Development and Evaluation of Multiple Droplet Digital PCR Method for Specific Detection and Differentiation of Brucella melitensis and Brucella abortus

Brucellosis remains a major global public health concern, with <i>B. melitensis</i> and <i>B. abortus</i> being the primary causative agents in China. This study describes the development of a multiplex droplet digital PCR (ddPCR) assay for the simultaneous detection and differentiation of <i>B. melitensis</i> and <i>B. abortus</i>. The assay employs TaqMan probes targeting the <i>bcsp31</i> gene (genus-specific), a transposase gene (<i>B. melitensis</i>-specific) and an autotransporter-associated beta strand repeat-containing protein gene (<i>B. abortus</i>-specific), each labeled with distinct fluorophores (FAM, HEX, ROX). The optimized assay exhibited no cross-reactivity with other pathogens and exhibited significantly higher sensitivity than both qPCR and conventional PCR, with detection limits as low as 2.54-3.11 copies/reaction. Repeatability was excellent, with intra- and inter-assay coefficients of variation below 9%. When validated on a panel of clinical nucleic acid samples, the ddPCR assay showed strong agreement with qPCR (kappa = 0.85), with a sensitivity of 100% (79.42%~100%, 95%CI) and specificity of 95.96% (92.08%~99.84%, 95%CI). These findings establish the multiplex ddPCR as a rapid, sensitive, and highly specific diagnostic platform that improves brucellosis detection accuracy and supports targeted control strategies.

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Publication Details

Journal
Animals
Published
2026-02-12
DOI
https://doi.org/10.3390/ani16040566
Primary Topic
Brucella: diagnosis, epidemiology, treatment
Type
article
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0.00
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article

Development and Evaluation of Multiple Droplet Digital PCR Method for Specific Detection and Differentiation of Brucella melitensis and Brucella abortus

Jihui Jin, Mengkun Huang, J. LI, Xin Yan et al.
Animals
Brucella: diagnosis, epidemiology, treatment
article

Development and Evaluation of Multiple Droplet Digital PCR Method for Specific Detection and Differentiation of Brucella melitensis and Brucella abortus

Jihui Jin, Mengkun Huang, J. LI, Xin Yan, H. Zhang, Wenlong Nan, Baoxu HUANG, Weixing Shao, Xiangxiang Sun, Yuning Liu, Xiaoxu Fan, Shufang Sun, Mengda Liu
article en

Abstract

Brucellosis remains a major global public health concern, with <i>B. melitensis</i> and <i>B. abortus</i> being the primary causative agents in China. This study describes the development of a multiplex droplet digital PCR (ddPCR) assay for the simultaneous detection and differentiation of <i>B. melitensis</i> and <i>B. abortus</i>. The assay employs TaqMan probes targeting the <i>bcsp31</i> gene (genus-specific), a transposase gene (<i>B. melitensis</i>-specific) and an autotransporter-associated beta strand repeat-containing protein gene (<i>B. abortus</i>-specific), each labeled with distinct fluorophores (FAM, HEX, ROX). The optimized assay exhibited no cross-reactivity with other pathogens and exhibited significantly higher sensitivity than both qPCR and conventional PCR, with detection limits as low as 2.54-3.11 copies/reaction. Repeatability was excellent, with intra- and inter-assay coefficients of variation below 9%. When validated on a panel of clinical nucleic acid samples, the ddPCR assay showed strong agreement with qPCR (kappa = 0.85), with a sensitivity of 100% (79.42%~100%, 95%CI) and specificity of 95.96% (92.08%~99.84%, 95%CI). These findings establish the multiplex ddPCR as a rapid, sensitive, and highly specific diagnostic platform that improves brucellosis detection accuracy and supports targeted control strategies.

AnimalsVol. 16(4)
Qingdao Agricultural University (CN), Northeast Agricultural University (CN), China Animal Health and Epidemiology Center (CN)
Good health and well-being, Partnerships for the goals
Openalex Percentile: Top 56%
Brucella: diagnosis, epidemiology, treatment
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Development and Evaluation of Multiple Droplet Digital PCR Method for Specific Detection and Differentiation of Brucella melitensis and Brucella abortus — Jihui Jin, Mengkun Huang, et al. · Animals (2026) | TGRS Research Map | TGRS