Deciphering the multifaceted role of double-stranded RNA sensor protein kinase R: pathophysiological function beyond the antiviral response
Bibliographic record
Abstract
Protein kinase R (PKR) is a serine/threonine kinase that recognizes double-stranded RNAs (dsRNAs) to initiate innate immune signalling during viral infection. PKR dimerizes on long dsRNAs and undergoes autophosphorylation. Phosphorylated/Activated PKR then catalyses the phosphorylation of numerous substrates to control global translation, inflammatory response, and cell signalling pathways. While primarily known for its antiviral role, emerging evidence suggests that PKR can play multifaceted roles in uninfected cells by interacting with cellular dsRNAs and protein regulators. The misactivation of PKR in uninfected cells is associated with many degenerative and inflammatory diseases. Even in healthy cells, PKR can affect gene expression by controlling mRNA splicing and gene-specific translation under stress. In addition, PKR can modulate cell cycle progression and promote cellular differentiation in several tissue types. This review explores PKR function in various pathological and physiological contexts in the absence of viral stimuli. By elucidating these diverse functions, we aim to highlight the perspectives in cellular dsRNA research and the therapeutic implications of targeting PKR, stimulating further research into this versatile and essential RNA-dependent kinase.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".