Resistance to Wheat streak mosaic virus identified in synthetic wheat lines

Citation: Shoup Rupp, J. L., Simon, Z. G., Gillett-Walker, B., & Fellers, J. P. (2014). Resistance to Wheat streak mosaic virus identified in synthetic wheat lines. Retrieved from http://krex.ksu.eduWheat streak mosaic virus (WSMV) is an important pathogen in wheat that causes significant yield losses each year. WSMV is typically controlled using cultural practices such as the removal of volunteer wheat. Genetic resistance is limited. Until recently, no varieties have been available with major resistance genes to WSMV. Two resistance genes have been derived from Thinopyrum intermedium through chromosome engineering, while a third gene was transferred from bread wheat through classical breeding. New sources of resistance are needed and synthetic wheat lines provide a means of accessing genetic variability in wheat progenitors. A collection of wheat synthetic lines was screened for WSMV resistance. Four lines, 07-SYN-27, -106, -164, and -383 had significant levels of resistance. Resistance was effective at 18 °C and virus accumulation was similar to the resistant control, WGGRC50 containing Wsm1. At 25 °C, resistance was no longer effective and virus accumulation was similar to the susceptible control, Tomahawk

A Game-Theoretic Model of Interactions between Hibiscus Latent Singapore Virus and Tobacco Mosaic Virus

Mixed virus infections in plants are common in nature and their interactions affecting host plants would depend mainly on plant species, virus strains, the order of infection and initial amount of inoculum. Hence, the prediction of outcome of virus competition in plants is not easy. In this study, we applied evolutionary game theory to model the interactions between Hibiscus latent Singapore virus (HLSV) and Tobacco mosaic virus (TMV) in Nicotiana benthamiana under co-infection in a plant host. The accumulation of viral RNA was quantified using qPCR at 1, 2 and 8 days post infection (dpi), and two different methods were employed to predict the dominating virus. TMV was predicted to dominate the game in the long run and this prediction was confirmed by both qRT-PCR at 8 dpi and the death of co-infected plants after 15 dpi. In addition, we validated our model by using data reported in the literature. Ten out of fourteen reported co-infection outcomes agreed with our predictions. Explanations were given for the four interactions that did not agree with our model. Hence, it serves as a valuable tool in making long term predictions using short term data obtained in virus co-infections