Rat1p and Rai1p function with the nuclear exosome in the processing and degradation of rRNA precursors
Bibliographic record
Abstract
Exoribonucleases function in the processing and degradation of a variety of RNAs in all organisms. These enzymes play a particularly important role in the maturation of rRNAs and in a quality-control pathway that degrades rRNA precursors upon inhibition of ribosome biogenesis. Strains with defects in 3'-5' exoribonucleolytic components of the RNA processing exosome accumulate polyadenylated precursor rRNAs that also arise in strains with ribosome biogenesis defects. These findings suggested that polyadenylation might target pre-rRNAs for degradation by the exosome. Here we report experiments that indicate a role for the 5'-3' exoribonuclease Rat1p and its associated protein Rai1p in the degradation of poly(A)(+) pre-rRNAs. Depletion of Rat1p enhances the amount of poly(A)(+) pre-rRNA that accumulates in strains deleted for the exosome subunit Rrp6p and decreases their 5' heterogeneity. Deletion of RAI1 results in the accumulation of poly(A)(+) pre-rRNAs, and inhibits Rat1p-dependent 5'-end processing and Rrp6p-dependent 3'-end processing of 5.8S rRNA. RAT1 and RAI1 mutations cause synergistic growth defects in the presence of rrp6-Delta, consistent with the interdependence of 5'-end and 3'-end processing pathways. These findings suggest that Rai1p may coordinate the 5'-end and 3'-end processing and degradation activities of Rat1p and the nuclear exosome.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".