Fine-tuning a blunt tool: Regulation of viral host shutoff RNases
Bibliographic record
Abstract
The evolutionary arms race between host and pathogen has resulted in the ability of many human viruses to alter the host gene expression profile during infection, in order to redirect cellular resources towards viral gene expression and inhibit cell-intrinsic host immune responses. In particular, multiple viruses globally reduce host gene expression in a process termed "host shutoff." Multiple mechanisms of host shutoff exist, including translational and transcriptional shutoff, but several viruses carry out host shutoff by encoding ribonucleases (RNases) that degrade host messenger RNAs (mRNAs). Viral host shutoff RNases include the influenza A virus polymerase acidic-X (PA-X) [1], the herpes simplex viruses (HSV-1 and -2) virion host shutoff protein (vhs) These RNases contribute to efficient formation of virions and/or reduction of innate immune signaling For example, in the absence of EBV BGLF5, the virus produces fewer mature capsids, many of which remain trapped in the nucleus Vhs-deficient HSV replicates well in many common tissue culture models [9] but shows replication defects in relevant cell types, such as cerebellar granule neurons Moreover, in mice, viruses lacking detectable host shutoff activity replicate to lower viral titers in neuronal tissue Influenza A virus PA-X also limits host antiviral and proinflammatory responses in several animal models Although host shutoff RNases are important for successful viral infection, their activity presents an interesting problem for the viruses that encode them. Unregulated RNase activity could degrade viral RNAs or host mRNAs encoding proteins that the virus needs. Moreover, drastic depletion of the mRNA pool by the virus could trigger antiviral host stress responses and cell death. It is thus unsurprising that evidence is now emerging that viruses posttranslationally regulate the activity of their host shutoff RNases through a variety of mechanisms, reviewed herein (Fig
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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) | 0.001 | 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 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".