UV damage induces G3BP1-dependent stress granule formation that is not driven by translation arrest via mTOR inhibition
Bibliographic record
Abstract
ABSTRACT Translation arrest is a part of the cellular stress response that decreases energy consumption and enables rapid reprioritisation of gene expression. Often translation arrest leads to condensation of untranslated messenger ribonucleoproteins (mRNPs) into stress granules (SGs). Studies into mechanisms of SG formation and functions are complicated because various types of stress cause formation of SGs with different properties and composition. In this work we focused on the mechanism of SG formation triggered by UV damage. We demonstrate that UV-induced inhibition of translation does not cause dissociation of the 48S preinitiation complexes. The catalytic activity of the general control non-derepressible 2 (GCN2) kinase contributes to UV-induced SG formation, which is independent of the GCN2-mediated phosphorylation of the eukaryotic translation initiation factor 2α. Like many other types of SGs, condensation of UV-induced granules specifically requires the Ras-GTPase-Activating Protein SH3-Domain-Binding Protein 1 (G3BP1). Our work reveals that in UV-treated cells the mechanisms of translation arrest and SG formation may be unlinked, resulting in condensation of ribonucleoproteins that do not represent the major type of polysome-free preinitiation complexes that accumulate in the cytoplasm.
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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 | 0.000 |
| 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".