Scleraxis Regulates the Cardiac Myofibroblast Phenotype
Bibliographic record
Abstract
Myofibroblasts mediate cardiac remodeling, but their persistence in the heart after proper wound healing is implicated in cardiac fibrosis, for which there is no treatment. Reducing myofibroblast presence in the myocardium represents a potential avenue for impairing, or even reversing, fibrosis. The transcription factor Scleraxis (SCX) is a regulator of collagen‐rich tissues, and its expression is increased by the same stimuli that induce conversion of fibroblasts to myofibroblasts. Cyclic stretch causes increased mRNA levels of SCX and myofibroblast markers, as well as activating the human SCX promoter in a luciferase assay. Thus we hypothesized that SCX regulates the myofibroblast phenotype. We observed that SCX over‐expression in primary rat cardiac fibroblasts (rCFs) increased mRNA and protein levels of key myofibroblast markers. Functionally, myofibroblasts differ from fibroblasts in their increased contractile ability and decreased motility, both of which were observed in rCFs over‐expressing SCX. Conversely, SCX knock‐down with shRNA adenovirus caused rCFs to become less contractile and more motile. Interestingly, knockdown of SCX, but not the use of a dominant negative DNA binding‐deficient version of SCX, prevented TGFβ‐induced contraction. Thus, SCX likely acts downstream of TGFβ during conversion of fibroblasts to myofibroblasts, perhaps independently of its ability to bind DNA. This is supported by the finding that TGFβ activates the SCX promoter. These results demonstrate that SCX promotes, and may in fact be required for, the myofibroblast phenotype. Thus, SCX may provide a novel drug target for the intervention treatment of cardiac fibrosis.
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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) | 0.002 | 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".