Homologous Recombination Initiated by Benzene Metabolites: A Potential Role of Oxidative Stress
Bibliographic record
Abstract
Benzene is a ubiquitous pollutant and known human leukemogen. Benzene can be enzymatically bioactivated to reactive intermediates that can lead to increased formation of reactive oxygen species (ROS). ROS formation can directly induce DNA double-strand breaks, and also oxidize nucleotides that are subsequently converted to double-strand breaks during DNA replication that can be repaired through homologous recombination, which is not error-free. Therefore increased DNA double-strand-break levels may induce hyper-recombination, which can lead to deleterious genetic changes. To test the hypothesis that benzene and its metabolites can initiate hyper-recombination and to investigate the potential role of ROS, a Chinese hamster ovary (CHO) cell line containing a neo direct repeat recombination substrate (CHO 3-6), was used to determine whether benzene or its metabolites phenol, hydroquinone, catechol, or benzoquinone initiated increased homologous recombination and whether this increase could be diminished by the coincubation of cells with the antioxidative enzyme catalase. Results demonstrated that cells exposed to benzene (1, 10, 30, or 100 micro M) for 24 h did not exhibit increased homologous recombination. Increased recombination occurred with exposure to phenol (1.8-, 2.6-, or 2.9-fold), catechol (1.9-, 2-, 5-, or 3.2-fold), or benzoquinone (2.7-, 5.5-, or 6.9-fold) at 1, 10, and 30- micro M concentrations, respectively, and with exposure to hydroquinone at 10 and 30 micro M concentrations (1.5-1.9-fold; p < 0.05). Studies investigating the effects of catalase demonstrated that increased homologous recombination due to exposure to phenol, hydroquinone, catechol, or benzoquinone (10 micro M) could be completely abolished by the addition of catalase. These data support the hypothesis that increased homologous recombination mediates benzene-initiated toxicity and supports a role for oxidative stress in this mechanism.
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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 | 0.000 | 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".