Optimizing gene expression by adapting splicing
Bibliographic record
Abstract
Abstract Can splicing be used by cells to adapt to new environmental challenges? While various adaptation mechanisms for regulating gene expression have been revealed for transcription and translation, the role of splicing and how it evolves to optimize gene-expression patterns has not been thoroughly investigated. To tackle this question, we employed a lab-evolution experimental approach that challenged yeast cells to increase expression levels of a gene that carries an inefficiently-spliced intron. We followed the evolution of multiple lines and found independent routes by which cells adapted. Surprisingly, we did not observe an intron loss event, a mechanism believed to be common in intron evolution. Instead, we identified mutations in cis that improved the intron’s splicing efficiency and increased the overall expression level of the entire gene. One of these cis -acting mutations occurred in an adjacent exon and hampered the functionality of the gene that was not under selection - demonstrating that adaptation of splicing efficiency may sometimes come at the expense of protein activity. Additionally, we observed adaptations in trans , which increased the cellular availability of the splicing machinery. These adaptations were achieved either by elevated expression levels of the splicing apparatus or, unexpectedly, by reduced expression levels of other intron-containing genes that are the natural consumers of this process. Ultimately, our work reveals novel molecular means by which the splicing machinery is changed by natural selection to optimize gene-expression patterns of cells.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".