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Gene regulatory network inference in human pathogenic fungi
Pathogenic fungi are a serious threat to people with impeded immune system, especially during organ transplantation and HIV infections. As the number of treatments that include a weakening of the patients immune system increase, so does the number of fungal infections. Often, the infection is opportunistic, meaning the pathogen already lives as a commensal in the host and uses the weak immune system to spread out and starts to colonise different parts of the host. These infections can lead to systemic, life-threatening infections, lowering the survival rate of the often already weakened host. Two of the most common human pathogens are Candida albicans and Aspergillus fumigatus. While C. albicans is a commensal and part of the healthy human flora, it can turn to an opportunistic pathogen, once the hosts immune system fails to contain it. Conidia of A. fumigatus are inhaled by humans every day and removed again by the immune system. In a weakened host, A. fumigatus can colonise the lung of the host and spread to other parts of the body, which can lead to fatal results, if no treatment is administered. The first part of this thesis aims to study the gene regulatory network of C. albicans on a genome-wide level, with a scale-free distribution of node degrees. These networks can be used to identify genes with central regulatory functions, called hubs, which are possible drug targets and can be the starting point for future studies. The modeling process included a large set of gene expression data measured by microarrays, the use of prior knowledge and a automatically harvested gold standard for the evaluation of the results. The final model is used to identify several hubs and is also able to reproduce current knowledge. A focused small-scale gene regulatory network is inferred for A. fumigatus while it is treated with the clinically applied drug caspofungin. The chapter describes the process from mapping of the RNA-Seq data over the selection of candidate genes and the harvest of prior knowledge to the application ...
Gene regulatory network inference in human pathogenic fungi
Pathogenic fungi are a serious threat to people with impeded immune system, especially during organ transplantation and HIV infections. As the number of treatments that include a weakening of the patients immune system increase, so does the number of fungal infections. Often, the infection is opportunistic, meaning the pathogen already lives as a commensal in the host and uses the weak immune system to spread out and starts to colonise different parts of the host. These infections can lead to systemic, life-threatening infections, lowering the survival rate of the often already weakened host. Two of the most common human pathogens are Candida albicans and Aspergillus fumigatus. While C. albicans is a commensal and part of the healthy human flora, it can turn to an opportunistic pathogen, once the hosts immune system fails to contain it. Conidia of A. fumigatus are inhaled by humans every day and removed again by the immune system. In a weakened host, A. fumigatus can colonise the lung of the host and spread to other parts of the body, which can lead to fatal results, if no treatment is administered. The first part of this thesis aims to study the gene regulatory network of C. albicans on a genome-wide level, with a scale-free distribution of node degrees. These networks can be used to identify genes with central regulatory functions, called hubs, which are possible drug targets and can be the starting point for future studies. The modeling process included a large set of gene expression data measured by microarrays, the use of prior knowledge and a automatically harvested gold standard for the evaluation of the results. The final model is used to identify several hubs and is also able to reproduce current knowledge. A focused small-scale gene regulatory network is inferred for A. fumigatus while it is treated with the clinically applied drug caspofungin. The chapter describes the process from mapping of the RNA-Seq data over the selection of candidate genes and the harvest of prior knowledge to the application ...
Gene regulatory network inference in human pathogenic fungi
Altwasser, Robert (author) / Guthke, Reinhard / Westra, Ronald / Figge, Marc Thilo Günter
2015-01-01
Theses
Electronic Resource
English
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