Protein tyrosine phosphatase-α amplifies transforming growth factor-β-dependent profibrotic signaling in lung fibroblasts
Bibliographic record
Abstract
Idiopathic pulmonary fibrosis (IPF) is a progressive, often fatal, fibrosing lung disease for which treatment remains suboptimal. Fibrogenic cytokines, including transforming growth factor-β (TGF-β), are central to its pathogenesis. Protein tyrosine phosphatase-α (PTPα) has emerged as a key regulator of fibrogenic signaling in fibroblasts. We have reported that mice globally deficient in PTPα ( Ptpra−/−) were protected from experimental pulmonary fibrosis, in part via alterations in TGF-β signaling. The goal of this study was to determine the lung cell types and mechanisms by which PTPα controls fibrogenic pathways and whether these pathways are relevant to human disease. Immunohistochemical analysis of lungs from patients with IPF revealed that PTPα was highly expressed by mesenchymal cells in fibroblastic foci and by airway and alveolar epithelial cells. To determine whether PTPα promotes profibrotic signaling pathways in lung fibroblasts and/or epithelial cells, we generated mice with conditional (floxed) Ptpra alleles ( Ptpraf/f). These mice were crossed with Dermo1-Cre or with Sftpc-CreERT2mice to delete Ptpra in mesenchymal cells and alveolar type II cells, respectively. Dermo1-Cre/ Ptpraf/fmice were protected from bleomycin-induced pulmonary fibrosis, whereas Sftpc-CreERT2/Ptpraf/fmice developed pulmonary fibrosis equivalent to controls. Both canonical and noncanonical TGF-β signaling and downstream TGF-β-induced fibrogenic responses were attenuated in isolated Ptpra−/−compared with wild-type fibroblasts. Furthermore, TGF-β-induced tyrosine phosphorylation of TGF-β type II receptor and of PTPα were attenuated in Ptpra−/−compared with wild-type fibroblasts. The phenotype of cells genetically deficient in PTPα was recapitulated with the use of a Src inhibitor. These findings suggest that PTPα amplifies profibrotic TGF-β-dependent pathway signaling in lung fibroblasts.
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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 | 0.001 |
| 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".