Real-life exposure to Fusarium toxins deoxynivalenol and zearalenone triggers apoptosis and activates NLRP3 inflammasome in bovine primary theca cells.
Bibliographic record
Abstract
Abstract Cattle are deemed less susceptible to mycotoxins due to the limited internal exposure resulting from rumen microbiota activity. However, the significant amounts of Fusarium mycotoxins deoxynivalenol (DON) and zearalenone (ZEN) frequently detected in bovine follicular fluid samples suggest that they could affect ovarian function. Both mycotoxins trigger several patterns of cell death and activate the NLRP3 inflammasome in the intestine. In vitro studies have reported a number of adverse effects on bovine oocytes. However, the biological relevance of such findings with regard to realistic concentrations of DON and ZEN in bovine follicular fluid is still not clear. Hence, it is important to better characterize the effects of dietary exposure to DON and ZEN on the bovine ovary. Using bovine primary theca cells, this study investigated the effects of real-life patterns for bovine ovary exposure to DON and ZEN, but also DON metabolite DOM-1, on cell death and NLRP3 inflammasome activation. Exposure to DON starting from 0.1µM significantly decreased theca cell viability. The kinetics of phosphatidylserine translocation and loss of membrane integrity showed that ZEN and DON, but not DOM-1, induce an apoptotic phenotype. qPCR analysis of the expression of NLRP3, PYCARD, IL-1β, IL-18, and GSDMD in primary theca cells at concentrations of mycotoxin previously reported in cow follicular fluid clearly indicated that DON and DOM-1 individually and in mixture, but not ZEN, activate NLRP3 inflammasome. Altogether, these results suggest that current European Union guidance values for DON may not protect the bovine ovary from inflammatory disorders.
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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) | 0.002 | 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".