RIPK2 Dictates Insulin Responses to Tyrosine Kinase Inhibitors in Obese Male Mice
Bibliographic record
Abstract
Tyrosine kinase inhibitors (TKIs) used in cancer are also being investigated in diabetes. TKIs can improve blood glucose control in diabetic cancer patients, but the specific kinases that alter blood glucose or insulin are not clear. We sought to define the role of Receptor Interacting Serine/Threonine Kinase 2 (RIPK2) in mouse models of insulin resistance. We tested the TKI gefitinib, which inhibits RIPK2 activity, in wild-type (WT), Nod1-/-, Nod2-/-, and Ripk2-/- mice fed an obesogenic high-fat diet. Gefitinib lowered blood glucose during a glucose tolerance test (GTT) in a nucleotide-binding oligomerization domain (NOD)-RIPK2-independent manner in all obese mice. However, gefitinib lowered glucose-stimulated insulin secretion only in obese Ripk2-/- mice. Gefitinib had no effect on insulin secretion in obese WT, Nod1-/-, or Nod2-/- mice. Hence, genetic deletion of Ripk2 promoted the insulin-sensitizing potential of gefitinib, since this TKI lowered both blood glucose and insulin only in Ripk2-/- mice. Gefitinib did not alter the inflammatory profile of pancreas, adipose, liver, or muscle tissues in obese Ripk2-/- mice compared with obese WT mice. We also tested imatinib, a TKI that does not inhibit RIPK2 activity, in obese WT mice. Imatinib lowered blood glucose during a GTT, consistent with TKIs lowering blood glucose independently of RIPK2. However, imatinib increased glucose-stimulated insulin secretion during the glucose challenge. These data show that multiple TKIs lower blood glucose, where actions of TKIs on RIPK2 dictate divergent insulin responses, independent of tissue inflammation. Our data show that RIPK2 limits the insulin sensitizing effect of gefitinib, whereas imatinib increased insulin secretion.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".