilase and the wild-type nitrilase transformed in slightly acidic buffer systems benzaldehyde plus cyanide preferentially to (R)-mandelic acid with ee-values > 95%. The combination of the (R)-specific oxynitrilase with the amide forming nitrilase variant gave whole cell catalysts which converted at pH-values ≤ pH 5 benzaldehyde plus cyanide with a high degree of enantioselectivity (ee > 90%) to (R)-mandelic acid amide. The acid and the amide forming catalysts also converted chlorinated benzaldehydes with cyanide to chlorinated mandelic acid or chlorinated mandelic acid amides. CONCLUSIONS: Efficient systems for the biocatalytic production of (R)-2-hydroxycarboxylic acids and (R)-2-hydroxycarboxamides were generated.

Symbolic Aggregate Approximation Improves Gap Filling in High-Resolution Mass Spectrometry Data Processing.

Nontargeted mass spectrometry (MS) is widely used in life sciences and environmental chemistry to investigate large sets of samples. A major problem for larger-scale MS studies is data gaps or missing values in aligned data sets. The main causes for these data gaps are the absence of the compound from the sample, issues related to chromatography or mass spectrometry (for example, broad peaks, early eluting peaks, ion suppression, low ionization efficiency), and issues related to software (mainly limitations of peak detection algorithms). While those algorithms are heuristic by necessity and should be used with strict settings to minimize the number of false positive and negative peaks in a data set, gap filling may be used to reduce missing data in single samples remaining after peak detection. In this study, we present a new gap filling algorithm. The method is based on the symbolic aggregation approximation (SAX) algorithm that was developed for the evaluation and classification of time series in data mining studies. We adopted SAX for liquid chromatography high-resolution MS nontarget screening to support the detection of missing peaks in aligned mass spectral data sets. The SAX-based algorithm improves the detection efficiency considerably compared to existing gap filling methods including the Peak Finder algorithm provided in MZmine.

Non-targeted mercapturic acid screening in urine using LC-MS/MS with matrix effect compensation by postcolumn infusion of internal standard (PCI-IS).

While the targeted analysis of mercapturic acid (MA) metabolites in human urine is used to assess exposure to selected chemicals, this compound class has only rarely been addressed in non-target screening utilizing diagnostic neutral loss liquid chromatography tandem mass spectrometry (LC-MS/MS). Additionally, this type of analysis is severely affected by matrix effects (MEs) causing poor comparability of samples and distortion of signal intensities. However, MEs have been neglected in urinary MA non-target screening so far. Therefore, we developed a non-target screening method relying on neutral loss scanning for MAs using post column infusion of an isotope-labelled standard. For signal correction, we synthesized a structural analogue to MAs, N-acetyl-S-methyl-homocysteine-D3, lacking the characteristic neutral loss of the MAs. For method development, 16 structurally different model MA compounds and 20 spiked urine samples were used. Twelve out of the 16 model compounds could be analysed by the developed method. We found severe matrix effects (largely signal suppression) for the spiked model compounds, with only 34% of all peaks' intensities changing by less than a factor of two. This could be compensated by the post column internal standard infusion with now 68% of all peaks' intensities changing by less than a factor of two. For three compounds, an over-compensation was observed resulting in an increase of signal of up to a factor of 16. In the 20 urine samples, altogether 558 native MAs (between 74 and 175 per sample) could be detected after ME compensation. These results indicate that a large number of so far uncharacterized MAs are present in urine, which yield a potential for biomarker discovery and pattern characterisation. Graphical Abstract.

An all-diamond X-ray position and flux monitor using nitrogen-incorporated ultra-nanocrystalline diamond contacts.

Diamond X-ray detectors with conducting nitrogen-incorporated ultra-nanocrystalline diamond (N-UNCD) films as electrodes were fabricated to measure X-ray beam flux and position. Structural characterization and functionality tests were performed for these devices. The N-UNCD films grown on unseeded diamond substrates were compared with N-UNCD films grown on a seeded silicon substrate. The feasibility of the N-UNCD films acting as electrodes for X-ray detectors was confirmed by the stable performance in a monochromatic X-ray beam. The fabrication process is able to change the surface status which may influence the signal uniformity under low bias, but this effect can be neglected under full collection bias.

