Chromatin-specific regulation of LEF-1–β-catenin transcription activation and inhibition in vitro
Bibliographic record
Abstract
Transcriptional activation of Wnt/Wg-responsive genes requires the stabilization and nuclear accumulation of beta-catenin, a dedicated coactivator of LEF/TCF enhancer-binding proteins. Here we report that recombinant beta-catenin strongly enhances binding and transactivation by LEF-1 on chromatin templates in vitro. Interestingly, different LEF-1 isoforms vary in their ability to bind nucleosomal templates in the absence of beta-catenin, owing to N-terminal residues that repress binding to chromatin, but not nonchromatin, templates. Transcriptional activation in vitro requires both the armadillo (ARM) repeats and the C terminus of beta-catenin, whereas the phosphorylated N terminus is inhibitory to transcription. A fragment spanning the C terminus (CT) and ARM repeats 11 and 12 (CT-ARM), but not the CT alone, functions as a dominant negative inhibitor of LEF-1-beta-cat activity in vitro and can block ATP-dependent binding of the complex to chromatin. LEF-1-beta-cat transactivation in vitro was also repressed by inhibitor of beta-catenin and Tcf-4 (ICAT), a physiological inhibitor of Wnt/Wg signaling that interacts with ARM repeats 11 and 12, and by the nonsteroidal anti-inflammatory compound, sulindac. None of these transcription inhibitors (CT-ARM, ICAT, or sulindac) could disrupt the LEF-1-beta-cat complex after it was stably bound to chromatin. We conclude that the CT-ARM region of beta-catenin functions as a chromatin-specific activation domain, and that several inhibitors of the Wnt/Wg pathway directly modulate LEF-1-beta-cat activity on chromatin.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".