Insight into antimicrobial resistance at a new beef cattle feedlot in western Canada
Bibliographic record
Abstract
ABSTRACT In North America, beef production relies on the administration of antimicrobials to manage disease. Bovine respiratory disease (BRD) is the most significant disease of beef cattle, and antimicrobial resistance (AMR) to conventional therapies presents an existential risk to animal welfare and food production. While AMR surveillance programs are poised to help facilitate antimicrobial stewardship and decision making at feedlots, monitoring strategies for large numbers of animals at an individual or group level are time consuming and costly. Accordingly, we completed a pilot investigation of feedlot water bowls, which is an understudied interface between cattle and bacteria. By performing cultivation-dependent and cultivation-independent studies, we demonstrate that water bowl-dwelling bacteria can act as sentinel organisms for clinically relevant antimicrobial resistance genes (ARGs) and that cattle have an impact on the microbial communities in the bowls. Moreover, by sampling water at a feedlot site before animal arrival, we detected resistance to two antibiotics: florfenicol and tulathromycin. After just 4 weeks of operation, multidrug-resistant bacteria were routinely found in most water bowls. A comparison of ARGs encoded by five water bowl bacterial isolates along with previously reported source and wastewater metagenomes to those found in BRD pathogens confirmed the utility of using water samples for AMR surveillance. IMPORTANCE A better understanding of how environmental reservoirs of ARGs in the feedlot relate to those found in animal pathogens will help inform and improve disease management, treatment strategies, and outcomes. Monitoring individual cattle or small groups is invasive, logistically challenging, expensive, and unlikely to gain adoption by the beef cattle industry. Wastewater surveillance has become standard in public health studies and has inspired similar work to better our understanding of AMR in feedlots. We derived our insights from sampling water bowls in a newly established feedlot: a unique opportunity to observe AMR prior to animal arrival and to monitor its development over 2 months. Importantly, the bacterial community of a single water bowl can be influenced by direct contact with hundreds of animals. Our results suggest that water bowl microbiomes are economical and pragmatic sentinels for monitoring relevant AMR mechanisms.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".