Modulation of RNA condensation by the DEAD-box protein eIF4A
Bibliographic record
Abstract
SUMMARY Stress granules are condensates of non-translating mRNAs and proteins involved in the stress response and neurodegenerative diseases. Stress granules form in part through intermolecular RNA-RNA interactions, although the process of RNA condensation is poorly understood. In vitro , we demonstrate that RNA is effectively recruited to the surfaces of RNA or RNP condensates. We demonstrate that the DEAD-box protein eIF4A reduces RNA condensation in vitro and limits stress granule formation in cells. This defines a purpose for eIF4A to limit intermolecular RNA-RNA interactions in cells, thereby allowing for proper RNP function. These results establish an important role for DEAD-box proteins as ATP-dependent RNA chaperones that can limit the intermolecular condensation and entanglement of RNA, analogous to the function of proteins like HSP70 in combatting protein aggregates. eTOC Blurb Stress granules are formed in part by the process of RNA condensation, which is mediated by and promotes trans RNA-RNA interactions. The essential DEAD-box protein and translation initiation factor eIF4A limits stress granule formation by reducing RNA condensation through its function as an ATP-dependent RNA binding protein, behaving analogously to how protein chaperones like HSP70 combat protein aggregates. Highlights RNA condensates promote intermolecular RNA-RNA interactions at their surfaces eIF4A limits the recruitment of RNAs to stress granules in cells eIF4A reduces the nucleation of stress granules in cells Recombinant eIF4A1 inhibits the condensation of RNA in vitro in an ATP-dependent manner
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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 | 0.000 |
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".