ArticlePLoS neglected tropical diseases2025
Establishment of an I-ELISA method based on multi-epitope fusion protein for diagnosis of human brucellosis.
Article in PLoS neglected tropical diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Application of Outer Membrane Protein Omp25 in Serological Diagnosis of Brucellosis.Infectious disease reports · 2026Review
- Development and Preliminary Evaluation of an OMP16-Targeting Trivalent Nanobody-HRP-Based cELISA for Serological Detection of Bovine Brucellosis.Animals : an open access journal from MDPI · 2026Article
- Article
- Epidemiology, diagnosis and emerging therapies for Lyme disease of the Northern Hemisphere.International journal of emergency medicine · 2026Review
- Optimizing ovine brucellosis serodiagnosis: evaluation of recombinantFrontiers in veterinary science · 2026Article
- Development and evaluation of novel Brucella diagnostic antigen protein ST.BMC infectious diseases · 2025Article
- Multiepitope fusion protein-based ELISA for enhanced brucellosis serodiagnosis.PLoS neglected tropical diseases · 2025Article
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Authors and funding
9 authors.
Funding
Abstract
backgroundBrucellosis is a significant zoonotic disease that impacts people globally, and its diagnosis has long posed challenges. This study aimed to explore the application value of multi-epitope fusion protein in the diagnosis of human brucellosis.
methodsEight important Brucella outer membrane proteins (OMPs) were selected: BP26, omp10, omp16, omp25, omp2a, omp2b, and omp31. Bioinformatics techniques were used to predict the immune epitopes of these proteins, and a multi-epitope fusion protein was designed. This fusion protein was used as the antigen for indirect enzyme-linked immunosorbent assay (iELISA) testing on 100 positive and 96 negative serum samples. The performance of the fusion protein in diagnosing brucellosis was evaluated using receiver operating characteristic (ROC) curves.
resultsA total of 31 epitopes were predicted from the eight proteins, and a multi-epitope fusion protein was successfully obtained. For the detection of human serum samples, the area under the ROC curve (AUC) of the fusion protein was 0.9594, with a positive diagnostic accuracy of 91.26% and a negative diagnostic accuracy of 93.55%. The area under the ROC curve (AUC) for lipopolysaccharides (LPS) was 0.9999, with a positive diagnostic accuracy of 100% and a negative diagnostic accuracy of 98.97%.
conclusionsThe fusion protein constructed using bioinformatics techniques, as the diagnostic antigen, showed significantly reduced cross-reactivity and enhanced specificity, improving diagnostic accuracy. This not only saves time but also avoids the preparation of LPS antigens, making the diagnostic process safer and more convenient.
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