Identification and Characterization of the Protein-associated Splicing Factor as a Negative Co-regulator of the Progesterone Receptor
Bibliographic record
Abstract
Progesterone is essential in all species for the maintenance of pregnancy, and its withdrawal is required to activate the myometrium and to initiate labor. However, unlike most other species, progesterone levels do not fall at term in humans, raising the paradox as to how labor can occur under the continued influence of progesterone. We hypothesized that an endogenous (myometrial) repressor of the progesterone receptor (PR) could induce a functional withdrawal of progesterone and hence lead to the initiation of labor. We used the human PR as bait in a protein pull-down assay and identified polypyrimidine tract-binding protein-associated splicing factor (PSF) as a PR-interacting protein. PSF functions as a potent inhibitor of PR (but not estrogen receptor) transcriptional activity in mammalian cells. It acts through two novel mechanisms, inducing degradation of the PR through the proteasomal pathway and also interfering with binding of PR to its DNA response element. Importantly, in vivo studies in rats demonstrated a dramatic increase in myometrial PSF expression at term that was temporally associated with reduced levels of the myometrial PR. Accordingly, we propose that PSF acts as a PR corepressor and contributes to the functional withdrawal of progesterone and the initiation of human labor.
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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".