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.

ReCodLiver0.9: Overcoming Challenges in Genome-Scale Metabolic Reconstruction of a Non-model Species.

The availability of genome sequences, annotations, and knowledge of the biochemistry underlying metabolic transformations has led to the generation of metabolic network reconstructions for a wide range of organisms in bacteria, archaea, and eukaryotes. When modeled using mathematical representations, a reconstruction can simulate underlying genotype-phenotype relationships. Accordingly, genome-scale metabolic models (GEMs) can be used to predict the response of organisms to genetic and environmental variations. A bottom-up reconstruction procedure typically starts by generating a draft model from existing annotation data on a target organism. For model species, this part of the process can be straightforward, due to the abundant organism-specific biochemical data. However, the process becomes complicated for non-model less-annotated species. In this paper, we present a draft liver reconstruction, ReCodLiver0.9, of Atlantic cod (Gadus morhua), a non-model teleost fish, as a practicable guide for cases with comparably few resources. Although the reconstruction is considered a draft version, we show that it already has utility in elucidating metabolic response mechanisms to environmental toxicants by mapping gene expression data of exposure experiments to the resulting model.

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.

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.

Terminal Deoxynucleotidyl Transferase (TdT) Inhibiti on of Cord Blood Derived B and T Cells Expansion.

Purpose: Terminal deoxynucleotidyl transferase(TdT) is a DNA polymerase that is present in immature pre-B and pre-T cells. TdT inserts N-nucleotides to the V (D) J gene segment during rearrangements of genes, therefore, it plays a vital role in the development and variation of the immune system in vertebrates. Here we evaluated the relationship between cytokines like interleukin-2 (IL-2), interleukin-7 (IL-7), and interleukin-15 (IL-15) and TdT expression in cord blood mononuclear cells and also effect of inhibition in the expansion of B and T cells derived from cord blood. Methodes: The cord blood mononuclear cells were cultured with different combination of cytokines for 21days, which they were harvested in definite days (7, 14 and 21) and evaluated by flow cytometry. Results: Our data indicated that TdT expression increased in cord blood mononuclear cells using immune cell key cytokines without being dependent on the type of cytokines. TdT inhibition reduced both the expansion of B and T cells derived from cord blood and also declined the apoptosis and proliferation. Considered together, TdT played an important role in the control of the expansion of B and T cells derived from cord blood. Conclusion: considered together, it was observed that TdT expression was increased by cytokines and TdT inhibition not only reduced B and Tcells derived from cord blood, but it also affected the rate of apoptosis and proliferation.

Impact of C-rel inhibition of cord blood-derived B-, T-, and NK cells.

The c-Rel transcription factor is a unique member of the nuclear factor (NF)-κB family that has a role in curtailing the proliferation, differentiation, cytokine production, and overall activity of B- and T-cells. In addition, c-Rel is a key regulator of apoptosis in that it influences the expression of anti-apoptotic genes such as Bcl-2 and Bcl-xL; conversely, inhibition of c-Rel increases cell apoptosis. To better understand the relationship between c-Rel expression and effects on B- and T-cell expansion, the current study evaluated c-Rel expression in cord blood mononuclear cells. This particular source was selected as cord blood is an important source of cells used for transplantation and immunotherapy, primarily in treating leukemias. As stem cell factor (SCF) and FLT3 are important agents for hematopoietic stem cell expansion, and cytokines like interleukin (IL)-2, -7, and -15 are essential for T- and B- (and also NK) cell development and proliferation, the current study evaluated c-Rel expression in cord blood mononuclear cells and CD34+ cells, as well as effects on B-, T-, and NK cells associated with alterations in c-Rel expression, using flow cytometry and PCR. The results showed c-Rel expression increased among cells cultured in the presence of SCF and FLT3 but was reduced when IL-2, IL-7, and IL-15 were used all together. Further, inhibition of c-Rel expression by siRNA reduced cord blood-derived B-, T-, and NK cell differentiation and expansion. These results indicated that with cells isolated from cord blood, c-Rel has an important role in B-, T-, and NK cell differentiation and, further, that agents (select cytokines/growth factors) that could impact on its expression might not only affect immune cell profiles in a host but could potentially also limit apoptotic activities in (non-)immune cells in that host. In the context of cancer (immuno)therapy, in particular, when cord blood is used an important source in stem cell transplantation in leukemia patients, such down-regulating changes in c-Rel levels could be counter-productive.