Multi-conflict islands are a widespread trend within Serratia spp.
Bibliographic record
Abstract
Bacteria carry numerous anti-phage systems in "defense islands" or hotspots. Recent studies have delineated the content and boundaries of these islands in various species, revealing instances of islands that encode additional factors, including antibiotic resistance genes, stress genes, type VI secretion system (T6SS)-dependent effectors, and virulence factors. Our study identifies three defense islands in the Serratia genus with a mixed cargo of anti-phage systems, virulence factors, and different types of anti-bacterial modules, revealing a widespread trend of co-accumulation that extends beyond T6SS-dependent effectors to colicins and contact-dependent inhibition systems. We further report the identification of four distinct anti-phage system/subtypes, including a previously unreported Toll/interleukin (IL)-1 receptor (TIR)-domain-containing system with population-wide immunity, and two loci co-opting a predicted T6SS-related protein for phage defense. This study enhances our understanding of the protein domains that can be co-opted for phage defense, resulting in a highly diversified anti-phage arsenal.
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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 | 0.001 |
| 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 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".