Establishment of a 23S rRNA assay for Brucella and its application in evaluating bacterial growth status.

Abstract

Brucellosis presents notable diagnostic challenges, particularly during the chronic phase of infection, due to the typically low bacterial load in tissues, which may evade detection by conventional Polymerase Chain Reaction (PCR) methods. Traditional PCR techniques are susceptible to cross-reactions and can produce false-positive or false-negative results. Furthermore, bacteriological and serological tests have recognized limitations in sensitivity and specificity, potentially complicating accurate diagnosis. We developed a novel reverse transcription quantitative PCR (RT-qPCR) assay targeting Brucella species' highly abundant and conserved 23S rRNA gene to address these challenges. The 23S rRNA gene was selected as an optimal molecular target due to the approaches' number and genetic stability across Brucella strains. Primers were designed based on a conserved region (Sequence ID: NR_103168.2) to ensure broad applicability, and their specificity was verified through BLAST analysis. A key feature of the 23S-RT-qPCR method is incorporating a reverse transcription step to convert RNA into complementary DNA (cDNA), which may enhance detection sensitivity. Using the Brucella S2 vaccine strain as a template, experimental results indicated that the 23S-RT-qPCR method reduced cycle threshold (Ct) values by approximately 2-3 units (14.67-16.74) compared to conventional qPCR. Statistical analysis using a T-test showed that this reduction was significant (P < 0.05). The performance of the 23S-RT-qPCR method was further evaluated using clinical samples and compared with the IS711 detection method. The 23S-RT-qPCR method demonstrated a lower false-positive rate (2.6%) and false-negative rate (7.6%) compared to the IS711 method, which had a false-positive rate of 5.2% and a false-negative rate of 7.6%. These results suggest that the 23S-RT-qPCR method offers improved sensitivity and specificity, reducing false-positive and false-negative rates in clinical sample analysis. Overall, this method may provide a more reliable approach for diagnosing chronic brucellosis and could have broader applicability in detecting other bacterial pathogens.