Bacterial Microbiota in Soil Amended with Deoxynivalenol-Contaminated Wheat
Bibliographic record
Abstract
Feed contaminated with the mycotoxin deoxynivalenol (DON) can negatively impact livestock health and performance. Bacteria capable of degrading DON present a method of mitigating its harmful effects. This study aimed to identify microbial consortia from soil samples that could degrade DON. Soil from central (Lacombe, LA) and southern (Lethbridge, LE) Alberta were used as microbial inoculant. The soils were mixed with DON-contaminated wheat (18 ppm/kg) on day 0, and each soil type was divided into triplicate pots (180 g) and placed in a controlled environment for 32 d. Control pots of each soil type were included, which contained no DON-contaminated wheat. On days 0, 7, 14, and 32, 1 g subsamples were collected from pots, serially diluted in a limited medium containing DON (10 µg/mL) as the only carbon source, and incubated for 2 weeks (30 °C). DNA was extracted from the pots across time, as well as the subsample consortia grown in DON-amended medium, and was analyzed for bacterial changes after 16S rRNA gene sequencing. The relative abundance of bacterial genera in soil samples after enrichment with DON-contaminated wheat increased across time compared to the baseline day 0 time point. DON-degrading activity (26%) was only detected in LA soil suspension on day 7, and was highest after 14 days of incubation. The most abundant bacteria in the LA DON-degrading consortia belonged to the Pseudomonas (8.8%), Delftia (7.4%), Acinetobacter (6.4%), Comamonas (5.7%), Stenotrophomonas (5.5%), Shinella (5.5%), Ensifer (5.1%), Agrobacterium (5.0%), Achromobacter (4.7%), and Rhizobium (3.7%) genera. Pseudomonas aeruginosa (n = 9) and Serratia liquefaciens (n = 3) strains isolated from the LA consortia did not degrade DON. Overall, this study shows that the soil contained bacteria capable of degrading DON; however, variation existed depending on the soil’s source.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".