Diacylglycerol Kinase ϵ Is Selective for Both Acyl Chains of Phosphatidic Acid or Diacylglycerol
Bibliographic record
Abstract
The phosphatidylinositol (PI) cycle mediates many cellular events by controlling the metabolism of many lipid second messengers. Diacylglycerol kinase epsilon (DGK epsilon) has an important role in this cycle. DGK epsilon is the only DGK isoform to show inhibition by its product phosphatidic acid (PA) as well as substrate specificity for sn-2 arachidonoyl-diacylglycerol (DAG). Here, we show that this inhibition and substrate specificity are both determined by selectivity for a combination of the sn-1 and sn-2 acyl chains of PA or DAG, respectively, preferring the most prevalent acyl chain composition of lipids involved specifically in the PI cycle, 1-stearoyl-2-arachidonoyl. Although the difference in rate for closely related lipid species is small, there is a significant enrichment of 1-stearoyl-2-arachidonoyl PI because of the cyclical nature of PI turnover. We also show that the inhibition of DGK epsilon by PA is competitive and that the deletion of the hydrophobic segment and cationic cluster of DGK epsilon does not affect its selectivity for the acyl chains of PA or DAG. Thus, this active site not only recognizes the lipid headgroup but also a combination of the two acyl chains in PA or DAG. We propose a mechanism of DGK epsilon regulation where its dual acyl chain selectivity is used to negatively regulate its enzymatic activity in a manner that ensures DGK epsilon remains committed to the PI turnover cycle. This novel mechanism of enzyme regulation within a signaling pathway could serve as a template for the regulation of enzymes in other pathways in the cell.
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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 | 0.001 |
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".