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Quantitative Measurement Technique for Transcription Factor Profiles

Zuyi (Jacky) Huang, Department of Chemical Engineering, Texas A& M University, 225 Jack E. Brown Engineering Building, 3122 TAMU, College Station, TX 77843, Fatih Senocak, Texas A&M University, Department of Chemical Engineering, 3122 TAMU, College Station, TX 77840, Arul Jayaraman, Department of Chemical Engineering, Texas A&M University, College Station, TX 77843-3122, and Juergen Hahn, Department of Chemical Engineering, Texas A& M University, College Station, TX 77843-3122.

Control of gene expression by transcription factors is an integral component of cell signaling and gene expression regulation (Hoffmann et al., 2007). Different transcription factors exhibit different expression and activation dynamics, and together govern the expression of specific genes and cellular phenotypes (Grove and Walhout, 2008). An important requirement for the development of these signal transduction models is the ability to quantitatively describe the activation dynamics of transcriptions so that parameters can be estimated for model development. The activation of transcription factors under different conditions have been conventionally monitored using protein binding techniques such as electrophoretic mobility shift assay or chromatin immunoprecipitation (Elnitski et al., 2006). While these techniques provide snapshots of activation at a small set of single time points, they can yield only qualitative or semi-quantitative data at best. This approach also requires the use of multiple cell populations for each time point at which transcription factor activation is to be measured, and often, the true dynamics of transcription factors are not captured due to limited sampling points and frequencies. Hence, these methods are not ideal for investigating time-dependent activation of transcription factors in a quantitative manner.

More recently, fluorescence-based reporter systems have been developed for continuous and non-invasive monitoring of transcription factors and elucidation of regulatory molecule dynamics. Recent studies (Thompson et al., 2004; Wieder et al., 2005, King et al., 2007) have used green fluorescent protein (GFP) as a reporter molecule for continuously monitoring activation of a panel of transcription factors, underlying the inflammatory response in hepatocytes for 24 h. These systems involve expressing GFP under the control of a minimal promoter such that GFP expression and fluorescence is observed only when a transcription factor is activated (i.e., when the transcription factor binds to its specific DNA binding sequence and induces expression from a minimal promoter). The dynamics of GFP fluorescence is used as the indicator for dynamics of the transcription factor being profiled. The primary drawback with this approach is that it does not provide direct activation rates of the transcription factors being investigated. Even though transcription factor dynamics influence GFP dynamics, the relationship between the two is non-trivial as the induction of GFP fluorescence itself involves multiple steps (i.e., transcription of GFP mRNA, GFP protein translation, post-translational processing, etc) (Subramanian and Srienc, 1996), and not all of these steps contribute equally to regulation of GFP expression. The observed fluorescence dynamics in GFP reporter cell systems is the result of two different dynamics: (i) the dynamics of transcription factor activation by a soluble stimulus-mediated signal transduction pathway and (ii) the dynamics of GFP expression, folding, and maturation. Therefore, it is necessary to uncouple the effects of these independent systems in order to quantitatively determine transcription factor activation profiles underlying cellular phenotpyes.

In this work, we develop a strategy for determining transcription factor concentrations from fluorescence microscopy data. This technique is based upon the following steps:

1) Development of an image analysis method based on K-means clustering and Principal Component Analysis (PCA) to obtain a fluorescence intensity profile from the fluorescence microscopy images. This method distinguishes the regions of the image with similar brightness and then groups them into the same cluster.

2) Based on the model initially presented by Subramanian and Srienc (1996), a model for transcription, translation, and activation of GFP is derived to correlate transcription factor concentration with fluorescence intensity.

3) A procedure for solving an inverse problem involving the model developed in 2) and the fluorescence data derived from the image analysis from step 1) is presented. This procedure computes the transcription factor profiles from fluorescence intensity data.

The technique has been implemented to derive quantitative concentration of NF-kB from fluorescence microscopy images of hepatocytes stimulated by TNF-a with four different concentrations. Part of the derived NF-kB data is then used to develop and refine a model of the TNF-a signaling pathway. The refined model of the TNF-a signaling pathway is tested on data not used for parameter estimation and it is found that it can predict the dynamics of TNF-a signaling pathway very well.


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