This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. In viruses, large population sizes, high mutation rates, and relatively small genome sizes facilitate rapid and extensive exploration of the local adaptive landscape. It has generally been assumed that evolution has too many degrees of freedom to allow the molecular basis of change to be predictable. However, the molecular changes leading to short-term viral evolution may be predictable when there are limited alternative solutions to an adaptive challenge, or when population size and mutation rate allow the virus to quickly find the best of many possible solutions. This project uses a bacteriophage model system to address questions about the generality of rules of viral evolution. Progress was made on several Aims: i) Laboratory adaptation of wild phage shows that initial fitness is not a good predictor of final fitness, but that fitness improvement is highly correlated with the number of genetic changes seen in the lab adaptations. ii) Molecular dissection of host attachment sites shows that some amino acid substitutions allow attachment but not high fitness, suggesting that they affect some viral trait in addition to attachment. iii) Analysis of phage competition in spatially structured and unstructured environments suggests that both spatial and temporal components strongly affect the outcome. Evolution in a structured environment can quickly change the dynamics of these competitive interactions. In addition, we carried out an empirical test of a mathematical model based on extreme-value theory that predicts the average size of the first step in an adaptive walk. We find this theory to be a good predictor if transition / transversions bias is incorporated into the model. Models that can predict evolution but do not depend on the specific biological details of the system may be widely applicable to problems in medicine, agriculture, industry and basic population biology.
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