Salmonella Typhimurium is a major cause of morbidity and mortality in humans. It is also a commonly used model organism for intracellular Gram negative pathogens, a group of bacteria that is becoming increasingly resistant to available antibiotics. Systemic Salmonella infection involves proliferation in the small intestine followed by infection of epithelial and later macrophage host cells. In order to advance the understanding of the r^ole of metabolism in virulence, a genome-scale metabolic model of S. Typhimurium was constructed, based on genomic and biochemical data obtained from public databases. A method for modelling metabolic interactions between cells was developed and applied to models of S. Typhimurium and the probiotic Lactobacillus plan-tarum, in order to simulate the intestinal stage of infection. The analysis indicated that interactions, involving the transfer of glycolate from L. plantarum to S. Typhimurium, that favour growth of S. Typhimurium, are possible, by unlikely to occur in vivo. Data from Phenotype Microarray (PM), as well as DNA microarray data obtained during infection of cultured macrophage cells, was integrated with the S. Typhimurium model. The PM data was largely in agreement with model results for growth on carbon and nitrogen sources, and indicated moderate agreement for sulphur and phosphorus sources. A model-based method for analysis of nutrient availability during growth inside host cells, based on PM and DNA microarray data, was developed. This environment is poorly characterised and direct experimental methods for obtaining this information are not available. The analysis indicated a nutritionally complex host environment, dominated by glycerol 3-phosphate and certain nucleosides and amino acids. Owing to the complexity of the host environment, a method for identication of a sub-network of the model, required for viability on all growth supporting carbon sources was developed. The impact of sequentially removing combinations of reactions in the sub-network from the genome-scale model was evaluated. This analysis suggested approximately 60 reactions that in various combinations could be of relevance for designing antimicrobial intervention strategies, including antimicrobial agents and live attenuated vaccines.
Hartman, H
Faculty of Health and Life SciencesDepartment of Biological and Medical Sciences
Year: 2013
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