Sonic hedgehog promotes autophagy of vascular smooth muscle cells
Bibliographic record
Abstract
Sonic hedgehog (Shh) is a morphogen critically involved in development that is reexpressed in atherosclerotic lesions. It also stimulates proliferation of vascular smooth muscle cells (SMCs). Autophagy in vascular SMCs is known to promote SMC survival and increase plaque stability. The aim of this study was to investigate whether Shh induces autophagy of vascular SMCs. Our study showed that both Shh protein and microtubule-associated protein 1 light chain 3 (LC3)-II were increased in SMCs within neointimal lesions of mouse common carotid arteries. In cultured mouse aortic SMCs, recombinant mouse Shh stimulated LC3-II levels. Overexpression of wild-type mouse Shh through the tetracycline-regulated expression-inducible system in human aortic SMCs time-dependently increased the levels of LC3-II and also stimulated protein kinase B (AKT) phosphorylation. Pretreatment with AKT inhibitor IV (AKTI IV) inhibited AKT phosphorylation and the increase in LC3-II. Shh-induced autophagy was further confirmed by the formation of autophagosomes as detected by immunostaining and transmission electron microscopy, which was inhibited by AKTI IV. Shh further increased SMC LC3-II in the presence of bafilomycin A1, (2S,3S)-trans-epoxysuccinyl-L-leucylamido-3-methylbutane ethyl ester, and pepstatin A or siRNA for the autophagy-related gene 7 (ATG7). In addition, Shh induced SMC proliferation, which was inhibited not only by AKTI IV but also by cyclopamine, an inhibitor of Shh receptor. Inhibition of autophagy with 3-methyladenine (3-MA), bafilomycin A1, or ATG7 siRNA resulted in inhibition of cell proliferation. Treatment with 3-MA, AKTI IV, or cyclopamine inhibited neointima formation in mouse common carotid arteries. Taken together, our results have shown that Shh induces autophagy of vascular SMCs involving AKT activation, suggesting a role of autophagy in Shh-induced cellular responses.
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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".