Engineering and characterization of human β-defensin-3 and its analogues and microcin J25 peptides against Mannheimia haemolytica and bovine neutrophils
Bibliographic record
Abstract
Mannheimia haemolytica-induced bovine respiratory disease causes loss of millions of dollars to Canadian cattle industry. Current antimicrobials are proving to be ineffective and leave residues in meat. Antimicrobial peptides (AMPs) may be effective against M. haemolytica while minimizing the risk of drug residues. Cationic AMPs can kill bacteria through interactions with the anionic bacterial membrane. Human β-Defensin 3 (HBD3) and microcin J25 (MccJ25) are AMPs with potent activity against many Gram-negative bacteria. We tested the microbicidal activity of wild-type HBD3, three HBD3 peptide analogues (28 amino acid, 20AA, and 10AA) derived from the sequence of natural HBD3, and MccJ25 in vitro against M. haemolytica. Three C-terminal analogues of HBD3 with all cysteines replaced with valines were manually synthesized using solid phase peptide synthesis. Since AMPs can act as chemoattractant we tested the chemotactic effect of HBD3, 28AA, 20AA, and 10AA peptides on bovine neutrophils in Boyden chamber. Minimum bactericidal concentration (MBC) assay showed that M. haemolytica was intermediately sensitive to HBD3, 28AA and 20AA analogues with an MBC of 50 µg/mL. The 10AA analogue had MBC 6.3 µg/mL which is likely a result of lower final inoculum size. MccJ25 didn't have significant bactericidal effect below an MBC < 100 µg/mL. Bovine neutrophils showed chemotaxis towards HBD3 and 20AA peptides (P < 0.05) but not towards 28AA analogue. Co-incubation of neutrophils with any of the peptides did not affect their chemotaxis towards N-formyl-L-methionyl-L-leucyl-phenylalanine (fMLP). The data show that these peptides are effective against M. haemolytica and are chemotactic for neutrophils in vitro.
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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) | 0.000 | 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".