Mind the gap - Relevant design for laboratory oil exposure of fish as informed by a numerical impact assessment model.

Laboratory experiments provide knowledge of species-specific effects thresholds that are used to parameterize impact assessment models of oil contamination on marine ecosystems. Such experiments typically place individuals of species and life stages in tanks with different contaminant concentrations. Exposure concentrations are usually fixed, and the individuals experience a shock treatment being moved from clean water directly into contaminated water and then back to clean water. In this study, we use a coupled numerical model that simulates ocean currents and state, oil dispersal and fate, and early life stages of fish to quantify oil exposure histories, specifically addressing oil spill scenarios of high rates and long durations. By including uptake modelling we also investigate the potential of buffering transient high peaks in exposure. Our simulation results are the basis for a recommendation on the design of laboratory experiments to improve impact assessment model development and parameterization. We recommend an exposure profile with three main phases: i) a gradual increase in concentration, ii) a transient peak that is well above the subsequent level, and iii) a plateau of fixed concentration lasting ∼3 days. In addition, a fourth phase with a slow decrease may be added.

Integrative omics-analysis of lipid metabolism regulation by peroxisome proliferator-activated receptor a and b agonists in male Atlantic cod.

Lipid metabolism is essential in maintaining energy homeostasis in multicellular organisms. In vertebrates, the peroxisome proliferator-activated receptors (PPARs, NR1C) regulate the expression of many genes involved in these processes. Atlantic cod (Gadus morhua) is an important fish species in the North Atlantic ecosystem and in human nutrition, with a highly fatty liver. Here we study the involvement of Atlantic cod Ppar a and b subtypes in systemic regulation of lipid metabolism using two model agonists after in vivo exposure. WY-14,643, a specific PPARA ligand in mammals, activated cod Ppara1 and Ppara2 in vitro. In vivo, WY-14,643 caused a shift in lipid transport both at transcriptional and translational level in cod. However, WY-14,643 induced fewer genes in the fatty acid beta-oxidation pathway compared to that observed in rodents. Although GW501516 serves as a specific PPARB/D ligand in mammals, this compound activated cod Ppara1 and Ppara2 as well as Pparb in vitro. In vivo, it further induced transcription of Ppar target genes and caused changes in lipid composition of liver and plasma. The integrative approach provide a foundation for understanding how Ppars are engaged in regulating lipid metabolism in Atlantic cod physiology. We have shown that WY-14,643 and GW501516 activate Atlantic cod Ppara and Pparb, affect genes in lipid metabolism pathways, and induce changes in the lipid composition in plasma and liver microsomal membranes. Particularly, the combined transcriptomic, proteomics and lipidomics analyses revealed that effects of WY-14,643 on lipid metabolism are similar to what is known in mammalian studies, suggesting conservation of Ppara functions in mediating lipid metabolic processes in fish. The alterations in the lipid profiles observed after Ppar agonist exposure suggest that other chemicals with similar Ppar receptor affinities may cause disturbances in the lipid regulation of fish. Model organism: Atlantic cod (Gadus morhua). LSID: urn:lsid:zoobank.org:act:389BE401-2718-4CF2-BBAE-2E13A97A5E7B. COL Identifier: 6K72F.

An annual profile of the impacts of simulated oil spills on the Northeast Arctic cod and haddock fisheries.

We simulate the combined natural and pollutant-induced survival of early life stages of NEA cod and haddock, and the impact on the adult populations in response to the time of a major oil spill in a single year. Our simulations reveal how dynamic ocean processes, controlling both oil transport and fate and the frequency of interactions of oil with drifting fish eggs and larvae, mediate the magnitude of population losses due to an oil spill. The largest impacts on fish early life stages occurred for spills initiated in Feb-Mar, concomitant with the initial rise in marine productivity and the earliest phase of the spawning season. The reproductive health of the adult fish populations was maintained in all scenarios. The study demonstrates the application of a simulation system that provides managers with information for the planning of development activities and for the protection of fisheries resources from potential impacts.

New Conceptual Toxicokinetic Model to Assess Synergistic Mixture Effects between the Aromatic Hydrocarbon ß-Naphthoflavone and the Azole Nocodazole on the CYP1A Biomarker in a Fish Cell Line.

Toxicokinetic interactions with catabolic cytochrome P450 (CYP) enzymes can inhibit chemical elimination pathways and cause synergistic mixture effects. We have created a mathematical bottom-up model for a synergistic mixture effect where we fit a multidimensional function to a given data set using an auxiliary nonadditive approach. The toxicokinetic model is based on the data from a previous study on a fish cell line, where the CYP1A enzyme activity was measured over time after exposure to various combinations of the aromatic hydrocarbon ß-naphthoflavone and the azole nocodazole. To describe the toxicokinetic mechanism in this pathway and how that affects the CYP1A biomarker, the model uses ordinary differential equations. Local sensitivity and identifiability analyses revealed that all the 10 parameters estimated in the model were identified uniquely while fitting the model to the data for measuring the CYP1A enzyme activity. The model has a good prediction power and is a promising tool to test the synergistic toxicokinetic interactions between different chemicals.

