β-Arrestin2 Regulates Lysophosphatidic Acid-Induced Human Breast Tumor Cell Migration and Invasion via Rap1 and IQGAP1
Bibliographic record
Abstract
β-Arrestins play critical roles in chemotaxis and cytoskeletal reorganization downstream of several receptor types, including G protein-coupled receptors (GPCRs), which are targets for greater than 50% of all pharmaceuticals. Among them, receptors for lysophosphatidic acid (LPA), namely LPA(1) are overexpressed in breast cancer and promote metastatic spread. We have recently reported that β-arrestin2 regulates LPA(1)-mediated breast cancer cell migration and invasion, although the underlying molecular mechanisms are not clearly understood. We show here that LPA induces activity of the small G protein, Rap1 in breast cancer cells in a β-arrestin2-dependent manner, but fails to activate Rap1 in non-malignant mammary epithelial cells. We found that Rap1A mRNA levels are higher in human breast tumors compared to healthy patient samples and Rap1A is robustly expressed in human ductal carcinoma in situ and invasive tumors, in contrast to the normal mammary ducts. Rap1A protein expression is also higher in aggressive breast cancer cells (MDA-MB-231 and Hs578t) relative to the weakly invasive MCF-7 cells or non-malignant MCF10A mammary cells. Depletion of Rap1A expression significantly impaired LPA-stimulated migration of breast cancer cells and invasiveness in three-dimensional Matrigel cultures. Furthermore, we found that β-arrestin2 associates with the actin binding protein IQGAP1 in breast cancer cells, and is necessary for the recruitment of IQGAP1 to the leading edge of migratory cells. Depletion of IQGAP1 blocked LPA-stimulated breast cancer cell invasion. Finally, we have identified that LPA enhances the binding of endogenous Rap1A to β-arrestin2, and also stimulates Rap1A and IQGAP1 to associate with LPA(1). Thus our data establish novel roles for Rap1A and IQGAP1 as critical regulators of LPA-induced breast cancer cell migration and invasion.
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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".