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Dr Chris Knight - postgraduate opportunities

(NERC DTP) Experimenting with evolution: adaptation in spatially structured environments

Introduction

Understanding how spatial structure influences evolution is vital to describing, understanding and predicting the likely impacts of environmental change on biodiversity. This is particularly true for microbes, which occupy environments with a wide range of spatial structuring from the fine-structure of soil to the fluid structure of the atmosphere. The fact that many microbes may readily be brought into the laboratory and studied with approaches developed in cell biology enables questions of ecology and evolution to be addressed in ways impossible in other systems (e.g. see Replansky et al. 2008). However, this is typically done using unstructured, shaken liquid cultures, uncharacteristic most natural environments. Novel approaches are being developed to high throughput growth of microbes in spatially structured colonies on agar plates (Bean et al. 2014). Here we will combine the power of such high throughput analysis with approaches to assessing competitive fitness within microbial colonies (Hallatschek and Nelson 2010), to address fundamental questions about the role of spatial structure in microbial evolution.

 

Project Summary

We have recently acquired a robot that can propagate >6,000 microbial colonies on a single agar plate (www.singerinstruments.com). The student will use this robot to carry out experimental evolution in spatially structured environments and to test the fitness effects of specific beneficial mutations. This will build on, and involve direct comparison with, existing work in the lab using unstructured environments to identify the influences of the environment on evolution. Specifically we have used budding yeast (Saccharomyces cerevisiae) and alcohol, as a stressor important in yeast evolution, to test the genetic basis of adaptation in the lab. Such approaches, combined with whole-genome sequencing and analysis will enable the student to ask questions such as ‘What is the effect of spatial structure on the rate, fitness effects and nature and of beneficial mutations?’, which leads into wider issues of understanding phenotypic plasticity and environmental change (Chevin et al. 2010). The ideal student will have a background in evolutionary biology and genetics, with good quantitative skills and be willing to learn and develop a range of experimental and analytical techniques. Experience of microbiology and computational expertise would be advantageous.

Bean, G.J. et al. (2014) Development of ultra-high-density screening tools for microbial "omics". PLoS One, 9, e85177.

Chevin, L.M. et al. (2010) Adaptation, plasticity, and extinction in a changing environment: towards a predictive theory. PloS Biol, 8, e1000357.

Hallatschek, O. and Nelson, D.R. (2010) Life at the front of an expanding population. Evolution, 64, 193-206.

Replansky, T. et al. (2008) Saccharomyces sensu stricto as a model system for evolution and ecology. Trends Ecol Evol, 23, 494-501.

(NERC DTP) Survival of the flattest and its relevance for conservation biology

Introduction

Loss of genetic diversity through inbreeding is well known for small populations, but our recent theoretical findings (Aston et al. 2013) suggest that there may be another 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 threatened species. 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 (). 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).

 

Project Summary

The student will initially use experimental evolution to test whether the combination of high sharp peaks and low broad peaks required for survival of the flattest do in fact occur in realised fitness landscapes. They will go on to test whether this does in fact occur, using experimental manipulations, phenotypic tests and whole genome sequence analysis. Several appropriate microbial systems, both eukaryotic and bacterial, are available in the Knight and Delneri laboratories. 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 ofmicrobiology and statistical/computing skills plus an interest in conservation biology would all be advantageous

Aston, E. et al. (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. et al. (2014) Mutation rate plasticity in rifampicin resistance depends on Escherichia colicell–cell interactions. Nature Communications, 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.

(NERC DTP) The role of genetic architecture and constraint in the maintenance of genetic diversity

Project Summary

We believe that a novel approach is needed to address the existence of such natural variation or allow for its persistence in the face of selection, where one considers both the nature of genetic variation in populations and the underlying genetic architecture of social traits, since these ultimately determines how evolution can shape the system. We therefore propose to employ a uniquely powerful integration of computational, genomic and experimental approaches using the social amoeba D. discoideum to achieve the following goals:

 

1.1 Genome-wide identification of loci responsible for patterns of natural variation in social and non-social traits. You will integrate high throughput phenotyping (whole genome next generation sequencing) and large-scale genotyping of a panel of natural isolates to identify sequence variation associated with variation in fitness related traits. These data will provide unprecedented insights into the genetic architecture of natural variation in social and fitness related traits.

 

1.2. Understand the importance of pleiotropy in shaping variation. You will examine different fitness related traits to determine the degree to which variation in traits presumed to be under selection are controlled by the same loci. These results will provide insights into the genetic constraints (trade-offs) that potentially shape patterns of variation in social and non-social traits.

