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Rapid reverse genetics systems for N. furzeri, a suitable model organism to study vertebrate aging
Masayuki Oginuma, Moana Nishida, Tomomi Ohmura-Adachi, Kota Abe, Shohei Ogamino, Chihiro Mogi, Hideaki Matsui, Tohru Ishitani
Research Square · 2022
Abstract
Abstract The African turquoise killifish (N. furzeri) is a useful model organism for studying aging, age-related diseases, and embryonic diapause. CRISPR/Cas9-mediated gene knockout and Tol2 transposon-mediated transgenesis in N. furzeri have been reported previously. However, these methods take time to generate knockout and transgenic fish. In addition, knock-in technology that inserts large DNA fragments as fluorescent reporter constructs into the target gene in N. furzeri has not yet been established. Here, we show that triple-target CRISPR-mediated single gene disruption efficiently produces whole-body biallelic knockout and enables the examination of gene function in the F0 generation. In addition, we developed a method for creating the knock-in reporter N. furzeri without crossing by optimizing the CRISPR/Cas9 system. These methods drastically reduce the experimental duration and we think that these will accelerate aging and developmental studies using N. furzeri.
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- 10.21203/rs.3.rs-1467860/v1
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- 2026-10-13 MST
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APA
Oginuma, M., Nishida, M., Ohmura-Adachi, T., Abe, K., Ogamino, S., Mogi, C., Matsui, H., & Ishitani, T. (2022). Rapid reverse genetics systems for N. furzeri, a suitable model organism to study vertebrate aging. <em>Research Square</em>. https://doi.org/10.21203/rs.3.rs-1467860/v1
Vancouver
Oginuma M, Nishida M, Ohmura-Adachi T, Abe K, Ogamino S, Mogi C, et al. Rapid reverse genetics systems for N. furzeri, a suitable model organism to study vertebrate aging. Research Square. 2022. doi:10.21203/rs.3.rs-1467860/v1.
BibTeX
@unpublished{masayuki2022Rapidr,
title = {Rapid reverse genetics systems for N. furzeri, a suitable model organism to study vertebrate aging},
author = {Masayuki Oginuma and Moana Nishida and Tomomi Ohmura-Adachi and Kota Abe and Shohei Ogamino and Chihiro Mogi and Hideaki Matsui and Tohru Ishitani},
journal = {Research Square},
year = {2022},
doi = {10.21203/rs.3.rs-1467860/v1},
}
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