Faculty Opinions recommendation of RNA catalyses nuclear pre-mRNA splicing.
Bibliographic record
Abstract
In nuclear pre-messenger RNA splicing, introns are excised by the spliceosome, a multimegadalton machine composed of both proteins and small nuclear RNAs (snRNAs).Over thirty years ago, following the discovery of self-splicing group II intron RNAs, the snRNAs were hypothesized to catalyze splicing.However, no definitive evidence for a role of either RNA or protein in catalysis by the spliceosome has been reported to date.By using metal rescue strategies, here we show that the U6 snRNA catalyzes both splicing reactions by positioning divalent metals that stabilize the leaving groups during each reaction.Strikingly, all of the U6 catalytic metal ligands we identified correspond to the ligands observed to position catalytic, divalent metals in crystal structures of a group II intron RNA.These findings indicate that group II introns and the spliceosome share common catalytic mechanisms, and likely common evolutionary origins.Our results demonstrate that RNA mediates catalysis within the spliceosome.Nuclear pre-mRNA splicing (Fig. 1a) is a crucial determinant of the export, translation, stability, and diversity of eukaryotic messages 1 , but the spliceosome is the only major cellular machinery 2 required for gene expression for which the catalytic components remain undefined.Nevertheless, for three decades, there has been widespread speculation that nuclear pre-mRNA splicing is catalyzed by RNA.Users may view, print, copy, download and text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.394 | 0.321 |
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".