Type IV-A3 CRISPR-Cas systems drive inter-plasmid conflicts by acquiring spacers <i>in trans</i>
Bibliographic record
Abstract
ABSTRACT Type IV-A CRISPR-Cas systems are primarily encoded on plasmids and form multi-subunit ribonucleoprotein complexes with unknown biological functions. In contrast to other CRISPR-Cas types, they lack the archetypical CRISPR acquisition module and encode a DinG helicase instead of a nuclease component. Type IV-A3 systems are carried by large conjugative plasmids that often harbor multiple antibiotic-resistance genes. Although their CRISPR array contents suggest a role in inter-plasmid conflicts, this function and the underlying mechanisms have remained unexplored. Here, we demonstrate that a plasmid-encoded type IV-A3 CRISPR-Cas system co-opts the type I-E adaptation machinery from its clinical Klebsiella pneumoniae host to update its CRISPR array. Furthermore, we demonstrate that robust interference of conjugative plasmids and phages is elicited through CRISPR RNA-dependent transcriptional repression. By targeting plasmid core functions, type IV-A3 can prevent the uptake of incoming plasmids, limit their horizontal transfer, and destabilize co-residing plasmids, altogether supporting type IV-A3’s involvement in plasmid competition. Collectively, our findings shed light on the molecular mechanisms and ecological function of type IV-A3 systems and have broad implications for understanding and countering the spread of antibiotic resistance in clinically relevant strains.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".