| Literature DB >> 35891340 |
Abhinay Gontu1,2, Erika A Marlin1,3, Santhamani Ramasamy1, Sabarinath Neerukonda4, Gayatri Anil1, Jasmine Morgan1, Meysoon Quraishi1, Chen Chen5, Veda Sheersh Boorla5, Ruth H Nissly2, Padmaja Jakka1,2, Shubhada K Chothe1, Abirami Ravichandran6, Nishitha Kodali1,7, Saranya Amirthalingam1,7, Lindsey LaBella1, Kathleen Kelly2, Pazhanivel Natesan8, Allen M Minns7,9, Randall M Rossi7, Jacob R Werner10, Ernest Hovingh1, Scott E Lindner7,9, Deepanker Tewari11, Vivek Kapur7,10,12, Kurt J Vandegrift7,12,13, Costas D Maranas5, Meera Surendran Nair1,2, Suresh V Kuchipudi1,2,7,12.
Abstract
Multiple domestic and wild animal species are susceptible to SARS-CoV-2 infection. Cattle and swine are susceptible to experimental SARS-CoV-2 infection. The unchecked transmission of SARS-CoV-2 in animal hosts could lead to virus adaptation and the emergence of novel variants. In addition, the spillover and subsequent adaptation of SARS-CoV-2 in livestock could significantly impact food security as well as animal and public health. Therefore, it is essential to monitor livestock species for SARS-CoV-2 spillover. We developed and optimized species-specific indirect ELISAs (iELISAs) to detect anti-SARS-CoV-2 antibodies in cattle, swine, and chickens using the spike protein receptor-binding domain (RBD) antigen. Serum samples collected prior to the COVID-19 pandemic were used to determine the cut-off threshold. RBD hyperimmunized sera from cattle (n = 3), swine (n = 6), and chicken (n = 3) were used as the positive controls. The iELISAs were evaluated compared to a live virus neutralization test using cattle (n = 150), swine (n = 150), and chicken (n = 150) serum samples collected during the COVID-19 pandemic. The iELISAs for cattle, swine, and chicken were found to have 100% sensitivity and specificity. These tools facilitate the surveillance that is necessary to quickly identify spillovers into the three most important agricultural species worldwide.Entities:
Keywords: ELISA; SARS-CoV-2; cattle; chicken; serology; surveillance; swine
Mesh:
Substances:
Year: 2022 PMID: 35891340 PMCID: PMC9317974 DOI: 10.3390/v14071358
Source DB: PubMed Journal: Viruses ISSN: 1999-4915 Impact factor: 5.818
Figure 1Performance of iELISAs on the serum samples collected from cattle, swine, and chicken. Pre-pandemic (known negatives) and pandemic-era serum samples from domestic animals were tested on species-specific iELISAs for cattle, swine, and chicken. The cut-offs of the iELISAs were determined by testing pre-pandemic sera from each species. Hyperimmune serum (HIS) samples were used as positive controls for the ELISAs. Bar heights represent the mean absorbance of the samples read at 450 nm. Error bars represent the standard deviation. Dashed lines indicate the cut-off for each of the assays.
Estimation of sensitivity and specificity of iELISAs.
| VNT Status | Total | Positive | Negative | Sensitivity/Specificity |
|---|---|---|---|---|
|
| ||||
| Positive sera * | 13 | 13 | 0 | 100% (13/13; sensitivity) |
| Negative sera | 150 | 0 | 150 | 100% (150/150; specificity) |
|
| ||||
| Positive sera | 6 | 6 | 0 | 100% (6/6; sensitivity) |
| Negative sera | 150 | 0 | 150 | 100% (150/150; specificity) |
|
| ||||
| Positive sera * | 24 | 24 | 0 | 100% (24/24; sensitivity) |
| Negative sera | 150 | 0 | 150 | 100% (150/150; specificity) |
* Analyses conducted on serum samples from bleeds collected at multiple time points from three cattle, six pigs, and three chickens.
Figure 2Correlation of cattle, swine, and chicken iELISAs with VNT. Correlation analyses were performed on the antibody titers from iELISAs and virus neutralization (VN) assays for each of the species: cattle, swine, and chicken. Strong correlation was observed between the titers from the assays (Pearson’s r: >0.8). Dots represent each sample of hyperimmune serum or IgY tested. Solid lines represent the linear regression curves of the titers of the samples analyzed by the assays. Shaded area indicates 95% confidence interval of the linear regression curve. R2 is the correlation coefficient from the linear regression curve.