A Role for ATP-Citrate Lyase, Malic Enzyme, and Pyruvate/Citrate Cycling in Glucose-induced Insulin Secretion
Bibliographic record
Abstract
In pancreatic beta-cells, metabolic coupling factors generated during glucose metabolism and pyruvate cycling through anaplerosis/cataplerosis processes contribute to the regulation of insulin secretion. Pyruvate/citrate cycling across the mitochondrial membrane leads to the production of malonyl-CoA and NADPH, two candidate coupling factors. To examine the implication of pyruvate/citrate cycling in glucose-induced insulin secretion (GIIS), different steps of the cycle were inhibited in INS 832/13 cells by pharmacological inhibitors and/or RNA interference (RNAi) technology: mitochondrial citrate export, ATP-citrate lyase (ACL), and cytosolic malic enzyme (ME1). The inhibitors of the di- and tri-carboxylate carriers, n-butylmalonate and 1,2,3-benzenetricarboxylate, respectively, reduced GIIS, indicating the importance of transmitochondrial transport of tri- and dicarboxylates in the action of glucose. To directly test the role of ACL and ME1 in GIIS, small hairpin RNA (shRNA) were used to selectively decrease ACL or ME1 expression in transfected INS 832/13 cells. shRNA-ACL reduced ACL protein levels by 67%, and this was accompanied by a reduction in GIIS. The amplification/K(ATP)-independent pathway of GIIS was affected by RNAi knockdown of ACL. The ACL inhibitor radicicol also curtailed GIIS. shRNA-ME1 reduced ME1 activity by 62% and decreased GIIS. RNAi suppression of either ACL or ME1 did not affect glucose oxidation. However, because ACL is required for malonyl-CoA formation, inhibition of ACL expression by shRNA-ACL decreased glucose incorporation into palmitate and increased fatty acid oxidation in INS 832/13 cells. Taken together, the results underscore the importance of pyruvate/citrate cycling in pancreatic beta-cell metabolic signaling and the regulation of GIIS.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".