叶柄(昆虫解剖学)
生物
砧木
园艺
塔克曼
植物
聚合酶链反应
生物化学
基因
膜翅目
作者
Karandeep Chahal,Ethan J. Wachendorf,Laura A. Miles,Adam M. Grove,Martin I. Chilvers,Timothy D. Miles
出处
期刊:Phytopathology
[American Phytopathological Society]
日期:2025-03-12
标识
DOI:10.1094/phyto-08-24-0253-r
摘要
Oak wilt, caused by the fungal pathogen Bretziella fagacearum, spreads via root grafts and insect vectors, threating oaks (Quercus spp.) and chestnuts (Castanea spp.) in the United States. Detection and management of B. fagacearum are crucial, as oak wilt can devastate forested and urban ecosystems. However, diagnosing oak wilt presents challenges and requires laboratory confirmation due to symptom similarities with other stressors. Common detection methods also have limitations. In this study, we optimized and validated an existing TaqMan real-time PCR assay, comparing it with a culture-based method and using nested PCR as gold standard. We also developed a novel non-destructive sampling technique. Our optimized real-time PCR assay demonstrated a consistent 100% detection rate and accuracy across all branch sapwood samples. In contrast, the culture-based method varied significantly, achieving 100% detection rate and accuracy only for fresh samples displaying sapwood discoloration. In the absence of sapwood discoloration, the culture detection rate and accuracy were 80% and 90%, respectively. For dry samples, these rates decreased significantly to 22% and 52%. The novel non-destructive sampling method used leaf petioles of fallen leaves to detect B. fagacearum from two tree hosts, using both optimized real-time PCR and culture-based methods. Our real-time PCR consistently outperformed the culture-based method, regardless of symptom severity in leaf samples. The real-time PCR offers improved efficiency, specificity, sensitivity, and turnaround time compared to nested PCR and culture-based methods. Our findings highlight the potential of detecting vascular-inhabiting pathogens from leaf petiole samples, particularly in scenarios requiring non-destructive sampling and high-throughput screening.
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