TAILS proteomics reveals dynamic changes in airway proteolysis controlling protease activity and innate immunity during COPD exacerbations
Bibliographic record
Abstract
Dysregulated protease activity is thought to cause parenchymal and airway damage in chronic obstructive pulmonary disease (COPD). Multiple proteases have been implicated in COPD, and identifying their substrates may reveal new disease mechanisms and treatments. However, as proteases interact with many substrates that may be protease inhibitors or proteases themselves, these webs of protease interactions make the wider consequences of therapeutically targeting proteases difficult to predict. We therefore used a systems approach to determine protease substrates and protease activity in COPD airways. Protease substrates were determined by proteomics using the terminal amine isotopic labeling of substrates (TAILS) methodology in paired sputum samples during stable COPD and exacerbations. Protease activity and specific protein degradation in airway samples were assessed using Western blotting, substrate assays, and ex vivo cleavage assays. Two hundred ninety-nine proteins were identified in human COPD sputum, 125 of which were proteolytically processed, including proteases, protease inhibitors, mucins, defensins, and complement and other innate immune proteins. During exacerbations, airway neutrophils and neutrophil proteases increased and more proteins were cleaved, particularly at multiple sites, consistent with degradation and inactivation. During exacerbations, different substrates were processed, including protease inhibitors, mucins, and complement proteins. Exacerbations were associated with increasing airway elastase activity and increased processing of specific elastase substrates, including secretory leukocyte protease inhibitor. Proteolysis regulates multiple processes including elastase activity and innate immune proteins in COPD airways and differs during stable disease and exacerbations. The complexity of protease, inhibitor, and substrate networks makes the effect of protease inhibitors hard to predict which should be used cautiously.
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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".