Incorporating the plasmidome into antibiotic resistance surveillance in animal agriculture
Bibliographic record
Abstract
Mobile genetic elements (MGE) carrying resistance genes represent a unique challenge to risk assessment and surveillance of antimicrobial resistance (AMR). Yet determining the mobility of resistance genes within animal microbiomes is essential to evaluating the potential dissemination from livestock to potential human pathogens, as well as evaluating co-selection mechanisms that may impact persistence of resistance genes with changing antibiotic use patterns. Current surveillance efforts utilize phenotypic testing and sequencing of individual isolates for tracking of AMR in livestock. In this work, we investigated the utility of using long-read sequencing of the plasmids from mixed Enterobacterales enrichments of swine fecal samples as a surveillance strategy for AMR plasmids. Enrichments were performed in either MacConkey broth without selection or with selection by addition of tetracycline or ceftriaxone, and plasmids were extracted and sequenced in order to evaluate the diversity of plasmids enriched by each method. Intact resistance plasmids were successfully assembled, as well as complex resistance transposons carrying multiple repeated elements that would interfere with assembly by short read sequencing technologies. Comparison of the assembled plasmids with representatives from public databases confirmed the quality of the assemblies and also revealed the occurrence of IncI2 plasmids carrying blaCMY-2 in Ontario swine samples, which have not been found in previous studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".