Proteostasis regulation through ribosome quality control and no‐go‐decay
Bibliographic record
Abstract
Cell functionality relies on the existing pool of proteins and their folding into functional conformations. This is achieved through the regulation of protein synthesis, which requires error-free mRNAs and ribosomes. Ribosomes are quality control hubs for mRNAs and proteins. Problems during translation elongation slow down the decoding rate, leading to ribosome halting and the eventual collision with the next ribosome. Collided ribosomes form a specific disome structure recognized and solved by ribosome quality control (RQC) mechanisms. RQC pathways orchestrate the degradation of the problematic mRNA by no-go decay and the truncated nascent peptide, the repression of translation initiation, and the recycling of the stalled ribosomes. All these events maintain protein homeostasis and return valuable ribosomes to translation. As such, cell homeostasis and function are maintained at the mRNA level by preventing the production of aberrant or unnecessary proteins. It is becoming evident that the crosstalk between RQC and the protein homeostasis network is vital for cell function, as the absence of RQC components leads to the activation of stress response and neurodegenerative diseases. Here, we review the molecular events of RQC discovered through well-designed stalling reporters. Given the impact of RQC in proteostasis, we discuss the relevance of identifying endogenous mRNA regulated by RQC and their preservation in stress conditions. This article is categorized under: RNA Turnover and Surveillance > Turnover/Surveillance Mechanisms Translation > Regulation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".