Genomic islands in <i>Pseudomonas</i> encode modular hotspots of defence and anti-defence systems
Bibliographic record
Abstract
Abstract Bacteria use diverse defence systems to resist phage predation, many of which cluster within mobile genetic elements (MGEs) and defence islands. In Pseudomonas aeruginosa, genomic and pathogenicity islands—such as the pathogenicity islands (PAPI), genomic islands (PAGI), and Liverpool epidemic strain islands (LESGI)—have been linked to virulence and adaptation, but their contribution to the organization and spread of defence systems remains unexplored. Here, we show that these islands serve as hubs for the assembly and spread of defence systems, revealing an underappreciated role in shaping the bacterium’s antiviral arsenal. We identify 11 conserved hotspots that encode defence and anti-defence genes, but rarely co-occur with virulence factors, resistance genes, or interbacterial competition modules. The frequent co-occurrence of defence and anti-defence genes within these loci points to an ongoing, intense molecular arms race between bacteria, MGEs, and lytic phages. Notably, these hotspots are found beyond their original island contexts, appearing across diverse Pseudomonas species and, in some cases, other genera. Together, our findings expand the known bacterial immunity landscape in P. aeruginosa, redefine the roles of these islands as defence and anti-defence reservoirs, and establish a framework for scalable discovery and annotation of novel defence and anti-defence systems in bacterial genomes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".