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. Author manuscript; available in PMC: 2021 May 1.
Published in final edited form as: J Mol Endocrinol. 2020 May;64(4):235–248. doi: 10.1530/JME-20-0031

Pancreatic Islet Beta Cell-Specific Deletion of G6pc2 Reduces Fasting Blood Glucose

Karin J Bosma 1, Mohsin Rahim 2, Kritika Singh 3, Slavina B Goleva 3, Martha L Wall 2, Jing Xia 4, Kristen E Syring 1, James K Oeser 1, Greg Poffenberger 3, Owen P McGuinness 1, Anna L Means 5, Alvin C Powers 1,3,6, Wen-hong Li 4, Lea K Davis 1,3, Jamey D Young 2, Richard M O’Brien 1,§
PMCID: PMC7331801  NIHMSID: NIHMS1603054  PMID: 32213654

Abstract

The G6PC1, G6PC2 and G6PC3 genes encode distinct glucose-6-phosphatase catalytic subunit (G6PC) isoforms. In mice, germline deletion of G6pc2 lowers fasting blood glucose (FBG) without affecting fasting plasma insulin (FPI) while, in isolated islets, glucose-6-phosphatase activity and glucose cycling are abolished and glucose-stimulated insulin secretion (GSIS) is enhanced at submaximal but not high glucose. These observations are all consistent with a model in which G6PC2 regulates the sensitivity of GSIS to glucose by opposing the action of glucokinase. G6PC2 is highly expressed in human and mouse islet beta cells, however, various studies have shown trace G6PC2 expression in multiple tissues raising the possibility that G6PC2 also affects FBG through non-islet cell actions. Using real time PCR we show here that expression of G6pc1 and/or G6pc3 are much greater than G6pc2 in peripheral tissues whereas G6pc2 expression is much higher than G6pc3 in both pancreas and islets with G6pc1 expression not detected. In adult mice, beta cell-specific deletion of G6pc2 was sufficient to reduce FBG without changing FPI. In addition, electronic health record-derived phenotype analyses showed no association between G6PC2 expression and phenotypes clearly unrelated to islet function in humans. Finally, we show that germline G6pc2 deletion enhances glycolysis in mouse islets and that glucose cycling can also be detected in human islets. These observations are all consistent with a mechanism by which G6PC2 action in islets is sufficient to regulate the sensitivity of GSIS to glucose and hence influence FBG without affecting FPI.

Introduction

Glucose-6-phosphatase catalyzes the hydrolysis of glucose-6-phosphate (G6P) to glucose and inorganic phosphate and is located in the endoplasmic reticulum (ER) membrane (Hutton and O’Brien 2009). It is thought to exist as a multi-component enzyme system in which a G6P transporter, encoded by the SLC37A4 gene, delivers substrate from the cytosol to the active site of a glucose-6-phosphatase catalytic subunit (G6PC) in the lumen of the ER with transporters for inorganic phosphate and glucose returning the reaction products back to the cytosol (Hutton and O’Brien 2009). The G6PC gene family is comprised of three members, G6PC1, G6PC2 and G6PC3 (Hutton and O’Brien 2009). G6PC1, also known as G6PC or G6Pase, is predominantly expressed in liver and kidney where it catalyzes the terminal step in the gluconeogenic and glycogenolytic pathways (Hutton and O’Brien 2009). G6PC3 is widely expressed, with especially high expression in kidney, testis, skeletal muscle and brain (Hutton and O’Brien 2009). While G6PC3 catalyzes G6P hydrolysis in vitro (Shieh, et al. 2003), its key substrate in vivo is thought to be the G6P analog 1,5-anhydroglucitol-6-phosphate (Veiga-da-Cunha, et al. 2019). Based on Northern blotting and immunohistochemistry, G6PC2, also known as IGRP, was thought to be only expressed in pancreatic islets in mice (Arden, et al. 1999) and humans (Martin, et al. 2001). However, human RNA-Seq data, available through the GTEx database, suggest very low expression of G6PC2 in other tissues, including liver, skeletal muscle, and hypothalamus, raising the possibility that G6PC2 is functionally important in non-islet tissues.

The beta cell glucose sensor, glucokinase, catalyzes the formation of G6P from glucose and was thought to be the key factor that determines the rate of beta cell glycolytic flux (Iynedjian 2009; Matschinsky and Wilson 2019). This glycolytic rate determines the sensitivity of glucose-stimulated insulin secretion (GSIS) to glucose and hence the influence of beta cells on fasting blood glucose (FBG) (Iynedjian 2009; Matschinsky and Wilson 2019). However, in isolated germline G6pc2 knockout (KO) islets glucose-6-phosphatase activity (Pound, et al. 2013) and glucose cycling (Wall, et al. 2015), calculated as the percentage of the G6P generated from glucose that is converted back to glucose, are abolished. This results in a leftward shift in the dose response curve for GSIS (Pound et al. 2013) such that under fasting conditions, insulin levels are the same in wild type (WT) and germline G6pc2 KO mice but FBG is reduced in KO mice (Pound et al. 2013; Wang, et al. 2007). These data challenge the existing dogma and suggest a new paradigm in which a glucokinase/G6PC2 futile cycle, rather than glucokinase alone, determines the rate of beta cell glycolytic flux and hence the sensitivity of GSIS to glucose. Molecular studies have shown that the ‘A’ allele of the rs560887 single nucleotide polymorphism (SNP) in the G6PC2 gene allele reduces G6PC2 RNA splicing (Baerenwald, et al. 2013) and hence expression of full length G6PC2. Consistent with the new paradigm, genome wide association studies (GWAS) have linked the rs560887 ‘A’ allele to reduced FBG (Bouatia-Naji, et al. 2008; Chen, et al. 2008).

