Activation of NF-κB by Double-Stranded RNA (dsRNA) in the Absence of Protein Kinase R and RNase L Demonstrates the Existence of Two Separate dsRNA-Triggered Antiviral Programs
Bibliographic record
Abstract
Double-stranded RNA (dsRNA) of viral origin triggers two programs of the innate immunity in virus-infected cells. One is intended to decrease the rate of host cell protein synthesis and thus to prevent viral replication. This program is mediated by protein kinase R (PKR) and by RNase L and contributes, eventually, to the self-elimination of the infected cell via apoptosis. The second program is responsible for the production of antiviral (type I) interferons and other alarmone cytokines and serves the purpose of preparing naive cells for the viral invasion. This second program requires the survival of the infected cell and depends on the expression of antiapoptotic genes through the activation of the NF-kappaB transcription factor. The second program therefore relies on ongoing transcription and translation. It has been proposed that PKR plays an essential role in the activation of NF-kappaB by dsRNA. Here we present evidence that the dsRNA-induced NF-kappaB activity and the expression of beta interferon and inflammatory cytokines do not require either PKR or RNase L. Our results indicate, therefore, that the two dsRNA-activated programs are separate and can function independently of each other.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".