Role of scleraxis in mechanical stretch-mediated regulation of cardiac myofibroblast phenotype
Bibliographic record
Abstract
The phenotype conversion of fibroblasts to myofibroblasts plays a key role in the pathogenesis of cardiac fibrosis. Numerous triggers of this conversion process have been identified, including plating of cells on solid substrates, cytokines such as transforming growth factor-β, and mechanical stretch; however, the underlying mechanisms remain incompletely defined. Recent studies from our laboratory revealed that the transcription factor scleraxis is a key regulator of cardiac fibroblast phenotype and extracellular matrix expression. Here we report that mechanical stretch induces type I collagen expression and morphological changes indicative of cardiac myofibroblast conversion, as well as scleraxis expression via activation of the scleraxis promoter. Scleraxis causes phenotypic changes similar to stretch, and the effect of stretch is attenuated in scleraxis null cells. Scleraxis was also sufficient to upregulate expression of vinculin and F-actin, to induce stress fiber and focal adhesion formation, and to attenuate both cell migration and proliferation, further evidence of scleraxis-mediated regulation of fibroblast to myofibroblast conversion. Together, these data confirm that scleraxis is sufficient to promote the myofibroblast phenotype and is a required effector of stretch-mediated conversion. Scleraxis may thus represent a potential target for the development of novel antifibrotic therapies aimed at inhibiting myofibroblast formation.
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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".