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Title: Ecological conditions leading to the seep of antibiotic resistance genes in the model-type bacterium Escherichia coli
Author: Reding Roman, Rafael Carlos
ISNI:       0000 0004 5371 7291
Awarding Body: University of Exeter
Current Institution: University of Exeter
Date of Award: 2015
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In antibiotic therapy design, conventional wisdom holds that higher antibiotic dosages always leads to the observation of fewer bacterial cells, resulting in a monotonic decay in cell number as a function of increasing antibiotic dose; accordingly, throughout this thesis, we will call this phenomenon a monotone dose-response profile. When we analysed the evolution of antibiotic resistance mediated by the multi-drug efflux pump AcrAB-TolC in Escherichia coli to study if such a monotone dose-response is maintained at all times, our analysis showed that higher dosages can, in fact, lead to higher bacterial loads. This is because selection for drug resistance is mediated by the duplication of the genes, AcrAB-TolC, that encode the aforementioned efflux pump. As explained in detail below, our work highlights the idea that Darwinian selection on additional copies of AcrAB-TolC is a non-linear function of antibiotic dose and that the observed transition from monotone to non-monotone dose-response is a consequence of AcrAB-TolC being strongly selected at very specific dosages. We term this phenomenon an ‘evolutionary hotspot’. Next, we extended the above experimental system to solid media to study how selection on resistance mediated by AcrAB-TolC leads to a ‘spatio-genomic patterning’ effect that we call a ‘bullseye’. Using a bespoke culture device developed as part of this PhD, we show that spatial selection on resistance also depends non-linearly on the distance of the cell from an antibiotic source, and that the non-linearity can be multi-modal as a function of distance, and therefore also of antibiotic dose. This result also contradicts the aforementioned principle that higher antibiotic dosages necessarily lead to fewer bacterial cells. Following on from this, we then studied the ability of microbial competitors for resources to modulate the antibiotic sensitivity of a particular strain of E. coli, namely Tets , using a range of multi-species experiments. We measured the sensitivity to antibiotics of Tets both with, and without, one bacterial or fungal competitor. When that competitor was equally sensitive to the antibiotic, we observed that Tets was less sensitive to it, in part due to an ‘antibiotic sinking’ effect carried out by the competitor strain. However, when the competitor was not sensitive to the antibiotic, Tets was, accordingly, more sensitive than in the absence of competition. In this latter case, the competitor seemed to reduce the growth of Tets by carbon theft as part of a phenomenon known as ‘competitive suppression’. Moreover, this ecological effect is one that synergises with the action of the antibiotic. Finally, we turned to a study of an ecological trade-off motivated by ribosome-binding antibiotics. So, by manipulating the content of ribosomal RNA in the E. coli cell, a large and essential molecule that is bound by antibiotics such as tetracycline or erythromycin, we could subsequently manipulate what is known as a metabolic trade-off between growth rate and growth yield. The latter is the number of cells produced per molecule of carbon found in the extracellular environment of the bacterial population. Using glucose as carbon source we therefore constructed an empirical fitness landscape that shows how the optimum number of ribosomal rRNA operons depends on extracellular glucose concentration. Whilst this study does not relate directly to the presence of an antibiotic, it does show that by altering the number of operons in a manner that is known to affect antibiotic susceptibility, we can also mediate important growth parameters like cell yield, aka efficiency, and growth rate.
Supervisor: Beardmore, Robert E. Sponsor: Engineering and Physical Sciences Research Council (EPSRC)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: antibiotic resistance ; trade-off ; mutation window