Genomic characterization of antimicrobial resistance and mobile genetic elements in swine gut bacteria isolated from a Canadian research farm
Bibliographic record
Abstract
INTRODUCTION: The widespread use of antimicrobials in the livestock industry has raised global concerns regarding the emergence and spread of antimicrobial resistance genes (ARGs). Comprehensive databases of ARGs specific to different farm animal species can greatly improve the surveillance of ARGs within the agri-food sector and beyond. In particular, defining the association of ARGs with mobile genetic elements (MGEs)-the primary agents responsible for the spread and acquisition of resistant phenotypes among bacterial populations-could help assess the transmissibility potential of clinically relevant ARGs. Recognizing the gut microbiota as a vast reservoir of ARGs, we aimed to generate a representative isolate collection and genome database of the swine gut microbiome, enabling high-resolution characterization of ARGs in relation to bacterial host range and their association with MGEs. RESULTS: We generated a biobank of bacteria from different sections of the gastrointestinal tracts of four clinically healthy pigs housed at a research farm in Ontario, Canada. The culturing was performed under anaerobic conditions using both selective and general enrichment media to ensure the capture of a diverse range of bacterial families within the swine gut microbiota. We sequenced the genomes of 129 unique isolates encompassing 44 genera and 25 distinct families of the swine gut microbiome. Approximately 85.3% (110 isolates) contained one or more ARGs, with a total of 246 ARGs identified across 38 resistance gene families. Tetracycline and macrolide resistance genes were the most prevalent across different lineages of the swine gut microbiota. Additionally, we observed a wide range of MGEs, including integrative conjugative elements, plasmids, and phages, frequently associated with ARGs, indicating that the swine gut ecosystem is conducive to the horizontal transfer of ARGs. High-throughput alignment of the identified ARG-MGE complexes to large-scale metagenomics datasets of the swine gut microbiome suggests the presence of highly prevalent and conserved resistome sequences across diverse pig populations. CONCLUSION: Our findings reveal a highly diverse and relatively conserved reservoir of ARGs and MGEs within the gut microbiome of pigs. A deeper understanding of the microbial host range and potential transmissibility of prevalent ARGs in the swine microbiome can inform development of targeted antimicrobial resistance surveillance and disease control programs.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".