A novel profibrotic mechanism mediated by <scp>TGFβ</scp>‐stimulated collagen prolyl hydroxylase expression in fibrotic lung mesenchymal cells
Bibliographic record
Abstract
Idiopathic pulmonary fibrosis is a severe chronic lung disease with a high mortality rate. Excessive TGFβ signalling is recognized as a central player in lung fibrosis. However, the related mechanisms remain unclear. Herein we used a novel Tbx4 lung enhancer-driven Tet-On transgenic system to inhibit TGFβ signalling in mouse lung-resident mesenchymal cells at different stages of bleomycin-induced fibrosis, by conditionally knocking out TGFβ receptor II or expressing a dominant-negative TGFβ receptor II. Abrogation of mesenchymal TGFβ signalling markedly attenuated bleomycin-induced fibrotic pathology, which was independent of altered early inflammation. Furthermore, a novel TGFβ downstream target gene P4HA3 (an α-subunit of collagen prolyl hydroxylase) was identified, and its expression was significantly increased in fibroblastic foci of both bleomycin-induced fibrotic mouse lungs and idiopathic pulmonary fibrosis patients' lungs. The relationship between activated TGFβ signalling, up-regulation of P4HA3 and increased hydroxyproline/collagen production was further verified in cultured lung fibroblasts. Moreover, inhibition of collagen prolyl hydroxylase by pyridine-2,5-dicarboxylate attenuated TGFβ-stimulated collagen production in both cultured fibroblasts and bleomycin-induced mouse lung fibrosis. These data indicate that increased expression and activity of collagen prolyl hydroxylase is one of the important mechanisms underlying TGFβ-mediated profibrotic effects. Inhibition of collagen prolyl hydroxylase may be a new, promising approach for preventing and treating pulmonary fibrosis.
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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".