Differential expression of duplicated ribosomal protein genes modifies ribosome composition in response to stress
Bibliographic record
Abstract
In Saccharomyces cerevisiae, most ribosomal proteins are synthesized from duplicated genes, increasing the potential for ribosome heterogeneity. However, the contribution of these duplicated genes to ribosome production and the mechanism determining their relative expression remain unclear. Here we demonstrate that in most cases, one of the two gene copies generate the bulk of the active ribosomes under normal growth conditions, while the other copy is favored only under stress. To understand the origin of these differences in paralog expression and their contribution to ribosome heterogeneity we used RNA polymerase II ChIP-Seq, RNA-seq, polyribosome association and peptide-based mass-spectrometry to compare their transcription potential, splicing, mRNA abundance, translation potential, protein abundance and incorporation into ribosomes. In normal conditions a post-transcriptional expression hierarchy of the duplicated ribosomal protein genes is the product of the efficient splicing, high stability and efficient translation of the major paralog mRNA. Exposure of the cell to stress modifies the expression ratio of the paralogs by repressing the expression of the major paralog and thus increasing the number of ribosomes carrying the minor paralog. Together the data indicate that duplicated ribosomal protein genes underlie a modular network permitting the modification of ribosome composition in response to changing growth conditions.
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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) | 0.000 | 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".