Mobility of β-Lactam Resistance Under Bacterial Co-infection and Ampicillin Treatment in a Mouse Model
Bibliographic record
Abstract
Ingestion of food- or waterborne antibiotic-resistant bacteria may lead to the dissemination of antibiotic-resistance genes in the gut microbiota and the development of antibiotic-resistant bacterial infection, a significant threat to animal and public health. Food or water may be contaminated with multiple resistant bacteria, but animal models on gene transfer were mainly based on single-strain infections. In this study, we investigated the mobility of 𝛽-lactam resistance following infection with single- versus multi-strain of resistant bacteria under ampicillin treatment. We characterized three bacterial strains isolated from food-animal production systems, Escherichia coli O80:H26 and Salmonella enterica serovars Bredeney and Heidelberg. Each strain carries at least one conjugative plasmid that encodes a 𝛽-lactamase. We orally infected mice with each or all three bacterial strain(s) in the presence or absence of ampicillin treatment. We assessed plasmid transfer from the three donor bacteria to an introduced E. coli CV601gfp recipient in the mouse gut, and evaluated the impacts of the bacterial infection on gut microbiota and gut health. In the absence of ampicillin treatment, none of the donor or recipient bacteria established in the normal gut microbiota and plasmid transfer was not detected. In contrast, the ampicillin treatment disrupted the gut microbiota and enabled S. Bredeney and Heidelberg to colonize and transfer their plasmids to the E. coli CV601gfp recipient. E.coli O80:H26 on its own failed to colonize the mouse gut. However, during co-infection with the two Salmonella strains, E.coli O80:H26 colonized and transferred its plasmid to the E. coli CV601gfp recipient and a residential E. coli O2:H6 strain. The co-infection significantly increased plasmid transfer frequency, enhanced Proteobacteria expansion and resulted in inflammation in the mouse gut. Our findings suggest that single-strain infection models for evaluating in vivo gene transfer may underrepresent the consequences of multi-strain infections following the consumption of heavily contaminated food or water.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".