<scp>TGF</scp>β‐<scp>TAZ</scp>/<scp>SRF</scp> signalling regulates vascular smooth muscle cell differentiation
Bibliographic record
Abstract
Vascular smooth muscle cells (VSMCs) do not terminally differentiate; they modulate their phenotype between proliferative and differentiated states, which is a major factor contributing to vascular diseases. TGFβ signalling has been implicated in inducing VSMC differentiation, although the exact mechanism remains largely unknown. Our goal was to assess the network of transcription factors involved in the induction of VSMC differentiation, and to determine the role of TAZ in promoting the quiescent VSMC phenotype. TGFβ robustly induces VSMC marker genes in 10T1/2 mouse embryonic fibroblast cells and the potent transcriptional regulator TAZ has been shown to retain Smad complexes on DNA. Thus, the role of TAZ in regulation of VSMC differentiation was studied. Using primary aortic VSMCs coupled with siRNA-mediated gene silencing, our studies reveal that TAZ is required for TGFβ induction of smooth muscle genes and is also required for the differentiated VSMC phenotype; synergy between TAZ and SRF, and TAZ and Myocardin (MyoC856), in regulating smooth muscle gene activation was observed. These data provide evidence of components of a novel signalling pathway that links TGFβ signalling to induction of smooth muscle genes through a mechanism involving regulation of TAZ and SRF proteins. In addition, we report a physical interaction of TAZ and MyoC856. These observations elucidate a novel level of control of VSMC induction which may have implications for vascular diseases and congenital vascular malformations.
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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".