Control of hyperglycemia in male mice by leflunomide: mechanisms of action
Bibliographic record
Abstract
p70 S6 kinase (S6K1) is a serine/threonine kinase that phosphorylates the insulin receptor substrate-1 (IRS-1) at serine 1101 and desensitizes insulin receptor signaling. S6K1 hyperactivation due to overnutrition leads to hyperglycemia and type 2 diabetes. Our recent study showed that A77 1726, the active metabolite of the anti-rheumatoid arthritis (RA) drug leflunomide, is an inhibitor of S6K1. Whether leflunomide can control hyperglycemia and sensitize the insulin receptor has not been tested. Here we report that A77 1726 increased AKT S473/T308 and S6K1 T389 phosphorylation but decreased S6 S235/236 and IRS-1 S1101 phosphorylation in 3T3-L1 adipocytes, C2C12 and L6 myotubes. A77 1726 increased insulin receptor tyrosine phosphorylation and binding of the p85 subunit of the PI-3 kinase to IRS-1. A77 1726 enhanced insulin-stimulated glucose uptake in L6 myotubes and 3T3-L1 adipocytes, and enhanced insulin-stimulated glucose transporter type 4 (GLUT4) translocation to the plasma membrane of L6 cells. Finally, we investigated the anti-hyperglycemic effect of leflunomide on ob/ob and high-fat diet (HFD)-induced diabetes mouse models. Leflunomide treatment normalized blood glucose levels and overcame insulin resistance in glucose and insulin tolerance tests in ob/ob and HFD-fed mice but had no effect on mice fed a normal chow diet (NCD). Leflunomide treatment increased AKT S473/T308 phosphorylation in the fat and muscle of ob/ob mice but not in normal mice. Our results suggest that leflunomide sensitizes the insulin receptor by inhibiting S6K1 activity in vitro , and that leflunomide could be potentially useful for treating patients with both RA and diabetes.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".