Filamin A regulates platelet shape change and contractile force generation via phosphorylation of the myosin light chain
Bibliographic record
Abstract
Platelets are critical mediators of hemostasis and thrombosis. Platelets circulate as discs in their resting form but change shape rapidly upon activation by vascular damage and/or soluble agonists such as thrombin. Platelet shape change is driven by a dynamic remodeling of the actin cytoskeleton. Actin filaments interact with the protein myosin, which is phosphorylated on the myosin light chain (MLC) upon platelet activation. Actin-myosin interactions trigger contraction of the actin cytoskeleton, which drives platelet spreading and contractile force generation. Filamin A (FLNA) is an actin cross-linking protein that stabilizes the attachment between subcortical actin filaments and the cell membrane. In addition, FLNA binds multiple proteins and serves as a critical intracellular signaling scaffold. Here, we used platelets from mice with a megakaryocyte/platelet-specific deletion of FLNA to investigate the role of FLNA in regulating platelet shape change. Relative to controls, FLNA-null platelets exhibited defects in stress fiber formation, contractile force generation, and MLC phosphorylation in response to thrombin stimulation. Blockade of Rho kinase (ROCK) and protein kinase C (PKC) with the inhibitors Y27632 and bisindolylmaleimide (BIM), respectively, also attenuated MLC phosphorylation; our data further indicate that ROCK and PKC promote MLC phosphorylation through independent pathways. Notably, the activity of both ROCK and PKC was diminished in the FLNA-deficient platelets. We conclude that FLNA regulates thrombin-induced MLC phosphorylation and platelet contraction, in a ROCK- and PKC-dependent manner.
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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) | 0.001 | 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".