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Mol Cell Biol. Dec 2005; 25(24): 11102–11112.
PMCID: PMC1316956
Evidence for High-Capacity Bidirectional Glucose Transport across the Endoplasmic Reticulum Membrane by Genetically Encoded Fluorescence Resonance Energy Transfer Nanosensors
Marcus Fehr, Hitomi Takanaga, David W. Ehrhardt, and Wolf B. Frommer*
Carnegie Institution, 260 Panama St., Stanford, California 94305
*Corresponding author. Mailing address: Carnegie Institution, 260 Panama St., Stanford, CA 94305. Phone: (650) 325-1521. Fax: (650) 325-6857. E-mail: wfrommer/at/stanford.edu.
Authors contributed equally.
Received June 14, 2005; Revised July 20, 2005; Accepted October 5, 2005.
Glucose release from hepatocytes is important for maintenance of blood glucose levels. Glucose-6-phosphate phosphatase, catalyzing the final metabolic step of gluconeogenesis, faces the endoplasmic reticulum (ER) lumen. Thus, glucose produced in the ER has to be either exported from the ER into the cytosol before release into circulation or exported directly by a vesicular pathway. To measure ER transport of glucose, fluorescence resonance energy transfer-based nanosensors were targeted to the cytosol or the ER lumen of HepG2 cells. During perfusion with 5 mM glucose, cytosolic levels were maintained at ~80% of the external supply, indicating that plasma membrane transport exceeded the rate of glucose phosphorylation. Glucose levels and kinetics inside the ER were indistinguishable from cytosolic levels, suggesting rapid bidirectional glucose transport across the ER membrane. A dynamic model incorporating rapid bidirectional ER transport yields a very good fit with the observed kinetics. Plasma membrane and ER membrane glucose transport differed regarding sensitivity to cytochalasin B and showed different relative kinetics for galactose uptake and release, suggesting catalysis by distinct activities at the two membranes. The presence of a high-capacity glucose transport system on the ER membrane is consistent with the hypothesis that glucose export from hepatocytes occurs via the cytosol by a yet-to-be-identified set of proteins.
Cellular glucose levels must be maintained within a narrow range, since low supply leads to cell dysfunction and elevated levels are cytotoxic. Glucose homeostasis is maintained by the liver, which clears glucose from the blood when serum glucose is high and provides glucose from stores when serum glucose is low. At high serum levels, transport of glucose into hepatocytes is accomplished by GLUT2 (31). Following uptake, glucose is phosphorylated by glucokinase as the initial step in glucose utilization (16). At low blood glucose levels, glucose-6-phosphate (G6P) is produced in the cytosol of hepatocytes from gluconeogenic precursors and glycogen. However, the active site of G6P-phosphatase, which catalyzes the dephosphorylation of G6P to generate glucose, faces the lumen of the endoplasmic reticulum (ER) (18). To deliver the substrate to this luminal active site, G6P is imported into the ER lumen by a G6P translocator. Genes for both the translocator and G6P phosphatase have been identified and functionally characterized (34); defects in either phosphatase or G6P translocase genes lead to type I glycogen storage diseases (34).
Two pathways for the export of glucose formed inside the ER are discussed. In the first, glucose is transported across the ER membrane into the cytosol, where it is subsequently released by GLUT2 into circulation. This pathway requires a glucose transport activity at the ER membrane. Glucose transport activities have been detected by transport assays using microsomes, but their existence is controversial, and neither the respective genes nor a respective form of glycogen storage disease has been identified (4). Microsomal studies of transport are complicated by the fact that microsomal fractions are comprised of membranes from many intracellular compartments, thus presenting challenges for determining the original intracellular location of a measured transport activity. Further, glucose efflux across the plasma membrane is controversial. While GLUT2 appears to be the major glucose transporter expressed in the liver, surprisingly, glucose release from hepatocytes of GLUT2 knockout mice appeared unaffected, indicating the existence of an alternative pathway for glucose release (17). The proposed alternative pathway for glucose efflux from liver cells involves direct vesicular transport of glucose from the ER to the plasma membrane, thus not requiring a glucose transport system at the ER membrane (19). In this model, the combined activities of the G6P translocator and G6P phosphatase would concentrate glucose in the ER lumen, permitting an efficient second efflux route which would be active, allowing the export of glucose into the serum against a concentration gradient. As of yet, all cases of type I glycogen storage diseases are linked to defects in either the glucose-6-phosphatase or the G6P translocase gene, suggesting that mutations in glucose transport across the ER membrane are lethal or that ER transport systems are functionally redundant (28, 30, 35).
Given the controversies and the limitations of transport studies with microsomes, the aim of this study was to characterize glucose transport across the ER membrane by using an independent method that permits direct observation of ER glucose levels in vivo. Recently, genetically encoded fluorescence resonance energy transfer (FRET)-based nanosensors for a variety of sugars and amino acids were developed. The nanosensors consist of the bacterial periplasmic binding proteins as a recognition element coupled allosterically to a set of two spectral variants of the green fluorescent protein (GFP) as reporter elements (13, 14, 20, 29). The hinge-twist motion induced by ligand binding to the recognition element is translated into a change in fluorescence resonance energy transfer between attached enhanced cyan fluorescent protein and enhanced yellow fluorescent protein moieties, permitting noninvasive measurements of analyte levels in living cells (14). For determination of analyte levels inside organelles, e.g., glucose in the nuclear compartment or glutamate at the cell surface, these genetically encoded nanosensors can be targeted to respective subcellular compartments (11, 29).
To analyze ER glucose transport and metabolism in the ER lumen of intact cells, a FRET-based glucose nanosensor with an affinity for glucose of 590 μM was targeted to the ER lumen (FLIPglu-600μER) of HepG2 cells, a human hepatoma cell line. Steady-state glucose levels and kinetics of glucose accumulation and release in the ER were compared with changes observed in the cytosol by using a cytosolically localized sensor (FLIPglu-600μ). Glucose accumulation in the ER was indistinguishable from that in the cytosol, regarding both steady-state concentration and kinetics. The data suggest that the ER membrane contains a high-capacity glucose transport system. The similarity of accumulation kinetics of the ER and those of the cytosol is compatible with both facilitated uniport of glucose across the ER membrane and a concentrative uptake mechanism, which is compensated by rapid glucose efflux through a potential vesicular efflux route. However, the similarity between kinetics of efflux from the ER and the combined kinetics of efflux and metabolism in the cytosol can only be explained by a rapid bidirectional transport of glucose across the ER membrane. Moreover, ER transport appears insensitive to cytochalasin B (CytB), a membrane-permeable noncompetitive inhibitor of GLUT uniporters, which blocks transport by binding at or close to the sugar efflux site on the cytosolic domain of GLUTs (7). Since the sensor FLIPglu-600μ recognizes galactose with an affinity of 850 μM (14), it was possible to obtain insights into the specificity of the ER glucose transport system. While the kinetics of galactose accumulation and loss were overall very similar in the ER and the cytosol, both processes were significantly slower in the ER. Thus, regarding both selectivity towards galactose and sensitivity to CytB, ER glucose transport differs from the GLUT activities present at the plasma membrane. All experimental data could be fitted to a numerical model consisting of reversible GLUT-mediated plasma membrane transport, phosphorylation in the cytosol, and high-capacity bidirectional transport across the ER membrane. We hypothesize that ER transport is catalyzed by a yet-unknown transporter or a by a subset of GLUTs that is different from those present at the plasma membrane.
