基于病菌孢子捕捉和real-time PCR技术的田间空气中小麦白粉病菌孢子动态监测及病情估计模型研究

利用Burkard定容式孢子捕捉器结合real-time PCR定量技术,分别对种植高抗、中感和高感白粉病小麦品种的田间空气中白粉病菌分生孢子浓度进行监测,结果表明,real-time PCR定量与传统的显微观察计数两种方法测得的孢子浓度呈显著正相关(P≤0.01),且两种病菌孢子计数方法在同一抗性品种上监测到的孢子浓度动态相近。此外,两种方法测得的孢子浓度与各气象因子的相关性分析结果一致,空气中的白粉病菌孢子浓度主要与空气相对湿度显著正相关。在此基础上,利用两种方法测定的田间空气中白粉病菌孢子浓度分别建立了基于累积孢子浓度的田间病情估计模型。分析发现,基于两种孢子浓度测定方法建立的病情估计模型间无显著性差异,表明real-time PCR定量技术测定的孢子浓度在构建白粉病病情估计模型上具有一定可行性。该结果为real-time PCR定量技术与病菌孢子捕捉技术相结合用于小麦白粉病的监测和预测提供理论依据。

Dynamic monitoring of aerial conidia and disease estimation models for wheat powdery mildew in fields using pathogen spore traps coupled with real-time PCR technique

In this study, a combination of real-time PCR assay and Burkard 7-day recording spore traps was used to monitor airborne Blumeria graminis f.sp. tritici (Bgt) conidia in the fields across three wheat varieties exhibiting highly resistant, moderately susceptible and highly susceptible levels. There were significant correlations (P≤0.01) between conidia concentrations obtained by using a compound microscope and a real-time PCR assay. The dynamics of airborne Bgt conidia concentrations on same resistant varieties were found to be similar when determined by two methods. Furthermore, the correlations between meteorological factors and conidia concentrations, as determined by the two methods, were almost identical, with conidia concentrations mainly exhibiting significant positive correlations with humidity. Subsequently, disease estimation models for wheat powdery mildew between disease index and accumulated conidia concentration were constructed using two methods, respectively. Parallel curve analysis showed that there were no significant differences in the fitted models established based on two methods for measuring accumulated conidia concentration. The conidia concentration determined by the real-time PCR technique for fitting mildew estimation models has proven to be practical and efficient. These results suggested that the pathogen spore trap technique coupled with real-time quantitative PCR offers a potential tool for both monitoring and predicting wheat powdery mildew.