Water transport through tall trees: A vertically explicit, analytical model of xylem hydraulic conductance in stems.

Trees grow by vertically extending their stems, so accurate stem hydraulic models are fundamental to understanding the hydraulic challenges faced by tall trees. Using a literature survey, we showed that many tree species exhibit continuous vertical variation in hydraulic traits. To examine the effects of this variation on hydraulic function, we developed a spatially explicit, analytical water transport model for stems. Our model allows Huber ratio, stem-saturated conductivity, pressure at 50% loss of conductivity, leaf area, and transpiration rate to vary continuously along the hydraulic path. Predictions from our model differ from a matric flux potential model parameterized with uniform traits. Analyses show that cavitation is a whole-stem emergent property resulting from non-linear pressure-conductivity feedbacks that, with gravity, cause impaired water transport to accumulate along the path. Because of the compounding effects of vertical trait variation on hydraulic function, growing proportionally more sapwood and building tapered xylem with height, as well as reducing xylem vulnerability only at branch tips while maintaining transport capacity at the stem base, can compensate for these effects. We therefore conclude that the adaptive significance of vertical variation in stem hydraulic traits is to allow trees to grow tall and tolerate operating near their hydraulic limits.

Performance of combined fragmentation and retention prediction for the identification of organic micropollutants by LC-HRMS.

In nontarget screening, structure elucidation of small molecules from high resolution mass spectrometry (HRMS) data is challenging, particularly the selection of the most likely candidate structure among the many retrieved from compound databases. Several fragmentation and retention prediction methods have been developed to improve this candidate selection. In order to evaluate their performance, we compared two in silico fragmenters (MetFrag and CFM-ID) and two retention time prediction models (based on the chromatographic hydrophobicity index (CHI) and on log D). A set of 78 known organic micropollutants was analyzed by liquid chromatography coupled to a LTQ Orbitrap HRMS with electrospray ionization (ESI) in positive and negative mode using two fragmentation techniques with different collision energies. Both fragmenters (MetFrag and CFM-ID) performed well for most compounds, with average ranking the correct candidate structure within the top 25% and 22 to 37% for ESI+ and ESI- mode, respectively. The rank of the correct candidate structure slightly improved when MetFrag and CFM-ID were combined. For unknown compounds detected in both ESI+ and ESI-, generally positive mode mass spectra were better for further structure elucidation. Both retention prediction models performed reasonably well for more hydrophobic compounds but not for early eluting hydrophilic substances. The log D prediction showed a better accuracy than the CHI model. Although the two fragmentation prediction methods are more diagnostic and sensitive for candidate selection, the inclusion of retention prediction by calculating a consensus score with optimized weighting can improve the ranking of correct candidates as compared to the individual methods. Graphical abstract Consensus workflow for combining fragmentation and retention prediction in LC-HRMS-based micropollutant identification.

A dynamic bioenergetic model for coral-Symbiodinium symbioses and coral bleaching as an alternate stable state.

Coral reef ecosystems owe their ecological success - and vulnerability to climate change - to the symbiotic metabolism of corals and Symbiodinium spp. The urgency to understand and predict the stability and breakdown of these symbioses (i.e., coral 'bleaching') demands the development and application of theoretical tools. Here, we develop a dynamic bioenergetic model of coral-Symbiodinium symbioses that demonstrates realistic steady-state patterns in coral growth and symbiont abundance across gradients of light, nutrients, and feeding. Furthermore, by including a mechanistic treatment of photo-oxidative stress, the model displays dynamics of bleaching and recovery that can be explained as transitions between alternate stable states. These dynamics reveal that "healthy" and "bleached" states correspond broadly to nitrogen- and carbon-limitation in the system, with transitions between them occurring as integrated responses to multiple environmental factors. Indeed, a suite of complex emergent behaviors reproduced by the model (e.g., bleaching is exacerbated by nutrients and attenuated by feeding) suggests it captures many important attributes of the system; meanwhile, its modular framework and open source R code are designed to facilitate further problem-specific development. We see significant potential for this modeling framework to generate testable hypotheses and predict integrated, mechanistic responses of corals to environmental change, with important implications for understanding the performance and maintenance of symbiotic systems.

