BMP promotes motility and represses growth of smooth muscle cells by activation of tandem Wnt pathways
Bibliographic record
Abstract
We present a novel cell-signaling paradigm in which bone morphogenetic protein 2 (BMP-2) consecutively and interdependently activates the wingless (Wnt)-β-catenin (βC) and Wnt-planar cell polarity (PCP) signaling pathways to facilitate vascular smooth muscle motility while simultaneously suppressing growth. We show that BMP-2, in a phospho-Akt-dependent manner, induces βC transcriptional activity to produce fibronectin, which then activates integrin-linked kinase 1 (ILK-1) via α4-integrins. ILK-1 then induces the Wnt-PCP pathway by binding a proline-rich motif in disheveled (Dvl) and consequently activating RhoA-Rac1-mediated motility. Transfection of a Dvl mutant that binds βC without activating RhoA-Rac1 not only prevents BMP-2-mediated vascular smooth muscle cell motility but promotes proliferation in association with persistent βC activity. Interfering with the Dvl-dependent Wnt-PCP activation in a murine stented aortic graft injury model promotes extensive neointima formation, as shown by optical coherence tomography and histopathology. We speculate that, in response to injury, factors that subvert BMP-2-mediated tandem activation of Wnt-βC and Wnt-PCP pathways contribute to obliterative vascular disease in both the systemic and pulmonary circulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".