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Possible pitfalls investigating cell death responses in genetically engineered mouse models and derived cell lines
Claudia Manzl1, Florian Baumgartner, Lukas Peintner
1Division of Developmental Immunology, Biocenter, Medical University of Innsbruck, Innsbruck, Austria.
Methods (San Diego, Calif.)
|March 5, 2013
Summary
Genetic background significantly impacts cell death sensitivity in mouse models. Mixed backgrounds confer resistance to apoptosis, while inbred C57BL/6 mice show higher susceptibility to radiation-induced cell death.
Area of Science:
- Cell Biology
- Immunology
- Genetics
Background:
- Genetically engineered mouse models are crucial for studying cell death pathways.
- Targeting apoptotic proteins has been a long-standing approach in research.
- Understanding cell death regulation is vital for disease pathology.
Purpose of the Study:
- To highlight the impact of genetic background on cell death sensitivity.
- To present methods for investigating cell death responses in various cell types.
- To demonstrate how genetic background influences apoptosis in hematopoietic and non-hematopoietic cells.
Main Methods:
- Utilizing genetically engineered mouse models.
- Investigating cell death responses in hematopoietic and non-hematopoietic cells.
- Comparing apoptosis sensitivity across different mouse genetic backgrounds (inbred vs. mixed).
Main Results:
- Hematopoietic cells from mixed genetic backgrounds (C57BL/6:129/SvJ) show increased resistance to spontaneous and DNA-damage induced apoptosis compared to inbred C57BL/6.
- C57BL/6 mice exhibit higher susceptibility to gamma-irradiation-induced cell death and subsequent T cell lymphomagenesis in vivo.
- Genetic background significantly modulates cell death sensitivity, affecting experimental outcomes.
Conclusions:
- The genetic background of mouse models is a critical factor influencing cell death sensitivity.
- Researchers must carefully consider genetic background when interpreting data from knockout mice and cell lines.
- Variations in genetic background can lead to unexpected phenotypes and affect disease modeling.
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