Wnt-5a-induced Phosphorylation of DARPP-32 Inhibits Breast Cancer Cell Migration in a CREB-dependent Manner
Bibliographic record
Abstract
Tumor cell migration plays a central role in the process of cancer metastasis. We recently identified dopamine and cAMP-regulated phosphoprotein of 32 kDa (DARPP-32) as an antimigratory phosphoprotein in breast cancer cells. Here we link this effect of DARPP-32 to Wnt-5a signaling by demonstrating that recombinant Wnt-5a triggers cAMP elevation at the plasma membrane and Thr34-DARPP-32 phosphorylation in MCF-7 cells. In agreement, both protein kinase A (PKA) inhibitors and siRNA-mediated knockdown of Frizzled-3 receptor or Galpha(s) expression abolished Wnt-5a-induced phosphorylation of DARPP-32. Furthermore, Wnt-5a induced DARPP-32-dependent inhibition of MCF-7 cell migration. Phospho-Thr-34-DARPP-32 interacted with protein phosphatase-1 (PP1) and potentiated the Wnt-5a-mediated phosphorylation of CREB, a well-known PP1 substrate, but had no effect on CREB phosphorylation by itself. Moreover, inhibition of the Wnt-5a/DARPP-32/CREB pathway, by expression of dominant negative CREB (DN-CREB), diminished the antimigratory effect of Wnt-5a-induced phospho-Thr-34-DARPP-32. Phalloidin-staining revealed that that the presence of phospho-Thr-34-DARPP-32 in MCF-7 cells results in reduced filopodia formation. In accordance, the activity of the Rho GTPase Cdc42, known to be crucial for filopodia formation, was reduced in MCF-7 cells expressing phospho-Thr-34-DARPP-32. The effects of DARPP-32 on cell migration and filopodia formation could be reversed in T47D breast cancer cells that were depleted of their endogenous DARPP-32 by siRNA targeting. Consequently, Wnt-5a activates a Frizzled-3/Galpha(s)/cAMP/PKA signaling pathway that triggers a DARPP-32- and CREB-dependent antimigratory response in breast cancer cells, representing a novel mechanism whereby Wnt-5a can inhibit breast cancer cell migration.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".