c-Jun Homodimers Can Function as a Context-Specific Coactivator
Bibliographic record
Abstract
Transcription factors can function as DNA-binding-specific activators or as coactivators. c-Jun drives gene expression via binding to AP-1 sequences or as a cofactor for PU.1 in macrophages. c-Jun heterodimers bind AP-1 sequences with higher affinity than homodimers, but how c-Jun works as a coactivator is unknown. Here, we provide in vitro and in vivo evidence that c-Jun homodimers are recruited to the interleukin-1beta (IL-1beta) promoter in the absence of direct DNA binding via protein-protein interactions with DNA-anchored PU.1 and CCAAT/enhancer-binding protein beta (C/EBPbeta). Unexpectedly, the interaction interface with PU.1 and C/EBPbeta involves four of the residues within the basic domain of c-Jun that contact DNA, indicating that the capacities of c-Jun to function as a coactivator or as a DNA-bound transcription factor are mutually exclusive. Our observations indicate that the IL-1beta locus is occupied by PU.1 and C/EBPbeta and poised for expression and that c-Jun enhances transcription by facilitating a rate-limiting step, the assembly of the RNA polymerase II preinitiation complex, with minimal effect on the local chromatin status. We propose that the basic domain of other transcription factors may also be redirected from a DNA interaction mode to a protein-protein interaction mode and that this switch represents a novel mechanism regulating gene expression profiles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".