In vitro reconstitution of yeast splicing with U4 snRNA reveals multiple roles for the 3′ stem–loop
Bibliographic record
Abstract
U4 small nuclear RNA (snRNA) plays a fundamental role in the process of premessenger RNA splicing, yet many questions remain regarding the location, interactions, and roles of its functional domains. To address some of these questions, we developed the first in vitro reconstitution system for yeast U4 small nuclear ribonucleoproteins (snRNPs). We used this system to examine the functional domains of U4 by measuring reconstitution of splicing, U4/U6 base-pairing, and triple-snRNP formation. In contrast to previous work in human extracts and Xenopus oocytes, we found that the 3' stem-loop of U4 is necessary for efficient base-pairing with U6. In particular, the loop is sensitive to changes in both length and sequence. Intriguingly, a number of mutations that we tested resulted in more stable interactions with U6 than wild-type U4. Nevertheless, each of these mutants was impaired in its ability to support splicing, indicating that these regions of U4 have functions subsequent to base pair formation with U6. Our data suggest that one such function is likely to be in tri-snRNP formation, when U5 joins the U4/U6 di-snRNP. We have identified two regions, the upper stem of the 3' stem-loop and the central domain, that promote tri-snRNP formation. In addition, the loop of the 3' stem-loop promotes di-snRNP formation, while the central domain and the 3'-terminal domain appear to antagonize di-snRNP formation.
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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) | 0.001 | 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 | 0.001 | 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".