Deletion of <i>Pten</i> in Pancreatic β-Cells Protects Against Deficient β-Cell Mass and Function in Mouse Models of Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: Type 2 diabetes is characterized by diminished pancreatic β-cell mass and function. Insulin signaling within the β-cells has been shown to play a critical role in maintaining the essential function of the β-cells. Under basal conditions, enhanced insulin-PI3K signaling via deletion of phosphatase with tensin homology (PTEN), a negative regulator of this pathway, leads to increased β-cell mass and function. In this study, we investigated the effects of prolonged β-cell-specific PTEN deletion in models of type 2 diabetes. RESEARCH DESIGN AND METHODS: Two models of type 2 diabetes were employed: a high-fat diet (HFD) model and a db/db model that harbors a global leptin-signaling defect. A Cre-loxP system driven by the rat insulin promoter (RIP) was employed to obtain mice with β-cell-specific PTEN deletion (RIPcre(+) Pten(fl/fl)). RESULTS: PTEN expression in islets was upregulated in both models of type 2 diabetes. RIPcre(+) Pten(fl/fl) mice were completely protected against diabetes in both models of type 2 diabetes. The islets of RIPcre(+) Pten(fl/fl) mice already exhibited increased β-cell mass under basal conditions, and there was no further increase under diabetic conditions. Their β-cell function and islet PI3K signaling remained intact, in contrast to HFD-fed wild-type and db/db islets that exhibited diminished β-cell function and attenuated PI3K signaling. These protective effects in β-cells occurred in the absence of compromised response to DNA-damaging stimuli. CONCLUSIONS: PTEN exerts a critical negative effect on both β-cell mass and function. Thus PTEN inhibition in β-cells can be a novel therapeutic intervention to prevent the decline of β-cell mass and function in type 2 diabetes.
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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) | 0.000 | 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) | 0.000 | 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".