The In Vivo Kinetics of RNA Polymerase II Elongation during Co-Transcriptional Splicing
Bibliographic record
Abstract
RNA processing events that take place on the transcribed pre-mRNA include capping, splicing, editing, 3' processing, and polyadenylation. Most of these processes occur co-transcriptionally while the RNA polymerase II (Pol II) enzyme is engaged in transcriptional elongation. How Pol II elongation rates are influenced by splicing is not well understood. We generated a family of inducible gene constructs containing increasing numbers of introns and exons, which were stably integrated in human cells to serve as actively transcribing gene loci. By monitoring the association of the transcription and splicing machineries on these genes in vivo, we showed that only U1 snRNP localized to the intronless gene, consistent with a splicing-independent role for U1 snRNP in transcription. In contrast, all snRNPs accumulated on intron-containing genes, and increasing the number of introns increased the amount of spliceosome components recruited. This indicates that nascent RNA can assemble multiple spliceosomes simultaneously. Kinetic measurements of Pol II elongation in vivo, Pol II ChIP, as well as use of Spliceostatin and Meayamycin splicing inhibitors showed that polymerase elongation rates were uncoupled from ongoing splicing. This study shows that transcription elongation kinetics proceed independently of splicing at the model genes studied here. Surprisingly, retention of polyadenylated mRNA was detected at the transcription site after transcription termination. This suggests that the polymerase is released from chromatin prior to the completion of splicing, and the pre-mRNA is post-transcriptionally processed while still tethered to chromatin near the gene end.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".