Rapid Identification of Brucella Genus and Species In Silico and On-Site Using Novel Probes with CRISPR/Cas12a

Author:

Zhang Yan12,Lyu Yufei23ORCID,Wang Dongshu2ORCID,Feng Meijie12,Shen Sicheng2,Zhu Li2ORCID,Pan Chao2ORCID,Zai Xiaodong2ORCID,Wang Shuyi2,Guo Yan2,Yu Shujuan2,Gong Xiaowei4,Chen Qiwei4ORCID,Wang Hengliang123ORCID,Wang Yuanzhi5,Liu Xiankai23

Affiliation:

1. College of Food Science and Technology, Shanghai Ocean University, 999 Hucheng Huan Road, Lingang New City, Shanghai 201306, China

2. State Key Laboratory of Pathogens and Biosecurity, Beijing Institute of Biotechnology, 20 Dongdajie Street, Fengtai District, Beijing 100071, China

3. Laboratory of Advanced Biotechnology, 20 Dongdajie Street, Fengtai District, Beijing 100071, China

4. State Key Laboratory for Animal Disease Control and Prevention, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Chengguan District, Lanzhou 730046, China

5. School of Medicine, Shihezi University, Xinjiang Uygur Autonomous Region, Shihezi 832002, China

Abstract

Human brucellosis caused by Brucella is a widespread zoonosis that is prevalent in many countries globally. The high homology between members of the Brucella genus and Ochrobactrum spp. often complicates the determination of disease etiology in patients. The efficient and reliable identification and distinction of Brucella are of primary interest for both medical surveillance and outbreak purposes. A large amount of genomic data for the Brucella genus was analyzed to uncover novel probes containing single-nucleotide polymorphisms (SNPs). GAMOSCE v1.0 software was developed based on the above novel eProbes. In conjunction with clinical requirements, an RPA-Cas12a detection method was developed for the on-site determination of B. abortus and B. melitensis by fluorescence and lateral flow dipsticks (LFDs). We demonstrated the potential of these probes for rapid and accurate detection of the Brucella genus and five significant Brucella species in silico using GAMOSCE. GAMOSCE was validated on different Brucella datasets and correctly identified all Brucella strains, demonstrating a strong discrimination ability. The RPA-Cas12a detection method showed good performance in detection in clinical blood samples and veterinary isolates. We provide both in silico and on-site methods that are convenient and reliable for use in local hospitals and public health programs for the detection of brucellosis.

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Publisher

MDPI AG

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3