LARP1 facilitates translational recovery after amino acid refeeding by preserving long poly(A)-tailed TOP mRNAs
Bibliographic record
Abstract
ABSTRACT Occasionally, cells must adapt to an inimical growth conditions like amino acid starvation (AAS) by downregulating protein synthesis. A class of transcripts containing 5’terminal oligopyrimidine (5’TOP) motif encodes translation-related proteins such as ribosomal proteins (RPs) and elongation factors, and therefore, their translation is severely repressed during AAS to conserve energy 1 . The RNA-binding protein LARP1 transduces amino acid signaling to TOP gene expression by controlling translation and stability of TOP mRNAs 2-6 . When released from AAS, translation machineries in turn have to be restored, however, the underlying mechanism of such re-adaptation is largely unknown. Here we show that LARP1 preserves TOP mRNAs in a long polyadenylated state during long-term AAS. We found that TOP mRNAs become highly polyadenylated when cells are in AAS or treated with the mTOR (mechanistic target of rapamycin) inhibitor Torin1. Importantly, depletion of LARP1 completely abrogated the polyadenylation of TOP mRNAs. Comprehensive analysis of poly(A) tail length using the Nanopore direct RNA sequencing revealed that TOP mRNAs are selectively polyadenylated under mTOR inhibition. Since a long poly(A) tail confers increased stability and polysome formation of TOP mRNAs, we predict that LARP1-dependent preservation of TOP mRNAs enables rapid translational resumption after the release from AAS.
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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 | 0.001 |
| 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".