FUS contributes to mTOR-dependent inhibition of translation
Bibliographic record
Abstract
arcoma) has been implicated in several aspects of RNA regulation, including mRNA translation. The mechanism by which FUS affects the translation of polyribosomes has not been established. Here we show that FUS can associate with stalled polyribosomes and that this association is sensitive to mTOR (mammalian target of rapamycin) kinase activity. Specifically, we show that FUS association with polyribosomes is increased by Torin1 treatment or when cells are cultured in nutrient-deficient media, but not when cells are treated with rapamycin, the allosteric inhibitor of mTORC1. Moreover, we report that FUS is necessary for efficient stalling of translation because deficient cells are refractory to the inhibition of mTOR-dependent signaling by Torin1. We also show that ALS-linked FUS mutants R521G and P525L associate abundantly with polyribosomes and decrease global protein synthesis. Importantly, the inhibitory effect on translation by FUS is impaired by mutations that reduce its RNA-binding affinity. These findings demonstrate that FUS is an important RNA-binding protein that mediates translational repression through mTOR-dependent signaling and that ALS-linked FUS mutants can cause a toxic gain of function in the cytoplasm by repressing the translation of mRNA at polyribosomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.002 | 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".