miR-146a regulates glucose induced upregulation of inflammatory cytokines extracellular matrix proteins in the retina and kidney in diabetes
Bibliographic record
Abstract
Hyperglycemic damage to the endothelial cells (ECs) leads to increased synthesis of inflammatory cytokines. We have previously shown miR-146a downregulation in ECs and in the tissues of diabetic mice. Here we investigated the role of miR-146a, in the production of specific inflammatory cytokines and extracellular matrix (ECM) proteins in retina and kidneys in diabetes. We generated an endothelial specific miR-146a overexpressing transgenic mice (TG). We investigated these mice and wild type (WT) controls with or without streptozotocin (STZ) induced diabetes. Retinal and renal cortical tissues from the mice were examined for mRNAs for specific inflammatory markers, (ECM) proteins and inflammation inducible transcription factor by real time RT-PCR. Corresponding proteins, where possible, were examined using immunofluorescence or ELISA. In parallel, we examined ECs following incubation with various levels of glucose with or without miR-146a mimic transfection. In the retina and kidneys of WT mice with diabetes, increased expression of inflammatory markers (IL-6, TNFα, IL1β) in association augmented expression of ECM proteins (collagen 1αIV, fibronectin) and NF κB-P65 were observed, compared to WT non-diabetic controls. These changes were prevented in diabetic miR-146a TG mice along with retinal and renal functional and structural changes. In vitro studies showed similar changes in the ECs exposed to high glucose. Such changes were corrected in the cells following miR-146a mimic transfection. Further analyses of renal cortical tissues showed diabetes induced significant upregulation of two regulators of NFκB, namely Interleukin-1 associated Kinase 1 and tumour necrosis factor receptor associated factor. Such changes were prevented in diabetic TG animals. These data indicate that augmented production of inflammatory cytokines and ECM proteins in the retina and kidneys in diabetes are regulated through endothelium derived miR-146a. Identification of such novel mechanisms may potentially lead to the development of novel therapies.
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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 | 0.001 |
| 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".