Antibiotic resistance has emerged as one of the World's leading public health challenges. The inexorable emergence of drug resistant pathogens, combined with a steep decline in antibacterial drug discovery, has led to a major crisis. One of the most common drug resistance mechanisms involves bacteria adapting to reduce intracellular drug accumulation. To understand these resistance mechanisms, one needs quantitative information about the membrane permeability of drugs. In this Thesis, we develop a novel optofluidic permeability assay that allows us to quantify the permeability coefficient of drugs crossing lipid membranes. Lipid vesicles are used as model systems and drug molecules are tracked directly using their autofluorescence in the ultraviolet. The permeability coefficient of the drug is inferred by studying the increase in drug autofluorescence intensity within vesicles as they traverse a microfluidic network while exposed to the drug for well defined times. This provides a novel platform from which we can develop membrane models for understanding drug permeability. We incorporate the Escherichia coli outer membrane protein OmpF in vesicles and quantify its role in the transport of fluoroquinolone antibiotics. We provide direct visualisation of OmpF mediated fluoroquinolone transport. We study the pH dependence of antibiotic transport both through pure phospholipid membranes and through OmpF, and present a physical mechanism to explain the pH dependence of E. coli fluoroquinolone susceptibility. We also show the importance of lipid composition on drug permeability - changing the lipid composition of the membrane is shown to change antibiotic permeability by over an order of magnitude. Finally, we report on the discovery of a novel signalling mechanism in E. coli that relies on the transport of small drug-like molecules, and discuss the role it plays in stress response in the microbial community.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:681250 |
| Date | January 2016 |
| Creators | Cama, Jehangir |
| Publisher | University of Cambridge |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://www.repository.cam.ac.uk/handle/1810/254296 |
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