The Efficiency of Global Genome‐Nucleotide Excision Repair is Linked to the Fraction of Open rRNA Gene Chromatin, in Yeast<sup>†</sup>
Bibliographic record
Abstract
The yeast rDNA locus is a suitable model to study nucleotide excision repair (NER) in chromatin. A portion of rRNA genes is transcribed and largely depleted of nucleosomes, the remaining genes are not transcribed and folded in nucleosomes. In G1-arrested cells, most rRNA genes do not have nucleosomes. TC-NER removes UV-induced DNA lesions from the transcribed strand of active genes. GG-NER is less efficient and removes DNA lesions from the nontranscribed strand of active genes and from the inactive genome. Different from mammalian cells, in yeast, the rRNA gene-transcribed strand is repaired by RNA polymerase-I-dependent TC-NER. The opposite nontranscribed strand is repaired faster than both strands of inactive rRNA genes. In log-phase cells, RNA polymerase-I are dislodged from the damaged transcribed strand and partially replaced by nucleosomes. Contrary to log-phase cells, in G1-phase cells few, if any, histones are deposited on the open rRNA genes during NER. In this study, we compared GG-NER efficiency in the rRNA gene coding region: without nucleosomes, partially loaded or wholly loaded with nucleosomes. The results indicate that in log-phase cells histones obstruct GG-NER, whereas in G1-phase cells GG-NER is as efficient as TC-NER.
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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".