Bioactivation and DNA adduction as a rationale for ochratoxin A carcinogenesis
Bibliographic record
Abstract
Ochratoxin A (OTA) is a para-chlorophenolic mycotoxin produced by strains of Aspergillus and Penicillium that is widely found as a contaminant of improperly stored food products. The toxin is a potent renal carcinogen in rats, especially male, and has an implicated role in the etiology of Balkan endemic nephropathy and its associated urinary tract tumours. Although the mechanism of OTA-mediated tumour formation is not fully understood, and represents a hotly debated topic, bioactivation and subsequent DNA adduction through covalent attachment of electrophilic OTA species remains a viable mechanism for OTA-mediated carcinogenesis. In this paper we outline the established chemistry for the bioactivation of chlorophenol carcinogens and demonstrate how this chemistry relates to the bioactivation of OTA. From this basis it is predicted that OTA will form a benzoquinone electrophile following activation by cytochrome P450 enzymes and radical species following activation by enzymes with peroxidase activities. These electrophiles react preferentially with deoxyguanosine (dG) to form benzetheno adducts and C8- dG adducts, respectively. Analysis of OTA-mediated DNA adduction using the 32P-postlabelling method correlates with OTA chemistry and adduct spots derived from the quinone electrophile are generated following activation by cytochrome P450, while a C8-OTA adduct is formed following activation of OTA by peroxidase enzymes. These same adduct spots are also produced in animal (rat and pig) and human tumoral kidney tissue. This model for OTAmediated carcinogenesis is consistent with established structure-activity relationships for covalent attachment of OTA analogues and OTA toxicity. The model also provides a rationale for the synergistic effect observed for OTA in the presence of the mycotoxin citrinin and for the sexual differences observed in rat carcinogenesis where the male is particularly susceptible to OTA-mediated tumour formation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".