Effect of the Abortive Infection Mechanism and Type III Toxin/Antitoxin System AbiQ on the Lytic Cycle of Lactococcus lactis Phages
Bibliographic record
Abstract
To survive in phage-containing environments, bacteria have evolved an array of antiphage systems. Similarly, phages have overcome these hurdles through various means. Here, we investigated how phages are able to circumvent the Lactococcus lactis AbiQ system, a type III toxin-antitoxin with antiviral activities. Lactococcal phage escape mutants were obtained in the laboratory, and their genomes were sequenced. Three unrelated genes of unknown function were mutated in derivatives of three distinct lactococcal siphophages: orf38 of phage P008, m1 of phage bIL170, and e19 of phage c2. One-step growth curve experiments revealed that the phage mutations had a fitness cost while transcriptional analyses showed that AbiQ modified the early-expressed phage mRNA profiles. The L. lactis AbiQ system was also transferred into Escherichia coli MG1655 and tested against several coliphages. While AbiQ was efficient against phages T4 (Myoviridae) and T5 (Siphoviridae), escape mutants of only phage 2 (Myoviridae) could be isolated. Genome sequencing revealed a mutation in gene orf210, a putative DNA polymerase. Taking these observations together, different phage genes or gene products are targeted or involved in the AbiQ phenotype. Moreover, this antiviral system is active against various phage families infecting Gram-positive and Gram-negative bacteria. A model for the mode of action of AbiQ is proposed.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".