Aminopeptidase N-null neonatal piglets are protected from transmissible gastroenteritis virus but not porcine epidemic diarrhea virus
Bibliographic record
Abstract
Swine enteric diseases have caused significant economic loss and have been considered as the major threat to the global swine industry. Several coronaviruses, including transmissible gastroenteritis virus (TGEV) and porcine epidemic diarrhea virus (PEDV), have been identified as the causative agents of these diseases. Effective measures to control these diseases are lacking. The major host cells of transmissible gastroenteritis virus and porcine epidemic diarrhea virus have thought to be epithelial cells on small intestine villi. Aminopeptidase-N (APN) has been described as the putative receptor for entry of transmissible gastroenteritis virus and porcine epidemic diarrhea virus into cells in vitro. Recently, Whitworth et al. have reported that APN knockout pigs are resistant to TGEV but not PEDV after weaning. However, it remains unclear if APN-null neonatal pigs are protected from TGEV. Here we report the generation of APN-null pigs by using CRISPR/Cas9 technology followed by somatic cell nuclear transfer. APN-null pigs are produced with normal pregnancy rate and viability, indicating lack of APN is not embryonic lethal. After viral challenge, APN-null neonatal piglets are resistant to highly virulent transmissible gastroenteritis virus. Histopathological analyses indicate APN-null pigs exhibit normal small intestine villi, while wildtype pigs show typical lesions in small intestines. Immunochemistry analyses confirm that no transmissible gastroenteritis virus antigen is detected in target tissues in APN-null piglets. However, upon porcine epidemic diarrhea virus challenge, APN-null pigs are still susceptible with 100% mortality. Collectively, this report provides a viable tool for producing animals with enhanced resistance to TGEV and clarifies that APN is dispensable for the PEDV infection in pigs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".