Rapid Identification of Brucella Genus and Species In Silico and On-Site Using Novel Probes with CRISPR/Cas12a.
Yan ZhangYufei LyuDongshu WangMeijie FengSicheng ShenLi ZhuChao PanXiaodong ZaiShuyi WangYan GuoShujuan YuXiaowei GongQiwei ChenHengliang WangYuanzhi WangXiankai LiuPublished in: Microorganisms (2024)
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.
Keyphrases
- loop mediated isothermal amplification
- crispr cas
- public health
- genome editing
- real time pcr
- label free
- healthcare
- end stage renal disease
- single molecule
- small molecule
- molecular docking
- escherichia coli
- chronic kidney disease
- sensitive detection
- fluorescence imaging
- endothelial cells
- newly diagnosed
- high resolution
- risk assessment
- gene expression
- big data
- genetic diversity
- artificial intelligence
- molecular dynamics simulations
- patient reported