Microbial population diversity as a driver of antibiotic resistance evolution

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, recent discovery of sustained diversity over thousands of generations in simple abiotic environments (Good et al. 2017) challenges this view. The extent of diversity can significantly influence whether populations are able to 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 a diverse set of Escherichia coli to investigate how different 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.

DR Gifford et al. (2018a) Environmental pleiotropy and demographic history direct adaptation under antibiotic selection, Heredity (online early)
DR Gifford et al. (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
MA Gómez-Llano et al. (2016) The coevolution of sexual imprinting by males and females. Ecology and Evolution, 6(19), 7113-7125, doi:10.1002/ece3.2409
BH Good, et al. (2017). The dynamics of molecular evolution over 60,000 generations. Nature, 551(7678), p.45.
R Krasovec et al. (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
KS Ramirez 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
Whelan et al. 2017. Longitudinal sampling of the lung microbiota in individuals with cystic fibrosis. PLoS One, 12(3), p.e0172811.