Diacylglycerols Containing Omega 3 and Omega 6 Fatty Acids Bind to RasGRP and Modulate MAP Kinase Activation
Bibliographic record
Abstract
We elucidated the effects of different diacylglycerols (DAGs), i.e. 1-stearoyl-2-arachidonoyl-sn-glycerol (SAG), 1-stearoyl-2-docosahexaenoyl-sn-glycerol (SDG), and 1-stearoyl-2-eicosapentaenoyl-sn-glycerol (SEG), on [3H]PDBu binding to RasGRP. The competition studies with these DAGs on [3H]PDBu binding to RasGRP revealed different Ki values for these DAG molecular species. Furthermore, we transfected human Jurkat T cells by a plasmid containing RasGRP and assessed the implication of endogenous DAGs on activation of MAP kinases ERK1/ERK2, induced by phorbol-12-myristate-13-acetate (PMA). In control cells, GF109203X, a protein kinase C inhibitor, inhibited ERK1/ERK2 activation. However, this agent curtailed but failed to completely diminish ERK1/ERK2 phosphorylation in RasGRP-overexpressing cells, though calphostin C, a DAG binding inhibitor, suppressed the phosphorylation of MAP kinases in these cells. In cells incubated with arachidonic acid (AA), docosahexaenoic acid (DHA), and eicosapentaenoic acid (EPA), PMA induced the production of endogenous DAGs containing these fatty acids, respectively: DAG-AA, DAG-DHA, and DAG-EPA. The inhibition of production of DAG-AA and DAG-DHA significantly inhibited MAP kinase activation in RasGRP overexpressing, but not in control, cells. Our study demonstrates that three DAG molecular species bind to RasGRP, but only DAG-AA and DAG-DHA participate in the modulation of RasGRP-mediated activation of MAP kinases in Jurkat T cells.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".