Priming in a permissive type I-C CRISPR–Cas system reveals distinct dynamics of spacer acquisition and loss
Bibliographic record
Abstract
CRISPR–Cas is a bacterial and archaeal adaptive immune system that uses short, invader-derived sequences termed spacers to target invasive nucleic acids. Upon recognition of previously encountered invaders, the system can stimulate secondary spacer acquisitions, a process known as primed adaptation. Previous studies of primed adaptation have been complicated by intrinsically high interference efficiency of most systems against bona fide targets. As such, most primed adaptation to date has been studied within the context of imperfect sequence complementarity between spacers and targets. Here, we take advantage of a native type I-C CRISPR–Cas system in Legionella pneumophila that displays robust primed adaptation even within the context of a perfectly matched target. Using next-generation sequencing to survey acquired spacers, we observe strand bias and positional preference that are consistent with a 3′–5′ translocation of the adaptation machinery. We show that spacer acquisition happens in a wide range of frequencies across the plasmid, including a remarkable hotspot that predominates irrespective of the priming strand. We systematically characterize protospacer sequence constraints in both adaptation and interference and reveal extensive flexibilities regarding the protospacer adjacent motif in both processes. Lastly, in a strain with a genetically truncated CRISPR array, we observe increased interference efficiency, which, when coupled with forced maintenance of a targeted plasmid, provides a useful experimental system to study spacer loss. Based on these observations, we propose that the Legionella pneumophila type I-C system represents a powerful model to study primed adaptation and the interplay between CRISPR interference and adaptation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".