The Interleukin 1beta-Induced Expression of Human Prostaglandin F2alpha Receptor Messenger RNA in Human Myometrial-Derived ULTR Cells Requires the Transcription Factor, NFkappaB1
Bibliographic record
Abstract
The molecular mechanisms that regulate the expression of genes involved in parturition are poorly understood. The mRNA expression of the prostaglandin F(2alpha) receptor (PTGFR), a uterine activating gene, is increased at labor and is required for uterine contractile activity in numerous animal models, although the signaling pathways responsible for this increased expression have not been identified. Proinflammatory cytokines have been proposed to regulate the expression of the uterine activating genes via activation of the nuclear transcription factor, NFkappaB, and initiate labor. However, it is uncertain whether uterine PTGFR is regulated this way. In this report, we demonstrate for the first time that treatment of immortalized human myometrial-derived ULTR cells with the proinflammatory cytokine IL1beta causes an increase in PTGFR mRNA levels. Furthermore, IL1beta treatment increased the nuclear levels of the RELA subunit of NFkappaB and increased binding of RELA to the NFkappaB DNA-binding site. Inhibition of NFkappaB activation with either the proteasome inhibitor MG132 or phenethyl caffeiate reduced PTGFR mRNA levels, which indicates that this transcription factor is important for basal transcription. Furthermore, this inhibition prevented IL1beta induction ofPTGFRmRNA, which confirms that NFkappaB is required for the IL1beta-induced increase inPTGFR. These results are consistent with the proposal that proinflammatory cytokines directly regulate uterine activation genes and that the transcription factor NFkappaB is involved in both basal and IL1beta-stimulated transcription of the PTGFR gene.
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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".