Evaluating the Effects of Antimicrobial Drug Use On the Ecology of Antimicrobial Resistance and Microbial Community Structure in Beef Feedlot Cattle
Bibliographic record
Abstract
Abstract BackgroundAntimicrobial drugs (AMDs) are used in beef production to treat clinical disease and to prevent or control infections in groups of cattle. Use of AMDs in food producing animals has received increasing scrutiny because of concerns about antimicrobial resistance (AMR) that might affect consumers. Previously, investigations regarding AMR have focused largely on phenotypes of selected pathogens and indicator bacteria, but genes that confer AMR are known to be distributed and shared throughout microbial communities. Use of high-throughput metagenomic sequencing provides a holistic perspective on AMR ecology by examining determinants within the entire microbiome. The primary objective of this study was to employ metagenomic sequencing to investigate the effects of AMD use on the microbiome and resistome in beef feedlot cattle. ResultsThis study leveraged the use of archived samples that were collected during a previous longitudinal study of cattle at beef feedlots in Canada. This included fecal samples collected from randomly selected individual cattle, as well as composite-fecal samples from randomly selected pens of cattle. All AMD use was recorded and characterized across different drug classes using animal defined daily dose (ADD) metrics. Samples were analyzed using AMR target-enriched shotgun sequencing to characterize the fecal resistome and 16S rRNA gene sequencing to characterize the microbiome. Overall, the fecal resistome composition was dominated by alignments to gene accessions conferring resistance to tetracycline and macrolide-lincosamide-streptogramin (MLS) drug classes. The diversity of bacterial phyla was greater early in the feeding period and decreased over time as the microbiome shifted toward a similar composition dominated by Proteobacteria and Firmicutes. Antimicrobial drug exposures in individuals and groups were associated with explaining a statistically significant proportion of the variance in the resistome, but their contribution to the variance was small compared to other factors measured in this study.ConclusionsTime in the feedlot was associated with greater changes in the microbiome and resistome for both individual animals and pen-floor samples, although the proportion of the variance associated with this factor was small. Results of this study are consistent with other investigations showing that AMD exposures did not have strong effects on the microbial ecology of beef cattle.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".