<i>Listeria</i> phages induce Cas9 degradation to protect lysogenic genomes
Bibliographic record
Abstract
SUMMARY Bacterial CRISPR-Cas systems employ RNA-guided nucleases to destroy foreign DNA. Bacteriophages, in turn, have evolved diverse “anti-CRISPR” proteins (Acrs) to counteract acquired immunity. In Listeria monocytogenes , prophages encode 2-3 distinct anti-Cas9 proteins, with acrIIA1 always present; however, its mechanism is unknown. Here, we report that AcrIIA1 binds with high affinity to Cas9 via the catalytic HNH domain and, in Listeria , triggers Cas9 degradation. AcrIIA1 displays broad-spectrum inhibition of Type II-A and II-C Cas9s, including an additional highly-diverged Listeria Cas9. During lytic infection, AcrIIA1 is insufficient for rapid Cas9 inactivation, thus phages require an additional “partner” Acr that rapidly blocks Cas9-DNA-binding. The AcrIIA1 N-terminal domain (AcrIIA1 NTD ) is dispensable for anti-CRISPR activity; instead it is required for optimal phage replication through direct transcriptional repression of the anti-CRISPR locus. AcrIIA1 NTD is widespread amongst Firmicutes , can repress anti-CRISPR deployment by other phages, and has been co-opted by hosts potentially as an “anti-anti-CRISPR.” In summary, Listeria phages utilize narrow-spectrum inhibitors of DNA binding to rapidly inactivate Cas9 in lytic growth and the broad-spectrum AcrIIA1 to stimulate Cas9 degradation for protection of the Listeria genome in lysogeny.
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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 | 0.001 |
| 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".