Baculovirus Variant Detection from Transient CRISPR-Cas9-Mediated Disruption of gp64 at Different Gene Locations
Bibliographic record
Abstract
The Baculovirus Expression Vector System (BEVS) is an important protein and complex biologics production platform. The baculovirus GP64 protein is the major envelope glycoprotein that aids in virus entry and is required for cell-to-cell transmission in cell culture. Several studies have developed strategies around gp64 gene disruption in an attempt to minimize baculovirus co-production. Here, we investigate the result of transiently targeting the baculovirus gp64 gene with CRISPR-Cas9 during infection. Because not all genomes are effectively disrupted, we describe a variant calling methodology that allows the detection of the targeted mutations in gp64 even though these mutations are not the dominant sequences. Using a transfection-infection assay (T-I assay), the AcMNPV gp64 gene was targeted at six different locations to evaluate the effects of single and multiple targeting sites, and we demonstrated a reduction in the levels of baculovirus vectors while maintaining or enhancing foreign protein production when protein was driven by a p6.9 promoter. Viral genomes were subsequently isolated from the supernatant and cell pellet fractions, and our sequencing pipeline successfully detected indel mutations within gp64 for most of the single-guide RNA (sgRNA) targets. We also observed that 68.8% of variants found in the virus stock were conserved upon virus propagation in cell culture, thus indicating that they are not detrimental to viral fitness. This work provides a comprehensive assessment of CRISPR-Cas9 genome editing of baculovirus vectors, with potential applications in enhancing the efficiency of the BEVS.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".