Generation of FLIPglu-600μER and FLIPglu-170nER.
The cassettes encoding FLIPglu-600μ and FLIPglu-170n were transferred by PCR-based cloning using FLIPglu-600μ and FLIPglu-170n in pRSET as templates, omitting the initiator and terminator codons from the sensor portions and adding SalI and NotI sites into pEF/myc/ER (Invitrogen), which provides ER targeting and retention signals (12). The correct sequences were confirmed. The Prl ss-mRFP1-KDEL ER marker and the RFP1-N1-GalT Golgi marker were generous gifts from George Patterson and Jennifer Lippincott-Schwartz (NIH, Bethesda, Md.). The constructs are based on mRFP1-C1, driven by the strong cytomegalovirus promoter, and contain the monomeric DsRed. The sensor FLIPglu-600μ cloned in pcDNA3.1(−) for cytosolic expression has been described previously (14).
Confocal microscopy.
Stably transformed HepG2 cell lines expressing FLIPglu-600μER were transiently transfected with the ER and Golgi red fluorescent protein (RFP) markers. Transformed cells were imaged 48 h after transfection by using a Nipkow spinning disk confocal microscope. Incident argon (488-nm) and krypton (458-nm) ion laser (Coherent, Inc.) beams were coupled to a modified Yokogawa spinning disk confocal scan head (Yokogawa Electric, Japan, and Solamere Technology) via an acoustical optical tunable filter (NEOS). The confocal head was mounted on an inverted microscope (DM IRE2; Leica, Germany) equipped with a 63× glycerin immersion objective (numerical aperture 1.3, HCX PL APO, 21°C; Leica, Germany) and a motorized Z-stage. Fluorescence images (band pass filters of 525/50 nm for yellow fluorescent protein [YFP] and 620/60 nm for RFP) were acquired with a cooled on-chip multiplication gain Cascade 512B digital camera (Roper Scientific). Instrumentation was driven using Metamorph version 6.1r5 software (Universal Imaging Corp.).
Cell culture and transfection.
HepG2 cells were grown in minimal essential medium with 10% fetal calf serum and 50 U/ml penicillin and 50 μg/ml streptomycin (Sigma). Cells were cultured at 37°C and 5% CO2. Stable cell lines were generated using Ca phosphate transfection followed by selection on 400 μg/ml Geneticin for 2 weeks. For imaging, cells were cultured either in Lab-Tek chambered no. 1 borosilicate cover glass systems (Nalge Nunc, NY) or on round cover glasses (5-mm diameter) in 24-well plates. On the day of imaging, the cover glasses were mounted in a PH5 microchamber (Warner Instruments) for rapid perfusion.
RT-PCR.
RNA was extracted from HepG2 cells using an RNAeasy kit (QIAGEN). Reverse transcriptase (RT) reactions were performed using a Protoscript kit (New England Biolabs) according to the manufacturer's protocols. cDNAs were amplified (2 min at 94°C; 40 cycles of 30 s at 94°C, 30 s at 55 to 60°C, and 1 min at 72°C; 10 min at 72°C) using Taq DNA polymerase (New England Biolabs). Primers were designed for annealing to exon sequences with the lowest possible degree of homology to distinguish between GLUT1 to GLUT6, GLUT8 to GLUT14, and SGLT1 to SGLT3. The primers used for GLUT3 and GLUT14 did not discriminate. Quantitative PCR (qPCR) to measure transcript abundance of GLUT1, GLUT2, SGLT1, SGLT2, SGLT3, and GAPDH (gene expressing glyceraldehyde-3-phosphate dehydrogenase) was performed with cDNA of HepG2 and a DyNAmo HS SYBR green qPCR kit (New England BioLabs). Real-time PCR was performed with a DNA Engine Opticon 2 system (MJ Research, Waltham, Mass.). qPCR involved a denaturation (10 min at 94°C), followed by 40 cycles of amplification and quantification (10 s at 94°C, 15 s at 55 to 58°C, and 15 s at 72°C, with a single fluorescence measurement), and an incubation of 10 min at 72°C, followed by a melting-curve program (65 to 95°C, with a heating rate of 0.2°C and continuous fluorescence measurements). The same primers described above for RT-PCR were used for qPCR.
Ratio imaging.
Ratio imaging was performed with an inverted fluorescence microscope (DM IRE 2; Leica) with a cooled CoolSnap HQ digital camera (Photometrics). Dual emission intensity ratios were simultaneously recorded using a DualView with OI-5-EM filters (Optical Insights) and Metafluor 6.1r1 (Universal Imaging). A Sutter Instruments Lambda DG-4 wavelength switcher attenuated to 30% transmission provided excitation. Images were acquired within the linear detection range of the camera at intervals of 5 to 20 s. Depending on expression level, exposure times varied between 0.2 and 1 s. Perfusions were performed with a peristaltic pump at 1 ml/min in Hanks' buffered saline. CytB stock solutions were dissolved in methyl sulfoxide and added to Hanks' buffered saline at the indicated concentrations. To enable modeling of the perfusion curves, the baselines throughout the experiment were corrected using a third-order polynomial fit of the ratios measured in the absence of glucose. The obtained function describes the baseline aberration (photo bleaching) as a function of time during perfusion. To correct for this effect, the difference between the ratio at the beginning of the experiment [r(0)] and the baseline aberration [f(t)] was calculated at each time point of the measurement and added to the value of the measured ratio at the respective time point [r(t)], as shown by the following equation: rcorr(t) = r(t) + [r(0) − f(t)].
Rates of ratio change in the cytosol and ER.
The rates of ratio change (dr/dt) were determined by linear regression and averaged over three perfusion experiments for each compartment comprising 2 to 17 cells in the field of view. For normalization, the obtained values were divided by the absolute ratio changes of presence and absence of sugar for each experiment. Statistical significance was analyzed using the t test for independent samples.
Numerical model for glucose homeostasis.
