Carcinogenic metals and the epigenome: understanding the effect of nickel, arsenic, and chromium
Bibliographic record
Abstract
Carcinogenic metals, such as nickel, arsenic, and chromium, are widespread environmental and occupational pollutants. Chronic exposure to these metals has been connected with increased risks of numerous cancers and as well as non-carcinogenic health outcomes, including cardiovascular disease, neurologic deficits, neuro-developmental deficits in childhood, and hypertension. However, currently the specific molecular targets for metal toxicity and carcinogenicity are not fully understood. Here, we propose that the iron- and 2-oxoglutarate-dependent dioxygenase family enzymes, as well as, other histone modifying enzymes are important intracellular targets that mediate the toxicity and carcinogenicity of nickel, and maybe potential targets in chromium and arsenic induced carcinogenesis. Our data demonstrate that all three metals are capable of inducing post-translational histone modifications and affecting the enzymes that modulate them (i.e. the iron- and 2-oxoglutarate-dependent dioxygenase family, including HIF-prolyl hydroxylase PHD2, histone demethylase JHDM2A/JMJD1A, and DNA repair enzymes ABH3 and ABH2, and histone methyltransferases, G9a). Given the effects that these metals can exert on the epigenome, future studies of their involvement in histone modifying enzymes dynamics would deepen our understanding on their respective toxicities and carcinogenicities.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".