Analysis on the Metabolic Capabilities of five Salmonella Strains through Genome-Scale Metabolic Models

Abstract

In every country of the world, foodborne diseases caused by Salmonella represent a severe problem to the food supply as well as the public health. The work presented here in this dissertation, looks to investigate food safety related to sustainable farming practices, genome evolution of pathogenic bacteria during host-interactions, and harness post-genomic data to use systems biology methods to elucidate differentiating metabolic capabilities and targets of control of numerous Salmonella serovars. The first chapter introduces detailed information about the background information about Salmonella as a foodborne pathogen. The second examines computational methods to determine if we can accurately predict genome evolution of pathogenic Escherichia coli and Salmonella during host interactions in niches in humans. The third chapter examines the food safety risks associated with the use of chicken manure for agricultural sustainable farming practices in Minnesota. Pathogenic bacteria including Salmonella are also a concern for sustainable farming in which organic fertilizers such as animal wastes are utilized. An analysis on microbiological hazards for such a sustainable farming system was presented in the third chapter. Finally, systems biology approaches were used in the study described in Chapter 4 to analyze strain to strain differences of metabolism of these pathogenic microorganisms. Throughout evolution bacteria have gained or lost certain metabolic properties to better compete with other microorganisms in the changing living condition found in environmental niches found in hosts. Therefore, to develop advanced strategies fighting against pathogenic bacteria, a solid understanding must be obtained on their capability to metabolize available nutrients within different hosts or environmental niches during infection. The genome-scale metabolic models (GEMs) constructed in silico allow us to conduct simulations mimicking real-life situation by interpreting complex bacterial metabolic systems to conduct predictions during bacteria-host/environment interactions. A publication reprinted in Chapter 2 presents work that we conducted to analyze the metabolism-related genes essential to various Salmonella and Escherichia coli species under simulated environments found in three niches where they cause disease. Chapter 4 discussed a study on analyzing five different Salmonella strains’ metabolic capabilities through a systems biology approach. The objective of the study was to gain a better understanding of differentiating metabolic capabilities among various Salmonella strains through efficient model construction and accurate prediction. Overall, the GEMs generated in this study can make good predictions when compared to experimental results, showing their great potentials in analyzing pathogenic bacteria and developing related pathogen control strategies, and the usefulness of this approach for the future examination of 100’s to 1,000s of genomes of Salmonella spp..