Genome Engineering of Virulent Lactococcal Phages Using CRISPR-Cas9
Bibliographic record
Abstract
Phages are biological entities found in every ecosystem. Although much has been learned about them in past decades, significant knowledge gaps remain. Manipulating virulent phage genomes is challenging. To date, no efficient gene-editing tools exist for engineering virulent lactococcal phages. Lactococcus lactis is a bacterium extensively used as a starter culture in various milk fermentation processes, and its phage sensitivity poses a constant risk to the cheese industry. The lactococcal phage p2 is one of the best-studied models for these virulent phages. Despite its importance, almost half of its genes have no functional assignment. CRISPR-Cas9 genome editing technology, which is derived from a natural prokaryotic defense mechanism, offers new strategies for phage research. Here, the well-known Streptococcus pyogenes CRISPR-Cas9 was used in a heterologous host to modify the genome of a strictly lytic phage. Implementation of our adapted CRISPR-Cas9 tool in the prototype phage-sensitive host L. lactis MG1363 allowed us to modify the genome of phage p2. A simple, reproducible technique to generate precise mutations that allow the study of lytic phage genes and their encoded proteins in vivo is described.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".