Interested individuals must follow the "how to apply" link on the Geosciences E4 Doctoral Training Partnership web page: http://www.ed.ac.uk/e4-dtp/how-to-apply
This project will use large-scale analyses of viral genome sequence data to understand the selection pressures acting on viral proteins, and how these shape long-term virus evolution.
Viruses are the most numerous and ubiquitous form of life, but they are totally dependent on their hosts for replication. This suggests there must be conflict between the ‘interests’ of hosts and viruses, and such conflict is clearly evident in viral diseases and antiviral immune responses. In general, we expect this to drive antagonistic co-evolution between hosts and viruses, so that both are locked into a never-ending arms-race. This is often naively pictured as a simple tit-for-tat process of reciprocal fixation, with both parties finding that (like Alice’s Red Queen) “… it takes all the running you can do, to keep in the same place.”.
However, with a few clear exceptions, there is little evidence for this in the long-term evolution of virus proteins. This may suggest that the appealing and intuitive ‘arms-race’ model is wrong—or at least too simple to apply to viruses. For a start, RNA viruses and multicellular hosts cannot both be ‘running as fast as they can’, because the viruses can ‘run’ (evolve) many thousands of times faster than the hosts. In addition, analyses using ancient sequences suggest that, in striking contrast to host immunity genes and to our naïve prediction, virus proteins actually show very high levels of constraint, and little-to-no adaptive substitution in the long term.
Which viruses and virus proteins show evidence of long-term positive selection?
What is the frequency of adaptive fixation in viruses, and how does it compare to that of hosts?
What is the distribution of fitness effects for new mutations in viruses?
This work will use computational and statistical approaches to understand patterns of virus evolution. These will include the application well-established phylogenetic methods to large samples of publicly-available virus sequence data, and the development of new population-genetic methods. In the first year the work will focus on obtaining, cleaning, and analyzing public data using established methods. In the second and third year the work will focus on extending current methods (e.g. the McDonald-Kreitman framework) to obtain estimates of the frequency of adaptive substitution, and developing new methods to infer the distribution of fitness effects. Depending on the students interests, work in years 2 and 3 could also include a modelling component.
A comprehensive training programme will be provided comprising both specialist scientific training and generic transferable and professional skills.
This project is likely to suit students with a strong background and interest in evolutionary genetics and/or computational biology. Alternatively, it could suit students with a strong computational or mathematical background (such as those from the physical sciences) who wish to gain expertise in evolutionary biology.