Abstract
Lumpy skin disease virus (LSDV) infection, accompanied by loss of hide quality, poor reproductive efficiency, consistent degenerative emaciation, and milk yield reduction of animals, causes severe economic implications in endemic zones. The heterologous attenuated goat pox (GTPV) vaccine (AV41 strain) was used in China to prevent LSDV infection. Only a few LSDV detection methods that distinguish LSDV from GTPV vaccine strains have been reported before. For simple, rapid, and specific detection of LSDV, the real-time recombinase polymerase amplification (RPA) method was established with the specific primers and probes designed according to the conserved regions of ORF132 gene sequences. The assay could be finished within 20 min at a constant temperature (39 °C). This method had a limit of detection (LOD) of 15 copies/μL for LSDV and no cross-reaction with the nucleic acids of goat pox virus, infectious bovine rhinotracheitis virus, Pasteurella multocida, and bovine healthy tissue. Furthermore, 43 clinical samples were detected by this method and the real-time PCR recommended by the World Organisation for Animal Health (WOAH), with a kappa value, was 0.94. These results demonstrated that the real-time RPA method for detecting LSDV developed in this study was characterized by high sensitivity and specificity, which has wide application value in the clinical diagnosis and detection of LSDV in China.
Funding Information
  • Guangzhou Basic and Applied Basic Research Foundation (202201011182, XTXM202202, XT202208, 2023KJ114, 2023KJ119, 2023B04J0137, 202024)
  • Collaborative Innovation Center Fund (202201011182, XTXM202202, XT202208, 2023KJ114, 2023KJ119, 2023B04J0137, 202024)
  • Department of Agriculture and Rural Affairs of Guangdong Province (202201011182, XTXM202202, XT202208, 2023KJ114, 2023KJ119, 2023B04J0137, 202024)
  • Guangzhou Science and Technology Bureau (202201011182, XTXM202202, XT202208, 2023KJ114, 2023KJ119, 2023B04J0137, 202024)
  • Project of President Funding of Guangdong Academy of Agricultural Sciences (202201011182, XTXM202202, XT202208, 2023KJ114, 2023KJ119, 2023B04J0137, 202024)

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