miR-146a–Mediated Extracellular Matrix Protein Production in Chronic Diabetes Complications
Bibliographic record
Abstract
OBJECTIVE: MicroRNAs (miRNAs), through transcriptional regulation, modulate several cellular processes. In diabetes, increased extracellular matrix protein fibronectin (FN) production is known to occur through histone acetylator p300. Here, we investigated the role of miR-146a, an FN-targeting miRNA, on FN production in diabetes and its relationship with p300. RESEARCH DESIGN AND METHODS: miR-146a expressions were measured in endothelial cells from large vessels and retinal microvessels in various glucose levels. FN messenger RNA expression and protein levels with or without miR-146a mimic or antagomir transfection were examined. A luciferase assay was performed to detect miR-146a's binding to FN 3'-untranslated region (UTR). Likewise, retinas from type 1 diabetic rats were studied with or without an intravitreal injection of miR-146a mimic. In situ hybridization was used to localize retinal miR-146a. Cardiac and renal tissues were analyzed from type 1 and type 2 diabetic animals. RESULTS: A total of 25 mmol/L glucose decreased miR-146a expression and increased FN expression compared with 5 mmol/L glucose in both cell types. miR-146a mimic transfection prevented such change, whereas miR-146a antagomir transfection in the cells in 5 mmol/L glucose caused FN upregulation. A luciferase assay confirmed miR-146a's binding to FN 3'-UTR. miR-146a was localized in the retinal endothelial cells and was decreased in diabetes. Intravitreal miR-146a mimic injection restored retinal miR-146a and decreased FN in diabetes. Additional experiments showed that p300 regulates miR-146a. Similar changes were seen in the retinas, kidneys, and hearts in type 1 and type 2 diabetic animals. CONCLUSIONS: These studies showed a novel, glucose-induced molecular mechanism in which miR-146a participates in the transcriptional circuitry regulating extracellular matrix protein production in diabetes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".