Cells resist starvation through a nutrient stress splice switch
Bibliographic record
Abstract
Introns are common features of eukaryotic genes, typically removed through splicing to produce functional RNAs. In yeast, some introns play roles beyond host gene expression, mediating cellular responses to nutrient depletion. However, the mechanisms underlying these functions remain unclear. Here, we show that intron-dependent resistance to starvation is mediated by changes in spliceosome stoichiometry driven by a differential increase in the abundance of U1 small nuclear ribonucleoprotein (snRNP). Increased levels of U1 snRNP enhance its binding to, and promote splicing of, introns needed for improved tolerance to starvation. Nutrient depletion both increases and decreases the removal of different sets of introns. Remarkably, only introns that are more efficiently spliced out under starvation conditions are essential for resisting starvation. By investigating the mechanism using immunoprecipitation assays of different spliceosomal components, we found that the two sets of introns are differentially bound by U1 snRNP: starvation-induced introns are highly bound by U1, whereas underspliced introns bind less U1 snRNP in nutrient-limited conditions. Consistently, disrupting U1 interactions by mutating the 5' splice site or deleting nonessential U1 components significantly impairs starvation tolerance. These findings reveal a spliceosome-driven mechanism in which selective U1 recruitment to specific introns adapts cells to nutrient stress.
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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".