Effective Antimicrobial Activity of Plectasin-Derived Antimicrobial Peptides against Staphylococcus aureus Infection in Mammary Glands
Bibliographic record
Abstract
Staphylococcus aureus (S. aureus) is the causative agent for a wide variety of illnesses ranging from minor skin infections to life-threatening diseases. Development of antibiotic resistance by the bacteria has rendered many antibiotics ineffective. It has been known that plectasin-derived antimicrobial peptides (NZ2114 and MP1102) are promising alternatives to antibiotics. However, their activities against S. aureus in mammary glands were unknown. Our objective was to assess the antimicrobial activities of NZ2114 and MP1102 against S. aureus in milk, in cultured mammary epithelial cells, and in a mouse model in order to evaluate their potentials as anti-mastitis agents. NZ2114 and MP1102 showed in vitro bactericidal effects against S. aureus both in the culture medium and milk. NZ2114 and MP1102 at the concentration of 100μg/mL reduced the number of S. aureus by almost 100% within 4h in processed bovine milk. Similarly, both NZ2114 and MP1102 were efficient to reduce the number of internalized S. aureus in cultured mammary epithelial cells. Finally, both antimicrobial peptides significantly reduced the S. aureus load and concentrations of TNF-α and IL-6 in mammary glands, compared to a buffer control in the mouse model. Our results suggest that NZ2114 and MP1102 may be used to treat S. aureus -induced mastitis.
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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 | 0.000 | 0.000 |
| 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".