Expansion of targetable sites for the ribonucleoprotein-based CRISPR/Cas9 system in the silkworm Bombyx mori
Bibliographic record
Abstract
BACKGROUND: With the emergence of CRISPR/Cas9 technology, multiple gene editing procedures became available for the silkworm. Although binary transgene-based methods have been widely used to generate mutants, delivery of the CRISPR/Cas9 system via DNA-free ribonucleoproteins offers several advantages. However, the T7 promoter that is widely used in the ribonucleoprotein-based method for production of sgRNAs in vitro requires a 5' GG motif for efficient initiation. The resulting transcripts bear a 5' GG motif, which significantly constrains the number of targetable sites in the silkworm genome. RESULTS: In this study, we used the T7 promoter to add two supernumerary G residues to the 5' end of conventional (perfectly matched) 20-nucleotide sgRNA targeting sequences. We then asked if sgRNAs with this structure can generate mutations even if the genomic target does not contain corresponding GG residues. As expected, 5' GG mismatches depress the mutagenic activity of sgRNAs, and a single 5' G mismatch has a relatively minor effect. However, tests involving six sgRNAs targeting two genes show that the mismatches do not eliminate mutagenesis in vivo, and the efficiencies remain at useable levels. One sgRNA with a 5' GG mismatch at its target performed mutagenesis more efficiently than a conventional sgRNA with 5' matched GG residues at a second target within the same gene. Mutations generated by sgRNAs with 5' GG mismatches are also heritable. We successfully obtained null mutants with detectable phenotypes from sib-mated mosaics after one generation. CONCLUSIONS: In summary, our method improves the utility and flexibility of the ribonucleoprotein-based CRISPR/Cas9 system in silkworm.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".