Molecular Triggers of Granule Formation in Megakaryocytes and Platelets
Bibliographic record
Abstract
Abstract Abstract SCI-34 Platelet secretory granules develop within maturing bone marrow-resident megakaryocytes, where α-granules, δ-granules, and lysosomes are transported to extending proplatelets (1) and undergo further maturation after platelets are released into the circulation. Mature platelets contain 50 to 80 membrane-enclosed α-granules, three to eight dense (δ-) granules, and a few lysosomes. δ-granules store calcium, phosphate, ADP, ATP, and serotonin, which play important roles during platelet activation. α-granules store numerous soluble and membrane-bound proteins, including adhesion molecules, cytokines, chemokines, coagulation and fibrinolytic proteins, immunologic modulators, and an assortment of complement, growth, and pro- and antiangiogenic factors. These play important roles in clotting, angiogenesis, inflammation, wound healing, and bone remodeling, and provide defenses against infections. Insights into megakaryocyte and platelet δ-granule development have come from studying inherited δ-granule deficiencies such as Hermansky-Pudlak syndrome (HPS) and Chediak-Higashi syndrome (CHS; MIM214500), for which mouse models also exist. Several genes/proteins linked to the regulation of vesicle trafficking have been implicated in δ-granule formation. These include components of BLOC (biogenesis of lysosome-related organelles complex) protein complexes (BLOC-1, −2, and −3), known vesicle-trafficking proteins (VPS33A and the β3A and δ subunit of AP-3), and the BEACH domain, containing protein LYST. Less is known about α-granule development, in which two inherited disorders result in platelets lacking α-granules: ARC syndrome (Arthrogryposis, Renal dysfunction, and Cholestasis; MIM208085) and gray platelet syndrome (GPS; MIM139090). GPS is characterized by variable thrombocytopenia and large, gray-appearing platelets on blood smears, with α-granules and α-granule proteins markedly decreased or absent. We and others recently determined that GPS is caused by mutations in NBEAL2, encoding a BEACH protein (2, 3, 4). Our work has also shown that the large α-granule-deficient platelets in ARC syndrome can arise due to mutations in VPS33B, encoding the Sec1/Munc18 (SM) protein VPS33B involved in vesicular trafficking (5). SM proteins are known to interact with membrane-associated soluble N-ethylmaleimide-sensitive fusion (NSF)-attachment protein receptors (SNAREs) of the syntaxin subfamily. Recently we have also identified VPS16B as a VPS33B-binding protein. A patient with homozygous missense mutations in C14orf133, encoding VPS16B, has ARC syndrome, with platelets lacking α-granules and stored α-granule proteins. Thus VPS16B is also required for megakaryocyte and platelet α-granule formation, and, in contrast to GPS, in which platelets have α-granule membrane proteins such as P-selectin, VPS16 null platelets lack P-selectin. The observation that GPS and ARC platelets lack α-granules but contain δ-granules, while HPS platelets are devoid of δ-granules but contain α-granules, suggests there are distinct pathways for δ-granule and α-granule biogenesis in maturing megakaryocytes. Immunofluorescence microscopy suggests that VPS16B and VPS33B act along the trans-Golgi network/late endosome/α-granule vesicular trafficking pathway during formation of α-granules in megakaryocytes. It is predicted that complexes containing VPS33B and VPS16B facilitate docking and fusion of intracellular vesicles during α-granule formation, while NBEAL2 promotes the maturation of nascent α-granule vesicles. 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".