Whereas the external glucose concentration is given by the concentration in the perfusion medium, the rates of glucose concentration ([G]) change inside the cytosol (d[G]cyt/dt) and ER (d[G]ER/dt) can be expressed by the equations d[G]cyt/dt = νTPM − νTER − νhex and d[G]ER/dt = νTER × F, respectively, where νTPM is the rate of glucose transport across the plasma membrane, νTER is the rate of glucose transport across the ER membrane, and νhex is the rate of glucose phosphorylation by hexokinase. νTPM and νTER can be described by the reversible Michaelis-Menten equation, using Vmax(TPM) and Km(TPM) for νTPM and Vmax(TER) and Km(TER) for νTER as shown by the following two equations: νTPM = {Vmax(TPM) × [glc]out/Km(TPM)Vmax(TPM) × [glc]cyt/Km(TPM)}/{1 + [glc]out/Km(TPM) + [glc]cyt/Km(TPM) + [glc]out × [glc]cyt/Km(TPM)2} and νTER = {Vmax(TER) × [glc]cyt/Km(TER)Vmax(TER) × [glc]ER/Km(TER)}/{1 + [glc]cyt/Km(TER) + [glc]ER/Km(TER) + [glc]cyt × [glc]ER/Km(TLR)22}. νhex with Vmax(hex) and Km(hex) is determined by the irreversible Michaelis-Menten equation νhex = Vmax(hex) × [glc]cyt/{Km(hex) + [glc]cyt}. Transport across the plasma membrane and ER was modeled as symmetric (identical values for import and export). To account for the different volumes of the cytosol and ER, a volume factor, F, was introduced (F = volumecyt/volumeER) and set to 2.6 according to the relative subcellular volumes (23). To enable modeling of perfusion experiments, the resulting glucose concentrations were transformed into fractional saturation of the sensor based on the sensor's affinity (0.59 mM) and the equation S = [G]/(Kd + [G]), where Kd is the affinity of the sensor. The fractional saturation values were converted into ratios (r) using the minimum (Rmin) and maximum (Rmax) ratios of the individual perfusion experiment and the following equation: r = RmaxS × (RmaxRmin). Differential equations were integrated using the Runge-Kutta algorithm with a step size of 0.1 s using Microsoft Excel. Km(hex) was set to 50 μM, the affinity of hexokinase, and Km(TPM) was set to 1.0 mM, slightly above the affinity of GLUT1, the most abundant GLUT in HepG2 cells (27). To assign values to the unknown parameters, HepG2 cells expressing the glucose sensor inside the cytosol and ER were perfused with glucose. The rates of change in ratio for three perfusions were determined by linear regression and normalized by the ratio change between the steady states at 0 and 5 mM external glucose (dr/dt) (see Fig. Fig.4C).4C). The dr/dt for the increase in glucose was −0.79/min ± 0.01 inside the ER and −0.77/min ± 0.08 inside the cytosol. The dr/dt for the decrease in glucose was 0.43/min ± 0.01 in the ER and 0.39/min ± 0.05 in the cytosol. Km(TER) was therefore set to the same value as Km(TPM), but Vmax(TER) was set to 10 times the value of Vmax(TPM). Subsequently, the experimental perfusion curves were overlaid with the model. Values for Vmax(hex) and Vmax(TPM) were varied until the largest overlap between the experimental and modeled curves was achieved (Table (Table1;1; see also Fig. Fig.5A).5A). Since Vmax(TER) was set to 10 times the value of Vmax(TPM), the ratios inside the cytosol and ER were very similar, leading to overlapping curves. Table Table11 summarizes the parameters used for the modeling.
FIG. 4.
FIG. 4.
Perfusion of individual HepG2 cells expressing FLIPglu-600μER. Panel A shows results for FLIPglu-600μER (n = 1) with 10 mM glucose (glc) in the presence of 20 μM CytB. The same protocol as described in the legend for Fig. (more ...)
TABLE 1.
TABLE 1.
Parameters for modeling glucose homeostasis in HepG2 cells
FIG. 5.
FIG. 5.
(A) Perfusion of HepG2 cells expressing FLIPglu-600μER and FLIPglu-600μ with 5 mM external galactose. Galactose levels in the ER (green squares) changed more slowly than levels in the cytosol (blue squares). (B) The normalized (norm.) (more ...)
Construction of an ER-targeted, genetically encoded glucose nanosensor.
To measure glucose levels in the ER, the cassette encoding the glucose nanosensor FLIPglu-600μ was subcloned into pEF/myc/ER (Invitrogen), providing ER targeting and ER retention signals, to create FLIPglu-600μER (12-14, 20). HepG2 cells were stably transfected with FLIPglu-600μ and FLIPglu-600μER. To verify ER localization of FLIPglu-600μER, fluorescence was analyzed by confocal microscopy. By contrast to the cytosolically expressed sensor (11, 14), fluorescence derived from the ER-targeted sensor was highly reticular, consistent with ER localization (Fig. (Fig.1A).1A). The sensor colocalized with the mRFP1 ER marker Prl ss-mRFP1-KDEL, expressed from the same cytomegalovirus promoter (Fig. 1B and C). A line scan across a region containing fluorescent labeling of the ER suggests that ≥88% of the sensor is ER localized (Fig. 1D and E) (the actual percentage is expected to be higher, since out-of-focal-plane fluorescence from the densely distributed ER contributes to the signal in the cytosolic domains). The ER localization is supported further by the notion that permeabilization of cells expressing the cytosolic sensor with digitonin leads to rapid loss of fluorescence, while cells expressing the ER-targeted sensor retain fluorescence in vesicular bodies (data not shown). A Golgi marker, RFP1-N1-GalT, did not colocalize with the sensor (Fig. (Fig.1F).1F). Together, these data suggest that the sensor is efficiently targeted and retained in the ER lumen.
FIG. 1.
FIG. 1.
Localization of the ER-targeted sensor, FLIPglu-600μER, in HepG2 cells. Confocal imaging of (A) the sensor FLIPglu-600μER by YFP excitation in the stably expressing cell line and (B) the Prl ss-RFP1-KDEL ER marker after transient expression (more ...)
Glucose transport and metabolism in the cytosol of HepG2 cells.
Transport of glucose in mammalian cells is accomplished by members of the SLC2 (GLUT) and SGLT (sodium/glucose cotransporter) families. To obtain an indication of the mechanisms of glucose uptake in HepG2 cells, transcript levels were analyzed. Using RT-PCR, transcripts for all known GLUTs (GLUT1 to GLUT6 and GLUT8 to GLUT13) were detected (Fig. (Fig.2A).2A). Quantitative RT-PCR showed that SGLT1 and SGLT3 were expressed at very low levels, while GLUT2 and SGLT2 were expressed at slightly higher levels. However, consistent with previous observations of various cancer cell lines, GLUT1 levels were 25-fold higher than SGLT2 levels (Fig. (Fig.2B).2B). In agreement with the expression data, uptake of glucose was insensitive to the SGLT inhibitor phloridzin at levels that do not affect GLUT activity (Fig. (Fig.2C).2C). These data strongly suggest that the contribution of active transport to glucose translocation in HepG2 cells is minor and that uptake is dominated by the combined activities of multiple GLUT family members (33, 36). Moreover, uptake was sensitive to 20 μM CytB, consistent with GLUTs functioning as the predominant uptake system (Fig. (Fig.2D).2D). To analyze reversibility of glucose transport, CytB was added after the cells were loaded with glucose. Despite the block in transport, the ratio inside the cytosol increased, reflecting decreasing glucose levels due to metabolism or compartmentation of glucose (Fig. (Fig.2D).2D). The observed rate of increase in ratio was slower than that observed without inhibitor, suggesting that export of glucose across the plasma membrane dominates the decrease in cytosolic glucose levels in the absence of the inhibitor.
