1.
Sugar flux and signaling in plant-microbe interactions.
Bezrutczyk, M, Yang, J, Eom, JS, Prior, M, Sosso, D, Hartwig, T, Szurek, B, Oliva, R, Vera-Cruz, C, White, FF, et al
The Plant journal : for cell and molecular biology. 2018;(4):675-685
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Abstract
Plant breeders have developed crop plants that are resistant to pests, but the continual evolution of pathogens creates the need to iteratively develop new control strategies. Molecular tools have allowed us to gain deep insights into disease responses, allowing for more efficient, rational engineering of crops that are more robust or resistant to a greater number of pathogen variants. Here we describe the roles of SWEET and STP transporters, membrane proteins that mediate transport of sugars across the plasma membrane. We discuss how these transporters may enhance or restrict disease through controlling the level of nutrients provided to pathogens and whether the transporters play a role in sugar signaling for disease resistance. This review indicates open questions that require further research and proposes the use of genome editing technologies for engineering disease resistance.
2.
Threshold microsclerotial inoculum for cotton verticillium wilt determined through wet-sieving and real-time quantitative PCR.
Wei, F, Fan, R, Dong, H, Shang, W, Xu, X, Zhu, H, Yang, J, Hu, X
Phytopathology. 2015;(2):220-9
Abstract
Quantification of Verticillium dahliae microsclerotia is an important component of wilt management on a range of crops. Estimation of microsclerotia by dry or wet sieving and plating of soil samples on semiselective medium is a commonly used technique but this method is resource-intensive. We developed a new molecular quantification method based on Synergy Brands (SYBR) Green real-time quantitative polymerase chain reaction of wet-sieving samples (wet-sieving qPCR). This method can detect V. dahliae microsclerotia as low as 0.5 CFU g(-1) of soil. There was a high correlation (r=0.98) between the estimates of conventional plating analysis and the new wet-sieving qPCR method for 40 soil samples. To estimate the inoculum threshold for cotton wilt, >400 soil samples were taken from the rhizosphere of individual plants with or without visual wilt symptoms in experimental and commercial cotton fields at the boll-forming stage. Wilt inoculum was estimated using the wet-sieving qPCR method and related to wilt development. The estimated inoculum threshold varied with cultivar, ranging from 4.0 and 7.0 CFU g(-1) of soil for susceptible and resistant cultivars, respectively. In addition, there was an overall relationship of wilt incidence with inoculum density across 31 commercial fields where a single composite soil sample was taken at each field, with an estimated inoculum threshold of 11 CFU g(-1) of soil. These results suggest that wilt risk can be predicted from the estimated soil inoculum density using the new wet-sieving qPCR method. We recommend the use of 4.0 and 7.0 CFU g(-1) as an inoculum threshold on susceptible and resistant cultivars, respectively, in practical risk prediction schemes.