PSIII-25 Isolation of a deoxynivalenol (DON)-degrading microbial consortia from soil
Bibliographic record
Abstract
Abstract Mycotoxins, such as deoxynivalenol (DON) produced by Fusarium, are harmful fungal secondary metabolic products that cause economic losses and health risks to humans and livestock. Measures to control and prevent feed contamination with DON are necessary to ensure the safety of livestock. These include strategies based on preventing fungal contamination of feed or limiting contaminants in feed through mycotoxin adsorption and degradation. Soil is a valuable source of diverse microbial communities, potentially harboring DON-degrading bacteria that can be developed into feed additives to mitigate DON. This study aimed to identify microbial consortia from diverse soil samples, that could degrade DON. Soil from central (Lacombe; LA) and southern (Lethbridge; LE) Alberta were used as microbial inoculant. These soils were suspected to host Fusarium spp. which could produce DON in cereal crops growing upon them. The LE samples were further categorized into soil amended with manure (LE-MA) or non-manure (LE-UF). Prior to the study, soil pH and moisture content were measured. After soil collection, field-moist samples were mixed with DON-contaminated wheat (10% wt/wt; d 0) and each soil type was divided into triplicate pots and placed in a controlled environment for 32 d. On d 0, 7, 14 and 32, 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 wk (30°C). The degradation of DON in bacterial cultures was calculated using ELISA. The LA soil was slightly acidic (pH 6.4) compared with LE-MA (7.3) and LE-UF (pH 7.0), while moisture content was greater in LA (18.4%) than LE-MA (14.7%) and LE-UF (13.5%) soils. DON-degrading activity was only detected in LA soil samples and was greatest after 7 d of incubation (21%). We are currently isolating individual bacteria from LA soil for identification and are characterizing the soil microbiota using 16S rRNA sequencing. Overall, this study showed that soil contained bacteria capable of degrading DON, however variation existed depending on soil source.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".