Butyrate reduces epithelial barrier dysfunction induced by the foodborne mycotoxin deoxynivalenol in cell monolayers derived from pig jejunum organoids
Bibliographic record
Abstract
species threats animal and human health through disruption of the intestinal barrier. Targeting the gut microbiota and its products appears as a promising strategy to mitigate DON intestinal toxicity. In this study, we investigated whether the bacterial metabolite butyrate could alleviate epithelial barrier disruption induced by DON. We used a model of cell monolayers derived from porcine jejunum organoids allowing to reproduce the cellular complexity of the intestinal epithelium. Our results show that DON dose-dependently disrupted the epithelial barrier integrity, reduced epithelial differentiation, and altered innate immune defenses. Butyrate attenuated the DON-induced increase in paracellular permeability. Butyrate also prevented epithelial barrier dysfunction triggered by anisomycin, a ribosome inhibitor like DON. Moreover, butyrate partially counteracted the effects of DON on tight junctions (TJP1, OCLN), innate epithelial defenses (PTGS2, CD14, TLR4, TLR5), and absorptive cell functions (CA2, VIL1, NHE3, CFTR). In contrast, butyrate did not prevent the toxic effects of DON on mitochondrial metabolism, proliferation and goblet cell functions. Taken together, our results demonstrate that the bacterial metabolite butyrate is able to reduce DON-induced epithelial barrier disruption.
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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 | 0.001 |
| 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".