Project 1: Evolution by adaptive loss of gene function
Paradoxically, the first step of organisms' evolutionary adaptation to a new environment often involves losing existing genes, rather than gaining new ones—a phenomenon observed across the tree of life (Albalat and Cañestro 2016). Adaptation via loss-of-function is a particularly common process in microbial evolution observed for diverse selective pressures, e.g. novel carbon sources (Gifford et al. 2016), toxins such as antibiotics (Gifford et al. 2018) or heavy metals (Gorter et al. 2018), and new host environments (Winstanley et al. 2016). Despite the ubiquity of this process, we currently know little about how different environments shape loss of gene function, and the consequences of different loss-of-function mutations for organismal fitness in different environments.
Understanding evolution via loss-of-function will help us gain a handle not only on these important processes in natural selection, but will also have practical applications in, e.g. bioengineering, where preventing microbes from losing introduced genes is critical. We will first characterise fitness effects of single-gene deletant Escherichia coli strains in the Keio collection (Yamamoto et al. 2009) in a diverse set of environmental conditions, to identify the fitness effects of loss of gene function. Subsequently, we will constrast evolution at the genomic scale in high and low fitness strains in these environments. Finally, we will evolve wild-type strains with diverse genetic backgrounds in the same environments, to determine the probability that evolution would actually proceed via loss of function in the first instance.
This research will fill a gap in our current knowledge about the repeatability of adaptation via loss of function, its contribution to the adaptive process relative to other types of DNA changes, and its knock-on effects for survival and adaptation in other environments. Microbial experimental evolution offers the tools and throughput needed to uncover these processes over evolutionary time. Genomic and experimental tools, including high-throughput sequencing and fitness assays, will enable us to address clear hypotheses, filling important gaps in our current knowledge on this key adaptive process.The successful candidate will be someone motivated by fundamental questions in evolution, with an interest in working at the interface of microbial genomics and ecology. They will have the opportunity to explore these questions using established microbial model systems, while being supported with training in both in microbiology, experimental evolution and bioinformatics.
- lbalat, R. and Cañestro, C. (2016) Evolution by gene loss. Nature Reviews Genetics, doi:10.1038/nrg.2016.39
- Gifford, D.R., Toll‐Riera, M., and MacLean, R.C. (2016) Epistatic interactions between ancestral genotype and beneficial mutations shape evolvability in Pseudomonas aeruginosa. Evolution, 70(7), 1659-1666, doi:10.1111/evo.12958
- Gifford, D.R., Furió, V., Papkou, A., Vogwill, T., Oliver, A., and MacLean, R.C. (2018) Identifying and exploiting genes that potentiate the evolution of antibiotic resistance. Nature Ecology & Evolution, 2(6), 1033, doi:10.1038/s41559-018-0547-x
- Gorter, F.A., Derks, M.F., van den Heuvel, J., Aarts, M.G., Zwaan, B.J., de Ridder, D., and De Visser, J.A.G.M. (2017). Genomics of adaptation depends on the rate of environmental change in experimental yeast populations. Molecular Biology and Evolution, 34(10), 2613-2626, doi:10.1093/molbev/msx185
- Yamamoto, N., Nakahigashi, K., Nakamichi, T., Yoshino, M., Takai, Y., Touda, Y., ... and Datsenko, K. A. (2009). Update on the Keio collection of Escherichia coli single‐gene deletion mutants. Molecular systems biology, 5(1), 335, doi:10.1038/msb.2009.92
- Cao, H., Butler, K., Hossain, M., and Lewis, J. D. (2014). Variation in the fitness effects of mutations with population density and size in Escherichia coli. PLOS ONE, 9(8), e105369.