The VPS33B-binding protein VPS16B is required in megakaryocyte and platelet α-granule biogenesis
Bibliographic record
Abstract
Patients with platelet α or dense δ-granule defects have bleeding problems. Although several proteins are known to be required for δ-granule development, less is known about α-granule biogenesis. Our previous work showed that the BEACH protein NBEAL2 and the Sec1/Munc18 protein VPS33B are required for α-granule biogenesis. Using a yeast two-hybrid screen, mass spectrometry, coimmunoprecipitation, and bioinformatics studies, we identified VPS16B as a VPS33B-binding protein. Immunoblotting confirmed VPS16B expression in various human tissues and cells including megakaryocytes and platelets, and also in megakaryocytic Dami cells. Characterization of platelets from a patient with arthrogryposis, renal dysfunction, and cholestasis (ARC) syndrome containing mutations in C14orf133 encoding VPS16B revealed pale-appearing platelets in blood films and electron microscopy revealed a complete absence of α-granules, whereas δ-granules were observed. Soluble and membrane-bound α-granule proteins were reduced or undetectable, suggesting that both releasable and membrane-bound α-granule constituents were absent. Immunofluorescence microscopy of Dami cells stably expressing GFP-VPS16B revealed that similar to VPS33B, GFP-VPS16B colocalized with markers of the trans-Golgi network, late endosomes and α-granules. We conclude that VPS16B, similar to its binding partner VPS33B, is essential for megakaryocyte and platelet α-granule biogenesis.
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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) | 0.001 | 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) | 0.002 | 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".