Modeling the Infection Dynamics of Bacteriophages in Enteric Escherichia coli: Estimating the Contribution of Transduction to Antimicrobial Gene Spread
Bibliographic record
Abstract
Animal-associated bacterial communities are infected by bacteriophages, although the dynamics of these infections are poorly understood. Transduction by bacteriophages may contribute to transfer of antimicrobial resistance genes, but the relative importance of transduction among other gene transfer mechanisms is unknown. We therefore developed a candidate deterministic mathematical model of the infection dynamics of enteric coliphages in commensal Escherichia coli in the large intestine of cattle. We assumed the phages were associated with the intestine and were predominantly temperate. Model simulations demonstrated how, given the bacterial ecology and infection dynamics, most (>90%) commensal enteric E. coli bacteria may become lysogens of enteric coliphages during intestinal transit. Using the model and the most liberal assumptions about transduction efficiency and resistance gene frequency, we approximated the upper numerical limits ("worst-case scenario") of gene transfer through specialized and generalized transduction in E. coli by enteric coliphages when the transduced genetic segment is picked at random. The estimates were consistent with a relatively small contribution of transduction to lateral gene spread; for example, generalized transduction delivered the chromosomal resistance gene to up to 8 E. coli bacteria/hour within the population of 1.47 × 10(8) E. coli bacteria/liter luminal contents. In comparison, the plasmidic blaCMY-2 gene carried by ~2% of enteric E. coli was transferred by conjugation at a rate at least 1.4 × 10(3) times greater than our generalized transduction estimate. The estimated numbers of transductants varied nonlinearly depending on the ecology of bacteria available for phages to infect, that is, on the assumed rates of turnover and replication of enteric E. coli.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".