UNR, a new partner of poly(A)-binding protein, plays a key role in translationally coupled mRNA turnover mediated by the c-<i>fos</i> major coding-region determinant
Bibliographic record
Abstract
Messenger RNA decay mediated by the c-fos major protein coding-region determinant of instability (mCRD) is a useful system for studying translationally coupled mRNA turnover. Among the five mCRD-associated proteins identified previously, UNR was found to be an mCRD-binding protein and also a PABP-interacting protein. Interaction between UNR and PABP is necessary for the full destabilization function of the mCRD. By testing different classes of mammalian poly(A) nucleases, we identified CCR4 as a poly(A) nuclease involved in the mCRD-mediated rapid deadenylation in vivo and also associated with UNR. Blocking either translation initiation or elongation greatly impeded poly(A) shortening and mRNA decay mediated by the mCRD, demonstrating that the deadenylation step is coupled to ongoing translation of the message. These findings suggest a model in which the mCRD/UNR complex serves as a "landing/assembly" platform for formation of a deadenylation/decay mRNA-protein complex on an mCRD-containing transcript. The complex is dormant prior to translation. Accelerated deadenylation and decay of the transcript follows ribosome transit through the mCRD. This study provides new insights into a mechanism by which interplay between mRNA turnover and translation determines the lifespan of an mCRD-containing mRNA in the cytoplasm.
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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".