基于CP基因的云南烟草花叶病毒RT-LAMP快速检测体系的构建OA北大核心CSTPCD
Development of Reverse Transcription Loop-Mediated Isothermal Amplification(RT-LAMP)System for Rapid Detection of Yunnan Tobacco Mosaic Virus Based on CP Gene
为快速检测云南烟草花叶病毒(Tobacco mosaic virus,TMV),本研究根据来自云南烟草TMV分离物的外壳蛋白(CP)基因保守核苷酸序列,设计了 5 组引物进行筛选,并采用单一变量法对反应的温度、时间、甜菜碱浓度、dNTPs浓度、Mg2+浓度及内、外引物浓度比等进行逐一优化,建立了云南烟草TMV逆转录环介导等温扩增(RT-LAMP)检测体系.结果表明,最佳引物组为第 4 组,最适反应温度为 60℃,甜菜碱、dNTPs、Mg2+的最佳反应浓度分别为 0.6、0.4、2.0 mmol/L,最佳内、外引物浓度比为 4∶1,最佳反应时间 40 min.优化后的RT-LAMP经SYBR Green I染色可肉眼判断结果,特异性高,灵敏度是常规RT-PCR的 10 倍.RT-LAMP检测体系的建立为云南烟草TMV的检测提供了一种便捷、高效、可靠的方法.
In order to rapidly detect tobacco mosaic virus(TMV)in Yunnan tobacco,five sets of prim-ers were designed for screening based on the conserved nucleotide sequence of the coat protein(CP)gene from Yunnan tobacco TMV isolate,the reaction temperature,time,betaine concentration,dNTPs concentra-tion,Mg2+concentration,and the ratio of internal to external primer concentrations were optimized by the sin-gle variable method,and then a reverse transcription loop mediated isothermal amplification(RT-LAMP)de-tection system for Yunnan tobacco TMV was established.The results showed that the optimal primer group was the fourth group,the optimal reaction temperature was 60℃,and the optimal final concentrations of Betaine,dNTPs and Mg2+were 0.6,0.4 and 2.0 mmol/L,respectively.The optimal concentration ratio of internal prim-er to external primer was 4∶1,and the optimal reaction time was 40 minutes.The results of optimized RT-LAMP could be visualized after stained by SYBR Green Ⅰ,which showed high specificity to TMV and the sensitivity was 10 times that of conventional RT-PCR.This study provided a convenient,efficient and reliable method for the detection of TMV in Yunnan tobacco.
赵正婷;盖晓彤;张俊蕾;卢灿华;姜宁;刘雅婷
云南农业大学农学与生物技术学院,云南 昆明 650201云南省烟草农业科学研究院,云南 昆明 650021云南农业大学植物保护学院,云南 昆明 650201云南农业大学烟草学院,云南 昆明 650201
植物保护学
烟草花叶病毒CP基因RT-LAMP特异性灵敏度
Tobacco mosaic virus(TMV)CP GeneRT-LAMPSpecificitySensitivity
《山东农业科学》 2024 (005)
154-162 / 9
国家自然科学基金项目(32260681);云南省烟草公司科技计划项目(2021530000242031);云南省科技厅基础研究专项(202101AU070129)
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