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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".