Food-borne microbes influence conjugative transfer of antimicrobial resistance plasmids in pre-disturbed gut microbiome
Bibliographic record
Abstract
Ingestion of antibiotic-resistant bacteria following antibiotic treatments may lead to the transfer of antimicrobial resistance genes (ARGs) within a disturbed gut microbiota. However, it remains unclear whether and how microbes present in food matrices influence ARG transfer. Thus, a previously established mouse model, which demonstrated the conjugative transfer of a multi-drug resistance plasmid (pIncA/C) from Salmonella Heidelberg (donor) to Salmonella Typhimurium (recipient), was used to assess the effects of food-borne microbes derived from fresh carrots on pIncA/C transfer. Mice were pre-treated with ampicillin, streptomycin, sulfamethazine, or left untreated as a control to facilitate bacterial colonization. Contrary to previous findings where high-density colonization of the donor and recipient bacteria occurred in the absence of food-borne microbes, the presence of these microbes resulted in a low abundance of S. Typhimurium and no detection of S. Typhimurium transconjugants in the fecal samples from any of the mice. However, in mice pre-treated with streptomycin, a significant reduction in microbial species richness allowed for the significant enrichment of Enterobacteriaceae and pIncA/C transfer to bacteria from the genera Escherichia, Enterobacter, Citrobacter, and Proteus. These findings suggest that food-borne microbes may enhance ARG dissemination by influencing the population dynamics of bacterial hosts within a pre-disturbed gut microbiome.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".