FIG. 2.
FIG. 2.
(A) RT-PCR analysis of RNA from HepG2 cells. The PCR products obtained for GLUTs correspond to the predicted lengths. Molecular size markers (in base pairs) are shown at left. (B) Comparison of mRNA abundance levels of GLUT1, GLUT2, SGLT1, SGLT2, and (more ...)
To measure the relative rates of uptake and metabolism in the cytosol, stably transformed HepG2 cells expressing cytosolic FLIPglu-600μ were perfused with gradual, stepwise increases of external glucose (Fig. (Fig.3A).3A). After a constant ratio was reached, glucose was removed by washing with glucose-free saline. A perfusion protocol using increments of external glucose between 0.25 and 10 mM led to reversible ratio changes corresponding to increasing steady-state glucose levels in the cytosol. The glucose response curves describing the ratio changes were obtained by integration over all cells in the ratio images (Fig. (Fig.3C).3C). The fractional saturation of ~75 to 80% at physiologically relevant concentrations between 4 and 10 mM indicates a lower relative metabolic activity for HepG2 cells than for COS-7 cells (14) (Fig. (Fig.3D).3D). These data were used as a basis for analysis of glucose accumulation in the ER.
FIG. 3.
FIG. 3.
(A and B) Perfusion of HepG2 cells stably expressing (A) FLIPglu-600μ (n = 6) and (B) FLIPglu-600μER (n = 5). The ratio was integrated over all cells in the pseudocolored ratio images. The gray bars indicate the presence (more ...)
Glucose concentrations in the ER and cytosol are similar.
The same stepped perfusion protocol was applied to stably transfected HepG2 cells expressing the ER-targeted sensor, FLIPglu-600μER. Again, increasing increments of external glucose led to reversible ratio changes, demonstrating that externally supplied glucose is rapidly transported into the ER (Fig. (Fig.3B).3B). To allow comparison, the steady-state ratio changes derived from measurements in both compartments were normalized (fractional saturation of the sensor) after calibrating the signal by determining the minimum ratio in the absence of glucose (Δrmin) and the maximum ratio at glucose saturation (Δrmax), as described in reference 14 (Fig. (Fig.3C).3C). The fractional saturations at the respective external levels were very similar in both compartments, indicating similar steady-state glucose levels, at ~75 to 80% relative to external supply (Fig. (Fig.3D).3D). The rates of the respective concentration changes for the uptake phases were also similar (Fig. 3E and F). Since the steady-state concentrations, at least at low levels of external supply in the ER and the cytosol, are similar, the simplest interpretation is that glucose uptake into the ER lumen is not concentrative. Moreover, the reverse kinetics (efflux from the ER and reduction in steady-state levels in the cytosol) are also highly similar. This observation is compatible with high-capacity efflux from the ER lumen into the cytosol but incompatible with a significant contribution of an independent vesicular efflux pathway. The existence of a parallel vesicular pathway (19) that compensates for a concentrative step of ER uptake should lead to significantly faster loss of glucose from the ER.
To rule out the influence of changes in cellular parameters other than glucose inside the ER lumen on the sensor response (such as pH or ionic conditions), the ultra-high-affinity FLIPglu-170nER, which was obtained by subcloning FLIPglu-170n into pEF/myc/ER, was expressed in the ER of HepG2 cells (13, 14). Due to its high affinity of 170 nM, considerably below the Km of hexokinases, FLIPglu-170nER is saturated even in the absence of external glucose, and no glucose-dependent ratio change is expected upon addition of external glucose. During perfusion with 10 mM glucose, the ratio remained constant, showing that environmental factors did not affect the glucose measurements (Fig. (Fig.3G3G).
Transport across the ER membrane is insensitive to cytochalasin B.
It is conceivable that GLUTs are functional when present in the ER on their way to the plasma membrane. To determine whether plasma membrane and ER transport are both sensitive to the GLUT inhibitor CytB, HepG2 cells expressing FLIPglu-600μ and FLIPglu-600μER were perfused with external glucose in the presence of CytB. Because CytB binds to the cytosolic face of the GLUTs and inhibits transport by a noncompetitive mechanism (5, 10), it must be membrane permeable and diffusible in the cytosol. Thus, externally CytB should have access to the cytosolic faces of both plasma membrane and ER-localized transporters. Cells expressing the ER-targeted sensor, FLIPglu-600μER, did not show a ratio change in the presence of CytB, consistent with the inhibition of plasma membrane glucose transport (Fig. (Fig.4A;4A; cf. Fig. Fig.2D).2D). To analyze reversibility of glucose transport, CytB was added after the cell was loaded with external glucose (Fig. (Fig.4A).4A). The ratio increased at a lower rate than that for results obtained in the absence of the inhibitor. Because glucose can be metabolized only in the cytoplasm, these results suggest that CytB did not block export of glucose from the ER into the cytoplasm and indicate that transport across the ER membrane is catalyzed by an activity different from that present at the plasma membrane. When the concentration of glucose was reduced to 5 mM in the presence of a low level of CytB (2 μM) (Fig. (Fig.4B),4B), a ≥50% inhibition of uptake was observed (ER levels of ~2 mM, significantly slowed kinetic). Under these conditions, glucose was still imported into the cytosol and ER (Fig. (Fig.4B).4B). Accordingly, luminal glucose levels were almost in a steady state when CytB was added after the ER had been filled with glucose (Fig. (Fig.4B).4B). However, removal of glucose led to a rapid loss of glucose, again consistent with an uninhibited glucose efflux into the cytosol. An intermediate inhibition was obtained at intermediate glucose and CytB concentrations (Fig. (Fig.4C).4C). These results are consistent with previous observations using microsome tracer studies, which suggest that ER glucose transport is insensitive to CytB (4). The results may suggest that the high-capacity facilitative diffusion transporters present on the ER membrane are different from the GLUTs present at the plasma membrane.
Transport of galactose across the ER membrane.
