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Title: Whole genome sequencing and resistance in Escherichia coli
Author: Davies, Timothy J.
ISNI:       0000 0004 8507 1106
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2019
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Rising antimicrobial resistance is an increasing problem worldwide. Escherichia coli presents a particular challenge as it spreads resistance rapidly via mobile genetic elements. Combatting this requires fast, detailed identification of resistance to prevent its spread and inform treatment. Culture-based antimicrobial susceptibility testing (AST) is limited by the time required for bacterial growth and does not directly identify mechanisms of resistance. However, many molecular techniques require specific targets, limiting their routine use in clinical microbiology. Whole genome sequencing (WGS) offers an alternative, solving many of these issues. However, evidence of WGS's ability to identify clinically significant resistance in E. coli is limited. This thesis investigates this in detail. Using methods similar to those from "proof-of-principle" studies, beta-lactam resistance was predicted from WGS data in several sets of E. coli, including a large set of unselected bloodstream infection isolates. While agreement between WGS-predicted and observed AST was high for many antibiotics, for some, including several commonly used broad spectrum antibiotics, there was significant discrepancy. Focussing on amoxicillin-clavulanate resistance due to the extent of disparity and its clinical significance, the causes of discrepancy were investigated. Instead of the discrepancy being due to the bioinformatic approach, incomplete knowledge of resistance mechanisms or phenotyping errors as is commonly believed, results suggested oversimplifications of both phenotype and genotype were the major cause. Future work is needed to investigate WGS-based prediction for other antibiotic and antimicrobial combinations. This work demonstrates the feasibility of WGS-based prediction for a phenotype widely regarded as one of the most complex. However, it also highlights many barriers WGS-based prediction will need to overcome prior to clinical implementation. These include not only technical problems such as how to best curate and use genetic data for resistance prediction, but also non-technical problems such as the need to challenge long held assumptions regarding the nature of antimicrobial susceptibility testing.
Supervisor: Walker, Sarah ; Peto, Timothy ; Anjum, Muna ; Stoesser, Nicole ; Woodford, Neil Sponsor: HPRU
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Microbiology