The experiments described here provide further support for this new model by showing that glucose cycling also exists in human islets and that germline deletion of G6pc2 enhances glycolysis in mouse islets. In addition, gene expression analyses, electronic health record-derived phenotype analyses, and studies in a novel mouse model in which G6pc2 is specifically deleted in beta cells all suggest that G6PC2 is not active in non-islet tissues, and instead imply that an islet-specific action of G6PC2 is sufficient to mediate the effect of G6PC2 on FBG.

Materials and Methods

Animal Care

The Vanderbilt University Medical Center Animal Care and Use Committee approved all protocols used. Mice were maintained on a standard rodent chow diet (calorie contributions: 28% protein, 12% fat, 60% carbohydrate (14% disaccharides); LabDiet 5001; PMI Nutrition International). Food and water were provided ad libitum.

Generation of Germline G6pc2 KO Mice

The generation of germline G6pc2 KO mice on a pure C57BL/6J genetic background has been previously described (Pound et al. 2013; Wang et al. 2007). The generation of beta cell-specific (BCS) G6pc2 KO mice is described in the Supplemental Material.

Mouse and Human Islet Isolation

Mouse islets were isolated by the Vanderbilt Islet Procurement and Analysis Core as previously described (Syring, et al. 2016). Human islets were obtained by A.C.P. through the NIDDK-funded Integrated Islet Distribution Program (https://iidp.coh.org/) and handled and assessed as previously described (Kayton, et al. 2015). Human islets designated as ‘Group 1’ (Kayton et al. 2015) based on islet perifusion analyses (Supplemental Fig. 4) were used for these studies.

Analysis of Gene Expression

Tissue RNA was isolated using the ToTALLY RNA kit whereas islet RNA was isolated using the RNAqueous kit (Ambion, Carlsbad, CA). The Turbo DNA-free DNAse Treatment Kit (Ambion, Carlsbad, CA) was then used to remove trace genomic DNA followed by cDNA generation using the iScript DNA Synthesis Kit (Bio-Rad, Hercules, CA). Gene expression was then quantitated by PCR using the dUTP-containing FastStart SYBR Green Master Mix in conjunction with Uracil-DNA-Glycosylase (Roche, Nutley, NJ). Fold induction of gene expression was calculated using the 2(-ΔΔC(T)) method (Livak and Schmittgen 2001). PCR primer sequences are provided in Supplemental Material.

Phenotypic Analysis of Fasted Germline and BCS G6pc2 KO Mice

Mice were fasted for 5 hours and then weighed. After an additional hour of fasting, mice were anesthetized using isoflurane and blood samples were isolated from the retro-orbital venous plexus. Glucose concentrations were measured in whole blood using a glucose monitor (Accu-Check Advantage; Roche, Indianapolis, USA). EDTA (5 μl; 0.5 M) was then added to blood samples prior to isolation of plasma by centrifugation. Insulin samples were assayed using RIA (Morgan and Lazarow 1963) by the Vanderbilt Hormone Assay and Analytical Services Core.

Hematoxylin and eosin analysis of tissue morphology

Mice were euthanized at 8 months of age and the pancreata were formalin-fixed. Pancreata were then paraffin-embedded and sections cut at a depth of 5 μm, dewaxed and stained in hematoxylin and eosin by the Vanderbilt Translational Pathology Shared Resource. Details on the analysis of pancreatic cryo-sections by immunofluorescence are provided in Supplemental Material.

Electronic Health Record (EHR)-Based Analyses of Human Research Subjects

EHR-based analyses were conducted using data on human subjects in the Vanderbilt University Medical Center (VUMC) BioVU DNA databank. Genotyping data in BioVU is linked to the Synthetic Derivative (SD), a de-identified version of the VUMC EHR repository. Detailed descriptions of program operations, ethical considerations, and continuing oversight and patient engagement have been published (Pulley, et al. 2010; Roden, et al. 2008). The methods used to perform phenome-wide association studies (PheWAS) have been previously published (Denny, et al. 2010; Denny, et al. 2013). The methods used to perform laboratory value-wide association studies (LabWAS) are described in the GitHub repository [https://bitbucket.org/juliasealock/labwas/src/master/].

The BioVU sample was restricted to a homogenous European American (EA) population (N = 50,115) and an African American (AA) population (N = 9,640), based on global genetic ancestry estimated from principal components. We used the PheWAS package in R to perform 1,212 logistic regressions to determine whether any Phecodes (hierarchical clustering of International Classification of Disease (ICD) codes) were significantly associated with our SNPs of interest after adjusting for sex, age (defined for each individual as median age across their medical record) and the top four principal components of ancestry (Denny et al. 2010; Denny et al. 2013). For each phenotype, we required a minimum number of 100 cases for inclusion in the PheWAS. We used the LabWAS package to perform 453 linear regressions to determine whether any laboratory values were associated with SNP genotype. The SNPs tested were common in both EA and AA populations.

Glucose Cycling

Glucose cycling was analyzed as previously described (Wall et al. 2015). Briefly, aliquots of ~100 islets were incubated for 24 or 72 hr at 28°C in RPMI-1640 medium containing 5 mM or 11 mM glucose in a volume of 175 μl. Islets were incubated in either naturally labeled glucose or [1,2,3,4,5,6,6-2H7]glucose (D7-glucose) (98% isotopic purity per site; Cambridge Isotope Laboratories, Inc., Andover, MA). Following the 24 or 72 hr incubation, islets were resuspended by pipetting and pelleted by centrifugation. The supernatant was retained for GC-MS analysis of glucose concentration and mass isotopomer distribution, with glucose concentration determined through comparison to a standard curve. Glucose cycling and glucose uptake rates were calculated as described previously (Wall et al. 2015).

