Acute Inhibition of Adipose Triglyceride Lipase by NG497 Dysregulates Insulin and Glucagon Secretion From Human Islets
Bibliographic record
Abstract
Adipose triglyceride lipase (ATGL), which catalyzes the breakdown of triglycerides in lipid droplets (LDs), plays a critical role in releasing fatty acids to support insulin secretion in pancreatic β cells. Based on genetic downregulation of ATGL in β cells, multiple mechanisms are proposed that acutely or chronically regulate insulin secretion. Currently, the contribution of acute vs chronic mechanisms in the regulation of insulin secretion is unclear. Also, little is known whether ATGL affects α-cell function. Using the human-specific ATGL inhibitor, NG497, this study investigates the impact of acute inhibition of ATGL on hormone secretion from human islets. In addition, morphological differences in LDs were assessed in confocal images of β and α cells. β cells exposed to NG497 overnight showed notable increases in LD size and number under glucose-sufficient culture. The effect of NG497 on LD accumulation in α cells was more prominent under fasting-simulated conditions than glucose-sufficient conditions, pointing toward a critical role for ATGL lipolysis under conditions that stimulate hormone secretion in β and α cells. When exposed to NG497 acutely, human islets reduced glucose-stimulated insulin secretion mildly, particularly first-phase insulin secretion, to an extent somewhat less pronounced than the impacts of chronic ATGL downregulation. Thus, chronic mechanisms may play a predominant role in reducing insulin secretion when ATGL is downregulated. Acute exposure of human islets to NG497 significantly reduced amino acid stimulated glucagon secretion at low glucose concentration, highlighting an important potential role of ATGL lipolysis in promoting hormone secretion acutely from α cells.
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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) | 0.001 | 0.000 |
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".