Histone Deacetylases Inhibit IFN-γ-Inducible Gene Expression in Mouse Trophoblast Cells
Bibliographic record
Abstract
Trophoblast cells are the first cells to differentiate from the developing mammalian embryo, and they subsequently form the blastocyst-derived component of the placenta. IFN-gamma plays critical roles in activating innate and adaptive immunity, as well as apoptosis. In mice, IFN-gamma is produced in the pregnant uterus, and is essential for formation of the decidual layer of the placenta and remodeling of the uterine vasculature. Responses of mouse trophoblast cells to IFN-gamma appear to be selective, for IFN-gamma activates MHC class I expression and enhances phagocytosis, but fails to activate either MHC class II expression or apoptosis in these cells. To investigate the molecular basis for the selective IFN-gamma responsiveness of mouse trophoblast cells, IFN-gamma-inducible gene expression was examined in the trophoblast cell lines SM9 and M-11, trophoblast stem cells, and trophoblast stem cell-derived giant cells. IFN-gamma-inducible expression of multiple genes, including IFN regulatory factor-1 (IRF-1), was significantly reduced in trophoblast cells compared with fibroblast cells. Decreased IRF-1 mRNA expression in trophoblast cells was due to a reduced rate of IRF-1 transcription relative to fibroblast cells. However, no impairment of STAT-1 tyrosine phosphorylation or DNA-binding capacity was observed in IFN-gamma-treated mouse trophoblast cells. Importantly, histone deacetylase (HDAC) inhibitors significantly enhanced IFN-gamma-inducible gene expression in trophoblast cells, but not fibroblasts. Our collective studies demonstrate that IFN-gamma-inducible gene expression is repressed in mouse trophoblast cells by HDACs. We propose that HDAC-mediated inhibition of IFN-gamma-inducible gene expression in mouse trophoblast cells may contribute to successful pregnancy by preventing activation of IFN-gamma responses that might otherwise facilitate the destruction of the placenta.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".