Substrate stiffness engineered to replicate disease conditions influence senescence and fibrotic responses in primary lung fibroblasts
Bibliographic record
Abstract
Abstract In idiopathic pulmonary fibrosis (IPF) there is excessive ECM deposition, increased stiffness and ultimately destruction of lung parenchyma. IPF presents mainly in the elderly, implying that senescence, a hallmark of ageing, contributes to disease progression. Several studies have reported that IPF is characterised by increased senescence and accumulating evidence suggests that structural changes, such as increased stiffness may contribute to senescence. This study therefore investigated if increased tissue stiffness could modulate markers of senescence and/or fibrosis in primary lung fibroblasts. Using hydrogels representing healthy and fibrotic stiffnesses, we cultured primary fibroblasts from non-diseased lung tissue on top of these hydrogels for up to seven days before assessing senescence and fibrosis markers. Fibroblasts cultured on stiff (±15kPa) hydrogels showed higher Yes-associated protein-1 (YAP) nuclear translocation compared to soft hydrogels. When looking at senescence-associated proteins we also found higher secretion of receptor activator of nuclear factor kappa-B ligand (RANKL) but no change in transforming growth factor-β1 (TGF-β1) or connective tissue growth factor (CTGF) expression and higher decorin protein deposition on stiff matrices. With respect to genes associated with fibrosis, fibroblasts on stiff hydrogels compared to soft had higher expression of smooth muscle alpha (α)-2 actin ( ACTA2), collagen (COL) 1A1 and fibulin-1 (Fbln1) and higher Fbln1 protein deposition after seven days. Our results show that exposure of lung fibroblasts to fibrotic stiffness activates genes and secreted factors that are part of fibrotic responses and part of the senescence-associated secretory profile (SASP). This overlap may contribute to the creation of a feedback loop whereby fibroblasts create a perpetuating cycle reinforcing disease progression in IPF.
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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".