From functional genomics of vero cells to CRISPR‐based genomic deletion for improved viral production rates
Bibliographic record
Abstract
Abstract Despite their wide use in the vaccine manufacturing field for over 40 years, one of the main limitations to recent efforts to develop Vero cells as high‐throughput vaccine manufacturing platforms is the lack of understanding of virus‐host interactions during infection and cell‐based virus production in Vero cells. To overcome this limitation, this manuscript uses the recently generated reference genome for the Vero cell line to identify the factors at play during influenza A virus (IAV) and recombinant vesicular stomatitis virus (rVSV) infection and replication in Vero host cells. The best antiviral gene candidate for gene editing was selected using Differential Gene Expression analysis, Gene Set Enrichment Analysis and Network Topology‐based Analysis. After selection of the ISG15 gene for targeted CRISPR genomic deletion, the ISG15 genomic sequence was isolated for CRISPR guide RNAs design and the guide RNAs with the highest knockout efficiency score were selected. The CRISPR experiment was then validated by confirmation of genomic deletion via PCR and further assessed via quantification of ISG15 protein levels by western blot. The gene deletion effect was assessed thereafter via quantification of virus production yield in the edited Vero cell line. A 70‐fold and an 87‐fold increase of total viral particles productions in ISG15 −/− Vero cells was achieved for, respectively, IAV and rVSV while the ratio of infectious viral particles/total viral particles also significantly increased from 0.0316 to 0.653 for IAV and from 0.0542 to 0.679 for rVSV‐GFP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".