Sorcin Links Pancreatic β-Cell Lipotoxicity to ER Ca2+ Stores
Bibliographic record
Abstract
Preserving β-cell function during the development of obesity and insulin resistance would limit the worldwide epidemic of type 2 diabetes. Endoplasmic reticulum (ER) calcium (Ca(2+)) depletion induced by saturated free fatty acids and cytokines causes β-cell ER stress and apoptosis, but the molecular mechanisms behind these phenomena are still poorly understood. Here, we demonstrate that palmitate-induced sorcin downregulation and subsequent increases in glucose-6-phosphatase catalytic subunit-2 (G6PC2) levels contribute to lipotoxicity. Sorcin is a calcium sensor protein involved in maintaining ER Ca(2+) by inhibiting ryanodine receptor activity and playing a role in terminating Ca(2+)-induced Ca(2+) release. G6PC2, a genome-wide association study gene associated with fasting blood glucose, is a negative regulator of glucose-stimulated insulin secretion (GSIS). High-fat feeding in mice and chronic exposure of human islets to palmitate decreases endogenous sorcin expression while levels of G6PC2 mRNA increase. Sorcin-null mice are glucose intolerant, with markedly impaired GSIS and increased expression of G6pc2 Under high-fat diet, mice overexpressing sorcin in the β-cell display improved glucose tolerance, fasting blood glucose, and GSIS, whereas G6PC2 levels are decreased and cytosolic and ER Ca(2+) are increased in transgenic islets. Sorcin may thus provide a target for intervention 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".