A dynamic role for transcription factors in restoring transcription through mitosis
Bibliographic record
Abstract
Mitosis involves intricate steps, such as DNA condensation, nuclear membrane disassembly, and phosphorylation cascades that temporarily halt gene transcription. Despite this disruption, daughter cells remarkably retain the parent cell's gene expression pattern, allowing for efficient transcriptional memory after division. Early studies in mammalian cells suggested that transcription factors (TFs) mark genes for swift reactivation, a phenomenon termed 'mitotic bookmarking', but conflicting data emerged regarding TF presence on mitotic chromosomes. Recent advancements in live-cell imaging and fixation-free genomics challenge the conventional belief in universal formaldehyde fixation, revealing dynamic TF interactions during mitosis. Here, we review recent studies that provide examples of at least four modes of TF-DNA interaction during mitosis and the molecular mechanisms that govern these interactions. Additionally, we explore the impact of these interactions on transcription initiation post-mitosis. Taken together, these recent studies call for a paradigm shift toward a dynamic model of TF behavior during mitosis, underscoring the need for incorporating dynamics in mechanistic models for re-establishing transcription post-mitosis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".