Core defense hotspots within <i>Pseudomonas aeruginosa</i> are a consistent and rich source of anti-phage defense systems
Bibliographic record
Abstract
Bacteria use a diverse arsenal of anti-phage immune systems, including CRISPR-Cas and restriction enzymes. Recent advances in anti-phage system discovery and annotation tools have unearthed many unique systems, often encoded in horizontally transferred defense islands, which can be horizontally transferred. Here, we developed Hidden Markov Models (HMMs) for defense systems and queried microbial genomes on the NCBI database. Out of the 30 species with >200 completely sequenced genomes, our analysis found Pseudomonas aeruginosa exhibits the greatest diversity of anti-phage systems, as measured by Shannon entropy. Using network analysis to identify the common neighbors of anti-phage systems, we identified two core defense hotspot loci (cDHS1 and cDHS2). cDHS1 is up to 224 kb (median: 26 kb) with varied arrangements of more than 30 distinct immune systems across isolates, while cDHS2 has 24 distinct systems (median: 6 kb). Both cDHS regions are occupied in a majority of P. aeruginosa isolates. Most cDHS genes are of unknown function potentially representing new anti-phage systems, which we validated by identifying a novel anti-phage system (Shango) commonly encoded in cDHS1. Identifying core genes flanking immune islands could simplify immune system discovery and may represent popular landing spots for diverse MGEs carrying anti-phage systems.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".