Effect of Antimicrobial Use in Conventional Versus Natural Cattle Feedlots on the Microbiome and Resistome
Bibliographic record
Abstract
Antimicrobial use (AMU) in the livestock industry has been associated with increased levels of antimicrobial resistance. Recently, there has been an increase in the number of “natural” feedlots in the beef cattle sector that raise cattle without antibiotics. Shotgun metagenomics was employed to characterize the impact of AMU in feedlot cattle on the microbiome, resistome, and mobilome. Sequenced fecal samples identified a decline (q < 0.01) in the genera Methanobrevibacter and Treponema in the microbiome of naturally vs. conventionally raised feedlot cattle, but this difference was not (q > 0.05) observed in catch basin samples. No differences (q > 0.05) were found in the class-level resistome between feedlot practices. In fecal samples, decreases from conventional to natural (q < 0.05) were noted in reads for the antimicrobial-resistant genes (ARGs) mefA, tet40, tetO, tetQ, and tetW. Plasmid-associated ARGs were more common in feces from conventional than natural feedlot cattle. Interestingly, more chromosomal- than plasmid-associated macrolide resistance genes were observed in both natural and conventional feedlots, suggesting that they were more stably conserved than the predominately plasmid-associated tetracycline resistance genes. This study suggests that generationally selected resistomes through decades of AMU persist even after AMU ceases in natural production systems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".