Transcription factors induce differential splicing of duplicated ribosomal protein genes during meiosis
Bibliographic record
Abstract
In baker's yeast, genes encoding ribosomal proteins often exist as duplicate pairs, typically with one 'major' paralog highly expressed and a 'minor' less expressed paralog that undergoes controlled expression through reduced splicing efficiency. In this study, we investigate the regulatory mechanisms controlling splicing of the minor paralog of the uS4 protein gene (RPS9A), demonstrating that its splicing is repressed during vegetative growth but upregulated during meiosis. This differential splicing of RPS9A is mediated by two transcription factors, Rim101 and Taf14. Deletion of either RIM101 or TAF14 not only induces the splicing and expression of RPS9A with little effect on the major paralog RPS9B, but also differentially alters the splicing of reporter constructs containing only the RPS9 introns. Both Rim101 and Taf14 co-immunoprecipitate with the chromatin and RNA of the RPS9 genes, indicating that these transcription factors may affect splicing co-transcriptionally. Deletion of the RPS9A intron, RIM101 or TAF14 dysregulates RPS9A expression, impairing the timely expression of RPS9 during meiosis. Complete deletion of RPS9A impairs the expression pattern of meiotic genes and inhibits sporulation in yeast. These findings suggest a regulatory strategy whereby transcription factors modulate the splicing of duplicated ribosomal protein genes to fine-tune their expression in different cellular states.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".