Host-Symbiont Interaction Model Explains Non-monotonic Response of Soybean Growth and Seed Production to Nano-CeO2 Exposure.

Recent nanotoxicity studies have demonstrated non-monotonic dose-response mechanisms for planted soybean that have a symbiotic relationship with bacteroids in their root nodules: reduction of growth and seed production was greater for low, as compared to high, exposures. To investigate mechanistic underpinnings of the observed patterns, we formulated an energy budget model coupled to a toxicokinetic module describing bioaccumulation, and two toxicodynamic modules describing toxic effects on host plant and symbionts. By fitting data on plants exposed to engineered CeO2 nanoparticles to the newly formulated model, we show that the non-monotonic patterns can be explained as the interaction of two, individually monotonic, dose-response processes: one for the plant and the other for the symbiont. We further validate the newly formulated model by showing that, without the need for additional parameters, the model successfully predicts changes in dinitrogen fixation potential as a function of exposure (dinitrogen fixation potential data not used in model fitting). The symbiont buffers overall toxicity only when, in the absence of exposure to a toxicant, it has a parasitic interaction with the host plant. If the interaction is mutualistic or commensal, there is no buffering and only monotonic toxic responses are possible. Because the model is based on general biological principles, we expect it to be applicable to other similar symbiotic systems, especially other nodule-forming legumes.

The implications of reduced metabolic rate in resource-limited corals.

Many organisms exhibit depressed metabolism when resources are limited, a change that makes it possible to balance an energy budget. For symbiotic reef corals, daily cycles of light and periods of intense cloud cover can be chronic causes of food limitation through reduced photosynthesis. Furthermore, coral bleaching is common in present-day reefs, creating a context in which metabolic depression could have beneficial value to corals. In the present study, corals (massive Porites spp.) were exposed to an extreme case of resource limitation by starving them of food and light for 20 days. When resources were limited, the corals depressed area-normalized respiration to 37% of initial rates, and coral biomass declined to 64% of initial amounts, yet the corals continued to produce skeletal mass. However, the declines in biomass cannot account for the declines in area-normalized respiration, as mass-specific respiration declined to 30% of the first recorded time point. Thus, these corals appear to be capable of metabolic depression. It is possible that some coral species are better able to depress metabolic rates than others; such variation could explain differential survival during conditions that limit resources (e.g. shading). Furthermore, we found that maintenance of existing biomass, in part, supports the production of skeletal mass. This association could be explained if maintenance supplies needed energy (e.g. ATP) or inorganic carbon (i.e. CO2) that otherwise limits the production of skeletal mass. Finally, the observed metabolic depression can be explained as a change in pool sizes, and does not require a change in metabolic rules.

Feedbacks and tipping points in organismal response to oxidative stress.

Biological feedbacks play a crucial role in determining effects of toxicants, radiation, and other environmental stressors on organisms. Focusing on reactive oxygen species (ROS) that are increasingly recognized as a crucial mediator of many stressor effects, we investigate how feedback strength affects the ability of organisms to control negative effects of exposure. We do this by developing a general theoretical framework for describing effects of a wide range of stressors and species. The framework accounts for positive and negative feedbacks representing cellular processes: (i) production of ROS due to metabolism and the stressor, (ii) ROS reactions with cellular compounds that cause damage, and (iii) cellular control of both ROS and damage. We suggest functional forms that capture generic properties of cellular control mechanisms constituting the feedbacks, assess stability of equilibrium states in the resulting models, and investigate tipping points at which cellular control breaks down causing unregulated increase of ROS and damage. Depending on the chosen functional forms, the models can have zero, one, or two positive steady states; except in one singular case, the steady state with lowest values of ROS and damage is locally stable. We found two types of tipping points: those induced by changes in the parameters (including the stressor intensity), and those induced by the history of exposure, i.e. ROS and damage levels. The relatively simple models effectively describe several patterns of cellular responses to stress, such as the covariation of ROS and damage, the break-down of cellular control leading to explosion of ROS and/or damage, increase in damage even when ROS is (near)-constant, and the effects of exposure history on the ability of the cell to handle additional stress. The models quantify dynamics of cellular control, and could therefore be used to estimate the metabolic costs of stress and help integrate them into models that use energetic considerations to model organism's response to the environment. Although developed with unicellular organisms in mind, our models can be applied to all multicellular organisms that utilize similar feedbacks when dealing with stress.

