Retrospective analysis of antimicrobial resistance associated with bovine respiratory disease
Bibliographic record
Abstract
ABSTRACT The administration and utility of antibiotics to control and treat bovine respiratory disease (BRD) in beef cattle feedlots is a growing concern. Antimicrobial resistance (AMR) among BRD-associated bacterial pathogens has been the subject of cultivation-dependent and cultivation-independent surveillance. Bacterial genome sequencing and metagenomic approaches facilitate the characterization of AMR in the beef industry; however, the current collection of cattle-associated AMR research programs lack connections to each other. A more integrated view of how antimicrobial use (AMU) is related to resistance at a gene level is needed. We sought to establish a catalog of commonly observed AMR genes (ARGs) in opportunistic bacterial pathogens that contribute to BRD using publicly available data sets that were generated by the scientific community with and without AMU in mind. The presence of these clinically relevant ARGs appeared to differ by geography. Greater sampling in North America facilitated the generation of a list of ARGs often encoded by Mannheimia haemolytica and Pasteurella multocida . Detection of clinically relevant ARGs in shotgun metagenomes of cattle-associated and accessible feedlot samples such as water, soil, and feces was possible but limited by relative sequence read abundance. An exception was the tylosin esterase-encoding gene estT , which is among the most frequently observed ARGs in M. haemolytica and feedlot-related metagenomic data sets. Finally, by re-evaluating studies on the impact of AMU on AMR in beef production systems, we show that conventional practices, including in-feed antibiotic use, increase the relative abundance of ARGs in animal-derived samples. IMPORTANCE This retrospective analysis delivers a list of ARGs found in opportunistic pathogens that contribute to BRD. The high incidence of BRD in North America is linked to the origin and implementation of metaphylaxis to mitigate detrimental animal losses at feedlots. Notably, ARGs commonly observed in these pathogens isolated in North America were not conserved across the globe, underscoring the relationship between regional AMU and AMR. A positive relationship was also observed between the relative abundance of ARGs in cattle-associated metagenomes with greater exposure to antibiotics. Overall, this analysis should help to guide future surveillance efforts and experimental designs to more directly evaluate the impacts of feedlot practices on AMR.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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 | 0.000 |
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".