Glycolysis

Glycolysis was analyzed as described (Ashcroft, et al. 1972; Tamarit-Rodriguez, et al. 1998; Wang and Iynedjian 1997). Briefly, aliquots of ~100 WT and germline G6pc2 KO islets were incubated for 2 hrs at 37°C in Krebs bicarbonate buffer containing 5.6 mM glucose spiked with 1 μl D-[5-3H]glucose (Perkin Elmer, Waltham, MA; 10Ci/mmol; 1 mCi/ml) in a volume of 100 μl. Following the incubation, islets were pelleted by centrifugation. The supernatant was retained for analysis of 3H2O generation whereas DNA content of the cell pellet was measured using the Hoechst reagent (Sigma, St. Louis, MO) as described (Ashcroft et al. 1972).

Statistical Analyses

Mouse data were analyzed either using a Student’s t-test, two sample assuming equal variance or a one-way ANOVA, assuming normal distribution and equal variance, as indicated. Post hoc analyses were performed using the Bonferroni correction for multiple comparisons. P values indicating the level of significance are shown in the Figure Legends. EHR associations were analyzed with logistic and linear regressions using the PheWAS package in R and the LabWAS package in R. PheWAS and LabWAS results were deemed significant if the p-value of the association passed a Bonferroni multiple testing correction to account for 1,212 logistic and 453 linear regressions that were performed, respectively.

Results

G6pc Isoforms Exhibit Tissue Specific Expression in Male Mice

While published data strongly suggest that G6PC2 is active in islets, a major unanswered question is whether it is also active in other tissues. The observation from GTEx data that G6PC2 is expressed at low levels in multiple tissues raise this possibility, though it is important to note that there is currently no evidence for detectable G6PC2 protein expression outside of islets. Whether G6PC2 is functionally active in these tissues is likely to depend in part on its expression relative to other G6PC isoforms. We therefore used real time PCR to compare the tissue specific expression of mouse G6pc1 (Fig. 1A), G6pc2 (Fig. 1B), and G6pc3 (Fig. 1C), as well as Slc37a4 (Fig. 1D). Of the tissues tested, G6pc2 expression was only detected in pancreas (Fig. 1B). In contrast, G6pc1 was expressed predominantly in liver and kidney (Fig. 1A), whereas G6pc3 expression was detected in all tissues examined (Fig. 1C). Similarly, expression of Slc37a4, which is known to couple with G6PC1 and G6PC3 (Pan, et al. 2011), was detected in all tissues examined (Fig. 1D). These real time PCR data are similar to the results of expression analyses for G6pc1 (Shelly, et al. 1993), G6pc2 (Arden et al. 1999), G6pc3 (Boustead, et al. 2004) and Slc37a4 (Gerin, et al. 1997) performed using Northern blotting. These results may indicate that the pattern of G6pc2 expression differs between mouse and human or, more likely, that this apparent difference simply reflects the increased sensitivity of RNA-Seq versus real time PCR with the former method being able to detect extremely low expression. For example, RNA-Seq data demonstrate that G6PC2 is expressed in liver at ~0.018% of the level in islets (Ku, et al. 2012), a difference too small to easily detect using real time PCR. Even if G6pc2 is expressed at trace levels in these non-pancreatic tissues, because both G6PC1 (Lei, et al. 1993) and G6PC3 (Shieh et al. 2003) have much higher catalytic activity than G6PC2 (Petrolonis, et al. 2004), these results suggest that G6PC2 is highly unlikely to directly affect metabolism in these tissues.

Figure 1. Comparison of Tissue-Specific G6pc Isoform Expression in Male Mice.

Figure 1.

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Comparison of G6pc1 (Panel A), G6pc2 (Panel B), G6pc3 (Panel C) and Slc37a4 (Panel D) expression in brain, heart, kidney, liver, pancreas and testis from 2 month old non-fasted male mice. G6pc and Slc37a4 expression were quantitated relative to Ppia (cyclophilin A) expression in the indicated tissue and then expressed relative to that in either kidney (Panel A) or pancreas (Panels B-D). Comparison of G6pc1, G6pc2 and G6pc3 expression in pancreas (Panel E), islets (Panel F), liver (Panel G) and kidney (Panel H) from 2 month old non-fasted male mice. G6pc expression was quantitated relative to G6pc2 expression in pancreas (Panel E) or islet (Panel F) or relative to G6pc1 expression in liver (Panel G) or kidney (Panel H). Results represent the mean data ± S.E.M. derived from tissues isolated from 3 male mice.

The Relative Expression of G6pc Isoforms Varies within Specific Tissues in Male Mice

We next examined the relative expression of G6pc isoforms within individual tissues. Within pancreas (Fig. 1E) and islets (Fig. 1F) G6pc2 expression is higher than that of G6pc3, and G6pc1 expression is not detected. These results suggest that G6PC3 is unlikely to compensate for the absence of G6PC2 in pancreatic islets. Indeed, glucose-6-phosphatase activity (Pound et al. 2013) and glucose cycling (Wall et al. 2015) are abolished rather than reduced in germline G6pc2 KO islets. Similarly, G6pc1 expression is higher than that of G6pc3 in both liver (Fig. 1G) and kidney (Fig. 1H) suggesting that G6PC3 is unlikely to be able to compensate for the absence of G6PC1 in either tissue, explaining why inactivating mutations in G6PC1 result in glycogen storage disease type 1a despite the presence of G6PC3 (Chou and Mansfield 2008).

