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High-efficiency CAG-FLPe deleter mice in C57BL/6J background
Hiroaki Kanki1, Hitomi Suzuki, Shigeyoshi Itohara
1Laboratory for Behavioral Genetics, Brain Science Institute (BSI), The Institute of Physical and Chemical Research (RIKEN), Wako-shi, Saitama, Japan.
Experimental Animals
|May 3, 2006
Summary
New FLP deleter mice on a C57BL/6J background efficiently remove FRT-flanked sequences, making them valuable tools for conditional knockout studies, especially in behavioral research.
Area of Science:
- Genetics
- Molecular Biology
- Neuroscience
Background:
- Conditional knockout (KO) technology is increasingly vital for genetic research.
- Efficient FLP deleter mouse lines are in high demand for precise genetic modifications.
- Existing FLP deleters may not be optimized for specific genetic backgrounds like those used in behavioral studies.
Purpose of the Study:
- To generate and characterize novel FLP deleter transgenic mice on a C57BL/6J genetic background.
- To assess the recombination efficiency of these new FLP deleter lines for conditional gene targeting.
- To provide a valuable resource for researchers conducting behavioral studies with conditional KO mice.
Main Methods:
- Generation of CAG-FLPe transgenic (Tg) mouse lines.
- Crossing CAG-FLPe-Tg lines with mice carrying an FRT-PGK-neo-FRT cassette.
- Evaluation of recombination efficiency and patterns in offspring using established genetic assays.
Main Results:
- Four out of five generated CAG-FLPe lines demonstrated high recombination efficiency (91%-100%) in progeny.
- Mosaic recombination patterns were observed in a small fraction (0%-30%) of animals, varying by line.
- The generated FLP deleters are highly effective in removing FRT cassettes.
Conclusions:
- The novel CAG-FLPe transgenic mice on a C57BL/6J background are potent tools for FRT cassette deletion.
- These mice are well-suited for behavioral studies requiring conditional gene knockout strategies.
- The developed FLP deleter lines represent a significant advancement for genetic research and model generation.