Conditional Reverse Tet-Transactivator Mouse Strains for the Efficient Induction of TRE-Regulated Transgenes in Mice
Bibliographic record
Abstract
Tetracycline or doxycycline (dox)-regulated control of genetic elements allows inducible, reversible and tissue specific regulation of gene expression in mice. This approach provides a means to investigate protein function in specific cell lineages and at defined periods of development and disease. Efficient and stable regulation of cDNAs or non-coding elements (e.g. shRNAs) downstream of the tetracycline-regulated element (TRE) requires the robust expression of a tet-transactivator protein, commonly the reverse tet-transactivator, rtTA. Most rtTA strains rely on tissue specific promoters that often do not provide sufficient rtTA levels for optimal inducible expression. Here we describe the generation of two mouse strains that enable Cre-dependent, robust expression of rtTA3, providing tissue-restricted and consistent induction of TRE-controlled transgenes. We show that these transgenic strains can be effectively combined with established mouse models of disease, including both Cre/LoxP-based approaches and non Cre-dependent disease models. The integration of these new tools with established mouse models promises the development of more flexible genetic systems to uncover the mechanisms of development and disease pathogenesis.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".