TBC1D1, a Rab‐GTPase Activating Protein, is Critical for Maintaining β‐cell Mass and Glucose Homeostasis
Bibliographic record
Abstract
Understanding the mechanisms regulating islet function is crucial for establishing novel therapeutic modalities to combat diabetes. TBC1D1, a Rab‐GTPase activating protein involved in skeletal muscle GLUT4 trafficking events, was recently identified withinβ‐cells and implicated in cell proliferation. However, the in vivo function of TBC1D1 remains to be elucidated. Therefore, we addressed the role of TBC1D1 in the pancreas utilizing a rat knockout (KO) model. Wild‐type and TBC1D1 KO rats were maintained on a chow or high‐fat diet. We demonstrated that TBC1D1 KO rats were glucose intolerant and displayed reduced insulin levels during a glucose challenge, suggesting that their phenotype prominently manifests within the pancreas. Next, we examined islet function and β‐cell mass. While, glucose‐stimulated insulin secretion from harvested islets was increased in TBC1D1 KO rats, we observed an overall decrease in β‐cell mass by ~30%. In addition, consumption of a high‐fat diet did not influence these changes observed in the KO rats. Altogether, our data suggests that in vivo impaired glucose homeostasis observed in TBC1D1 KO rats is a consequence of altered islet mass, thereby establishing a fundamental in vivo role for TBC1D1 in maintaining β‐cell mass. Therefore, pancreatic TBC1D1 may represent an attractive target to improve β‐cell function and stability to treat and prevent 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".