Unifying concept of DNA repair: The polymerase scanning hypothesis
Bibliographic record
Abstract
According to a series of experiments on untransformed mouse embryonic fibroblasts, quiescent mouse cells lack global genomic repair (GGR) of premutagenic DNA damage. The gene used to assess mutation and premutagenic DNA damage was the lacI transgene incorporated permanently in the DNA in a lambda shuttle vector. The transgene lacks mammalian transcription signals and thus is unexpressed in the cells. Although the cells conducted transcription-coupled repair (TCR) of UV damage, the transgene was not repaired over a 4-day interval. These cells are not terminally differentiated and can readily be induced to resume cellular division. In this article, we discuss the interpretation of these results and suggest a new hypothesis for DNA scanning, the mechanism by which cells discover DNA damage and initiate DNA repair. Our hypothesis, which we call the polymerase scanning hypothesis, is that GGR is initiated in very much the same way as TCR, by a polymerase complex encountering the damage. We call the two together polymerase-coupled repair (PC repair). In the case of GGR, it would be the DNA replication complex during the S-phase. This is, we suggest, the dominant mechanism of repair of DNA at low doses for untranscribed genes. Evidence contrary to this hypothesis exists, which we discuss, but it should be noted that existing hypotheses about DNA scanning and DNA repair cannot account for the results that we have obtained.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| 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".