Constitutive Protease-activated Receptor-2-mediated Migration of MDA MB-231 Breast Cancer Cells Requires Both β-Arrestin-1 and -2
Bibliographic record
Abstract
Protease-activated receptor-2 (PAR-2) is activated by trypsin-like serine proteases and can promote cell migration through an ERK1/2-dependent pathway, involving formation of a scaffolding complex at the leading edge of the cell. Previous studies also showed that expression of a dominant negative fragment of beta-arrestin-1 reduces PAR-2-stimulated internalization, ERK1/2 activation, and cell migration; however, this reagent may block association of many proteins, including beta-arrestin-2 with clathrin-coated pits. Here we investigate the role of PAR-2 in the constitutive migration of a metastatic breast cancer cell line, MDA MB-231, and use small interfering RNA to determine the contribution of each beta-arrestin to this process. We demonstrate that a trypsin-like protease secreted from MDA MB-231 cells can promote cell migration through autocrine activation of PAR-2 and this correlates with constitutive localization of PAR-2, beta-arrestin-2, and activated ERK1/2 to pseudopodia. Addition of MEK-1 inhibitors, trypsin inhibitors, a scrambled PAR-2 peptide, and silencing of beta-arrestins with small interfering RNA also reduce base-line migration of MDA MB-231 cells. In contrast, a less metastatic PAR-2 expressing breast cancer cell line does not exhibit constitutive migration, pseudopodia formation, or trypsin secretion; in these cells PAR-2 is more uniformly distributed around the cell periphery. These data demonstrate a requirement for both beta-arrestins in PAR-2-mediated motility and suggest that autocrine activation of PAR-2 by secreted proteases may contribute to the migration of metastatic tumor cells through beta-arrestin-dependent ERK1/2 activation.
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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".