Attenuation of DNA damage‐induced p53 expression by arsenic: A possible mechanism for arsenic co‐carcinogenesis
Bibliographic record
Abstract
Inhibition of DNA repair processes has been suggested as one predominant mechanism in arsenic co-genotoxicity. However, the underlying mode of action responsible for DNA repair inhibition by arsenic remains elusive. To further elucidate the mechanism of repair inhibition by arsenic, we examined the effect of trivalent inorganic and methylated arsenic metabolites on the repair of benzo(a)pyrene diol epoxide (BPDE)-DNA adducts in normal human primary fibroblasts and their effect on repair-related protein expression. We observed that monomethylarsonous acid (MMA(III)) was the most potent inhibitor of the DNA repair. MMA(III) did not change the expression levels of some key repair proteins involved upstream of the dual incision in the global nucleotide excision repair (NER) pathway, including p48, XPC, xeroderma pigmentosum complementation group A (XPA), and p62-TFIIH. However, it led to a marked impairment of p53 induction in response to BPDE treatment. The abrogated p53 expression translated into reduced p53 DNA-binding activity, suggesting a possibility of downregulating downstream repair genes by p53. A p53-null cell line failed to exhibit the inhibitory effect of MMA(III) on NER, implicating a role for p53 in the NER inhibition by MMA(III). Further investigation revealed that MMA(III) dramatically inhibited p53 phosphorylation at serine 15, implying that MMA(III) destabilized p53 by inhibiting its phosphorylation. Because p53 is required for proficient global NER, our data suggest that arsenic inhibits NER through suppressing p53 induction in response to DNA damage in cells with normal p53 gene expression.
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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) | 0.000 | 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) | 0.001 | 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".