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Record W2558867271 · doi:10.1182/blood.v126.23.417.417

Glucose Transporter 3 in Platelets Facilitates Alpha-Granule Mediated Glucose Uptake, Driving Intragranular Glycolysis That Is Required for Platelet Degranulation and Activation

2015· article· en· W2558867271 on OpenAlexaff
Fidler P.L Trevor, Elizabeth A. Middleton, Jesse W. Rowley, Luc H. Boudreau, Robert A. Campbell, Rhonda Souvenir, Éric Boilard, E. Dale Abel, Andrew S. Weyrich

Bibliographic record

VenueBlood · 2015
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsGLUT3Glucose transporterPlateletGlucose uptakeGlycolysisInternal medicinePlatelet activationEndocrinologyGlycogenBiologyDegranulationGLUT1Carbohydrate metabolismChemistryBiochemistryMetabolismInsulinMedicine

Abstract

fetched live from OpenAlex

Abstract Patients with diabetes display increased thrombosis and platelet activation. Preliminary metabolomics analysis of platelets from patients with type 2 diabetes revealed an accumulation of glycolytic and TCA intermediates relative to healthy controls. In vitro studies of platelets under hyperglycemic conditions suggest that glucose metabolism may lead to increased platelet activation. Platelets import glucose via two glucose transporters GLUT1, which is expressed on the plasma membrane, and GLUT3, which is expressed on the plasma membrane (15%) and the remaining 85% on α-granule membranes. Following stimulation, platelet α-granules translocate to the plasma membrane and release their cargo. To better understand the consequences of glucose metabolism on platelet function we generated a platelet specific knockout of GLUT3 using a Pf4 Cre recombinase transgenic mouse crossed to mice that harbor floxed GLUT3 alleles. GLUT3 KO platelets displayed a 23% reduction in basal glucose uptake compared to littermate controls. Control platelets stimulated with thrombin displayed a significant increase in glucose uptake whereas KO platelets failed to show any change. Additionally, platelet glycogen content and glycolysis intermediates were significantly reduced in KO platelets, which exhibited reduced glycolytic rates following metabolic stress (mitochondrial uncoupling). Because GLUT3 KO platelets had only a minor decrease in glucose uptake under basal conditions, but platelet glucose metabolism was dramatically altered when stimulated, we hypothesized that under basal conditions, GLUT3 facilitates glucose uptake into α-granules, to generate glycogen and fuel intragranular glycolysis that generates the energy required for α-granule degranulation. To test this hypothesis we permeabilized platelet plasma membranes, but not α-granule membranes using saponin and incubated the platelets with C13-glucose. Under these conditions, control platelets produced 2.5-fold more C13 -lactic acid than KO platelets. In vitro, GLUT3 knockout platelets display a 90% reduction in spreading on fibrinogen and collagen matrices and significant reductions in α-granule degranulation as marked by CD62p surface translocation, platelet factor 4 release, and the persistence of α-granules observed in electron micrographs of stimulated platelets. In vivo in a KBx/N model of rheumatoid arthritis, which is dependent in part on platelet activation, GLUT3 KO mice exhibited significantly reduced severity of disease. Analysis of GLUT3 mice in models of arterial thrombosis, deep vein thrombosis and tail-bleeding indicated no alteration in thrombosis between littermate controls and KO mice. Together these data indicate that GLUT3 mediated α-granule glucose uptake is essential for platelet activation and degranulation. Moreover reducing platelet GLUT3 may ameliorate the course of rheumatoid arthritis. 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 score0.001
Threshold uncertainty score0.004

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 science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.258
Teacher spread0.228 · 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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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