Transcription Promotes the Interaction of the FAcilitates Chromatin Transactions (FACT) Complex with Nucleosomes in <i>Saccharomyces cerevisiae</i>
Bibliographic record
Abstract
Abstract FACT (FAcilitates Chromatin Transactions) is an abundant and conserved complex that is essential for cell viability. FACT binds to highly expressed genes and facilitates transcription while maintaining chromatin structure, but how it is targeted... The FACT (FAcilitates Chromatin Transactions) complex is a conserved complex that maintains chromatin structure on transcriptionally active genes. Consistent with this, FACT is enriched on highly expressed genes, but how it is targeted to these regions is unknown. In vitro, FACT binds destabilized nucleosomes, supporting the hypothesis that FACT is targeted to transcribed chromatin through recognition of RNA polymerase (RNAP)-disrupted nucleosomes. In this study, we used high-resolution analysis of FACT occupancy in Saccharomyces cerevisiae to test this hypothesis. We demonstrate that FACT interacts with nucleosomes in vivo and that its interaction with chromatin is dependent on transcription by any of the three RNAPs. Deep sequencing of micrococcal nuclease-resistant fragments shows that FACT-bound nucleosomes exhibit differing nuclease sensitivity compared to bulk chromatin, consistent with a modified nucleosome structure being the preferred ligand for this complex. Interestingly, a subset of FACT-bound nucleosomes may be “overlapping dinucleosomes,” in which one histone octamer invades the ∼147-bp territory normally occupied by the adjacent nucleosome. While the differing nuclease sensitivity of FACT-bound nucleosomes could also be explained by the demonstrated ability of FACT to alter nucleosome structure, transcription inhibition restores nuclease resistance, suggesting that it is not due to FACT interaction alone. Collectively, these results are consistent with a model in which FACT is targeted to transcribed genes through preferential interaction with RNAP-disrupted nucleosomes.
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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 | 0.001 | 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".