Substrate stiffness engineered to replicate disease conditions influence senescence and fibrotic responses in primary lung fibroblasts
Bibliographic record
Abstract
In fibrosis remodelling of ECM leads to changes in composition and stiffness. Such changes can have a major impact on cell functions including proliferation, secretory profile and differentiation. Several studies have reported that fibrosis is characterised by increased senescence and accumulating evidence suggests that changes to the ECM including altered composition and increased stiffness may contribute to premature cellular senescence. This study investigated if increased stiffness could modulate markers of senescence and/or fibrosis in primary human lung fibroblasts. Using hydrogels representing stiffnesses that fall within healthy and fibrotic ranges, we cultured primary fibroblasts from non-diseased lung tissue on top of these hydrogels for up to 7 days before assessing senescence and fibrosis markers. Fibroblasts cultured on stiffer (±15 kPa) 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 stiffer matrices. With respect to genes associated with fibrosis, fibroblasts on stiffer 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 7 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 phenotype (SASP). This overlap may contribute to the creation of a feedback loop whereby fibroblasts create a perpetuating cycle reinforcing progression of a fibrotic response.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".