Addressing bacterial antibiotic resistance by the optimisation of a dynamic drug environment

Yoshida, Mari (2017) Addressing bacterial antibiotic resistance by the optimisation of a dynamic drug environment. PhD thesis, University of Glasgow.

Due to Embargo and/or Third Party Copyright restrictions, this thesis is not available in this service.

Abstract

The work detailed in this thesis aimed to address antibiotic resistance - a pressing health issue on a global scale - through three different approaches: identifying the mechanisms of bacterial resistance evolution; modifying antibiotic treatment strategies to limit, or redirect, the course of resistance evolution, and; developing an effective method to find potential compounds for new therapeutic agents. Initially, a systematic investigation was conducted to monitor the emergence of bacterial antibiotic resistance when bacteria were exposed to a single antibiotic. In order to study the dynamics of bacterial resistance emergence, we constructed a morbidostat device to continuously monitor bacterial growth, and regulate drug concentrations to maintain an antibiotic-induced selection pressure. The resistance evolution was then examined with eight antibiotics from different classes. During 12 days of antibiotic exposure, resistance levels increased dramatically, exhibiting various evolutionary patterns depending on which antibiotics were administered. The mechanisms underlying these various evolutionary changes were further investigated by genotypic and phenotypic characterisation experiments. Secondly, the effect of an alternating antibiotic treatment, in which antibiotics were administered sequentially with periodic switching, was examined. The intention was to identify a dosing regimen that could suppress or control the resistance evolution, using only existing antibiotics. Antibiotic-sensitive bacteria had their resistance evolution tested in response to a range of alternating treatments that employed seven antibiotics and three different cycling interval times. We found that the development of antibiotic resistance could be reduced, or even reversed, by using certain antibiotic pairs. Additionally, we also identified the optimal interval time to further increase the degree of resistance evolution suppression. Further genotypic and phenotypic assays were then conducted to gain insight into the population dynamics occurring during antibiotic cycling and the underlying mechanisms that enabled the manipulation of antibiotic resistance. Finally, the optimisation of antimicrobial peptide sequences (AMPs) was achieved, demonstrating the potential to rapidly discover new antibiotic candidates. The conventional approaches to drug discovery are extremely time and labour-intensive, and hence struggle to cope with the accelerated development of bacterial drug resistance. AMPs demonstrate great promise as a potential new class of antibiotics; they display a range of modes-of-action against bacterial pathogens, and there are an astronomical number of possible sequences available to explore (e.g. 20^13 ≈ 8×10^16 combinations for a 13-mer peptide). An evolution-based algorithm was developed to effectively find the most potent antimicrobial peptides whilst requiring only a small number of experimental evaluations. The algorithm was subsequently validated by conducting an optimisation experiment, whereby, a naturally-occurring 13-mer AMP was taken as a starting point, and optimised over three generations. Finally, the physicochemical properties of the identified potent peptides were examined to gain insights into the underlying mechanisms responsible for the improvement in antimicrobial activity.

Item Type: Thesis (PhD)
Qualification Level: Doctoral
Keywords: Microbial evolution, antibiotic resistance.
Subjects: Q Science > QD Chemistry
Q Science > QR Microbiology
R Medicine > R Medicine (General)
Colleges/Schools: College of Science and Engineering > School of Chemistry
Funder's Name: UNSPECIFIED
Supervisor's Name: Cronin, Professor Leroy
Date of Award: 2017
Embargo Date: 1 June 2020
Depositing User: Dr Mari Yoshida
Unique ID: glathesis:2017-8207
Copyright: Copyright of this thesis is held by the author.
Date Deposited: 01 Jun 2017 11:04
Last Modified: 25 Jul 2017 13:32
URI: http://theses.gla.ac.uk/id/eprint/8207

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