Specific Reaction Patterns to Distinct Positive Emotional Cues Related to Incentive Motivation in Dependence of the Taq1A-Polymorphism: Molecular Genetic Associations of Early and Late Event-Related Potentials.

BACKGROUND/AIMS: Early and late event-related potential (ERP) responses, representing early subconscious and late motivational processes, were recorded for positive emotional words related to 'wanting' and 'liking', in dependence of the dopamine-related Taq1A genotype (ANKK1/DRD2). Research suggests that 'wanting' as opposed to 'liking' is related to dopaminergic processes. Therefore, it was hypothesized that risk allele carriers of the Taq1A polymorphism exhibit late ERP changes in reaction to words representing incentive motivation, i.e. 'wanting' (word categories 'lust' and 'anticipation'), but not to words representing 'liking' ('closeness'). METHODS: Seventy-two male participants performed an emotional-word Stroop task during EEG recording and were genotyped according to the Taq1A polymorphism of ANKK1/DRD2. RESULTS: Positive emotional words related to anticipation and lust revealed blunted responses in the late positive potential (LPP) in carriers of the A1 allele, an effect absent in response to 'liking'-related words. These differences were not evident in the earlier posterior negativity (EPN). CONCLUSION: As no differences in dependence of the Taq1A genotype were observed in reaction to 'wanting'- and 'liking'-related words in the EPN, but merely in the LPP, it can be assumed that incentive-motivational stimuli only modify motivation-related ERP responses in carriers of the A1 allele of the Taq1A polymorphism, indicating the role of dopamine in late ERP components.

Daphnia magna's sense of competition: intra-specific interactions (ISI) alter life history strategies and increase metals toxicity.

This work investigates whether the scale-up to multi-animal exposures that is commonly applied in genomics studies provides equivalent toxicity outcomes to single-animal experiments of standard Daphnia magna toxicity assays. Specifically, we tested the null hypothesis that intraspecific interactions (ISI) among D. magna have neither effect on the life history strategies of this species, nor impact toxicological outcomes in exposure experiments with Cu and Pb. The results show that ISI significantly increased mortality of D. magna in both Cu and Pb exposure experiments, decreasing 14 day LC50 s and 95 % confidence intervals from 14.5 (10.9-148.3) to 8.4 (8.2-8.7) µg Cu/L and from 232 (156-4810) to 68 (63-73) µg Pb/L. Additionally, ISI potentiated Pb impacts on reproduction eliciting a nearly 10-fold decrease in the no-observed effect concentration (from 236 to 25 µg/L). As an indication of environmental relevance, the effects of ISI on both mortality and reproduction in Pb exposures were sustained at both high and low food rations. Furthermore, even with a single pair of Daphnia, ISI significantly increased (p < 0.05) neonate production in control conditions, demonstrating that ISI can affect life history strategy. Given these results we reject the null hypothesis and conclude that results from scale-up assays cannot be directly applied to observations from single-animal assessments in D. magna. We postulate that D. magna senses chemical signatures of conspecifics which elicits changes in life history strategies that ultimately increase susceptibility to metal toxicity.

Pixelated transmission-mode diamond X-ray detector.

