Osteopontin Expression Is Required for Myofibroblast Differentiation
Bibliographic record
Abstract
Osteopontin (OPN) is a multifunctional cytokine that is strongly expressed in healing wounds and fibrotic lesions, both of which are characterized by the formation of myofibroblasts. We examined the role of OPN in myofibroblast differentiation induced by the profibrotic cytokine transforming growth factor-beta1. In cultured cardiac or dermal fibroblasts treated with transforming growth factor-beta1, there was a 2- to 5-fold increase in the expression of the myofibroblast markers alpha-smooth muscle actin and extradomain A fibronectin but no significant increase of these proteins in OPN-null fibroblasts. Phalloidin staining for actin filaments and immunostaining for alpha-smooth muscle actin and focal adhesion proteins showed reduced stress fibers, focal adhesions, and lamellipodia in OPN-null fibroblasts compared with wild-type cells. OPN-null fibroblasts exhibited 40% to 60% less spreading, 50% less resistance to detachment by shear force, and a approximately 3-fold reduction in collagen gel contraction. These defects were partially rescued by ectopic expression of OPN. Mass spectrometric analysis of proteins in focal adhesions formed on collagen type I beads revealed an enrichment of HMGB1 protein in wild-type cells, whereas HMGB1 was not detected in OPN-null cells. Treatment of wild-type cells with small interfering RNA to knock down OPN reduced transforming growth factor-beta1-induced alpha-smooth muscle actin and HMGB1 to levels observed in OPN-null cells. These studies demonstrate that OPN is required for the differentiation and activity of myofibroblasts formed in response to the profibrotic cytokine transforming growth factor-beta1.
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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 | 0.001 |
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".