A p38 Mitogen-Activated Protein Kinase-Regulated Myocyte Enhancer Factor 2–β-Catenin Interaction Enhances Canonical Wnt Signaling
Bibliographic record
Abstract
Canonical Wnt/β-catenin signaling plays a major role in various biological contexts, such as embryonic development, cell proliferation, and cancer progression. Previously, a connection between p38 mitogen-activated protein kinase (MAPK) signaling and Wnt-mediated activation of β-catenin was implied but poorly understood. In the present study, we investigated potential cross talk between p38 MAPK and Wnt/β-catenin signaling. Here we show that a loss of p38 MAPK α/β function reduces β-catenin nuclear accumulation in Wnt3a-stimulated primary vascular smooth muscle cells (VSMCs). Conversely, active p38 MAPK signaling increases β-catenin nuclear localization and target gene activity in multiple cell types. Furthermore, the effect of p38 MAPK α/β on β-catenin activity is mediated through phosphorylation of a key p38 MAPK target, myocyte enhancer factor 2 (MEF2). Here we report a p38 MAPK-mediated, phosphorylation-dependent interaction between MEF2 and β-catenin in multiple cell types and primary VSMCs that results in (i) increased β-catenin nuclear retention, which is reversed by small interfering RNA (siRNA)-mediated MEF2 gene silencing; (ii) increased activation of MEF2 and Wnt/β-catenin target genes; and (iii) increased Wnt-stimulated cell proliferation. These observations provide mechanistic insight into a fundamental level of cross talk between p38 MAPK/MEF2 signaling and canonical Wnt signaling.
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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) | 0.001 | 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) | 0.003 | 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".