Knockdown of specific host factors protects against influenza virus-induced cell death
Bibliographic record
Abstract
Cell death is a characteristic consequence of cellular infection by influenza virus. Mounting evidence indicates the critical involvement of host-mediated cellular death pathways in promoting efficient influenza virus replication. Furthermore, it appears that many signaling pathways, such as NF-κB, formerly suspected to solely promote cell survival, can also be manipulated to induce cell death. Current understanding of the cell death pathways involved in influenza virus-mediated cytopathology and in virus replication is limited. This study was designed to identify host genes that are required for influenza-induced cell death. The approach was to perform genome-wide lentiviral-mediated human gene silencing in A549 cells and determine which genes could be silenced to provide resistance to influenza-induced cell death. The assay proved to be highly reproducible with 138 genes being identified in independent screens. The results were independently validated using siRNA to each of these candidates. Graded protection was observed in this screen with the silencing of any of 19 genes, each providing > 85% protection. Three gene products, TNFSF13 (APRIL), TNFSF12-TNFSF13 (TWE-PRIL) and USP47, were selected because of the high levels of protection conferred by their silencing. Protein and mRNA silencing and protection from influenza-induced cell death was confirmed using multiple shRNA clones and siRNA, indicating the specificity of the effects. USP47 knockdown prevented proper viral entry into the host cell, whereas TNFSF12-13/TNFSF13 knockdown blocked a late stage in viral replication. This screening approach offers the means to identify a large number of potential candidates for the analysis of viral-induced cell death. These results may also have much broader applicability in defining regulatory mechanisms involved in cell survival.
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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) | 0.000 | 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) | 0.001 | 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".