mTORC1 promotes TOP mRNA translation through site-specific phosphorylation of LARP1
Bibliographic record
Abstract
LARP1 is a key repressor of TOP mRNA translation. It binds the m7Gppp cap moiety and the adjacent 5'TOP motif of TOP mRNAs, thus impeding the assembly of the eIF4F complex on these transcripts. mTORC1 controls TOP mRNA translation via LARP1, but the details of the mechanism are unclear. Herein we elucidate the mechanism by which mTORC1 controls LARP1's translation repression activity. We demonstrate that mTORC1 phosphorylates LARP1 in vitro and in vivo, activities that are efficiently inhibited by rapamycin and torin1. We uncover 26 rapamycin-sensitive phospho-serine and -threonine residues on LARP1 that are distributed in 7 clusters. Our data show that phosphorylation of a cluster of residues located proximally to the m7Gppp cap-binding DM15 region is particularly sensitive to rapamycin and regulates both the RNA-binding and the translation inhibitory activities of LARP1. Our results unravel a new model of translation control in which the La module (LaMod) and DM15 region of LARP1, both of which can directly interact with TOP mRNA, are differentially regulated: the LaMod remains constitutively bound to PABP (irrespective of the activation status of mTORC1), while the C-terminal DM15 'pendular hook' engages the TOP mRNA 5'-end to repress translation, but only in conditions of mTORC1 inhibition.
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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".