Receptor-Ligand Interaction Mediates Targeting of Endothelial Colony Forming Cell-derived Exosomes to the Kidney after Ischemic Injury
Bibliographic record
Abstract
Endothelial colony forming cell (ECFC)-derived exosomes protect mice against ischemic kidney injury, via transfer of microRNA-(miR)-486-5p. Mechanisms mediating exosome recruitment to tissues are unclear. We hypothesized that ECFC exosomes target ischemic kidneys, involving interaction between exosomal CXC chemokine receptor type 4 (CXCR4) and stromal cell-derived factor (SDF)-1α. Ischemia-reperfusion was induced in mice by bilateral renal vascular clamp, with intravenous infusion of exosomes at reperfusion. Optical imaging determined exosome biodistribution, and miR-486-5p was measured by real-time PCR. Human umbilical vein endothelial cells (HUVECs) were cultured to study the CXCR4/SDF-1α interaction. Targeting of administered exosomes to ischemic kidneys was detected 30 min and 4 hrs after reperfusion. Exosomes increased miR-486-5p levels only in kidneys, within proximal tubules, glomeruli, and endothelial cells. Uptake of fluorescently-labeled exosomes into HUVECs, and exosomal transfer of miR-486-5p were enhanced by hypoxia, effects blocked by neutralizing antibody to SDF-1α or by the CXCR4 inhibitor plerixafor. Infusion of ECFC exosomes prevented ischemic kidney injury in vivo, an effect that was not observed when exosomes were pre-incubated with plerixafor. These data indicate that ECFC exosomes selectively target the kidneys after ischemic injury, with rapid cellular transfer of miR486-5p. Targeting of exosomes may involve interaction of CXCR4 with endothelial cell SDF-1α.
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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".