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CORP: Using transgenic mice to study skeletal muscle physiology.
C Brooks Mobley1, Ivan J Vechetti1, Taylor R Valentino1
1Department of Physiology, College of Medicine, University of Kentucky, Lexington, Kentucky.
Journal of Applied Physiology (Bethesda, Md. : 1985)
|February 28, 2020
Summary
This review details using inducible transgenic mice for skeletal muscle research. It covers Cre-loxP and Tet systems for gene manipulation and cell studies in myofibers and satellite cells.
Area of Science:
- Molecular Biology
- Genetics
- Physiology
Background:
- Tissue-specific inducible transgenic mice are crucial for studying gene function and cell biology.
- These models allow temporal and spatial control over genetic modifications in living animals.
- Skeletal muscle research benefits significantly from precise genetic manipulation tools.
Purpose of the Study:
- To provide a comprehensive guide on utilizing two inducible transgenic systems for skeletal muscle research.
- To detail the application of Cre-loxP and Tet-ON/OFF systems in studying skeletal muscle physiology.
- To facilitate the integration of these advanced mouse models into skeletal muscle research programs.
Main Methods:
- Description of myofiber- and satellite cell-specific Cre-loxP transgenic mice for gene knockout and cell depletion.
- Explanation of a myofiber-specific Tet-ON system for gene overexpression and myonuclei labeling.
- Guidance on breeding and genotyping these specialized transgenic mouse lines.
Main Results:
- Demonstration of how Cre-loxP systems enable gene knockout in adult skeletal muscle myofibers.
- Illustration of satellite cell depletion using specific Cre-loxP mouse models.
- Showcasing the utility of Tet-ON systems for inducible gene overexpression and myonuclei labeling in myofibers.
Conclusions:
- Inducible transgenic mouse models offer powerful, versatile tools for skeletal muscle research.
- The Cre-loxP and Tet systems provide precise control over gene function studies in skeletal muscle.
- This review equips researchers with the foundational knowledge to implement these models effectively.

