Translational coregulation of 5′TOP mRNAs by TIA-1 and TIAR
Bibliographic record
Abstract
The response of cells to changes in their environment often requires coregulation of gene networks, but little is known about how this can occur at the post-transcriptional level. An important example of post-transcriptional coregulation is the selective translational regulation in response to growth conditions of mammalian mRNAs that encode protein biosynthesis factors and contain hallmark 5'-terminal oligopyrimidine tracts (5'TOP). However, the responsible trans-factors and the mechanism by which they coregulate 5'TOP mRNAs have remained elusive. Here we identify stress granule-associated TIA-1 and TIAR proteins as key factors in human 5'TOP mRNA regulation, which upon amino acid starvation assemble onto the 5' end of 5'TOP mRNAs and arrest translation at the initiation step, as evidenced by TIA-1/TIAR-dependent 5'TOP mRNA translation repression, polysome release, and accumulation in stress granules. This requires starvation-mediated activation of the GCN2 (general control nonderepressible 2) kinase and inactivation of the mTOR (mammalian target of rapamycin) signaling pathway. Our findings provide a mechanistic explanation to the long-standing question of how the network of 5'TOP mRNAs are coregulated according to amino acid availability, thereby allowing redirection of limited resources to mount a nutrient deprivation response. This presents a fundamental example of how a group of mRNAs can be translationally coregulated in response to changes in the cellular environment.
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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".