Assessment of diagnostic tests for bovine brucellosis: systematic review, development and validation

Abstract

Bovine brucellosis is a zoonotic disease caused mainly by Brucella abortus, with significant economic and health impacts on cattle herds, particularly in developing countries. To support surveillance and control programs, diagnostic tests especially serological assays are essential for monitoring herd health and guiding decision-making. However, several of these tests still present limitations that require further investigation. In the first chapter of this dissertation, a systematic review was conducted to identify key parameters in the development of Lateral Flow Assays (LFA) for the diagnosis of bovine brucellosis. Indirect LFAs were the most frequently reported (96%), with bovine serum as the primary sample type (95.83%), immunoglobulin- binding proteins as bioconjugates (58.33%), smooth lipopolysaccharide (sLPS) as the test-line antigen (62.5%), and colloidal gold (62.5%) or quantum dots (25%) as detection particles. In the second chapter, five indirect enzyme-linked immunosorbent assays (iELISAs) were evaluated to validate and compare diagnostic accuracy, considering potential interference from vaccine antigens and cross-reactions with antibodies against other Gram-negative bacteria. The assays developed with polysaccharide antigens (sLPS and OPS) achieved the highest performance, with diagnostic sensitivity and specificity above 99%. In contrast, assays employing crude antigens presented reduced performance (DSe and DSp ~96%), and the inactivated acetone antigen demonstrated poor results (below 80%), rendering it unsuitable for diagnostic purposes. These findings contribute to the field of bovine brucellosis diagnosis by identifying key methodological parameters that can support the future development of more robust and standardized LFA platforms and by demonstrating the superior accuracy of polysaccharide-based iELISAs. Together, these results provide important guidance for selecting antigens and detection strategies, improving the reliability of diagnostic tools and, consequently, enhancing surveillance and control programs for the disease.