Identification of novel proteins associated with yeast snR30 small nucleolar RNA
Bibliographic record
Abstract
H/ACA small nucleolar RNPs (snoRNPs) that guide pseudouridylation reactions are comprised of one small nucleolar RNA (snoRNA) and four common proteins (Cbf5, Gar1, Nhp2 and Nop10). Unlike other H/ACA snoRNPs, snR30 is essential for the early processing reactions that lead to the production of 18S ribosomal RNA in the yeast Saccharomyces cerevisiae. To determine whether snR30 RNP contains specific proteins that contribute to its unique functional properties, we devised an affinity purification strategy using TAP-tagged Gar1 and an RNA aptamer inserted in snR30 snoRNA to selectively purify the RNP. Northern blotting and pCp labeling experiments showed that S1-tagged snR30 snoRNA can be selectively purified with streptavidin beads. Protein analysis revealed that aptamer-tagged snR30 RNA was associated with the four H/ACA proteins and a number of additional proteins: Nop6, ribosomal proteins S9 and S18 and histones H2B and H4. Using antibodies raised against Nop6 we show that endogenous Nop6 localizes to the nucleolus and that it cosediments with snR30 snoRNA in sucrose density gradients. We demonstrate through primer extension experiments that snR30 snoRNA is required for cleavages at site A0, A1 and A2, and that the absence of Nop6 decreases the efficiency of cleavage at site A2. Finally, electron microscopy analyses of chromatin spreads from cells depleted of snR30 snoRNA show that it is required for SSU processome assembly.
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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 | 0.001 |
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".