Very-Long-Chain Unsaturated Sphingolipids Mediate Oleate-Induced Rat β-Cell Proliferation
Bibliographic record
Abstract
Fatty acid (FA) signaling contributes to β-cell mass expansion in response to nutrient excess, but the underlying mechanisms are poorly understood. In the presence of elevated glucose, FA metabolism is shifted toward synthesis of complex lipids, including sphingolipids. Here, we tested the hypothesis that sphingolipids are involved in the β-cell proliferative response to FA. Isolated rat islets were exposed to FA and 16.7 mmol/L glucose for 48-72 h, and the contribution of the de novo sphingolipid synthesis pathway was tested using the serine palmitoyltransferase inhibitor myriocin, the sphingosine kinase (SphK) inhibitor SKI II, or knockdown of SphK, fatty acid elongase 1 (ELOVL1) and acyl-CoA-binding protein (ACBP). Rats were infused with glucose and the lipid emulsion ClinOleic and received SKI II by gavage. β-Cell proliferation was assessed by immunochemistry or flow cytometry. Sphingolipids were analyzed by liquid chromatography-tandem mass spectrometry. Among the FAs tested, only oleate increased β-cell proliferation. Myriocin, SKI II, and SphK knockdown all decreased oleate-induced β-cell proliferation. Oleate exposure did not increase the total amount of sphingolipids but led to a specific rise in 24:1 species. Knockdown of ACBP or ELOVL1 inhibited oleate-induced β-cell proliferation. We conclude that unsaturated very-long-chain sphingolipids produced from the available C24:1 acyl-CoA pool mediate oleate-induced β-cell proliferation in rats.
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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".