Targeted transgenesis at the HPRT locus: an efficient strategy to achieve tightly controlled in vivo conditional expression with the tet system
Bibliographic record
Abstract
The tet-inducible system has been widely used to achieve conditional gene expression in genetically modified mice. To alleviate the frequent difficulties associated with recovery of relevant transgenic founders, we tested whether a controlled strategy of transgenesis would support reliable cell-specific, doxycycline (Dox)-controlled transgene expression in vivo. Taking advantage of the potent hypoxanthine-aminopterin-thymidine selection strategy and an embryonic stem (ES) cell line supporting efficient germ-line transmission, we used hypoxanthine phosphoribosyltransferase (HPRT) targeting to insert a single copy tet-inducible construct designed to allow both glucocorticoid receptor (GR) and beta-galactosidase (beta-Gal) expression. Conditional, Dox-dependent GR and beta-Gal expression was evidenced in targeted ES cells. Breeding ES-derived single copy transgenic mice with mice bearing appropriate tet transactivators resulted in beta-Gal expression both qualitatively and quantitatively similar to that observed in mice with random integration of the same construct. Interestingly, GR expression in mice was dependent on transgene orientation in the HPRT locus while embryonic stem cell expression was not. Thus, a conditional construct inserted in single copy and in predetermined orientation at the HPRT locus demonstrated a Dox-dependent gene expression phenotype in adult mice suggesting that controlled insertion of tet-inducible constructs at the HPRT locus can provide an efficient alternative strategy to reproducibly generate animal models with tetracycline-induced transgene expression.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 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".