Several GLUT transporters also mediate uptake of galactose (33). FLIPglu-600μ binds galactose with an affinity of 850 μM (14), permitting the analysis of selectivity of glucose transport across the ER membrane. Upon perfusion with 5 mM external galactose, the fluorescence intensity ratios in HepG2 cells expressing either FLIPglu-600μ or FLIPglu-600μER increased, indicating that galactose is taken up first into the cytosol and then into the ER (Fig. (Fig.5A).5A). The rate of galactose decrease after removal of extracellular sugar was significantly slower than the rate of glucose decrease in both cytosol and ER, consistent with a slower rate of metabolism of galactose than of glucose. The rates for ratio changes were determined by linear regression and normalized by the ratio change between the steady state at 0 and 5 mM external galactose (dr/dt) (Fig. (Fig.5B).5B). The rates for uptake and release from the ER were lower than those in the cytosol, indicating that the galactose transport rate across the ER membrane is lower relative to plasma membrane transport. The difference in rates suggests that the transport activity in the ER membrane discriminates between glucose and galactose.
Sugar homeostasis model comprising the facilitative ER transport component.
The steady-state ratios and the ratio changes observed when the system is moving towards the steady state reflect the sum of all processes acting on metabolite levels. Thus, each perfusion step comprises the entire information of glucose homeostasis, providing a unique opportunity to build models that may contribute to a better understanding of the interactions between the component catalytic and transport activities. Parameters for the model can be derived from the known kinetic properties of the transporters and enzymes expressed in HepG2 cells. RT-PCR detected transcripts for high-affinity hexokinase I but not for glucokinase (data not shown). The simplest model incorporating the results consists of three compartments: the external medium, the cytosol, and the ER. Assuming the absence of catalytic activities for glucose metabolism in the ER, the ER constitutes a buffer compartment for glucose (Fig. (Fig.6A).6A). Integration of differential equations based on Michaelis-Menten kinetics (see Materials and Methods) using parameters for glucose uptake and phosphorylation from the literature generates a close fit with the experimental data only if the ER buffer is included in the models (Fig. (Fig.6B).6B). The properties of the unknown ER transport system were set to an affinity similar to the uptake across the plasma membrane, and Vmax was set to a value higher than the plasma membrane transport, based on the finding that ER transport velocity rates must be equal to or higher than that of the plasma membrane (Fig. (Fig.6C).6C). Using the same parameters, the ratios representing the glucose concentrations in the cytosol and ER at various levels of external supply could be modeled (Fig. 6B, C, and E), as could the fractional saturation of the sensors in both compartments (Fig. 6D and F). To test the model, it was applied to the data obtained for CytB inhibition at different glucose and CytB concentrations. To account for the noncompetitive inhibition of plasma membrane transport, a factor (I) as coefficient to Vmax was introduced:
equation M1
equation M2
equation M3
to reduce the transport rate when CytB is present (6). Using the parameters depicted in Fig. Fig.6,6, the model successfully described all experiments. Both titration curves could be modeled assuming 20% residual transport activity in the presence of 2 μM CytB and complete inhibition in the presence of 8 μM of the inhibitor (Fig. 4A to C).
FIG. 6.
FIG. 6.
(A) Three-compartment model for glucose (glc) homeostasis in HepG2 cells. Glucose is transported reversibly across the plasma and ER membranes and is phosphorylated irreversibly in the cytosol. (B) The modeled ratios for glucose in the cytosol and ER (more ...)
Due to the important role of glucose as an energy source, glucose homeostasis is of central importance for the function of an organism. Before we can understand the regulation of glucose homeostasis in whole organisms, it is essential to characterize the processes that control the various components at the cellular level. Three principal components determine glucose homeostasis within a cell: (i) transport across the plasma membrane, (ii) biosynthesis and metabolism, and (iii) compartmentation. It is conceivable that all three processes are regulated and that both extracellular and intracellular glucose levels as well as downstream metabolic intermediates can affect the individual metabolic and transport reactions. To model glucose metabolism, it would be useful to know all of the relevant parameters, such as concentration and flux. However, it is difficult to measure the key metabolites directly. While tracer studies with nonmetabolizable analogs provide indications for affinity and velocity of the uptake process, these parameters may not represent the true values due to differences in recognition of substrate and analog and due to slower metabolism of the tracer. The efflux parameters are even more difficult to determine. One way to analyze the parameters would be to preload the cells with substrate and to determine the release, but this approach is complicated by the difficulty of manipulating intracellular analyte levels. Compartmentation is also difficult to measure. The isolation of organelles may affect the transport and metabolic properties of the organelle, or purification of the compartment may be partial, especially in the case of the ER, where, typically, microsomal fractions are used as proxies. Moreover, regulatory metabolites may be lost during purification. Therefore, the regulation of the three components, despite their relevance for disease, is still poorly understood.
Noninvasive methods that dynamically report glucose levels in the cytosol as well as within cell compartments are thus required. FRET-based sensors provide the opportunity to measure all parameters simultaneously in different compartments, since these genetically encoded sensors can be targeted to organelles and report glucose levels with high temporal and spatial resolution (21). In previous studies, FRET sensors for glucose were developed and used to measure glucose homeostasis in COS-7 cells (14) The sensor was targeted to both the cytosol and the nucleus, showing that steady-state glucose levels were identical in the two compartments and suggesting that nuclei can be used as proxies for the levels in the cytosol (11). In this study, the FLIPglu-600μ sensor was expressed in the cytosol or targeted to the ER lumen to address the question of glucose flux across the ER membrane in vivo.
As outlined above, hepatocytes function as transient glucose buffering systems. When serum levels drop, the liver compensates by releasing glucose. For reasons not fully understood, the ER plays a crucial role during gluconeogenesis and thus for the release of glucose into the bloodstream from these cells. According to “Arion's model,” the ER membrane G6P translocator and the ER lumen G6P phosphatase are essential for glucose production from glycogen (2). According to this model, which has been sustained by measurements of glucose transport across microsomal vesicles, the ER membrane also contains a glucose transport system for efflux of glucose produced in the ER lumen to the cytosol. This predicted ER transport system for glucose may thus represent a potential target for drug development aiming at control of blood glucose levels.
As a first step towards a better understanding of glucose homeostasis in liver, HepG2 cells were used as a model. HepG2 cells are liver derived but, as with many immortalized cell cultures, display properties of tumor cells, especially the upregulation of GLUT1 gene expression (15). The advantage for imaging studies is that HepG2 cells are easy to culture and transform, adhere to coverslip surfaces, and thus can be perfused with various glucose and inhibitor concentrations. Such an analysis may be used to unravel the principal components and regulatory networks involved in glucose homeostasis, to identify novel genes involved in transport, metabolism, and regulation, and to develop drug targets, but the results may not be directly applicable to normal hepatocytes in the context of the liver. Insights into altered glucose homeostasis in immortalized cells may also be highly relevant for the development of treatments for cancer. Cancer cells typically show elevated uptake and metabolism of glucose (15). Many studies of cancer tissue support the hypothesis that the control over glycolytic flux resides primarily at the glucose uptake and phosphorylation steps.
