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ABSTRACT Brucellosis is a zoonotic disease caused by Brucella spp. and is a major threat to human and livestock health. The accurate and rapid detection of Brucella DNA is essential for the diagnosis and treatment of this disease. However, traditional diagnostic methods, such as culture and serological tests, have limitations in terms of sensitivity, specificity, and time consumption. To address these challenges, we developed a highly sensitive one-tube nested quantitative real-time PCR (qPCR) method to detect Brucella DNA using a closed-tube approach with two primers and two probes that sequentially react with encoding a Brucella outer membrane protein. The analytical sensitivity of this one-tube nested qPCR approach was 100 fg/μL, which is 100-fold higher than that of a conventional qPCR. Intra-batch and inter-batch replicates showed low coefficients of variation, both less than 5%. The study included 250 clinical samples, showing that this one-tube nested qPCR method has a specificity of 100% and a sensitivity of 98.6%, exceeding the sensitivity of conventional qPCR (84.1%) at a cycle threshold (CT) cutoff of 38. More importantly, the one-tube nested qPCR reduced the CT values by an average of 6.4 compared to conventional qPCR, significantly improving the detection rate of low-load samples (CT > 35). In conclusion, the bcsp31 one-tube nested qPCR method is a promising tool for the detection of brucellosis, owing to its high sensitivity and operational simplicity. This study highlights the potential of qPCR-based methods to improve the accuracy and speed of brucellosis diagnoses. IMPORTANCE This study developed a highly sensitive and efficient method for the detection of brucellosis by introducing a one-tube nested quantitative real-time PCR (qPCR) approach, representing a remarkable advance in the field. The method demonstrated an impressive analytical sensitivity of 100 fg/μL, surpassing conventional qPCR and enabling the detection of even low levels of Brucella DNA. In addition, the study’s comprehensive evaluation of 250 clinical samples revealed a specificity of 100% and a sensitivity of 98.6%, underscoring its reliability and accuracy. Most importantly, the new method significantly improved the detection rate of low-burden samples, reducing cycle threshold values by an average of 6.4. These results underscore the immense potential of this approach to facilitate rapid and accurate brucellosis diagnosis, which is critical for effective disease management and control. |
