Salmonella enterica serovar Dublin (S. Dublin) is a host-adapted cattle pathogen that causes systemic disease, chronic carriage, and environmental contamination. Early detection of subclinical infections remains challenging due to low bacterial loads and the limitations of conventional diagnostics. We developed a duplex TaqMan real-time PCR assay targeting a chromosomal locus (SeDA1104) and a plasmid-encoded gene (vagD). The assay demonstrated strong linearity (R² > 0. 99) with amplification efficiencies of 91%-102% and detection limits as low as 1. 9 × 10¹ genome copies per reaction. Specificity was confirmed in silico and against 20 non-Dublin Salmonella serovars, with no cross-amplification. Intra- and inter-assay variability was consistently below 3%. Field testing of 40 samples from subclinical cattle and farm environments demonstrated detection in multiple matrices, including feces (50% positive), vaginal swabs (40%), nasal swabs (30%), and environmental boot-swabs (75%). The dual-target design provides improved confidence in detection, reduces false positives, and supports early detection and control of S. Dublin in cattle herds and associated environments, thereby contributing to reduced zoonotic transmission and enhancing food safety within a One Health framework. IMPORTANCEEarly detection of Salmonella Dublin is critical for controlling disease in cattle and reducing zoonotic risk. Existing diagnostics often fail to identify subclinical carriers or low-level environmental contamination, which sustain transmission within herds. We developed a dual-target duplex quantitative real-time PCR assay that simultaneously amplifies a chromosomal locus and a plasmid-encoded gene, improving specificity and reducing false positives caused by genetically related Salmonella serovars. Unlike many current tools, this assay is validated for direct use with challenging samples, including feces, nasal and vaginal swabs, and environmental boot swabs, where inhibitors and low pathogen load commonly interfere with detection. By enhancing sensitivity and reliability across diverse matrices, this method supports farm-level surveillance, rapid outbreak response, and risk-based control strategies. Broader implementation can improve herd health, limit economic losses, and reduce the risk of human exposure through food or environmental sources.
Arevalo-Mayorga et al. (Thu,) studied this question.