The Tetracycline-Controlled Transactivator (Tet-On/Off) System in β-Cells Reduces Insulin Expression and Secretion in Mice
Bibliographic record
Abstract
Controllable genetic manipulation is an indispensable tool in research, greatly advancing our understanding of cell biology and physiology. However in β-cells, transgene silencing, low inducibility, ectopic expression, and off-targets effects are persistent challenges. In this study, we investigated whether an inducible Tetracycline (Tet)-Off system with β-cell–specific mouse insulin promoter (MIP)-itTA–driven expression of tetracycline operon (TetO)-CreJaw/J could circumvent previous issues of specificity and efficacy. Following assessment of tissue-specific gene recombination, β-cell architecture, in vitro and in vivo glucose-stimulated insulin secretion, and whole-body glucose homeostasis, we discovered that expression of any tetracycline-controlled transactivator (e.g., improved itTA, reverse rtTA, or tTA) in β-cells significantly reduced Insulin gene expression and decreased insulin content. This translated into lower pancreatic insulin levels and reduced insulin secretion in mice carrying any tTA transgene, independent of Cre recombinase expression or doxycycline exposure. Our study echoes ongoing challenges faced by fundamental researchers working with β-cells and highlights the need for consistent and comprehensive controls when using the tetracycline-controlled transactivator systems (Tet-On or Tet-Off) for genome editing.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".