Cross-talk between TGF-β and PDGFRα signaling pathways regulates the fate of stromal fibro–adipogenic progenitors
Bibliographic record
Abstract
Fibro-adipogenic progenitors (FAPs) are tissue-resident mesenchymal stromal cells (MSCs) required for proper skeletal muscle development, regeneration and maintenance. However, FAPs are also responsible for fibro-fatty scar deposition following chronic damage. We aimed to investigate the role of functional cross-talk between TGF-β and PDGFRα signaling pathways in the fate of FAPs. Here, we show that the number of FAPs correlates with TGF-β levels and with extracellular matrix deposition during regeneration and repair. Interestingly, the expression of PDGFRα changed dynamically in the fibroblast lineage after injury. Furthermore, PDGFRα-dependent immediate early gene expression changed during regeneration and repair. We also found that TGF-β signaling reduces PDGFRα expression in FAPs, mouse dermal fibroblasts and in two related mesenchymal cell lines. Moreover, TGF-β promotes myofibroblast differentiation of FAPs but inhibits their adipogenicity. Accordingly, TGF-β impairs the expression of PDGFRα-dependent immediate early genes in a TGFBR1-dependent manner. Finally, pharmacological inhibition of PDGFRα activity with AG1296 impaired TGF-β-induced extracellular matrix remodeling, Smad2 signaling, myofibroblast differentiation and migration of MSCs. Thus, our work establishes a functional cross-talk between TGF-β and PDGFRα signaling pathways that is involved in regulating the biology of FAPs and/or MSCs.This article has an associated First Person interview with the first author of the paper.
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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 | 0.001 |
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".