Autophagy and the unfolded protein response promote profibrotic effects of TGF-β<sub>1</sub> in human lung fibroblasts
Bibliographic record
Abstract
Idiopathic pulmonary fibrosis (IPF) is a lethal fibrotic lung disease in adults with limited treatment options. Autophagy and the unfolded protein response (UPR), fundamental processes induced by cell stress, are dysregulated in lung fibroblasts and epithelial cells from humans with IPF. Human primary cultured lung parenchymal and airway fibroblasts from non-IPF and IPF donors were stimulated with transforming growth factor-β1 (TGF-β1) with or without inhibitors of autophagy or UPR (IRE1 inhibitor). Using immunoblotting, we monitored temporal changes in abundance of protein markers of autophagy (LC3βII and Atg5-12), UPR (BIP, IRE1α, and cleaved XBP1), and fibrosis (collagen 1α2 and fibronectin). Using fluorescent immunohistochemistry, we profiled autophagy (LC3βII) and UPR (BIP and XBP1) markers in human non-IPF and IPF lung tissue. TGF-β1-induced collagen 1α2 and fibronectin protein production was significantly higher in IPF lung fibroblasts compared with lung and airway fibroblasts from non-IPF donors. TGF-β1 induced the accumulation of LC3βII in parallel with collagen 1α2 and fibronectin, but autophagy marker content was significantly lower in lung fibroblasts from IPF subjects. TGF-β1-induced collagen and fibronectin biosynthesis was significantly reduced by inhibiting autophagy flux in fibroblasts from the lungs of non-IPF and IPF donors. Conversely, only in lung fibroblasts from IPF donors did TGF-β1 induce UPR markers. Treatment with an IRE1 inhibitor decreased TGF-β1-induced collagen 1α2 and fibronectin biosynthesis in IPF lung fibroblasts but not those from non-IPF donors. The IRE1 arm of the UPR response is uniquely induced by TGF-β1 in lung fibroblasts from human IPF donors and is required for excessive biosynthesis of collagen and fibronectin in these cells.
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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".