Co-transcriptional recruitment of Puf6 by She2 couples translational repression to mRNA localization
Bibliographic record
Abstract
Messenger RNA (mRNA) localization is coupled to the translational repression of transcripts during their transport. It is still unknown if this coupling depends on physical interactions between translational control and mRNA localization machineries, and how these interactions are established at the molecular level. In yeast, localization of transcripts like ASH1 to the bud depends on the RNA-binding protein She2. During its transport, ASH1 mRNA translation is repressed by Puf6. Herein, we report that She2 recruits Puf6 on ASH1 co-transcriptionally. The recruitment of Puf6 depends on prior co-transcriptional loading of Loc1, an exclusively nuclear protein. These proteins form a ternary complex, in which Loc1 bridges Puf6 to She2, that binds the ASH1 3'UTR. Using a genome-wide ChIP-chip approach, we identified over 40 novel targets of Puf6, including several bud-localized mRNAs. Interestingly, the co-transcriptional recruitment of Puf6 on genes coding for these bud-localized mRNAs is also She2- and Loc1-dependent. Our results suggest a coordinated assembly of localization and translational control machineries on localized mRNAs during transcription, and underline the importance of co-transcriptional events in establishing the cytoplasmic fate of mRNAs.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".