Takanaga Hitomi, Chaudhuri Bhavna, Frommer Wolf B
Carnegie Institution for Science, 260 Panama Street, Stanford CA 94305, USA.
Biochim Biophys Acta. 2008 Apr;1778(4):1091-9. doi: 10.1016/j.bbamem.2007.11.015. Epub 2007 Dec 14.
Genetically encoded FRET glucose nanosensors have proven to be useful for imaging glucose flux in HepG2 cells. However, the dynamic range of the original sensor was limited and thus it did not appear optimal for high throughput screening of siRNA populations for identifying proteins involved in regulation of sugar flux. Here we describe a hybrid approach that combines linker-shortening with fluorophore-insertion to decrease the degrees of freedom for fluorophore positioning leading to improved nanosensor dynamics. We were able to develop a novel highly sensitive FRET nanosensor that shows a 10-fold higher ratio change and dynamic range (0.05-11 mM) in vivo, permitting analyses in the physiologically relevant range. As a proof of concept that this sensor can be used to screen for proteins playing a role in sugar flux and its control, we used siRNA inhibition of GLUT family members and show that GLUT1 is the major glucose transporter in HepG2 cells and that GLUT9 contributes as well, however to a lower extent. GFP fusions suggest that GLUT1 and 9 are preferentially localized to the plasma membrane and thus can account for the transport activity. The improved sensitivity of the novel glucose nanosensor increases the reliability of in vivo glucose flux analyses, and provides a new means for the screening of siRNA collections as well as drugs using high-content screens.
基因编码的荧光共振能量转移(FRET)葡萄糖纳米传感器已被证明可用于对HepG2细胞中的葡萄糖通量进行成像。然而,原始传感器的动态范围有限,因此对于高通量筛选参与糖通量调节的蛋白质的siRNA群体而言,它似乎并非最佳选择。在此,我们描述了一种混合方法,该方法将连接子缩短与荧光团插入相结合,以减少荧光团定位的自由度,从而改善纳米传感器的动态性能。我们能够开发出一种新型的高灵敏度FRET纳米传感器,其在体内显示出高10倍的比率变化和动态范围(0.05 - 11 mM),允许在生理相关范围内进行分析。作为该传感器可用于筛选在糖通量及其控制中起作用的蛋白质的概念验证,我们使用siRNA抑制葡萄糖转运蛋白(GLUT)家族成员,并表明GLUT1是HepG2细胞中的主要葡萄糖转运蛋白,GLUT9也有贡献,不过程度较低。绿色荧光蛋白(GFP)融合表明GLUT1和9优先定位于质膜,因此可以解释转运活性。新型葡萄糖纳米传感器灵敏度的提高增加了体内葡萄糖通量分析的可靠性,并为使用高内涵筛选技术筛选siRNA文库以及药物提供了一种新方法。