Systems Biology of Microbiota Metabolites and Adipocyte Transcription Factor Network
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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.
adipocyte transcription factor network
stochastic simulation algorithm
Choi, Kyungoh (2013). Systems Biology of Microbiota Metabolites and Adipocyte Transcription Factor Network. Doctoral dissertation, Texas A & M University. Available electronically from