Glucose export from the ER.
Mammalian cells are unique in that gluconeogenesis involves import of G6P into the ER, where it is dephosphorylated, and an unknown function, named the T3 component according to Arion's model, that exports glucose from the ER back into the cytosol (reviewed in references 3 and 34). However, glucose transport across the ER membrane has been a matter of debate. Some studies suggested that the ER (as derived from microsome tracer studies) was not permeable to glucose, while others found evidence for glucose transport with properties differing from those of GLUTs (4). Moreover, while glycogen storage disease cases can be related to mutations in either the G6P translocator or the G6P phosphatase, no mutations affecting T3 have been described. An alternative model was suggested by Guillam et al., based on findings with GLUT2 knockout mice (17). While facilitated diffusion of 3-O-methylglucose in glut2 hepatocytes was reduced by 95%, efflux of d-glucose was indistinguishable from that of the wild type (19). This finding led to the suggestion of a vesicular pathway from ER effluxing glucose from the cells (33). This pathway appeared different from the typical Golgi vesicle trafficking since it was Brefeldin A independent and nocodazole sensitive. Nocodazole was, however, shown to affect GLUT function directly; thus, the existence of this ER-based vesicular pathway is still controversial (26).
Vesicular secretion pathways typically make use of active transporters for loading the vesicles. One would thus expect a concentrative step of the ER for efficient export of glucose from the cell to the serum. Concentration of glucose inside the ER lumen can be achieved by the combined actions of the G6P translocator and the G6P phosphatase. Therefore, the presence of a facilitative glucose transport system like that posited in Arion's model would represent a leak, leading to reduction of the glucose gradient and the production of ER-derived vesicles with glucose concentrations similar to that in the cytosol. One of the aims of this study was therefore to determine whether such a leak can be detected by directly comparing cytosolic and luminal ER glucose levels.
Targeting of a glucose nanosensor to the ER lumen.
To measure glucose transport across the ER membrane the FRET glucose sensor FLIPglu-600μ was targeted to the ER lumen of HepG2 cells. ER signal sequences have been extensively used to target heterologous proteins, including GFP variants, to the ER lumen and to retain them in the ER by using the KDEL retention signal (22). Confocal images support colocalization of FLIPglu-600μER with an ER marker, while no colocalization was observed with a Golgi marker. Permeabilization of the cells with digitonin leads to rapid loss (<2 min) of fluorescence in cells expressing the sensor in the cytosol, while cells expressing the senor in the ER retain fluorescence in vesicle-like structures that form after addition of digitonin for longer periods (>10 min). Moreover, the difference in galactose accumulation between ER and cytosol supports a differential localization. The data do not exclude the possibility that cotranslational import of the sensor is blocked after interaction with the recognition particle, leading to partial import and thus exposure of sensor domains to the cytosol. However, since GFP and other proteins are efficiently imported into the ER, one would expect that at least the N-terminally fused cyan fluorescent protein should be able to enter the ER lumen. Subsequently, one may envisage that sequences in the periplasmic glucose binding protein lead to a block of transfer. However, in such a scenario, the sensor would not be folded correctly, thus being unable to undergo the conformational change that leads to a FRET change, and would be nonfunctional. The data presented here regarding both localization and functionality indicate that the sensor FLIPglu-600μER is efficiently imported and retained in a fully functional conformation in the ER lumen.
Properties of the glucose transport system detected at the ER membrane.
The observed changes in steady-state glucose levels in response to perfusion of the cells with glucose shown here permit the following conclusions: (i) the ER of HepG2 cells contains a function for mediating uptake of glucose; (ii) glucose uptake across the ER membrane is faster than transport across the plasma membrane, since no difference is observed between accumulation rates of glucose in the cytosol and the ER; and (iii) the predominant mechanism for efflux of glucose from the ER is facilitated diffusion of glucose.
The hypothesis that ER efflux is mediated by facilitated diffusion across the ER membrane rather than vesicular efflux is based on a number of observations. Given that the glucose concentrations in the ER and cytosol as derived from fractional saturation of the sensor in vivo are similar, two possible scenarios can be envisaged. If efflux through an independent vesicular efflux pathway as suggested by Hosokawa and Thorens exists (19), then this efflux must compensate for concentrative uptake into the ER; otherwise, we would observe higher or lower glucose levels inside the ER than in the cytosol. Alternatively, rapid equilibration occurs by a facilitator, as suggested by Banhegyi and Mandl (3). While the measured concentrations and kinetics do not exclude a vesicular route, the similarity in uptake rates at the different external levels does not seem highly probable. More importantly, also the rates of decline of glucose levels after removal of extracellular glucose are highly similar for ER and cytosol. The decline of glucose levels after withdrawal of glucose from the medium in the cytosol is driven by cellular efflux and metabolism via hexokinase I. In contrast, the ER does not contain known enzymes for glucose metabolism; thus, the decline in glucose levels during withdrawal is assumed to be due to efflux through the vesicular or ER transport systems. Again, here it does not seem probable that the vesicular efflux component shows rates similar to the combined efflux from the cytosol and cytosolic metabolism. If the facilitator and vesicular paths coexist, as suggested by Hosokawa and Thorens (19), fluxes out of the ER should be additive; thus, the decline in ER levels is expected to be faster than decline in the cytosol. The observed relative glucose concentrations and kinetics can however be simply explained by the presence of a high-capacity bidirectional glucose facilitator.
Evidence for glucose transport across the ER membrane was derived from biochemical studies using microsomal membrane fractions from homogenized hepatocytes. The data were interpreted as evidence for pore-like glucose transporter selecting solutes on the basis of size (25, 34). Another group defined at least two kinetic components for glucose transport across the ER membrane (4): the slower component had affinities for influx and efflux of glucose in the range of 100 mM, was insensitive to CytB, and was more selective in discriminating between glucose and galactose. Moreover, both data sets concluded that rat liver microsomal vesicles are heterogeneous concerning their glucose transport properties.
The data presented here suggest that the ER glucose facilitator is insensitive to CytB but may also be permeable to galactose. Galactose uptake and efflux kinetics of the ER membrane were significantly slower than the transport across the plasma membrane, suggesting that affinity and Vmax of the facilitator differ for glucose and galactose. Alternatively, it is possible that the ER contains separate transporters for glucose and galactose. Taken together, the differences in the kinetics between glucose and galactose transport would argue against the presence of a nonselective pore.
Numerical model for glucose homeostasis.
