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Genomic Determinants of Phage Activity Against Pseudomonas aeruginosa: Roles of Receptors, Defence Systems, and Anti-Defences

TL;DR

Pseudomonas aeruginosa is a priority pathogen in chronic and multidrug-resistant infections, yet therapeutic phages targeting this organism often exhibit variable and unpredictable efficacy. A mechanistic understanding of the genomic determinants governing phage-host interactions is therefore critical for the rational design of robust phage therapeutics. Here, we systematically dissected the genetic architecture underlying lytic outcomes across a diverse panel of P. aeruginosa-infecting phages.

Credibility Assessment Preliminary — 34/100
Study Design
Rigor of the research methodology
5/20
Sample Size
Whether the study was sufficiently powered
7/20
Peer Review
Review status and journal reputation
4/20
Replication
Has this finding been independently reproduced?
6/20
Transparency
Funding disclosure and data availability
12/20
Overall
Sum of all five dimensions
34/100

Pseudomonas aeruginosa is a priority pathogen in chronic and multidrug-resistant infections, yet therapeutic phages targeting this organism often exhibit variable and unpredictable efficacy. A mechanistic understanding of the genomic determinants governing phage-host interactions is therefore critical for the rational design of robust phage therapeutics. Here, we systematically dissected the genetic architecture underlying lytic outcomes across a diverse panel of P. aeruginosa-infecting phages. We comprehensively annotated receptor-binding proteins (RBPs), bacterial defence systems and phage-encoded anti-defence genes, and experimentally defined host receptor usage for each phage, linking receptor specificity to cognate RBPs. We identified multiple anti-defence systems-including vcrx089, acrIIA24, atd1, gnarl1, klcA, darA and the nmna (NARP2-associated)-and RBPs targeting lipopolysaccharide, type IV pili and flagella that are associated with enhanced lytic activity and represent tractable engineering targets. Across 174 genomic features analysed, 110 significantly influenced phage activity, with heterogeneous and context-dependent effects. Leveraging these features, we trained a machine-learning classifier that accurately predicted phage-host outcomes (AUC-ROC = 0.875), demonstrating that interaction phenotypes are encoded in definable genomic signatures. Together, our findings reveal the quantitative contribution of phage anti-defence systems to infectivity in P. aeruginosa and define a genomic framework for predicting and engineering lytic success. These results establish a foundation for the rational design of synthetic phages with enhanced host ranges.

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