PGC-1 coactivators in β-cells regulate lipid metabolism and are essential for insulin secretion coupled to fatty acids
Bibliographic record
Abstract
OBJECTIVES: Peroxisome proliferator-activated receptor γ coactivator 1 (PPARGCA1, PGC-1) transcriptional coactivators control gene programs important for nutrient metabolism. Islets of type 2 diabetic subjects have reduced PGC-1α expression and this is associated with decreased insulin secretion, yet little is known about why this occurs or what role it plays in the development of diabetes. Our goal was to delineate the role and importance of PGC-1 proteins to β-cell function and energy homeostasis. METHODS: We investigated how nutrient signals regulate coactivator expression in islets and the metabolic consequences of reduced PGC-1α and PGC-1β in primary and cultured β-cells. Mice with inducible β-cell specific double knockout of Pgc-1α/Pgc-1β (βPgc-1 KO) were created to determine the physiological impact of reduced Pgc1 expression on glucose homeostasis. RESULTS: Pgc-1α and Pgc-1β expression was increased in primary mouse and human islets by acute glucose and palmitate exposure. Surprisingly, PGC-1 proteins were dispensable for the maintenance of mitochondrial mass, gene expression, and oxygen consumption in response to glucose in adult β-cells. However, islets and mice with an inducible, β-cell-specific PGC-1 knockout had decreased insulin secretion due in large part to loss of the potentiating effect of fatty acids. Consistent with an essential role for PGC-1 in lipid metabolism, β-cells with reduced PGC-1s accumulated acyl-glycerols and PGC-1s controlled expression of key enzymes in lipolysis and the glycerolipid/free fatty acid cycle. CONCLUSIONS: These data highlight the importance of PGC-1s in coupling β-cell lipid metabolism to promote efficient insulin secretion.
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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) | 0.000 | 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 | 0.001 |
| 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".