The ratio changes and steady-state ratios measured by the nanosensors reflect all components acting on metabolite levels, offering an opportunity to come to a better understanding of homeostasis by building and testing models. The perfusion curves with external glucose could be simulated on the basis of a very simple model comprising reversible transport of glucose across the plasma and ER membranes together with phosphorylation inside the cytosol. Transport across the ER membrane was faster than transport across the plasma membrane, which exceeded phosphorylation of glucose. In this model, the ER constitutes a buffer compartment expanding the accessible space for glucose inside the cell. The decrease in glucose levels inside the cytosol and ER under conditions where transport across the plasma membrane is blocked could be explained simply by phosphorylation of glucose in the cytosol. Internal glucose levels in HepG2 cells were ~75 to 80% of external supply and thus higher than in COS-7 cells, where cytosolic levels were ~50% of external glucose (14). This is in agreement with the rates of increase in ratio measured for both cell lines after loading with glucose and blocking export across the plasma membrane. The model can be further improved by determining the actual subcellular volumes in HepG2 cells rather than estimating these values by comparison to measurements made for other cell types.
Molecular nature of the predicted glucose facilitator in the ER membrane.
Glucose uniporters have been identified for Saccharomyces cerevisiae and mammals as members of the MFS (major facilitator superfamily) (33). Many of these yeast Hxt and mammalian GLUT transporters are located on the plasma membrane. Plasma membrane targeting of transporters occurs typically via the ER-Golgi pathway; thus, the transporters transiently pass through the ER. If these transport proteins are active in the ER, they might well be responsible for the observed glucose uniport across the ER membrane. Moreover, some members of the GLUT family were found in the ER (33).
The redundancy of GLUTs could also explain why mutants in the T3 component of “Arion's cycle” have not been found. GLUTs were found to be differentially sensitive to CytB and to differ in selectivity. However, the CytB insensitivity of ER glucose transport and the differences between plasma membrane and ER transport between glucose and galactose suggest that either a different set of proteins is responsible for ER glucose transport or, at least, the sets of GLUTs contributing to plasma membrane and ER transport are different (Fig. (Fig.7).7). Direct analysis of cells lacking GLUT transporters may be a way to characterize the potential role of different GLUT members in this function.
FIG. 7.
FIG. 7.
Glucose (glc) transport across the ER membrane is catalyzed by (A) a different subset of GLUT transporters or (B) a transport protein different from that for transport across the plasma membrane. (C) Import into and export from the ER are catalyzed by (more ...)
Potential role of Arion's cycle.
The rapid equilibration of glucose between the cytosol and the ER abolishes differences in glucose levels between the compartments, which result from the different subcellular localizations of glucose-6-phosphatase and hexokinases. Thus, one may speculate that the distinct localizations of G6P forming and hydrolyzing activities are instead involved in compartmentalization of G6P. G6P is not only an intermediate of glucose utilization and production but also a regulator of gene expression and enzyme activity (1). This hypothesis would be consistent with studies that provided indirect evidence for multiple G6P pools (8, 32).
Conclusions.
Taken together, the use of genetically encoded nanosensors for glucose targeted to the cytosol and ER provides a unique tool permitting measurements of glucose homeostasis in both compartments and analysis of the exchange of glucose between the compartments. The analysis provides direct evidence for the presence of high-capacity glucose transport systems for import into and export from the ER. The identification of a bidirectional high-capacity glucose transport system suggests that glucose produced in the ER during gluconeogenesis is exported into the cytosol before it is released into circulation. The characterization of ER glucose transport provides a basis for identifying the responsible proteins, which may represent important drug targets for diabetes treatment. One may hypothesize that the ER leak detected here with HepG2 cells is suppressed in the liver and that a vesicular glucose efflux pathway is activated in liver cells. The next step will thus be the analysis of primary hepatocytes or even liver tissue from mice transformed with the sensor. Further optimization of the dynamic range of the glucose sensors has been achieved by semirational design, providing the opportunity to detect more subtle changes (9). Moreover, novel sensors for metabolites downstream of glucose, i.e., G6P and pyruvate, may be useful to obtain deeper insights into homeostasis. These novel sensors may be engineered on the basis of existing nanosensor scaffolds, using rational redesign of the binding pocket to change the substrate specificity (24). Such a set of FRET sensors will undoubtedly help to unravel the open questions regarding sugar homeostasis and its regulation and provide a new means for the development of drugs using high-content screens.
Acknowledgments
We are very grateful to George Patterson and Jennifer Lippincott-Schwartz (NIH, Bethesda) for providing the RFP ER and RFP Golgi markers.
This work was made possible by grants to W.B.F. from NIH (Roadmap Initiative “Metabolomics technology development” R33DK070272) and DOE (DE-FG02-04ER15542).
1. Agius, L., J. Centelles, and M. Cascante. 2002. Multiple glucose 6-phosphate pools or channelling of flux in diverse pathways? Biochem. Soc. Trans. 30:38-43. [PubMed]
2. Arion, W. J., A. J. Lange, H. E. Walls, and L. M. Ballas. 1980. Evidence for the participation of independent translocases for phosphate and glucose 6-phosphate in the microsomal glucose-6-phosphatase system. Interactions of the system with orthophosphate, inorganic pyrophosphate, and carbamyl phosphate. J. Biol. Chem. 255:10396-10406. [PubMed]
3. Banhegyi, G., and J. Mandl. 2001. The hepatic glycogenoreticular system. Pathol. Oncol. Res. 7:107-110. [PubMed]
4. Banhegyi, G., P. Marcolongo, A. Burchell, and A. Benedetti. 1998. Heterogeneity of glucose transport in rat liver microsomal vesicles. Arch. Biochem. Biophys. 359:133-138. [PubMed]
