Thesis (Ph.D.)--University of Alabama at Birmingham, 2009. / Title from first page of PDF file (viewed on June 10, 2009). Includes bibliographical references.

Thesis (M.S.)--Brigham Young University. Dept. of Microbiology and Molecular Biology, 2007. / Includes bibliographical references (p. 29-31).

Philosophiae Doctor - PhD / Supervised machine learning approaches have been applied successfully to the prediction of protein-protein interactions (PPIs) within a single organism, i.e., intra-species predictions. However, because of the absence of large amounts of experimentally validated PPIs data for training and testing, fewer studies have successfully applied these techniques to host-pathogen PPI, i.e., inter-species comparisons. Among the host-pathogen studies, most of them have focused on human-virus interactions and specifically human-HIV PPI data. Additional improvements to machine learning techniques and feature sets are important to improve the classification accuracy for host-pathogen protein-protein interactions prediction. The primary aim of this bioinformatics thesis was to develop a binary classifier with an appropriate feature set for host-pathogen protein-protein interaction prediction using published human-Hepatitis C virus PPI, and to test the model on available host-pathogen data for human-Bacillus anthracis PPI. Twelve different feature sets were compared to find the optimal set. The feature selection process reveals that our novel quadruple feature (a subsequence of four consecutive amino acid) combined with sequence similarity and human interactome network properties (such as degree, cluster coefficient, and betweenness centrality) were the best set. The optimal feature set outperformed those in the relevant published material, giving 95.9% sensitivity, 91.6% specificity and 89.0% accuracy. Using our optimal features set, we developed a neural network model to predict PPI between human-Mycobacterium tuberculosis. The strategy is to develop a model trained with intra-species PPI data and extend it to inter-species prediction. However, the lack of experimentally validated PPI data between human-Mycobacterium tuberculosis (Mtuberculosis), leads us to first assess the feasibility of using validated intra-species PPI data to build a model for inter-species PPI. In this model we used human intra-species PPI combined with Bacillus anthracis intra-species data to develop a binary classification model and extend the model for human-Bacillus anthracis inter-species prediction. Thus, we test our hypotheses on known human-Bacillus anthracis PPI data and the result shows good performance with 89.0% as average accuracy. The same approach was extended to the prediction of PPI between human-Mycobacterium tuberculosis. The predicted human-M-tuberculosis PPI data were further validated using functional enrichment of experimentally verified secretory proteins in M-tuberculosis, cellular compartment analysis and pathway enrichment analysis. Results show that five of the M-tuberculosis secretory proteins within an infected host macrophage that correspond to the mycobacterial virulent strain H37Rv were extracted from the human-M- tuberculosis PPI dataset predicted by our model. Finally, a web server was created to predict PPIs between human and Mycobacterium tuberculosis which is available online at URL:http://hppredict.sanbi.ac.za. In summary, the concepts, techniques and technologies developed as part of this thesis have the potential to contribute not only to the understanding PPI analysis between human and Mycobacterium tuberculosis, but can be extended to other pathogens. Further materials related to this study are available at ftp://ftp.sanbi.ac.za/machine learning. / National Research Foundation (NRF) and SANBI

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Bacillus anthracis is a Gram positive, spore forming, non-motile, rod shaped, soil bacterium, and is endemic worldwide. Currently, the biology of B. anthracis is poorly understood. B. anthracis is one of many biological weapons used today. A -/- mutant strain of B. anthracis that lacks the pathogenic plasmids was created by serial culture at 42°C. Key DNA replication genes were identified by homology as targets. The dnaB gene, essential for B. subtilis initiation of DNA replication, was my focus. A vector system was created by polymerase chain reaction (PCR) with the pMUTIN4 integration vector and the promoter region of dnaB to study the genetics of B. anthracis. An electro-transformation system was formulated to knock-out the -/- B. anthracis dnaB gene. We have successfully incorporated the pMUTIN4 vector into the chromosomal DNA of B. anthracis. We also have formulated an electro-transformation system and vector system for use in B. anthracis.

Bacteriophages (phages) are the smallest biological entity on the planet. They provide vast amounts of valuable knowledge to biologists. Phage genomes are relatively simple compared to the organisms they infect (prokaryotes) and yet continually point to the complexity surrounding molecular- and microbiological mechanisms of life. By studying phages we can learn of the systems of gene expression, protein interaction and DNA organization. Phages are useful not only from an academic perspective, but may also have useful clinical applications. In the face of the rise of antibiotic-resistant bacterial “super pathogens”, scientists and researchers turn to phages as alternative treatments to these types of infections. Phages are capable of infecting and killing even the deadliest of bacterial pathogens, such as carbapenem-resistant Enterobacteriaceae (CRE) or Bacillus anthracis, and may prove increasingly useful in the future for combatting harmful pathogens. This thesis looks at several aspects of phage biology—from the underlying genetics contributing to phage virulence, to the clinical application of phage therapy to treat infections. First, a look at CRE-Klebsiella pneumoniae isolates and phages capable of infecting some strains may reveal a potential therapeutic approach in the future. Additionally, genomic analysis reveals interesting features that may explain aspects of phage virulence and evolutionary history. Then, a collection of genetically diverse phages is used in infection assays on pathogenic strains of Bacillus anthracis to establish the first-reported phages capable of infecting these strains. Finally, the process of preparing phage samples for therapeutic application is explored in-depth to conclude with discussion of clinical application. During the course of these projects over 25 phages were isolated, as many phage genomes were assembled and annotated, resulting in the preparation of two genome announcements and near-completion of two publishable first-author papers (chapters II and III). In addition, participation in a variety of collaborative efforts may lead to a handful of co-author papers and on various topics, including phage biology and application.

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