Analysis of viromes and microbiomes from pig fecal samples reveals that phages and prophages rarely carry antibiotic resistance genes
Bibliographic record
Abstract
Understanding the transmission of antibiotic resistance genes (ARGs) is critical for human health. For this, it is necessary to identify which type of mobile genetic elements is able to spread them from animal reservoirs into human pathogens. Previous research suggests that in pig feces, ARGs may be encoded by bacteriophages. However, convincing proof for phage-encoded ARGs in pig viromes is still lacking, because of bacterial DNA contaminating issues. We collected 14 pig fecal samples and performed deep sequencing on both highly purified viral fractions and total microbiota, in order to investigate phage and prophage-encoded ARGs. We show that ARGs are absent from the genomes of active, virion-forming phages (below 0.02% of viral contigs from viromes), but present in three prophages, representing 0.02% of the viral contigs identified in the microbial dataset. However, the corresponding phages were not detected in the viromes, and their genetic maps suggest they might be defective. We conclude that among pig fecal samples, phages and prophages rarely carry ARG. Furthermore, our dataset allows for the first time a comprehensive view of the interplay between prophages and viral particles, and uncovers two large clades, inoviruses and Oengus-like phages.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".