Enhancing the antibacterial activities of sow milk via site-specific knock-in of a lactoferrin gene in pigs using CRISPR/Cas9 technology
Bibliographic record
Abstract
Colostrum quality is a vital factor in mortality and growth performance for piglets. Lactoferrin is an immuno-active milk protein that contributes to the formation of a protective layer above intestinal mucosa, possesses the antibacterial and antiviral activities that are favorable for piglet development. However, there is a notable reduction in lactoferrin in sow milk during lactation after the first few days, which causes many piglets to fail to ingest enough colostrum thereby leading to an increase in piglet mortality. In this study, we successfully constructed genome-edited Large-White pigs with marker-free site-specific knock-in of lactoferrin gene in the 3'-end of Casein alpha-s1 via CRISPR/Cas9 mediated homologous recombination. Thus, the lactoferrin protein can be expressed in the mammary gland in the control of Casein alpha-s1 promoter. As expected, the lactoferrin protein in genetically modified pigs sustained high expression in both colostrum and milk when compared with wild-type pigs. Moreover, the bacterial plate assay indicated that the milk from genetically modified pigs showed bacteriostatic effects when compared with control pigs. Taken together, our study demonstrated that the milk from genetically modified pigs had antibacterial activity which may reduce the costs of veterinary drug and improve the surviving rate of piglets, which is promising for pig breeding.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".