<i>De novo</i> synthesis of ß‐catenin <i>via</i> H‐Ras and MEK regulates airway smooth muscle growth
Bibliographic record
Abstract
beta-Catenin is a component of adherens junctions that also acts as a transcriptional coactivator when expressed in the nucleus. Growth factors are believed to regulate the nuclear expression of beta-catenin via inactivation of glycogen synthase kinase 3 (GSK-3) by phosphorylation, resulting in increased beta-catenin protein stability. Here, we report on a novel pathway that regulates the expression and nuclear presence of beta-catenin. In proliferating human airway smooth muscle cells, we observed increased expression of beta-catenin, which was required for proliferation. Interestingly, increased beta-catenin expression was accompanied by an increase in beta-catenin mRNA and was independent of beta-catenin liberation from the plasma membrane, suggesting a role for de novo synthesis. This was confirmed using actinomycin D and cycloheximide, which abrogated the induction and nuclear localization of beta-catenin protein. GSK-3 inhibition using SB216763 failed to regulate beta-catenin mRNA. However, expression of dominant negative H-Ras or pharmacological inhibition of MEK reduced serum and TGF-beta-induced beta-catenin mRNA and protein. Collectively, these data indicate that beta-catenin is an important signaling intermediate in airway smooth muscle growth and that its cellular accumulation and nuclear localization require de novo protein synthesis effected, in part, via H-Ras and MEK.-Gosens, R., Baarsma, H. A., Heijink, I. H., Oenema, T. A., Halayko, A. J., Meurs, H., Schmidt, M. De novo synthesis of beta-catenin via H-Ras and MEK regulates airway smooth muscle growth.
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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 | 0.001 |
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".