Systems Biology of Microbiota Metabolites and Adipocyte Transcription Factor Network

The overall goal of this research is to understand roles of gut microbiota metabolites and adipocyte transcription factor (TF) network in health and disease by developing systematic analysis methods. As microbiota can perform diverse biotransformation reactions, the spectrum of metabolites present in the gastrointestinal (GI) tract is extremely complex but only a handful of bioactive microbiota metabolites have been identified. We developed a metabolomics workflow that integrates in silico discovery with targeted mass spectrometry. A computational pathway analysis where microbiota metabolisms are modeled as a single metabolic network is utilized to predict a focused set of targets for multiple reaction monitoring (MRM) analysis. We validated our methodology by predicting, quantifying in murine cecum and feces and characterizing tryptophan (TRP)-derived metabolites as ligands for the aryl hydrocarbon receptor. The adipocyte process of lipid droplet accumulation and differentiation is regulated by multiple TFs that function together in a network. Although individual TF activation is previously reported, construction of an integrated network has been limited due to different measurement conditions. We developed an integrated network model of key TFs - PPAR, C/EBP, CREB, NFAT, FoxO1, and SREBP-1c - underlying adipocyte differentiation. A hypothetic model was determined based on literature, and stochastic simulation algorithm (SSA) was applied to simulate TF dynamics. TF activation profiles at different stages of differentiation were measured using 3T3-L1 reporter cell lines where binding of a TF to its DNA binding element drives expression of the Gaussia luciferase gene. Reaction trajectories calculated by SSA showed good agreement with experimental measurement. The TF model was further validated by perturbing dynamics of CREB using forskolin, and comparing the predicted response with experimental data. We studied the molecular recognition mechanism underlying anti-inflammatory function of a bacterial metabolite, indole in DC2.4 cells. The indole treatment attenuated the fraction of cells that were producing the pro-inflammatory cytokine, TNFα and knockdown of nuclear receptor related 1 (Nurr1; NR4A2) resulted in less indole-derived suppression of TNFα production. The first discovery of NR4A2 as a molecular mediator of the endogenous metabolite, indole is expected to provide a new strategy for treatment of inflammatory disorders

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

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

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

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

Validation of TF reporter constructs.

<p>(A) Foxo1, CREB, SREBP-1c, and NFAT3 reporter plasmids were validated by co-transfecting plasmids for constitutive expression of each TFs (pCMV5-FLAG-FoxO1, pCMV-Sport6-CREB, pSV Sport SREBP-1c, pEGFP-C1 NFAT3) and corresponding reporter plasmids into 293T/17 cells, and monitoring the TF-mediated Gaussia luciferase (Gluc) activity. A yellow fluorescence protein expressing plasmid (pEYFP-N1) plasmid was co-transfected with each reporter plasmid as a control. (B) The PPARγ reporter construct was verified by transfecting a plasmid containing the PPARγ gene was into 3T3-L1 PPARγ reporter cells, and activating PPARγ with 25 µM of rosiglitazone (RGZ). (C) The C/EBPβ reporter construct was validated by activating C/EBPβ in 3T3-L1 preadipocyte C/EBPβ reporter cells with the cytokine oncostatin M (OSM). Data represent mean ± SD. *: p<0.05.</p