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Record W3032452569 · doi:10.1161/atvbaha.118.314087

S100A9-RAGE Axis Accelerates Formation of Macrophage-Mediated Extracellular Vesicle Microcalcification in Diabetes Mellitus

2020· article· en· W3032452569 on OpenAlexaff
Ryo Kawakami, Shunsuke Katsuki, Richard J. Travers, Dayanna Romero, Dakota Becker-Greene, Lívia Silva Araújo Passos, Hideyuki Higashi, Mark C. Blaser, Galina K. Sukhova, Josef Buttigieg, David Kopriva, Ann Marie Schmidt, Daniel G. Anderson, Sasha A. Singh, Luís Cardoso, Sheldon Weinbaum, Peter Libby, Masanori Aikawa, Kevin Croce, Elena Aïkawa

Bibliographic record

VenueArteriosclerosis Thrombosis and Vascular Biology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Glycation End Products research
Canadian institutionsUniversity of SaskatchewanRegina Qu'Appelle Health RegionUniversity of Regina
FundersNational Heart, Lung, and Blood Institute
KeywordsRage (emotion)EndocrinologyInternal medicineProinflammatory cytokineS100A9InflammationGlycationMedicineDiabetes mellitusBiology

Abstract

fetched live from OpenAlex

Objective: Vascular calcification is a cardiovascular risk factor and accelerated in diabetes mellitus. Previous work has established a role for calcification-prone extracellular vesicles in promoting vascular calcification. However, the mechanisms by which diabetes mellitus provokes cardiovascular events remain incompletely understood. Our goal was to identify that increased S100A9 promotes the release of calcification-prone extracellular vesicles from human macrophages in diabetes mellitus. Approach and Results: Human primary macrophages exposed to high glucose (25 mmol/L) increased S100A9 secretion and the expression of receptor for advanced glycation end products (RAGE) protein. Recombinant S100A9 induced the expression of proinflammatory and osteogenic factors, as well as the number of extracellular vesicles with high calcific potential (alkaline phosphatase activity, P <0.001) in macrophages. Treatment with a RAGE antagonist or silencing with S100A9 siRNA in macrophages abolished these responses, suggesting that stimulation of the S100A9-RAGE axis by hyperglycemia favors a procalcific environment. We further showed that an imbalance between Nrf-2 (nuclear factor 2 erythroid related factor 2) and NF-κB (nuclear factor-κB) pathways contributes to macrophage activation and promotes a procalcific environment. In addition, streptozotocin-induced diabetic Apoe −/− S100a9 −/− mice and mice treated with S100a9 siRNA encapsulated in macrophage-targeted lipid nanoparticles showed decreased inflammation and microcalcification in atherosclerotic plaques, as gauged by molecular imaging and comprehensive histological analysis. In human carotid plaques, comparative proteomics in patients with diabetes mellitus and histological analysis showed that the S100A9-RAGE axis associates with osteogenic activity and the formation of microcalcification. Conclusions: Under hyperglycemic conditions, macrophages release calcific extracellular vesicles through mechanisms involving the S100A9-RAGE axis, thus contributing to the formation of microcalcification within atherosclerotic plaques.

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 score0.002
Threshold uncertainty score0.005

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)0.0020.000

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 head0.038
GPT teacher head0.288
Teacher spread0.250 · 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

Citations94
Published2020
Admission routes1
Has abstractyes

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