255 A Bacterial Serine Protease Inhibitor, Ecotin Inhibits Neutrophil Elastase in Cystic Fibrosis Airway Samples
Bibliographic record
Abstract
OBJECTIVES/GOALS: Ecotin is a potent periplasmic serin protease inhibitor that was first described in Escherichia coli (E. coli). Ecotin is known to inhibit a broad range of serine proteases including NE. Therefore, we hypothesized that ecotin can inhibit the NE activity present in CF clinical sputum samples. METHODS/STUDY POPULATION: To investigate the function of ecotin on CF sputum, orthologues from Campylobacter rectus and Campylobacter showae were recombinantly expressed and purified from E. coli to generate C. rectus ecotin, C. showae ecotin, and E. coli ecotin. Ecotin was added to CF sputum supernatant samples at various concentrations and NE enzymatic activity was measured by a fluorometric assay kit. In addition, CF sputum and healthy sputum was added to PMNs isolated from healthy donors for 3.5 hours and NE was measured with immunofluorescence staining. The sputum was washed two times prior to measuring NE released and on the PMNs, and PBMCs was used as a control. RESULTS/ANTICIPATED RESULTS: Our results indicate a clear inhibition of NE activity in CF sputum supernatant. C. showae ecotin showed the greatest inhibitory effect on NE activity in CF sputum supernatant. We also saw that CF sputum supernatant causing healthy PMNs to release more NE suggesting that NE in the CF airway may trigger more NE release from newly incoming PMNs to the lung. Our next steps will be to determine if ecotin can inhibit NE on the PMN surface and released from PMNs. DISCUSSION/SIGNIFICANCE: Neutrophil elastases are produced by PMNs to kill microbial pathogens in the lung, however in CF, the pathogens are unable to be killed by NE and instead causes severe lung damage. PMNs and NE has been shown to be elevated in the sputum of CF patients and there is currently no NE therapeutic inhibitor that has been effective in these patients.
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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) | 0.002 | 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".