Adipose Triglyceride Lipase Is Implicated in Fuel- and Non-fuel-stimulated Insulin Secretion
Bibliographic record
Abstract
Reduced lipolysis in hormone-sensitive lipase-deficient mice is associated with impaired glucose-stimulated insulin secretion (GSIS), suggesting that endogenous beta-cell lipid stores provide signaling molecules for insulin release. Measurements of lipolysis and triglyceride (TG) lipase activity in islets from HSL(-/-) mice indicated the presence of other TG lipase(s) in the beta-cell. Using real time-quantitative PCR, adipose triglyceride lipase (ATGL) was found to be the most abundant TG lipase in rat islets and INS832/13 cells. To assess its role in insulin secretion, ATGL expression was decreased in INS832/13 cells (ATGL-knockdown (KD)) by small hairpin RNA. ATGL-KD increased the esterification of free fatty acid (FFA) into TG. ATGL-KD cells showed decreased glucose- or Gln + Leu-induced insulin release, as well as reduced response to KCl or palmitate at high, but not low, glucose. The K(ATP)-independent/amplification pathway of GSIS was considerably reduced in ATGL-KD cells. ATGL(-/-) mice were hypoinsulinemic and hypoglycemic and showed decreased plasma TG and FFAs. A hyperglycemic clamp revealed increased insulin sensitivity and decreased GSIS and arginine-induced insulin secretion in ATGL(-/-) mice. Accordingly, isolated islets from ATGL(-/-) mice showed reduced insulin secretion in response to glucose, glucose + palmitate, and KCl. Islet TG content and FFA esterification into TG were increased by 2-fold in ATGL(-/-) islets, but glucose usage and oxidation were unaltered. The results demonstrate the importance of ATGL and intracellular lipid signaling for fuel- and non-fuel-induced insulin secretion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".