Nucleolar Pol II interactome reveals TBPL1, PAF1, and Pol I at intergenic rDNA drive rRNA biogenesis
Bibliographic record
Abstract
Abstract Nucleolar ribosomal DNA (rDNA) repeats control ribosome manufacturing. rDNA harbors a ribosomal RNA (rRNA) gene and an intergenic spacer (IGS). RNA polymerase (Pol) I transcribes rRNA genes yielding the rRNA components of ribosomes. Pol II at the IGS induces rRNA production by preventing Pol I from excessively synthesizing IGS non-coding RNAs (ncRNAs) that can disrupt nucleoli. At the IGS, Pol II regulatory processes and whether Pol I function can be beneficial remain unknown. Here, we identify IGS Pol II regulators, uncovering nucleolar optimization via IGS Pol I. Compartment-enriched proximity-dependent biotin identification (compBioID) showed enrichment of the TATA-less promoter-binding TBPL1 and transcription regulator PAF1 with IGS Pol II. TBPL1 localizes to TCT motifs, driving Pol II and Pol I and maintaining its baseline ncRNA levels. PAF1 promotes Pol II elongation, preventing unscheduled R-loops that hyper-restrain IGS Pol I and its ncRNAs. PAF1 or TBPL1 deficiency disrupts nucleolar organization and rRNA biogenesis. In PAF1-deficient cells, repressing unscheduled IGS R-loops rescues nucleolar organization and rRNA production. Depleting IGS Pol I-dependent ncRNAs is sufficient to compromise nucleoli. We present the interactome of nucleolar Pol II and show its control by TBPL1 and PAF1 ensures IGS Pol I ncRNAs maintaining nucleolar structure and operation.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".