About the Project
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.
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.
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.
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
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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