Diacylglycerol Kinase Delta Promotes Lipogenesis
Bibliographic record
Abstract
We have studied the relationship between diacylglycerol kinase delta (DGKδ) and lipogenesis. There is a marked increase in the expression of DGKδ during the differentiation of 3T3-L1 cells to adipocytes, as well as in the synthesis of neutral and polar lipids. When 3T3-L1 undifferentiated fibroblasts are transfected to express DGKδ, there is increased triglyceride synthesis without differentiation to adipocytes. Hence, expression of DGKδ promotes lipogenesis. Lipid synthesis is decreased in DGKδ knockout mouse embryo fibroblasts, especially for lipids with shorter acyl chains and limited unsaturation. This reduction occurs for both neutral and polar lipids. These findings suggest reduced de novo lipid synthesis. This is confirmed by measuring the incorporation of glycerol into polar and neutral lipids, which is higher in the wild type cells than in the DGKδ knockouts. In comparison, there was no change in lipid synthesis in DGKε knockout mouse embryo fibroblasts. We also demonstrate that the DGKδ knockout cells had a lower expression of acetyl-CoA carboxylase and fatty acid synthase as well as a lower degree of activation by phosphorylation of ATP citrate lyase. These three enzymes are involved in the synthesis of long chain fatty acids. Our results demonstrate that DGKδ markedly increases lipid synthesis, at least in part as a result of promoting the de novo synthesis of fatty acids.
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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 | 0.000 |
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".