The monoacylglycerol acyltransferase pathway contributes to triacylglycerol synthesis in HepG2 cells
Bibliographic record
Abstract
The monoacylglycerol acyltransferase (MGAT) pathway has a well-established role in the small intestine where it facilitates the absorption of dietary fat. In enterocytes, MGAT participates in the resynthesis of triacylglycerol using substrates (monoacylglycerol and fatty acids) generated in the gut lumen from the breakdown of triacylglycerol consumed in the diet. MGAT activity is also present in the liver, but its role in triacylglycerol metabolism in this tissue remains unclear. The predominant MGAT isoforms present in human liver appear to be MGAT2 and MGAT3. The objective of this study was to use selective small molecule inhibitors of MGAT2 and MGAT3 to determine the contributions of these enzymes to triacylglycerol production in liver cells. We found that pharmacological inhibition of either enzyme had no effect on TG mass in HepG2 cells but did alter lipid droplet size and number. Inhibition of MGAT2 did result in decreased DG and TG synthesis and TG secretion. Interestingly, MGAT2 preferentially utilized 2-monoacylglycerol derived from free glycerol and not from exogenously added 2-monoacylglycerol. In contrast, inhibition of MGAT3 had very little effect on TG metabolism in HepG2 cells. Additionally, we demonstrated that the MGAT activity of DGAT1 only makes a minor contribution to TG synthesis in intact HepG2 cells. Our data demonstrated that the MGAT pathway has a role in hepatic lipid metabolism with MGAT2, more so than MGAT3, contributing to TG synthesis and secretion.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".