Improvement of the reverse tetracycline transactivator by single amino acid substitutions that reduce leaky target gene expression to undetectable levels
Bibliographic record
Abstract
Conditional gene expression systems that enable inducible and reversible transcriptional control are essential research tools and have broad applications in biomedicine and biotechnology. The reverse tetracycline transcriptional activator is a canonical system for engineered gene expression control that enables graded and gratuitous modulation of target gene transcription in eukaryotes from yeast to human cell lines and transgenic animals. However, the system has a tendency to activate transcription even in the absence of tetracycline and this leaky target gene expression impedes its use. Here, we identify single amino-acid substitutions that greatly enhance the dynamic range of the system in yeast by reducing leaky transcription to undetectable levels while retaining high expression capacity in the presence of inducer. While the mutations increase the inducer concentration required for full induction, additional sensitivity-enhancing mutations can compensate for this effect and confer a high degree of robustness to the system. The novel transactivator variants will be useful in applications where tight and tunable regulation of gene expression is paramount.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 | 0.000 |
| 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".