Spliceosome assembly and regulation: insights from analysis of highly reduced spliceosomes
Bibliographic record
Abstract
Premessenger RNA splicing is catalyzed by the spliceosome, a multimegadalton RNA–protein complex that assembles in a highly regulated process on each intronic substrate. Most studies of splicing and spliceosomes have been carried out in human orS. cerevisiaemodel systems. There exists, however, a large diversity of spliceosomes, particularly in organisms with reduced genomes, that suggests a means of analyzing the essential elements of spliceosome assembly and regulation. In this review, we characterize changes in spliceosome composition across phyla, describing those that are most frequently observed and highlighting an analysis of the reduced spliceosome of the red algaCyanidioschyzon merolae. We used homology modeling to predict what effect splicing protein loss would have on the spliceosome, based on currently available cryo-EM structures. We observe strongly correlated loss of proteins that function in the same process, for example, in interacting with the U1 snRNP (which is absent inC. merolae), regulation of Brr2, or coupling transcription and splicing. Based on our observations, we predict splicing inC. merolaeto be inefficient, inaccurate, and post-transcriptional, consistent with the apparent trend toward its elimination in this lineage. This work highlights the striking flexibility of the splicing pathway and the spliceosome when viewed in the context of eukaryotic diversity.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".