Modulation of Porcine β-Defensins 1 and 2 upon Individual and Combined Fusarium Toxin Exposure in a Swine Jejunal Epithelial Cell Line
Bibliographic record
Abstract
Defensins are small antimicrobial peptides (AMPs) that play an important role in the innate immune system of mammals. Since the effect of mycotoxin contamination of food and feed on the secretion of intestinal AMPs is poorly understood, the aim of this study was to elucidate the individual and combined effects of four common Fusarium toxins, deoxynivalenol (DON), nivalenol (NIV), zearalenone (ZEA), and fumonisin B1 (FB1), on the mRNA expression, protein secretion, and corresponding antimicrobial effects of porcine β-defensins 1 and 2 (pBD-1 and pBD-2) using a porcine jejunal epithelial cell line, IPEC-J2. In general, upregulation of pBD-1 and pBD-2 mRNA expression occurred following exposure to Fusarium toxins, individually and in mixtures (P < 0.05). However, no significant increase in secreted pBD-1 and pBD-2 protein levels was observed, as measured by enzyme-linked immunosorbent assay (ELISA). Supernatants from IPEC-J2 cells exposed to toxins, singly or in combination, however, possessed significantly less antimicrobial activity against Escherichia coli than untreated supernatants. When single toxins and two-toxin combinations were assessed, toxicity effects were shown to be nonadditive (including synergism, potentiation, and antagonism), suggesting interactive toxin effects when cells are exposed to mycotoxin combinations. The results show that Fusarium toxins, individually and in mixtures, activate distinct antimicrobial defense mechanisms possessing the potential to alter the intestinal microbiota through diminished antimicrobial effects. Moreover, by evaluating toxin mixtures, this improved understanding of toxin effects will enable more effective risk assessments for common mycotoxin combinations observed in contaminated food and feed.
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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) | 0.000 | 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".