A Possible Role of Ku in Mediating Sequential Repair of Closely Opposed Lesions
Bibliographic record
Abstract
One of the hallmarks of ionizing radiation exposure is the formation of clustered damage that includes closely opposed lesions. We show that the Ku70/80 complex (Ku) has a role in the repair of closely opposed lesions in DNA. DNA containing a dihydrouracil (DHU) close to an opposing single strand break was used as a model substrate. It was found that Ku has no effect on the enzymatic activity of human endonuclease III when the substrate DNA contains only DHU. However, with DNA containing a DHU that is closely opposed to a single strand break, Ku inhibited the nicking activity of human endonuclease III as well as the amount of free double strand breaks induced by the enzyme. The inhibition on the formation of a free double strand break by Ku was found to be much greater than the inhibition of human endonuclease III-nicking activity by Ku. Furthermore, there was a concomitant increase in the formation of Ku-DNA complexes when endonuclease III was present. Similar results were also observed with Escherichia coli endonuclease III. These results suggest that Ku reduces the formation of endonuclease III-induced free double strand breaks by sequestering the double strand breaks formed as a Ku-DNA complex. In doing so, Ku helps to avoid the formation of the intermediary free double strand breaks, possibly helping to reduce the mutagenic event that might result from the misjoining of frank double strand breaks.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".