Phage efficacy in infecting dual-strain biofilms of <i>Pseudomonas aeruginosa</i>
Bibliographic record
Abstract
Bacterial viruses, or phage, play a key role in shaping natural microbial communities. Yet much research on bacterial-phage interactions has been conducted in liquid cultures involving single bacterial strains. Critically, phage often have a very narrow host range meaning they can only ever target a subset of strains in a community. Here we explore how strain diversity affects the success of lytic phage in structured communities. In particular, we infect a susceptible Pseudomonas aeruginosa strain PAO1 with lytic phage Pseudomonas 352 in the presence versus absence of an insensitive P. aeruginosa strain PA14, in liquid culture versus colonies growing on agar. We find that competition between the two bacterial strains reduces the likelihood of the susceptible strain evolving resistance to the phage. This result holds in liquid culture and in colonies. However, while in liquid the phage eliminate the whole sensitive population, colonies contain refuges wherein bacteria can remain sensitive yet escape phage infection. These refuges form mainly due to reduced growth in colony centers. We find little evidence that the presence of the insensitive strain provides any additional protection against phage. Our study reveals that living in a spatially structured population can protect bacteria against phage infection, while the presence of competing strains may instead reduce the likelihood of evolving resistance to phage, if encountered.
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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) | 0.000 | 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 | 0.000 |
| 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".