Whole genome sequencing (WGS) has transformed molecular infectious diseases epidemiology in the last five years, and represents a high resolution means by which to catalogue the genetic content and variation in bacterial pathogens. This thesis utilises WGS to enhance our understanding of antimicrobial resistance in two clinically important members of the Enterobacteriaceae family of bacteria, namely Escherichia coli and Klebsiella pneumoniae. These organisms cause a range of clinical infections globally, and are increasing in incidence. The rapid emergence of multi-drug resistance in association with infections caused by them represents a major threat to the effective management of a range of clinical conditions. The reliability of sequencing and bioinformatic methods in the analysis of E. coli and K. pneumoniae sequence data is assessed in chapter 4, and provides a context for the subsequent study chapters, investigating resistance genotype prediction, outbreak epidemiology in two different contexts, and population structure of an important global drug-resistant E. coli lineage, ST131 (5-8). In these, the advantages (and limitations) of short-read, high-throughput, WGS in defining resistance gene content, associated mobile genetic elements and host bacterial strains, and the relationships between them, are discussed. The overarching conclusion is that the dynamic between all the components of the genetic hierarchy involved in the transmission of important antimicrobial resistance elements is extremely complicated, and encompasses almost every imaginable scenario. Complete/near-complete assessment of the genetic content of both chromosomal and episomal components will be a prerequisite to understanding the evolution and spread of antimicrobial resistance in these organisms.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:640085 |
| Date | January 2014 |
| Creators | Stoesser, Nicole Elinor |
| Contributors | Crook, Derrick; Peto, Tim; Donnelly, Peter |
| Publisher | University of Oxford |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://ora.ox.ac.uk/objects/uuid:10ed1097-b2a1-4e3e-a4b3-58318d325f89 |
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