Reprint of: miRNA-1 regulates endothelin-1 in diabetes
Bibliographic record
Abstract
AIMS: MicroRNAs (miRNAs) play important roles in several biological processes. In this study, we investigated the role of miR-1, an endothelin-1 (ET-1) targeting miRNA, in endothelial cells (ECs) and tissues of diabetic animals. ET-1 is known to be of pathogenetic significance in several chronic diabetic complications. MAIN METHODS: PCR array was used to identify alterations of miRNA expression in ECs exposed to glucose. miR-1 expression was validated by TaqMan real-time PCR assay. Human retinal ECs (HRECs) and human umbilical vein ECs (HUVECs) exposed to various glucose levels with or without miR-1 mimic transfection, and tissues from streptozotocin-induced diabetic animals after two months of follow-up, were examined for miR-1 expression, as well as ET-1 and fibronectin (FN) mRNA and protein levels. KEY FINDINGS: Array analyses showed glucose-induced alterations of 125 miRNAs (out of 381) in ECs exposed to 25 mM glucose compared to 5 mM glucose. Fifty-one miRNAs were upregulated and 74 were downregulated. 25 mM glucose decreased miR-1 expression and increased ET-1 mRNA and protein levels. miR-1 mimic transfection prevented HG-induced ET-1 upregulation. Furthermore, glucose induced upregulation of FN, which is mediated partly by ET-1, was also prevented by such transfection. Diabetic animals showed decreased miR-1 expression in the retina, heart and kidneys. In parallel, ET-1 mRNA expressions were increased in these tissues of diabetic animals, in association with upregulation of FN. SIGNIFICANCE: These results indicate a novel glucose-induced mechanism of tissue damage, in which miR-1 regulates ET-1 expressions in diabetes. Identifying such mechanisms may lead to RNA based treatment for diabetic complications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.084 | 0.039 |
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".