Cytoplasmic protein-free mRNA induces stress granules by two G3BP1/2-dependent mechanisms
Bibliographic record
Abstract
Abstract Stress granules are cytoplasmic membraneless organelles that sequester proteins and non-translating mRNAs in response to various stressors. To assess the contributions of mRNA and RNA-binding proteins to stress granule formation, we use microinjection to deliver protein-free mRNA into the cytoplasm in a controlled manner. We demonstrate that mRNAs trigger stress granule formation through two mechanisms that are enhanced by the presence of G3BP1 and G3BP2. Low concentrations of in vitro transcribed mRNA activated protein kinase R (PKR), leading to phosphorylation and inhibition of the eukaryotic translation initiation factor eIF2α and stress granule formation. This was inhibited by replacing uridine with pseudouridine in the mRNA or by treating it with RNase III, which cleaves double-stranded RNA. High concentrations of mRNA triggered stress granule formation by a mechanism that was independent of PKR and enhanced by G3BP1/2, highlighting the importance of both protein-free mRNA and RNA-binding proteins in stress granule formation. Graphical Abstract/Model Summary Microinjected mRNA induces stress granules in mammalian cells by two G3BP1/2-dependent mechanisms: one requires the stress-sensing protein kinase PKR to phosphorylate the translation initiation factor eIF2α, and the other is independent of PKR and phospho-eIF2α and acts when the cytoplasmic concentration of ribosome-free mRNA is increased acutely.
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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) | 0.000 | 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) | 0.006 | 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".