 

1.3. Validate the causal role of genes associated with natural variation in fitness related traits. You will generate gene knock-out and allelic replacement strains to experimentally confirm the causal influence and pleiotropic effects of genes putatively underlying natural genetic diversity.

1.4. Examine signatures of selection on social and non-social genes. You will model sequence evolution to understand the processes shaping diversity in genes identified by association analyses, as well as those predicted or previously confirmed to play a role in social and non-social traits by experimental approaches. Most importantly, we will apply tests to contrast predictions from a series of models to understand the evolutionary origins and maintenance of genetic variation.

Research training: Training in quantitative, computational and wet lab experimental skills is crucial to ensure UK scientists continue to remain world leading. This interdisciplinary project is therefore ideally suited to meet these professional development needs. The student will be trained in cutting edge quantitative skills related to genomic and phenotypic data manipulation, association mapping, maximum likelihood based mixed modelling, and computer simulation. The student will also develop analytical skills through the integration of mathematical theory with the analysis of experimental results. These skills will provide the student with a multifaceted toolkit that will prepare them for a research career.

Li SI, Buttery NJ, Thompson CR, Purugganan MD (2014) .Sociogenomics of self vs. non-self cooperation during development of Dictyostelium discoideum. BMC Genomics. Jul 21;15:616. doi: 10.1186/1471-2164-15-616.

Parkinson, K., Buttery, N., Wolf, J. & Thompson, C (2011). A simple mechanism for complex social behavior. PLoS Biol, 9(3), e1001039. eScholarID:125874 | PMID:21468302 | DOI:10.1371/journal.pbio.1001039

Buttery, N., Rozen, D., Wolf, J. & Thompson, C (2009). Quantification of social behavior in D. discoideum reveals complex fixed and facultative strategies. Curr Biol, 19(16), 1373-7. eScholarID:76937 | PMID:19631539 | DOI:10.1016/j.cub.2009.06.058

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.

 

Biological approaches to lignocellulose for sustainable biofuel production

As fossil fuel reserves decrease and human emissions of greenhouse gas cause climate change, the development of new sustainable fuel sources is essential. Biofuels are a promising alternative to fossil fuel, but their development presents challenges for food security and land use. Therefore, it is important to find new sources of biomass that do not compete with food production and increase the efficiency of biomass conversion into biofuel. Second-generation biofuel can be produced from lignocellulosic feedstocks such as wood, straw and solid waste, which can be a by-product of crop cultivation, offering better sustainability. However, bioethanol production from lignocellulosic biomass requires additional processing to extract fermentable sugars. Pre-treatment is required to alter the structure of lignin and hemicelluloses to make cellulose more accessible for subsequent hydrolysis.

 

Currently, the main methods used for pre-treatment are physical, such as milling, or chemical, such as acid pre-treatment. However, biological methods are a promising alternative since no harmful chemicals are used and less energy input is required. This project will investigate how different communities of microorganisms can be used to optimise this process.

 

We will be using a combination of modelling and experimental analyses. Training will be provided in using biological data (including meta-genomic DNA sequences) to build mathematical models of metabolic pathways in biofuel feedstocks. Experiments will be conducted to test how modifications in microbial communities affect, and are affected by, degradation of feedstock material. The results will open new ways to manipulate microbiological communities in order to improve biofuel production.

 

http://www.bioinf.man.ac.uk/schwartz/

http://tinyurl.com/KnightFLS

  • Menon V, Rao M (2012). Trends in bioconversion of lignocellulose: Biofuels, platform chemicals & biorefinery concept. Progress in Energy and Combustion Science 38: 522-550.
  • Chico-Santamarta et al. (2011). Microbial changes during the on-farm storage of canola (oilseed rape) straw bales and pellets. Biomass and Bioenergy 35: 2939-2949.
  • Schwartz JM, Gaugain C (2011). Genome-scale integrative data analysis and modeling of dynamic processes in yeast. Methods in Molecular Biology 759: 427-443.
  • Shrestha P, Khanal SK, Pometto AL, van Leeuwen JH (2009). Enzyme production by wood-rot and soft-rot fungi cultivated on corn fiber followed by simultaneous saccharification and fermentation. Journal of Agricultural and Food Chemistry 57: 4156-4161.
  • Schwartz JM, Gaugain C, Nacher JC, de Daruvar A, Kanehisa M (2007). Observing metabolic functions at the genome scale. Genome Biology 8: R123.

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.