Hypoxia Promotes Invadosome Formation by Lung Fibroblasts
Bibliographic record
Abstract
Lung parenchymal hypoxia has emerged as a cardinal feature of idiopathic pulmonary fibrosis (IPF). Hypoxia promotes cancer cell invasion and metastasis through signaling that is dependent upon the lysophosphatidic acid (LPA) receptor, LPA1 (LPAR1). Abundant data indicate that LPA1-dependent signaling also enhances lung fibrogenesis in IPF. We recently reported that fibroblasts isolated from the lungs of individuals with IPF have an increased capacity to form subcellular matrix-degradative structures known as invadosomes, an event that correlates with the degree of lung fibrosis. We therefore hypothesized that hypoxia promotes invadosome formation in lung fibroblasts through LPA1-dependent signaling. Here, it is demonstrated that invadosome formation by fibroblasts from the lungs of individuals with advanced IPF is inhibited by both the tyrosine receptor kinase inhibitor nintedanib and inhibition of LPA1. In addition, exposure of normal human lung fibroblasts to either hypoxia or LPA increased their ability to form invadosomes. Mechanistically, the hypoxia-induced invadosome formation by lung fibroblasts was found to involve LPA1 and PDGFR-Akt signaling. We concluded that hypoxia increases the formation of invadosomes in lung fibroblasts through the LPA1 and PDGFR-Akt signaling axis, which represents a potential target for suppressing lung 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".