Trans geometric isomers of EPA decrease LXRα-induced cellular triacylglycerol via suppression of SREBP-1c and PGC-1β
Bibliographic record
Abstract
Dietary mono- or di-trans fatty acids with chain lengths of 18-22 increase the risk of cardiovascular diseases because they increase LDL cholesterol and decrease HDL cholesterol in the plasma. However, the effects of trans isomers of PUFAs on lipid metabolism remain unknown. Dietary PUFAs, especially eicosapentaenoic acid (EPA) in marine oils, improve serum lipid profiles by suppressing liver X receptor alpha (LXRalpha) activity in the liver. In this study, we compared the effects of trans geometric isomers of eicosapentaenoic acid (TEPA) on triacylglycerol synthesis induced by a synthetic LXRalpha agonist (T0901317) with the effects of EPA in HepG2 cells. TEPA significantly decreased the amount of cellular triacylglycerol and the expression of mRNAs encoding fatty acid synthase, stearoyl-CoA desaturase-1, and glycerol-3-phosphate acyltransferase induced by T0901317 compared with EPA. However, there was no significant difference between the suppressive effect of TEPA or EPA on the expression of sterol-regulatory element binding protein-1c (SREBP-1c) induced by T0901317. We found that TEPA, but not EPA, decreased the mRNA expression of peroxisome proliferator-activated receptor gamma coactivator 1beta (PGC-1beta), which is a coactivator of both LXRalpha and SREBP-1. These results suggest that the hypolipidemic effect of TEPA can be attributed to a decrease not only in SREBP-1 but also in PGC-1beta expression.
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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 | 0.000 |
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".