Preferential lipolysis of DGAT1 over DGAT2 generated triacylglycerol in Huh7 hepatocytes
Bibliographic record
Abstract
Abstract Hepatic steatosis is defined by accumulation of neutral lipids in lipid droplets (LDs) including triacylglycerol (TG) and steryl esters. Two distinct diacylglycerol acyltransferases (DGAT1 and DGAT2) catalyze synthesis of TG in hepatocytes. TG formed through either DGAT1 or DGAT2 appears to be preferentially directed to distinct intercellular fates, such as fatty acid production for oxidation or very-low density lipoprotein assembly, respectively. Because of the preferential use of TG generated by DGAT1 and DGAT2, we hypothesized that targeting/association of lipolytic machinery to LDs would differ depending on whether the TG stores were generated through DGAT1 or DGAT2 activities. Inhibition of DGAT1 or DGAT2 in human hepatoma cells (Huh7) incubated with oleic acid resulted in only a small change in TG accretion suggesting that the two DGATs can compensate for each other in fatty acid esterification. This compensation was not accompanied by changes in DGAT1 or DGAT2 mRNA expression. DGAT1 inhibition (TG synthesized by DGAT2) resulted in large LDs, whereas DGAT2 inhibition (TG synthesized by DGAT1) caused the accumulation of numerous small LDs. Oleic acid treatment increased mRNA and protein expression of the LD-associated protein PLIN2 but not PLIN5 or the lipase ATGL and its activator ABHD5/CGI-58. Inactivation of DGAT1 or DGAT2 did not alter expression (mRNA or protein) of ATGL, ABHD5/CGI-58, PLIN2 or PLIN5, but inactivation of both DGATs increased PLIN2 abundance despite a dramatic reduction in the number of LDs. ATGL localized preferentially to DGAT1-made LDs rather than to DGAT2-made LDs, and TG in these LDs was preferentially used for fatty acid (FA) oxidation. A combination of DGAT2 inhibitor and the pan lipase inhibitor E600 resulted in large LDs, suggesting that the small size of DGAT1-made LDs is due to a lipolytic process.
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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 | 0.000 |
| 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".