A Handy and Visual Detection Method for Potato Late Blight Utilizing Multi-Copy Specific Sequences and RPA-LFD Technology
Potato late blight, caused by <i>Phytophthora infestans</i>, is one of the most destructive diseases affecting global potato production. Rapid and accurate detection of the pathogen is essential for effective disease prevention and control. In this study, a novel multi-copy and specific DNA sequence, designated <i>SSPI1</i> (<i>Specific Sequence of Phytophthora infestans 1</i>), was identified through bioinformatic analysis. Using seven closely related Phytophthora species and two additional common potato pathogens as controls, PCR assays demonstrated that <i>SSPI1</i> offers higher sensitivity than <i>PiYPT1</i> and improved specificity over <i>PiITS</i>. Based on this novel multi-copy sequence, we developed a rapid, user-friendly, and highly sensitive detection method for potato late blight by combining recombinase polymerase amplification (RPA) with lateral flow dipstick (LFD) technology. The entire diagnostic process-including crude DNA extraction, RPA amplification, and visual detection-can be completed within 35 min without the need for specialized equipment. Optimization results indicated that among nine candidate RPA primer pairs, RPA-F1/RPA-R2 exhibited the highest amplification efficiency. The optimal RPA reaction temperature range was determined to be 27-42°C, with a detection limit of 11 copies/μl for <i>SSPI1</i>. This method could detect as little as 100 pg of genomic DNA, identify samples collected as early as 24 h post-inoculation, and even detect infections in asymptomatic leaves under field conditions, demonstrating superior sensitivity compared to the <i>PiYPT1</i>-targeted RPA-LFD assay. Overall, this study presents a highly sensitive, specific, practical, and efficient tool for the early and rapid detection of potato late blight.
Authors
- Lei Chen (ORCID: https://orcid.org/0009-0000-7585-7891)
- Yi Que
- Zhiwei Zhang (ORCID: https://orcid.org/0000-0001-9257-4092)
- Xia Wang
- Guangjin Fan
- Li Sheng
- RongSheng Wang
Institutions
- Chongqing University of Science and Technology (CN)
- Guizhou University (CN)
- Quality Enhancement Research Initiative (US)
- Chongqing Academy of Agricultural Sciences (CN)
Publication Details
- Journal
- Journal of Microbiology and Biotechnology
- Published
- 2026-02-12
- DOI
- https://doi.org/10.4014/jmb.2509.09040
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00