Nol9 is a Spatial Regulator for the Human ITS2 pre-rRNA Processing Complex
Bibliographic record
Abstract
Abstract The ribosome plays a universal role in translating the cellular proteome. Defects in the ribosome assembly factor Las1L are associated with congenital lethal motor neuron disease and X-linked intellectual disability disorders, yet its role in processing precursor ribosomal RNA (pre-rRNA) is largely unclear. The Las1L endoribonuclease associates with the Nol9 polynucleotide kinase to form the internal transcribed spacer 2 (ITS2) pre-rRNA processing machinery. Together, Las1L-Nol9 catalyzes RNA cleavage and phosphorylation to mark the ITS2 for degradation. While ITS2 processing is critical for the production of functional ribosomes, the regulation of mammalian Las1L-Nol9 remains obscure. Here we characterize the human Las1L-Nol9 complex and identify critical molecular features that regulate its assembly and spatial organization. We establish that Las1L and Nol9 form a higher-order complex and identify the regions responsible for orchestrating this intricate architecture. Structural analysis by high-resolution imaging defines the intricate spatial pattern of Las1L-Nol9 within the nucleolar sub-structure linked with late pre-rRNA processing events. Furthermore, we uncover a Nol9 encoded nucleolar localization sequence that is responsible for nucleolar transport of the assembled LasL-Nol9 complex. Together, these data provide a mechanism for the assembly and nucleolar localization of the human ITS2 pre-rRNA processing complex.
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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".