Human Pre-Elafin Inhibits a <i>Pseudomonas aeruginosa</i> -Secreted Peptidase and Prevents Its Proliferation in Complex Media
Bibliographic record
Abstract
Pseudomonas aeruginosa is a life-threatening opportunist human pathogen frequently associated with lung inflammatory diseases, namely, cystic fibrosis. Like other species, this gram-negative bacteria is increasingly drug resistant. During the past decade, intensive research efforts have been focused on the identification of natural innate defense molecules with broad antimicrobial activities, collectively known as antimicrobial peptides. Human pre-elafin, best characterized as a potent inhibitor of neutrophil elastase with anti-inflammatory properties, was also shown to possess antimicrobial activity against both gram-positive and gram-negative bacteria, including P. aeruginosa. Its mode of action was, however, not known. Using full-length pre-elafin, each domain separately, and mutated variants of pre-elafin with attenuated antipeptidase activity toward neutrophil elastase, we report here that both pre-elafin domains contribute, through distinct mechanisms, to its antibacterial activity against Pseudomonas aeruginosa. Most importantly, we demonstrate that the whey acidic protein (WAP) domain specifically inhibits a secreted peptidase with the characteristics of arginyl peptidase (protease IV). This is the first demonstration that a human WAP-motif protein inhibits a secreted peptidase to prevent bacterial growth in vitro. Since several WAP-motif proteins from various species demonstrate antimicrobial function with variable activities toward bacterial species, we suggest that this mechanism may be more common than initially anticipated.
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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".