How do interactions in the microbiome shape the evolution of antibiotic resistance in bacterial pathogens?

About the Project

Antibiotic resistance in pathogenic bacteria poses a fundamental threat to human health. It is estimated that antibiotic resistant infections are already directly responsible for at least one million deaths per annum and this number is predicted to rise dramatically by 2050.

Resistance spreads in bacterial pathogen populations as a result of both evolutionary and ecological processes. Bacterial pathogens exist rarely in isolation, but are often embedded in broader microbial communities (i.e. the microbiome). Microbes existing in the same space will interact, such as through signalling molecules, the exchange of metabolites or genetic material, and through competition for resources and space. All of these interactions from may change how a focal pathogen responds to antibiotics.

The goal of this PhD project is to investigate how interactions in the microbiome shape the evolution of antibiotic resistance in bacterial pathogens, working with Pseudomonas aeruginosa in the context of the respiratory microbiome as the model system to explore this in. This will involve:

(i) Screening and characterising mechanisms of interactions between P. aeruginosa and members of the respiratory microbiome.

(ii) Using experimental evolution to assess how the presence/absence of different microbes from the microbiome changes the evolvability of antibiotic resistance in P. aeruginosa.

(iii) Identifying and comparing the underlying mechanisms of resistance by whole genome sequencing and genome analysis.

This project will involve both computational and experimental microbiology approaches, including high-throughput adaptive laboratory evolution experiments in which the dynamics of selection can be dissected in high resolution. The PhD student will have the opportunity to develop skills in wet lab microbiology, evolution experiments, microbial ecology, bacterial genetics, and bioinformatics, along with training in statistical analyses, scientific writing, and presentation skills.

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