A pooled genome-wide screening strategy to identify and rank influenza host restriction factors in cell-based vaccine production platforms
Bibliographic record
Abstract
Cell-derived influenza vaccines provide better protection and a host of other advantages compared to the egg-derived vaccines that currently dominate the market, but their widespread use is hampered by a lack of high yield, low cost production platforms. Identification and knockout of innate immune and metabolic restriction factors within relevant host cell lines used to grow the virus could offer a means to substantially increase vaccine yield. In this paper, we describe and validate a novel genome-wide pooled CRISPR/Cas9 screening strategy that incorporates a reporter virus and a FACS selection step to identify and rank restriction factors in a given vaccine production cell line. Using the HEK-293SF cell line and A/PuertoRico/8/1934 H1N1 influenza as a model, we identify 64 putative influenza restriction factors to direct the creation of high yield knockout cell lines. In addition, gene ontology and protein complex enrichment analysis of this list of putative restriction factors offers broader insights into the primary host cell determinants of viral yield in cell-based vaccine production systems. Overall, this work will advance efforts to address the public health burden posed by influenza.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".