TGFβ<sub>1</sub>regulates Scleraxis expression in primary cardiac myofibroblasts by a Smad-independent mechanism
Bibliographic record
Abstract
In cardiac wound healing following myocardial infarction (MI), relatively inactive resident cardiac fibroblasts phenoconvert to hypersynthetic/secretory myofibroblasts that produce large quantities of extracellular matrix (ECM) and fibrillar collagen proteins. Our laboratory and others have identified TGFβ1 as being a persistent stimulus in the chronic and inappropriate wound healing phase that is marked by hypertrophic scarring and eventual stiffening of the entire myocardium, ultimately leading to the pathogenesis of heart failure following MI. Ski is a potent negative regulator of TGFβ/Smad signaling with known antifibrotic effects. Conversely, Scleraxis is a potent profibrotic basic helix-loop-helix transcription factor that stimulates fibrillar collagen expression. We hypothesize that TGFβ1 induces Scleraxis expression by a novel Smad-independent pathway. Our data support the hypothesis that Scleraxis expression is induced by TGFβ1 through a Smad-independent pathway in the cardiac myofibroblast. Specifically, we demonstrate that TGFβ1 stimulates p42/44 (Erk1/2) kinases, which leads to increased Scleraxis expression. Inhibition of MEK1/2 using U0126 led to a sequential temporal reduction of phospho-p42/44 and subsequent Scleraxis expression. We also found that adenoviral Ski expression in primary myofibroblasts caused a significant repression of endogenous Scleraxis expression at both the mRNA and protein levels. Thus we have identified a novel TGFβ1-driven, Smad-independent, signaling cascade that may play an important role in regulating the fibrotic response in activated cardiac myofibroblasts following cardiac injury.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".