Retinoic acid modulates chromatin to potentiate tumor necrosis factor alpha signaling on the DIF2 promoter
Bibliographic record
Abstract
Transcriptional activation by nuclear hormone receptors is well characterized, but their cooperation with other signaling pathways to activate transcription remains poorly understood. Tumor necrosis factor alpha (TNFalpha) and all-trans retinoic acid (RA) induce monocytic differentiation of acute promyelocytic leukemia (APL) cells in a synergistic manner. We used the promoter of DIF2, a gene involved in monocytic differentiation, to model the mechanism underlying the cooperative induction of target genes by RA and TNFalpha. We show a functional RA response element in the DIF2 promoter, which is constitutively bound by PML/RARalpha in APL cells. RA stimulates release of corepressors and recruitment of chromatin modifying proteins and additional transcription factors to the promoter, but these changes cause only a modest induction of DIF2 mRNA. Co-stimulation with RA plus TNFalpha facilitates binding of NF-kappaB to the promoter, which is crucial for full induction of transcription. Furthermore, RA plus TNFalpha greatly enhanced the level of RNA Pol II phosphorylation on the DIF2 promoter, via synergistic recruitment of TFIIH. We propose that RA mediates remodeling of chromatin to facilitate binding of transcription factors, which cooperate to enhance Pol II phosphorylation, providing a mechanism whereby nuclear receptors interact with other signaling pathways on the level of transcription.
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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) | 0.003 | 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".