Escherichia coli (E.coli) 0157:H7, a pathogen belonging to the enterohemorrhagic group of E.coli, has long been a concern to human health. The pathogen causes a myriad of symptoms in humans, ranging from diarrhea and malaise to renal failure. Human infection with the spread of the pathogen is mainly attributed to consumption of contaminated food material such as meat. Decontamination of meat via sprays have to date been the most commonly practiced method to reduce contamination, which now has little relevance in the face of developing resistance by the pathogen. In the following study we investigated FlhC mediated gene regulation in E. coli 0157:H7 on the surface of meat, in an attempt to recognize FlhC regulated targets, which may ultimately serve as targets for the development of novel decontaminating sprays. Microarray experiments were conducted to compare gene expression levels between a parental E. coli 0157:H7 strain and its isogenic flhC mutant, both grown on meat. Putative FlhC targets were then grouped based on their function. Realtime PCR experiment was done to confirm the regulation. Additionally, experiments were done to investigate the phenotypic effects of the regulation. To test the effect of FlhC on biofilm formation, an ATP based assay was first developed in E.coli K-12, which has been detailed in the following dissertation. This assay was used to quantify biofilm biomass in E. coli 0157. Microarray experiments revealed 287 genes as being down regulated by FlhC. These genes belonged to functions relating to cell division, metabolism, biofilm formation and pathogenicity. Real-time PCR confirmed the regulation of 87% of the tested genes. An additional 13 genes were tested with real-time PCR. These belonged to the same functional groups, but were either not spotted on the microarray chips or had missing data points and were hence not included in the analysis. All 13 of these genes appeared to be regulated by FlhC. The phenotypic experiments performed elucidated that the FlhC mutants divided to 20 times higher cell densities, formed five times more biofilm biomass and were twice as pathogenic in a chicken embryo lethality assay, when compared to the parental strain. The following dissertation also reports the development of a combination assay for the quantification of biofilm that takes advantage of the previously mentioned ATP assay and the PhenotypeMicroarray TM (PM) system. The assay was developed using the parental E. coli strain AJW678 and later applied to its isogenic flhD mutant to elaborate on the differences in nutritional requirements between the two strains during biofilm formation. Metabolic modeling and statistical testing was also applied to the data obtained. This assay will be used in the future to study biofilm formation by the parental strain E. coli 0157:H7 strain and its isogenic FlhC mutants on single carbon sources, hence identifying potential metabolites which differentially support biofilm formation in the parental and the mutant strain.

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Microarray gene expression data for thousands of genes in many organisms is quickly becoming available. The information this data can provide the experimental biologist is powerful. This data may provide information clarifying the regulatory linkages between genes within a single metabolic pathway, or alternative pathway routes under different environmental conditions, or provide information leading to the identification of genes for selection in animal and plant genetic improvement programs or targets for drug therapy. Many analysis methods to unlock this information have been both proposed and utilized, but not evaluated under known conditions (e.g. simulations). Within this dissertation, an analysis method is proposed and evaluated for identifying independent and linked metabolic pathways and compared to a popular analysis method. Also, this same analysis method is investigated for its ability to identify regulatory linkages within a single metabolic pathway. Lastly, a variant of this same method is used to analyze time series microarray data. In Chapter 2, Factor Analysis is shown to identify and group genes according to membership within independent metabolic pathways for steady state microarray gene expression data. There were cases, however, where the allocation of all genes to a pathway was not complete. A competing analysis method, Hierarchical Clustering, was shown to perform poorly when negatively correlated genes are assumed unrelated, but performance improved when the sign of the correlation coefficient was ignored. In Chapter 3, Factor Analysis is shown to identify regulatory relationships between genes within a single metabolic pathway. These relationships can be explained using metabolic control analysis, along with external knowledge of the pathway structure and activation and inhibition of transcription regulation. In this chapter, it is also shown why factor analysis can group genes by metabolic pathway using metabolic control analysis. In Chapter 4, a Bayesian exploratory factor analysis is developed and used to analyze microarray gene expression data. This Bayesian model differs from a previous implementation in that it is purely exploratory and can be used with vague or uninformative priors. Additionally, 95% highest posterior density regions can be calculated for each factor loading to aid in interpretation of factor loadings. A correlated Bayesian exploratory factor analysis model is also developed in this chapter for application to time series microarray gene expression data. While this method is appropriate for the analysis of correlated observation vectors, it fails to group genes by metabolic pathway for simulated time series data. / Ph. D.

published_or_final_version / Biochemistry / Doctoral / Doctor of Philosophy

published_or_final_version / Pathology / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / toc / Zoology / Master / Master of Philosophy