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Development of rapid and visual Erwinia amylovora detection technology using recombinase polymerase amplification
Peijie Gong1, Shuyu Liu1,2, Chenyang Han2
1Institute of Plant Protection, Jiangsu Academy of Agricultural Sciences/Jiangsu Key Laboratory for Food Quality and Safety-State Key Laboratory Cultivation Base of Ministry of Science and Technology, Nanjing, China.
Background:
Erwinia amylovora, a highly destructive bacterial disease affecting pears, apples, and other rosaceous plants, has been reported in over 60 countries. Accurate identification during monitoring is essential to prevent its spread to new regions, underscoring the need for efficient, reliable detection techniques. Conventional methods are often instrument-dependent and unsuitable for early field detection, so establishing a rapid, sensitive, field-adaptable assay is crucial for timely pathogen detection and integrated green control, especially under resource-limited conditions.
Results:
A sensitive, rapid detection system was developed by combining recombinase polymerase amplification (RPA) with a colloidal gold-based lateral flow dipstick (LFD) for specific E. amylovora identification. The assay demonstrated high specificity when evaluated against 22 bacterial species (such as Erwinia pyrifoliae, Dickeya fangzhongdai, Bacillus velezensis, etc.), four pear pathogenic fungi (such as Valsa pyri, Botryosphaeria dothidea, Alternaria alternata, etc.), and four pear DNA samples. The entire procedure can be completed within 1 h, providing a simple and rapid detection platform, with broad operational flexibility (effective amplification at 25-45 °C, 10-30 min), and a detection sensitivity of 10 fg μL-1 genomic DNA, 5 × 103 CFU mL-1, or 1000-fold diluted crude extracts.
Conclusion:
The RPA-LFD system achieved a 94.6% positive detection rate for artificially inoculated samples with visual LFD readout. This system enables rapid on-site detection of E. amylovora and provides a practical tool for implementing timely phytosanitary interventions and science-based control strategies. Furthermore, the technique shows significant potential for widespread application in fire blight monitoring and integrated disease management. © 2026 Society of Chemical Industry.
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