Adenosine Triphosphate Activates Mitogen-Activated Protein Kinase in Human Granulosa-Luteal Cells*
Bibliographic record
Abstract
ATP has been shown to activate the phospholipase C/diacylglycerol/protein kinase C (PKC) pathway. However, little is known about the downstream signaling events. The present study was designed to examine the effect of ATP on activation of the mitogen-activated protein kinase (MAPK) signaling pathway and its physiological role in human granulosa-luteal cells. Western blot analysis, using a monoclonal antibody that detected the phosphorylated forms of extracellular signal-regulated kinase-1 and -2 (p42(mapk) and p44 (mapk), respectively), demonstrated that ATP activated MAPK in a dose- and time-dependent manner. Treatment of the cells with suramin (a P2 purinoceptor antagonist), neomycin (a phospholipase C inhibitor), staurosporin (a PKC inhibitor), or PD98059 (an MAPK/ERK kinase inhibitor) significantly attenuated the ATP-induced activation of MAPK. In contrast, ATP-induced MAPK activation was not significantly affected by pertussis toxin (a G(i) inhibitor). To examine the role of G(s) protein, the intracellular cAMP level was determined after treatment with ATP or hCG. No significant elevation of intracellular cAMP was noted after ATP treatment. To determine the role of MAPK in steroidogenesis, human granulosa-luteal cells were treated with ATP, hCG, or ATP plus hCG in the presence or absence of PD98059. RIA revealed that ATP alone did not significantly affect the basal progesterone concentration. However, hCG-induced progesterone production was reduced by ATP treatment. PD98059 reversed the inhibitory effect of ATP on hCG-induced progesterone production. To our knowledge, this is the first demonstration of ATP-induced activation of the MAPK signaling pathway in the human ovary. These results support the idea that the MAPK signaling pathway is involved in mediating ATP actions in the human ovary.
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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 | 0.001 |
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".