Evolutionary responses of microbiomes during therapeutic interventions

The rapid rise of antibiotic resistance has made many clinical antibiotics ineffective. One way to increase the efficiency of antibiotics is to use them in combination with bacteria-specific parasitic viruses, phages. Mechanistically, this is thought to function due to collateral sensitivity and evolutionary trade-offs whereby selection by one agent makes the pathogen more susceptible to selection by the other. However, it is unclear how phage-antibiotic synergies are realised in bacterial microbiomes, and to what extent pathogen evolutionary responses, such as resistance evolution, are shaped by the presence of other bacteria.

Pseudomonas aeruginosa is a human opportunistic pathogen characterised by high levels of antibiotic resistance. While being the main source of morbidity in Cystic Fibrosis (CF) lung infections, it is typically embedded in polymicrobial communities whose presence might affect the success of therapeutic interventions. The proposed multidisciplinary PhD project will combine evolutionary microbiology with mathematical modelling to predict the success of therapeutic interventions in highly dynamic CF lung bacterial communities (a model system already established in Friman-Wood lab). Specifically, we will aim to test novel phage-antibiotic therapy to control human pathogen outbreaks in model CF lung community and use this biologically-derived information to model predicted outcomes. The key aims include:

1) Studying the effectiveness of phage-antibiotic synergy across CF community diversity gradient in terms of survival of the P. aeruginosa focal pathogen

2) Quantifying the evolutionary responses of the P. aeruginosa focal pathogen across the CF community diversity gradient

3) Using predictive mathematical modelling to estimate successful therapeutic interventions as a function of microbiome community complexity and the likelihood of focal pathogen resistance evolution.

This project will draw on ecological and evolutionary theory, microbial ecology and mathematical modelling. In addition to having an opportunity to develop skills in all these areas, you will have the possibility to sequence evolved focal pathogen strains and to learn and develop bioinformatics and statistical skills. Moreover, supervisors will support and encourage you to develop your own research ideas and hypotheses as a part of extended Friman-Wood research group at York.

As this is an interdisciplinary project ideal candidates will have a background in at least one of the main subject areas, e.g. in microbiology, community ecology, experimental evolution or mathematical modelling, and a willingness to develop skills in the other areas.

We strongly encourage you to email the project supervisor prior to submitting an application to discuss your suitability for this project. Please email: [Email Address Removed]