TGF-β-driven downregulation of the Wnt/β-Catenin transcription factor TCF7L2/TCF4 in PDGFRα <sup>+</sup> fibroblasts
Bibliographic record
Abstract
ABSTRACT Mesenchymal stromal/stem cells (MSCs) are multipotent progenitors essential ororganogenesis, tissue homeostasis, regeneration, and scar formation. Tissue injury upregulates TGF-β signaling, which modulates myofibroblast fate, extracellular matrix remodeling, and fibrosis. However, the molecular determinants of MSCs differentiation and survival remain poorly understood. The canonical Wnt Tcf/Lef transcription factors regulate development and stemness, but the mechanisms by which injury-induced cues modulate their expression remain underexplored. Here, we studied the cell-specific gene expression of Tcf/Lef and, more specifically, we investigated whether damage-induced TGF-β impairs the expression and function of TCF7L2, using several models of MSCs, including skeletal muscle fibro-adipogenic progenitors. We show that Tcf/Lefs are differentially expressed and that TGF-β reduces the expression of TCF7L2 in MSCs but not in myoblasts. We also found that the ubiquitin-proteasome system regulates TCF7L2 proteostasis and participates in TGF-β-mediated TCF7L2 protein downregulation. Finally, we show that TGF-β requires HDACs activity to repress the expression of TCF7L2. Thus, our work found a novel interplay between TGF-β and Wnt canonical signaling cascades in PDGFRα + fibroblasts and suggests that this mechanism could be targeted in tissue repa ir and regeneration. Summary statement TGF-β signaling suppresses the expression of the Wnt transcription factor TCF7L2 and compromises TCF7L2-dependent functions in tissue-resident PDGFRα + fibroblasts.
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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".