Threshold Microsclerotial Inoculum for Cotton Verticillium Wilt Determined Through Wet-Sieving and Real-Time Quantitative PCR

文献类型： 外文期刊

第一作者： Feng Wei

作者： Feng Wei;Rong Fan;Haitao Dong;Wenjing Shang;Xiangming Xu;Heqin Zhu;Jiarong Yang;Xiaoping Hu

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关键词： disease severity;proportional odds model

期刊名称：PHYTOPATHOLOGY （ 影响因子：4.025； 五年影响因子：4.394 ）

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收录情况： SCI

摘要： 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 semi-selective 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.

分类号： S432.1

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