Temporal Production of the Signaling Lipid Phosphatidic Acid by Phospholipase D2 Determines the Output of Extracellular Signal-Regulated Kinase Signaling in Cancer Cells
Bibliographic record
Abstract
The Ras-extracellular signal-regulated kinase (ERK) cascade is an important signaling module in cells. One regulator of the Ras-ERK cascade is phosphatidic acid (PA) generated by phospholipase D (PLD) and diacylglycerol kinase (DGK). Using a newly developed PA biosensor, PASS (phosphatidic acid biosensor with superior sensitivity), we found that PA was generated sequentially by PLD and DGK in epidermal growth factor (EGF)-stimulated HCC1806 breast cancer cells. Inhibition of PLD2, one of the two PLD members, was sufficient to eliminate most of the PA production, whereas inhibition of DGK decreased PA production only at the later stages of EGF stimulation, suggesting that PLD2 precedes DGK activation. The temporal production of PA by PLD2 is important for the nuclear activation of ERK. While inhibition of both PLD and DGK had no effect on the overall ERK activity, inhibition of PLD2 but not PLD1 or DGK blocked the nuclear ERK activity in several cancer cell lines. The decrease of active ERK in the nucleus inhibited the activation of Elk1, c-fos, and Fra1, the ERK nuclear targets, leading to decreased proliferation of HCC1806 cells. Together, these findings reveal that PA production by PLD2 determines the output of ERK in cancer cell growth factor signaling.
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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".