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Inference of gene-regulatory networks in primary human hepatocytes
182 Seiten, Dissertation Eberhard-Karls-Universität Tübingen (2011), Softcover, A5
The liver is among the largest and most complex organs in the human body. It is responsible for the detoxification, metabolization, and elimination of thousands of chemically diverse substances. This essential functionality is mainly accomplished by hepatocytes, the most abundant cell type in human liver tissue. An arsenal of liver proteins that are sensitive to chemical compounds, orchestrates the activation of genes and thus the production of specific enzymes, transporters, and other proteins. These proteins transform endogenous and exogenous chemical substances into less toxic and more water soluble metabolites and promote the efflux of these compounds. The complex architecture of these regulatory programs in human liver cells is widely unknown. Defects in the regulatory mechanisms are known to be involved in the development of a variety of liver diseases. Understanding the structure and functionality of gene-regulatory networks in human liver cells is, therefore, of high medical relevance.
The work at hand presents several new approaches to the reconstruction of gene-regulatory networks in primary human hepatocytes. The main building blocks of this kind of networks are transcription factors, which are able to activate genes by recognizing and binding to specific DNA sequence elements in the regulatory region of these genes. In this work, first, a new approach for the prediction of transcription factor binding specificities is proposed. Next, a new method for the reconstruction of transcriptional regulatory networks is presented that mines for patterns of transcription factor binding sites in sets of co-expressed genes. The application of this method to drug-response gene expression data revealed several new regulatory relationships that were successfully validated in wet lab experiments. Finally, genome-wide association analyses are performed to identify new associations between genetic markers and quantitative gene expression levels based on a cohort of 150 human liver samples.