Generation and characterization of a Cre-inducible ZNF768 overexpression mouse model
Bibliographic record
Abstract
Abstract Zinc-finger protein 768 (ZNF768) is an emerging transcription factor regulating cell proliferation and senescence. Although the role of ZNF768 in regulating cell fate decision has been demonstrated in vitro, its importance in controlling physiological and pathophysiological processes in vivo is still unclear. Here, we report the generation of a transgenic mouse model allowing the conditional overexpression of ZNF768. This was achieved by inserting an inverted Znf768 coding sequence surrounded by heterologous Cre recognition sites in the Gt(ROSA)26Sor mouse locus (FLExZnf768). To study the impact linked to systemic overexpression of ZNF768, mice carrying the FLExZnf768 allele were crossed with CMV-Cre mice to produce a whole-body ZNF768 transgenic mouse (WB-ZNF768-Tg). As expected, WB-ZNF768-Tg mice showed higher ZNF768 levels in various tissues. These mice were born at the expected Mendelian ratio and did not display apparent phenotypes. Because ZNF768 levels are often overexpressed in cancer, we assessed tumor development in WB-ZNF768-Tg mice. However, ZNF768 overexpression was not sufficient to promote 3-methylcholantrene-induced fibrosarcoma and KRASG12D-induced lung adenocarcinoma in mice. Overall, we report the generation of a conditional mouse for ZNF768 overexpression and reveal that forcing ZNF768 expression is not sufficient to alter tumour development 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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".