生物技术通报 ›› 2025, Vol. 41 ›› Issue (10): 186-195.doi: 10.13560/j.cnki.biotech.bull.1985.2025-0421

• 技术与方法 • 上一篇    下一篇

四种主要黄萎病致病菌微滴数字PCR检测方法的建立及应用

翁慧婷1,2(), 郭惠明3, 程红梅3, 李君2, 张超2, 刘海洋1(), 苏晓峰3()   

  1. 1.新疆维吾尔自治区农业科学院,乌鲁木齐 830091
    2.河北农业大学生命科学学院,保定 071001
    3.中国农业科学院生物技术研究所 农业微生物资源发掘与利用全国重点实验室,北京 100081
  • 收稿日期:2025-04-22 出版日期:2025-10-26 发布日期:2025-10-28
  • 通讯作者: 苏晓峰,男,博士,研究员,研究方向 :植物与微生物互作;E-mail: suxiaofeng@caas.cn
    刘海洋,男,博士,研究员,研究方向 :植物保护;E-mail: liuhaiyang001@163.com
  • 作者简介:翁慧婷,女,博士研究生,研究方向 :植物学;E-mail: wht_bio@163.com
  • 基金资助:
    国家自然科学基金项目(32160624);中国农业科学院重大任务(CAAS-ZDRW202308)

Establishment and Application of Droplet Digital PCR Detection Methods for Four Major Verticillium Wilt Pathogens

WENG Hui-ting1,2(), GUO Hui-ming3, CHENG Hong-mei3, LI Jun2, ZHANG Chao2, LIU Hai-yang1(), SU Xiao-feng3()   

  1. 1.Xinjiang Uygur Autonomous Region Academy of Agricultural Sciences, Urumqi 830091
    2.College of Life Sciences, Hebei Agricultural University, Baoding 071001
    3.National Key Laboratory of Agricultural Microbial Resources Exploration and Utilization, Institute of Biotechnology, Chinese Academy of Agricultural Sciences, Beijing 100081
  • Received:2025-04-22 Published:2025-10-26 Online:2025-10-28

摘要:

目的 建立一种同时检测4种黄萎病致病菌的微滴式数字PCR(ddPCR)方法,为及时、准确定量监测该病原真菌的生长动态,进行早期诊断和风险评估奠定基础。 方法 通过比对4种黄萎病致病菌大丽轮枝菌(Verticillium dahliae, Vd)、长孢轮枝菌(V. longisporum, Vl)、非苜蓿轮枝菌(V. nonalfalfae, Vna)和黑白轮枝菌(V. albo-atrum, Vaa)的ITS(Internal transcribed spacer)序列(Vd,KY039312.1;Vl,KX058040.1;Vna,KT362917.1和Vaa,MH856937.1),选取保守区域设计引物和探针。结合微滴式数字PCR和实时荧光定量PCR(qPCR)筛选最佳引物,优化ddPCR最佳反应体系,并测定方法的特异性与灵敏度。 结果 建立方法的最佳引物/探针组为Ver5;最佳退火温度为58 ℃,引物浓度为500 nmol/L和探针浓度为250 nmol/L。特异性检测结果显示,该方法能够特异性识别4种黄萎病致病菌,对包括7种真菌和6种细菌在内的非靶标微生物无交叉扩增;对于VdVlVnaVaa的检测限分别为2.1×10-6、1.6×10-6、6.9×10-4、3.6×10-5 ng/μL。选取50个棉花和50份土壤样品展开检测分析,结果表明相较于qPCR,ddPCR方法的检出率呈现出显著优势,且检测灵敏度分别提高了46%和51%。 结论 建立的ddPCR方法检测4种黄萎病致病菌特异性强,灵敏度高,稳定可靠,为黄萎病的精准检测提供了重要的技术手段。该方法有利于海关检验检疫与植物病虫害监管等领域,提高病害防控的科学性与时效性。

关键词: 作物黄萎病, 轮枝菌, 数字PCR, 实时荧光定量, 精准检测

Abstract:

Objective A droplet digital PCR (ddPCR) method was established for the simultaneous detection of four Verticillium wilt pathogens, which may lay the foundation for timely and accurate quantitative monitoring of pathogen growth dynamics, early diagnosis, and risk assessment. Method Based on the alignment of the internal transcribed spacer (ITS) sequences of four Verticillium wilt pathogens-Verticillium dahliae (Vd, KY039312.1), V. longisporum (Vl, KX058040.1), V. nonalfalfae (Vna, KT362917.1), and V. albo-atrum (Vaa, MH856937.1)-conserved regions were selected for the design of primers and probes. Droplet digital PCR (ddPCR) and real-time quantitative PCR (qPCR) were used to screen for the optimal primers, optimize the ddPCR reaction system, and evaluate the specificity and sensitivity of the method. Result The optimal primer/probe set for the established method was Ver5; the optimal annealing temperature was 58 ℃, with primer and probe concentrations of 500 nmol/L and 250 nmol/L, respectively. Specificity testing showed that this method specifically identified the four Verticillium wilt pathogens without cross-amplification for non-target microbes, including 7 fungal and 6 bacterial species. The detection limits for Vd, Vl, Vna, and Vaa were 2.1×10-⁶, 1.6×10-⁶, 6.9×10-⁴, and 3.6×10-⁵ ng/μL, respectively. Detection analysis of 50 cotton and 50 soil samples demonstrated that, compared to qPCR, the ddPCR method showed a significant advantage in detection rate, with sensitivities improved by 46% and 51%, respectively. Conclusion The established ddPCR method for detecting the four Verticillium wilt pathogens demonstrates high specificity, excellent sensitivity, and robust reliability, providing an important technical tool for the accurate detection of Verticillium wilt. This method is advantageous for applications in customs inspection and quarantine, as well as in the monitoring and regulation of plant diseases and pests, thereby enhancing the scientific accuracy and timeliness of disease prevention and control.

Key words: crop Verticillium wilt, Verticillium, ddPCR, qPCR, precise detection