Lack of cathepsin activities alter or prevent the development of lung granulomas in a mouse model of sarcoidosis
Bibliographic record
Abstract
BACKGROUND: Remodeling of lung tissues during the process of granuloma formation requires significant restructuring of the extra-cellular matrix and cathepsins K, L and S are among the strongest extra-cellular matrix degrading enzymes. Cathepsin K is highly expressed in various pathological granulomatous infiltrates and all three enzymes in their active form are detected in bronchoalveolar lavage fluids from patients with sarcoidosis. Granulomatous inflammation is driven by T-cell response and cathepsins S and L are actively involved in the regulation of antigen presentation and T-cell selection. Here, we show that the disruption of the activities of cathepsins K, L, or S affects the development of lung granulomas in a mouse model of sarcoidosis. METHODS: Apolipoprotein E-deficient mice lacking cathepsin K or L were fed Paigen diet for 16 weeks and lungs were analyzed and compared with their cathepsin-expressing littermates. The role of cathepsin S in the development of granulomas was evaluated using mice treated for 8 weeks with a potent and selective cathepsin S inhibitor. RESULTS: When compared to wild-type litters, more cathepsin K-deficient mice had lung granulomas, but individually affected mice developed smaller granulomas that were present in lower numbers. The absence of cathepsin K increased the number of multinucleated giant cells and the collagen content in granulomas. Cathepsin L deficiency resulted in decreased size and number of lung granulomas. Apoe-/- mice treated with a selective cathepsin S inhibitor did not develop lung granulomas and only individual epithelioid cells were observed. CONCLUSIONS: Cathepsin K deficiency affected mostly the occurrence and composition of lung granulomas, whereas cathepsin L deficiency significantly reduced their number and cathepsin S inhibition prevented the formation of granulomas.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".