Antibiotic resistance is a major threat to public health, compromising treatment and prevention of bacterial infections previously well-controlled by antibiotics. Existing antibiotic dosing regimens are not necessarily optimised, and the best way to avoid emergence of resistance remains particularly controversial.
Antibiotic efficacy strongly depends on infection context, including bacterial density. Ecological interactions among bacteria sharing a common environment can drastically impact their survival and growth, and in turn selection for resistance. Previous work considering ecological context has emphasised how sensitive bacteria act as competitors to resistant bacteria, implying that a larger sensitive population suppresses emergence of resistance [1]. These findings suggest that lower antibiotic doses may outperform conventionally recommended higher doses at long-term infection control.
However, sufficiently dense bacterial populations can also survive antibiotic doses that kill isolated cells, a phenomenon called “collective antibiotic tolerance” [2]. Underlying mechanisms include enzymatic degradation and titration of antibiotics by binding to cellular targets, essentially reducing the ambient antibiotic concentration experienced by all bacteria present. While density-dependent antibiotic tolerance among genetically-sensitive bacteria is widely known, we recently uncovered a surprising additional effect: sufficiently dense sensitive populations can also “protect” genetically-resistant cells, i.e., increase their chances of survival and growth during antibiotic treatment [3]. Proof-of-principle of this protective effect was demonstrated under particular experimental conditions in one model system, and it remains unclear how widely it arises.
The aim of this PhD is to understand the conditions under which protection occurs and outweighs competitive suppression. More broadly: when does a larger sensitive population help versus hinder emergence of resistance, and what does this mean for optimal antibiotic dosing? We will quantify:
(i) effective reduction in antibiotic concentration due to uptake by bacteria
(ii) how emergence of resistance is altered by presence of sensitive bacteria
and elucidate dependencies on initial bacterial density, timing and dose of antibiotic treatment, and timing of appearance of resistant cells. We will address these questions with wet-lab experiments, primarily using the opportunistic pathogen Pseudomonas aeruginosa, combined with advanced data analysis methods, cf. [3].
The ideal candidate would have experience in microbiological lab work, statistics and coding. These skills will be further developed throughout the PhD. The candidate should also be keen to engage with an interdisciplinary theoretical/experimental research group, including related mathematical modelling.
https://www.ed.ac.uk/biology/groups/alexander
https://www.ed.ac.uk/biology/groups/moule
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