GLUT1 and GLUT9 as major contributors to glucose influx in HepG2 cells identified by a high sensitivity intramolecular FRET glucose sensor - PubMed (original) (raw)

GLUT1 and GLUT9 as major contributors to glucose influx in HepG2 cells identified by a high sensitivity intramolecular FRET glucose sensor

Hitomi Takanaga et al. Biochim Biophys Acta. 2008 Apr.

Abstract

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.

PubMed Disclaimer

Figures

Figure 1

Figure 1

In vivo detection range of FLIP nanosensors. The detection range of FLII12Pglu-700μδ 6 (pink) and FLIPglu-600μ (blue) was determined using in vivo FRET measurements (shown in Fig. 3). The eCFP/(Ctrine-eYFP) emission ratio was normalized to the starting ratio. Baseline noise was ~0.1. The reliable concentration ranges of FLII12Pglu-700μδ6 and FLIPglu-600μ were 0.074 to 6.1 mM and 0.05 to 9.6 mM, respectively. The in vivo glucose concentration was calculated using the equation [gluc]cyt = _K_d x (r - 1)/(Rmax - r). Rmax is the maximum ratio change; r is the ratio. The _K_d was assumed to be identical as under in vitro conditions.

Figure 2

Figure 2

FLII12Pglu series nanosensor with enhanced in vitro signal change. (A) FLII12Pglu construct consisting of N-terminal His-tag, N-terminal 12 amino acids of MglB (pink), eCFP (cyan), 13-304 amino acid of MglB (pink), a C-terminal composite linker, and Citrine-eYFP (yellow). (B) Top graph: Effect of number of amino acid deletion on Δratio change in 20 mM MOPS buffer. The Δratio of each nanosensor was determined by titration with glucose (0 – 100mM). Closed circles show deletion constructs, open circle shows synthetic linker deletion constructs, the open triangle shows the original nanosensor FLII12Pglu-600μ. Bottom graph: Effect of the number of amino acids deleted on Δratio change in Hanks’ balanced buffer (pH 7.4). (C) Correlation of Δratio change between in Hanks’ balanced buffer and artificial cytosol. Data were fitted by linear regression.

Figure 3

Figure 3

In vivo and in vitro assay of FLII12Pglu series nanosensors. (A) Time dependent ratio change of HepG2 cells expressing the FLII12Pglu series nanosensors perfused with increasing external glucose concentrations. Boxes indicate the loading time of external glucose concentrations (mM) during continuous perfusion with Hanks’ balanced buffer. HepG2 cells expressing FLII12Pglu series nanosensors were perfused with gradually increasing concentration of external glucose (0.1 to 25 mM). After each glucose loading (2 min), glucose was withdrawn by perfusion with Hanks’ balanced buffer. FRET images were acquired every 10 sec (600 ms exposure time) and the cytosolic glucose change was analyzed (FLII12Pglu600μ magenta, FLII12Pglu-10aa red, FLII12Pglu-15aa green, FLII12D183A cyan, FLII12Pglu-δ4 yellow, FLII12Pglu-δ6 blue). (B) In vitro glucose binding titration curves of FLII12Pglu series nanosensors. Labeling as in (A). Data was fitted to a single site binding isotherm: S = (r - _r_apo)/(_r_sat - _r_apo) = [gluc] / (Kd + [gluc]), where S is saturated-binding portion; [gluc], glucose concentration; r, ratio; _r_apo, ratio in the absence of glucose; and _r_sat, ratio at saturation with glucose. (C) In vivo glucose apparent flux titration curve of FLII12Pglu series nanosensors. To determine the in vivo apparent Kd, the initial phase was plotted by using ratio change in 30 sec after glucose loading (Fig. 3A).

Figure 4

Figure 4

(A) Localization of the cytosolic FLII12Pglu-700μδ6 in HepG2 cells. Confocal images of the nanosensor FLII12Pglu-700μδ6 by eCFP excitation in the stably expressing cell line. Bar: 10 μm (83 pixels). (B) Ratio change of HepG2 cell expressing FLII12Pglu-700μδ6 in HepG2 cells perfused with increasing external glucose concentrations. Gray bars at the bottom of the trace indicate the duration and external glucose concentrations (mM) during perfusion with Hanks’ balanced buffer (mean ± SD, n = 5) (C) Michaelis-Menten kinetics of glucose flux measured by FLII12Pglu-700μδ6 in HepG2 cells.

Figure 5

Figure 5. siRNA inhibition of GLUT-mediated glucose transport

(A) Images of the cytosolic FLII12Pglu-700μδ6 in HepG2 cells. eCFPex/eCFPem, eCFPex/eYFPem, images of the nanosensor FLII12Pglu700μ-δ6 by eCFP excitation in the stably expressing cell line, and Cy3-labeled control oligonucleotide. Stably transfected HepG2 cells expressing FLII12Pglu700μ-™6 were cotransfected with siRNA and Cy3-labeled control oligonucleotides to identify transfected cells. Bar: 100 μm (50 pixels). (B) Effect of siRNA silencing on the FRET response after addition of 0.5 mM and 20 mM glucose using the ImageXpress. Representative time courses for siRNA treated cells are shown for GLUT1 and GLUT9 siRNAs. (C) Steady-state glucose levels were compared at different time points (0.5 min, 0 mM; 2 min, 0.5 mM; 3 min, 20 mM). Statistical analysis used Students _t_-test (n = 4 - 18, * P < 0.05, ** P < 0.01).

Figure 6

Figure 6

Localization of GLUT1-eGFP and GLUT9-eGFP fusion protein and cytosolic localized mCherry in HepG2 cells using spinning disk confocal microscopy. Bar: 10 μm (83 pixels).

Similar articles

Cited by

References

    1. Sanz P. Yeast as a model system to study glucose-mediated signalling and response. Front Biosci. 2007;12:2358–2371. - PubMed
    1. Galvez T, Teruel MN, Heo WD, Jones JT, Kim ML, Liou J, Myers JW, Meyer T. siRNA screen of the human signaling proteome identifies the PtdIns(3,4,5)P3-mTOR signaling pathway as a primary regulator of transferrin uptake. Genome Biology. 2007;19:R142. - PMC - PubMed
    1. Deuschle K, Fehr M, Hilpert M, Lager I, Lalonde S, Looger LL, Okumoto S, Persson J, Schmidt A, Frommer WB. Genetically encoded sensors for metabolites. Cytometry Part A. 2005;64A:3–9. - PMC - PubMed
    1. Medintz IL. Recent progress in developing FRET-based intracellular sensors for the detection of small molecule nutrients and ligands. Trends Biotechnol. 2006;24:539–542. - PubMed
    1. Fehr M, Frommer WB, Lalonde S. Visualization of maltose uptake in living yeast cells by fluorescent nanosensors. Proceedings of the National Academy of Sciences of the United States of America. 2002;99:9846–9851. - PMC - PubMed

Publication types

MeSH terms

Substances

Grants and funding

LinkOut - more resources