CRISPR-Cas: an efficient tool for genome engineering of virulent bacteriophages
Bibliographic record
Abstract
Bacteriophages are now widely recognized as major players in a wide variety of ecosystems. Novel genes are often identified in newly isolated phages as well as in environmental metavirome studies. Most of these novel viral genes have unknown functions but appear to be coding for small, non-structural proteins. To understand their biological role, very efficient genetic tools are required to modify them, especially in the genome of virulent phages. We first show that specific point mutations and large deletions can be engineered in the genome of the virulent phage 2972 using the Streptococcus thermophilus CRISPR-Cas Type II-A system as a selective pressure to increase recombination efficiencies. Of significance, all the plaques tested contained recombinant phages with the desired mutation. Furthermore, we show that the CRISPR-Cas engineering system can be used to efficiently introduce a functional methyltransferase gene into a virulent phage genome. Finally, synthetic CRISPR bacteriophage insensitive mutants were constructed by cloning a spacer-repeat unit in a low-copy vector illustrating the possibility to target multiple regions of the phage genome. Taken together, this data shows that the CRISPR-Cas system is an efficient and adaptable tool for editing the otherwise intractable genomes of virulent phages and to better understand phage-host interactions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".