New insights into the biogenesis of nuclear RNA polymerases?This paper is one of a selection of papers published in this special issue entitled “Canadian Society of Biochemistry, Molecular & Cellular Biology 52nd Annual Meeting — Protein Folding: Principles and Diseases” and has undergone the Journal's usual peer review process.
Bibliographic record
Abstract
More than 30 years of research on nuclear RNA polymerases (RNAP I, II, and III) has uncovered numerous factors that regulate the activity of these enzymes during the transcription reaction. However, very little is known about the machinery that regulates the fate of RNAPs before or after transcription. In particular, the mechanisms of biogenesis of the 3 nuclear RNAPs, which comprise both common and specific subunits, remains mostly uncharacterized and the proteins involved are yet to be discovered. Using protein affinity purification coupled to mass spectrometry (AP-MS), we recently unraveled a high-density interaction network formed by nuclear RNAP subunits from the soluble fraction of human cell extracts. Validation of the dataset using a machine learning approach trained to minimize the rate of false positives and false negatives yielded a high-confidence dataset and uncovered novel interactors that regulate the RNAP II transcription machinery, including a set of proteins we named the RNAP II-associated proteins (RPAPs). One of the RPAPs, RPAP3, is part of an 11-subunit complex we termed the RPAP3/R2TP/prefoldin-like complex. Here, we review the literature on the subunits of this complex, which points to a role in nuclear RNAP biogenesis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".