Membrane morphology determines diacylglycerol kinase α substrate acyl chain specificity
Bibliographic record
Abstract
Diacylglycerol kinases catalyze the ATP-dependent phosphorylation of diacylglycerol (DAG) to produce phosphatidic acid (PA). In humans, the alpha isoform (DGKα) has emerged as a potential target in the treatment of cancer due to its anti-tumor and pro-immune responses. However, its mechanism of action at a molecular level is not fully understood. In this work, a systematic investigation of the role played by the membrane in the regulation of the enzymatic properties of human DGKα is presented. By using a cell-free system with purified DGKα and model membranes of variable physical and chemical properties, it is shown that membrane physical properties determine human DGKα substrate acyl chain specificity. In model membranes with a flat morphology; DGKα presents high enzymatic activity, but it is not able to differentiate DAG molecular species. Furthermore, DGKα enzymatic properties are insensitive to membrane intrinsic curvature. However, in the presence of model membranes with altered morphology, specifically the presence of physically curved membrane structures, DGKα bears substrate acyl chain specificity for palmitic acid-containing DAG. The present results identify changes in membrane morphology as one possible mechanism for the depletion of specific pools of DAG as well as the production of specific pools of PA by DGKα, adding an extra layer of regulation on the interconversion of these two potent lipid-signaling molecules. It is proposed that the interplay between membrane physical (shape) and chemical (lipid composition) properties guarantee a fine-tuned signal transduction system dependent on the levels and molecular species of DAG and PA.
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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".