The α Isoform of Diacylglycerol Kinase Exhibits Arachidonoyl Specificity with Alkylacylglycerol
Bibliographic record
Abstract
We compared the diacylglycerol kinase (DGK) catalyzed phosphorylation of 1-O-hexanoyl-2-oleoylglycerol (HOG) with 1-O-hexanoyl-2-arachidonoylglycerol (HAG). We assayed the activity of DGKalpha and DGKzeta using a liposomal-based assay system. Liposomal assays show that the DGKalpha and, to a lesser extent, DGKzeta preferentially act on substrates containing an arachidonoyl group when this group is incorporated into alkylacylglycerols. The activity of DGKalpha was 82 times greater with HAG compared to HOG. DGKzeta is 10 times more active in catalyzing the phosphorylation of HAG compared to HOG. Although diacylglycerols were better substrates for both DGKalpha and DGKzeta than the alkylacylglycerols, no specificity was exhibited for arachidonoyl-containing diacylglycerols. However, this specificity for HAG over HOG is modulated by the phospholipid composition of the liposome. Addition of cholesterol and/or phosphatidylethanolamine partially reduces the substrate selectivity. We also analyzed the kinetic constants for the phosphorylation of both diacylglycerol and 1-alkyl-2-acylglycerol catalyzed by the alpha, epsilon, or zeta isoforms using a soluble Triton mixed micelle system. We found that all three isoforms of DGK can phosphorylate 1-alkyl-2-acylglycerols but generally at a lower rate than for the corresponding diacylglycerol. The specificity of DGKepsilon for diacylglycerols containing an arachidonoyl group was retained when the ester group in the C-1 position is replaced with an ether linkage. In contrast, DGKalpha and, to a lesser extent, DGKzeta had greater specificity for arachidonoyl-containing 1-alkyl-2-acylglycerols than for arachidonoyl-containing diacylglycerols. This demonstrates that the acyl chain specificity is affected by the structure of the lipid headgroup.
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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".