Transforming Growth Factor-β Signaling in Myogenic Cells Regulates Vascular Morphogenesis, Differentiation, and Matrix Synthesis
Bibliographic record
Abstract
OBJECTIVE: Transforming growth factor-β (TGF-β) signaling is required for normal vascular development. We aimed to discover the role of TGF-β signaling in embryonic smooth muscle cells (SMCs). METHODS AND RESULTS: We bred mice with smooth muscle (SM) 22α-Cre and Tgfbr2(flox) alleles to generate embryos in which the type II TGF-β receptor (TGFBR2; required for TGF-β signaling) was deleted in SMCs. Embryos were harvested between embryonic day (E) 9.5 and E18.5 and examined grossly, microscopically, and by histochemical and RNA analyses. SM22α-Cre(+/0) Tgfbr2(flox/flox) (knockout [KO]) embryos died before E15.5 with defects that included cardiac outflow tract abnormalities, persistence of the right dorsal aorta, and dilation of the distal aorta. Histological analyses suggested normal expression of SMC differentiation markers in KO aortas; however, RNA analyses showed that SMC differentiation markers were increased in KO cardiac outflow vessels but decreased in the descending aorta. KO aortas had only rare mature elastin deposits and contained abnormal aggregates of extracellular matrix proteins. Expression of several matrix proteins was significantly decreased in KO descending aortas but not in cardiac outflow vessels. CONCLUSIONS: TGF-β signaling in SMCs controls differentiation, matrix synthesis, and vascular morphogenesis. Effects of TGF-β on SMC gene expression appear to differ depending on the location of SMCs in the aorta.
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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) | 0.001 | 0.000 |
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".