The great phage escape: Activating and escaping lactococcal antiphage systems
Bibliographic record
Abstract
In recent years, the number of newly discovered systems that bacteria use to combat bacteriophages is increasing at an impressive rate. To obtain mechanistic insights into several antiphage systems identified in previous studies, we isolated 66 phage escape mutants which had become insensitive to 13 distinct, plasmid-encoded lactococcal phage resistance systems (i.e. Rhea, Kamadhenu, Rugutis, Audmula, PARIS, type II CBASS, Septu, AbiA, AbiB, AbiD/F, AbiG, AbiJ, AbiP). Genome analysis of these phage escape mutants identified a total of 15 mutated genes. Six of the encoded proteins appear to activate specific antiphage systems. Furthermore, AbiA escape mutants were found to be insensitive to AbiJ, while distinct antiphage systems (AbiG and AbiP) were observed to be activated by a major phage tail protein, indicating mechanistic commonalities. PARIS homologues encoded by members of different bacterial genera appear to share similar sensing mechanisms, whereas our data indicate mechanistic differences between Septu homologues from different genera. Based on our escape mutant sequence analysis, previously predicted domains, and experimental data using the purified endolysin of phage c2, we propose that Audmula modifies the cell wall of the host bacterium, delaying cell lysis and release of progeny phages, protecting the host cell by a heretofore unknown mode of action. The obtained advances in our understanding of lactococcal antiphage mechanisms provide fundamental insights into phage-host interactions, which undoubtedly benefits the dairy industry but may also be useful for biotechnological or biomedical applications.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".