DGKα, Bridging Membrane Shape Changes with Specific Molecular Species of DAG/PA: Implications in Cancer and Immunosurveillance
Bibliographic record
Abstract
Cancer immunotherapy has revolutionized the oncology field. Despite the success, new molecular targets are needed to increase the percentage of patients that benefits from this therapy. Diacylglycerol kinase α (DGKα) has gathered great attention as a potential molecular target in immunotherapy because of its role in cancer proliferation and immunosuppression. DGKα catalyzes the ATP-dependent phosphorylation of diacylglycerol (DAG) to produce phosphatidic acid (PA). Since both lipids are potent signaling messengers, DGKα acts as a switch between different signaling pathways. Its role in cancer and immunosuppression has long been ascribed to the regulation of DAG/PA levels. However, this paradigm has been challenged with the identification of DGKα substrate acyl chain specificity, which suggests its role in signaling could be specific to DAG/PA molecular species. In several biological processes where DGKα plays a role, large membrane morphological changes take place. DGKα substrate specificity depends on the shape of the membrane that the enzyme binds to. Hence, DGKα can act as a bridge between large membrane morphological changes and the regulation of specific molecular species of DAG/PA. Bearing in mind the potential therapeutic benefits of targeting DGKα, here, the role of DGKα in cancer and T cell biology with a focus on the modulation of its enzymatic properties by membrane shape is reviewed. The goal is to contribute to a global understanding of the molecular mechanisms governing DGKα biology. This will pave the way for future experimentation and, consequently, the design of better, more potent therapeutic strategies aiming at improving the health outcomes of cancer patients.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".