MicroRNA-223 is essential for maintaining functional β-cell mass during diabetes through inhibiting both FOXO1 and SOX6 pathways
Bibliographic record
Abstract
The initiation and development of diabetes are mainly ascribed to the loss of functional β-cells. Therapies designed to regenerate β-cells provide great potential for controlling glucose levels and thereby preventing the devastating complications associated with diabetes. This requires detailed knowledge of the molecular events and underlying mechanisms in this disorder. Here, we report that expression of microRNA-223 (miR-223) is up-regulated in islets from diabetic mice and humans, as well as in murine Min6 β-cells exposed to tumor necrosis factor α (TNFα) or high glucose. Interestingly, miR-223 knockout (KO) mice exhibit impaired glucose tolerance and insulin resistance. Further analysis reveals that miR-223 deficiency dramatically suppresses β-cell proliferation and insulin secretion. Mechanistically, using luciferase reporter gene assays, histological analysis, and immunoblotting, we demonstrate that miR-223 inhibits both forkhead box O1 (FOXO1) and SRY-box 6 (SOX6) signaling, a unique bipartite mechanism that modulates expression of several β-cell markers (pancreatic and duodenal homeobox 1 (PDX1), NK6 homeobox 1 (NKX6.1), and urocortin 3 (UCN3)) and cell cycle-related genes (cyclin D1, cyclin E1, and cyclin-dependent kinase inhibitor P27 (P27)). Importantly, miR-223 overexpression in β-cells could promote β-cell proliferation and improve β-cell function. Taken together, our results suggest that miR-223 is a critical factor for maintaining functional β-cell mass and adaptation during metabolic stress.
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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".