Basic Domains Target Protein Subunits of the RNase MRP Complex to the Nucleolus Independently of Complex Association
Bibliographic record
Abstract
The RNase MRP and RNase P ribonucleoprotein particles both function as endoribonucleases, have a similar RNA component, and share several protein subunits. RNase MRP has been implicated in pre-rRNA processing and mitochondrial DNA replication, whereas RNase P functions in pre-tRNA processing. Both RNase MRP and RNase P accumulate in the nucleolus of eukaryotic cells. In this report we show that for three protein subunits of the RNase MRP complex (hPop1, hPop4, and Rpp38) basic domains are responsible for their nucleolar accumulation and that they are able to accumulate in the nucleolus independently of their association with the RNase MRP and RNase P complexes. We also show that certain mutants of hPop4 accumulate in the Cajal bodies, suggesting that hPop4 traverses through these bodies to the nucleolus. Furthermore, we characterized a deletion mutant of Rpp38 that preferentially associates with the RNase MRP complex, giving a first clue about the difference in protein composition of the human RNase MRP and RNase P complexes. On the basis of all available data on nucleolar localization sequences, we hypothesize that nucleolar accumulation of proteins containing basic domains proceeds by diffusion and retention rather than by an active transport process. The existence of nucleolar localization sequences is discussed.
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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) | 0.002 | 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".