摘要
本试验的目的是建立更加灵敏的检测牛冠状病毒(BCoV)的RT-PCR方法,并对川西北草原牦牛病料进行BCoV病原检测。选择BCoV聚合酶基因Nsp7-Nsp9片段设计引物,通过反应条件和体系优化,建立检测BCoV的RT-PCR方法并应用于临床样本检测。结果显示:所建方法特异性和重复性好,灵敏性达到1×10-2pg·μL-1;该方法对肉牛和牦牛BCoV都有良好的检测效果,优于比较的两种以BCoV N基因为靶点的RT-PCR方法;对2016年采集的川西北草原牦牛病料进行了BCoV的检测,结果显示:125份腹泻牦牛粪便样本中BCoV的检出率为71.20%,98份患呼吸道疾病的牦牛鼻腔棉拭子中BCoV的检出率为72.45%。此次建立的BCoV RT-PCR检测方法特异性和重复性好、灵敏度高;BCoV是当前川西北草原牦牛腹泻和呼吸道疾病综合征的重要病原。
The aim of this study was to establish a more sensitive RT-PCR assay for detecting BCoV and to detect CoV from clinically ill yak in the northwest grasslands of Sichuan.The RT-PCR assay was established through designing primers targeted to Nsp 7-Nsp 9 fragment of BCoV polymerase gene and optimizing the reaction conditions and system,and the clinical samples were detected by the RT-PCR assay.Results revealed that the RT-PCR assay have good specificity and stability,and the detection limit of viral nucleic acid of the assays was 1×10^-2 pg·μL-1.Comparing to two RT-PCR assays targeted to the N gene of BCoV,the RT-PCR assay in this study has a remarkable detection rate for BCoV in clinical samples both of bovine and yak.In 125 diarrhea samples of yak in the northwest grasslands of Sichuan in 2016,the CoV detection rate was 71.20%,In 98 nasal cavity samples of yak in the northwest grasslands of Sichuan in 2016,the CoV detection rate was 72.45%.The RT-PCR assay for detecting BCoV established in this study has a good specificity and stability.BCoV is an important causative agent of yak diarrhea and respiratory disease syndrome in the northwest grasslands of Sichuan.
作者
何琪富
郭紫晶
李然
周军
岳华
张斌
汤承
HE Qi-fu;GUO Zi-jing;LI Ran;ZHOU Jun;YUE Hua;ZHANG Bin;TANG Cheng(College of Life Science and Technology,Southwest Minzu University, Chengdu 610041,China;Innovation Team for Animal Epidemic Diseases Prevention and Control on Qinghai-Tibet Plateau,State Ethnic Affairs Commission,Chengdu 610041,China)
出处
《畜牧兽医学报》
CAS
CSCD
北大核心
2018年第10期2292-2298,共7页
ACTA VETERINARIA ET ZOOTECHNICA SINICA
基金
十三五国家重点研发计划课题(2016YFD0500907)
国家民委"青藏高原动物疫病防控创新团队"(13TD0057)
西南民族大学研究生"创新型科研项目"(CX2017SZ061)
