TNAP limits TGF-β-dependent cardiac and skeletal muscle fibrosis by inactivating SMAD2/3 transcription factors
Bibliographic record
Abstract
Abstract Fibrosis is associated with almost all forms of chronic cardiac and skeletal muscle diseases. The accumulation of extracellular matrix impairs the contractility of muscle cells contributing to organ failure. Transforming growth factor beta (TGF-β) plays a pivotal role in fibrosis, activating pro-fibrotic gene programs via phosphorylation of SMAD2/3 transcription factors. However, the mechanisms that control de-phosphorylation of SMAD2/3 have remained poorly characterized. Here we show that tissue non-specific alkaline phosphatase (TNAP) is highly upregulated in hypertrophic hearts and in dystrophic skeletal muscles, and the abrogation of TGF-β signalling in TNAP positive cells reduces vascular and interstitial fibrosis. We show that TNAP co-localizes and interacts with SMAD2. TNAP inhibitor MLS-0038949 increases SMAD2/3 phosphorylation, while TNAP overexpression reduces SMAD2/3 phosphorylation and the expression of downstream fibrotic genes. Overall our data demonstrate that TNAP negatively regulates TGF-β signalling and likely represents a mechanism to limit fibrosis. Summary statement This paper shows that tissue non-specific alkaline phosphatase negatively regulates TGF-β signalling and may represent a mechanism to limit fibrosis through SMAD dephosphorylation.
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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) | 0.005 | 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".