RNA Structure Directs RNA Partitioning and is Actively Disrupted inside Stress Granules to Enable Cellular Recovery
Bibliographic record
Abstract
Abstract RNA structures play important roles in liquid-liquid phase separation. However, how it is regulated during stress response and stress granule formation is still under studied. Here, we performed in vivo RNA structure probing before and after sodium arsenite treatment, and in stress granules. While RNAs generally become more double-stranded upon stress, they maintain their single-strandedness inside stress granules. We showed that RNA single-strandedness enables increased inclusion inside stress granules and that stress granule-enriched RNAs form fewer intra- and intermolecular RNA-RNA interactions. Additionally, several RNA binding proteins including SRSF1 are enriched in differential structure regions. eCLIP analysis revealed that SRSF1 binds to single-stranded regions along RNAs, and increased SRSF1 binding enabled better inclusion of RNAs in stress granules, whereas depletion of SRSF1 decreased stress granule formation under mild oxidative stress. We also observed the active unwinding of RNAs inside stress granules regulated by helicases, including DDX3X, and showed that inhibition of DDX3X results in slower dissolution of stress granules during recovery. Our study reveals the existence of multiple mechanisms to maintain RNA single-strandedness inside stress granules and to allow reversibility of stress granule formation, highlighting the importance of regulating RNA structure to enable cellular plasticity and stress response.
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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) | 0.001 | 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".