Mobile element warfare via CRISPR and anti-CRISPR in <i>Pseudomonas aeruginosa</i>
Bibliographic record
Abstract
SUMMARY Bacteria deploy multiple defense mechanisms to prevent the invasion of mobile genetic elements (MGEs). CRISPR-Cas systems use RNA-guided nucleases to target MGEs, which in turn produce anti-CRISPR (Acr) proteins that inactivate Cas protein effectors. The minimal component Type I-C CRISPR-Cas subtype is highly prevalent in bacteria, and yet a lack of a tractable in vivo model system has slowed its study, the identification of cognate Acr proteins, and thus our understanding of its true role in nature. Here, we describe MGE-MGE conflict between a mobile Pseudomonas aeruginosa Type I-C CRISPR-Cas system always encoded on pKLC102-like conjugative elements, which are large mobile islands, and seven new Type I-C anti-CRISPRs (AcrIF2*, AcrIC3-IC8) encoded by phages, other mobile islands, and transposons. The P. aeruginosa Type I-C system possesses a total of 300 non-redundant spacers (from 980 spacers total) across the 42 genomes analyzed, predominantly targeting P. aeruginosa phages. Of the seven new Type I-C anti-CRISPRs, all but one are highly acidic, and four have surprisingly broad inhibition activity, blocking multiple distantly related P. aeruginosa Type I CRISPR system subtypes (e.g. I-C and I-F, or I-C and I-E), including AcrIF2 (now, AcrIF2*), a previously described DNA mimic. Anti-type I-C activity of AcrIF2* was far more sensitive to mutagenesis of acidic residues in AcrIF2* than anti-type I-F activity, suggesting distinct binding mechanisms for this highly negatively charged protein. Five of the seven Acr proteins block DNA-binding, while the other two act downstream of DNA-binding, likely by preventing Cas3 recruitment or activity. For one such Cas3 inhibitor (AcrIC3), we identify a novel anti-CRISPR evasion strategy: a cas3-cas8 gene fusion, which also occurs in nature. Collectively, the Type I-C CRISPR spacer diversity and corresponding anti-CRISPR response, all occurring on Pseudomonas MGEs, demonstrates an active co-evolutionary battle between parasitic elements.
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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 | 0.001 |
| 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".