The orphan nuclear receptor Nor1/Nr4a3 is a negative regulator of β-cell mass
Bibliographic record
Abstract
The Nr4a subfamily of nuclear receptor comprises three members in mammalian cells: Nur77/Nr4a1, Nurr1/Nr4a2, and Nor1/Nr4a3. Nr4a proteins play key roles in the regulation of glucose homeostasis in peripheral metabolic tissues. However, their biological functions in β-cells remain relatively uncharacterized. Here we sought to investigate the potential role of Nor1 in the regulation of β-cell mass and, in particular, β-cell survival/apoptosis. We used histological analysis to examine the consequences of genetic deletion of either Nur77 and Nor1 on β-cell mass, investigated the expression patterns of Nr4as in human islets and INS cells and performed gain- and loss-of-function experiments to further characterize the role of Nor1 in β-cell apoptosis. Surprisingly, Nor1 knockout mice displayed increased β-cell mass, whereas mice with genetic deletion of Nur77 did not exhibit any significant differences compared with their WT littermates. The increase in β-cell mass in Nor1 knockout mice was accompanied by improved glucose tolerance. A gene expression study performed in both human islets and INS cells revealed that Nor1 expression is significantly increased by pro-inflammatory cytokines and, to a lesser extent, by elevated concentrations of glucose. Nor1 overexpression in both INS and human islet cells caused apoptosis, whereas siRNA-mediated Nor1 knockdown prevented cytokine-induced β-cell death. Finally, Nor1 expression was up-regulated in islets of individuals with type 2 diabetes. Altogether, our results uncover that Nor1 negatively regulates β-cell mass. Nor1 represents a promising molecular target in diabetes treatment to prevent β-cell destruction.
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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) | 0.002 | 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".