Progesterone regulates HLA-G gene expression through a novel progesterone response element
Bibliographic record
Abstract
BACKGROUND: We have previously demonstrated that progesterone has a stimulatory effect on HLA-G gene expression. Because this effect was abolished by the anti-progestin, RU486, we hypothesize that this effect is through receptor-mediated up-regulation of the HLA-G gene. The objective of this study was to explore the molecular mechanisms of this effect. METHODS: The transient transfection of a chloramphenicol acetyltransferase (CAT) construct containing a fragment of the HLA-G gene promoter into the JEG-3 choriocarcinoma cell line was performed. An electrophoretic mobility shift assay (EMSA) and a DNA fragment-binding enzyme-linked immunosorbent assay (ELISA) were carried out to locate a specific progesterone response element (PRE) in the HLA-G gene promoter region. RESULTS: Progesterone treatment of JEG-3 cells transfected with the HLA-G gene promoter-CAT construct resulted in an increase of CAT synthesis, whereas RU486 blocked this transcriptional activation. A novel PRE-binding site sequence, with 60% homology to that of wild-type mouse mammary tumour virus (MMTV) PRE, was discovered in this region. CONCLUSION: The effect of progesterone on HLA-G gene expression is through progesterone receptor (PR) activation, followed by binding to a novel PRE in the HLA-G promoter region. Therefore, one of the mechanisms of immunomodulation by progesterone during pregnancy may be through the regulation of HLA-G gene expression via this novel PRE.
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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".