Dramatic Differences in the Roles in Lipid Metabolism of Two Isoforms of Diacylglycerol Kinase
Bibliographic record
Abstract
Lipid species changes for SV40-transformed fibroblasts from wild-type or from diacylglycerol kinase-epsilon (DGKepsilon) or diacylglycerol kinase-alpha (DGKalpha) knockout mice were determined for glycerophospholipids, polyphosphatidylinositides (GPInsP n ) and diacylglycerol (DAG) using direct infusion mass spectrometry. Dramatic differences in arachidonate (20:4 fatty acid)-containing lipids were observed for multiple classes of glycerophospholipids and polyphosphatidylinositides between wild-type and DGKepsilon knockout cells. However, no difference was observed in either the amount or the acyl chain composition of DAG between DGKepsilon knockout and wild-type cells, suggesting that DGKepsilon catalyzed the phosphorylation of a minor fraction of the DAG in these cells. The differences in arachidonate content between the two cell lines were greatest for the GPInsP n lipids and lowest for DAG. These findings indicate that DGKepsilon plays a significant role in determining the enrichment of GPInsP n with 20:4 and that there is a pathway for the selective translocation of arachidonoyl phosphatidic acid from the plasma membrane to the endoplasmic reticulum. In contrast, no substantial difference was observed in the acyl chain composition of any class of glycerophospholipid or diacylglycerol between lipid extracts from fibroblasts from wild-type mice or from DGKalpha knockout mice. However, the cells from the DGKalpha knockout mice had a higher concentration of DAG, consistent with the lack of downregulation of the major fraction of DAG by DGKalpha, in contrast with DGKepsilon that is primarily responsible for enrichment of GPInsP n with arachidonoyl acyl chains.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".