A tetracycline‐inducible and skeletal muscle‐specific Cre recombinase transgenic mouse
Bibliographic record
Abstract
We have generated a transgenic mouse that expresses Cre recombinase only in skeletal muscle and only following tetracycline treatment. This spatiotemporal specificity is achieved using two transgenes. The first transgene uses the human skeletal actin (HSA) promoter to drive expression of the reverse tetracycline-controlled transactivator (rtTA). The second transgene uses a tetracycline responsive promoter to drive the expression of Cre recombinase. We monitored transgene expression in these mice by crossing them with ROSA26 loxP-LacZ reporter mice, which express beta-galactosidase when activated by Cre. We find that the expression of this transgene is only detectable within skeletal muscle and that Cre expression in the absence of tetracycline is negligible. Cre is readily induced in this model with tetracycline analogs at a range of embryonic and postnatal ages and in a pattern consistent with other HSA transgenic mice. This mouse improves upon existing transgenic mice in which skeletal muscle Cre is expressed throughout development by allowing Cre expression to begin at later developmental stages. This temporal control of transgene expression has several applications, including overcoming embryonic or perinatal lethality due to transgene expression. This mouse is especially suited for studies of steroid hormone action, as it uses tetracycline, rather than tamoxifen, to activate Cre expression. In summary, we find that this transgenic induction system is suitable for studies of gene function in the context of hormonal regulation of skeletal muscle or interactions between muscle and motoneurons in mice.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".