The interleukin 6 <i>trans</i>-signaling increases prostaglandin E2 production in human granulosa cells
Bibliographic record
Abstract
As a potent autocrine regulator, the proinflammatory cytokine interleukin 6 (IL6) is expressed in granulosa cells and is involved in the modulation of various follicular functions, including follicular development and ovulation. At present, the detailed molecular mechanisms by which IL6 regulates the event of ovulation remain to be elucidated. In the present study, primary and immortalized (SVOG) human granulosa-lutein (hGL) cells were used to investigate the effects of IL6 on the expression of prostaglandin-endoperoxide synthase 2 (PTGS2) and the subsequent synthesis of prostaglandin E2 (PGE2) and to investigate the underlying molecular mechanisms. We found that instead of classic signaling, IL6/soluble form of the IL6 receptor (sIL-6Ralpha) trans-signaling induced the expression of PTGS2 and production of PGE2 in both SVOG cells and primary hGL cells. Moreover, IL6/sIL-6Ralpha activated the phosphorylation of Janus-activated kinase 2 (JAK2) and signal transducer and activator of transcription 3 (STAT3), which in turn induced STAT3 nuclear translocation. In addition, these effects were suppressed by the addition of inhibitors (AG490 for JAK2 and C188-9 for STAT3) and by the small interfering RNA-mediated knockdown of STAT3. In addition, suppressor of cytokine signaling 3 (SOCS3) acts as a negative-feedback regulator in IL6/sIL-6Ralpha-induced cellular activities, including the activation and nuclear translocation of STAT3, upregulation of PTGS2 expression, and increase in PGE2 production in SVOG cells. In conclusion, IL6 trans-signaling upregulates the expression of PTGS2 and increases the production of PGE2 via the JAK2/STAT3/SOCS3 signaling pathway in hGL cells. Our findings provide insights into the molecular mechanisms by which IL6 trans-signaling may potentially modulate the event of ovulation in human ovaries.
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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) | 0.002 | 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".