G6pc Isoform Expression, FBG and FPI Change with Aging in Male Mice

To potentially further establish a critical role for pancreatic G6PC2 in the regulation of FBG, we examined the correlation between the induction of G6pc2 expression during development with the ability of G6PC2 to regulate FBG. G6pc2 expression is markedly induced during the weaning period between days 12 and 21 after birth relative to Slc2a2, which encodes the GLUT2 glucose transporter (Fig. 2A). Ins2 expression shows a modest induction over this same time period (Fig. 2B). G6pc2 expression changes little between day 21 and day 56 (2 months) (Fig. 2A) and remains unchanged even in old mice (17 months) (Fig. 2C). In contrast, expression of G6pc1 (Fig. 2D) and G6pc3 (Fig. 2E) decline markedly in liver and kidney in old mice. G6pc3 expression remains unchanged in other tissues in old mice (Supplemental Figure 1) while Slc37a4 expression is selectively reduced in kidney (Fig. 2). The induction of G6pc2 expression at P21 did not correlate with a reduction in FBG in germline G6pc2 KO relative to WT mice; a reduction was only apparent at P56 (Fig. 2G). Since FBG (Fig. 2G) and especially FPI (Fig. 2H) increase markedly between P21 and P56, we hypothesize that the absence of a difference in FBG between WT and germline G6pc2 KO mice at P21 is simply because the dose response curve for GSIS is at a low point where it is not affected by G6PC2 (Supplemental Figure 2). A trend towards reduced blood glucose levels is also observed in non-fasted G6pc2 KO mice (Fig. 2I), with no change in plasma insulin (Fig. 2J) or glucagon (Fig. 2K). Since the weaning period is associated with the switch to a relatively high carbohydrate diet, we hypothesize that this induction of G6pc2 represents part of the beta cell maturation process (Stolovich-Rain, et al. 2015) and is important for enabling the independent offspring to tightly regulate blood glucose levels.

Figure 2. Analysis of Changes in G6pc Isoform Expression, FBG and FPI with Aging in Male Mice.

Figure 2.

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Comparison of pancreatic G6pc2 (Panel A) and Ins2 (Panel B) expression in P12 (non-fasted) and 6 hr fasted P21 and P56 male mice. G6pc2 and Ins2 expression were quantitated relative to Slc2a2 expression and then expressed relative to that at P12. Results represent the mean data ± S.E.M. derived from pancreata isolated from 3–5 mice. *p < 0.05 vs P12, one-way ANOVA. Comparison of G6pc2 (Panel C), G6pc1 (Panel D), G6pc3 (Panel E) and Slc37a4 (Panel F) expression in pancreas, kidney or liver in 2 versus 17 month old non-fasted male mice. G6pc and Slc37a4 expression were quantitated relative to Ppia (cyclophilin A) expression in the indicated tissue and then expressed relative to expression at 2 months. Results represent the mean data ± S.E.M. derived from tissues isolated from 3 male mice. *p < 0.05 vs 2 months. Comparison of FBG (Panel G) and FPI (Panel H) in P12 (non-fasted) and 6 hr fasted P21 and P56 male WT and germline G6pc2 KO mice. FBG data represent the mean ± S.E.M. derived from WT, n=10; KO, n=7 (P12), WT, n=9; KO, n=9 (P21) and WT, n=21; KO, n=24 (P56) mice. FPI data represent the mean ± S.E.M. derived from WT, n=10; KO, n=7 (P12), WT, n=9; KO, n=9 (P21) and WT, n=15; KO, n=23 (P56) mice. *p < 0.05 vs WT. Comparison of glucose (Panel I), insulin (Panel J) and glucagon (Panel K) in 14–16 week old non-fasted WT (n=7) and germline G6pc2 KO (n=7) mice. Data represent the mean ± S.E.M.

FBG is Reduced in Male Beta Cell-Specific (BCS) G6pc2 KO Mice

We reasoned that a second approach for addressing whether G6PC2 is also active in other tissues would be through the generation and analysis of beta cell-specific (BCS) G6pc2 KO mice. If the effect of G6pc2 deletion on FBG were retained it would argue that G6PC2 primarily regulates FBG through an islet-dependent mechanism. BCS G6pc2 KO mice were generated by breeding mice with a floxed G6pc2 allele in which LoxP sites surround G6pc2 exon 3, which encodes the third transmembrane domain (Arden et al. 1999), with MIP1-CreERT2 knock in mice in which expression of the CreERT2 recombinase gene is driven by the endogenous mouse insulin1 promoter (Thorens, et al. 2015). Treatment of these mice with tamoxifen allows for the removal of exon 3 from the floxed G6pc2 allele in beta cells in adult mice thereby inactivating the gene. Sequence analysis indicates that, were splicing of G6pc2 exon 2 to exon 4 to occur, an in-frame G6PC2 variant lacking only exon 3 encoded amino acids would not be generated.

Neither the presence of LoxP sites in the G6pc2 gene nor the presence of the MIP1-CreERT2 gene prior to tamoxifen treatment had major effects on pancreatic G6pc2, Slc37a4 or Ins2 RNA expression in homozygous floxed relative to WT mice (Figs. 3A–F). In contrast, tamoxifen treatment (by three days of IP injections) of homozygous floxed mice expressing the MIP1-CreERT2 gene led to a selective ~60% reduction in pancreatic G6pc2 RNA (Figs. 3G–I) and protein (Figs. 3J & K) expression in floxed mice relative to WT mice, similar to the efficiency of gene deletion reported by Thorens et al. (Thorens et al. 2015) using these MIP1-CreERT2 mice.

Figure 3. Analysis of G6pc2 RNA and G6pc2 protein expression in Floxed G6pc2 Male Mice.

Figure 3.

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Comparison of pancreatic G6pc2 (Panels A, D & G), Slc37a4 (Panels B, E & H) and Ins2 (Panels C, F & I) expression in 16 week old 6 hr fasted male mice in the presence or absence of the MIP1-CreERT2 allele and in the presence or absence of tamoxifen (Tam) treatment. Pancreatic G6pc2, Ins2 and Slc37a4 expression were quantitated relative to Ppia (cyclophilin A) and then expressed relative to that in WT mice. Results represent the mean data ± S.E.M. derived from tissues isolated from the indicated number of male mice. Comparison of insulin and G6PC2 protein expression in representative sections of pancreas isolated from WT, germline and BCS G6pc2 KO mice (Panel J). Quantitation of G6PC2 protein expression (Panel K). Data represent the mean ± S.E.M. from 100 cells for each group. FL, floxed; KD, knockdown.

