Year: 2025
Type: article
Abstract: Background Brucellosis, a zoonotic bacterial infection caused by Brucella species, exhibits a global distribution. The Brucella S2 vaccine strain is known to cause brucellosis. Current serological antibody assays cannot distinguish between infections caused by the S2 strain and those caused by wild-type Brucella. Objective To develop a diagnostic method capable of specifically detecting S2 vaccine strain infections. Methods Two probes were designed targeting single nucleotide polymorphism (SNP) loci upstream of the sugar ABC gene; quantitative polymerase chain reaction (qPCR) and droplet digital polymerase chain reaction (ddPCR) methods were established. The performances of these methods were evaluated. The transient stem-loop structure of the DNA template was predicted, and the impact of probe overlap with the transient stem-loop structure on detection sensitivity was analyzed. Clinical applicability was assessed using 50 blood samples from brucellosis patients. Results Both types of methods demonstrated high specificity. However, MGB-SNPdd showed greater sensitivity than other detection methods. Reduction of overlap between the probe sequence and the transient stem-loop structure enhanced detection sensitivity. In the clinical applicability analysis, ddPCR methods exhibited higher rates of S2 vaccine strain detection compared with qPCR methods. Conclusion SNP-based ddPCR methods demonstrate higher sensitivity than qPCR methods and enable specific detection of brucellosis caused by the S2 vaccine strain. Reduction of probe overlap with the transient stem-loop structure improves detection sensitivity, providing valuable insights for enhanced PCR amplification efficiency.
Source: Frontiers in Veterinary Science
Authors: Xingya Wang, Xiaowei Tian, Wanyang Li, Yuanchao Yang, Shuai Zhang, Hui Wang, Wanru Geng, Jingbo Zhai
Language: English
Subfield: Small Animals
Field: Veterinary
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,801
Award: G3122094059
OpenAlex ID: W4411464772
