Population Dynamics between Erwinia amylovora, Pantoea agglomerans and Bacteriophages: Exploiting Synergy and Competition to Improve Phage Cocktail Efficacy
Bibliographic record
Abstract
Bacteriophages are viruses capable of recognizing with high specificity, propagating inside of, and destroying their bacterial hosts. The phage lytic life cycle makes phages attractive as tools to selectively kill pathogenic bacteria with minimal impact on the surrounding microbiome. To effectively harness the potential of phages in therapy, it is critical to understand the phage–host dynamics and how these interactions can change in complex populations. Our model examined the interactions between the plant pathogen Erwinia amylovora, the antagonistic epiphyte Pantoea agglomerans, and the bacteriophages that infect and kill both species. P. agglomerans strains are used as a phage carrier; their role is to deliver and propagate the bacteriophages on the plant surface prior to the arrival of the pathogen. Using liquid cultures, the populations of the pathogen, carrier, and phages were tracked over time with quantitative real-time PCR. The jumbo Myoviridae phage ϕEa35-70 synergized with both the Myoviridae ϕEa21-4 and Podoviridae ϕEa46-1-A1 and was most effective in combination at reducing E. amylovora growth over 24 h. Phage ϕEa35-70, however, also reduced the growth of P. agglomerans. Phage cocktails of ϕEa21-4, ϕEa46-1-A1, and ϕEa35-70 at multiplicities of infections (MOIs) of 10, 1, and 0.01, respectively, no longer inhibited growth of P. agglomerans. When this cocktail was grown with P. agglomerans for 8 h prior to pathogen introduction, pathogen growth was reduced by over four log units over 24 h. These findings present a novel approach to study complex phage–host dynamics that can be exploited to create more effective phage-based therapies.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".