Sitagliptin (MK0431) Inhibition of Dipeptidyl Peptidase IV Decreases Nonobese Diabetic Mouse CD4+ T-Cell Migration Through Incretin-Dependent and -Independent Pathways
Bibliographic record
Abstract
OBJECTIVE: Treatment of NOD mice with the dipeptidyl peptidase-IV (DPP-IV) inhibitor sitagliptin preserved islet transplants through a pathway involving modulation of splenic CD4(+) T-cell migration. In the current study, effects of sitagliptin on migration of additional subsets of CD4(+) T-cells were examined and underlying molecular mechanisms were further defined. RESEARCH DESIGN AND METHODS: Effects of sitagliptin on migration of NOD mouse splenic, thymic, and lymph node CD4(+) T-cells were determined. Signaling modules involved in DPP-IV-, Sitagliptin- and incretin-mediated modulation of CD4(+) T-cell migration were studied using Western blot and Rac1 and nuclear factor-kappaB (NF-kappaB) activity assays. RESULTS: Migration of splenic and lymph node CD4(+) T-cells of diabetic NOD mice was reduced by sitagliptin treatment. In vitro treatment of splenic, but not thymic or lymph node CD4(+) T-cells, from nondiabetic NOD mice with soluble (s) DPP-IV increased migration. Sitagliptin abolished sDPP-IV effects on splenic CD4(+) T-cell migration, whereas incretins decreased migration of lymph node, but not splenic, CD4(+) T-cells. Splenic CD4(+) T-cells demonstrating increased in vitro migration in response to sDPP-IV and lymph node CD4(+) T-cells that were nonresponsive to incretins selectively infiltrated islets of NOD mice, after injection. Sitagliptin decreases migration of splenic CD4(+) T-cells through a pathway involving Rac1/vasodilator-stimulated phosphoprotein, whereas its inhibitory effects on the migration of lymph node CD4(+) T-cells involve incretin-activation of the NF-kappaB pathway. CONCLUSIONS: Benefits of sitagliptin treatment in diabetic NOD mice may be mediated through selective effects on subpopulations of T-cells that are related to autoimmunity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".