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Yusaku Katada, Maki Miyauchi, Yukihiro Miwa, Xiaoyan Jiang, Kiwako Mori, Hidemasa Torii, Kenji F Tanaka, Kazuo Tsubota, Toshihide Kurihara; Establishment of retinal ganglion cell-specific gene recombination murine models using tet system. Invest. Ophthalmol. Vis. Sci. 2016;57(12):747. doi: https://doi.org/.
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© ARVO (1962-2015); The Authors (2016-present)
It is generally difficult to obtain an efficient gene recombination in a specifically targeted cell population in order to generate conditional transgenic mice. To establish both highly specific and sufficient gene recombination simultaneously, we utilize a tetracycline-controllable gene expression system (tet system, Fig. 1) in which the amount of gene expression have been much improved and previously reported (KENGE-tet system). In the present study we establish retinal ganglion cell (RGC)-specific gene recombination murine models with high efficiency using this system.
We employed two different mouse lines which express the gene encoding tetracycline transactivation (tTA) protein under the control of a cell-type-specific promoter, muscarinic acetylcholine receptor M4 or serotonin receptor 5B control region. Those mice were further crossed with another transgenic mouse line which contains the yellow cameleon (YC) fluorescent gene connected into the downstream of the tet operator (tetO) promoter. The YC gene expression was induced only by the presence of tTA protein in the double transgenic mice (M4-tTA::tetO-YC or 5B-tTA::tetO-YC). The expression of YC was observed in the double transgenic mouse retina with a fluorescence microscope.
In the M4-tTA::tetO-YC mouse retina we identified the expression of YC mainly in RGC and in some population of amacrine cells, whereas RGC specific expression of YC was observed in the 5B-tTA::tetO-YC mouse retina .
Using the tet system, RGC was visualized and the cell-type-specific gene recombination was confirmed. These results suggest that the current tetO-based system is useful to modify gene expression specifically in RGC.
This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.
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