The amino acid sensor GCN2 suppresses terminal oligopyrimidine (TOP) mRNA translation via La-related protein 1 (LARP1)
Bibliographic record
Abstract
La-related protein 1 (LARP1) has been identified as a key translational inhibitor of terminal oligopyrimidine (TOP) mRNAs downstream of the nutrient sensing protein kinase complex, mTORC1. LARP1 exerts this inhibitory effect on TOP mRNA translation by binding to the mRNA cap and the adjacent 5'TOP motif, resulting in the displacement of the cap-binding protein eIF4E from TOP mRNAs. However, the involvement of additional signaling pathway in regulating LARP1-mediated inhibition of TOP mRNA translation is largely unexplored. In the present study, we identify a second nutrient sensing kinase GCN2 that converges on LARP1 to control TOP mRNA translation. Using chromatin-immunoprecipitation followed by massive parallel sequencing (ChIP-seq) analysis of activating transcription factor 4 (ATF4), an effector of GCN2 in nutrient stress conditions, in WT and GCN2 KO mouse embryonic fibroblasts, we determined that LARP1 is a GCN2-dependent transcriptional target of ATF4. Moreover, we identified GCN1, a GCN2 activator, participates in a complex with LARP1 on stalled ribosomes, suggesting a role for GCN1 in LARP1-mediated translation inhibition in response to ribosome stalling. Therefore, our data suggest that the GCN2 pathway controls LARP1 activity via two mechanisms: ATF4-dependent transcriptional induction of LARP1 mRNA and GCN1-mediated recruitment of LARP1 to stalled ribosomes.
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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 | 0.000 |
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 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".