Neither the presence of LoxP sites in the G6pc2 gene nor the presence of the MIP1-CreERT2 gene prior to tamoxifen treatment affected FBG and FPI (Figs. 4A–D). In contrast, tamoxifen treatment led to a clear reduction in FBG in floxed mice (Fig. 4E) with no change in FPI (Fig. 4F) indicating that the loss of G6PC2 selectively in beta cells is sufficient to regulate FBG. As expected, the difference in FBG between WT and floxed mice (~15 mg/dl; WT n=14; floxed n=17) (Fig. 4E) is slightly less than that observed in germline G6pc2 KO mice (~21 mg/dl; WT n=29; KO n=16) (Pound et al. 2013; Wang et al. 2007), consistent with the difference between a partial reduction in G6pc2 expression in BCS G6pc2 KO mice compared to a complete loss of G6pc2 expression in germline KO mice. These data also suggest that developmental compensation, a common feature in germline KO mouse studies, has not affected the phenotype of germline G6pc2 KO mice, consistent with the absence of evidence for compensatory changes in pancreatic, islet and liver gene expression comparing WT and G6pc2 KO mice (Supplemental Figure 3).

Figure 4. Analysis FBG and FPI in Floxed G6pc2 Male Mice.

Figure 4.

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Comparison of FBG (Panels A, C & E) and FPI (Panels B, D & F) in 16 week old 6 hr fasted male mice in the presence or absence of the MIP1-CreERT2 allele and in the presence or absence of tamoxifen (Tam) treatment. Results represent the mean data ± S.E.M. derived from the indicated number of male mice. *p < 0.05 vs WT.

Human Biobank Studies Find No Evidence for Extra-Pancreatic Actions of G6PC2

To complement our mouse studies we searched for evidence for potential extra-pancreatic functions of G6PC2 in humans using Vanderbilt University’s Medical Center (VUMC) BioVU Biobank, a DNA biobank linked to a de-identified version of the Vanderbilt electronic health records, called the Synthetic Derivative (SD) (Pulley et al. 2010; Roden et al. 2008). Systematic and efficient approaches have been developed that involve screening the SD with specific SNPs to identify both novel phenotype-variant associations and plasma hormone/metabolite associations, referred to as PheWAS and LabWAS analyses, respectively (Denny et al. 2010; Denny, et al. 2011; Denny et al. 2013; Ritchie, et al. 2013; Shameer, et al. 2014). For these analyses we used the G6PC2 rs560887 SNP that has been shown to affect G6PC2 RNA splicing (Baerenwald et al. 2013) and has been linked by GWAS to variations in FBG (Bouatia-Naji et al. 2008; Chen et al. 2008) and hemoglobin A1c (HbA1c) (Soranzo, et al. 2010). PheWAS analyses showed that the G6PC2 rs560887 ‘A’ allele is associated with increased risk for acute pancreatitis in non-diabetic European Americans (EAs) (G6PC2 OR = 1.39, CI = 1.20 – 1.62, p = 2.05E-05) after Bonferroni correction for the 1212 phenotypes tested (Table 1). Similar, though less statistically significant, trends were observed in the AA sample and in the total (multi-ancestry) population (Table 1). The association with acute pancreatitis was not observed in EAs with type 2 diabetes (T2D) (Table 1) or in African Americans with T2D (AAs; data not shown). Because G6PC2 is not expressed in pancreatic exocrine or ductal tissue (Hutton and Eisenbarth 2003), this suggests that reduced islet G6PC2 expression can influence non-islet pancreatic tissue function. G6PC2 was not associated with T2D in the total population (Table 1). In contrast, BioVU analyses using the established rs13266634 T2D-associated SNP in the SLC30A8 gene (Yaghootkar and Frayling 2013) did detect an association with T2D (Table 1).

Table 1. Analysis of the Association Between G6PC2 SNP rs560887 with Disease Phenotypes Using Electronic Health Record (EHR)-Derived Analyses.

Data show an association between G6PC2 SNP rs560887 and acute pancreatitis in European Americans (EAs) without T2D and no association with T2D, in contrast to SLC30A8 SNP rs13266634. ‘All’ refers to the total EHR population. ‘Phecodes’ refer to the numerical abbreviation for individual phenotypes in the Vanderbilt EHRs. All associations were adjusted for the covariates median age of record, sex, and EHR-reported race using linear regression. SE, standard error; OR, odds ratio; N_no_SNP, number of records with a missing predictor; FDR, false discovery rate; LCI, lower confidence interval; UCI, upper confidence interval.

