Biotransformation of Deoxynivalenol to the Novel Metabolite Deoxynivalenol-8,15-hemiketal-7-glucoside by the <i>Bacillus subtilis</i> Glycosyltransferase YjiC
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The mycotoxin deoxynivalenol (DON) is a chronic problem in cereals in temperate areas worldwide. Above regulatory levels, DON contamination can result in significant economic loss both to the primary producer and the feed industry in terms of increased costs. Here we report the enzymatic biotransformation of DON to a novel stable metabolite by a soil-borne strain of Bacillus subtilis . Proteomic analysis of activity-enriched protein fractions from this B. subtilis strain identified the glycosyltransferase YjiC as the enzyme responsible for the observed DON biotransformation. Liquid chromatography high-resolution tandem mass spectrometry and NMR spectroscopic analysis demonstrated that YjiC glycosylates DON at the 7-hydroxyl position, producing the novel metabolite DON-8,15-hemiketal-7-glucoside (HKDON7G). In toxicity experiments, duckweed exposed to 20 μM HKDON7G showed no phytotoxicity when compared to DON. Stability testing of HKDON7G demonstrated that it is significantly more resistant to enzymatic and microbial hydrolysis compared to DON-3-glucoside. This study is the first to report a chemical modification to the 7-hydroxyl position of DON and presents a novel mechanism for the detoxification of DON-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) | 0.001 | 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".