Ribosome Quality Control Mechanism Mitigates the Cytotoxic Impacts of Ribosome Collisions Induced by 5-Fluorouracil
Bibliographic record
Abstract
Abstract Translation of aberrant or damaged mRNAs results in ribosome stalling and collisions. The Ribosome Quality Control (RQC) mechanism detects collided ribosomes and removes aberrant mRNAs and nascent peptides, thus preventing their cytotoxic effects. Conversely, excessive or unresolved ribosome collisions can induce apoptosis. 5-Fluorouracil (5FU) forms the backbone of standard-of-care chemotherapeutic regimens for several types of cancer. Although best known for its incorporation into DNA and inhibition of thymidylate synthase, a major determinant of 5FU’s anticancer activity is its incorporation into RNAs. Nevertheless, the mechanism(s) underlying RNA-dependent 5FU cytotoxicity and the cellular response to its impact on RNA metabolism remain unclear. Here, we report a key role for RQC in mitigating the cytotoxic effects of 5FU-induced dysregulation of mRNA translation. We show that acute 5FU treatment results in the rapid induction of the mTOR signalling pathway, an enhanced rate of mRNA translation initiation, and increased ribosome collisions that trigger RQC. We also found that RQC deficiency, caused by the depletion of ZNF598, results in increased 5FU-induced cell death, a phenotype that is reversed by inhibition of mTOR or repression of mRNA translation initiation. Importantly, 5FU treatment enhances the expression of key RQC factors, including ZNF598 and GIGYF2, via an mTOR-dependent post-translational regulation mechanism. This acute adaptation likely mitigates the cytotoxic consequences of increased ribosome collisions upon 5FU treatment. Overall, our data indicate a heretofore unknown mTOR-dependent mechanism that augments the RQC process, mitigating the cytotoxicity of 5FU and undermining its anticancer efficacy.
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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".