Gene SNP Population Phecode Description Beta SE OR P N_total N_cases N_controls N_no_snp LCI UCI Bonferroni FDR
G6PC2 rs560887 Total 577.1 Acute pancreatitis 0.13 0.05 1.14 0.00709997 37343 411 36932 10 1.04 1.26 FALSE FALSE
G6PC2 rs560887 All EA 577.1 Acute pancreatitis 0.15 0.05 1.16 0.00606234 31878 339 31539 6 1.04 1.28 FALSE FALSE
G6PC2 rs560887 EA Non-T2D 577.1 Acute pancreatitis 0.33 0.08 1.39 2.05E-05 21357 145 21212 5 1.20 1.62 TRUE TRUE
G6PC2 rs560887 EAT2D 577.1 Acute pancreatitis 2.5E-03 0.09 1.00 0.97696475 6155 140 6015 0 0.85 1.19 FALSE FALSE
G6PC2 rs560887 Total 250.2 Type 2 diabetes 0.01 0.01 1.01 0.43847308 32759 7026 25733 9 0.98 1.04 FALSE FALSE
SLC30A8 rs13266634 Total 250.2 Type 2 diabetes −0.06 0.01 0.94 3.56E-05 32782 7035 25747 16 0.92 0.97 TRUE TRUE
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LabWAS analyses showed that the G6PC2 rs560887 ‘A’ allele was associated with reduced blood glucose (Effect estimate = −0.05, SE = 0.01, p = 6.56E-24) in the total population. Similar trends were observed in the total EA population and in non-diabetic EAs but not EAs with T2D (Table 2) or in AAs (data not shown). Given the differences in sample sizes we cannot rule out the possibility that the lack of association in these groups is simply related to power. The G6PC2 rs560887 ‘A’ allele was also associated with reduced hemoglobin A1c (HbA1c) but only in non-diabetic EAs (Table 2). These results are consistent with GWAS data (Bouatia-Naji et al. 2008; Chen et al. 2008; Soranzo et al. 2010) and suggest that the influence of G6PC2 on blood glucose and HbA1c is lost under diabetic conditions. Surprisingly, the G6PC2 rs560887 ‘A’ allele was associated with increased taurine levels in the total population (Effect estimate = 0.32, SE = 0.08, p = 7.28E-05) with similar trends in the total EA and non-diabetic EA populations (Table 2). It is well established that insulin regulates amino acid metabolism (Rooyackers and Nair 1997), so the simplest hypothesis is that reduced islet G6PC2 expression influences the kinetics of insulin secretion which then affects amino acid metabolism. Overall these PheWAS and LabWAS results complement the conclusions of the mouse studies by not finding evidence for extra-pancreatic functions of G6PC2.

Table 2. Analysis of the Association Between G6PC2 SNP rs560887 with Laboratory Analytes Using Electronic Health Record (EHR)-Derived Analyses.

Data show an association between G6PC2 SNP rs560887 and blood glucose, hemoglobin A1c (HbA1c) and blood taurine in either the total EHR population (All) or specific European American (EA) sub-populations. No associations were observed in the African American (AA) population or separately in AAs with or without T2D (data not shown). SE, standard error; FDR, false discovery rate. For each laboratory value, we tested for associations between the median of all lab values for an individual against the number of minor alleles for that individual. All associations were adjusted for covariates median age of record, sex, and EHR-reported race using linear regression. The units for all measurements in any given lab were consistent within the lab (e.g. all blood glucose values were reported in mg/dL).

Gene SNP Population Analyte Effect Estimate SE OR P N LCI UCI Bonferroni FDR
G6PC2 rs560887 Total GLUCOSE BLOOD −0.05 0.01 0.95 6.56E-24 38301 0.94 0.96 TRUE TRUE
G6PC2 rs560887 Total HGB A1C GLYCATED −0.01 0.01 0.99 0.21060335 14328 0.97 1.01 FALSE FALSE
G6PC2 rs560887 Total TAURINE 0.32 0.08 1.38 7.28E-05 158 1.22 1.53 TRUE TRUE
G6PC2 rs560887 All EA GLUCOSE BLOOD −0.05 0.01 0.95 2.08E-22 32686 0.94 0.96 TRUE TRUE
G6PC2 rs560887 All EA HGB A1C GLYCATED −0.01 0.01 0.99 0.15780832 12271 0.97 1.00 FALSE FALSE
G6PC2 rs560887 All EA TAURINE 0.27 0.08 1.31 0.00171088 132 1.15 1.47 FALSE FALSE
G6PC2 rs560887 Non T2D EA GLUCOSE BLOOD −0.07 0.01 0.93 3.97E-38 21728 0.92 0.94 TRUE TRUE
G6PC2 rs560887 Non T2D EA HGB A1C GLYCATED −0.05 0.01 0.96 3.83E-05 4353 0.93 0.98 TRUE TRUE
G6PC2 rs560887 Non T2D EA TAURINE 0.29 0.09 1.34 0.00243233 114 1.16 1.53 FALSE FALSE
G6PC2 rs560887 T2D EA GLUCOSE BLOOD 0.01 0.01 1.01 0.18306124 6444 0.99 1.04 FALSE FALSE
G6PC2 rs560887 T2D EA HGB A1C GLYCATED 0.02 0.01 1.02 0.18513367 5159 0.99 1.04 FALSE FALSE
G6PC2 rs560887 T2D EA TAURINE 0.30 0.51 1.35 0.58240028 12 0.36 2.35 FALSE FALSE
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To follow up on these observations from human population studies, we examined whether germline deletion of G6pc2 results in pancreatitis in mice. Pancreatic sections from ~8 month old 6 hour fasted mice showed no evidence of pancreatitis, with no necrosis, edema, hemorrhage, steatosis, or fibrosis within the pancreatic parenchyma and no increase in inflammatory infiltrate compared to WT controls. Furthermore, acinar cell morphology appeared normal with no evidence of degranulation, vacuolization, or luminal dilation (Fig. 5A). However, expression of Prss1, which encodes trypsinogen, was elevated in 16 week old 6 hour fasted germline G6pc2 KO mice (Fig. 5B). The expression of Ctrb1 (chymotrypsinogen), Amy2a2 (amylase) and Cpa1 (carboxypeptidase A1) were unchanged (Fig. 5B) as were Ins2, Slc30a8 (ZnT8) and Slc37a4, though Slc2a2 expression was slightly reduced (Fig. 5C). Surprisingly the opposite result was observed in 16 week old 6 hour fasted BCS G6pc2 KO mice in which expression of Prss1 was reduced (Fig. 5D).

Figure 5. Analysis Pancreatitis and Acinar Gene Expression in Germline G6pc2 KO and Floxed G6pc2 Male Mice.

Figure 5.

