209 Prevalence of Antimicrobial Resistance Genes and Mobile Genetic Elements in the Swine Gut Microbiome
Bibliographic record
Abstract
Abstract Antimicrobial resistance (AMR) is one of the most pressing threats to public health. It contributes to over a million deaths a year currently and by 2050 it is expected to cause more annual death than cancer. The swine industry has taken important steps to address the AMR crisis by phasing out the use of antimicrobial growth promoters. Nonetheless, the use of antimicrobials for controlling infectious diseases on swine farms remains high, particularly during the nursery stage of pig production. This has raised concerns about the potential role of the swine industry in the emergence and spread of antimicrobial resistance genes (ARGs) capable of conferring resistance to medically important antimicrobials. While traditionally ARGs have been studied in the context of pathogens, there is growing evidence that the natural microbiome of humans and animals can also serve as a significant reservoir of ARGs. Of particular importance is the association of ARGs with different classes of mobile genetic elements (MGEs) which can facilitate the horizontal transfer of genes among commensal bacteria and pathogens. In this study, we evaluated the diversity and distribution of ARGs and their associated MGEs in the swine gut microbiome. To achieve this, we isolated and sequenced the whole genomes of 130 unique bacterial isolates from stool, digesta, and mucosa of healthy adult sows and their piglets. Screening of genomes for the presence of ARGs was performed using the Resistance Gene Identifier (RGI) and Comprehensive Antibiotic Resistance Database (CARD), resulting in the identification of 323 ARGs across 117 genomes. Among these, genes predicted to confer resistance against tetracycline, lincosamide, beta-lactams, and fluoroquinolone were the most prevalent across all genomes. Importantly, we observed that several strictly anaerobic species belonging to gut-specific lineages including Lachnospiraceae, Megasphaeraceae, and Desulfovibrionaceae contained large ARGs pools spanning more than five different classes of antimicrobials. Further, we screened the flanking regions of ARGs for the presence of signature genes of MGEs including recombinases, phage integrases and capsids, and type IV secretion system. A total of 60 ARGs were identified to be associated with different classes of MGEs, including integrative and conjugative elements (ICEs; n = 30), bacteriophages (n = 8), integrative mobile elements (IMEs; n = 6), and plasmids (n = 3). Overall, our study highlights that ARGs, including those conferring resistance to medically important antimicrobials, are prevalent across different lineages of the swine gut microbiota. The association of the identified ARGs with MGEs warrants further in vitro experiments to evaluate the risk of transmission of resistance to zoonotic pathogens capable of infecting humans and animals.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".