Insight into crRNA Processing in Streptococcus mutans P42S and Application of SmutCas9 in Genome Editing
Bibliographic record
Abstract
CRISPR-Cas is an adaptive immune system found in bacteria and archaea that provides resistance against invading nucleic acids. Elements of this natural system have been harnessed to develop several genome editing tools, including CRISPR-Cas9. This technology relies on the ability of the nuclease Cas9 to cut DNA at specific locations directed by a guide RNA. In addition, the nuclease activity of Cas9 requires the presence of a short nucleotide motif (5′-NGG-3′ for Cas9 from Streptococcus pyogenes) called PAM, flanking the targeted region. As the reliance on this PAM is typically strict, diverse Cas9 variants recognising different PAM motifs have been studied to target a broader range of genomic sites. In this study, we assessed the potential of Cas9 from Streptococcus mutans strain P42S (SmutCas9) in gene editing. SmutCas9 recognises the rarely targeted 5′-NAA-3′ and 5′-NGAA-3′ PAMs. To test its efficacy, two genes of the virulent lactococcal phage p2 were edited, thereby demonstrating the potential of SmutCas9 for gene editing purposes, particularly in AT-rich genomes. Sequencing of total RNA also revealed the RNA components of this system, allowing further molecular characterisation of the type II-A CRISPR-Cas system of S. mutans.
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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".