La-related Protein 1 (LARP1) Represses Terminal Oligopyrimidine (TOP) mRNA Translation Downstream of mTOR Complex 1 (mTORC1)
Bibliographic record
Abstract
The mammalian target of rapamycin complex 1 (mTORC1) is a critical regulator of protein synthesis. The best studied targets of mTORC1 in translation are the eukaryotic initiation factor-binding protein 1 (4E-BP1) and ribosomal protein S6 kinase 1 (S6K1). In this study, we identify the La-related protein 1 (LARP1) as a key novel target of mTORC1 with a fundamental role in terminal oligopyrimidine (TOP) mRNA translation. Recent genome-wide studies indicate that TOP and TOP-like mRNAs compose a large portion of the mTORC1 translatome, but the mechanism by which mTORC1 controls TOP mRNA translation is incompletely understood. Here, we report that LARP1 functions as a key repressor of TOP mRNA translation downstream of mTORC1. Our data show the following: (i) LARP1 associates with mTORC1 via RAPTOR; (ii) LARP1 interacts with TOP mRNAs in an mTORC1-dependent manner; (iii) LARP1 binds the 5'TOP motif to repress TOP mRNA translation; and (iv) LARP1 competes with the eukaryotic initiation factor (eIF) 4G for TOP mRNA binding. Importantly, from a drug resistance standpoint, our data also show that reducing LARP1 protein levels by RNA interference attenuates the inhibitory effect of rapamycin, Torin1, and amino acid deprivation on TOP mRNA translation. Collectively, our findings demonstrate that LARP1 functions as an important repressor of TOP mRNA translation downstream of mTORC1.
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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".