Thrombospondin 1 is a key mediator of transforming growth factor β-mediated cell contractility in systemic sclerosis via a mitogen-activated protein kinase kinase (MEK)/extracellular signal-regulated kinase (ERK)-dependent mechanism
Bibliographic record
Abstract
BACKGROUND: The mechanism underlying the ability of fibroblasts to contract a collagen gel matrix is largely unknown. Fibroblasts from scarred (lesional) areas of patients with the fibrotic disease scleroderma show enhanced ability to contract collagen relative to healthy fibroblasts. Thrombospondin 1 (TSP1), an activator of latent transforming growth factor (TGF)β, is overexpressed by scleroderma fibroblasts. In this report we investigate whether activation of latent TGFβ by TSP1 plays a key role in matrix contraction by normal and scleroderma fibroblasts. METHODS: We use the fibroblast populated collagen lattices (FPCL) model of matrix contraction to show that interfering with TSP1/TGFβ binding and knockdown of TSP1 expression suppressed the contractile ability of normal and scleroderma fibroblasts basally and in response to TGFβ. Previously, we have shown that ras/mitogen-activated protein kinase kinase (MEK)/extracellular signal-regulated kinase (ERK) mediates matrix contraction basally and in response to TGFβ. RESULTS: During mechanical stimulation in the FPCL system, using a multistation tensioning-culture force monitor (mst-CFM), TSP1 expression and p-ERK activation in fibroblasts are enhanced. Inhibiting TSP1 activity reduced the elevated activation of MEK/ERK and expression of key fibrogenic proteins. TSP1 also blocked platelet-derived growth factor (PDGF)-induced contractile activity and MEK/ERK activation. CONCLUSIONS: TSP1 is a key mediator of matrix contraction of normal and systemic sclerosis fibroblasts, via MEK/ERK.
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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".