Analysis of transcription factor network underlying 3T3-L1 adipocyte differentiation.

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/EBPß, 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 Gaussia 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/EBPß, 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/EBPß 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.

Proteomic analysis of 3T3-L1 adipocyte mitochondria during differentiation and enlargement.

The increase in adipose tissue mass arises in part from progressive lipid loading and triglyceride accumulation in adipocytes. Enlarged adipocytes produce the highest levels of pro-inflammatory molecules and reactive oxygen species (ROS). Since mitochondria are the site for major metabolic processes (e.g., TCA cycle) that govern the extent of triglyceride accumulation as well as the primary site of ROS generation, we quantitatively investigated changes in the adipocyte mitochondrial proteome during different stages of differentiation and enlargement. Mitochondrial proteins from 3T3-L1 adipocytes at different stages of lipid accumulation (days 0-18) were digested and labeled using the iTRAQ 8-plex kit. The labeled peptides were fractionated using a liquid phase isoelectric fractionation system (MSWIFT) to increase the depth of proteome coverage and analyzed using LC-MS/MS. A total of 631 proteins in the mitochondrial fraction, including endoplasmic reticulum-associated and golgi-related mitochondrial proteins, were identified and classified into 12 functional categories. A total of 123 proteins demonstrated a statistically significant change in expression in at least one of the time points over the course of the experiment. The identified proteins included enzymes and transporters involved in the TCA cycle, fatty acid oxidation, and ATP synthesis. Our results indicate that cultured adipocytes enter a state of metabolic-overdrive where increased flux through the TCA cycle and increased fatty acid oxidation occur simultaneously. The proteomic data also suggest that accumulation of reduced electron carriers and the resultant oxidative stress may be attractive targets for modulating adipocyte function in metabolic disorders.

Using the Tet-On system to develop a procedure for extracting transcription factor activation dynamics.

The regulation of gene expression by transcription factors through different expression and activation dynamics is an important aspect of genomics and systems biology. Reporter systems using green fluorescent protein (GFP) or luciferase are often used to infer transcription factor dynamics. We recently used an inverse problem solution of GFP reporter profiles to demonstrate that the activation dynamics of a model transcription actor (NF-kappaB) can be reconstructed from GFP data. This approach assumes that the general nature of the transcription factor dynamics is known; however, it is non-trivial to determine the exact nature of the transcription factor dynamics as it often depends upon the cell type and the stimulus used to activate the transcription factor. This, in turn, limits the determination of accurate transcription factor dynamics from reporter data, especially since the model used for solution of an inverse problem needs to be verified. To address this point, we developed a reporter cell line for expressing GFP using an inducible, artificial transcription factor (tTA) and minimal promoter system. The artificial transcription factor can be activated independent of the cellular regulatory machinery through addition of doxycycline. This allows us to directly control the dynamics of the artificial transcription factor, and thereby, develop a model describing its activation dynamics from reporter data. Our experimental data and model predictions are in good agreement, and illustrate the utility of our approach. Future work will focus on using the developed approach, i.e. solution of an inverse problem involving the model describing expression of GFP, to extract the dynamics of transcription factors that are currently uncharacterized.

Emerging affinity-based techniques in proteomics.

Proteomes of interest, such as the human proteome, have such complexity that no single technique is adequate for the complete analysis of the constituents. Depending on the goal (e.g., identification of a novel protein vs measurement of the level of a known protein), the tools required can vary significantly. While existing methods provide valuable information, their limitations drive the development of complementary, innovative methods to achieve greater breadth of coverage, dynamic range or specificity of analysis. We will discuss affinity-based methods and their applications, focusing on their unique advantages. In addition, we will describe emerging methods with potential value to proteomics, as well as the challenges that remain for proteomic studies.

Effect of uncoupling protein-1 expression on 3T3-L1 adipocyte gene expression.

The mitochondrial respiratory uncoupling protein 1 (UCP1) partially uncouples substrate oxidation and oxidative phosphorylation to promote the dissipation of cellular biochemical energy as heat in brown adipose tissue. We have recently shown that expression of UCP1 in 3T3-L1 white adipocytes reduces the accumulation of triglycerides. Here, we investigated the molecular basis underlying UCP1 expression in 3T3-L1 adipocytes. Gene expression data showed that forced UCP1 expression down-regulated several energy metabolism pathways; but ATP levels were constant. A metabolic flux analysis model was used to reflect the gene expression changes onto metabolic processes and concordance was observed in the down-regulation of energy consuming pathways. Our data suggest that adipocytes respond to long-term mitochondrial uncoupling by minimizing ATP utilization.