This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Microbial evolution occurs on a time scale that is rapid relative to the human lifespan and can lead to changes in virulence, host and drug resistance. The long term goal of this project is to determine whether there are general principles that can be used to predict the molecular basis of short term adaptive evolution. The focus of the proposal is to develop a deeper understanding of the mechanistic basis of adaptive evolution using icosahedral bacteriophage as a model system.
The specific aims are 1) to determine the structural basis of protein adaptation using biochemical and biophysical principles;and 2) to determine the contribution of regulatory substitutions to adaptive evolution. To address the first aim, we will identify amino acid substitutions that increase or decrease temperature range in evolved phage and dissect the biophysical mechanisms underlying these adaptations. We will use this information to build a predictive model of thermal adaptation and test this model by predicting the relative temperature range of phages taken from the environment, and by constructing predicted thermal mutants in a related phage. To address the second aim, we will use gene expression and phenotypic assays to determine which experimentally observed changes in regulatory regions exert their adaptive effects by altering gene expression. We will also determine the magnitude of the changes and the context in which they are adaptive and identify selective conditions that promote changes in gene regulation. Successful achievement of these aims will enhance our ability to predict the probable pathways of adaptive evolution.
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