TGF-β Suppresses IFN-γ-STAT1-Dependent Gene Transcription by Enhancing STAT1-PIAS1 Interactions in Epithelia but Not Monocytes/Macrophages
Bibliographic record
Abstract
IFN-gamma and TGF-beta are important regulators of mucosal immunity, typically functioning in opposition to each other. In this study, we assessed whether TGF-beta could modulate IFN-gamma-induced STAT1 signaling. Model epithelial cell lines (HEp-2, HT-29, and T84) or monocytes/macrophages (THP-1 cell line, human blood mononuclear cells) were pretreated with TGF-beta (1 ng/ml; 5-60 min), followed by IFN-gamma exposure (20 ng/ml; 30 min), and then STAT1 transcriptional activity, DNA-binding activity, phosphorylation, and methylation were assessed. Some epithelia were transfected with an expression plasmid encoding SMAD7 to block TGF-beta-SMAD signaling. Epithelia, but not macrophages, pretreated with TGF-beta were hyporesponsive to IFN-gamma stimulation as indicated by reduced expression of four STAT1-regulated genes and reduced STAT1 DNA binding on EMSA. However, STAT1 Tyr(701)-, Ser(727) phosphorylation, and nuclear recruitment of STAT1 were not significantly different in IFN-gamma with or without TGF-beta-treated cells, indicating that the effects of TGF-beta are downstream of IFN-gammaR-JAK-STAT1 interaction. The TGF-beta effect was not dependent on ERK1/2, p38, or JNK activation but was prevented by overexpression of the inhibitory SMAD7 protein. Additional studies suggest that TGF-beta blockade of IFN-gamma activity in epithelia is via enhanced sequestering of STAT1 by pre-existing protein inhibitor of activated STAT1. These results demonstrate that TGF-beta rapidly suppresses IFN-gamma-driven STAT1 signaling by reducing DNA binding via promotion of STAT1-protein inhibitor of activated STAT1 interactions and not inhibition of STAT1 activation; an event that may be specific to epithelia and represent a novel mode of action of TGF-beta.
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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 | 0.001 |
| 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".