Fabrication and testing of a prototype transmission-mode pixelated diamond X-ray detector (pitch size 60-100 µm), designed to simultaneously measure the flux, position and morphology of an X-ray beam in real time, are described. The pixel density is achieved by lithographically patterning vertical stripes on the front and horizontal stripes on the back of an electronic-grade chemical vapor deposition single-crystal diamond. The bias is rotated through the back horizontal stripes and the current is read out on the front vertical stripes at a rate of ∼ 1 kHz, which leads to an image sampling rate of ∼ 30 Hz. This novel signal readout scheme was tested at beamline X28C at the National Synchrotron Light Source (white beam, 5-15 keV) and at beamline G3 at the Cornell High Energy Synchrotron Source (monochromatic beam, 11.3 keV) with incident beam flux ranges from 1.8 × 10(-2) to 90 W mm(-2). Test results show that the novel detector provides precise beam position (positional noise within 1%) and morphology information (error within 2%), with an additional software-controlled single channel mode providing accurate flux measurement (fluctuation within 1%).

Relating suborganismal processes to ecotoxicological and population level endpoints using a bioenergetic model.

Ecological effects of environmental stressors are commonly evaluated using organismal or suborganismal data, such as standardized toxicity tests that characterize responses of individuals (e.g., mortality and reproduction) and a rapidly growing body of "omics" data. A key challenge for environmental risk assessment is relating such information to population dynamics. One approach uses dynamic energy budget (DEB) models that relate growth and reproduction of individuals to underlying flows of energy and elemental matter. We hypothesize that suborganismal information identifies DEB parameters that are most likely impacted by a particular stressor and that the DEB model can then project suborganismal effects on life history and population endpoints. We formulate and parameterize a model of growth and reproduction for the water flea Daphnia magna. Our model resembles previous generic bioenergetic models, but has explicit representation of discrete molts, an important feature of Daphnia life history. We test its ability to predict six endpoints commonly used in chronic toxicity studies in specified food environments. With just one adjustable parameter, the model successfully predicts growth and reproduction of individuals from a wide array of experiments performed in multiple laboratories using different clones of D. magna raised on different food sources. Fecundity is the most sensitive endpoint, and there is broad correlation between the sensitivities of fecundity and long-run growth rate, as is desirable for the default metric used in chronic toxicity tests. Under some assumptions, we can combine our DEB model with the Euler-Lotka equation to estimate longrun population growth rates at different food levels. A review of Daphnia gene-expression experiments on the effects of contaminant exposure reveals several connections to model parameters, in particular a general trend of increased transcript expression of genes involved in energy assimilation and utilization at concentrations affecting growth and reproduction. The sensitivity of fecundity to many model parameters was consistent with frequent generalized observations of decreased expression of genes involved in reproductive physiology, but interpretation of these observations requires further mechanistic modeling. We thus propose an approach based on generic DEB models incorporating few essential species-specific features for rapid extrapolation of ecotoxicogenomic assays for Daphnia-based population risk assessment.

Quantitative Adverse Outcome Pathway Analysis of Hatching in Zebrafish with CuO Nanoparticles.

This study develops and evaluates a mechanistic model of the hatching of zebrafish eggs that were exposed to CuO engineered nanoparticles (ENP) in a high-throughput screening system and places this model in an adverse outcome pathway (AOP) that also includes CuO ENP dissolution and Cu bioaccumulation. Cu(2+) inhibits the proteolytic activity of Zebrafish Hatching Enzyme 1 and thereby delay or impair hatching success. This study demonstrates that noncompetitive inhibition kinetics describe the impact of dissolved Cu on hatching; it is estimated that indefinitely long exposure to 1.88 µM dissolved Cu in the environment reduces hatching enzyme activity by 50%. The complexity arising from CuO ENP dissolution and CuO ENP assisted bioaccumulation of Cu has led to apparently contradictory findings about ion versus "nano" effects on hatching. Model-mediated data analyses indicate that, relative to copper salts, CuO ENPs increase the uptake rates of Cu into the perivitelline space up to 8 times. The toxicity assessment framework in this study can be adapted to accommodate other types of toxicant, environmental samples and other aquatic oviparous species.

