Contrasting the incorporation of glycerol into lipids caused by the presence of two isoforms of diacylglycerol kinase (605.20)
Bibliographic record
Abstract
The mammalian isoforms of diacylglycerol kinase (DGK) include the epsilon isoform (DGKε) and the delta isoform (DGKδ). DGKε has specificity for substrates with an arachidonoyl group, while DGKδ shows no acyl chain specificity for the substrate, but it promotes fatty acid synthesis resulting in an increased fraction of lipids with shorter and less unsaturated acyl chains. We have compared the effects of knocking out each of these isoforms individually using SV40 transformed mouse embryo fibroblasts (MEFs). We compared wild‐type (WT) MEFs with those from knock‐out (KO) mice. We find that DGKδ‐KO MEFs have lower incorporation of 3 H‐glycerol into lipids compared with their WT counterparts. This is consistent with our recent demonstration (Biochemistry (2013) 52 , 7766) that the depletion of this DGK isoform results in less fatty acid synthesis. This change is not caused by a decreased expression of glycerol kinase in the KO cells. In contrast, the DGKε‐KO MEFs show greater incorporation of 3 H‐glycerol into lipids compared with their WT counterparts, with no change in the relative amounts of various lipids between the DGKε‐KO and WT MEFs. This result is explained by our observation that glycerol kinase is more highly expressed in the DGKε‐KO cells than in their WT counterparts.
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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 | 0.000 |
| 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".