Regulating expression of mistranslating tRNAs by readthrough RNA polymerase II transcription
Bibliographic record
Abstract
ABSTRACT Transfer RNA (tRNA) variants that alter the genetic code increase protein diversity and have many applications in synthetic biology. Since the tRNA variants can cause a loss of proteostasis, regulating their expression is necessary to achieve high levels of novel protein. Mechanisms to positively regulate transcription with exogenous activator proteins like those often used to regulate RNA polymerase II (RNAP II) transcribed genes are not applicable to tRNAs as their expression by RNA polymerase III requires elements internal to the tRNA. Here, we show that tRNA expression is repressed by overlapping transcription from an adjacent RNAP II promoter. Regulating the expression of the RNAP II promoter allows inverse regulation of the tRNA. Placing either Gal4 or TetR-VP16 activated promoters downstream of a mistranslating tRNA Ser variant that mis-incorporates serine at proline codons in Saccharomyces cerevisiae allows mistranslation at a level not otherwise possible because of the toxicity of the unregulated tRNA. Using this inducible tRNA system, we explore the proteotoxic effects of mistranslation on yeast cells. High levels of mistranslation cause cells to arrest in G1 phase. These cells are impermeable to propidium iodide, yet growth is not restored upon repressing tRNA expression. High levels of mistranslation increase cell size and alter cell morphology. This regulatable tRNA expression system can be applied to study how native tRNAs and tRNA variants affect the proteome and other biological processes. Variations of this inducible tRNA system should be applicable to other eukaryotic cell types.
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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".