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UBC Theses and Dissertations

Network-based integration and visualization of high-throughput datasets in Pseudomonas aeruginosa Castillo Arnemann, Javier José


Pseudomonas aeruginosa is a clinically-important, opportunistic pathogen that is the third leading cause of hospital infections in North America, the major cause of life-threatening chronic infections in patients with cystic fibrosis, and a major threat due to its high level of antibiotic resistance. To understand the complexity behind the adaptive behaviours of P. aeruginosa it is necessary to employ systems biology methods made possible by the ongoing revolution in high-throughput omics technologies. One powerful systems biology approach leverages existing molecular interaction databases to generate networks showing the interactions between the identified molecules. However, most existing interaction databases are focused on data for humans and other well-studied organisms; thus, there is a lack of systems biology tools to study medically-important bacterial pathogens such as P. aeruginosa. I developed the Pseudomonas aeruginosa Interaction Database, PaIntDB, to fill in this gap. It is an intuitive web-based tool for network-based systems biology analyses using protein-protein interactions (PPI). It enables the interpretation and visualization of omics studies including proteomics, RNA-Seq, and Tn-Seq. These high-throughput datasets are mapped onto PPI networks, which can be explored visually and filtered to uncover putative molecular pathways related to the conditions of study. PaIntDB employs the most comprehensive P. aeruginosa interactome to date, collected from a variety of resources, including interactions predicted computationally to further expand analysis capabilities. Two case studies demonstrate how PaIntDB can be used to quickly identify functional gene groups involved in growth in physiologically-relevant conditions and biofilm formation, and use these insights to derive new hypotheses about the underlying biology.

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