There is currently an intense focus on antimicrobial resistance (AMR) in medical, scientific and political arenas owing to its broad and potentially devastating public health consequences. While we are beginning to understand aspects of a drugs mechanism of action and what pathogen traits lead to faster or slower resistance evolution, one major factor has been largely ignored is inter-host variation. Studies of diverse infectious diseases have shown that there is huge variation among individual hosts in their propensity to transmit infection, with the most infectious individuals being termed “super-spreaders”. This project will explore a similar concept for antimicrobial resistance evolution. The overarching goal of the project will be to identify the factors that may make an individual a “super-selector” – an individual in which there is a very high probability of infecting pathogens evolving antimicrobial resistance.
Some specific questions the project will address include:
1) How does inter-individual variation in immunity and microbiome composition affect the probability of infections evolving antimicrobial resistance?
2) Does inter-individual variation in immunity and microbiome composition alter the mechanisms via which pathogens evolve antimicrobial resistance?
3) How frequent are “super-selectors” in human populations?
4) In the presence of both “super-spreaders” and “super-selectors” what epidemiological interventions can best slow the spread of antimicrobial resistance?
The project will make use of a mixture of mathematical modelling of within-host resistance evolution and the epidemiological spread of resistance, meta-analysis of published studies, statistical modelling of large clinical datasets, and/or experimental evolution using a fly infection model depending on the skills and training desires of the successful candidate. As such this project would be suitable for a student with either a background in biology or a quantitative discipline such as mathematics, statistics or computer science.
There will also be opportunity for collaboration with experimentalists within both the McNally and Vale labs if the student wishes, and opportunities to interact with our clinical collaborators in Edinburgh to understand the public health implications of the project.
The student will have the opportunity to gain skills in evolutionary and epidemiological modelling, data science (including coding, meta-analysis techniques, and statistical modelling), bioinformatics, experimental evolution and microbiology depending on their specific interests.
This project will be joint supervised between the McNally and Vale labs. Informal enquiries to Luke McNally ([email protected]
) are encouraged.
McNally lab website: http://lukemcnally.wordpress.com/
Vale lab website: http://pedrovale.bio.ed.ac.uk/
Stein, Richard A. "Super-spreaders in infectious diseases." International Journal of Infectious Diseases 15.8 (2011): e510-e513.
Sommer, Morten OA, et al. "Prediction of antibiotic resistance: time for a new preclinical paradigm?." Nature Reviews Microbiology (2017): nrmicro-2017.
Leventhal, Gabriel E., et al. "Evolution and emergence of infectious diseases in theoretical and real-world networks." Nature communications 6 (2015): 6101