ERK Modulates DNA Bending and Enhancesome Structure by Phosphorylating HMG1-Boxes 1 and 2 of the RNA Polymerase I Transcription Factor UBF
Bibliographic record
Abstract
Transcription of the ribosomal RNA genes of mammals by RNA polymerase I is rapidly activated by epidermal growth factor via the MAP-kinase (ERK) signaling cascade. This activation is mediated by direct phosphorylation of the HMG box DNA binding domains of the architectural transcription factor UBF. Mutation of the ERK sites of UBF inhibits its normal function and blocks growth factor activation of ribosomal transcription. UBF has little or no DNA sequence selectivity and binds throughout the ribosomal genes, defining a specialized chromatin. Indeed, the HMG boxes of UBF induce looping of the ribosomal DNA to create the enhancesome, a structure somewhat reminiscent of the nucleosome. Here, we show that both ERK phosphorylation and mutations that simulate this phosphorylation decrease the affinity of the individual HMG boxes of UBF for linear ribosomal DNA but have little or no effect on the capacity of these HMG boxes to bind to pre-bent DNA and do not affect the overall binding constant of UBF for the DNA. Electron spectroscopic imaging showed that ERK site UBF mutants do not induce the characteristic DNA looping of the enhancesome and associate with no more than half of the enhancesomal DNA. The data demonstrate that ERK phosphorylation of UBF prevents DNA bending by its first two HMG boxes, leading to a cooperative unfolding of the enhancesome.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".