Development of novel Competitive Enzyme-linked immunosorbent assays to detect SARS-CoV-2-specific antibodies in animals
Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the COVID-19-causing virus, is a zoonotic pathogen. There is concern about the virus spilling over from humans into wildlife species, which may then serve as reservoirs for future infection of humans and other animals. Furthermore, the level of exposure of potentially susceptible wildlife species is currently not known. There is, therefore, an urgent need to develop a single test that could be used for the serosurveillance of multiple wildlife species for exposure to SARS-CoV-2. Although there are serological techniques to detect the exposure of humans to the virus, few assays have the capacity to detect antibodies in a wide variety of species. Here, I describe the development of a competitive enzyme-linked immunosorbent assay (cELISA) to detect SARS-CoV-2 antibodies in mammals for which species-specific reagents are not available. Therefore, cELISAs were developed to detect SARS-CoV-2 spike S1 and S2 domains and nucleocapsid (N) specific antibodies and were validated using sera from experimentally infected hamsters. We further validated our cELISA by comparing it with results obtained from the surrogate virus neutralization test (cPASS, GenScript) and indirect ELISA using anti-hamster horse radish peroxidase (HRP) conjugated reagents. Our initial cELISA was based on the ability of test antibodies to displace the binding of commercially obtained rabbit antibodies against viral proteins coated on the ELISA plate. Rabbit antibody reagents are expensive and anti-rabbit detection antibody may cross-react with other mammalian antibodies. Therefore, I explored the use of antibodies produced in hen eggs (IgY) as a substitute for rabbit sera. Hens were immunized against SARS-CoV-2 antigens: S1, S2 and N. IgY antibodies were purified from egg yolk, and the assay was optimized to use specific antibody and antigen combinations. Among S1, S2 and N-IgYs, only the S2-IgY based cELISA was specific and comparable with both the rabbit anti serum based cELISA and the surrogate virus neutralization test (cPASS). This assay will be a valuable tool which can be implemented in surveillance programs investigating exposure to and transmission of SARS-CoV-2 in multiple domestic, captive, or wildlife species.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".