β-Arrestin Regulation of Myosin Light Chain Phosphorylation Promotes AT1aR-mediated Cell Contraction and Migration
Bibliographic record
Abstract
Over the last decade, it has been established that G-protein-coupled receptors (GPCRs) signal not only through canonical G-protein-mediated mechanisms, but also through the ubiquitous cellular scaffolds β-arrestin-1 and β-arrestin-2. Previous studies have implicated β-arrestins as regulators of actin reorganization in response to GPCR stimulation while also being required for membrane protrusion events that accompany cellular motility. One of the most critical events in the active movement of cells is the cyclic phosphorylation and activation of myosin light chain (MLC), which is required for cellular contraction and movement. We have identified the myosin light chain phosphatase Targeting Subunit (MYPT-1) as a binding partner of the β-arrestins and found that β-arrestins play a role in regulating the turnover of phosphorylated myosin light chain. In response to stimulation of the angiotensin Type 1a Receptor (AT1aR), MLC phosphorylation is induced quickly and potently. We have found that β-arrestin-2 facilitates dephosphorylation of MLC, while, in a reciprocal fashion, β-arrestin 1 limits dephosphorylation of MLC. Intriguingly, loss of either β-arrestin-1 or 2 blocks phospho-MLC turnover and causes a decrease in the contraction of cells as monitored by atomic force microscopy (AFM). Furthermore, by employing the β-arrestin biased ligand [Sar(1),Ile(4),Ile(8)]-Ang, we demonstrate that AT1aR-mediated cellular motility involves a β-arrestin dependent component. This suggests that the reciprocal regulation of MLC phosphorylation status by β-arrestins-1 and 2 causes turnover in the phosphorylation status of MLC that is required for cell contractility and subsequent chemotaxic motility.
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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".