Optimising antibiotic treatment regimens by understanding epistasis of multidrug resistance.

The development of antimicrobial resistance (AMR) often incurs a fitness cost to the bacterial cell. Multiple AMR sometimes results in more, or less of a fitness cost than expected. This project is aimed at understanding this phenomenon, known as epistasis, specifically for clinically relevant resistances so we can determine optimal drug combinations for treatment of infectious disease.

Where does this project lie in the translational pathway? 

T1 – Basic Research in Medecine

 

T3 Evidence into Practice

 

Proof of concept studies which would translate rapidly to evidence based treatment choice. We aim to determine antibiotic combinations which would lead to less fit, multiple resistant strains which would not persist in the environment once cessation of antibiotic treatment had occurred. Once reproducible evolutionary trajectories, based on drug resistance, had been identified we would aim to implement a change in clinical use to determine if background resistance to these drugs reduced.

Methodological aspects of the PhD project

Microbiology, molecular biology, mathematical modelling of resistance emergence and epidemiology

Expected outputs of the PhD project

High quality publications in an emerging area of AMR research, further funding once proof of principle has been demonstrated, policy change in terms of treatment choice for individual patients, policy change, following epidemiological modelling, for entire geographic areas depending on the background resistance of circulating strains.

External industry links or training opportunities available for the student 

Opportunity for training at the Malawi Liverpool Wellcome Trust.

Required skills/experience/aptitudes

Microbiology experience is essential, knowledge of AMR, molecular microbiology, bioinformatics, genome analysis, modelling useful.

Key publications that relate to this proposed project

1.

Wong (2017) Epistasis and the Evolution of Antimicrobial Resistance, front Microbiol.  doi: 10.3389/fmicb.2017.00246.

2.

Lescat et al., (2017) Using long-term experimental evolution to uncover the patterns and determinants of molecular evolution of an Escherichia coli natural isolate in the streptomycin-treated mouse gut. Mol Ecol  doi: 10.1111/mec.13851.

3.

Moura de Sousa et al., (2017) Multidrug-resistant bacteria compensate for the epistasis between resistances. PloS Biol.  doi: 10.1371/journal.pbio.2001741.

4.

Vogwill & MacLean, (2015) The genetic basis of the fitness costs of antimicrobial resistance: a meta-analysis approach. Evol Appl  doi: 10.1111/eva.12202.

5.

San Millan et al (2014) Positive epistasis between co-infecting plasmids promotes plasmid survival in bacterial populations. ISME J.  doi: 10.1038/ismej.2013.

The deadline for applications is Friday 20th July 2018 23.45 BST.

More information on this programme can be found here

www.pillsbank.net

here

https://medicaments-24.com