Interference-driven spacer acquisition is dominant over naive and primed adaptation in a native CRISPR–Cas system
Bibliographic record
Abstract
CRISPR-Cas systems provide bacteria with adaptive immunity against foreign nucleic acids by acquiring short, invader-derived sequences called spacers. Here, we use high-throughput sequencing to analyse millions of spacer acquisition events in wild-type populations of Pectobacterium atrosepticum. Plasmids not previously encountered, or plasmids that had escaped CRISPR-Cas targeting via point mutation, are used to provoke naive or primed spacer acquisition, respectively. The origin, location and order of spacer acquisition show that spacer selection through priming initiates near the site of CRISPR-Cas recognition (the protospacer), but on the displaced strand, and is consistent with 3'-5' translocation of the Cas1:Cas2-3 acquisition machinery. Newly acquired spacers determine the location and strand specificity of subsequent spacers and demonstrate that interference-driven spacer acquisition ('targeted acquisition') is a major contributor to adaptation in type I-F CRISPR-Cas systems. Finally, we show that acquisition of self-targeting spacers is occurring at a constant rate in wild-type cells and can be triggered by foreign DNA with similarity to the bacterial chromosome.
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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".