APPL1 potentiates insulin secretion in pancreatic β cells by enhancing protein kinase Akt-dependent expression of SNARE proteins in mice
Bibliographic record
Abstract
Insulin resistance and defective insulin secretion are the two major features of type 2 diabetes. The adapter protein APPL1 is an obligatory molecule in regulating peripheral insulin sensitivity, but its role in insulin secretion remains elusive. Here, we show that APPL1 expression in pancreatic β cells is markedly decreased in several mouse models of obesity and diabetes. APPL1 knockout mice exhibit glucose intolerance and impaired glucose-stimulated insulin secretion (GSIS), whereas transgenic expression of APPL1 prevents high-fat diet (HFD)-induced glucose intolerance partly by enhancing GSIS. In both pancreatic islets and rat β cells, APPL1 deficiency causes a marked reduction in expression of the exocytotic machinery SNARE proteins (syntaxin-1, synaptosomal-associated protein 25, and vesicle-associated membrane protein 2) and an obvious decrease in the number of exocytotic events. Such changes are accompanied by diminished insulin-stimulated Akt activation. Furthermore, the defective GSIS and reduced expression of SNARE proteins in APPL1-deficient β cells can be rescued by adenovirus-mediated expression of APPL1 or constitutively active Akt. These findings demonstrate that APPL1 couples insulin-stimulated Akt activation to GSIS by promoting the expression of the core exocytotic machinery involved in exocytosis and also suggest that reduced APPL1 expression in pancreatic islets may serve as a pathological link that couples insulin resistance to β-cell dysfunction in type 2 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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".