Bile acid preconditions does not induce a shift from apoptosis to necrosis when HepG2.rNtcp cells are exposed to 200 μM GCDCA.

<p>HepG2.rNtcp cells were preconditioned with CDCA, GCDCA, CA, TCA, UDCA or TUDCA (25 μM / 24 h), without challenge with GCDCA (Control, black bars) or challenged with the pro-apoptotic concentration (200 μM) GCDCA for 4 h (grey bars). LDH activity was measured in medium alone and in medium from digitonin-treated cells (100% leakage). LDH leakage is shown as percentage of total LDH. Experiments were performed in n = 3 for each condition. Error bars represent the SEM.</p

GCDCA, TCA and TUDCA preconditioning are most protective against GCDCA-induced apoptosis, compared to preconditioning with CDCA, CA or UDCA.

<p>HepG2.rNtcp cells were preconditioned with CDCA, GCDCA, CA, TCA, UDCA or TUDCA (25 μM / 24 h), without challenge with GCDCA (Control, black bars) or challenged with 200 μM GCDCA for 4 h (grey bars). Caspase-3/7 activity is shown as fold change compared to control (no preconditioning and no GCDCA challenge), control values were set as one. Experiment where performed n = 3–10 depending on the condition. Error bars represent the SEM. *** = P<0.001, No challenge versus GCDCA challenge counterpart. # = P<0.05, ## = P<0.01, ### = P<0.001, compared to no preconditioning (24 hr) / GCDCA challenge.</p

The protective effect of GCDCA preconditioning is time- and concentration-dependent.

<p>HepG2.rNtcp cells were preconditioned with GCDCA for different periods and in different concentrations. <b>(A)</b> Cells were preconditioned with 25 μM GCDCA for 2, 6, 12, 24, and 48 h, without challenge (control, black bars) or challenged 200 μM GCDCA for 4 h (grey bars). <b>(B)</b> Cells were preconditioned with 0.25, 1, 5, or 25 μM GCDCA for 24 h without challenge (control, black bars) or challenged 200 μM GCDCA for 4 h (grey bars). Caspase-3/7 activity is shown as fold change compared to control (no preconditioning and no GCDCA challenge), control values were set as one. Experiments were performed in n = 3 for each condition. Error bars represent the SEM. * = P<0.05, *** = P<0.001 No challenge versus GCDCA challenge counterpart. ### = P<0.001 compared to no preconditioning (24 h) / no GCDCA challenge.</p

GCDCA preconditioning does not increase the efflux of GCDCA after the challenge.

<p>HepG2.rNtcp cells were preconditioned with GCDCA (25 μM / 24 h) without challenge with GCDCA (Control, black bars) or challenged with the pro-apoptotic concentration (200 μM) GCDCA for 4 h (grey bars). UHPLC-MS/MS was used to determine bile acid concentrations, intracellular GCDCA concentrations are shown as relative peak area. Error bars represent the SEM, experiment was performed in n = 3. ** = P<0.01, No challenge vs GCDCA challenge counterpart.</p

Table1.xlsx

<p>Immunity and cellular metabolism are tightly interconnected but it is not clear whether different pathogens elicit specific metabolic responses. To address this issue, we studied differential metabolic regulation in peripheral blood mononuclear cells (PBMCs) of healthy volunteers challenged by Candida albicans, Borrelia burgdorferi, lipopolysaccharide, and Mycobacterium tuberculosis in vitro. By integrating gene expression data of stimulated PBMCs of healthy individuals with the KEGG pathways, we identified both common and pathogen-specific regulated pathways depending on the time of incubation. At 4 h of incubation, pathogenic agents inhibited expression of genes involved in both the glycolysis and oxidative phosphorylation pathways. In contrast, at 24 h of incubation, particularly glycolysis was enhanced while genes involved in oxidative phosphorylation remained unaltered in the PBMCs. In general, differential gene expression was less pronounced at 4 h compared to 24 h of incubation. KEGG pathway analysis allowed differentiation between effects induced by Candida and bacterial stimuli. Application of genome-scale metabolic model further generated a Candida-specific set of 103 reporter metabolites (e.g., desmosterol) that might serve as biomarkers discriminating Candida-stimulated PBMCs from bacteria-stimuated PBMCs. Our analysis also identified a set of 49 metabolites that allowed discrimination between the effects of Borrelia burgdorferi, lipopolysaccharide and Mycobacterium tuberculosis. We conclude that analysis of pathogen-induced effects on PBMCs by a combination of KEGG pathways and genome-scale metabolic model provides deep insight in the metabolic changes coupled to host defense.</p

Rise and fall periods of metabolic concentrations, parameters, and fluxes.

<p>The rise and fall periods are represented by light-gray and dark-gray bars (median median absolute deviation), respectively. The rise period is defined as the time period during which a trajectory rises from to of its maximal value. Similarly, the fall period is defined as the time period during which a trajectory falls from to of its maximal value.</p

The VLDL particle secretion is reduced upon LXR activation.

<p> histograms were calculated from the acceptable sets to determine the density of trajectories during the treatment period. A darker color represents a higher density of trajectories in that specific region and time point. The white lines enclose the central of the densities. A) VLDL particle secretion. B) VLDL-TG production. The data is represented by mean standard deviation. C) VLDL-CE production. D) Ratio of VLDL-TG production to VLDL particle secretion. E) Ratio of VLDL-CE production to VLDL particle secretion.</p

Sensitivity analysis of the hepatic triglyceride accumulation.

<p>A sensitivity analysis was performed to identify adapting processes for which the hepatic triglyceride level is sensitive. The quantity of interest is given by the total hepatic triglyceride pool (). A hundred batches, each containing thousand randomly selected optimized trajectories, were generated. Subsequently, for each batch the temporal sensitivities were calculated. Top) mean distances for all states, parameters, and fluxes. Bottom) Three examples of dynamic sensitivity profiles. A distance was considered significant when it exceeds the critical value indicated by the dotted lines (obtained from the Kolmogorov distribution using a significance level of ).</p

Computational workflow of ADAPT to analyze the effects of a treatment intervention.

<p><i>Step 1</i>. Quantitative experimental data was generated at different stages of a treatment intervention. <i>Step 2</i>. Cubic smoothing splines were calculated that describe the dynamic trend of the experimental data. To account for experimental and biological uncertainties a collection of splines was calculated using a Monte Carlo approach. <i>Step 3</i>. The cubic splines were used as input for the computational approach to iteratively estimate dynamic trajectories of metabolic parameters and fluxes. The additional insights obtained via the computational analysis could be used to design new experiments, and repeat the mentioned steps. For each step an example is given. The data is represented by means standard deviations.</p

Estimation of time-dependent parameters.

<p>The progression of adaptations induced by a treatment intervention is predicted by identifying necessary dynamic changes in the model parameters to describe the transition between experimental data obtained during different stages of the treatment. The time-dependency of the parameters is introduced by dividing a simulation in steps of time period. Initially () the system is in steady-state and corresponding parameters are estimated to describe the experimental data of the untreated phenotype. Subsequently, for each step the system is simulated for a time period of using the final values of the model states of the previous step as initial conditions (B). Simultaneously, parameters are estimated (A) by minimizing the difference between the data interpolants and corresponding model outputs (C). Here, the previously estimated parameter set was provided as initial set for the optimization algorithm.</p