Inhibition of the inflammatory response to stress by targeting interaction between PKR and its cellular activator PACT
Bibliographic record
Abstract
PKR is a cellular kinase involved in the regulation of the integrative stress response (ISR) and pro-inflammatory pathways. Two N-terminal dsRNA Binding Domains (DRBD) are required for activation of PKR, by interaction with either dsRNA or PACT, another cellular DRBD-containing protein. A role for PKR and PACT in inflammatory processes linked to neurodegenerative diseases has been proposed and raised interest for pharmacological PKR inhibitors. However, the role of PKR in inflammation is subject to controversy. We identified the flavonoid luteolin as an inhibitor of the PKR/PACT interaction at the level of their DRBDs using high-throughput screening of chemical libraries by homogeneous time-resolved fluorescence. This was further validated using NanoLuc-Based Protein Complementation Assay. Luteolin inhibits PKR phosphorylation, the ISR and the induction of pro-inflammatory cytokines in human THP1 macrophages submitted to oxidative stress and toll-like receptor (TLR) agonist. Similarly, luteolin inhibits induction of pro-inflammatory cytokines in murine microglial macrophages. In contrast, luteolin increased activation of the inflammasome, in a PKR-independent manner. Collectively, these data delineate the importance of PKR in the inflammation process to the ISR and induction of pro-inflammatory cytokines. Pharmacological inhibitors of PKR should be used in combination with drugs targeting directly the inflammasome.
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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 | 0.001 |
| 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".