Multi-species ELISA for the detection of antibodies against SARS-CoV-2 in animals
2020 | 2021
Wernike, Kerstin | Aebischer, Andrea | Michelitsch, Anna | Hoffmann, Donata | Freuling, Conrad | Balkema-Buschmann, Anne | Graaf, Annika | Müller, Thomas | Osterrieder, Nikolaus | Rissmann, Melanie | Rubbenstroth, Dennis | Schön, Jacob | Schulz, Claudia | Trimpert, Jakob | Ulrich, Lorenz | Volz, Asisa | Mettenleiter, Thomas | Beer, Martin
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic with millions of infected humans and hundreds of thousands of fatalities. As the novel disease - referred to as COVID-19 - unfolded, occasional anthropozoonotic infections of animals by owners or caretakers were reported in dogs, felid species and farmed mink. Further species were shown to be susceptible under experimental conditions. The extent of natural infections of animals, however, is still largely unknown. Serological methods will be useful tools for tracing SARS-CoV-2 infections in animals once test systems are evaluated for use in different species. Here, we developed an indirect multi-species ELISA based on the receptor-binding domain (RBD) of SARS-CoV-2. The newly established ELISA was evaluated using 59 sera of infected or vaccinated animals including ferrets, raccoon dogs, hamsters, rabbits, chickens, cattle and a cat, and a total of 220 antibody-negative sera of the same animal species. Overall, a diagnostic specificity of 100.0% and sensitivity of 98.31% was achieved, and the functionality with every species included in this study could be demonstrated. Hence, a versatile and reliable ELISA protocol was established that enables high-throughput antibody detection in a broad range of animal species, which may be used for outbreak investigations, to assess the seroprevalence in susceptible species or to screen for reservoir or intermediate hosts.
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Библиографическая информация
Издатель Wiley-Blackwell ; Hindawi
ISSN 1865-1682 | 1865-1674 | 1865-1682Эту запись предоставил Friedrich-Loeffler-Institut