Layer chicken manure as a hotspot for the dissemination of microbial communities and antimicrobial resistance genes: metagenomic insights
Bibliographic record
Abstract
Abstract Manure contains a diverse range of microbial communities and antimicrobial resistant genes (ARGs), and the use of poultry manure in agricultural fields for soil enrichment may lead to environmental contamination, posing a risk to human and animal health. The current study profiled the microbiome and resistome of manure collected from poultry operations across Alberta, Canada. The sampling was conducted from 15 (cage and floor housed layer chickens) barns across Alberta from 2022–24 and processed via shotgun metagenomics. Taxonomic alignment was performed after mapping the reads via Kraken 2. The resistome of the assembled contig files was analyzed via the AMRFinderPlus database. Bacillota, Actinomycetota and Pseudomonadota were the most relatively abundant phyla in the study samples. ESPEC pathogens ( Enterococcus faecium , Staphylococcus aureus , Klebsiella pneumoniae , Acinetobacter baumannii , and Pseudomonas aeruginosa ) were detected in both types of housing systems, and the relative abundances of S. aureus, P. multocida, and A. baumannii were greater in manure from cage housing, whereas E. coli and K. pneumoniae were relatively more abundant in floor-housed manure. lnu C , aad 9 , aph3-IIIa , sat 4 , and tet W ARG subtypes were found in all 15 samples. The analysis of ARGs revealed that resistance was associated mainly with the tetracycline, aminoglycoside and lincosamide classes of antibiotics. Overall, manure contains potential opportunistic pathogens and ARGs that are resistant to various classes of antibiotics. This study provides strong evidence for the need for policy change regarding the treatment of litter and manure prior to their application on agricultural land in Alberta.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".