FlnA binding to PACSIN2 F-BAR domain regulates membrane tubulation in megakaryocytes and platelets
Bibliographic record
Abstract
Bin-Amphiphysin-Rvs (BAR) and Fes-CIP4 homology BAR (F-BAR) proteins generate tubular membrane invaginations reminiscent of the megakaryocyte (MK) demarcation membrane system (DMS), which provides membranes necessary for future platelets. The F-BAR protein PACSIN2 is one of the most abundant BAR/F-BAR proteins in platelets and the only one reported to interact with the cytoskeletal and scaffold protein filamin A (FlnA), an essential regulator of platelet formation and function. The FlnA-PACSIN2 interaction was therefore investigated in MKs and platelets. PACSIN2 associated with FlnA in human platelets. The interaction required FlnA immunoglobulin-like repeat 20 and the tip of PACSIN2 F-BAR domain and enhanced PACSIN2 F-BAR domain membrane tubulation in vitro. Most human and wild-type mouse platelets had 1 to 2 distinct PACSIN2 foci associated with cell membrane GPIbα, whereas Flna-null platelets had 0 to 4 or more foci. Endogenous PACSIN2 and transfected enhanced green fluorescent protein-PACSIN2 were concentrated in midstage wild-type mouse MKs in a well-defined invagination of the plasma membrane reminiscent of the initiating DMS and dispersed in the absence of FlnA binding. The DMS appeared less well defined, and platelet territories were not readily visualized in Flna-null MKs. We conclude that the FlnA-PACSIN2 interaction regulates membrane tubulation in MKs and platelets and likely contributes to DMS formation.
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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".