Discovery and identification of a novel canine coronavirus causing a diarrhea outbreak in Vulpes
Bibliographic record
Abstract
Cross-species transmission of viruses from wildlife animal reservoirs, such as bats, poses a threat to human and domestic animal health. Previous studies have shown that domestic animals have important roles as intermediate hosts, enabling the transmission of genetically diverse coronaviruses from natural hosts to humans. Here, we report the identification and characterization of a novel canine coronavirus (VuCCoV), which caused an epidemic of acute diarrhea in Vulpes (foxes) in Shenyang, China. The epidemic started on November 8, 2019, and caused more than 39,600 deaths by January 1, 2022. Full-length viral genomic sequences were obtained from 15 foxes with diarrhea at the early stage of this outbreak. The VuCCoV genome shared more than 90% nucleotide identity with canine coronavirus (CCoV) for three of the four structural genes, with the S gene showing a larger amount of divergence. In addition, 67% (10/15) of the VuCCoV genomes contained an open reading frame (ORF3) gene, which was previously only detected in CCoV-I genomes. Notably, VuCCoV had only two to three amino acid differences at the partial RNA-dependent RNA polymerase (RdRp) level to bat CoV, suggesting a close genetic relationship. Therefore, these novel VuCCoV genomes represent a previously unsampled lineage of CCoVs. We also show that the VuCCoV spike protein binds to canine and fox aminopeptidase N (APN), which may allow this protein to serve as an entry receptor. In addition, cell lines were identified that are sensitive to VuCCoV using a pseudovirus system. These data highlight the importance of identifying the diversity and distribution of coronaviruses in domestic animals, which could mitigate future outbreaks that could threaten livestock, public health, and economic growth.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".