Novel insights into RNA polymerase II transcription regulation: transcription factors, phase separation, and their roles in cardiovascular diseases
Bibliographic record
Abstract
Transcription factors (TFs) are specialized proteins that bind DNA in a sequence-specific manner and modulate RNA polymerase II (Pol II) in multiple steps of the transcription process. Phase separation is a spontaneous or driven process that can form membrane-less organelles called condensates. By creating different liquid phases at active transcription sites, the formation of transcription condensates can reduce the water content of the condensate and lower the dielectric constant in biological systems, which in turn alters the structure and function of proteins and nucleic acids in the condensate. In RNA Pol II transcription, phase separation formation shortens the time at which TFs bind to target DNA sites and promotes transcriptional bursting. RNA Pol II transcription is engaged in developing several diseases, such as cardiovascular disease, by regulating different TFs and mediating the occurrence of phase separation. This review aims to summarize the advances in the molecular mechanisms of RNA Pol II transcriptional regulation, in particular the effect of TFs and phase separation. The role of RNA Pol II transcriptional regulation in cardiovascular disease will be elucidated, providing potential therapeutic targets for the management and treatment of cardiovascular disease.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".