Cholesterol Elevation Impairs Glucose-Stimulated Ca2+ Signaling in Mouse Pancreatic β-Cells
Bibliographic record
Abstract
Recent studies have demonstrated that cholesterol elevation in pancreatic islets is associated with a reduction in glucose-stimulated insulin secretion, but the underlying cellular mechanisms remain elusive. Here, we show that cholesterol enrichment dramatically reduced the proportion of mouse β-cells that exhibited a Ca(2+) signal when stimulated by high glucose. When cholesterol-enriched β-cells were challenged with tolbutamide, there was a decrease in the amplitude of the Ca(2+) signal, and it was associated with a reduction in the cell current density of voltage-gated Ca(2+) channels (VGCC). Although the cell current densities of the ATP-dependent K(+) channels and the delayed rectifier K(+) channels were also reduced in the cholesterol-enriched β-cells, glucose evoked only a small depolarization in these cells. In cholesterol-enriched cells, the glucose-mediated increase in cellular ATP content was dramatically reduced, and this was related to a decrease in glucose uptake via glucose transporter 2 and an impairment of mitochondrial metabolism. Thus, cholesterol enrichment impaired glucose-stimulated Ca(2+) signaling in β-cells via two mechanisms: a decrease in the current density of VGCC and a reduction in glucose-stimulated mitochondrial ATP production, which in turn led to a smaller glucose-evoked depolarization. The decrease in VGCC-mediated extracellular Ca(2+) influx in cholesterol-enriched β-cells was associated with a reduction in the amount of exocytosis. Our findings suggest that defect in glucose-stimulated Ca(2+) signaling is an important mechanism underlying the impairment of glucose-stimulated insulin secretion in islets with elevated cholesterol level.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".