Heat shock transcription factors demonstrate a distinct mode of interaction with mitotic chromosomes
Bibliographic record
Abstract
A large number of transcription factors have been shown to bind and interact with mitotic chromosomes, which may promote the efficient reactivation of transcriptional programs following cell division. Although the DNA-binding domain (DBD) contributes strongly to TF behavior, the mitotic behaviors of TFs from the same DBD family may vary. To define the mechanisms governing TF behavior during mitosis in mouse embryonic stem cells, we examined two related TFs: Heat Shock Factor 1 and 2 (HSF1 and HSF2). We found that HSF2 maintains site-specific binding genome-wide during mitosis, whereas HSF1 binding is somewhat decreased. Surprisingly, live-cell imaging shows that both factors appear excluded from mitotic chromosomes to the same degree, and are similarly more dynamic in mitosis than in interphase. Exclusion from mitotic DNA is not due to extrinsic factors like nuclear import and export mechanisms. Rather, we found that the HSF DBDs can coat mitotic chromosomes, and that HSF2 DBD is able to establish site-specific binding. These data further confirm that site-specific binding and chromosome coating are independent properties, and that for some TFs, mitotic behavior is largely determined by the non-DBD regions.
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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) | 0.000 | 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".