Advances in whole genome sequencing (WGS) technologies have created an era in which WGS can routinely be integrated into disease outbreak investigations for the rapid detection and characterization of the causative agents. Although most genomic investigations of outbreaks to date focus on using single nucleotide variations to help track the spread of disease, this dissertation focuses on efforts to improve the characterization of large clusters of horizontally-acquired genes, named genomic islands (GIs), that may cause large phenotypic changes. Such mobile elements contribute a fundamental role in the rapid adaptation of microbial life to various changes in the environment and are known to encode genes involved virulence, antimicrobial resistance (AMR) and alternative metabolism. I present the integration of rich gene annotations of virulence factors (VFs), AMR genes, and pathogen-associated genes into IslandViewer, a web server for the prediction of GIs in addition to the re-design of the web server to now include an interactive genome visualization library named GenomeD3Plot. I also present the application of IslandViewer for GI analysis on real outbreak data from multiple Listeria monocytogenes food-borne outbreaks from across Canada to show that isolates from geographically and temporally distinct outbreaks have unique sets of GIs. In addition, I present an analysis coupling the rich AMR gene annotations with GI predictions over a large collection of diverse microbial genera that revealed AMR genes as a whole are not over-represented within GIs, in contrast to VFs as have been previously studied. However, upon breaking down the dataset, certain classes of resistance were found to be associated with such mobile regions. Lastly, I present a WGS study of L. monocytogenes to elucidate the contribution of genetic changes to the ability of this pathogen to tolerate and grow in harsh environments, especially cold temperatures, that are important for its role in causing disease. Overall, this work contributes to improved characterization of GIs as well as a better understanding of trends in the role of GIs and mobile regions in the context of AMR and infectious disease.
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Thesis advisor: Brinkman, Fiona S. L.
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