摘要
水稻潜根线虫(Hirschmanniellaoryzae)在水稻产区普遍存在,是水稻(Oryzaspp.)的主要植物寄生线虫,在世界范围内造成产量损失。本研究通过比较GenBank数据库中潜根属线虫相关序列,以水稻潜根线虫ITS-rDNA序列为靶标,设计了水稻潜根线虫LAMP的一组特异性引物,包括一对外引物(F3:5′-ATCTTGTCCTTTGGCACG-3′,B3:5′-CGGTTGAACAAACAACGT-3′)和一对内引物(FIP:5′-CAGCATAGCAACAGAATGAATTCACGGTCGTAAACCTAATACGCG-3′,BIP:5′-TTGTACTACAATGGATTGTTTTCGCCTGATCCATCCACCCATG-3′);用琼脂凝胶电泳法和SYBR Green I染色法筛选合适的引物和扩增条件,建立了优化的水稻潜根线虫LAMP检测条件:扩增温度为57℃,反应时间为45 min,反应体系中dNTPs和MgSO_(4)的浓度分别为1.4 mmol·L^(-1)和7 mmol·L^(-1)。本研究建立的检测方法可以检测鉴定水稻潜根线虫不同发育时期虫态(雌虫、雄虫、幼虫)个体,还可以从近缘种和其他植物寄生线虫混合的样品及水稻根组织样品中直接检测出水稻潜根线虫,且检测灵敏度达到1/1 000条成虫DNA。
Hirschmanniella oryzae is widely distributed in rice producing areas,and it has been affecting the growth of rice and causing some yield losses worldwide.The specific primers for the loop-mediated isothermal amplification(LAMP)were designed based on rDNA ITS sequences of H.oryzae by comparing with related sequences of Hirschmanniella spp.deposited in GenBank.Two external Primers(F3:5′-ATCTTGTCCTTTGGCACG-3′,B3:5′-CGGTTGAACAAACAACGT-3′)and two inner primers(FIP:5′-CAGCATAGCAACAGAATGAATTCACGGTCGTAAACCTAATACGCG-3′,BIP:5′-TTGTACTACAATGGATTGTTTTCGCCTGATCCATCCACCCATG-3′)were designed.The optimal reaction conditions were screened based on electrophoresis detection and visual observation with SYBR Green I staining method,and the optimal conditions are:LAMP amplification under 57℃for 45 min,the concentration of dNTPs and MgSO4 used in the reaction volume is 1.4 mmol·L^(-1) and 7 mmol·L^(-1),respectively.The results showed that the LAMP method could detect and identify single juvenile,female or male of H.oryzae,and also could directly detect H.oryzae from mixed nematode species and rice root samples,the sensitivity of LAMP assay was 1/1000 of single nematode DNA.
作者
何晋
刘淑婷
陈淳
丁善文
谢辉
徐春玲
HE Jin;LIU Shu-ting;CHEN Chun;DING Shan-wen;XIE Hui;XU Chun-ling(College of Agriculture/Research Center of Nematodes of Plant Quarantine,South China Agricultural University,Guangzhou 510642,China)
出处
《植物病理学报》
CAS
CSCD
北大核心
2021年第4期626-635,共10页
Acta Phytopathologica Sinica
基金
国家自然科学基金项目(31871939)
科技部基础资源调查专项(2018FY100300)。