Dynamic energy budget modeling reveals the potential of future growth and calcification for the coccolithophore Emiliania huxleyi in an acidified ocean.

Ocean acidification is likely to impact the calcification potential of marine organisms. In part due to the covarying nature of the ocean carbonate system components, including pH and CO2 and CO3(2-) levels, it remains largely unclear how each of these components may affect calcification rates quantitatively. We develop a process-based bioenergetic model that explains how several components of the ocean carbonate system collectively affect growth and calcification rates in Emiliania huxleyi, which plays a major role in marine primary production and biogeochemical carbon cycling. The model predicts that under the IPCC A2 emission scenario, its growth and calcification potential will have decreased by the end of the century, although those reductions are relatively modest. We anticipate that our model will be relevant for many other marine calcifying organisms, and that it can be used to improve our understanding of the impact of climate change on marine systems.

Environmental Risk Assessment of Time-Variable Toxicant Exposure with Toxicokinetic-Toxicodynamic Modeling of Sublethal Endpoints and Moving Time Windows: A Case Study with Ceriodaphnia dubia.

Toxicokinetic-toxicodynamic (TKTD) modeling has received increasing attention in terms of the regulatory environmental risk assessment of chemicals. This type of mechanistic model can integrate all available data from individual-level bioassays into a single framework and enable refined risk assessments by extrapolating from laboratory results to time-variable exposure scenarios, based, for instance, on surface water exposure modeling (e.g., FOCUS). Dynamic energy budget (DEB) models coupled with TKTD modules (DEB-TKTD) constitute the leading approach to assess and predict sublethal effects of chemicals on individual organisms. However, thorough case studies are rare. We provide a state-of-the-art example with the standard aquatic test species Ceriodaphnia dubia and the fungicide azoxystrobin, including all steps, from bespoke laboratory toxicity tests to model calibration and validation, through to environmental risk assessment. Following the framework proposed in the European Food Safety Authority Scientific Opinion from 2018, we designed bespoke good laboratory practice-compliant laboratory toxicity studies based on test guideline 211 of the Organisation for Economic Co-operation and Development and then identified robust parameter values from those data for all relevant model parameters through model calibration. The DEB-TKTD model, DEBtox2019, then informed the design of the validation experiment. Once validated, the model was used to perform predictions for a time-variable exposure scenario generated by FOCUS. A moving time-window approach was used to perform the environmental risk assessment. This assessment method reduces uncertainty in the risk assessment while maintaining consistency with the traditional measures of risk. Environ Toxicol Chem 2024;43:2409-2421. © 2024 Syngenta Crop Protection AG. ibacon GmbH and The Author(s). Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Connecting Suborganismal Data to Bioenergetic Processes: Killifish Embryos Exposed to a Dioxin-Like Compound.

A core challenge for ecological risk assessment is to integrate molecular responses into a chain of causality to organismal or population-level outcomes. Bioenergetic theory may be a useful approach for integrating suborganismal responses to predict organismal responses that influence population dynamics. We describe a novel application of dynamic energy budget (DEB) theory in the context of a toxicity framework (adverse outcome pathways [AOPs]) to make quantitative predictions of chemical exposures to individuals, starting from suborganismal data. We use early-life stage exposure of Fundulus heteroclitus to dioxin-like chemicals (DLCs) and connect AOP key events to DEB processes through "damage" that is produced at a rate proportional to the internal toxicant concentration. We use transcriptomic data of fish embryos exposed to DLCs to translate molecular indicators of damage into changes in DEB parameters (damage increases somatic maintenance costs) and DEB models to predict sublethal and lethal effects on young fish. By changing a small subset of model parameters, we predict the evolved tolerance to DLCs in some wild F. heteroclitus populations, a data set not used in model parameterization. The differences in model parameters point to reduced sensitivity and altered damage repair dynamics as contributing to this evolved resistance. Our methodology has potential extrapolation to untested chemicals of ecological concern. Environ Toxicol Chem 2023;42:2040-2053. © 2023 Oak Ridge National Laboratory and The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.