The environment is increasingly recognised as a key player in the emergence and mobilisation of antibiotic resistance, which negatively impacts human health, healthcare systems, and farming practices worldwide. Recent work has demonstrated concentrations of antibiotics in the natural environment may select for resistance in situ, but a scarcity of meaningful data has prevented rigorous environmental risk assessment of antibiotics. Without such data, mitigation strategies, such as improved antibiotic stewardship or environmental discharge limits, cannot be effectively designed or implemented. This thesis designed and developed two methods for determining effect concentrations of antibiotics in complex microbial communities, thereby generating a significant amount of data to address this knowledge gap. Minimal selective concentrations (MSCs) were determined in long term selection experiments for four classes of antibiotic at concentrations as low as 0.4 μg/L, which is below many measured environmental concentrations. Lowest observed effect concentrations were determined using a short term, growth based assay which were highly predictive of MSCs. A novel finding was significant selection for cefotaxime resistance occurred at a wide range of antibiotic concentrations, from 125 μg/L - 64 mg/L, which has important clinical implications. Determination of MSC in single species assays was also shown to be a poor predictor of MSC in a complex microbial community. Co-selection for antimicrobial resistance was demonstrated in selection experiments and through improved understanding of class 1 integron evolution, assessing selective effects on resistance gene acquisition using a novel PCR method and next-generation sequencing. In the final study, a novel resistance determinant (UDP-galactose 4-epimerase) conferring cross-resistance to biocides and antibiotics was discovered, providing a target for further study. These findings indicate selection and co-selection for antimicrobial resistance is likely to occur in the environment, and provides the means to rapidly generate further data to aid in the development of appropriate mitigation strategies.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:761733 |
| Date | January 2017 |
| Creators | Murray, Aimee Kaye |
| Contributors | Gaze, William Hugo ; Zhang, Lihong ; Snape, Jason |
| Publisher | University of Exeter |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/10871/30325 |
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