Influence of three different manure treatments on antimicrobial resistance genes and mobile genetic elements
Bibliographic record
Abstract
There is a growing concern about the potential dissemination of antimicrobial resistance into agricultural fields due to the application of manure as crop fertilizer. While reducing the usage of antibiotics in livestock production stands as the first alternative to prevent this problem, there is evidence that this may not be enough to eliminate antimicrobial resistance elements already present in manure microbiomes. This study employed a metagenomic approach to investigate the impact of common manure treatments, including aerobic storage, mesophilic anaerobic digestion (MAD), and solid-liquid separation, on the presence and abundance of antimicrobial resistance genes (ARGs), bactericides, and heavy metal resistance genes (BacMet), and mobile genetic elements (MGEs) in manure from three different farms, including one operating in an antibiotic-free environment. The results indicated that MAD was the best method to reduce the numbers of ARGs, BacMet, and MGEs, achieving reduction rates greater than 40%, 89%, and 68%, respectively. Manure storage significantly reduced BacMet levels (over 30%) and MGEs (28%) but had no significant effect on total ARG levels. Solids recovered through solid-liquid separation exhibited elevated levels of ARGs, BacMet, and MGEs, while the liquid fraction displayed levels similar to untreated manures. Correlation and co-occurrence modeling analyses indicated that changes in microbial communities, particularly fluctuations in aerobic and facultative communities belonging to Bacillota, Actinomycetota, and Pseudomonadota phyla, played a significant role in driving changes in ARGs, BacMet, and MGEs. The results also showed the presence of toxin-antitoxin and transposon systems near different ARGs. Overall, the results confirmed that genes conferring resistance to various antimicrobials and MGE capable of mobilizing them are widely spread in dairy farms; that even under the absence of antibiotics, the use of heavy metals and disinfectants may promote the maintenance of ARGs and MGEs, and; that treatment such as anaerobic digestion could reduce the risk of the spread of antimicrobial resistance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".