Influenza Promotes Collagen Deposition via αvβ6 Integrin-mediated Transforming Growth Factor β Activation
Bibliographic record
Abstract
Influenza infection exacerbates chronic pulmonary diseases, including idiopathic pulmonary fibrosis. A central pathway in the pathogenesis of idiopathic pulmonary fibrosis is epithelial injury leading to activation of transforming growth factor β (TGFβ). The mechanism and functional consequences of influenza-induced activation of epithelial TGFβ are unclear. Influenza stimulates toll-like receptor 3 (TLR3), which can increase RhoA activity, a key event prior to activation of TGFβ by the αvβ6 integrin. We hypothesized that influenza would stimulate TLR3 leading to activation of latent TGFβ via αvβ6 integrin in epithelial cells. Using H1152 (IC50 6.1 μm) to inhibit Rho kinase and 6.3G9 to inhibit αvβ6 integrins, we demonstrate their involvement in influenza (A/PR/8/34 H1N1) and poly(I:C)-induced TGFβ activation. We confirm the involvement of TLR3 in this process using chloroquine (IC50 11.9 μm) and a dominant negative TLR3 construct (pZERO-hTLR3). Examination of lungs from influenza-infected mice revealed augmented levels of collagen deposition, phosphorylated Smad2/3, αvβ6 integrin, and apoptotic cells. Finally, we demonstrate that αvβ6 integrin-mediated TGFβ activity following influenza infection promotes epithelial cell death in vitro and enhanced collagen deposition in vivo and that this response is diminished in Smad3 knock-out mice. These data show that H1N1 and poly(I:C) can induce αvβ6 integrin-dependent TGFβ activity in epithelial cells via stimulation of TLR3 and suggest a novel mechanism by which influenza infection may promote collagen deposition in fibrotic lung disease.
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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".