MétaCan
Menu
Back to cohort

Glucose‐Derived Extracellular Endothelial Microvesicles Induce a Pathologic Endothelial Phenotype

2021· article· en· W3171782059 on OpenAlexaff
Vinícius P. Garcia, Kelly A. Stockelman, L. Madden Brewster, Anabel Goulding, Noah M. DeSouza, Jared J. Greiner, Jamie G. Hijmans, Jonathan P. Little, Isaac T. S. Li, Christopher A. DeSouza

Bibliographic record

VenueThe FASEB Journal · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsMicrovesiclesInflammationExtracellularOxidative stressUmbilical veinEndothelial stem cellNitric oxideEndotheliumChemistryEndocrinologyCell biologyInternal medicineImmunologyBiologyBiochemistryMedicineIn vitromicroRNA

Abstract

fetched live from OpenAlex

Hyperglycemia is associated with an increased risk and prevalence of cardiovascular disease (CVD), due in large part to the adverse effects of glucose on endothelial cells. Indeed, hyperglycemic conditions can damage endothelial cells, in turn, impairing endothelial vasomotor and fibrinolytic function, exacerbating inflammatory and oxidative processes and propagating a proapoptotic phenotype. In addition to these primary effects, glucose also stimulates the release of extracellular microvesicles from endothelial cells. Extracellular endothelial microvesicles (EMVs) have emerged as causative agents in vascular pathology owing to their numerical increase in disease states, including diabetes, and cellular effects. The effects of glucose‐derived EMVs on endothelial cell function is not clear. The experimental aim of this study was to determine, in vitro, the effect of glucose‐derived EMVs on endothelial cell inflammation, oxidative stress, and nitric oxide (NO) production. Human umbilical vein endothelial cells (HUVECs) were cultured (3 rd passage) and incubated with RPMI 1640 media containing 25mM D‐glucose (concentration representing a diabetic glycemic state) or 5mM D‐glucose (control, normoglycemic condition) for 48 h to generate EMVs. EMVs (CD144‐PE) were counted and isolated by flow cytometry. Thereafter, HUVECs (2 x10 6 cells/condition) were treated with EMVs (2:1 EMV:cell ratio) generated from either the high (hgEMV) or normal (ngEMV) glucose condition for 24 h. EMV release was markedly higher (~280%; P<0.05) in cells treated with high vs normal glucose (134.7±15.6 vs. 35.4±3.2 EMV/µL). hgEMVs induced significantly higher release of cytokines IL‐6 (26.6±1.5 vs. 20.7±1.6 pg/mL) and IL‐8 (40.5±4.1 vs. 26.4±2.9 pg/mL); in addition, expression of active NF‐κB p65 (Ser‐536) was higher in the hgEMV treated cells (7.6±1.0 vs. 3.4±0.5 AU; P<0.05). Intracellular ROS production was higher(~26%) in the cells treated with hgEMVs (72.5±3.4 %) vs ngEMVs (57.7±1.8%). Active endothelial nitric oxide synthase (p‐eNOS Ser1177) (10.6±1.6 vs 31.7±5.2 AU) and NO production (7.7±0.2 vs. 11.6±1.1 umol/L) were both significantly lower (~30% and 20%, respectively) in hgEMV treated cells. Blocking NF‐kB activation using IKK‐2 inhibitor IV abolished the increase in IL‐6 and IL‐8 release in response to hgEMVs, verifying hgEMV induced NF‐kB‐mediated inflammation. Inhibiting endocytosis prevented the proinflammatory, pro‐oxidative and anti‐NO effects of hgEMVs; whereas, RNase‐inactivation (RNase A/T1) of hgEMVs eliminated their negative effects on endothelial proteins regulating inflammation, oxidative stress and NO production. These data demonstrate that: 1)high glucose‐derived EMVs induce a proinflammatory, pro‐oxidative, proatherogenic endothelial phenotype; and 2) EMV internalization and intracellular delivery of RNA regulatory cargo are central mechanisms underlying these deleterious cellular effects.

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 integrity0.0000.000
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 head0.014
GPT teacher head0.237
Teacher spread0.223 · 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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueThe FASEB JournalSame topicExtracellular vesicles in diseaseFrench-language works237,207