G3BP1-linked mRNA partitioning supports selective protein synthesis in response to oxidative stress
Bibliographic record
Abstract
Cells limit energy-consuming mRNA translation during stress to maintain metabolic homeostasis. Sequestration of mRNAs by RNA binding proteins (RBPs) into RNA granules reduces their translation, but it remains unclear whether RBPs also function in partitioning of specific transcripts to polysomes (PSs) to guide selective translation and stress adaptation in cancer. To study transcript partitioning under cell stress, we catalogued mRNAs enriched in prostate carcinoma PC-3 cell PSs, as defined by polysome fractionation and RNA sequencing (RNAseq), and compared them to mRNAs complexed with the known SG-nucleator protein, G3BP1, as defined by spatially-restricted enzymatic tagging and RNAseq. By comparing these compartments before and after short-term arsenite-induced oxidative stress, we identified three major categories of transcripts, namely those that were G3BP1-associated and PS-depleted, G3BP1-dissociated and PS-enriched, and G3BP1-associated but also PS-enriched. Oxidative stress profoundly altered the partitioning of transcripts between these compartments. Under arsenite stress, G3BP1-associated and PS-depleted transcripts correlated with reduced expression of encoded mitochondrial proteins, PS-enriched transcripts that disassociated from G3BP1 encoded cell cycle and cytoprotective proteins whose expression increased, while transcripts that were both G3BP1-associated and PS-enriched encoded proteins involved in diverse stress response pathways. Therefore, G3BP1 guides transcript partitioning to reprogram mRNA translation and support stress adaptation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".