Prophages mediate defense against phage infection through diverse mechanisms
Bibliographic record
Abstract
The activity of bacteriophages poses a major threat to bacterial survival. Upon infection, a temperate phage can either kill the host cell or be maintained as a prophage. In this state, the bacteria carrying the prophage is at risk of superinfection, where another phage injects its genetic material and competes for host cell resources. To avoid this, many phages have evolved mechanisms that alter the bacteria and make it resistant to phage superinfection. The mechanisms underlying these phentoypic conversions and the fitness consequences for the host are poorly understood, and systematic studies of superinfection exclusion mechanisms are lacking. In this study, we examined a wide range of Pseudomonas aeruginosa phages and found that they mediate superinfection exclusion through a variety of mechanisms, some of which affected the type IV pilus and O-antigen, and others that functioned inside the cell. The strongest resistance mechanism was a surface modification that we showed is cost-free for the bacterial host in a natural soil environment and in a Caenorhabditis. elegans infection model. This study represents the first systematic approach to address how a population of prophages influences phage resistance and bacterial behavior in P. aeruginosa.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".