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Panel A: Comparison of pancreatic tissue in WT and germline G6pc2 KO mice. Results are representative of sections analyzed from 3 WT and 3 KO mice. Size bars represent 100 μm. Panels B & C: Comparison of pancreatic acinar gene expression in 6 hr fasted 16 week old male WT and germline G6pc2 KO mice. Gene expression was quantitated relative to Ppia (cyclophilin A) and then expressed relative to that in WT mice. Results represent the mean data ± S.E.M. derived from pancreata isolated from 5 mice. *p < 0.05 vs WT. Panel D: Comparison of pancreatic gene expression in 6 hr fasted 16 week old male WT and BCS G6pc2 KO mice following tamoxifen treatment. The RNA samples were a subset of those used in Figs. 5G-I. Gene expression was quantitated relative to Ppia (cyclophilin A) and then expressed relative to that in WT mice. Results represent the mean data ± S.E.M. derived from pancreata isolated from 5 mice. *p < 0.05 vs WT.

Glycolysis is Enhanced in Germline G6pc2 KO Relative to WT Islets

Our model predicts that deletion of G6pc2 will increase glycolytic flux in islets. While glucose cycling analyses can quantitate G6P production (Wall et al. 2015) such experiments do not quantitate the rate of glycolysis. It is important to address this key difference because glycolysis, rather than the rate of G6P production, is the determinant of the magnitude of GSIS (Iynedjian 2009; Matschinsky and Wilson 2019). This distinction arises because G6P has multiple alternate metabolic fates, including conversion to fructose-6-phosphate, glucose-1-phosphate, and inositol-3-phosphate. The well-established glycolytic assay that measures the generation of 3H2O from D-[5-3H]glucose (Ashcroft et al. 1972; Tamarit-Rodriguez et al. 1998; Wang and Iynedjian 1997) was used to compare glycolytic rates in WT and germline G6pc2 KO islets. Consistent with our model, glycolysis was elevated in KO islets at 5.6 mM glucose (Fig. 6).

Figure 6. Comparison of Glycolytic Rates in WT and Germline G6pc2 KO Islets.

Figure 6.

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Data show the rates of glycolysis in isolated islets from WT and germline G6pc2 KO mice at 5 mM glucose and represent the mean ± S.E.M. of 5 – 7 islet samples. *p < 0.05 vs WT.

Glucose Cycling Exists in Human Islets

Using a novel stable isotope-based methodology that eliminates assumptions associated with the use of radioisotopes, we have recently shown that mouse islets exhibit significant rates of glucose cycling (Wall et al. 2015). The same methodology was used to address the key question as to whether glucose cycling also occurs in human islets. Kayton et al. (Kayton et al. 2015) have recently used islet perifusion assays to define 5 patterns of GSIS in human islets, designated as Group 1 – 5. Group 1 islets exhibit optimal responses to secretagogues whereas GSIS shows distinct abnormalities in Group 2–5 islets (Kayton et al. 2015). Glucose uptake and cycling were compared at both 5 mM and 11 mM glucose in human islet preparations from four donors that were defined as Group 1 using perifusion assays (Supplemental Figure 4). Significant rates of glucose uptake and glucose cycling were detected and were typically greater at 11 mM than 5 mM though, for reasons that are unclear, this was not uniform and the islet preparation that exhibited decreased glucose uptake at 11 mM was distinct from the preparation that exhibited decreased glucose cycling at 11 mM (Fig. 7). At 5 mM glucose the level of glucose uptake inversely correlated with the level of glucose cycling (Fig. 7). Since the rate of glucose cycling is determined by quantitating the glucose isotopomers generated by cycling that are transported out of the islets and back into the media, one possible interpretation of this observation is that in cells with high glycolytic flux, re-cycled glucose preferentially re-enters the glycolytic pathway rather than leaving the cell, leading to an underestimate of the rate of cycling. Another possible interpretation is that the rate of G6P dephosphorylation is fairly constant despite changes in glucose uptake, so glucose cycling represents a smaller percentage of glucose uptake when the glucose uptake rate is high. The mean rate of glucose cycling is clearly lower in human (Fig. 7) than mouse (Wall et al. 2015) islets. This correlates with higher basal GSIS (Conrad, et al. 2016) and lower G6PC2 expression (Xin, et al. 2016) in human versus mouse islets as well as lower FBG in healthy humans (2019) than C57BL/6J mice (Pound et al. 2013).

Figure 7. Comparison of Glucose Uptake Cycling Rates in Group 1 Human Islets.

Figure 7.

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Data show the rates of glucose uptake (Panel A) and cycling (Panel B) in individual preparations of Group 1 islets at either 5 mM or 11 mM glucose from four donors (designated A-D). Information on the individual human donors is presented in Supplemental Table 1 and information of the insulin secretory profile of each islet preparation, as assessed by islet perifusion, is presented in Supplemental Figure 4.

Discussion

The experiments described here provide further support for the concept that G6PC2 activity in pancreatic islets creates a glucokinase/G6PC2 futile cycle that determines the rate of beta cell glycolytic flux and hence the sensitivity of GSIS to glucose rather than this rate being determined by glucokinase alone. Specifically, we show that deletion of G6pc2 enhances glycolysis in mouse islets (Fig. 6) and glucose cycling also exists in human islets (Fig. 7). In addition, gene expression analyses show that G6PC2 is unlikely to be important in non-islet tissues where the more active G6PC isoforms, G6PC1 and G6PC3, are more abundantly expressed (Fig. 1) and that deletion of G6pc2 specifically in beta cells is sufficient to reduce FBG (Figs. 3 & 4). Future studies will be designed to confirm that GSIS is enhanced at sub-maximal glucose in perfused pancreata and isolated islets from BCS G6pc2 KO mice as previously observed in germline G6pc2 KO mice (Pound, et al. 2013).

