Stress Granule Sequestration of CCR4–NOT Promotes Poly(A) Lengthening of Stress-Survival Transcripts
Bibliographic record
Abstract
Cells adapt to stress by rewiring their post-transcriptional gene regulation. Stress granules-biomolecular condensates composed of polyadenylated RNAs and RNA-binding proteins-are implicated in this process, yet their precise functional roles remain debated. To address this, we mapped the dynamic proteomic landscapes of stress granules formed under oxidative and hyperosmotic stress using multi-bait BioID proximity profiling coupled with quantitative mass spectrometry. This analysis revealed context-specific remodeling of proximal interaction networks and identified a conserved, stress-dependent shift in association with the CCR4-NOT deadenylase complex. A complementary genome-wide chemical genetic screen further implicated CCR4-NOT in stress granule biology, showing that reduced CCR4-NOT activity bypassed lipoamide-mediated inhibition of stress granule assembly. Microscopy showed sequestration of the CCR4-NOT complex into stress granules, and global transcriptomic analyses revealed that this relocalization promotes poly(A) tail lengthening and increased abundance of stress-induced survival transcripts. Together, integration of proteomics, chemical genetics, and transcriptomics uncovers a spatial mechanism by which stress granule assembly promotes cellular adaptation to stress through sequestration of CCR4-NOT from the cytosol and consequent remodeling of post-transcriptional regulation.
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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".