犬猫弓形虫病real-time荧光定量PCR检测方法的建立与应用
Establishment and application of real-time fluorescent quantitative PCR for detection of toxoplasmosis in dogs and cats
施远国 1胡潇予 2柯艳坤 1李颖鑫 1文青元 2杨子鹏 2杨留禄 2袁子国2
作者信息
- 1. 深圳市动物疫病预防控制中心,广东 深圳 518000
- 2. 华南农业大学 兽医学院,广东 广州 510642
- 折叠
摘要
为建立一种灵敏性高、特异性强、可自动化检测犬猫弓形虫的分子学诊断方法,本研究提取弓形虫RH虫株DNA,以致密颗粒蛋白 7(GRA7)基因保守区为靶标,设计特异性引物并构建标准质粒pMD18-GRA7,以标准质粒pMD18-GRA7 作为模板,建立SYBR-GreenⅠ荧光定量PCR(real-time PCR or qPCR)检测方法.结果显示,该方法特异性引物只能扩增出弓形虫DNA样品,对阴性样品及其他犬猫寄生虫阳性样品DNA则未见扩增曲线.所建qPCR方法的检测下限为 3 copies,当标准品浓度为 3 copies/μL时,检测Ct值为29.4.对犬猫临床样品进行检测,并与商品化试剂盒结果相比校,阴阳性符合率达 100%.上述结果表明,本研究建立的SYBR-Green Ⅰ荧光定量PCR方法特异性好,敏感性强,结果稳定,可用于犬猫弓形虫病的临床检测.
Abstract
To establish a highly sensitive,specific and automated molecular diagnostic method for detecting Toxoplasma gondii in dogs and cats,this study extracted the DNA of the RH strain of T.gondii and used the conserved region of the dense granular protein 7(GRA7)gene as the target.Specific primers were designed and a standard plasmid pMD18-GRA7 was constructed,A SYBR Green Ⅰ fluorescent quantitative PCR(real time PCR or qPCR)detection method was established using the standard plasmid pMD18-GRA7 as a template.The results showed that the specific primers of this method could only amplify DNA samples of T.gondii,and no amplification curve was found in the negative samples and other positive samples of dog and cat parasites.The detection limit of the established qPCR method is 3 copies,when the standard con-centration is 3 copies/μL,the Ct value detected is 29.4.The detection of clinical samples from dogs and cats showed a positive and negative coincidence rate of 100%compared to the results of commercial reagent kits.The above results indicate that the SYBR Green Ⅰ fluorescence quantitative PCR method established in this study has good specificity,strong sensitivity,and stable results,and can be used for clinical detection of canine and cat toxoplasmosis.
关键词
弓形虫/犬/猫/GRA7/实时荧光定量PCRKey words
Toxoplasma gondii/dogs/cats/GRA7/real-time PCR引用本文复制引用
基金项目
弓形虫检测技术研究项目(NJ-FW-20230068)
国家自然科学基金(31972707)
广东省基础与应用基础研究基金(2023A1515011795)
出版年
2024
国家科技期刊平台
NSTL
万方数据