Several interesting observations arose from the human biobank studies. First, we observed a strong association between G6PC2 and blood glucose levels (Table 2) even though the BioVU population represents individuals who visit VUMC with a wide range of medical issues and who were not all under overnight fasting conditions when blood was isolated. This suggests that G6PC2 can influence blood glucose under non-fasting conditions, consistent with the concept that altered G6PC2 expression affects the sensitivity of GSIS to glucose over a range of glucose concentrations rather than just at fasting glucose levels (Supplemental Fig. 2). Second, the results show that altered G6PC2 expression in humans can affect the risk of acute pancreatitis (Table 1) and that deletion of G6pc2 in mice alters Prss1 expression (Fig. 5B & D). Since altered Prss1 expression may affect the risk of pancreatitis (Boulling, et al. 2016; Le Marechal, et al. 2006) this provides a potential mechanistic link between these observations. Since G6PC2 is expressed selectively in pancreatic islet cells (Hutton and Eisenbarth 2003) the mechanism by which it affects Prss1 expression and especially why the direction of the effect differs with germline and BCS deletion is unclear but will be the subject of future studies. Finally, we observed that G6PC2 was not associated with altered risk for T2D (Table 1), matching GWAS data that have failed to establish a consistent link with T2D risk (Shi, et al. 2017). It will be of considerable interest to identify individuals with mutations that have a major effect on G6PC2 function to determine whether an association with T2D risk then emerges. Elevations in FBG in the prediabetic state correlate with (Brunzell, et al. 1976) and appear sufficient to drive (Brereton, et al. 2014; Wang, et al. 2014) the decline in beta cell function, likely mediated by reactive oxygen species (ROS), that underlies the progression from prediabetes to T2D (Halban, et al. 2014; Weir and Bonner-Weir 2004). However, unless G6PC2 has unknown additional effects on beta cell metabolic flux, a therapy designed to inhibit G6PC2 and thereby lower FBG may fail to prevent this progression because the reduced FBG would be associated with unchanged glycolytic flux and therefore unchanged generation of damaging ROS. Future studies will compare the generation of ROS in WT and KO islets. While a therapy directed at G6PC2 may not affect T2D risk it would likely still be beneficial in preventing cardiovascular-associated mortality (CAM) in individuals with elevated FBG. Thus, in Europeans, an increase in FBG levels from less than 90 mg/dl to between 99–108 mg/dl is associated with 30% increased risk of CAM (Coutinho, et al. 1999), and in Asians a reduction in FBG from 99 to 90 mg/dl is associated with a 25% reduction in the risk of CAM (Lawes, et al. 2004). The risk of CAM increases still further in individuals with the high FBG levels characteristic of diabetes (Coutinho et al. 1999; DECODE 2003; Khaw, et al. 2001; Lawes et al. 2004).

In mice, G6pc2 expression is ~20 fold higher in beta than alpha cells (Xin et al. 2016), G6PC2 protein is undetectable in alpha cells (Hutton and Eisenbarth 2003), and glucagon levels are unchanged in germline G6pc2 KO mice (Wang et al. 2007). The reduction in FBG in germline G6pc2 KO mice with unchanged FPI is therefore solely due to the shift in the sensitivity of GSIS to glucose, consistent with our glycolysis and BCS G6pc2 KO mouse data. In contrast, in humans G6PC2 expression is only ~5 fold higher in beta than alpha cells (Xin et al. 2016) such that altered alpha cell glycolysis could contribute to the reduction in FBG in humans with reduced G6PC2 expression.

In summary, these studies show that islet-specific G6PC2 activity is sufficient to regulate FBG, although the possibility that G6PC2 plays some role in tissue-specific metabolism outside the pancreas cannot be excluded.

Supplementary Material

Supplementary Text
Suppl Table 1
Suppl Fig 1
Suppl Fig 2
Suppl Fig 3
Suppl Fig 4

Acknowledgements

We thank Susan Hajizadeh, Tracy O’Brien and Brooke Olesnevich for performing insulin, glycogen and glucose cycling assays, respectively. We also thank Joshua C. Denny and Huan Mo for their insights on BioVU-related studies. This research was supported by the following grants: R.O’B., DK92589; O.P.M., DK043748 and DK078188; A.C.P., UC4 DK104211, DK106755, DK89572, and by grants from the JDRF and the Department of Veterans Affairs (BX000666); W.H.L. JDRF grant 1-SRA-2018-675-S-B; L.K.D., R56MH120736; J.D.Y DK106348. The isolation of mouse islets by the Vanderbilt Islet Procurement and Analysis Core and the measurement of plasma insulin by the Vanderbilt Hormone Assay & Analytical Services Core were supported by NIH grant P60 DK20593, to the Vanderbilt Diabetes Research Training Center. Human pancreatic islets were provided by the NIDDK-funded Integrated Islet Distribution Program at the City of Hope (NIH Grant # 2UC4 DK098085). K.J.B., S.B.G., M.L.W., and K.E.S. were supported by the Vanderbilt Molecular Endocrinology Training Program grant 5T32 DK07563.

R.O’B. is the guarantor of this work, had full access to all the data, and takes full responsibility for the integrity of data and the accuracy of data analysis.

Footnotes

Conflict of Interest Statement

There is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.

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Supplementary Materials

Supplementary Text
NIHMS1603054-supplement-Supplementary_Text.docx (32.6KB, docx)
Suppl Table 1
NIHMS1603054-supplement-Suppl_Table_1.pdf (51.1KB, pdf)
Suppl Fig 1
NIHMS1603054-supplement-Suppl_Fig_1.pdf (20KB, pdf)
Suppl Fig 2
NIHMS1603054-supplement-Suppl_Fig_2.pdf (21.2KB, pdf)
Suppl Fig 3
NIHMS1603054-supplement-Suppl_Fig_3.pdf (25.3KB, pdf)
Suppl Fig 4
NIHMS1603054-supplement-Suppl_Fig_4.pdf (87.2KB, pdf)

RESOURCES