5. Basketter, D. A., and W. F. Widdas. 1978. Asymmetry of the hexose transfer system in human erythrocytes. Comparison of the effects of cytochalasin B, phloretin and maltose as competitive inhibitors. J. Physiol. 278:389-401. [PubMed]
6. Bloch, R. 1973. Inhibition of glucose transport in the human erythrocyte by cytochalasin B. Biochemistry 12:4799-4801. [PubMed]
7. Cloherty, E. K., K. B. Levine, and A. Carruthers. 2001. The red blood cell glucose transporter presents multiple, nucleotide-sensitive sugar exit sites. Biochemistry 40:15549-15561. [PubMed]
8. Das, I., H. G. Sie, and W. H. Fishman. 1971. Labeling of rat liver glucose-1-phosphate, glucose-6-phosphate, uridine diphosphate glucose, and glycogen during glycogen synthesis. Arch. Biochem. Biophys. 144:715-722. [PubMed]
9. Deuschle, K., S. Okumoto, M. Fehr, L. L. Looger, L. Kozhukh, and W. B. Frommer. 2005. Construction and optimization of a family of genetically encoded metabolite sensors by semirational protein engineering. Protein Sci. 14:2304-2314. [PubMed]
10. Deves, R., and R. M. Krupka. 1978. Cytochalasin B and the kinetics of inhibition of biological transport: a case of asymmetric binding to the glucose carrier. Biochim. Biophys. Acta 510:339-348. [PubMed]
11. Fehr, M., D. W. Ehrhardt, S. Lalonde, and W. B. Frommer. 2004. Live imaging of glucose homeostasis in nuclei of COS-7 cells. J. Fluoresc. 14:603-609. [PubMed]
12. Fehr, M., D. W. Ehrhardt, S. Lalonde, and W. B. Frommer. 2004. Minimally invasive dynamic imaging of ions and metabolites in living cells. Curr. Opin. Plant Biol. 7:345-351. [PubMed]
13. Fehr, M., W. B. Frommer, and S. Lalonde. 2002. Visualization of maltose uptake in living yeast cells by fluorescent nanosensors. Proc. Natl. Acad. Sci. USA 99:9846-9851. [PubMed]
14. Fehr, M., S. Lalonde, I. Lager, M. W. Wolff, and W. B. Frommer. 2003. In vivo imaging of the dynamics of glucose uptake in the cytosol of COS-7 cells by fluorescent nanosensors. J. Biol. Chem. 278:19127-19133. [PubMed]
15. Gatenby, R. A., and R. J. Gillies. 2004. Why do cancers have high aerobic glycolysis? Nat. Rev. Cancer 4:891-899. [PubMed]
16. Grossbard, L., and R. T. Schimke. 1966. Multiple hexokinases of rat tissues. Purification and comparison of soluble forms. J. Biol. Chem. 241:3546-3560. [PubMed]
17. Guillam, M. T., R. Burcelin, and B. Thorens. 1998. Normal hepatic glucose production in the absence of GLUT2 reveals an alternative pathway for glucose release from hepatocytes. Proc. Natl. Acad. Sci. USA 95:12317-12321. [PubMed]
18. Hers, H. G., J. Berthet, L. Berthet, and C. De Duve. 1951. The hexose-phosphatase system. III. Intracellular localization of enzymes by fractional centrifugation. Bull. Soc. Chim. Biol. 33:21-41. [PubMed]
19. Hosokawa, M., and B. Thorens. 2002. Glucose release from GLUT2-null hepatocytes: characterization of a major and a minor pathway. Am. J. Physiol. Endocrinol. Metab. 282:E794-E801. [PubMed]
20. Lager, I., M. Fehr, W. B. Frommer, and S. Lalonde. 2003. Development of a fluorescent nanosensor for ribose. FEBS Lett. 553:85-89. [PubMed]
21. Lalonde, S., D. W. Ehrhardt, and W. B. Frommer. 2005. Shining light on signaling and metabolic networks by genetically encoded biosensors. Curr. Opin. Plant Biol. 8:574-581. [PMC free article] [PubMed]
22. Lippincott-Schwartz, J., T. H. Roberts, and K. Hirschberg. 2000. Secretory protein trafficking and organelle dynamics in living cells. Annu. Rev. Cell Dev. Biol. 16:557-589. [PubMed]
23. Lodish, H., A. Berk, L. S. Zipursky, P. Matsudaira, D. Baltimore, and J. Darnell. 2000. Molecular cell biology, 4th ed. W. H. Freeman and Company, New York, N.Y.
24. Looger, L. L., M. A. Dwyer, J. J. Smith, and H. W. Hellinga. 2003. Computational design of receptor and sensor proteins with novel functions. Nature 423:185-190. [PubMed]
25. Meissner, G., and R. Allen. 1981. Evidence for two types of rat liver microsomes with differing permeability to glucose and other small molecules. J. Biol. Chem. 256:6413-6422. [PubMed]
26. Molero, J. C., J. P. Whitehead, T. Meerloo, and D. E. James. 2001. Nocodazole inhibits insulin-stimulated glucose transport in 3T3-L1 adipocytes via a microtubule-independent mechanism. J. Biol. Chem. 276:43829-43835. [PubMed]
27. Mueckler, M., C. Caruso, S. A. Baldwin, M. Panico, I. Blench, H. R. Morris, W. J. Allard, G. E. Lienhard, and H. F. Lodish. 1985. Sequence and structure of a human glucose transporter. Science 229:941-945. [PubMed]
28. Nordlie, R. C., H. M. Scott, I. D. Waddell, R. Hume, and A. Burchell. 1992. Analysis of human hepatic microsomal glucose-6-phosphatase in clinical conditions where the T2 pyrophosphate/phosphate transport protein is absent. Biochem. J. 281:859-863. [PubMed]
29. Okumoto, S., L. L. Looger, K. D. Micheva, R. J. Reimer, S. J. Smith, and W. B. Frommer. 2005. Detection of glutamate release from neurons by genetically encoded surface-displayed FRET nanosensors. Proc. Natl. Acad. Sci. USA 102:8740-8745. [PubMed]
30. Rake, J. P., A. M. ten Berge, G. Visser, E. Verlind, K. E. Niezen-Koning, C. H. Buys, G. P. Smit, and H. Scheffer. 2000. Glycogen storage disease type Ia: recent experience with mutation analysis, a summary of mutations reported in the literature and a newly developed diagnostic flow chart. Eur. J. Pediatr. 159:322-330. [PubMed]
31. Thorens, B., H. K. Sarkar, H. R. Kaback, and H. F. Lodish. 1988. Cloning and functional expression in bacteria of a novel glucose transporter present in liver, intestine, kidney, and beta-pancreatic islet cells. Cell 55:281-290. [PubMed]
32. Threlfall, C. J., and D. F. Heath. 1968. Compartmentation between glycolysis and gluconeogenesis in rat liver. Biochem. J. 110:303-312. [PubMed]
33. Uldry, M., and B. Thorens. 2004. The SLC2 family of facilitated hexose and polyol transporters. Pflueg. Arch. Eur. J. Physiol. 448:259-260. [PubMed]
34. van Schaftingen, E., and I. Gerin. 2002. The glucose-6-phosphatase system. Biochem. J. 362:513-532. [PubMed]
35. Veiga-da-Cunha, M., I. Gerin, Y. T. Chen, P. J. Lee, J. V. Leonard, I. Maire, U. Wendel, M. Vikkula, and E. Van Schaftingen. 1999. The putative glucose 6-phosphate translocase gene is mutated in essentially all cases of glycogen storage disease type I non-a. Eur. J. Hum. Genet. 7:717-723. [PubMed]
36. Wright, E. M., D. D. Loo, M. Panayotova-Heiermann, M. P. Lostao, B. H. Hirayama, B. Mackenzie, K. Boorer, and G. Zampighi. 1994. ‘Active’ sugar transport in eukaryotes. J. Physiol. 196:197-212. [PubMed]
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