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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".