Analysis of Transcription Factor Network Underlying 3T3-L1 Adipocyte Differentiation

<div><p>Lipid accumulation in adipocytes reflects a balance between enzymatic pathways leading to the formation and breakdown of esterified lipids, primarily triglycerides. This balance is extremely important, as both high and low lipid levels in adipocytes can have deleterious consequences. The enzymes responsible for lipid synthesis and breakdown (lipogenesis and lipolysis, respectively) are regulated through the coordinated actions of several transcription factors (TFs). In this study, we examined the dynamics of several key transcription factors (TFs) - PPARγ, C/EBPβ, CREB, NFAT, FoxO1, and SREBP-1c - during adipogenic differentiation (week 1) and ensuing lipid accumulation. The activation profiles of these TFs at different times following induction of adipogenic differentiation were quantified using 3T3-L1 reporter cell lines constructed to secrete the <i>Gaussia</i> luciferase enzyme upon binding of a TF to its DNA binding element. The dynamics of the TFs was also modeled using a combination of logical gates and ordinary differential equations, where the logical gates were used to explore different combinations of activating inputs for PPARγ, C/EBPβ, and SREBP-1c. Comparisons of the experimental profiles and model simulations suggest that SREBP-1c could be independently activated by either insulin or PPARγ, whereas PPARγ activation required both C/EBPβ as well as a putative ligand. Parameter estimation and sensitivity analysis indicate that feedback activation of SREBP-1c by PPARγ is negligible in comparison to activation of SREBP-1c by insulin. On the other hand, the production of an activating ligand could quantitatively contribute to a sustained elevation in PPARγ activity.</p></div

A novel model for ex situ reperfusion of the human liver following subnormothermic machine perfusion

Machine perfusion-based organ preservation techniques are prudently transitioning into clinical practice. Although experimental data is compelling, the outcomes in the highly variable clinical donation-transplantation setting are unpredictable. Here, we offer an intermediate tool for pre-clinical assessment of human donor livers. We present a model for ex situ reperfusion of discarded human livers and report on its application in three human livers that have undergone subnormothermic (21 degrees C) machine perfusion as an experimental preservation method. During reperfusion, the livers macroscopically reperfused in the first 15 minutes, and remained visually well-perfused for 3 hours of ex situ reperfusion. Bile production and oxygen consumption were observed throughout ex situ reperfusion. ATP levels increased 4.25-fold during SNMP. Between the end of SNMP and the end of reperfusion ATP levels dropped 45%. ALT levels in blood increased rapidly in the first 30 minutes and ALT release continued to taper off towards the end of perfusion. Release of CRP, TNF-alpha, IL-1 ss, and IL-12, IFN-gamma was sustained during reperfusion. These findings support the use of this model for the evaluation of novel human liver preservation techniques

Sum of squared residuals (SSR) and adjusted R² values for the best ten mass action and Hill equation models representing different logical gate combinations.

<p>Sum of squared residuals (SSR) and adjusted R² values for the best ten mass action and Hill equation models representing different logical gate combinations.</p

Schematic of TF network model.

<p>Arrows indicate direction of interaction. Model parameters labeling the dotted arrows (k₂, k₅, k₈, and k₁₀) represent first-order decay rate constants for the TFs. The rate constants shown in the schematic refer to the mass action models. See text for abbreviations.</p

Gut Microbiota-Derived Tryptophan Metabolites Modulate Inflammatory Response in Hepatocytes and Macrophages

Summary: The gut microbiota plays a significant role in the progression of fatty liver disease; however, the mediators and their mechanisms remain to be elucidated. Comparing metabolite profile differences between germ-free and conventionally raised mice against differences between mice fed a low- and high-fat diet (HFD), we identified tryptamine and indole-3-acetate (I3A) as metabolites that depend on the microbiota and are depleted under a HFD. Both metabolites reduced fatty-acid- and LPS-stimulated production of pro-inflammatory cytokines in macrophages and inhibited the migration of cells toward a chemokine, with I3A exhibiting greater potency. In hepatocytes, I3A attenuated inflammatory responses under lipid loading and reduced the expression of fatty acid synthase and sterol regulatory element-binding protein-1c. These effects were abrogated in the presence of an aryl-hydrocarbon receptor (AhR) antagonist, indicating that the effects are AhR dependent. Our results suggest that gut microbiota could influence inflammatory responses in the liver through metabolites engaging host receptors. : Dysbiosis of the intestinal microbiota is an emerging factor contributing to the progression of fatty liver disease. Krishnan et al. utilize metabolomics and biochemical assays in conjunction with animal and cell culture models to identify microbiota-dependent metabolites that engage a host receptor to affect liver inflammatory responses under lipid loading. Keywords: nonalcoholic fatty liver disease, gut microbiota, metabolomics, indole-3-acetate, inflammation, aryl hydrocarbon recepto

Fraction of model calculated data points that lie outside of the 95% confidence interval of the baseline case after a parameter has been perturbed.

<p>Plus and minus columns refer to a 15% increase and decrease in the parameter value relative to the baseline case, respectively.</p

Perturbation of CREB activity profiles.

<p>(A) Perturbation of CREB activity profiles upon addition of 10 µM of forskolin or 0.1% DMSO at day 0 for 48 h. (B) and (C) show activation of C/EBPβ and PPARγ, respectively, with forskolin treatment as compared to DMSO control. The RLU/h/RFU for each time point was normalized to the value at the start of differentiation (day 0). Data are from two independent experiments and represent mean ± SD. *: p<0.05.</p

Simulated activity profiles for (A) CREB, (B) C/EBP, (C) PPARγ, and (D) SREBP-1c generated using the top five Hill equation models.

<p>The model numbers are shown in the figure legend, listed in order of increasing sum of squared residuals (SSR). The measured data (normalized mean Gluc activities, RFL/h/RLU) are shown as red dots. The specific combination of logic gates for these models can be determined from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100177#pone.0100177.s007" target="_blank">Tables S3</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100177#pone.0100177.s008" target="_blank">S4</a>.</p

Simulated activity profiles for (A) CREB, (B) C/EBP, and (C) PPARγ generated using the best fitting mass action model with added forskolin input.

<p>Forskolin treatment was modeled as a step increase in IBMX during the first induction period (days 0 to 2). All other model parameters (<i>k₁</i>–<i>k₁₃</i>) were kept at the same values that were estimated from the training data without forskolin. Dashed lines show 95% confidence intervals. The measured data (normalized mean Gluc activities, RFL/h/RLU) are shown as red dots.</p