Molecular Triggers of Granule Formation in Megakaryocytes and Platelets
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».