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Molecular Triggers of Granule Formation in Megakaryocytes and Platelets

2012· article· en· W2569883412 on OpenAlexaff
Walter H.A. Kahr

Bibliographic record

VenueBlood · 2012
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsPlateletHermansky–Pudlak syndromeCell biologyDense granuleGranule (geology)MegakaryocyteBiologyPlatelet activationBiogenesisChemistryImmunologyBiochemistryProgenitor cellStem cellPathologyGeneMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.010
GPT teacher head0.238
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2012
Admission routes1
Has abstractyes

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