Submitted to: Journal of Molecular Evolution
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/15/2008
Publication Date: 11/1/2008
Citation: Cavatorta, J., Savage, A., Yeam, I., Gray, S.M., Jahn, M. 2008. Positive Darwinian selection at single amino acid sites conferring plant virus resistance. Journal of Molecular Evolution. 67(5):551-559. Interpretive Summary: In recent years a plant protein that plays a critical role in the production of all proteins in the plant has been discovered to be an important factor in the resistance of plants to virus infection. Normally during virus infection the virus commandeers this plant protein to function in making more virus. However, plants have evolved to become resistant to virus infection by slightly altering the gene that encodes the protein and the mutated protein can no longer assist in making more virus. Our work has identified specific changes in this plant protein that are responsible for virus resistance using reverse genetic approaches that are not possible in many plant-virus systems. Yet the identification of the changes responsible for virus resistance is important in facilitating the identification of disease resistant plants. Using two computer programs that predict positive selection sites in proteins we were able to show that one of computer programs was able to accurately predict the amino acids in our protein of interest that were positively selected during plant evolution and that these were the amino acids that conferred resistance to virus infection. This is novel and important information to assist others in determining what changes in plant proteins may be important in the development and selection of virus resistance without the need for long term and expensive molecular studies.
Technical Abstract: Explicit evaluation of the accuracy and power of Maximum Likelihood and Bayesian methods for detecting site-specific positive Darwinian selection presents a challenge because selective consequences of single amino acid changes are generally unknown. We exploit extensive molecular and functional characterization of amino acid substitutions in the plant gene eIF4E to evaluate the performance of these methods in detecting site-specific positive selection. We document for the first time a molecular signature of positive selection within a recessive resistance gene in plants. We then use two statistical platforms, PAML and HyPhy, to look for site-specific positive selection. Their relative power and accuracy are assessed by comparing the sites they identify as being positively selected with those of resistance-determining amino acids. Our results indicate that, although both methods are surprisingly accurate in their identification of resistance sites, HyPhy appears to more accurately identify biologically-significant amino acids using our data set.