Deoxynivalenol Degradation by Various Microbial Communities and Its Impacts on Different Bacterial Flora
Bibliographic record
Abstract
Deoxynivalenol, a mycotoxin that may present in almost all cereal products, can cause huge economic losses in the agriculture industry and seriously endanger food safety and human health. Microbial detoxifications using microbial consortia may provide a safe and effective strategy for DON mitigation. In order to study the interactions involving DON degradation and change in microbial flora, four samples from different natural niches, including a chicken stable (expJ), a sheep stable (expY), a wheat field (expT) and a horse stable (expM) were collected and reacted with purified DON. After being co-incubated at 30 °C with 130 rpm shaking for 96 h, DON was reduced by 74.5%, 43.0%, 46.7%, and 86.0% by expJ, expY, expT, and expM, respectively. After DON (0.8 mL of 100 μg/mL) was co-cultivated with 0.2 mL of the supernatant of each sample (i.e., suspensions of microbial communities) at 30 °C for 96 h, DON was reduced by 98.9%, 99.8%, 79.5%, and 78.9% in expJ, expY, expT, and expM, respectively, and was completely degraded after 8 days by all samples except of expM. DON was confirmed being transformed into de-epoxy DON (DOM-1) by the microbial community of expM. The bacterial flora of the samples was compared through 16S rDNA flux sequencing pre- and post the addition of DON. The results indicated that the diversities of bacterial flora were affected by DON. After DON treatment, the most abundant bacteria belong to Galbibacter (16.1%) and Pedobacter (8.2%) in expJ; Flavobacterium (5.9%) and Pedobacter (5.5%) in expY; f_Microscillaceae (13.5%), B1-7BS (13.4%), and RB41 (10.5%) in expT; and Acinetobacter (24.1%), Massilia (8.8%), and Arthrobacter (7.6%) in expM. This first study on the interactions between DON and natural microbial flora provides useful information and a methodology for further development of microbial consortia for mycotoxin detoxifications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".