6-Mercaptopurine augments glucose transport activity in skeletal muscle cells in part via a mechanism dependent upon orphan nuclear receptor NR4A3
Bibliographic record
Abstract
The purine anti-metabolite 6-mercaptopurine (6-MP) is widely used for the treatment of leukemia and inflammatory diseases. The cellular effects of 6-MP on metabolism remain unknown; however, 6-MP was recently found to activate the orphan nuclear receptor NR4A3 in skeletal muscle cell lines. We have reported previously that NR4A3 (also known as NOR-1, MINOR) is a positive regulator of insulin sensitivity in adipocytes. To further explore the role of NR4A3 activation in insulin action, we explored whether 6-MP activation of NR4A3 could modulate glucose transport system activity in L6 skeletal muscle cells. We found that 6-MP increased both NR4A3 expression and NR4A3 transcriptional activity and enhanced glucose transport activity via increasing GLUT4 translocation in both basal and insulin-stimulated L6 cells in an NR4A3-dependent manner. Furthermore, 6-MP increased levels of phospho-AS160, although this effect was not modulated by NR4A3 overexpression or knockdown. These primary findings provide a novel proof of principle that 6-MP, a small molecule NR4A3 agonist, can augment glucose uptake in insulin target cells, although this occurs via both NR4A3-dependent and -independent actions; the latter is related to an increase in phospho-AS160. These results establish a novel target for development of new treatments for insulin resistance.
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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 | 0.001 |
| 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) | 0.002 | 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".