Enhanced Resistance to Bacterial Infection in Protegrin-1 Transgenic Mice
Bibliographic record
Abstract
Antibiotic-resistant bacteria have become a public health concern. It was suggested that one source of resistant pathogens may be food-producing animals. Alternative approaches are therefore needed to enhance the resistance of farm animals to bacterial infection. Protegrin-1 (PG-1) is a neutrophil-derived antimicrobial peptide that possesses activity against a wide range of bacteria and enveloped viruses. Here we report on the production of transgenic mice that ectopically expressed PG-1 and compare their susceptibilities to Actinobacillus suis infection with those of their wild-type (WT) littermates. Of the 126 mice that were challenged with A. suis, 87% of the transgenic mice survived, whereas 31% of their WT littermates survived. The PG-1 transgenic mice had significantly lower bacterial loads in their lungs and reduced numbers of pulmonary pathological lesions. The antimicrobial function of PG-1 was confirmed in vitro by using fibroblast cells isolated from the transgenic mice but not the WT mice. Moreover, differential blood cell counts in bronchoalveolar lavage fluid indicated greater number of neutrophils in PG-1 transgenic mice than in their WT littermates after bacterial challenge. Our data suggest that the ectopic expression of PG-1 in mice confers enhanced resistance to bacterial infection, laying the foundation for the development of livestock with improved resistance to infection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".