High-throughput technologies are generating a tremendous amount of data about cellular functions of human pathogens. However, despite the genome sequencing of hundreds of pathogens, the genomics approach has had limited success at identifying viable drug targets. One key hurdle has been that targets identified by sequencing analysis efforts do not take into account the network of interactions inside the cell;for instance, inhibiting the function of one protein may have no effect given the redundancy of pathways in the system. We propose to reconstruct and validate the metabolic and regulatory networks of Pseudomonas aeruginosa, with particular attention to previously identified mutants known to be critical for its virulence. In order to develop therapeutic strategies in the context of the intracellular networks in this human pathogen, there exists a significant need for a quantitative framework that can be used to contextualize high-throughput data, generate phenotypic predictions, and propose testable hypotheses regarding its physiology. Specifically, our proposed aims are to: (1) Reconstruct the metabolic and regulatory networks of P. aeruginosa to account for the function of 1500 genes, which will result in the largest network reconstruction of a pathogen to date;(2) Characterize the metabolic phenotypes under minimal media conditions of avirulent P. aeruginosa single-gene mutants identified previously in a signature-tagged mutagenesis screen;and (3) Analyze metabolic phenotypes of P. aeruginosa and avirulent mutant strains in cystic fibrosis-specific medium to develop possible therapeutic strategies based on conditionally essential genes. The outcome of this proposed program will be a well-characterized, well-validated model of P. aeruginosa that can be used for systematically identifying drug targets and key features of its pathogenicity, as well as a delineation of the key metabolic phenotypes (e.g., growth rate, byproduct secretions) of the STM-identified mutants that can be used potentially for the development of therapeutic strategies. The proposed program will lead to the most comprehensive reconstruction to date of a pathogen with a significant disease burden.
The proposed program will lead to the most comprehensive reconstruction to date of a human pathogen. Aside from its significant disease burden in hospital-acquired infections, Pseudomonas aeruginosa poses a significant problem in burn patients, individuals with cystic fibrosis, cancer patients on chemotherapy regimens, and other immuno-compromised individuals. Furthermore, drug resistance is already an important problem in P. aeruginosa infections and will certainly be an even greater challenge in the future. The outcome of this proposed program will be a well-characterized, well-validated model of P. aeruginosa that can be used for systematically identifying drug targets and key features of its pathogenicity.
|Bartell, Jennifer A; Blazier, Anna S; Yen, Phillip et al. (2017) Reconstruction of the metabolic network of Pseudomonas aeruginosa to interrogate virulence factor synthesis. Nat Commun 8:14631|
|Varga, John J; Barbier, Mariette; Mulet, Xavier et al. (2015) Genotypic and phenotypic analyses of a Pseudomonas aeruginosa chronic bronchiectasis isolate reveal differences from cystic fibrosis and laboratory strains. BMC Genomics 16:883|
|Jensen, Paul A; Dougherty, Bonnie V; Moutinho Jr, Thomas J et al. (2015) Miniaturized plate readers for low-cost, high-throughput phenotypic screening. J Lab Autom 20:51-5|
|Bartell, Jennifer A; Yen, Phillip; Varga, John J et al. (2014) Comparative metabolic systems analysis of pathogenic Burkholderia. J Bacteriol 196:210-26|
|Jensen, Paul A; Papin, Jason A (2014) MetDraw: automated visualization of genome-scale metabolic network reconstructions and high-throughput data. Bioinformatics 30:1327-8|
|Biggs, Matthew B; Papin, Jason A (2013) Novel multiscale modeling tool applied to Pseudomonas aeruginosa biofilm formation. PLoS One 8:e78011|
|Walpole, Joseph; Papin, Jason A; Peirce, Shayn M (2013) Multiscale computational models of complex biological systems. Annu Rev Biomed Eng 15:137-54|
|Schmidt, Brian J; Papin, Jason A; Musante, Cynthia J (2013) Mechanistic systems modeling to guide drug discovery and development. Drug Discov Today 18:116-27|
|Thiele, Ines; Swainston, Neil; Fleming, Ronan M T et al. (2013) A community-driven global reconstruction of human metabolism. Nat Biotechnol 31:419-25|
|Blais, Edik M; Chavali, Arvind K; Papin, Jason A (2013) Linking genome-scale metabolic modeling and genome annotation. Methods Mol Biol 985:61-83|
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