Recombinantly expressed virus-like particles (VLPs) of canine circovirus for development of an indirect ELISA

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Canine circovirus (CanineCV) is an emerging pathogen in domestic dogs, detected in multiple countries in association with varying clinical and pathological presentations including diarrhoea, vasculitis, granulomatous inflammation, and respiratory signs. Understanding the pathology of CanineCV is confounded by the fact that it has been detected in asymptomatic dogs as well as in diseased dogs concurrently infected with known pathogens. Recombinantly expressed self-assembling Virus-like particles (VLPs) lack viral genomic material but imitate the capsid surface conformations of wild type virion, allowing arrays of biological applications including subunit vaccine development and immunodiagnostics. In this study, full length CanineCV capsid gene was expressed in Escherichia coli followed by two-step purification process to yield soluble capsid protein in high concentration. Transmission electron microscopy (TEM) confirmed the capsid antigen self-assembled into 17-20 nm VLPs in glutathione S-transferase (GST) buffer, later utilised to develop an indirect enzyme-linked immunosorbent assay (iELISA). The respective sensitivity and specificity of the proposed iELISA were 94.10% and 88.40% compared with those obtained from Western blot. The mean OD450 value for western blot positive samples was 1.22 (range 0.12-3.39) and negative samples was 0.21 (range 0.07-0.41). An optimal OD450 cut-off of 0.35 was determined by ROC curve analysis. Median inter-assay and intra-assay validation revealed that the iELISA test results were reproducible with coefficients of variation 7.70 (range 5.6-11.9) and 4.21 (range 1.2-7.4). Our results demonstrated that VLP-based iELISA is a highly sensitive method for serological diagnosis of CanineCV infections in dogs, suitable for large-scale epidemiological studies.

Original languageEnglish
Pages (from-to)1121-1133
Number of pages13
JournalVeterinary Research Communications
Issue number2
Early online date02 Jan 2024
Publication statusPublished - Apr 2024


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