Phage reprogramming of <i>Pseudomonas aeruginosa</i> amino acid metabolism drives efficient phage replication
Bibliographic record
Abstract
ABSTRACT Phages have been shown to use diverse strategies to commandeer bacterial host cell metabolism during infection. However, for many of the physiological changes in bacteria during infection, it is often unclear if they are part of a bacterial response to infection or if they are actively driven by the phage itself. Here, we identify two phage proteins that promote efficient phage replication by reprogramming host amino acid metabolism. These proteins, Eht1 and Eht2, are expressed early in the infection cycle and increase the levels of key amino acids and the arginine-derived polyamine putrescine. This provides a fitness advantage as these metabolites are important for phage replication and are often depleted during infection. We provide evidence that Eht1 and Eht2 alter the expression of bacterial host metabolic genes, and their activities may impinge on metabolism-related signaling processes. This work provides new insight into how phages ensure access to essential host resources during infection and the competitive advantage this provides. IMPORTANCE Bacterial viruses, known as phages, are abundant in all environments that are inhabited by bacteria. During the infection process, phages exploit bacterial resources, resulting in notable changes to bacterial metabolism. However, precise mechanisms underlying these changes, and if they are driven by the phage or are a generalized bacterial response to infection, remain poorly understood. We characterized two proteins in Pseudomonas aeruginosa phage JBD44 whose activities alter bacterial host metabolism to optimize phage replication. Our work provides insight into how phages control bacterial processes to ensure access to essential host resources during infection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".