The Relative Contributions of DNA Strand Breaks, Base Damage and Clustered Lesions to the Loss of DNA Functionality Induced by Ionizing Radiation
Bibliographic record
Abstract
The majority of studies on lethal radiobiological damage have focused on double-strand breaks (DSBs), a type of clustered DNA damage and the evaluation of their toxicity, while other types of clustered DNA damage have received much less attention. The main purpose of this study is to evaluate the contribution of different lesions induced by ionizing radiation to the loss of plasmid DNA functionality. We employed a simple model system comprising E. coli transformed with an irradiated plasmid [pGEM-3Zf (-)] to determine the effect of DSBs and other lesions including base damage and clustered lesions on the functionality ("viability") of the plasmid. The yields of γ-radiation-induced single-strand breaks (SSBs) and DSBs were measured by gel electrophoresis. We found that the transformation efficiency decreases with radiation dose, but this decrease cannot be explained by the formation of DSBs. For example, at doses of 500 and 700 Gy, the relative transformation efficiency falls from 100% to 53% and 26%, respectively, while only 5.7% and 9.1% of the plasmids contain a DSB. In addition, it is also unlikely that randomly distributed base lesions could explain the loss of functionality of the plasmid, since cells can repair them efficiently. However, clustered lesions other than DSBs, which are difficult to repair and result in the loss of information on both DNA strands, have the potential to induce the loss of plasmid functionality. We therefore measured the yields of γ-radiation-induced base lesions and cluster damage, which are respectively converted into SSBs and DSBs by the base excision repair enzymes endonuclease III (Nth) and formamidopyrimidine-DNA glycosylase (Fpg). Our data demonstrate that the yield of cluster damage (i.e., lesions that yield DSBs following digestion) is 31 times higher than that of frank DSBs. This finding suggests that frank DSBs make a relatively minor contribution to the loss of DNA functionality induced by ionizing radiation, while other toxic lesions formed at a much higher frequencies than DSBs must be responsible for the loss of plasmid functionality. These lesions may be clustered lesions/locally multiply damaged sites (LMDS), including base damage, SSBs and/or intrastrand and interstrand crosslinks, leading to the loss of vital information in the DNA. Using a mathematical model, we estimate that at least three toxic lesions are required for the inactivation of plasmid functionality, in part because even these complex lesions can be repaired.
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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 | 0.003 | 0.002 |
| 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) | 0.000 | 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".