Establishment of an I-ELISA method based on multi-epitope fusion protein for diagnosis of human brucellosis.

Background: Brucellosis 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.

Methods: Eight 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.

Results: A 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%.

Conclusions: The 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.