A Novel Competitive ELISA for Specifically Measuring and Differentiating Immune Responses to Classical Swine Fever C-Strain Vaccine in Pigs

Abstract

Classical swine fever can be controlled effectively by vaccination with C-strain vaccine. In this study, we developed a novel competitive enzyme-linked immunosorbent assay (cELISA) based on a C-strain Erns specific monoclonal antibody (mAb 1504), aiming to serologically measure immune responses to C-strain vaccine in pigs, and finally to make the C-strain become a DIVA-compatible vaccine. The cELISA system was established based on the strategy that mAb 1504 will compete with the C-strain induced antibodies in the pig serum to bind the C-strain Erns protein. The cELISA was optimized and was further evaluated by testing different categories of pig sera. It can efficiently differentiate C-strain immunized from wild-type CSFV-infected pigs and lacks cross-reaction with other common swine viruses and viruses in genus Pestivirus such as Bovine viral diarrhea virus (BVDV). The C-strain antibody can be tested in pigs 7–14 days post vaccination with this cELISA. The sensitivity and specificity of the established cELISA were 100% (95% confidence interval: 95.60 to 100%) and 100% (95% confidence interval: 98.30 to 100%), respectively. This novel cELISA is a reliable tool for specifically measuring and differentiating immune responses to C-strain vaccine in pigs. By combining with the wild-type CSFV-specific infection tests, it can make the C-strain have DIVA capability.

Editorial: Advances and insights in the diagnosis of viral infections and vaccines development in animals

“…Clinical signs and pathological findings in viral infected animals are highly variable due to both viral and host factors, and often confused with other diseases of animals (Murcia et al, 2009 ; Wang et al, 2020 ). Well-established laboratory techniques like virus isolation, real-time polymerase chain reaction (RT-PCR), and enzyme-linked immunosorbent assay (ELISA) have improved diagnostic accuracy (Wang et al, 2020 , 2022 ; Mi et al, 2022 ; Azam et al, 2023 ). Recent advancements have taken this progress a step further by offering faster, more efficient methods.…”