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Dr Chris Knight - postgraduate opportunities

Behavioural evolution in yeast

Behavioural studies usually involve animals. However, the sorts of responses to environmental cues that make up behaviour in animals also occur in microbes. For asking about behavioural evolution, microbes have several advantages: manipulating their environment in controlled ways is much more straightforward than for animals; it is possible to test very large numbers of environmental cues and responses; available genomic knowledge offers more direct routes into understanding mechanism. This project will utilise all these advantages to ask how behaviour has evolved among genome-sequenced wild yeasts (Replansky et al. 2008).

 

The student will look broadly at a wide set of environments (Bochner et al. 2001) to construct behavioural networks of environmental cues and responses. This data will be used to construct networks of some of the most interesting, anticipatory microbial behaviour (Mitchell et al. 2009). A comparative approach will be used to ask, both experimentally and computationally, how those networks have evolved (Knight and Pinney 2009). Doing so will provide novel insight into how cellular networks are interconnected and into the ecology of these environmentally and economically important organisms. At the same time this project offers new routes to ask about both behavioural evolution and responses to environmental change more generally.

 

Applicants should have a background in biology or genetics with interests in evolution. The project will involve analytical and computational approaches as well as microbiology and molecular biology techniques. The successful applicant will interact with both laboratory-based and computational biologists and have the chance to contribute to the direction of research in a collaborative and inter-disciplinary laboratory.

 

 

  • Bochner, B.R., Gadzinski, P., and Panomitros, E. (2001) Phenotype microarrays for high-throughput phenotypic testing and assay of gene function. Genome Res 11: 1246-1255.
  • Knight, C.G., and Pinney, J.W. (2009) Making the right connections: biological networks in the light of evolution. BioEssays 31: 1080-1090.
  • Mitchell A, Romano GH, Groisman B, Yona A, Dekel E, et al. (2009) Adaptive prediction of environmental changes by microorganisms. Nature 460: 220-224.
  • Replansky, T., Koufopanou, V., Greig, D., and Bell, G. (2008) Saccharomyces sensu stricto as a model system for evolution and ecology. Trends Ecol Evol 23: 494-501.

 

Survival of the flattest

Loss of genetic diversity through inbreeding is well known for small populations, but our recent theoretical findings (Aston et al. 2013) suggest a novel genetic threat to small populations from mutation itself. In large populations, damaging mutations are readily removed by selection – only at unrealistically high mutation rates would mutation swamp an existing fit allele. At such high mutation rates, a high narrow peak in the fitness landscape may be lost in favour of a lower broader adaptive peak – so called ‘survival of the flattest’ (Wilke et al. 2001). It seems that the mutation rate above which this can happen drops drastically at low population sizes (Aston et al. 2013). This brings it into the range of biologically realistic mutation rates, meaning that survival of the flattest could be happening in organisms with normal mutation rates. i.e. Small populations could be cryptically losing fit alleles.

 

To test this hypothesis requires experiments where we can manipulate fitness landscapes, population sizes and monitor evolution experimentally. With microbes we can do this reproducibly in the lab (Buckling et al. 2009). This project will take an experimental approach to ask whether real biological fitness landscapes have the features that will enable survival of the flattest can occur and whether it is a real issue in practice. This will complement our on-going experimental approaches to mutation rates themselves (Krašovec et al. 2014).

 

The ideal student will have a background in evolutionary biology and genetics, be willing to learn a range of experimental and analytical techniques and to interact with the theoretical developments in building a cutting edge project. Experience of microbiology and statistical/computing skills would all be advantageous.

 

  • Aston, E. … & Knight, C. G. (2013) Critical Mutation Rate Has an Exponential Dependence on Population Size in Haploid and Diploid Populations. PLoS ONE, 8, e83438.

 

  • Buckling, A. et al. (2009) The Beagle in a bottle. Nature, 457, 824-829.

 

  • Krašovec R, … & Knight, C. G. (2014) Mutation rate plasticity in rifampicin resistance depends on Escherichia coli cell–cell interactions. Nature Commun 5, 3742.

 

  • Wilke, C.O. et al. (2001) Evolution of digital organisms at high mutation rates leads to survival of the flattest. Nature, 412, 331-333.

 

Testing mutation rate plasticity in yeast

Mutation rates in microbes are fundamental to their evolutionary adaptation, for instance in the evolution of antibiotic resistance. We have recently identified novel mechanisms by which a single genotype may modify its mutation rate to antibiotic resistance dependent upon its environment, including its social environment (Krašovec et al. 2014a). This opens up many interesting issues, both in terms of mechanism and the evolutionary processes involved (Krašovec et al. 2014b).

 

A key question is how widely such mutation rate plasticity occurs. Since we identified this effect motivated by mathematical modelling, not based on any particular organism (Belavkin et al. 2014), there is no reason to believe it is restricted to particular bacteria. There is known to be mutation rate plasticity in eukaryotes, including humans (Kong et al. 2012). This project would therefore use well-established approaches to assaying mutation rates in eukaryotic organisms (Lang and Murray 2008), starting with the budding yeast genetic model system (Saccharomyces cerevisiae).

 

Applicants should have a background in biology, genetics or evolution. Analytical and quantitative skills or microbiology and molecular biology experience would be an advantage. The successful applicant will interact with both laboratory-based and computational biologists and have the chance to contribute to the direction of research in a collaborative and inter-disciplinary laboratory.

 

  • Krašovec R, … & Knight, C. G. (2014)a Mutation rate plasticity in rifampicin resistance depends on Escherichia coli cell–cell interactions. Nature Commun 5, 3742.

 

  • Krašovec R, … & Knight, C. G. (2014)b Where antibiotic resistance mutations meet quorum-sensing. Microbial Cell. 1, 250-2.

 

  • Belavkin, R.V & Knight, C.G. (2014). Monotonicity of fitness landscapes and mutation rate control. Preprint: arXiv:1209.0514.

 

  • Kong A, et al. (2012) Rate of de novo mutations and the importance of father's age to disease risk. Nature. 488, 471-5.

 

  • Lang GI, Murray AW. (2008) Estimating the per-base-pair mutation rate in the yeast Saccharomyces cerevisiae. Genetics. 178, 67-82.