Induction of the Peroxisomal Glycerolipid-synthesizing Enzymes during Differentiation of 3T3-L1 Adipocytes
Bibliographic record
Abstract
The glycerophosphate backbone for triglyceride synthesis is commonly believed to be created through the conversion of dihydroxyacetone phosphate (DHAP) by glycerophosphate dehydrogenase (GPD) to sn-glycerol 3-phosphate (GP), which is then converted by glycerophosphate acyltransferase (GPAT) to 1-acyl-GP. Consistent with this, GPD and GPAT are highly induced during differentiation of mouse 3T3-L1 preadipocytes. While the acyl dihydroxyacetone phosphate (acyl-DHAP) pathway for glycerolipid synthesis is commonly believed to be involved only in glycerol ether lipid synthesis, we report here that during conversion of 3T3-L1 preadipocytes to adipocytes, the specific activity of peroxisomal DHAP acyltransferase (DHAPAT) is increased by 9-fold in 6 days, while acyl-DHAP:NADPH reductase is induced by 5-fold. A parallel increase in the catalase (the peroxisomal marker enzyme) activity is also seen. In contrast, the specific activity of alkyl-DHAP synthase, the enzyme catalyzing the synthesis of the ether bond, is decreased by 60% during the same period. Unlike microsomal GPAT, the induced DHAPAT is found to have high activity at pH 5.5 and is resistant to inhibition by sulfhydryl agents, heat, and proteolysis. On subcellular fractionation, DHAPAT is found to be associated with microperoxisomes whereas GPAT activity is mainly present in microsomes. Northern blot analyses reveal that induction of DHAPAT can be largely explained through increases in DHAPAT mRNA. A comparison of microsomal and peroxisomal glycerolipid synthetic pathways, using D-[3-(3)H, U-(14)C]glucose as the precursor of the lipid glycerol backbone shows that about 40-50% of triglyceride is synthesized via the acyl-DHAP pathway. These results indicate that the acyl-DHAP pathway is important not only for the synthesis of ether lipids, but also for the synthesis of triacylglycerol and other non-ether glycerolipids.
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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 | 0.001 |
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".