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(BBSRC DTP) Microbial population diversity as a driver of antibiotic resistance evolution

   Faculty of Biology, Medicine and Health

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  Dr Danna Gifford, Dr C Knight, Dr T Gilman  No more applications being accepted  Competition Funded PhD Project (European/UK Students Only)

About the Project

Classically, microbial evolution has been thought to progress via rapid sweeps of beneficial mutations that quickly fix within populations—the so-called ‘strong selection, weak mutation’ model. However, the recent discovery of sustained diversity over thousands of generations in simple abiotic environments (Good et al. 2017) challenges this view. The extent of diversity potentially significantly influences whether populations can adapt to novel conditions (Gifford et al. 2018a). In light of these findings, there is considerable need to understand how genomic and phenotypic diversity contributes to the evolution of populations. Particularly, we need to understand how variability in genomic content and phenotypic traits, such as growth and mutation rates, contribute to a population’s ability to adapt to new environmental conditions. Genomic content has considerable impact on the ability to evolve resistance in pure cultures (Gifford et al. 2018b), but whether this is true for mixed microbial populations is unknown. This question is equally important for evolution in wild microbial populations (Ramirez, Knight et al. 2018) and in clinical infections, which are known to harbour sustained diversity (Whelan et al. 2017).

This project will combine wet-lab experimental evolution and high-throughput genomics to understand how bacterial population diversity contributes to the evolution of antibiotic resistance. We will construct populations from diverse Escherichia coli strains (Galardini et al. 2017) to investigate how levels of diversity contribute to different evolutionary rates and potential, or collectively, ‘evolvability’ of populations. Populations consisting of different levels of diversity will be experimentally evolved in the presence of antibiotics. Questions addressed will include: how does diversity affect the repeatability of evolution on phenotypic and genomic scales? Are there genes that allow E. coli to adapt better to a diverse set of antibiotics? What genomic changes underlie adaptation in diverse populations: de novo mutations, horizontal gene transfer, or a mixture of both? What role(s) do mobile genetic elements, such as plasmids and bacteriophages, play in adaptation? Experimental evolution will be complemented with high performance computing simulations (of the sort used by Gómez‐Llano et. al 2016) where precise control over the distributions of traits can be achieved.

The successful applicant to this multi-disciplinary project will be motivated by fundamental questions in evolutionary biology, with a background in biology, microbiology, genetics or evolution. Analytical and quantitative skills and microbiology experience would be an advantage. Training will be provided to enable the successful applicant to interact with a cross-disciplinary team of laboratory-based and computational scientists.


Entry Requirements:
Applications are invited from UK/EU nationals only. Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.

Funding Notes

This project is to be funded under the BBSRC Doctoral Training Programme. If you are interested in this project, please make direct contact with the Principal Supervisor to arrange to discuss the project further as soon as possible. You MUST also submit an online application form - full details on how to apply can be found on the BBSRC DTP website www.manchester.ac.uk/bbsrcdtpstudentships

As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.


Supervisory Team References
DR Gifford, R Krašovec, E Aston, RV Belavkin, A Channon, and CG Knight (2018a) Environmental pleiotropy and demographic history direct adaptation under antibiotic selection, Heredity, doi:10.1038/s41437-018-0137-3

DR Gifford, V Furió, A Papkou, T Vogwill, A Oliver, RC MacLean (2018b) Identifying and exploiting genes that potentiate the evolution of antibiotic resistance, Nature Ecology and Evolution, 2, 1033-1039, doi:10.1038/s41559-018-0547-x

KS Ramirez, CG Knight, et al. (2018) Detecting macroecological patterns in bacterial communities across independent studies of global soils. Nature Microbiology, 3(2), 189-196, doi:10.1038/s41564-017-0062-x

R Krašovec, H Richards, DR Gifford, C Hatcher, KJ Faulkner, RV Belavkin, A Channon, E Aston, AJ McBain, CG Knight (2017) Spontaneous mutation rate is a plastic trait associated with population density across domains of life, PLoS Biology, 15(8), e2002731, doi:10.1371/journal.pbio.2002731

MA Gómez‐Llano, EM Navarro‐López, and RT Gilman (2016) The coevolution of sexual imprinting by males and females. Ecology and Evolution, 6(19), 7113-7125, doi:10.1002/ece3.2409

Additional References
BH Good, MJ McDonald, JE Barrick, RE Lenski, and MM Desai (2017) The dynamics of molecular evolution over 60,000 generations. Nature, 551(7678), 45-50, doi:10.1038/nature24287.

FJ Whelan, AA Heirali, L Rossi, RH Rabin, MD Parkins, and MG Surette (2017) Longitudinal sampling of the lung microbiota in individuals with cystic fibrosis. PLoS One, 12(3), e0172811, doi:10.1371/journal.pone.0172811

M Galardini, A Koumoutsi, L Herrera-Dominguez, JA Cordero Varela, A. Telzerow, O Wagih, M Wartel, O Clermont, E Denamur, A Typas, and P Beltrao (2017) Phenotype inference in an Escherichia coli strain panel. Elife, 6, e31035. doi:10.7554/eLife.31035
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