Circulating small extracellular vesicles mediate vascular hyperpermeability in diabetes
Bibliographic record
Abstract
AIMS/HYPOTHESIS: A hallmark chronic complication of type 2 diabetes mellitus is vascular hyperpermeability, which encompasses dysfunction of the cerebrovascular endothelium and the subsequent development of associated cognitive impairment. The present study tested the hypothesis that during type 2 diabetes circulating small extracellular vesicles (sEVs) exhibit phenotypic changes that facilitate pathogenic disruption of the vascular barrier. METHODS: sEVs isolated from the plasma of a mouse model of type 2 diabetes and from diabetic human individuals were characterised for their ability to disrupt the endothelial cell (EC) barrier. The contents of sEVs and their effect on recipient ECs were assessed by proteomics and identified pathways were functionally interrogated with small molecule inhibitors. RESULTS: ) displayed hyperpermeability of the cerebrovasculature. Enhanced vascular leakiness was recapitulated following i.v. injection of sEVs from diabetic mice into non-diabetic recipient mice. Characterisation of circulating sEV populations from the plasma of diabetic mice and humans demonstrated increased quantity and size of sEVs compared with those isolated from non-diabetic counterparts. Functional experiments revealed that sEVs from diabetic mice or humans induced the rapid and sustained disruption of the EC barrier through enhanced paracellular and transcellular leak but did not induce inflammation. Subsequent sEV proteome and recipient EC phospho-proteome analysis suggested that extracellular vesicles (sEVs) from diabetic mice and humans modulate the MAPK/MAPK kinase (MEK) and Rho-associated protein kinase (ROCK) pathways, cell-cell junctions and actin dynamics. This was confirmed experimentally. Treatment of sEVs with proteinase K or pre-treatment of recipient cells with MEK or ROCK inhibitors reduced the hyperpermeability-inducing effects of circulating sEVs in the diabetic state. CONCLUSIONS/INTERPRETATION: Diabetes is associated with marked increases in the concentration and size of circulating sEVs. The modulation of sEV-associated proteins under diabetic conditions can induce vascular leak through activation of the MEK/ROCK pathway. These data identify a new paradigm by which diabetes can induce hyperpermeability and dysfunction of the cerebrovasculature and may implicate sEVs in the pathogenesis of cognitive decline during type 2 diabetes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".