Identification of a VPS33B-Binding Protein That Facilitates Alpha Granule Formation In Human Megakaryocytes and Platelets.
Bibliographic record
Abstract
Abstract Abstract 1444 Human platelets contain α-granules, dense (δ-) granules and lysosomes that release their contents upon platelet activation. Platelet granule release is important for hemostasis, since patients with inherited granule defects have bleeding problems. α-granules are absent in the gray platelet and ARC syndromes, while deficient δ-granules are observed in isolation, in combination with α-granule deficiency, or as part of a syndrome in the Hermansky-Pudlak, Chediak-Higashi and Griscelli syndromes. The biogenesis of α-granules is poorly understood. Our laboratory has identified VPS33B as a central player in the formation of platelet α-granules. VPS33B has yet to be characterized in detail, however, its homolog VPS33A is known to be part of a multiprotein complex involved intracellular vesicle trafficking. Studies in our laboratory suggest that VPS33B is also part of a multiprotein complex. We performed a yeast two-hybrid library screen with VPS33B as bait and found another member of the complex: the unidentified gene product of chromosome 14 open reading frame 133 (C14orf133). Sequence analysis indicated this to be human VPS16B. Our studies show that VPS16B specifically binds to VPS33B but not its homologue, VPS33A. Furthermore, we show that VPS33B forms a distinct complex from that of its homologue VPS33A. VPS16B was found to co-localize with trans-Golgi, late endosome and α-granule markers in megakaryocytic Dami cells. Ongoing studies suggest that knockdown of VPS16B affects α-granule formation. We conclude that VPS16B, much like its binding partner VPS33B, plays a crucial role in megakaryocyte and platelet α-granule biogenesis. Disclosures: No relevant conflicts of interest to declare.
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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) | 0.002 | 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".