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.

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.

A comparison of Monte Carlo sampling methods for metabolic network models.

Reaction rates (fluxes) in a metabolic network can be analyzed using constraint-based modeling which imposes a steady state assumption on the system. In a deterministic formulation of the problem the steady state assumption has to be fulfilled exactly, and the observed fluxes are included in the model without accounting for experimental noise. One can relax the steady state constraint, and also include experimental noise in the model, through a stochastic formulation of the problem. Uniform sampling of fluxes, feasible in both the deterministic and stochastic formulation, can provide us with statistical properties of the metabolic network, such as marginal flux probability distributions. In this study we give an overview of both the deterministic and stochastic formulation of the problem, and of available Monte Carlo sampling methods for sampling the corresponding solution space. We apply the ACHR, OPTGP, CHRR and Gibbs sampling algorithms to ten metabolic networks and evaluate their convergence, consistency and efficiency. The coordinate hit-and-run with rounding (CHRR) is found to perform best among the algorithms suitable for the deterministic formulation. A desirable property of CHRR is its guaranteed distributional convergence. Among the three other algorithms, ACHR has the largest consistency with CHRR for genome scale models. For the stochastic formulation, the Gibbs sampler is the only method appropriate for sampling at genome scale. However, our analysis ranks it as less efficient than the samplers used for the deterministic formulation.

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.