Evaluation of an in-house indirect enzyme-linked immunosorbent assay of feline panleukopenia VP2 subunit antigen in comparison to hemagglutination inhibition assay to monitor tiger antibody levels by Bayesian approach

Abstract

Background: Feline panleukopenia virus (FPV) is an etiologic pathogen of feline panleukopenia that infects all members of Felidae including tigers (Panthera tigris). Vaccinations against FPV among wild felid species have long been practiced in zoos worldwide. However, few studies have assessed the tiger immune response post-vaccination due to the absence of a serological diagnostic tool. To address these limitations, this study aimed to develop an in-house indirect enzyme-linked immunosorbent assay (ELISA) for the monitoring of tiger antibody levels against the feline panleukopenia vaccine by employing the synthesized subunit capsid protein VP2. An in-house horseradish peroxidase (HRP) conjugated rabbit anti-tiger immunoglobulin G (IgG) polyclonal antibody (HRP-anti-tiger IgG) was produced in this study and employed in the assay. It was then compared to a commercial HRP-conjugated goat anti-cat IgG (HRPanti-cat IgG). Sensitivity and specificity were evaluated using the Bayesian model with preferential conditional dependence between HRP-conjugated antibody-based ELISAs and hemagglutination-inhibition (HI) tests. Results: The posterior estimates for sensitivity and specificity of two indirect ELISA HRP-conjugated antibodies were higher than those of the HI test. The sensitivity and specificity of the indirect ELISA for HRP-anti-tiger IgG and HRP-anticat IgG were 86.5, 57.2 and 86.7%, 64.6%, respectively, while the results of the HI test were 79.1 and 54.1%. In applications, 89.6% (198/221) and 89.1% (197/221) of the tiger serum samples were determined to be seropositive by indirect ELISA testing against HRP-anti-tiger and HRP-anti-cat, respectively.

Development of in-house ELISA for detection of antibodies against lumpy skin disease virus in cattle and assessment of its performance using a bayesian approach

“…The optimization of an in-house ELISA was modified from a previously described method [ 12 ]. A total of 50 sera samples were derived from infected cattle exhibiting classical cinical signs of LSDV infection and confirmed with the VN assay.…”

“…It has a cheaper cost than the VN assay and can be administered more efficiently to a large number of samples on 96 well-plates [ 8 ]. This method has been successfully applied and performed in the detection of certain antibodies to animal diseases [ [9] , [10] , [11] , [12] , [13] ] but would require the use of a standard coated antigen in the microtiter plates [ 8 ]. Previous investigations have demonstrated that ELISA can serve as an efficient diagnostic tool for antibody detection of LSD in animals [ 6 , 14 ].…”

“…This analysis has become increasingly used in veterinary epidemiology to evaluate the accuracy of a diagnostic test or the true disease prevalence when a gold standard is not available [ 15 , 16 ]. The Bayesian analysis has been employed to calculate the sensitivity and specificity of an in-house ELISA method in previous studies [ 9 , 12 , 13 ]. The Bayesian latent class model can then be performed to determine the sensitivity and specificity of the present in-house test kit when compared to the commercial test kit.…”

Development of Nonstructural Protein-Based Indirect ELISA to Identify Elephant Endotheliotropic Herpesvirus (EEHV) Infection in Asian Elephants (Elephas maximus)

“…The sera were obtained from the Sample Bank of the Veterinary Diagnostic Laboratory, Faculty of Veterinary Medicine, Chiang Mai University, Thailand. The percentage of binding (% binding) for the sera-to-antigen ratio was compared to that of the elephant at 100% of the 15 µg/mL antigen concentration, as has been previously described [ 36 ].…”

Validation of a Novel ELISA for the Diagnosis of Hemorrhagic Septicemia in Dairy Cattle from Thailand Using a Bayesian Approach

“…Thus, a Bayesian latent class analysis was performed to address these problems because this method has inferred posterior estimates from a combination of prior information and observed data to solve the uncertainty of unknown parameters such as test performance or disease prevalence [ 9 ]. This analysis has become increasingly used in veterinary epidemiology to evaluate the accuracy of a diagnostic test or the true disease prevalence when a gold standard may be impossible to obtain [ 9 , 10 , 11 , 12 ].…”