Characterization of human pre-elafin mutants: full antipeptidase activity is essential to preserve lung tissue integrity in experimental emphysema
Bibliographic record
Abstract
Pre-elafin is a tight-binding inhibitor of neutrophil elastase and myeloblastin; two enzymes thought to contribute to tissue damage in lung emphysema. Previous studies have established that pre-elafin is also an effective anti-inflammatory molecule. However, it is not clear whether both functions are linked to the antipeptidase activity of pre-elafin. As a first step toward elucidating the structure/function relationship of this protein, we describe here the construction and characterization of pre-elafin variants with attenuated antipeptidase potential. In these mutants, the P1' methionine residue of the inhibitory loop is replaced by either a lysine (pre-elafinM25K) or a glycine (pre-elafinM25G) residue. Both mutated variants are stable and display biochemical properties undistinguishable from WT (wild-type) pre-elafin. However, compared with WT pre-elafin, their inhibitory constants are increased by one to four orders of magnitude toward neutrophil elastase, myeloblastin and pancreatic elastase, depending on the variants and enzymes tested. As suggested by molecular modelling, this attenuated inhibitory potential correlates with decreased van der Waals interactions between the variants and the enzymes S1' subsite. In elastase-induced experimental emphysema in mice, only WT pre-elafin protected against tissue destruction, as assessed by the relative airspace enlargement measured using lung histopathological sections. Pre-elafin and both mutants prevented transient neutrophil alveolitis. However, even the modestly affected pre-elafinM25K mutant, as assayed in vitro with small synthetic substrates, was a poor inhibitor of the neutrophil elastase and myeloblastin elastolytic activity measured with insoluble elastin. We therefore conclude that full antipeptidase activity of pre-elafin is essential to protect against lung tissue lesions in this experimental model.
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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".