A Whole-Genome RNA Interference Screen for Human Cell Factors Affecting Myxoma Virus Replication
Bibliographic record
Abstract
Myxoma virus (MYXV) provides an important model for investigating host-pathogen interactions. Recent studies have also highlighted how mutations in transformed human cells can expand the host range of this rabbit virus. Although virus growth depends upon interactions between virus and host proteins, the nature of these interactions is poorly understood. To address this matter, we performed small interfering RNA (siRNA) screens for genes affecting MYXV growth in human MDA-MB-231 cells. By using siRNAs targeting the whole human genome (21,585 genes), a subset of human phosphatases and kinases (986 genes), and also a custom siRNA library targeting selected statistically significant genes ("hits") and nonsignificant genes ("nonhits") of the whole human genome screens (88 genes), we identified 711 siRNA pools that promoted MYXV growth and 333 that were inhibitory. Another 32 siRNA pools (mostly targeting the proteasome) were toxic. The overall overlap in the results was about 25% for the hits and 75% for the nonhits. These pro- and antiviral genes can be clustered into pathways and related groups, including well-established inflammatory and mitogen-activated protein kinase pathways, as well as clusters relating to β-catenin and the Wnt signaling cascade, the cell cycle, and cellular metabolism. The validity of a subset of these hits was independently confirmed. For example, treating cells with siRNAs that might stabilize cells in G(1), or inhibit passage into S phase, stimulated MYXV growth, and these effects were reproduced by trapping cells at the G(1)/S boundary with an inhibitor of cyclin-dependent kinases 4/6. By using 2-deoxy-D-glucose and plasmids carrying the gene for phosphofructokinase, we also confirmed that infection is favored by aerobic glycolytic metabolism. These studies provide insights into how the growth state and structure of cells affect MYXV growth and how these factors might be manipulated to advantage in oncolytic virus therapy.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".