Experimental design for parameter estimation in steady-state linear models of metabolic networks.

Metabolic networks are typically large, containing many metabolites and reactions. Dynamical models that aim to simulate such networks will consist of a large number of ordinary differential equations, with many kinetic parameters that must be estimated from experimental data. We assume these data to be metabolomics measurements made under steady-state conditions for different input fluxes. Assuming linear kinetics, analytical criteria for parameter identifiability are provided. For normally distributed error terms, we also calculate the Fisher information matrix analytically to be used in the D-optimality criterion. A test network illustrates the developed tool chain for finding an optimal experimental design. The first stage is to verify global or pointwise parameter identifiability, the second stage to find optimal input fluxes, and finally remove redundant measurements.

Effects of defined mixtures of POPs and endocrine disruptors on the steroid metabolome of the human H295R adrenocortical cell line.

The presence of environmental pollutants in our ecosystem may impose harmful health effects to wildlife and humans. Several of these toxic chemicals have a potential to interfere with the endocrine system. The adrenal cortex has been identified as the main target organ affected by endocrine disrupting chemicals. The aim of this work was to assess exposure effects of defined and environmentally relevant mixtures of chlorinated, brominated and perfluorinated chemicals on steroidogenesis, using the H295R adrenocortical cell line model in combination with a newly developed liquid chromatography tandem mass spectrometry (LC-MS/MS) method. By using this approach, we could simultaneously analyze 19 of the steroids in the steroid biosynthesis pathway, revealing a deeper insight into possible disruption of steroidogenesis. Our results showed a noticeable down-regulation in steroid production when cells were exposed to the highest concentration of a mixture of brominated and fluorinated compounds (10,000-times human blood values). In contrast, up-regulation was observed with estrone under the same experimental condition, as well as with some other steroids when cells were exposed to a perfluorinated mixture (1000-times human blood values), and the mixture of chlorinated and fluorinated compounds. Interestingly, the low concentration of the perfluorinated mixture alone produced a significant, albeit small, down-regulation of pregnenolone, and the total mixture a similar effect on 17-hydroxypregnenolone. Other mixtures resulted in only slight deviations from the control. Indication of synergistic effects were noted when we used a statistical model to improve data interpretation. A potential for adverse outcomes of human exposures is indicated, pointing to the need for further investigation into these mixtures.

Evaluating model reduction under parameter uncertainty.

BACKGROUND: The dynamics of biochemical networks can be modelled by systems of ordinary differential equations. However, these networks are typically large and contain many parameters. Therefore model reduction procedures, such as lumping, sensitivity analysis and time-scale separation, are used to simplify models. Although there are many different model reduction procedures, the evaluation of reduced models is difficult and depends on the parameter values of the full model. There is a lack of a criteria for evaluating reduced models when the model parameters are uncertain. RESULTS: We developed a method to compare reduced models and select the model that results in similar dynamics and uncertainty as the original model. We simulated different parameter sets from the assumed parameter distributions. Then, we compared all reduced models for all parameter sets using cluster analysis. The clusters revealed which of the reduced models that were similar to the original model in dynamics and variability. This allowed us to select the smallest reduced model that best approximated the full model. Through examples we showed that when parameter uncertainty was large, the model should be reduced further and when parameter uncertainty was small, models should not be reduced much. CONCLUSIONS: A method to compare different models under parameter uncertainty is developed. It can be applied to any model reduction method. We also showed that the amount of parameter uncertainty influences the choice of reduced models.

LC-MS/MS based profiling and dynamic modelling of the steroidogenesis pathway in adrenocarcinoma H295R cells.

Endocrine disrupting chemicals have been reported to exert effects directly on enzymes involved in steroid biosynthesis. Here, we present a new liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for profiling the steroid metabolome of H295R human adrenocarcinoma cells. Our method can simultaneously analyse 19 precursors, intermediates and end-products, representing the adrenal steroid biosynthesis pathway. In order to obtain better insights into the processes of steroidogenesis, we investigated the dose-response relationship of forskolin, an activator of adenylate cyclase, on steroid production in H295R cells. We observed that 1.5 µM forskolin stimulated steroid production at approximately 50% of the maximum rate for most steroids. Hence, we studied the time course for steroid synthesis over 72 h in H295R cells that were stimulated with forskolin. At 24 h, we observed a peak in steroid levels for the intermediate metabolites, such as progesterone and pregnenolone, while end-products such as testosterone and cortisol continued to increase until 72 h. Finally, we show how global data provide a unique basis to develop a comprehensive, dynamic model for steroidogenesis using first order kinetics. The timeline data made it possible to estimate all reaction rate constants of the network. We propose this method as a unique and sensitive screening tool to identify effects on adrenal steroidogenesis by endocrine disrupting compounds.